CA2793118A1 - Maintaining context information between user interactions with a voice assistant - Google Patents

Maintaining context information between user interactions with a voice assistant Download PDF

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Publication number
CA2793118A1
CA2793118A1 CA2793118A CA2793118A CA2793118A1 CA 2793118 A1 CA2793118 A1 CA 2793118A1 CA 2793118 A CA2793118 A CA 2793118A CA 2793118 A CA2793118 A CA 2793118A CA 2793118 A1 CA2793118 A1 CA 2793118A1
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Prior art keywords
user
task
parameter
assistant
input
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CA2793118A
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French (fr)
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CA2793118C (en
Inventor
Adam John Cheyer
Didier Rene Guzzoni
Thomas Robert Gruber
Christopher Dean Brigham
Dag Kittlaus
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Apple Inc
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Apple Inc
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Priority to CA2954559A priority Critical patent/CA2954559C/en
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Abstract

An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.

Description

MAINTAINING CONTEXT INFORMATINO BETWEEN
USER INTERACTIONS WITH A VOICE ASSISTANT

[0001] This is a divisional of Canadian application serial no. 2,787,351 which is the Canadian national phase of International application PCT/US2011/020861 filed 11 January 2011 (11.01.2011) and published 21 July 2011 under publication no.
WO 2011/088053.
FIELD OF THE INVENTION
[0002] The present invention relates to intelligent systems, and more specifically for classes of applications for intelligent automated assistants.
BACKGROUND OF THE INVENTION
[0003] Today's electronic devices are able to access a large, growing, and diverse quantity of functions, services, and information, both via the Internet and from other sources. Functionality for such devices is increasing rapidly, as many consumer devices, smartphones, tablet computers, and the like, are able to run software applications to perform various tasks and provide different types of information. Often, each application, function, website, or feature has its own user interface and its own operational paradigms, many of which can be burdensome to learn or overwhelming for users. In addition, many users may have difficulty even discovering what functionality and/or information is available on their electronic devices or on various websites; thus, such users may become frustrated or overwhelmed, or may simply be unable to use the resources available to them in an effective manner.
[0004] In particular, novice users, or individuals who are impaired or disabled in some manner, and/or are elderly, busy, distracted, and/or operating a vehicle may have difficulty interfacing with their electronic devices effectively, and/or engaging online services effectively. Such users are particularly likely to have difficulty with the large number of diverse and inconsistent functions, applications, and websites that may be available for their use.
[0005] Accordingly, existing systems are often difficult to use and to navigate, and often present users with inconsistent and overwhelming interfaces that often prevent the users from making effective use of the technology.

SUMMARY
[0006] According to various embodiments of the present invention, an intelligent automated assistant is implemented on an electronic device, to facilitate user interaction with a device, and to help the user more effectively engage with local and/or remote services. In various embodiments, the intelligent automated assistant engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions.
[0007] According to various embodiments, a method for operating an automated assistant, comprising: at an electronic device comprising a processor and memory storing instructions for execution by the processor: performing a first task using a first parameter; obtaining a text string from a speech input received from a user;
identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter; and performing the first task using the second parameter or performing the second task using the first parameter.
[0008] According to various embodiments, a system for operating an intelligent automated assistant, comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the processors to perform operations comprising: performing a first task using a first parameter; obtaining a text string from a speech input received from a user;
identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter; and performing the first task using the second parameter or performing the second task using the first parameter.
[0009] According to various embodiments, a computer readable storage medium storing instructions that, when executed by an electronic device with one or more processors, cause the processors to perform operations comprising: performing a first task using a first parameter; obtaining a text string from a speech input received from a user; identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter; and performing the first task using the second parameter or performing the second task using the first parameter.
[0010] According to various embodiments of the present invention, the intelligent automated assistant integrates a variety of capabilities provided by different software components (e.g., for supporting natural language recogni-2a tion and dialog, multimodal input, personal information management, task flow management, orchestrating distributed services, and the like;. Further-more, to offer intelligent interfaces and useful functionality to users, the intel-ligent automated assistant of the present invention may, in at least some em-bodiments, coordinate these components and services. The conversation in-terface, and the ability to obtain information and perform follow-on task, are implemented, in at least some embodiments, by coordinating various compo-nents such as language components, dialog components, task management components, information management components and/ or a plurality of ex-ternal services.
[0011] According to various embodiments of the present invention, intelli-gent automated assistant systems may be configtii ed, designed, and/or oper-able to provide various different types of operations, functionalities, and/
or features, and/or to combine a plurality of features, operations, and applica-tions of an electronic device on which it is installed. In some embodiments, the intelligent automated assistant systems of the present invention can per-form any or all of: actively eliciting input from a user, interpreting user intent, disambiguating among competing interpretations, requesting and receiving clarifying information as needed, and performing (or initiating) actions based on the discerned intent. Actions can be performed, for example, by activating and/or interfacing with any applications or services that may be available on an electronic device, as well as services that are available over an electronic network such as the Internet. In various embodiments, such activation of ex-ternal services can be performed via APIs or by any other suitable mecha-nism. In this manner, the intelligent automated assistant systems of various embodiments of the present invention can unify, simplify, and improve the user's experience with. respect to many different applications and functions of an electronic device, and with respect to services that may be available over the Internet. The user can thereby be relieved of the burden of learning what functionality may be available on the device and on web-connected services, WO 2011/088053 PCT/LTS2011ro20861 how to interface with such services to get what he or she wants, and how to interpret the output received from such services; rather, the assistant of the present invention can act as a go-between between the user and such diverse services.
[0012] In addition, in various embodiments, the assistant of the present invention provides a conversational interface that the user may find more in-tuitive and less burdensome than conventional graphical user interfaces. The user can engage in a form of conversational dialog with the assistant using any of a number of available input and output mechanisms, such as for ex-ample speech, graphical user interfaces (buttons and links), text entry, and the like. The system can be implemented using any of a number of different plat-forms, such as device APIs, the web, email, and the like, or anv% combination thereof. Requests for additional input can be presented to the user in the con-text of such a conversation. Short and long term memory can be engaged so that user input can be interpreted in proper context given previous events and communications within a given session, as well as historical and profile in-formation about the user.
[0013] In addition, in various embodiments, context information derived from user interaction with a feature, operation, or application on a device can be used to streamline the operation of other features, operations, or applica-tions on the device or on other devices. For example, the intelligent auto-mated assistant can use the context of a phone call (such as the person called) to streamline the initiation of a text message (for example to determine that the text message should be sent to the same person, without the user having to explicitly specify the recipient of the text message). The intelligent auto-mated assistant of the present invention can thereby interpret instructions such as "send him a text message", wherein the "him" is interpreted accord-ing to context information derived from a current phone call, and/or from any feature, operation, or application on the device. In various embodiments, the intelligent automated assistant takes into account various types of avail-able context data to determine which address book contact to use, which con-tact data to use, which telephone number to use for the contac" and the like, so that the user need not re-specify such information manualiv.
[0014] In various embodiments, the assistant can also take into account ex-ternal events and respond accordingly, for example, to initiate action, initiate communication with the user, provide alerts, and/or modify previously initi-ated action in view of the external events. If input is required from the user, a conversational interface can again be used.
[0015] In one embodiment, the system is based on sets of interrelated do-mains and tasks, and employs additional functionally powered by external services with which the system can interact. In various embodiments, these external services include web-enabled services, as well as functionality related to the hardware device itself. For example, in an embodiment where the in-ent automated assistant is implemented on a smartphone, personal digi-telligtal assistant, tablet computer, or other device, the assistant can control many operations and functions of the device, such as to dial a telephone number, send a text message, set reminders, add events to a calendar, and the like.
[0016] In various embodiments, the system of the present invention can be implemented to provide assistance in any of a number of different domains.
Examples include:

= Local Services (including location- and time-specific services such as restaurants, movies, automated teller machines (ATMs), events, and places to meet);

= Personal and Social Memory Services (including action items, notes, calendar events, shared links, and the like;;

= E-commerce (including online purchases of items such as books, DVDs, music, and the like);

= Travel Services (including flights, hotels, attractions, and the like).
[0017) One skilled in the art will recognize that the above list of domains is merely exemplary. In addition, the system of the present invention can be implemented in any combination of domains.
[0018) In various embodiments, the intelligent automated assistant sys-tems disclosed herein may be configured or designed to include functionality Tor automating the application of data and services available over the Internet to discover, find, choose among, purchase, reserve, or order products and services. In addition to automating the process of using these data and ser-vices, at least one intelligent automated assistant system embodiment dis-closed herein may also enable the combined use of several sources of data and services at once. For example, it may combine information about products from several review sites, check prices and availability from multiple distribu-tors, and check their locations and time constraints, and help a user find a personalized solution to their problem. Additionally, at least one intelligent automated assistant system embodiment disclosed herein may be configured or designed to include functionality for automating the use of data and ser-vices available over the Internet to discover, investigate, select among, re-serve, and otherwise learn about things to do (including but not limited to movies, events, performances, exhibits, shows and attractions); places to go (including but not limited to travel destinations, hotels and other places to stay, landmarks and other sites of interest, etc.); places to eat, or drink (such as restaurants and bars), times and places to meet others, and any other source of entertainment or social interaction which may be found on the Internet.
Additionally, at least one intelligent automated assistant system embodiment disclosed herein may be configured or designed to include functionality for enabling the operation of applications and services via natural language dia-log that may be otherwise provided by dedicated applications with graphical user interfaces including search (including location-based search); navigation (maps and directions); database lookup (such as finding businesses or people by name or other properties); getting weather conditions and forecasts, check-WO 2011/088053 PCT'/LTS2011/020861 in- the price of market items or status of financial transactions; monitoring traffic or the status of flights; accessing and updating calendars and sched-ules; managing reminders, alerts, tasks and proiects; communicating over email or other messaging platforms' and operating devices locally or remotely b I _ v (e.g., dialing telephones, controlling light and temperature, controlling home security devices, playing music or video, etc.). Further, at least one intelligent automated assistant system embodiment disclosed herein may he configured or designed to include functionality for identifying, generating, and/or pro-viding personalized recommendations for activities, products, services, source of entertainment, time management, or any other kind of recommendation service that benefits from an interactive dialog in natural language and auto-mated access to data and services.
[0019] In various embodiments, the intelligent automated assistant of the present invention can control many features and operations of an electronic device. For example, the intelligent automated assistant can call services that interface with functionality and applications on a device via APIs or by other means, to perform functions and operations that might otherwise be initiated using a conventional user interface on the device. Such functions and opera-tions may include, for example, setting an alarm, making a telephone call, sending a text message or email message, adding a calendar event, and the like. Such functions and operations may be performed as add-on functions in the context of a conversational dialog between a user and the assistant. Such functions and operations can be specified by the user in the context of such a dialog, or they may be automatically performed based on the context of the dialog. One skilled in the art will recognize that the assistant can thereby be used as a control mechanism for initiating and controlling various operations on the electronic device, which may be used as an alternative to conventional mechanisms such as buttons or graphical user interfaces.

WO 2011/088053 PCT/1,US2011/020861 BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The accompanying drawings illustrate several embodiments of the invention and, together" with the description, serve to explain the principles of the invention according to the embodiments. One skilled in the art will recon-nize that the particular embodiments illustrated in the drawings are merely exemplary, and are not intended to limit the scope of the present invention.
[0021] Fig. 1 is a block diagram depicting an example of one embodiment of an intelligent automated assistant system.
[0022] Fig. 2 illustrates an example of an interaction between a user and an intelligent automated assistant according to at least one embodiment.
[0023] Fig. 3 is a block diagram depicting a computing device suitable for implementing at least a portion of an intelligent automated assistant accord-ing to at least one embodiment.
[0024] Fig. 4 is a block diagram depicting an architecture for implementing at least a portion of an intelligent automated assistant on a standalone com-puting system, according to at least one embodiment.
[0025] Fig. 5 is a block diagram depicting an architecture for implementing at least a portion of an intelligent automated assistant on a distributed com-puting network, according to at least one embodiment.
[0026] Fig. 6 is a block diagram depicting a system architecture illustrating several different types of clients and modes of operation.
[0027] Fig. 7 is a block diagram depicting a client and a server, which com-municate with each other to implement the present invention according to one embodiment.
[0028] Fig. 8 is a block diagram depicting a fragment of an active ontology according to one embodiment.
[0029] Fig. 9 is a block diagram depicting an example of an alternative em-bodiment of an intelligent automated assistant system.
[0030] Fig. 10 is a flow diagram depicting a method of operation for active input elicitation component(s) according to one embodiment.

[00311 Fig. 11 is a flow diagram depicting a method for active tvped input elicitation according to one embodiment.

[0032] Fibs. 12 to 21 are screen shots iliust;-ating some portions of some or the procedures for active typed-input elicitation according to one embodi-ment.

[0033] Fib. 22 is a flow diagram depicting a method for active input elicita-tion for voice or speech input according to one embodiment.

[0034] Fig. 23 is a flow diagram depicting a method for active input elicita-tion for GUI-based input according to one embodiment.

[0035] Fig. 24 is a flow diagram depicting a method for active input elicita-tion at the level of a dialog flow according to one embodiment.

[0036] Fib. 25 is a flow diagram depicting a method for active monitoring for relevant events according to one embodiment.

[0037] Fig. 26 is a flow diagram depicting a method for multimodal active input elicitation according to one embodiment.

[0038] Fig. 27 is a set of screen shots illustrating an example of various types of functions, operations, actions, and/or other features which may be provided by domain models component(s) and services orchestration accord-ing to one embodiment.

[0039] Fib. 28 is a flow diagram depicting an example of a method for natural language processing according to one embodiment.

[0040] Fi. 29 is a screen shot illustrating natural language processing ac-C) to one embodiment.

[0041 ] Figs. 30 and 31 are screen shots illustrating an example of various types of functions, operations, actions, and/ or other features which may, be provided by dialog flow processor component(s) according to one embod-rn ent.

[0042] Fib. 32 is a flow diagram depicting a method of operation for dialog flow processor component(s) according to one embodiment.

[0043] Fig. 33 is a flow diagram depicting an automatic call and response procedure, according to one embodiment.

[0044] Fig. '4 is a flow diagram depicting an example of task flow for a constrained selection task according to one embodiment.

[0045] Figs. 35 and 36 are screen shots illustrating an example of the opera-tion of constrained selection task according to one embodiment.

[0046] Fig. 37 is a flow diagram depicting an example of a procedure for executing a service orchestration procedure according to one embodiment.
[0047] Fig. 38 is a flow diagram depicting an example of a service invoca-tion procedure according to one embodiment.

[0048] Fig. 39 is a flow diagram depicting an example of a multiphase out-put procedure according to one embodiment.

[0049] Figs. 40 and 41 are screen shots depicting examples of output proc-essing according to one embodiment.

[0050] Fig. 4? is a flow diagram depicting an example of multimodal out-put processing according to one embodiment.

[0051) Figs. 43A and 43B are screen shots depicting an example of the use of short term personal memory component(s) to maintain dialog context while changing location, according to one embodiment.

[0052] Figs. 44A through 44C are screen shots depicting an example of the use of long term personal memory component(s), according to one embodi-ment.

[0053] Fig. 45 depicts an example of an abstract model for a constrained selection task.

[0054] Fig. 46 depicts an example of a dialog flow model to help guide the user through a search process.

[0055] Fig. 47 is a flow diag.-am depicting a method of constrained selec-tion according to one embodiment.

DETAILED DESCRIPTION OF THE EMBODIMENTS

[0056] Various techniques will now be described in detail with reference to a few example embodiments tI ereof as illustrated in the accompanying draw-v ings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of one or more aspects and or features described or reference herein. It will be apparent, however, to one skilled in the art, that one or more aspects and/ or features described or refer-ence h~eerein may be practiced without some or all o' these specific details.
In other instances, well known process steps and or structures. have not been described in detail in order to not obscure some of the aspects and or features described or reference herein.

[0057] One or more different inventions may be described in the present application. Further, for one or more of the invention(s) described herein, numerous embodiments may be described in this patent application, and are presented for illustrative purposes only. The described embodiments are not intended to be limiting in any sense. One or more of the invention(s) may be widely applicable to numerous embodiments, as is readily apparent from the disclosure. These embodiments are described in sufficient detail to enable those skilled in the art to practice one or more of the invention (s), and it is to be understood that other embodiments may he utilized and that structural, logical, software, electrical and other changes may be made without departing from the scope of the one or more of the invention(s). Accordingly, those skilled in the art will recognize that the one or snore of the invention(s) may be practiced with various modifications and alterations. Particular features of one or more of the invention(s) may be described with reference to one or more particular embodiments or figures that form a part of the present disclo-sure, and in which are shown, by way of illustration, specific embodiments of one or more of the invention(s). It should be understood, however, that such features are not limited to usage in the one or more particular embodiments or figures with reference to which they are described. The present disclosure is neither a literal description of all embodiments of one or more of the inven-tion(s) nor a listing of features of one or more of the inventions s) L.Hat must be present in all embodiments.

[0058] rieadinzs of sections provided in this patent application and the title of this patent application are for convenience only, and are not to be taken as limiting the disclosure in and, way.

[0059] Devices that are in communication with each other need not be in continuous communication with each other, unless ex-Dressy specified other-wise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries.
[0060] A description of an embodiment with several components in com-munication with each other does not imply that all such components are re-quired. To the contrary, a variety of optional components are described to il-lustrate the wide variety of possible embodiments of one or more of the in-vention(s,.

[00611 Further, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and al-gorithms may be configured to work in alternate orders. In other words, any sequence or order of steps that may be described in this patent application does not, in and of itself, indicate a requirement that the steps be performed in that order. The steps of described processes may be performed in any order practical. Further, some steps may be performed simultaneously despite be-ing described or implied as occurring non-simultaneously (e.g., because one step is described after the other step). Moreover, the illustration of a process by its depiction in a drawing does not imply that the illustrated process is ex-clusive of other variations and modifications thereto, does not imply that the illustrated process or any of its steps are necessary to one or more of the in-vention(s), and does not imply that the illustrated process is preferred.

[0062] When a single device or article is described, it will be readily appar-ent that more than one device' article (whether or not they cooperate) may be used in place of a single device/ article. Similarly, where more than one device or article is described (whether or not they cooperate), it wibe readily a:`-parent that a single device, article may be used in Diace of the more than one device or article.

[0063] The functionality and/ o_ the features of a device may be alterna-tively embodied by one or more other devices that are not explicitly described as having such functionality; features. Thus, other embodiments of one or more of the invention(s) need not include the device itself.

[0064] Techniques and mechanisms described or reference herein will sometimes be described in singular- form for clarity. However, it should be noted that particular embodiments include multiple iterations of a technique or multiple instantiations of a mechanism unless noted otherwise.

[0065] Although described within the context of intelligent automated as-sistant technology, it may be understood that the various aspects and tech-niques described herein (such as those associated with active ontoiogies, for example) may also be deployed and/or applied in other fields of technology involving human and/or computerized interaction with software.

[0066] Other aspects relating to intelligent automated assistant technology-(e.(-., which may be utilized by, provided by, and/or" implemented at one or more intelligent automated assistant system embodiments described herein) are disclosed in one or more of the following references:

= U.S. Provisional Patent Application Serial No. 61 / 295,774 for "Intelligent Automated Assistant", attorney docket number SI.IPO03P, filed januara 18, 2010, the disclosure of which is incorporated herein by reference;

= U.S. Patent Application Serial No. 11/518,292 for "Method And Apparatus for Building an Intelligent Automated Assistant , filed September 8, 2006, the disclosure of which is incorporated herein by reference; and = U.S. Provisional Patent Application Seria No. 61/186,41_4 for "System and Method for Semantic Auto-Completion", filed June 1211 2009, the disclosure of which is incorporated herein by reference.

Hardware Architecture [00671 Generally, the intelligent automated assistant techniques disclosed herein may be implemented or, hardware or a combination of software and hardware. For example, they may be implemented in an operating system Kernel, in a separate user process, in a library package bound into network appllications, on a specially constructed machine, or on a network interface card. In a specific embodiment, the techniques disclosed herein mays be im-plemented in software such as an operating system or in an application run-ning on an operating system.

[0068] Software; hardware hybrid implementation(s) of at least some of the intelligent automated assistant embodiment(s) disclosed herein may be implemented on a programmable machine selectively activated or reconfig-ured by a computer program stored in memoir'. Such network devices may have multiple network interfaces which mar be configured or designed to utilize different types of network communication protocols. A general archi-tecture for some of these machines may appear from the descriptions dis-closed herein. According to specific embodiments, at least some of the fea-tures and/ or functionalities of the various intelligent automated assistant em-bodiments disclosed herein may be implemented on one or more general-purpose network host machines such as an end-user computer system, com-puter, network server or server system, mobile computing device (e.g., peI-sonal digital assistant, mobile phone, smartphone, laptop, tablet computer, or the like), consumer electronic device, music player, or any other suitable elec-t onic device, router, switch, or the like, or any combination thereof. in at least some embodiments, at least some of the features and for functionalities of the various intelligent automated assistant embodiments disclosed herein may be implemented in one or more virtualized computing environments (e.g., network computing clouds, or the like).

(0069] Referring now to Fig..3, there is shown a block diagram depicting a computing device 60 suitable for implementing at least a portion of the intel-inept automated assistant features and/or functionalities disclosed herein.
Computing device 60 may be, for example, an end L!ser computer system, network server or server system, mobile computing device e.g., persona' digital assistant, mobile phone. smartpnone, laptop, tablet computer, or the like), consumer electronic device, music player, or any' other suitable elec-tronic device, or any- combination or portion thereof. Computing device 60 may be adapted to communicate with other computing devices, such as cli-ents and/or servers, over a communications network such as the internet, us-ing known protocols for such communication, whether wireless or wired.
[0070] In one embodiment, computing device 60 includes central process-ing unit (CPU) 62, interfaces 68, and a bus 67 (such as a peripheral component interconnect (PCI) bus). When acting under the control of appropriate soft-ware or firmware, CPU 62 may be responsible for implementing specific func-tions associated with the functions of a specifically configured computing de-vice or machine. For example, in at least one embodiment, a user's personal digital assistant (PDA) may be configured or designed to function as an intel-ligent automated assistant system utilizing CPU 62, memory 61, 65, and inter-face(s) 68. In at least one embodiment, the CPU 62 may be caused to perform one or more of the different types of intelligent automated assistant functions and/or operations under the control of software modules/components, which for example, may include an operating system and anV appropriate applications software, drivers, and the like.

[0071] CPU 62 may include one or more processor(s) 63 such as, for exam-ple, a processor from the Motorola or Intel family of microprocessors or the MIPS family of microprocessors. In some embodiments, processor(s) 63 may include specially designed hardware (e.g., application-specific integrated cir-cuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable ;ate arrays (FPGAs), and the like) for con-trolling the operations of computing device 60. in a specific embodiment, a memory 61 (such as non-volatile random access memory (RAM) and/or read -1.5-WO 2011/088073 PCT[LS2011/020861 only memory (ROM)) also forms part of CPL 62. However, there are many different ways in which memory ma-,-'be coupled to the system, Memory block 61 max he used i0'" 0. variety of pui"poses suc as, for examply, Caching and/ or storing data, programming instructions, and the like.

[0072] As used herein, the term "processor" is not limited merely to those integrated circuits referred to in the art as a processor, but broadly refers to a microcontroller, a microcomputer, a programmable logic controller, an appl_ cation-specific integrated circuit, and any other programmable circuit.

[0073] in one embodiment, interfaces 68 are provided as interface cards sometimes referred to as "line cards"). Ge..n C
,~. ~erali~~, they control the sending and receiving of data packets over a computing network and sometimes sup-port other peripherals used with computing device 60. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable in-terfaces, DEL interfaces, token ring interfaces, and the like. in addition, vari-ous types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, Firewire, PCI, parallel, radio frequency (RF), Blue- near-field communications (e.g., using near-field magnetics;, 80?.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, asynchronous transfer mode (ATMI interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POE) interfaces, fiber data distrib-uted interfaces (FDDIs), and the like. Generally, such interfaces 68 max in-clude ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some inn-stances, volatile and/or non-volatile memory (e.g., RAM).

[00741 Although the system shown in Fig. 3 illustrates one specific archi-tecture for a computing device 60 for implementing the techniques of the in-vention described herein, it is by no means the only device architecture on which at least a portion of the features and techniques described 'Herein may be implemented. For example, architectures having one or any number of processors 60 can be used, and such processors 60 can be present in a single device or distributed among any number of devices. in one embodiment, a sin -le processor 63 handles communications as well as routing coznnutations.
In various embodiments, different types of intelligent automated assistant features and/ or functionaiities may be implemented in an intelligent auto-mated assistant system which includes a client device (such as a personal digital assistant or smartphone running client so:tware and serve'Svsterll(S) (such as a server system described in more detail below).

[0075] Regardless of network device configuration, the system of the pre-sent invention may employ one or more memories or memory modules (such as, for example, memory' block 65) configured to store data, program instruc-tions for the general-purpose network operations and/or other information relating to the functionality of the intelligent automated assistant techniques described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store data structures, Keyword taxon-omv information, advertisement information, user click and impression in-fornation, and /or other specific non-program information described herein.
[0076] Because such information and program instructions may be emr.-ployed to implement the systems/ methods described herein, at least some network device embodiments may include nontransitory machine-readable storage media, which, for example, may be configured or designed to store program instructions, state information, and the like for performing various operations described herein. Examples of such nontransitory machine-readable storage in edia include, but are not limited to, magnetic media such as hard disks, floppy disks, and magnetic tape; optical media such as CD-ROM disks; magneto optical media such as floptical disks, and hardware de vices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory.', meinristor memory, random access memory, (RAI,4), and the like. Examples of program instruc-tions include both machine code, such as produced by a compiler, and files WO 2011/088053 PCT/tIS2011/020861 containing nigher level code that mar be executed hr the computer using an interpreter.

[0077] In one embodiment, the system of the present invention is impie-mentea on a standalone computing system. Referrin now to L, there is shown a block- diagram depicting an architecture for implementing at least a portion of an intelligent automated assistant on a standalone computing sys-tem, according to at least one embodiment. Computing device 60 includes processor(s) 63 which run software for implementing intelligent automated assistant 1002. Input device 1206 can be of any type suitable for receiving user input, including for example a keyboard, touchscreen, microphone (for example, for voice input), mouse, touch-pad, trackball, five-way switciz, joy-stick, and/or any combination thereof. Output device 120; can be a screen, speaker, printer, and/or any combination thereof. Memory 1210 can be ran-dom-access memory having a structure and architecture as are known in the art, for use by processor(s) 63 in the course of running software. Storage de-vice 1208 can be any magnetic, optical, and," or electrical storage device for storage of data in digital form; exarn'DIes include flash memory, magnetic hard drive, CD-ROM, and/or the like.

[0078] In another embodiment, the system of the present invention is im-plemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to rig. 5, there is shown a block diagram depicting an architecture for implementing at least a portion or an intelligent automated assistant on a distributed computing network, ac-cording to at -least one embodiment.

[0079] in the arrangement shown in Fig. any number of clients 1304 are provided; each client 1304 may run software for implementing client-side por-tions of the present invention. In addition, any number of servers 1340 can be provided for handling requests received from clients 1304. Clients 1304 and servers 1340 can communicate with one another via electronic network 1361, WO 2011/088053 PC'T/1TS2011/020861 such as' the Internet. Network: 1361 may be implemented using any known network protocols, inciudinc_ ro_- example wired atria/or wireless protocols.
[0080] In addition, in one embodiment, servers 10 4C Can Cal! external se:"
vices 1360 when needed to obtain additional information or refer to store data concerning previous interactions with particular users. Communications with external services 1360 can take place, for example, via network 1361. In vari-ous embodiments, external services 1360 include web-enabled services and/or functionality related to or installed on the hardware device itself.

For example, in an embodiment where assistant 1002 is implemented on a smartphone or other electronic device, assistant 1002 can obtain information stored in a calendar application ("app"), contacts, and; or other sources.
[0081] In various embodiments, assistant 1002 can control many features and operations of an electronic device on which it is installed. For example, assistant 1002 can call external services 1360 that interface with functionality and applications on a device via APIs or by other means, to perform functions and operations that might otherwise be initiated using a conventional user interface on the device. Such functions and operations may include, for ex-ample, setting an alarm, making a telephone call, sending a text message or email message, adding a calendar- event, and the like. Such functions and op -erations may be performed as add-o] functions in the context of a conversa-tional dialog between a user and assistant 1002. Such functions and opera-tions can be specified by the user in the context of such a dialog, or they may be automatically performed based on the context of the dialog. One skilled in the art will recognize that assistant 1002 can thereby be used as a control mechanism for initiating and controlling various operations on the electronic device, which Mays be used as an alternative to conventional mechanisms such as buttons or graphical user interfaces.

[0082] For example, the user may provide input to assistant 100 such as "I
need to wake tomorrow at Sam". Once assistant 1002 has determined the user's intent, using the technioues described herein, assistant 1002 can call e>:-I C) 'J4/~ 2011/0880 3 PCT'/T 52011/020561 term' services 13 40 to interlace with an alarm clock function or application on the device. Assistant 1002 sets the alai in on behalf of the user. in this mar.
ner, the user can use assistant 1002 as a replacement for conventional mecha-nisms for setting the alarm or performing other runcrions on the device. L
'the use. -'s requests are ambiguous or need further clarification, assistant 1002 can use the various techniques described herein, including active e'licitatiolnn, paraphrasing, suggestions, and the like, to obtain the needed information so that the correct services 1340 are called and the intended action taken. In one embodiment, assistant 1002 may prompt the user for confirmation before call-ing a service 1340 to perform a function. In one embodiment, a user can selec-tively disable assistant's 1002 ability to call particular services 1340, or can disable all such service-calling if desired.

[0083] The system of the present invention can be implemented with many different types of clients 1304 and modes of operation. Referring now to Fig.
6, there is shown a block diagram depicting a system architecture illustrating several different types of clients 1304 and modes of operation. One skilled in the art will recognize that the various types of clients 1304 and modes of or.-eration shown in Fig. 6 are merely exemplary, and that the svsteni of the pre-sent invention can be implemented using clients 1304 and/or modes of opera-tion other than those depicted. Additionally, the system can include any or all of such clients 1304 and/or modes of operation, alone o,' in any corribina tion. Depicted examples include:

= Computer devices with input/output devices and/or sensors 1402.
A client component may be deployed on any such computer device 1402. At least one embodiment may be implemented using a web browser 1304A or other software application for enabling commu-nication with servers 1340 via network 1361. Inr)ut and output channels may of any type, includin ; for example visual and/or auditory channels. For example, in one embodiment, the system of the invention can be implemented using voice-based communica-tion methods, allowing for an embodiment of the assistant for the D 1 i n d whose eauivalent of a web browser is driven by speech and uses speech for output.

= Mobile Devices with l/ t") and sensors 1406, for which the client may be implemented as an application on the mobile device 13045. This includes, but is not limited to, mobile phones, smartphones, per-sonal digital assistants, tablet devices, networked game consoles, and the like.

= Consumer Appliances with 1/0 and sensors 1410, for which the cli-ent may be implemented as an embedded application on the appli-ance 1304C.

= Automobiles and other vehicles with dashboard interfaces and sen-sors 1414. for which the client may be implemented as an embed-ded system application 1304D. This includes, but is not limited to, car navigation systems, voice control systems, in-car entertainment systems, and the like.

= Networked computing devices such as routers 1413 or any other device that resides on or interfaces with a network, for which the client may be implemented as a device-resident application 1304E.

= Email clients 1424, for which an embodiment of the assistant is con-nected via an Email Modality Server 1426. Email Modality server 1426 acts as a communication bridge, for example taking input from the use,. as email messages sent to the assistant and sending output from the assistant to the user as replies.

= Instant messaging clients 1428, for which an embodiment of the as-sistant is connected via a Messaging Modality Server 1430. Messag-ing Modality server 1430 acts as a communication bridge, taking input from the user as messages sent to the assistant and sending output from the assistant to the user as messages in reply.

Voice telephones 1432, for which an embodiment o_` the assistant is connected via a Voice over Internet Protocol Modality Server' 1430. V oft Modality server 1430 acts as a communicatior. bridge, taKln`r input from the user as voice spoken to the a ssistan: and sending output from the assistant to the user, for example as syn-thesized speech, in reply.

[0084] For messaging platforms including but not limited to email, instant messaging, discussion forums, group chat sessions, live help or customer sup-port sessions and the like, assistant 1002 may act as a participant in the con-versations. Assistant 1002 may monitor the conversation and reply to indi-viduals or the group using one or more the techniques and methods described herein for one-to-one interactions.

[00851 In various embodiments, functionality for implementing the tech-niques of the present invention can be distributed among any number or cli-ent and/or server components. For example, various software modules can be implemented for performing various functions in connection with the pre-sent invention, and such modules can be variously imrhemented to run on server and/or client components. Referring now to Fig. 7, there is shown an example of a client 1304 and a server 1340, which communicate with each other to implement the present invention according to one embodiment. Fig.
7 depicts one possible arrangement by which software modules can be dis-tributed among client 1304 and server 1340. One skilled in the art will recog-nize that the depicted arrangement is merely exemplary, and that such mod-ules can be distributed in mam, different ways. in addition, any number of clients 1304 and/or servers 1340 can be provided, and the modules Cal-, be dis-tributed among these clients 1304 and/ or servers 1340 in any of a number of different ways.

[0086] In the example of rid. 7, input elicitation functionality and output processing functionality are distributed among client 1304 and server 1340, with client part of input elicitation 1094a and client part of output processing -22_ 1092a located at client 1304, and server part of input elicitation 1094b and server part of output processing 1D 2r located at server 1:40. The followinO
components are located at server 1340:

= complete vocabulary 10586;

= complete library of language pattern recognizers 10606;
= master version of short term personal memory 1052b;

= master version of long term personal memory 1054b.

[0087] In one embodiment, client 1304 maintains subsets and/or portions of these components locally, to improve responsiveness and reduce depend-ence on network communications. Such subsets and/or portions can be maintained and updated according to well known cache management tech-niques. Such subsets and/or rortions include, for example:

= subset of vocabulary 1058a;

= subset of library of language pattern recognizers 2,060L-,,-= cache of short term personal memory 20,52a,-= cache of long term personal memory 1054a.

[0088] Additional components may be implemented as part of server :34C, including for example:

= language interpreter 1070;
= dialog flow processor 1080;
= output processor 1090;

= domain entity databases 1072;
= task flow models 1036;

= services orchestration 10821*

= service capability models 1088.

[0089] Each of these components will be described in more detail below.
Server 1340 obtains additional information by interfacing with external ser-vices 1360 when needed.

Conceptual Architecture [0090] Referring now to Fi there is shown, a simplified block: diagram of a specific example embodiment or an intelligent automated assistant 1002.
As described in greater detail herein, different embodiments of intelligent automated assistant systems may be confi-"ured, designed, and/or operable to provide various different types of operations, fun.ctionalities, and/or fea-tures generally relating to intelligent automated assistant technolog~'. Fur-ther, as described in greater detail herein, many of the various operations, functionalities, and/ or features of the intelligent automated assistant svs-tem(s) disclosed herein may provide may enable or provide different types of advantages and/or benefits to different entities interacting with the intelligent automated assistant systein(s). The embodiment shown in Fib. 1 may be irn-piemented using any of the hardware architectures described above, or using a different type of hardware architecture.

[0091) For example, according to different embodiments, at least some in-telligent automated assistant system(s) may be COntigured, designed, and/or operable to provide various different types of operations, functionalities, and/or features, such as, for example, one or more of the following (or com-binations thereof,):

= automate the application of data and services available over the Internet to discover, find, choose among, purchase, reserve, or or-der products and services. In addition to automating the process of using these data and services, intelligent automated assistant 1002 may also enable the combined use of several sources of data and services at once. For example, it may combine information about products from several_ review sites, check prices and availability from multiple distributors, and check, their locations and time con-straints, and help a use, find a personalized solution to their prorb-lem.

= automate the use of data and services available over the Internet to discover, investigate, select among, reserve, and otherwise learn about things to do (including but not limited. to movies, events, per-formances, exhibits, snows and attractions); places to go (including but not limited to travel destinations, hotels and other daces to stay, landmarks and other sites of interest, and the iike; places to eat or drink (such as restaurants and bars), times and places to meet others, and any other source of entertainment or social interaction which may be found on the Internet.

enable the operation of applications and services via natural lan-guage dialog that are otherwise provided by dedicated applications with o aphical user interfaces including search (including location based search); navigation (maps and directions); database looikup r such as finding businesses or people by naive or other-properties);
getting weather- conditions and forecast, checking the price of market items or status of financial transactions; monitoring traffic or the status of flights; accessing and updating calendars and schedules; managing reminders, alerts, tasks and projects; commu-nicating over email or other messaging platforms; and operating devices locally or remotely (e.g., dialing telephones, controlling light and temperature, controlling home security devices, playing music or video, and the like. In one embodiment, assistant 1002 can be used to initiate, operate, and control many functions and apps available on the device.

a, offer personal recommendations for activities, products, services, source of entertainment, time management, or any other kind of r=
recommendation service that benefits from an interactive dialog in natural language and automated access to data and services.
[0092] According to different embodiments, at least a portion of the vari-ous types of functions, operations, actions, and/or other features provided by intelligent automated assistant 1002 may be implemented at one or more ci -ent SVStemS(S), at one o More Serve" s ~ stems (s) and, or combinations thereof.
[0093] According to different embodiments, at least a portion of the vari-ous types of functions, operations, actions, and/or other features provided by assistant 1002 may implement by at least one embodiment of an automated call and response procedure, such as that illustrated and described, for exam-ple, with respect to Fig. 33.

[0094] Additionally-, various embodiments of assistant 1002 described herein may include or provide a number of different advantages and,/'o:-benefits over currently existing intelligent automated assistant technology such as, for example, one or more of the following (or combinations thereof i The integration of speech-to-text and natural language understand-ing technology that is constrained by a set of explicit models of do-mains, tasks, services, and dialogs. Unlike assistant technology- that attempts to implement a general-purpose artificial intelligence sys-tem, the embodiments described herein may apply the multiple sources of constraints to reduce the number of solutions to a more tractable size. This results in fewer ambiguous interpretations of language, fewer relevant domains or tasks, and fewer ways to op-erationalize the intent in services. The focus on specific domains, tasks, and dialogs also makes it feasible to achieve coverage over domains and tasks with human-managed vocabulary and rnar.-pings from intent to services parameters.

The ability to solve user problems by invoking services on their be-half over the Internet, using APIs. Unlike search engines which only return links and content, some embodiments of automated as-sistants 1002 described herein may automate research and problem-solving activities. The ability to invoke multiple services for a given request also provides broader functionality to the user than is achieved by visiting a single Site, for instance to produce a 'oroduct or service or rind sometnin.~ to do.

The application of personal information and personal interaction history in the interpretation and execution or user recuests. -Unlike conventional search engines or Question answering Services, the embodiments described herein use information from personal in-teraction history (e.g., dialog history', previous selections from re-sults, and the like), personal physical context (e.g., user's location and time), and personal information gathered in the context of in-teraction name, email addresses, physical addresses, phone numbers, account numbers, preferences, and the like). Using these sources of information enables, for example, o better interpretation of user input (e.g., using personal his ton' and physical context when interpreting language-C-, more personalized results (e.., that bias toward preferences or recent Selections);

improved efficiency for the user (e.g., dv automating steps involving the signing up to services or filling out forms).
The use of dialog history in interpreting the natural language of user inputs. Because the embodiments may, keep personal history and apply natural language understanding on user inputs, they may also use dialog context such as current location, time, domain, task step, and task parameters to interpret the new inputs. Conven-tional search engines and command processors interpret at least one query independent of a dialog history,. The ability to use dialog history may make a more natural inter action possible, one which resembles normal human conversation.

Active input elicitation, in which assistant 100? actively, guides and constrains the input from the user, based on the same models and information used to interpret their input. For example, assistant 2 i 1002 may anpiv dialog models to suggest next steps in a dialog with tae user in which they are refining a request; offer completions to partially typed. input based on domain and context specific poss -biiities; or use semantic interpretation to select froth among ain-biguous interpretations of speech as text or text as intent.

The explicit modeling and dynamic management of services, with dynamic and robust services orchestration. The architecture of em-bodiments described enables assistant 1002 to interface with mane external services, dynamically Cleter1111ne which services may pro-vide information for a specific user request, map parameters of the user request to different service APIs, call multiple services at once, integrate results from multiple services, fail over gracefully on failed services, and/or efficiently maintain the implementation of services as their APIs and capabilities evolve.

= The use o active ontologles as c method and apparatus for building assistants 1002, which simplifies the software engineering and data maintenance of automated assistant systems. Active ontoloies are an integration of data modeling and execution environments for as-sistants. They provide a framework to tie together the various sources of models and data (domain concepts, task Flows, vocabu-iary, language pattern recognizers, dialog context, user personal in-formation, and mappings from domain and task requests to exter-nal services. Active ontologies and the other architectural innova-tions described herein make it practical to build deep functionality within domains, unifying multiple sources of information and ser-vices, and to do this across a set of domains.

[00951 In at least one embodiment, intelligent automated assistant 1002 may be operable to utilize and/ or generate various different types of data and/or other types of information when performing specific tasks and/or op-erations. This may include, for example, input data/information and; or out--)o WO 2011/088053 PCT/11S2011/02086i put data/ Information. For example, in at least one embodiment, intelligent automated assistant 1002 may be operable to access, process, and/or other-wise utilize information from one or more different types of sources, Such aS, 1 ies for example, One or more local and! o' remote _ ma ..mo_1.,,, devices and/ or S~C
~ _-tems. Additionally, in at least one embodiment, intelligent automated assis-tant 1002 mar be operable to generate one or more different types of output data; information, which, for example, may be stored in memory of one or more local and/or remote devices and/or systems.

[0096] Examples of different types of input data/information which mar be accessed and/or utilized by intelligent automated assistant 1002 may in-clude, but are not limited to, one or more of the following (or combinations thereof):

= Voice input: from mobile devices such as mobile telephones and tablets, computers with microphones, Bluetooth headsets, automo-bile voice control systems, over the telephone system, recordings on answering services, audio voicemail on integrated messaging ser-vices, consumer applications with voice input such as clock radios, telephone station, home entertainment control systems, and game consoles.

= Text input from keyboards on computers or mobile devices, key-pads on remote controls or other consumer electronics devices, email messages sent to the assistant, instant messages or similar short messages sent to the assistant, text received from players in multiuser game environments, and text streamed in message feeds.
= 1,ocation information coming from sensors or location-based s\'S-tems. Examples include Global Positioning System (GPs) and As-sisted GPS (A-GPS) on mobile phones. In one embodiment, loca-tion information is combined with explicit user input. in one em-bodiment, the system of the present invention is able to detect when a user is at home, based on known address information and current - ')q -WO 2011/088053 PCT/tJS2011/020861 location determination. In this manner, certain inferences ma v be made about the tvue of information the user might be interested in when at home as opposed to outside the home, ac well as the tvpe or services and actions that should be invoked on behalf o.` ti-te user depending on whether or not he or she is at home.

= Tillie information from clocks on client devices. This may include, for example, time from telephones or other` client devices indicating the local, time and time zone. In addition, time may be used in the context of user requests, such as for instance, to interpret phrases such as "in an hour" and "tonight".

= Compass, accelerometer, gyroscope, and/or travel velocity data, as well as other sensor data from mobile or handheld devices or em-bedded systems such as automobile control systems. This may also include device positioning data from remote controls to appliances and came consoles.

= Clicking and menu selection and other events from a g -aphical user interface (GUI) on any device having a GUI. Further examples in-clude touches to a touch screen.

= Events from sensors and other data-driven triggers, such as alarm clocks, calendar alerts, price change triggers, location triggers, push notification onto a device from servers, and the like.

[0097] The input to the embodiments described herein also includes the context of the user interaction history, including dialog and request history.
[0098] Examples of different types of output data/information which may be generated by intelligent automated assistant 100? may include, but are not limited to, one or more of the following (or combinations thereof:

= Text output sent directly to an output device arid/ or to the user in-terface of a device = Text and graphics sent to a user over email = Text and graphics send to a user over a messaging service WO 2011/088053 PCT/L'S2011/0208611 F Speech output, may include one or more of the following (or com-binations thereof;:

c Synthesized speech Sampled speech Recorded messages Graphical layout of information with photos, rich text, videos, sounds, and hyperiinks. For instance, the content rendered in a web browser.

Actuator output to control physical actions on a device, such as causing, it to turn on or off, make a sound, change color, vibrate, control a light, or the like.

Invoking other applications on a device, such as calling a mapping application, voice dialing a telephone, sending an email or instant message, playing media, making entries in calendars, task manag-ers, and note applications, and other applications.

Actuator output to control physical actions to devices attached or controlled by a device, such as operating a remote camera, control ling a wheelchair, playing music on remote speakers, playing yid eos on remote displays, and the like.

[0099] It may be appreciated that the intelligent automated assistant 1002 of Fig. 1 is but one example from a wide range of intelligent automated assis-tant system embodiments which may be implemented. Other embodiments of the intelligent automated assistant system (not shown j may include addi-tional, fewer and/or different components/features than those illustrated, for example, in the example intelligent automated assistant system embodiment of Fig. 1.

User interaction [0100] Referring now to Fib. 2, there is shown an example of an interaction between a user and at least one embodiment o. an intelligent automated aSSI~

WO 2011/088053 PCT!US2011/020861 tant 100?. 1 ne example of Fig. 2 assumes that a user is speaking to intelligent automated assistant 1002 using input device 1206, which may be a speed: in put mechanism, an the ou )ut i grail LLcai lavoll to output device 12C , which may be a scrollable screen. Conversad.on screen 101A features a con-versational user interface showing what the user said 101B ("I'd like a romaI -tic place for Italian roof. near in -v office") and assistant's 1002 response, tined is a summahv of its findings 101C ("OIL, I found these Italian restaurants which reviews say are romantic close to your work:") and a set of results 101D
(the first three of a list of restaurants are shown). In this example, the user clicks on the first result in the list, and the result automatically opens up to reveal more information about the restaurant, shown in information screen 101E. information screen 101E and conversation screen, 101A may appear on the same output device, such as a toucrtscreen or other display device; the e>:-amples depicted ii Fig. 2 are two different output states for the same output device.

[0101 ] In one embodiment, information screen 101E shows information gathered and combined from a variety of services, including for example, amp or all of the following:

= Addresses and geolocations of businesses;
Distance from user's current location;

= Reviews fr om a plurality of sow ces;

[0102] in one embodiment, information screen 101E also includes some examples of services that assistant 1002 might offer on behalf of the user, i~
cluding:

= Dial a telephone to call the business ("call"';

= Remember this restaurant for future reference ("save = Send an email to someone with the directions and information about this restaurant ( "share")) = Show the location of and directions to this restaurant on a map ("map it");

WO 2011/088(53 PCT /US2011/020861 = Save personal notes about this restaurant (" my notes").

[01031 As shown in the example of Fi`~. 2, in one embodiment, assistant 1002 includes intelligence beyond simple database applications, such as, for example, = Processing a statement o_` intent in a natural lan_uage lOIF, not lust keywords;

= Inferring semantic intent from that language input, such as inter-preting "place for Italian rood" as "Italian restaurants";

= Operationalizing semantic intent into a strategy for using online services and executing that strategy on behalf of the user (e.g., op-erationalizing the desire for a romantic place into the stratep v of checking online review sites for reviews that describe a place as `ro-mantic").

Intelligent Automated Assistant Components [0104) According to various embodiments, intelligent automated assistant 1002 may include a plurality of different types of components, devices, mod-ules, processes, systems, and the like, which, for example, may be imple-mented and/or instantiated via the use of hardware and, or combinations of hardware and software. For example, as illustrated in the example embodi-ment of Fig. 1, assistant 1002 may include one or more of the following types of s7 stems, components, devices, processes, and the like (or combinations thereof),:

= One or more active ontologies 1050;

= Active input elicitation components 1094 (mav include client par:
1094a and server part 1094b);

= Short term personal memory component(s) 1052 (mav include mas-ter version 1052b and cache 1052a);

= Long-term personal memory component(s) 1054 (may include mas-ter version 1052b and cache 1052a);

= Domain models component(s) 1056;

= Vocabulary component(s) 1058 (may include complete vocabulary 1058b and subset 1058aj;

Language pattern recognizer(s) component(s) 1060 (max include full library 1060b and subset 1560a);

= Language interpreter component(s) 1070;
= Domain entity database(s) 1072;

= Dialog flow processor component(s) 1.080;
= Services orchestration component(s) 1082;
= Services component(s) 1084;

= Task flow models component(s) 1086;

= Dialog flow models component(s) 108;;
= Service models component(s) 1088;

= Output processor component(s) 1090.

[0105] As described in connection with Fig. 7, in certain client/ server-, based embodiments, some or all of these components mar be distributed be-tween client 1304 and server 1340.

[0106] For purposes of illustration, at !east a portion of the different types of components of a specific example embodiment of intelligent automated as-sistant 1002 will now be described in greater detail with reference to the ex-ample intelligent automated assistant 100' embodiment of rig. 1.

Active Ontologies 1050 [0107] Active ontologies 1050 serve as a unifying infrastructure that inte-grates models, components, and/or data from other parts o= embodiments of intelligent automated assistants 1002. in the field of computer and informa-tion science, ontologies provide structures for data and knowledge represen-tation such as classes/ types, relations, attributes/properties and their instan-tiation in instances. Ontologies are used, for example, to build models of data and knowledge. In some embodiments of the intelligent automated system 1002, ontologies are part of the modeling framework in which to build models such as domain models.

[0108] Within the context o, the present invention, an active ontolog'"

1050 may also serve as an execution environment, in which distinct process-ing elements are arranged in an ontology-like manner (e.g., having distinct attributes and relations with other processing elements). These processing elements carry out at least some of the tasks of intelligent automated assistant 10021. And- number of active ontologies 1050 can be provided.

[0109] In at least one embodiment, active ontologies 1050 may be operable to perform and/or implement various types of functions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof):

= Act as a modeling and development environment, integrating mo els and data from various model and data components, including but not limited to o Domain models 1056 Vocabulary 1058 o Domain entity databases 107"
c Tasl: flow, models 1086 o Dialog flow models 1087 o Service capability models 1088 = Act as a data-modeling environment on which ontology-based edit-ing tools may operate to develop new models, data structures, da-tabase schemata, and representations.

= Act as a live execution environment, instantiating values for ele-ments of domain 1056, task 1086, and/or dialog models 1087, lan-guage pattern recognizers, and/or vocabulary 1058, and user-specific information such as that found in short term personal memorv 1052, long term personal memory 1054, and/or the results of service orchestration 1182. For example, some nodes of an active ontology- may correspond to domain concepts such as restaurant and its property restaurani name. During live execution, these active ontology nodes may be instantiate-,-' with the identity of a pa "ticuiar restaurant entity and its name, and how its name corresponds to words in a natural language input utterance. Thus, in this em bodiment, the active ontology is serving as both a modeling envi-ronment specifying the concept that restaurants are entities with identities that have names, and for storing dynamic bindings of those modeling nodes with data from entity databases and parses of natural language.

= Enable the communication and coordination among components and processing elements of an intelligent automated assistant, such as, for example, one or more of the following (or combinations thereof):

o Active input elicitation component(s) 1094 Language interpreter component(s) 1070 Dialog flow processor component(s) 1080 o Services orchestration component(s) 1082 o Services component(s) 1084 [0110] In one embodiment, at least a portion of the functions, operations, actions, and/or other features of active ontologies 1050 described herein ma' be implemented, at least in part, using various methods and apparatuses de-scribed in L.S. Patent Application Serial No. 11/518,292 for "Method and Ap-paratus for Building an Intelligent Automated Assistant", filed September 8, 2006.

[0111] In at least one embodiment, a given instance of active ontology 1050 may access and/or utilize information from one or more associated databases.
In at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/or remote mem-ory, devices. Examples of different types of data which may be accessed by active ontoloC-ies 1050 man include, but are not limited to, one or more of the following (or combinations thereof 1:

e Static data than i avai able frOn One o_" more com~)onents o intell_ gent automated assistant 100=;

= Data that is dynamically instantiated per user session, for exam ie, but not "limited to, maintaining the state of the LLser-speciric inputs and outputs exchanged among components of intelligent auto-mated assistant 1002, the contents of short term personal memory, the inferences made from previous states of the user session, and the like.

[0112] In this manner, active ontoiogies 1050 are used to unify elements of various components in intelligent automated assistant 1002. An active ontol-ogy 1050 allows an author, designer, or system builder to integrate compo-nents so that the elements of one component are identified with elements of other components. The author, designer, or system builder can thus combine and integrate the components more easily.

[0113] Referring now to Fig. 8, there is shown an example of a fragment of an active ontology 1050 according to one embodiment. This example is in-tended to help illustrate some of the various types of functions, operations, actions, and/or other features that may be provided by active ontoiogies 1050.
V
[0114] Active ontology 1050 in Fig. 8 includes representations of a restau-rant and meal event. in this example, a restaurant is a concept 1610 with properties such as its name 1612, cuisines served 1615, and its location 1613, which in turn might be modeled as a structured node with properties for street address 1614. The concept of a meal event might be modeled as a node 1616 including a dining party 1617 (which has a size 1619) and time period 1618.

Active onto ogies may include ands or make reference to domain models 1056. For example, Fig. 8 depicts a dining out domain model 1622 linked to restaurant concept 1610 and meal event con-cept 1616. In this instance, active ontology 1050 includes dining out domain model 1622; specifically, at least two nodes of active ontol-ogv 1050, namely restaurant 1610 and meal event 1616, art also in-cluded in and/ or referenced by dining, out domain model 1622.

This domain model represents, among other things, the idea that dining out involves meal event that occur at restaurants. The ac-tive ontology nodes restaurant 1610 and meal event 1616 are also included and/or referenced by other components of the intelligent automated assistant, a shown by dotted lines in Fig. S.

Active ontologies may include and/or make reference to task flow models 1086. For example, Fig. S depicts an event Manning task flow model 1630, which models the planning of events independent of domains, applied to a domain-specific kind of event: meal event 1616. Here, active ontology 1050 includes general event planning task flow model 1630, which comprises nodes representing events and other concepts involved in planning them. Active ontology 1070 also includes the node meal event 1616, which is a particular kind of event. In this example, meal event 1616 is included or made reference to by both domain model 1622 and task flow model 1630, and both of these models are included in and/or referenced by ac-tive ontology 1050. Again, meal event 1616 is an example of how active ontologies can unify elements of various components in-cluded and/or referenced by other components of the intelligent automated assistant, a shown by dotted lines in Fib. E.

Active ontologies may include and/or make reference to dialog flow models 108; . For example, Fig. 8 depicts a dialog flow model 1642 for getting the values of constraints required for a transaction instantiated on the constraint party size as represented in concept 1619. Again, active ontology 1050 provides a framework for relat-ing and unifying various components such as dialog flow models -3~-108 . In this case, dialog flow model 1642 has a general concept o a constraint that 1s instantiated in this particular example to the active ontology node party size 1619. This particular dialog flow model 1642 operates at the abstraction of constraints, independent of do-main. Active ontology 1050 represents party size property 1619 of party node 1617, which is related to meal event node 1616. In such an embodiment, intelligent automated assistant 1002 uses active on-tolog~, 1050 to uni~, the concept of constraint in dialog flow model 1642 with the property of party size 1619 as part of a cluster of nodes representing meal event concept 1616, which is part of the domain model 1622 for dining out.

= Active ontologies may include and/ or make reference to service models 1088. For example, rig. S depicts a model of a restaurant reservation service 167) associated with the dialog flow step for get-ting values required for that service to perform a transaction. In this instance, service model 1672 for a restaurant reservation service specifies that a reservation requires a value for party size 1619 (the number of people sitting at a table to reserve). The concept party size 1619, which is part of active ontology 1050, also is linked or re-lated to a general dialog flow model 1642 for asking the use, about the constraints for a transaction; in this instance, the party size is a required constraint for dialog flow model 1642.

Active ontologies may include and/'or make reference to domain entity databases 1072. For example, Fig. 8 depicts a domain entity database of restaurants 1652 associated with restaurant node 1610 in active ontology 1050. Active ontology 1050 represents the gen-eral concept of restaurant 1610, as may be used by the various com-ponents of intelligent automated assistant 1002, and it is instanti-ated by data about specific restaurants in restaurant database 1652.

e Active ontologies max include and /o, make reference to vocabu-la rv databases 1058. Fore amnie, Fl_. 8 denicts a vocabulary data-base of cuisines 1662; such ac_: Malian, French. and the 'like; and the words associated with each cuisine such as " Frenct , "contlnenLal~ , "provincial", and the like. Active ontology 1070 includes restaurant node 1610, which is related to cuisines served node 1617, which is associated with the representation of cuisines in cuisines database 1662. A specific entry in database 1662 for a cuisine, such as "French", is thus related through active ontology 1070 as an in-stance of the concept of cuisines served 1617.

= Active ontoiobies may include and/or make reference to any data-base that can be mapped to concepts or other representations in on-tolog~- 1050. Domain entity databases 1072 and vocabulary data-bases 1058 are merely two examples of how active ontology 1050 may integrate databases with each other and with other compo-nents of automated assistant 1002. Active ontoiobies allow the au-thor, designer, or system builder to specify a nontrivial mapping between representations in the database and representations in on-tolog- 1050. For example, the database schema for restaurants da-tabase 1652 may represent a restaurant as a table of strings and numbers, or as a projection from a larger database of business, or any other representation suitable for database 1652. In this example active ontology 1050, restaurant 1610 is a concept node with proper ties and relations, organized differently from the database tables. In this example, nodes of ontology 1050 are associated with elements of database schemata. The integration of database and ontology 1050 provides a unified representation for interpreting and acting on specific data entries in databases in terms of the larger sets of models and data in active ontology, 1050. For instance, the word "French" may be an entry in cuisines database 1662. Because, in this example, database 1662 is integrated in active ontology 1050, that same word "French" also has an interpretation as a possible' cuisine served at. a restaurant, which is involves: in planning meal events, and this cuisine serves as a constraint to use when using res-taurants reservation services, and so forth. Active ontologies can thus integrate databases into the modeling and execution environ-mentto inter-operate with other components of automated assistant 1002.

[0115] As described above, active ontology- 1050 allows the author, de-signer, or system builder to integrate components; thus, in the example of Fig.
8, the elements of a component such as constraint in dialog flow mode' 1642 can be identified with elements of other components such as required pa-rameter of restaurant reservation service 1672.

[0116] Active ontologies 1050 may be embodied as, for example, configu-rations of models, databases, and components in which the relationships among models, databases, and components are any of:

containership and,/ or inclusion;
relationship with links and/or pointers;

= interface over APis, both internal to a program and between pro-clams.

[0117] For example, referring now to Fir. 9, there is shown an example of an alternative embodiment of intelligent automated assistant system 1002, wherein domain models 1056, vocabulary 1058, language pattern recognizers 1060, short term personal memory 1052, and long term personal memory 1054 components are organized under a common container associated with active ontolog~,y 1050, and other components such as active input elicitation compo-nent(s) 1094, language interpreter 1070 and dialog flow processor 1080 are as-sociated with active ontology 1050 via API relationships.

WQ 2011/0880,3 PCT/US2011/020861 ,fictive Input Elicitation Component(s) 1094 [0118] In at least one embodiment, active input elicitation component(s) 1094 (which, as described above, maybe implemented in a stand-alone cony figuration or in a configuration ineludina both server and client components) may be Operable to perform, and or implement Various types Ot functions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof;:

= Elicit, facilitate and; or process input from the user or the user's en-vironment, and/or information about their need(s) or request(s).
For example, if the user is looking to find a restaurant, the input elicitation module may,, get information about the user's constraints or preferences for location, time, cuisine, price, and so forth.

= Facilitate different kinds of input from various sources, such as f07 example, one or more of the following (or combinations thereof):
input from keyboards or any other input device that gener-ates text o input from keyboards in user interfaces that offer dynamic suggested completions of partial input o input from voice or speech input systems o input from Graphical User Interfaces (GUis) in which users click, select, or otherwise directly manipulate graphical ob-jects to indicate choices o input from other applications. that venerate text and send it to the automated assistant, including email, text messaging, or other text communication platforms [0119] By performing active input elicitation, assistant 100? is able to dis-ambiguate intent at an early phase of input processing. For example, in an embodiment where input is provided by speech, the waveform might be sent to a server 1340 where words are extracted, and semantic interpretation ner-forme-d. The results of such semantic interpretation can then be used to drive WO 2011/0880,53 PCT/1 S2011/020861 active input elicitation, which mar offer the user alternative candidate words to choose among based on their de"-Tee of semantic fit as well as phonetic match.

[0120] In at least one embodiment, active input elicitation component(s) 1094 actively-, automaticaliy, ann dvnamically wide file user toward inputs that mar be acted upon by one or more of the services offered by embodi-ments of assistant 1002. Referring now to Fig. 10, there is shown a flow dia-gram depicting a method of operation for active input elicitation compo-nent(s) 1094 according to one embodiment.

[0121] The procedure begins 20. In step 21, assistant 1002 may offer inter-faces on one or more input channels. For example, a user interface may offer the user options to speak or type or tap at any stage of a conversational inter action. In step 22, the user selects an input channel by initiating input on one modality, such as pressing a button to start recording speech or to bring up an interface for typing.

[0122] In at least one embodiment, assistant 1002 offers default sugges-tions for the selected modality 25. That is, it offers options 24 that are relevant in the current context prior to the user entering any input on that modality.
For example, in a text input modality, assistant 1002 might offer a list of common words that would begin textual requests or commands such as, for example, one or more of the following (or combinations thereof): imperative verbs (e.g., find, buy, reserve, get, call, check, schedule, and the like), nouns (e.g., restaurants, movies, events, businesses, and the like), or menu-like op-tions naming domains of discourse (e.g., weather, sports, news, and the like) [0123] If the user selects one of the default options in 25, and a preference to autosubmit 30 is set, the procedure may return immediately. This is similar to the operation of a conventional menu selection.

[0124] However, the initial option may be taken as a partial input, or the user may have started to enter a partial input 26. At any point of input, in at least one embodiment, the user may choose to indicate that the nartiai input is complete 22, which causes the procedure to return.

[01251 In 28, the latest input, whether selected or entered, is added to the cumulative input.

[0126] In 29, the system suggestions next possible inputs that are relevant given the current inpu t and other sources of constraints on what constitutes relevant and/or meaningful input.

[01271 in at least one embodiment, the sources of constraints on user input (for example, which are used in steps 23 and 29) are one or more of the vari-ous models and data sources that may be included in assistant 1002, which may include, but are not limited to, one or more of the following (or combina-tions thereof):

= Vocabulary 1058. For example, words or phrases that match the current input may be suggested. In . at least one embodiment, vo-cabulary may be associated with any or one or more nodes of active ontologies, domain models, task models, dialog models, and/or service models.

= Domain models 1056, which may constrain the inputs that may in-stantiate or otherwise be consistent with the domain mode'. For example, in at ]east one embodiment, domain models 1056 mar be used to suggest concepts, relations, properties, and/or instances that would be consistent with the current input.

= Language pattern recognizers 1060, which mar be used to recog-nize idioms, phrases, grammatical constructs, or other patterns in the current input and be used to suggest completions that fill out the pattern.

= Domain entity databases 1.0%2, which may be used to suggest possi-ble entities in the domain that match the input (e.g., business names, movie names, event names, and the like).

= Short term memory 100?, which may be used to match am' prior input or portion of prior input, and/ or any other property or fact about the h story of interaction with a used. For example, partia input may be matrhe .~ against cities that the user has encountered in a session, whether hypothetically (e.g., mentioned in queries and/or phvsicallly (e.g., as determined from Iocation sensors;.

= In at least one embodiment, semantic paraphrases of recent inputs, request, or results may be matched against the current input. For example, if the user had previously request "live music" and ob-tained concert listing, and then typed "music" in an active input elicitation environment, suggestions may include "live music"
and/or "concerts".

= Long term personal memory 1054, which may be used to suggest matching items from long term memory. Such matching items mar include, for example, one or more or am, combination of: domain entities that are saved e.g., "favorite" restaurants, movies, theaters, venues, and the like), to-do items, list items, calendar entries, peo-pie names in contacts/ address books, street or city names men-tioned in contact/address books, and the like.

= Task flow models 1086, which may be used to suggest inputs based v`
on the next possible steps of in a task flow.

= Dialog flow models 1087, which may be used to suggest inputs based on the next possible steps of in a dialog floe,-.

= Service canabiiity models 1088, which maybe used to suggest pos-sible services to employ, by name, category, capability, or any other property in the model. For example, a user may type part of the name of a preferred review site, and assistant 1002 may suggest a complete command for querying that review site for review.

[01281 In at least one embodiment, active input elicitation component(s)) 1094 present to the user a conversational interface, for example, an interface in which the use., and assistant communicate by making utterances back and forth in a conversational manner. Active input elicitation component(s) 1094 may be Operable to ner torn: and/or implement various types of conversa-tional Interlaces.

[0129] In at least one embodiment, active input elicitation component(s) 1094 may be operable to perform and"'or implement Carious types or Conver-sational interfaces in which assistant 1002 uses plies of the conversation to prompt for information from the user according to dialog models. Dialog models may represent a procedure for executing a dialog, such as, for exam_-pie, a series of steps required to elicit the information needed to perform a service.

[0130] In at !east one embodiment, active input elicitation component(s) 1094 offer constraints and guidance to the user in real time, while the user is in the midst of typing, speaking, or otherwise creating input. For example, active elicitation may guide the user to type text inputs that are recognizable by an embodiment of assistant 1002 and/ or that may be serviced by one or more services offered by embodiments of assistant 1002. This is an advantage over passively waiting for unconstrained input from a user because it enables the user"s efforts to be focused on inputs that may or might be useful, and/or it enables embodiments of assistant 1002 to apply its interpretations of the in-put in real time as the user is inputting it.

[0131] At least a portion of the functions, operations, actions, and/ or other features of active input elicitation described herein may be implemented, at least in part, using various methods and apparatuses described in U.S. Patent Application Serial No. 11/518,292 for "Method and Apparatus for Building an Intelligent Automated Assistant", filed September 8, 2006.

[0132] According to specific embodiments, multiple instances or threads of active input elicitation component(s) 1094 may be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other coin-binations of hardware and/or hardware and software.

WO 2011/088053 PCT/ÃTS2011/020861 [0133) According to different embodiments, one or more different threads or instances of active input elicitation component(s) 1094 may- be initiated in response to detection of one or more conditions or events satisrving one or more different types of minimum threshold criteria for triggering initiation of at least one instance of active input elicitation components' 1094. Various ex-amples of conditions or events which may trigger initiation and; or imple-mentation of one or more different threads or instances of active input elicita-tion component(s) 1094 may include, but are not limited to, one or more of the following (or combinations thereof):

= Start of user session. For example, when the user session starts up an application that is an embodiment of assistant 1002, the interface may offer the opportunity for the user to initiate input, for example, by pressing a button to initiate a speech input system or clicking on a text field to initiate a text input session.

= user input detected.

= When assistant 1002 explicitly prompts the user for input, as when it requests a response to a question or offers a menu of next steps from which to choose.

= When assistant 1002 is helping the user perform a transaction and is gathering data for that transaction, e.g., filling in a form.

[0134] In at least one embodiment, a given instance of active input elicita-tion component(s) 1094 may access and/or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/or remote memory devices. Examples of different types of data which may be accessed by active input elicitation component(s) 1094 may include, but are not limited to, one or more of the following (or combina-tions thereof):

= database of possible words to use in a textual input;

= ammar of possible phrases to use in a textual input utterance;
_4~-= database of possible interpretations of speech input;

= database of previous inputs from a user or from other users;

= data from any of the various models and data sources that may be part of embodiments of assistant 1002, which may include, but are not limited to, one or more of the following (or combinations thereof):

= Domain models 1056;
= Vocabulary 1058;

= Language pattern recognizers 1060;
= Domain entity databases 1072;

= Short term memory 1052;

= Long term personal memory 1054;
= Task flow models 1086,-a Dialog flow models 1087;

= Service capability models 1088.

[0135] According to different embodiments, active input elicitation com-ponent(s) 1094 may apply active elicitation procedures to, for example, one or more of the following (or combinations thereof):

= typed input;
= speech input;

= input from g -aphical user interfaces (GUIs), including gestures;
= input from suggestions offered in a dialog; and = events from the computational and/or sensed environments.
Active Typed Input Elicitation [01361 Referring now to Fib. 11, there is shown a flow diagram depicting a method for active typed input elicitation according to one embodiment.
[011371 The method begins 110. Assistant 1002 receives 111 partial text in-put, for example via input device 1206. Partial text input may include, for ex-ample, the characters that have been typed so far in a text input field. At any time, a user may indicate that the typed input is complete 112, as, for exam-ple, by pressing an Enter ken. If not complete, a suggestion generator gener-ates 11= candidate suggestions 116. These suggestions may be syntactic, se-mantic, and/or other kinds of suggestion based any of the sources of informa-tion or constraints described herein. If the suggestion is selected 118, the in-put is transformed 117 to include the selected suggestion.

[0138] In at least one embodiment, the suggestions may include extensions to the current input. For example, a suggestion for "rest" max be ' restau-rants".

[01391 In at least one embodiment, the suggestions may include replace-ments of parts of the current input. For example, a suggestion for "rest" may be "places to eat".

[0140] In at least one embodiment, the suggestions may include replacing and rephrasing of parts of the current input. For example, if the current input is "find restaurants of style" a suggestion may be "italian" and when the sug-gestion is chosen, the entire input may be rewritten as "find Italian restau-rants".

[0141] In at least one embodiment, the resulting input that is returned is annotated 119, so that information about which choices were made in 118 is preserved along with the textual input. This enables, for example, the seman-tic concepts or entities underlying a string to be associated with the string when it is returned, which improves accuracy of subsequent language inter-pretation.

[0142] Referring now to Fibs. 12 to 21, there are shown screen shots illus-trating some portions of some of the procedures for active typed-input elicita-tion according to one embodiment. The screen shots depict an example of an embodiment of assistant 1002 as implemented on a smartphone such as the iPhone available from Apple Inc. of Cupertino, California. Input is provided to such device via a touchscreen, including on-screen keyboard functionality.
One skilled in the art will recognize that the screen shots depict an embod_ ment that is merely exemplary", and that the techniques o' the present inven-tion can be implemented on other devices and using other layouts and a-,,--[ 01431 In Fig. i2. screen 1201 includes a top-level set of suggestions 1202 shown when no input has been provided in field 1203. This corresponds to no-input step 23 of Fig. 10 applied to step 114 of Fib. 11 where there is no in-put.

[01441 In Fig. 13, screen 1301 depicts an example of the use of vocabulary to offer suggested completions 1303 of partial user input 1305 entered in field 1203 using on-screen keyboard 1304. These suggested completions 1303 may be part of the function of active input elicitation 1094. The user has entered partial user input 1305 including the string "eomm". Vocabulary component 1078 has provided a mapping of this string into three different kinds of in-stances, which are listed as suggested completions 1303: the phrase "commu-nity local events" is a category of the events domain; "chambers of com-merce" is a category of the local business search domain, and "Jewish Com munit), Center" is the name of an instance of local businesses. Vocabulary component 10.58 may provide the data I ookup and management of name spaces like these. The user can tap Go button 1306 to indicate that he or she has finished entering input; this causes assistant 1002 to proceed with the completed text string as a unit of user input.

[0145] In Fig. 14, screen 1401 depicts an example in which suggested se-inantic completions 1303 for a partial string "wh" 130u include entire phrases with typed parameters. These kinds o_~ suggestions may be enabled by the use of one or more of the various models and sources of input constraints de-scribed herein. For example, in one embodiment shown in Fig. 14, "what is happening in city" is an active elicitation of the location parameter of the Lo-cal Events domain; "where is business namc" is an active elicitation of the Business Name constraint of the Local Business Search domain; "what is showing at the venue name" is an active elicitation of the Venue Name con-straint of the Local Events domain; and "what is piavinr at the movie greater"

is an active elicitation of the Movie Theater Name constraint of the Local Events domain. These examples illustrate that the suggested completions are gei'leratecl b1 7710dells rather than simply drawn from a database of previously entered queries.

[0146] In Fig. 15, screen 1501 depicts a continuation of the same examnie, after the user has entered additional text 1305 in field 1203. Suggested com-pletions 1303 are updated to match the additional text 1305. In this example, data from a domain entity database 1072 were used: venues whose name starts with "f". Note that this is a significantly smaller and more semantically relevant set of suggestions than all words that begin with "f". Again, the su~-gestions are generated by applying a mode', in this case the domain model that represents Local Events as happening at Venues, which are Businesses with Names. The suggestions actively elicit inputs that would make potentially meaningful entries when using a Local Events serv,,ice.

[0147] In Fig. 16, screen 1601 depicts a continuation of the same example, after- the user has selected one of suggested completions 1303. Active elicits:-tion continues by prompting the user to further specify the type of informa-tion desired, hereby presenting a number of specifiers 1602 from which the user can select. In this example, these specifiers are generated by the domain.
task flow, and dialog flow models. The Domain is Local Events, which in-cludes Categories of events that happen on Dates in Locations and have Even, Names and Feature Performers. In this embodiment, the fact that these five options are offered to the user is generated from the Dialog Flow model that indicates that users should be asked for Constraints that they have not vet en-tered and from the Service Model that indicates that these five Constraints are parameters to Local Event services available to the assistant. Even the choice of preferred phrases to use as specifiers, such as "by category" and "fea-tured", are generated from the Domain Vocabulary databases.

WO 2011/08803 Pad/LJ~2011/(i2086~
[0148] In Fig. 17, screen 1701 depicts a continuation of the same example, after the user has selected one of specifiers 1602.

[0149] in Fib. 1S, screen 1801 depicts a continuation of the same example, wherein the selected specifier 1602 has been added to nerd i203, and add.'-tionai specifiers 1602 are presented. The use- can select one o specifiers and/ or provide additional text input via keyboard 1304.

[0150] In Fig. 19, screen 1901 depicts a continuation of the same example, wherein the selected specifier 1602 has been added to field 1203, and vet more specifiers 1602 are presented. In this example, previously, entered constraints are not actively elicited redundantly.

[0151] In Fig. 20, screen 2001 depicts a continuation of the same example, wherein the user has tapped the Go button 1306. The user's input is shown in box 2002, and a message is shown in box 2003, providing feedback to the user as to the query being performed in response to the user's input.

[0152] In Fib. 21, screen 2101 depicts a continuation of the same example, wherein results have been found. Message is shown in box 2102. Results 2103, including input elements allowing the user to view further details, save the identified event, buy tickets, add notes, or the like.

[0153] In one screen 2101, and other displayed screens, are scrollable, al-lowing the user to scroll upwards to see screen 2001 or other previously pre-sented screens, and to make changes to the query if desired.

Active Speech Input Elicitation [0154] Referring now to Fig. 22, there is shown a flow diagram depicting a method for active input elicitation for voice or speech input according to one embodiment.

[0155] The method begins 221. Assistant 1002 receives 121 voice or speech input in the form of an auditory signal. A speech-to-text service 122 or proc-essor generates a set of candidate text interpretations 124 of the auditory sig-nal. In one embodiment, speech-to-text service 122 is implemented using, for -D?-examnie, Nuance Recognizer, available from Nuance Communications, Inc. of Burlington, MA.

[01561 In one embodiment. assistant 1000 emniovs statistical language models to generate can idate Text interpretations 124 of speech input 12".
[0157] In addition, in one embodiment, the statlstlcal language models are tuned to loot: for words, names, and phrases that occur in the various models of assistant 1002 shown in Fig. S. For example. in at least one embodiment the statistical Language models are given words, names, and phrases from some or all of: domain models 1056 (e.g., words and phrases relating to restaurant and meal events}, task flow models 1086 (e.g., words and phrases relating to planning an event), dialog flow models 1087 (e.g.. words and phrases related to the constraints that are needed to gather the inputs for a restaurant reserva-tion), domain entity databases 1072 (e.g., names of restaurants), vocabulary databases 1058 (e.g., names of cuisines), service models 1088 (e.g., names of service provides such as OpenTable), and/ or any words, names, or phrases associated with anm' node of active ontology 1050.

[01581 In one embodiment, the statistical language 1 models are also tuned to look for words, names, and phrases from long-term personal memory 1054.
For example, statistical language models can be given text from to-do items, list items, personal notes, calendar entries, people names in contacts/address books, email addresses, street or city names mentioned in contact/address books, and the like.

[0159] A ranking component analyzes the candidate interpretations 124 and ranks 126 them according to how well they fit syntactic and/or semantic models of intelligent automated assistant 1002. Any sources of constraints on user input may be used. For example, in one embodiment, assistant 1002 may rank the output of the speech-to-text interpreter according to how well the in-terpretations parse in a syntactic and/or semantic sense, a domain model, task flow model, and/or dialog model, and/ or the like: it evaluates how well various combinations of words in the text interpretations 124 would fit the concepts, relations, entities, and properties of active ontology 1050 and its as-sociated models. For example, if speech-to-text service 122 generates the two candidate interpretations Italian food for lunch" and 'Italian shoes for lunch the ranking b~' semantic relevance 12b mlc"ht :"anl,~ "italian t00_-~ t0;"

I unch" higher if it better matches the nodes assistant's 1002 active ontology 1050 (e.g., the words "itaiian", "food" and "lunch" all match nodes in ontol-ogti' 1050 and they are all connected by relationships in ontology 1050, whereas the word "shoes" does not match ontology 1050 or matches a node that is not part of the dining out domain network).

[01601 In various embodiments, algorithms or procedures used by assis-tant 1002 for interpretation of text inputs, including any embodiment of the natural Language processing procedure shown in Fig. 28, can be used to rank and score candidate text interpretations 124 generated by speech-to-text ser-vice 122.

[0161 ] In one embodiment, if ranking component 126 determines 128 that the highest-ranking speech interpretation from interpretations 124 ranks above a specified threshold, the highest-,-anking interpretation may be auto-matically selected 130. If no interpretation ranks above a specified threshold, possible candidate interpretations of speech 134 are presented 132 to the user.
The user can then select 136 among the displayed choices.

[0162] In various embodiments, user selection 136 among the displayed choices can be achieved by any mode of input, including for example any of the modes of multimodal input described in connection with Fig. 16. Such input modes include, without limitation, actively elicited typed input 2610, actively elicited speech input 2620, activel_., presented GUI for input 2640, and/ or the like. In one embodiment, the user can select among candidate in-terpretations 134, for example by tapping or speaking. In the case of speak-ing, the possible interpretation of the new speech input is highly constrained by the small set of choices offered 134. For example, if offered "Did you mean italian food or italian shoes?" the user can just say "food" and the assistant WO 2011/088053 PCT/L'S2011/020861 can match this to the phrase "italian food" and not get it confused with other global interpretations of the input.

[0163] Whether input is automaticaliv selected 130 or selected 136 by the user, the resulting input 138 is returned. In at least one embodiment, the re-turned input is annotated 133, so that information about which choices were made in step 136 is preserved along with the textual input. This enables, for example, the semantic concepts or entities underlying a string to be associated with the string when it is returned, which improves accuracy of subsequent language interpretation. For example, it "Italian food" was offered as one or the candidate interpretations 134 based on a semantic interpretation of then the machine-readable semantic interpretation can be sent along with the user's selection of the string "Italian food" as annotated text input 138.

[0164] In at feast one embodiment, candidate text interpretations 124 are generated based on speech interpretations received as output of screech-tc-text service 122.

[0165] In at least one embodiment, candidate text interpretations 124 are generated by paraphrasing speech interpretations in terms of their semantic meaning. In some embodiments, there can be multiple paraphrases of the same speech interpretation, offering different word sense or homonym alter-natives. For example, if speech-to-text service 1_2 indicates "place for meet", the candidate interpretations presented to the user could be paraphrased as "place to meet (local bu.sinesses)" and "place for meat (restaurants)".

[0166] In at least one embodiment, candidate text interpretations 124 in-clude offers to correct substrings.

[0167] In at least one embodiment, candidate text interpretations 124 in-clude offers to correct substrings of candidate interpretations using syntactic and semantic analysis as described herein.

[0168] In at least one embodiment, when the user selects a candidate in-terpretation, it is returned.

WO 2011/088053 PCT/LTS2011/02086i [01 69] In at least one embodiment, the use_' is offered an interlace to edit the interpretation before it is returned.

[0170] In at least one embodiment, the user is offered an interface to con-tinue with more voice Input before input Is returned. This enables one to in-crementalix build up an input utterance, getting syntactic and semantic cor-rections, suggestions, and guidance at one iteration.

[0171] In at least one embodiment, the user is offered an interface to pro-ceed directly from, 136, to step 111 of a method of active typed input elicitation (described above in connection with Fig. 11). This enables one to interleave typed and spoken input, getting syntactic and semantic corrections, sugges-tions, and guidance at one step.

[0172] In at least one embodiment, the user is offered an interface to pro-ceed directly from step 111 of an embodiment of active typed input elicitation to an embodiment of active speech input elicitation. This enables one to inter-leave typed and spoken input, getting syntactic and semantic corrections, suggestions, and guidance at one step.

Active GUI-based Input Elicitation [0173] Referring now to Fib. 23, there is shown a flow diagram depicting a method for active input elicitation for GUI-based input according to one em-bodiment.

[0174] The method begins 14C. Assistant 1002 presents 141 b aphical user interface (GUI) on output device 1207, which may include, for example, links and buttons. The user interacts 142 with at least one GUI element. Data 144 is received, and converted 146 to a uniform format. The converted data is then returned.
[0175] in at least one embodimlent, some of the elements of the GUI are generated dynamically from the models of the active ontolog v, rather than written into a computer prod -am. For example, assistant 1002 can offer' a set of constraints to guide a restaurant reservation service as regions for tapping on a screen, with each region representing the name of t he constraint anJ or WO 2011/0880,53 PCT/US2011/020861 a value. For instance, the screen could have rows of a dynamically generated CLTI layout with regions for the constraints Cuisine, Location, and Price Rance. It the models of the active ontoio~m chance, the 'CLI screen would automatically Change without reprogramming.

Active Dialog Suggestion: input Elicitation [0176] Fig. 24 is a flow diagram depicting a method for active input elicita-tion at the level of a dialog flow according to one embodiment. Assistant 1002 suggests 151 possible responses 152. The user selects 154 a suggested re-sponse. The received input is converted 154 to a uniform format. The con-verted data is then returned.

[0177] in at least one embodiment, the suggestions offered in step 151 are offered as follow-up steps in a dialog and/or task fl ow.

P the suggestions 17 ~ in at least one ~ m bodiinent, th ~~iors offer options to refine a query,, for example using parameters from a domain and/or task model. For example, one may be offered to change the assumed location or time of a Fe-Quest.

[01791 In at least one embodiment, the suggestions offer options to choose among ambiguous alternative interpretations given by a language interpreta-tion procedure or component.

[01801 In at least one em:eodiment, the suggestions offer options to choose among ambiguous alternative interpretations given by a language interpreta-tion procedure or component.

[0181] In at least one embodiment, the suggestions offer options to choose among next steps in a workflow associated dialog flow model 1087. For ex-ample, dialog flow model 1087 may suggest that after gathering the con-strained for one domain (e.g., restaurant dining), assistant 1002 should sug-gest other related domains (e.g., a movie nearby).

Active Monitoring for Relevant events [0182] in at least one embodiment, asynchronous events may be treated as inputs in an analogous manner to the other modalities of active elicited input.
Thus, such events mad" be provld ed as inputs to assistant 1001. -ice litter preted, such events can be treated in a manner similar to any other input.

[0183] For example. a flight status change may initiate an alert notification.
to be sent to a user. If a flight is indicated as being late, assistant 1002 man continue the dialog by presenting alternative flights, making other sugges-tions, and the like, based on the detected event.

[0184] Such events can be of any type. For example, assistant 1002 might detect that the user just got home, or is lost (off a specified route), or that a stock price hit a threshold value, or that a television show the user is inter-ested in is starting, or that a musician of interest is touring in the area.
In any of these situations, assistant 1002 can proceed with a dialog in substantially the same manner as if the user had him- or herself initiated the inquiry. In one embodiment, events can even be based on data provided from other de-vices, for example to tell the user when a coworker has returned from lunch (the coworker'-, device can signal such an event to the user's device, at which time assistant 1002 installed on the user's device responds accordingly).
[0185] In one embodiment, the events can be notifications or alerts from a calendar, clock, reminder, or to-do application. For example, an alert from a calendar application about a dinner date can initiate a dialog with assistant 1002 about the dining event. The dialog can proceed as if the user had just spoken or typed the information about the upcoming dinner event, such as "dinner" for 2 in San Francisco".

[0186] In one embodiment, the context of possible event trigger 162 can include information about people, places, times, and other data. These data can be used as part of the input to assistant 1002 to use in various steps of processing.

[0187] In one embodiment, these data from the context of event trigger 162 can be used to disambiguate speech or text inputs from the user. For exam-ple. if a calendar" event alert includes the name of a person invited to the event, that information can help disambiguate input which might match Sev-eral people with the same or similar name.

[01881 Referring now to ig. 25, there is shown a flow dlanrarn depicting a b C v b method for active monitoring for relevant events according to one embodi-ment. In this example, event trigger events are sets of input 162. Assistant 1002 monitors 161 for such events. Detected events may be filtered and sorted 164 for semantic relevance using models, data and information available from other components in intelligent automated assistant 1002. For example, an event that reports a change in flight status may be given higher relevance if the short-term or long-term memory records for a user indicate that the user is on that fight and/ or have made inquiries about it to assistant 1002. This sorting and filtering may then present only the top events for review by the user, who may then choose to pick one or more and act on them.

[01891 Event data is converted 166 to a uniform input format, and re-turned.

[0190] In at least one embodiment, assistant 1002 may proactively offer services associated with events that were suggested for user attention. For e~

ample, if a flight status alert indicates a flight may be missed, assistant may suggest to the user a task flow for re-planning the itinerary or booking a hotel.

Input Elicitation Component(s) Example:

[01911 The following example is intended to help illustrate some of the various types of functions, operations, actions, and/or other features which Ma-\7 be provided by active input elicitation component(s) 1094.

Example: Command completion (what can the user say to assistant 1002?) [0192] The user is facing a text input box with the general instruction to enter "what do you want to do?" Depending on the context and the user in-- ;~9-put, any of several system responses are provided. Examples are shown be-low Case: Null input User Input System Response Comment With an empty field = Find restaurants. hotels, movies. Black text would be and no dialog context, ... inserted into the in-type a question or Book a table, a room, a seat, ... put field. Italicized space character, or en- j = Order food, flowers, ... text would be the ter nothing for some = Buy a ticket, a book, a DVU, ... follow-up sugges period of time. = Schedule a meeting, an even;,, ... tions once the text ~ = Remember to a thing to do... was inserted.

~= Explain results Case: First word input User Input System Response Comment With empty dialog = Find restaurants, hotels, movies, Black text would be context If the user (... inserted into the in-types the first letter of nut field. Italicized any command, it istext would be the suggested. For exam- follow-up sugges-pie tions once the text F1 was inserted.
Case: Key word input User Input System Response Comment With empty dialog = Remember to a thing to do... Black text would be context It the user = Find restaurants by location, cm- inserted into the it types the first two let- sine, put field. Italicized ters of any keyword, it = text would be the WO 2011/088053 PCT'/US2011/020861 is suggested as well as follow-up sugges-command. For exam- tions once the text pie was inserted.

Re Case: Prompting for arguments User Input System Response Comment The user has entered or = restaurants by location, cuisine, I Offer not only they selected a "command" ... selection class but verb and nothing else. = hotels by location, availability, ... advertise the con-For example, = movies by location, genre, ... straint options. -vote Find name of ` place that name of place is a just a prompt, and would not insert text.
Case: Suggesting criteria User input System Response Comment The user has already en- = in city, zip code Black text would be tered enough to establish = near home, office, place name inserted into the input a task and domain, and = named restaurant name field. Italicized text now is prompted for would be the follow-constraints in order. up suggestions once Find restaurants_ the text was inserted.
Case: Adding criteria User Input System Response Comment The user has said "in" and now = city, zip code Until they enter the next is prompted ompted for locations = Palo Alto, CA
word, the suggestions are a Find restaurants in_ , = Menlo Park, CA prompt for what kind of thing to type. The Places are cities from personal mem-orv.

The user has said "near" ana = my home The Places are one or more I I
now is prompted for locations = M,\- work places from personal mere Find restaurants near_ = Palo Alto, CA orv.

= Menlo Park, CA
Case: Adding location or other constraints User input System Response Comment Find restaurants in = serving food type or cuisine Prompt for other Palo Alto = with availability tonight, to- constraints morrow, ...

= described as romantic, good service I l Find restaurants in = tonight The 7:00 and 9:00 Palo Alto with avail- = tomorrow are our su ges-ability _ = at 7pm tions based on cur- at 9pm rent time = other time or date Case: Starting from constraint, unknown task or domain User Input System Response Comment romantic _ = restaurants by location, cul Constraint value from OT-!-sine, ... tolob 7 tells you which se-hotels by location, availabil- lection classes to use.

= movies by location, genre, ...

=

corned = -movies by location, enre, ... '" comedy" is a constraint = events by location, ... value for genre in movies, = clubs dv Iocation a Genre in events, and "comedy clubs is a local, directory business care-gold' Example: Name completion [01931 Here, the user has typed some text without accepting any of the commands; or he or she is just extending a command with an entity name.
The system may attempt to complete the names, depending on context. It also disambiguates the domain.

Case: words without context User Input System Response Comment it for = 11 Fornaio (restaurant) May require entity name = III Forgotten Gains lookup. Notice multi-word (movie) completion. Show domain as a suggestion:.

tom cruise = movies May require entity name lookup.
romantic = movies Based on ontology only = restaurants Case: name with context User Input System Response Comment Find restaurants = Il Forrlio (restaurant) May require entity name it for = lookup. uses selection class ~ I
and location context from cur-rent and past queries to con-OD -strain completion. show ao-main as a suggestion.

torn cruise = movies May require entity name lookup.
Find movies rom = romantic Based on ontology only = Roman Holiday (movie) g-rated = movies Example: Choosing values from a set [01941 Here, the user is responding to a system request to enter a value fora specific parameter, such as location, tune, cuisine, or genre. The user may either select from a list or enter a value. As she types, matching items from the list are shown as options. Examples are shown below.
Case: valueclass selection User input System Response Comment Restaurants serv- = burgers User may type anything e se and doeesn t ing = Ice cream have to pick from the menu.
i I
= Hot dogs Restaurants sere- = Hamburgers ing l1 = Hot dogs = Hot sauce Movies playing today = tonight j = Friday night Example: Reusing previous commands [01951 Previous queries are also options to complete on in an autocomplete interface. They may be just matched as strings (when the input field is empty WO 2011/088053 PCT/t;S2011/020861 and there are no known constraints) or they may be suggested as relevant when in certain situations.
Case: completion on previous queries User Input System Response Comment e 7 s g stl (~ l ( Ital I,ahan rsLaaiani ~nolmai com Lsin~ Inb irtat~lun~;
nletion) to retrieve previous j = Films starring Italian actors 're queries I - l cent auerv) Lunch = lunch places in marin (recent query) = buy the book naked lunch Example: Retrieving Personal Memory items [0196] Assistant 100? may remember certain events and or entities in per sonal memory associated with the user. Autocomplete can be performed based on such remembered items. Examples appear below.

Case: completion on events and entities in personal memory User Input System Response Comment Mare = lunch with Marv on Saturday (from M), Events) = films called "Something about Mary"
Lunch = lunch with Marv, on Saturday (from My Events) = bud- the book naked lunch (from My Todos) Hobee's Restaurant in Palo Alto (from My Hob R)eszaurants) Multimodal Active input Elicitation [019 7] In at least one embodiment, active input elicitation component(s) 1094 may process input from a plurality o input modalities At least one m0 dalit;y might be implemented with an active input elicitation procedure that takes advantages of the particular kinds of inputs and methods for selecting from suggested options. A described herein, they ma v be embodiments of procedures for active input elicitation for text input, speech, input, GUI-based input, input in the context of a dialog, and,/or input resulting from event trig-g" s.
c`
[01 98] In at least one embodiment, for a single instance of intelligent auto-mated assistant 1002, there may be support for one or more (or any combina-tion of) typed input, speech input, GUI input, dialog input, and/ or event in-put.

[01991 Referring now to Fig. 26, there is shown a flow diagram depicting a method for multimoda'_ active input elicitation according to one embodiment.
The method begins 100. Inputs may be received concurrently from one or more or any combination of the input modalities, in any sequence. Thus, the method includes actively eliciting typed input 261.0, speech input 2620, GUI-based input 2640, input in the context of a dialog 2650, and/or input resulting from event triggers 2660. Any or all of these input sources are unified into unified input format 2690 and returned. Unified input format 2690 enables the other components of intelligent automated assistant 1002 to be designed and to operate independently of the particular modality of the input.

[0200] Offering active guidance for multiple modalities and levels enables constraint and guidance on the input beyond those available to isolated mo-dalities. For example, the kinds of suggestions offered to choose among speech, text, and dialog steps are independent, so their combination is a sig-nificant improvement over adding active elicitation techniques to individual modalities or levels.

[0201] Combining multiple sources of ,constraints as described herein (svn-tactic/ linguistic, vocabulary, entity data bases, domain models, task models, service models, and the like) and multiple places where these Constraints max - T
be actively applied (speech, text, GUI, dialog, and asynchronous events; pro-vides a new level of functionality for human-machine interaction.

Domain Mode!s Component(s) 1056 [02021 Domain models 1056 component(s) include representations of the concepts, entities, relations, properties, and instances of a domain. For exam-ple, dining out domain model 1622 might include the concept of a restaurant as a business with a name and an address and phone number, the concept of a meal event with a party size and date and time associated with the restaurant.
[02031 in at least one embodiment, domain models component(s) 1056 of assistant 1002 mia`' be operable to perform and/or implement various types Of Functions, operations, actions, and/or other features such as, for exampi.e, one or more of the following (or combinations thereof):

= Domain model component(s) 1056 may be used by automated assis-tant 1002 for several processes, including: eliciting input 100, inter-preting natural language 200, dispatching to services 400, and gen-erating output 600.

= Domain model component(s) 1056 may provide lists of words that might match a domain concept or entity, such as names of restau-rants, which may be used for active elicitation of input 100 and natural language processing 200.

= Domain model component(s) 1056 mar classify candidate words in processes, for instance, to determine that a word is the name of a restaurant.

= Domain model component(s) 1056 max show the relationship be-tween partial information for interpreting natural language, for ex-ample that cuisine may be associated with business entities (e.g., WO 2011/0880-i, PCTIUS2011/020861 "local Mexican food" may be interpreted as "find restaurants with style = Mexican", and this inference is possible because of the in-formation in domain mode's 10561.

= Domain model component(s) 1056 may organize information about Services Used in se'"vice_ orchestration 108?, for example, that a par-ticular wev service may provide reviews of restaurants.

= Domain model component(s) 1056 may provide the information for generating natural language paraphrases and other output forinat-ting, for example, by providing canonical ways of describing con-cepts, relations, properties and instances.

[0204] According to specific embodiments, multiple instances or threads of the domain models component(s) 1056 may be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other com-binations of hardw are and or i Hardware and softw are. For example, in at leas` some embodiments, various aspects, features, and/or functionaiities of domain models component(s) 1056 may he performed, implemented and/or initiated by one or more of the following types or systems, components, sys-tems, devices, procedures, processes, and the like (or combinations thereof):

= Domain models component(s) 1056 may be i plemented as data structures that represent concepts, relations, properties, and in-stances. These data structures max be stored in memory, files, or databases.

= Access to domain model component(s) 1056 rnav be implemented through direct APIs, network APIs, database auerv interfaces, and/ or the like.

= Creation and maintenance of domain models component(s) 1056 may be achieved, for example, via direct editing of files, database transactions, and/ or through the use of domain model editing tools.

= Domain models component(s) 1056 may- be i niemented as part of or in association with active ontologies 1050, which combine mod-els with instantiations of the models for" servers and users.

[0205] According to various embodiments, one or more different threads or instances of domain models component(S) 1[,'-6 may be initiated in re-sponse to detection of one or more conditions or events satisfying one Or more different types of minimum threshold criteria for trigering initiation of a-1-least one instance of domain models component(s) 1056. For example, trigger initiation and/ or implementation of one or more different threads or in-stances of domain models component(s) 1056 may be triggered when domain model information is required, including during input elicitation, input inter-pretation, task and domain identification, natural language processing, ser-vice orchestration, and/ or formatting output for users.

[0206] In at least one embodiment, a given instance of domain models component(s) 1056 may access and/or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the da-tabase information mar be accessed via communication with one or more lo-cal and./ or remote memory devices. For example, data from domain model component(s) 1056 may be associated with other model modeling compo-nents including vocabulary 1058, language pattern recognizers 1060, dialog flow models 1087, task flow models 1086, service capability models 1088, do-main entity databases 1072, and the like. For example, businesses in domain entity databases 1072 that are classified as restaurants might be known by type identifiers which are maintained in the dining out domain model com-ponents.

Domain Models Component(s) Example:

[0207] Referring now to Fig. 27, there is shown a set of screen shots illus-trating an example of various types of functions, operations, actions, and/ or other features which may be provided by domain models component(s) 1056 according to one embodiment.

10208] In at least one embodiment, domain models component(s) 1056 are the unifying data representation that el' gables the aresentation or information shown in screens 103A and 103E about a restaurant, which combines data from several' distinct data sources and Services and L" hick includes, for exam-ple: name, address, business categories, phone number, identifier for saving to long term personal memory, identifier for sha=m L- over email, reviews from multiple sources, map coordinates, personal notes, and the like.

Language interpreter Component(s) 1070 [0209] In at least one embodiment, language interpreter component(s) 1070 of assistant 1002 may be operable to perform and/or implement various types of functions, operations, actions, and/or other features such as, for ex-ample, one or more of the following (or combinations thereof):

a Analyze user input and identify a set of parse results.

c User invut can include any information from the user and his/her device context that can contribute to understanding the user's intent, which can include, for example one or more of the following (or combinations thereof): sequences of words, the identity of gestures or GUI elements involved in eliciting the input, curl-ent context of the dialog, current de-vice application and its current data objects, and/ or any other personal dynamic data obtained about the user such as location, time, and the like. For example, in one embod _-ment, user input is in the form of the uniform annotated in-put format 2690 resulting from active input elicitation 1094, c Parse results are associations of data in the user input With concepts, relationships, properties, instances, and/or other nodes and/or data structures in models, databases, and/or other representations of user intent and/ context. Parse re-sult associations can be complex mappings from sets and se-quences of words, signals, and other elements of user input to one or more associated concerts, relations, properties, in-stances, other nodes, and/ or data structures described herein.

E Analyze user input and identify a set of syntactic parse results, which are parse results that associate data in the user input with structures that represent syntactic parts of speech, clauses and phrases including multiword names, sentence structure, and/or other grammatical b -aph structures. Syntactic parse results are de-scribed in element 212 of natural language processing procedure described in connection with Fig. 28.

= Analyze user input and identify a set of semantic parse results, which are parse results that associate data in the user input with structures that represent concepts, relationships, properties, enti-ties, quantities, propositions, and/or other representations of mean-ing and user intent. in one embodiment, these representations of meaning and intent are represented by sets of and/ or elements of and/or instances of models or databases and/or nodes in ontoio-gies, as described in element 220 of natural language processing procedure described in connection with Fig. 28.

= Disambiguate among alternative syntactic or semantic parse results as described in element 230 of natural language processing proce-dure described in connection with Fib;. 28.

= Determine whether a partially typed input is syntactically and/or semantically meaningful in an autocompiete procedure such as one described in connection with Fig. 11.

= Help generate suggested completions 114 in an autocompiete pro-cedure such as one described in connection with Fig. 11.

WO 2011/088053 PCT[US2011/020861 Determine whether interpretations Of spoken input are syntactically and/ or semantically meaningful in a speech input procedure such as one described in connection with Fi. 22.

[0210] According to specific embodiments, multiple instances or threads of language interpreter component(s) 1070 man be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other com-binations of hardware and/or hardware and software.

[02111 According to different embodiments, one or more different threads or instances of language interpreter component(s) 1070 may be initiated in re-sponse to detection of one or more conditions or events satisfying one or more different types of minimum threshold criteria for triggering initiation of at least one instance of language interpreter component(s) 1070. Various exam-pies of conditions or events which may trigger initiation and/ or implementa-tion of one oI more different threads or instances of language interpreter component(s) 1070 may include, but are not limited to, one or more of the roll-lowing (or combinations thereof:

= while eliciting input, including but not limited to o Su gesting possible completions of typed input 114 (Fig. 11);
o Ranking interpretations of speech 126 (Fig. ?2);

c, When offering ambiguities as suggested responses in dialog 152 (FI g. 24);

= when the result of eliciting input is available, including when input is elicited by any mode of active multimodal input elicitation 100.
[0212] In at least one embodiment, a given instance of language interpreter component(s) 1070 may access and; or utilize information from one or more associated databases. In at least one embodiment, at least a portion of such database information may be accessed via communication with one or more local and/or remote memo1y7 devices. Examples of different types of data which may be accessed by the Language Interpreter component(s) may in-elude, but are not limited to, one o,. more of the following or combinations thereof):

= Dornain models 1056;
= Vocabulary 1058;

= Domain entity databases 1072 = Short term memory 1072-= Long term personal memory 1054;
= Task flow models 1086;

= Dialog flow models 1087;

= Service capability models 1058.

[0213] Referring now also to Fig. 29, there is shown a screen shot illustrat-ing natural language processing according to one embodiment. The user has entered ;via voice or text' language input 2902 consisting of the phrase "who is playing this weekend at the filmore". This phrase is echoed back to the user on screen 2901. Language interpreter component(s) 1070 component process input 2902 and generates a parse result. The parse result associates that input with a request to show the local events that are scheduled fo:- any of the up-coming weekend days at any event venue whose name matches "filmore". A
paraphrase of the parse results is shown as 2903 on screen 2901.

[0214] Referring now also to Fig. 28, there is shown a flow diagram depict-ing an example of a method for natural language processing according to one embodiment.

[0215] The method begins 200. Language input 202 is received, such as the string "who is playing this weekend at the filmore" in the example of Fig. 29.
in one embodiment, the input is augmented by current context information, such as the current user location and local time. In word 'phrase matching 210, language interpreter component(s) 1070 find associations between user input and concepts. In this example, associations are found between the string "playing" and the concept of listings at event venues; the string "this weekend" (along with the current local time of the user) and an instantiation WO 2011/088053 PCT /uS2011/020861 of an approximate time period that represents the upcoming weekend; and the string "Himore" with the name o" a venue. W ora /phrase matching 210 may use data from, for example, language pattern recognizers 1060, vocanu-larv database 1058, active ontology 1050, short term personal memo.--\, 1052, and long term personal memory 1054.

[0216] Language interpreter component(s 1070 generate candidate svniac tic parses 212 which include the chosen parse result but may also include other parse results. For example, other parse results may include those wherein "playing" is associated with other domains such a s games or with a category of event such as sporting events.

[0217] Short- and,/or long-term memory 1052, 1054 can also be used by language interpreter component(s) 1070 in generating candidate syntactic parses 212. Thus, input that was provided previously in the same session, and/or known information about the use:-,, can be used. to improve perform-ance, reduce ambiguity, and reinforce the conversational nature of the interac-tion. Data from active ontology 1050, domain models 1056, and task flow models 1086 can also be used, to implement evidential reasoning in determin-ing valid candidate syntactic parses 212.

[0218] In semantic matching 220, language interpreter component(s) 1070 consider combinations of possible parse results according to how well they fit semantic models such as domain models and databases. In this case, the parse includes the associations (1) "playing" (a word in the user input) as "Lo-cal Event At Venue" (part of a domain mode? 1056 represented by a cluster of nodes in active ontology 1050) and (2) "filmore" (another word in the input) as a match to an entity name in a domain entity database 1072 for Local Event Venues, which is represented by a domain model element and active ontology node (Venue N'aine).

[0219] Semantic matching 220 may use data from, for example, active on-tology 10o0, short term personal memory 10a2, and Ion(, term personal mem-o,,7y 1054. For example, semantic matching 220 may use data from previous references to venues or local events in the dialog (from short term personal memory 10521 0. r)ersonaI favorite venues (from long term personal memory 10541.

[0220] A se of candidate, or potential, semantic ,-,arse result; is venerated [0221] In disambiguation step 230, language interpreter components; 1070 weigh the evidential strength of candidate semantic parse results 222. in this example, the combination of the parse of"playing" as "Local Event At Venue"
and the match of "filmore" as a Venue Name is a stronger match to a domain model than alternative combinations where, for instance, "playing" is associ-ated with a domain model for sports but there is no association in the sports domain for "filmore".

[0222] Disambiguation 230 may use data from, for example, the structure of active ontology 1050. in at least one embodiment, the connections between nodes in an active ontology provide evidential support for disambiguating among candidate semantic parse results 222. For example, in one embodi-ment, if three active ontology nodes are semantically matched and are all con-nected in active ontology 1050, this indicates higher evidential strength of the semantic parse than if these matching nodes were not connected or connected by Longer paths of connections in active ontology 1050. For example, in one embodiment of semantic matching 220, the parse that matches both Local Event At Venue and Venue Name is given increased evidential support because the combined representations of these aspects of the user intent are connected by links and/or relations in active ontology 1050: in this instance, the Loca Event node is connected to the Venue node which is connected to the Venue Name node which is connected to the entity name in the database of venue names.

[0223] In at least one embodiment, the connections between nodes in an active ontology that provide evidential support for disambiguating among candidate semantic parse results 22-2 c, an inference p.= are directed arcs, forming P .rence lattice, in which matching nodes provide evidence for nodes to Which they are connected by directed arcs.

[0224] in 232; language interpreter corn ~onent(s) 1070 sort and select 232 the top semantic parses as the representation of user intent 290.

Domain Entity Database(s) 1072 [0225] In at least one embodiment, domain entity database(s) 1072 mar be operable to perform and/or implement various types of functions, operations, actions, and/ or other features such as, for example, one or more of the follow-ing (or combinations thereof;:

= Store data about domain entities. Domain entities are things in the world or computing environment that may be modeled in domain models. Examples may include, but are not limited to, one or more of the following (or combinations thereof):

o Businesses of any kind;

o Movies, videos, songs and/ or other musical products, and j or any other named entertainment products;
o Products of any kind;
o Events;

o Calendar entries;

o Cities, states, countries, neighborhoods, and/or other geo-graphic, geopolitical, and/or geospatial points or regions;
o Named places such as landmarks, airports, and the like;

= Provide database Services On these databases, including but not lim-ited to simple and complex queries, transactions, trigYered events, and the like.

[0226] According to specific embodiments, multiple instances or threads of domain entity database(s) 1072 may be concurrently implemented and,/or ini-tiated via the use of one or more processors 63 and/or other combinations of hardware and/or hardware and software. For example, in at least some em-WO 2011/0880>3 PCT/US2011/020861 bodiments, various aspects, features, and/or functionalities of domain entity database( s 13;'2 may be performed, implemented and/or initiated by data-base software and/or hardware residing on ciientls) 1304 and/ or on server~s~
1340.

[02271 One example of a domain entity database 1072 that can be used in connection with the present invention according to one embodiment is a da-tabase of one or more businesses Storing, for example, their names and loca-tions. The database might be used, for example, to look up words contained in an input request for matching businesses and/or to look up the location of a business whose name is known. One skilled in the art will recognize that mane other arrangements and implementations are possible.

Vocabulary Component(s) 1058 [0228] In at least one embodiment, vocabulary component(s) 1075 may be operable to perform and/ or implement various types of functions, operations, actions, and/or other features such as, for example, one or more of the follow-ing (or combinations thereof):

= Provide databases associating words and strings with concepts, properties, relations, or instances of domain models or task models;
= Vocabulary from vocabulary components may be used by auto-mated assistant 1002 for several processes, including for exam-Die:
eliciting input, interpreting natural language, and generating out-put.

[02291 According to specific embodiments, multi-Die instances or threads of vocabulary component(s) 1058 may be concurrently implemented and or ini-tiated via the use of one or more processors 63 and/or other combinations of hardware and/ or hardware and software. For example, in at least some em-bodiments, various aspects, features, and/ or functionalities of vocabulary component(s) 10,-8 may be implemented as data structures that associate strings with the names of concepts, relations, properties, and instances.
These data structures may be stored in memory, files, or databases. Access to vo cabular component(s) 1058 may be implemented through direct APIs, net-work APIs, and/or database query interfaces. Creation and maintenance o:

vocabulary component(s) 1058 may be achieved via direct editing of files, da-tabase transactions, or through the use of domain model editing tools. V.~'o-cabularv component(s) 1058 may be implemented as part o or in association with active ontologies 1050. One skilled in the art will recognize that many other arrangements and implementations are possible.

[0230] According to different embodiments, one or more different threads or instances of vocabulary component(s) 1058 map be initiated in response to detection of one or more conditions or events satisfying one or more different types of minimum threshold criteria for triggering initiation of at least one in-stance of vocabulary component(s) 1058. In one embodiment, vocabulary component(s) 1058 are accessed whenever vocabulary information is re-quired, including, for example, during input elicitation, input interpretation, and "formatting output for users. One skilled in the art will recognize that other conditions or events may trigger initiation and; or implementation of one or more different threads or instances of vocabulary component(s) 1058.
[02311 In at least one embodiment, a given instance of vocabulary coinpo-nent(s) 1058 may access and/ or utilize information from one or more associ-ated databases. in at least one embodiment, at 'east a portion of the database information may be accessed via communication with one or more local and/or remote P }
d/o: memory device-s. In one embodiment, vocabulary compo-nent(s) 1058 may access data from external databases, for instance, from a data warehouse or dictionary.

Language Pattern Recognizer Component(s) 1060 [02321 In at least one embodiment, language pattern recognizer compo-nent(s) 1060 may be operable to perform and/or implement various types of functions, operations, actions, and/or other features such as, for example, looking for patterns in language or speech input that indicate aminatical, idiomatic, and or other composites of input tokens. These patterns corre-spond to, for example, one or more of the following OF combinations thereof",.
words, names, phrases, data, parameters, commands, and Or signais of speech acts.

[0233] According to specific embodiments, multiple instances or threads o pattern recognizer component(s) 1060 may be concurrent)y; implemented and/or initiated via the use of one or more processors 63 and/ or other com-binations of hardware and/or hardware and software. For example, in at least some embodiments, various aspects, features, and/or functionalities of language pattern recognizer component(s) 1060 may be performed, imple-mented and/or initiated by one or more files, databases, and/or programs containing expressions in a pattern matching language. In at least one em-bodiment, language pattern recognizer component(s) 1060 are represented declaratively, rather than as program code; this enables them to be created and maintained by editors and other tools other than programming tools. Ex-amples of declarative representations may include, but are not limited to, one or more of the following (or combinations thereof): regular expressions, pat-tern matching rules, natural language grammars, parsers based on state ma-chines and/ or other parsing models.

[0234] One skilled in the art will recognize that other types of systems, components, systems, devices, procedures, processes, and the like (or combi-nations thereof) can be used for implementing language pattern recognizer component(s) 1060.

[0235] According to different embodiments, one or more different threads or instances of language pattern recognizer component(s) 1060 may be initi-ated in response to detection of one or more conditions or events satisfying one or more different types of minimum threshold criteria for triggering ini-tiation of at least one instance of language pattern recogniZer Component(s) 1060. Various examples of conditions or events which may trigger initiation and/ or implementation of one or more different threads or instances of lan-guage pattern recognizer component(s) 1060 may include, but are not limited to, one or more of the following (or combinations thereof):

= during active elicitation of input, In which the structure of the lan-guage pattern recognizes may constrain and guide the input from the user;

= during natural language processing, in which the language pattern recognizers help interpret input as language;

= during the identification of tasks and dialogs, in which the lar:-guage pattern recognizers may help identify' tasks, dialogs, and/or steps therein.

[02361 In at least one embodiment, a given instance of language pattern recognizer component(s) 1060 may access and/or utilize information from one or more associated databases. in at least one embodiment, at least a por-tion of the database information may be accessed via communication with one or more local and/or remote memory devices. Examples of different types of data which may be accessed by language pattern recognaizer compo-nent(s) 1060 may include, but are not limited to, data from any of the models various models and data sources that may be part of embodiments of a ssis-tant 1002, which may include, but are not limited to, one or more of the fol-lowing (or combinations thereof):

= Domain models 1056;
= Vocabulary 1058;

= Domain entity databases 10721-= Short term memory 1052;

= Long term personal memory 1054;
= Task flow models 1086;

= Dialog flow models 1087;

= Service capability models 1088.

[0237] In one embodiment, access of data from other Harts o ."embodiments of assistant 1002 may- be coordinated b-, active ontologies 1050.

[0238] Referring again to Fig-. 14, there is showr, an examnvie of some o the various types of functions, operations, actions, and j or other features which may be provided by language pattern recognizes component(s) 1060. Fig. 14 illustrates language patterns that language pattern recognizes component(s) 1060 may recognize. For example, the idiom "what is happening" (in a cityy) may be associated with the task of event planning and the domain of local events.

Dialog Fiow Processor Component(s) 1 080 [0239] In at least one embodiment, dialog flow processor component(s) 1080 may be operable to perform and/or implement various types of func-tions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof):

Given a representation of the user intent 290 from language inter-pretation 200, identify the task a user wants performed and/ or a problem the user wants solved. For example, a task might be to find a restaurant.

= For a given problem or task, given a representation of user intent 290, identify parameters to the task or problem. For example, the user might be looking for a recommended restaurant that serves Italian food near the user's home. The constraints that a restaurant be recommended, serving Italian food, and near home are parame-ters to the task of finding a restaurant.

= Given the task interpretation and current dialog with the user, such as that which may be represented in personal short term memory 1052, select an appropriate dialog flow model and determine a step in the flow model corresponding to the current state.

[0240] According to specific embodiments, multiple instances or threads of dialog flow processor component(s) 1080 may be concurentiv implemented.
and; or initiated via the use of one or more processors 61,3 and /or other con.-binations of hardware and/ or hardware and software.

[024 E] in at least one embodiment, a given instance of dialog flow proces-sor com.ponent(s) 1080 may access and/ or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the database information maybe accessed via communication with one or more local and/or remote memory devices. Examples of different topes of data which may be accessed by dialog flow processor component(s) 1080 may include, but are not limited to, one or more of the following (or combinations thereof):

task flow models 1086;
domain models 1056;

= dialog flow models 1087.

[0242] Referring now to Figs. 30 and 31, ti Jere are shown screen shots illus trating an example of various types of j=unctions, operations. actions, and/or other features which may be provided by dialog flow processor component(s) according to one embodiment.

[0243] As shown in screen 3001, user requests a dinner reservation by pro-viding speech or text input "book me a table for dinner". Assistant 10C2 gen-erates a prompt 3003 asking the user to specify time and party size.

[0244] Once these parameters have been provided, screen 3101 is shown.
Assistant 1002 outputs a dialog CD ~D box 3102 indicating that results are being pre-sented, and a prompt 3103 asking the user to click a time. Listings 3104 are also displayed.

[0245] In one embodiment, such a dialog is implemented as follows. Dia-log flow processor component(s) 1080 are given a representation of user intent from Language interpreter component 1070 and determine that the appropri-ate response is to ask the user for information required to perform the next WO 2011/088053 PCB'/UTS2011/020861 step in a task flow. in this case, the domain is restaurants, the task is getting a reservation, and the dialog siep is to ask the user for information required to accornplish the next step in the task flow. ThLl; aialoO step is exemplirle:
ov prompt 3003 of screen 3001.

[0246] Referring now also to Fig. 32, there is shown a flow diagram depict-ing a method of operation for dialog flow processor component(s) 1080 ac-cording to one embodiment. The flow diagram of Fig. 32 is described in con-nection with the example shown in Figs. 30 and 31.

[0247] The method begins 200. Representation of user intent 290 is re-ceived. As described in connection with Fig. 28, in one embodiment, repre-sentation of user intent 290 is a set of semantic parses. For the example shown in Fibs. 30 and 31, the domain is restaurants, the verb is "book" associated.
with restaurant reservations, and the time parameter is the evening of the cur-rent day-.

[0248] In 310, dialog flow processor component(s) 1080 determine whether this interpretation of user intent is supported strongly enough to proceed, and/ or if it is better supported than alternative ambiguous parses. In the cur-rent example, the interpretation is strongly supported, with no competing ambiguous parses. If, on the other hand, there are competing ambiguities or sufficient uncertainty, then step 322 is performed, to set the dialog flow step so that the execution phase causes the dialog to output a prompt for more in-formation from the user.

[0249] In 312, the dialog flow processor component( s) 1080 determine the preferred interpretation of the semantic parse with other information to de-termine the task to perform and its parameters. Information may be ob-tained, for example, from domain models 1056, task flow models 1086, and/or dialog flow models 1087, or any combination thereof. In the current example, the task is identified as getting a reservation, which involves both finding a place that is reservable and available, and effecting a transaction to WO 2011/088053 PCT/L'S2011/020861 reserve a table. Task parameter's are the time constraint along with others that are inferred in step 312.

[02501 In 320, the task flow model is consulted to determine an appropri-ate next step. information may be obtained, for example, from domain models 1056, task flow models 1086, and/or dialog flow models 108;", or any combi-nation thereof. In the example, it is determined that in this task flow the next step is to elicit missing parameters to an availability search for restaurants, resulting in prompt 3003 illustrated in Fig. 30, requesting party size and time for a reservation.

[0251' As described above, Fi ;. 31 depicts screen 3101 is shown including dialog element 3102 that is presented after the user answers the request for the party size and reservation time. In one embodiment, screen 3101 is pre-sented as the result of another iteration through an automated call and re-sponse procedure, as described in connection with Fig. 33, which leads to an-other call to the dialog and flow procedure depicted in Fig. 32. In this instan-tiation of the dialog and flow procedure, after receiving the user preferences, dialog flow processor component(s) 1080 determines a different task flow step in step 320: to do an availability search. When request 390 is constructed, it includes the task parameters sufficient for dialog flow processor compo-nent(s) 1080 and services orchestration component(s) 1082 to dispatch to a res-taurant booking service.

Dialog Flow Models Component(s) 1087 [0252] In at least one embodiment, dialog flow models component(s) 1087 may be operable to provide dialog flow models, which represent the steps one takes in a particular kind of conversation between a user and intelligent auto-mated assistant 1002. For example, the dialog flow for the generic task of per-forming a transaction includes steps for getting the necessary data for the transaction and confirming the transaction parameters before committing it.

WO 2011!088053 PC T/LTS2011/020861 Task Flow Models Component(s) 1086 [0253] in at least one embodiment, task flow models component(s) 1086 may be operable to provide task flow models, which represent the steps one takes to solve a problem or address a need. For example, the task flow for getting a dinner reservation involves finding a desirable restaurant, checking availability, and doing a transaction to bet a reservation for a specific time with the restaurant.

[0254] According to specific embodiments, multiple instances or threads of task flow models component(s) 1086 may be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other coir:-binations of hardware and/or hardware and software. For example, in at least some embodiments, various aspects, features, and/or functionalities of task flow models component(s) 1086 may be may be implemented as pro-grams, state machines, or other ways of identifying an appropriate step in a flow graph.

[0255] In at least one embodiment, task flow models component(s) 1086 !nay use a task modeling framework called generic tasks. Generic tasks are abstractions that model the steps in a task and their required inputs and gen-erated outputs, without being specific to domains. For example, a generic task for transactions might include steps for gathering data required for the transaction, executing the transaction, and outputting results of the transac-tion -- all without reference to any particular transaction domain or service for implementing it. It might be instantiated for a domain such as shopping, but it is independent of the shopping domain and might equally well apply to domains of reserving, scheduling, and the like.

[0256] At least a portion of the functions, operations, actions, and; or other features associated with task flow models component(s) 1086 and/ or proce-dure(s) described herein may be implemented, at least in part, using concepts, features, components, processes, and/or other aspects disclosed herein in connection with generic task modeling framework.

[0257] Additionally, at least a portion of the functions, operations, actions, and/or other features associated with tasl< flow models component(s) 1086 and / or procedure(s) described ne"ein may" be implemented, at least In part, using concepts, reatures, components, -processes, and/ o,,, other aspects relat-ing to constrained selection tasks, as described herein. For example, one err:-bodiment of generic tasks max be implemented usine a const dined selection task model.

[0258] in at least one embodiment, a given instance of task flow models component(s) 1086 may access and/or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the da-tabase information may be accessed via communication with one or more lo-cal and/or remote memory devices. Examples of different types of data which may be accessed by task flow models component(s) 1086 may include, but are not limited to, one or more of the following (or combinations thereof):
= Domain models 1056;

= Vocabulary 1058;

= Domain entity databases 1072;
= Short term memory 1052;

= Long term personal memory 1054;
= Dialog flow models 108%;

= Service capability models 1088.

[0259] Referring now to Fig. 34, there is shown a flow diagram depicting an example of task flow for a constrained selection task 351 according to one embodiment.

[0260] Constrained selection is a kind of generic task in which the goal is to select some item from a set of items in the world based on a set of con-straints. For example, a constrained selection task 351 may be instantiated for the domain of restaurants. Constrained selection task 351 starts by soliciting criteria and constraints from the user 352. For example, the use.- might be in-terested in Asian food and may want a place to eat near his or her office.

[02611 In step 353, assistant 1002 presents items that meet the stated crite-ria anQ constraintfor the user to 'Drowse. this exam le, it may be a _ st of ~
restaurants and their properties which may be used to select among then.:.

[0262] In step 354, the user is oven an opportunity to refine criteria and constraints. For example, the user might reline the request by saying near my office". The system WoulQ then present a new set of results in step. 35,3.

[0263] Referring now also to Fig. 35, there is shown an example of screen 3501 including list 3502 of items presented by constrained selection task 351 according to one embodiment.

[0264] In step 355, the user can select among the matching items. And- of a number of follow-on tasks 359 may then be made available, such as for exam-pie book 356, remember 357, or share 358. In various embodiments, follow-on tasks 359 can involve interaction with web-enabled services, and,/or with functionality local to the device (such as setting a calendar appointment, mak-ing a telephone call, sending an email or text messa(re, setting an alarm, and the like).

[0265] In the example of Fig. 35, the user can select an item within list 3502 to see more details and to perform additional actions. Referring now also to Fig. 36, there is shown an example of screen 3601 after the user has selected an item from list 3502. Additional information and options corresponding to fol-low-on tasks 359 concerning the selected item are displayed.

[0266] In various embodiments, the flow steps may be offered to the user in any of several input modalities, including but not limited to any combina-tion of explicit dialog prompts and GUI links, Services Component(s) 1084 [0267] Services component(s) 1084 represent the set of services that intelli-gent automated assistant 1002 might call on behall of the user. Any; service that can be called may be offered in a services component 1084.

[0268] In at least one embodiment, services component(s) 1084 may, be op-erable to perform and/or implement various types of functions, operations, actions, and/or other features such as, for example; one or more of the follow-int (or combinations thereof;;

Provide the functions over an API that would normally be provided by a web-based user interface to a service. For example, a review website night provide a service API that would return reviews of a riven entity autoinaticaliv when called by a program. The API of-fers to intelligent automated assistant 1002 the services that a hu-man would otherwise obtain by operating the user interface of the website.

= Provide the functions over an API that would normally be provided by a user interface to an application. For example, a calendar appli cation might provide a service API that would return calendar en-tries automatically when called by a program. The API offers to in-telligent automated assistant 1002 the services that a human would otherwise obtain by operating the user interface of the application.
In one embodiment, assistant 1002 is. able to initiate and control ant' of a number of different functions available on the device. For ex-ample, if assistant 1002 is installed on a smartphone, personal dig -taI assistant, tablet computer, or other device, assistant 1002 can perform functions such as: initiate applications, make calls, send emails and/or text messages, add calendar' events, set alarms, and the like. In one embodiment, such functions are activated using services component(s) 1084.

Provide services that are not currently implemented in a user inter-face, but that are available through an API to assistant in larger tasks. For example, in one embodiment, an API to take a street ad-dress and return machine-readable geo-coordinates might be used by assistant 1002 as a service component 1084 ever if it has no d-_-' I

user inter ace on the web or a device.

[0269] According to specific embodiments, multiple instances or threads of services components l 1084 may be concurrently implemented and/ or initi-ated via the use of one or more processors 63 and/or other combinations of hardware and; or hardware and software. For example, in at least some em-bodiments, various aspects, features, and/ or functionalities of services com-ponent(s) 1084 may be performed, implemented and/ or initiated by one or more of the following types of systems, components, systems, devices, proce-dures, processes, and the like (or combinations thereof):

= implementation of an API exposed by a service, locally or remotely or any combination;

= inclusion of a database within automated assistant 1002 or a data-base service available to assistant 1002, [02701 For example, a website that offers users an interface for browsing movies might be used by an embodiment of intelligent automated assistant 1002 as a copy of the database used by the website. Services component(s) 1084 would then offer an internal API to the data, as if it were provided over a network API, even though the data is kept locally.

[0271) As another example, services component(s) 1084 for an intelligent automated assistant 1002 that helps with restaurant selection and meal plan-ning might include any or all of the foilowing set of services which are avail-able from third parries over the network:

= a set of restaurant listing services which lists restaurants matching name, location, or other constraints;

= a set of restaurant rating services which return rankings for named restaurants;

= a set of restaurant reviews services which returns written reviews for named restaurants;

= a geocoding service to locate restaurants on a map;

= a reservation service that enables programmatic reservation of ta-bles at restaurants.

Services Orchestration Component(s) 1062 [0272] Services orchestration component(s) 1052 of intelligent automated assistant 1002 executes a service orchestration procedure.

[0273] In at least one embodiment, services orchestration component(s) 1082 may be operable to perform and/ or implement various types of func-tions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof):

= Dynamically and automatically determine which services may meet the user's request and/or specified domain(s) and task(s);

= Dynamically and automatically call multiple services, in any com-bination of concurrent and sequential ordering;

= Dynamically and automatically transform task parameters and con-straints to meet input requirements of service APIs;

= Dvnamically and automatically monitor for and gather results from multiple services;

= Dynamically and automatically merge service results data from various services into to a unified result model, = Orchestrate a plurality of services to meet the constraints of a re-quest;

= Orchestrate a plurality of services to annotate an existing result set with auxiliary information;

= Output the result of calling a plurality of services in a unifo-m, ser-vice independent representation that unifies the results from the various services (for example, as a result of calling several restau-rant services that return lists of restaurants, merge the data on at least one restaurant from the several services, removing redun-dancy).

WO 2011/088"53 PCT/IIS2011/0208G1 [0274] For example, in some situations, there may be several ways to ac-comnlish a particular task. For exampie, user input such as "remind me to leave for 1m1my meetin across town at do Specines an action t -at car. ne aC-comollshed in at least three ways: Set alarm clock; create a calendar event;
or call a to-do manager. In one embodiment, services orchestration compo-nent(s) 1082 makes the determination as to which warr to best satisfy the re-quest.

[0275] Services orchestration component(s) 1082 can also make detei"inina-tions as to which combination of several services would be best to invoke in order to perform a given overall task. For example, to find and reserve a table for dinner, services orchestration component(s) 1082 would make determina-tions as to which services to call in order to perform such functions as l ooking up reviews, betting availability, and making a reservation. Determination of which services to use may depend on any of a number of different factors.

For example, in at least one embodiment, information about reliability, ability of service to handle certain types of requests, user feedback, and the like, can be used as factors in determining which service(s) is/are appropriate to in-voke.

[0276] According to specific embodiments, multiple instances or threads of services orchestration component(s) 1082 may be concurrently implemented and/or initiated via the use of one or more processors and/or other combina-tions of hardware and/or hardware and software.

[0277] In at least one embodiment, aiven instance of services orchestra-tion component(s) 1082 may use explicit service capability models 1088 to represent the capabilities and Other' properties of external services, and reason about these capabilities and properties while achieving the features of services orchestration component(s) 1082. This affords advantages over manually programming a set of services that may include, for example, one or more O.

the following (or combinations thereof,:
= Ease of development;

= Robustness and reliability in execution;

= The ability- to dynamically add and remove services without dis-rup ing code;

= The ability to implement general distributed query optimization al-gorithms that are driven by the properties and capabilities rather than hard coded to specific services or APIs.

[0278] In at least one embodiment, a given instance of services orchestra-tion component(s) 1082 may access and/or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/or remote memory devices. Examples of different types of data which may be accessed by services orchestration component(s) 1082 may include, but are not limited to, one or more of the following (or combinations thereof):

= Instantiations of domain models;

= Syntactic and semantic parses of natural language input;

= Instantiations of task models (with values for parameters);

= Dialog and task flow models and/ or selected steps within them;
= Service capability models 1088;

= Any other information available in an active ontology 1070.

[0279] Referring now to Fig. 37, there is shown an example of a procedure for executing a service orchestration procedure according to one embodiment.
[0280] In this particular example, it is assumed a single user is interesting in finding a good place for dinner at a restaurant, and is engaging intelligent automated assistant 1002 in a conversation to help provide this service.

[0281] Consider the task of finding restaurants that are of high quality, are well reviewed, near a particular location, available for reservation at a par-ticular time, and serve a particular kind of food. These domain and task pa-rameters are given as input 390.

WO 2011/088053 PCT/U~S2011/020861 [0282] The method begins 400. At 402, it is determined whether the given request may require any, services. In some situations, services delegation may not be required, for example if assistant 1002 is able to perform the desired task itself, For example, in one embodiment, assistant 1002 may be able to an-swer a factual question without invoking services delegation. Accordingly if the request does not require services, then standalone flow step is executed in 403 and its result 490 is returned. For example, if the task request was to ask for information about automated assistant 1002 itself, then the dialog re-sponse may be handled without invoking any external services.

[0283] If, in step 402, it is determined that services delegation is required, services orchestration component(s) 1082 proceed to step 404. In 404, services orchestration component(s) 1082 may match up the task requirements with declarative descriptions of the capabilities and properties of services in service capability models 1088. At least one service provider that might support the instantiated operation provides declarative, qualitative metadata detailing, for example, one or more of the following (or combinations thereof):

= the data fields that are returned with results;

= which classes of parameters the service provider is statically known to support;

= policy functions for parameters the service provider might be able to support after dynamic inspection of the parameter values;

= a performance rating defining how the service performs (e.g. rela-tional DB, web service, triple store, full-text index, or some comb-nation thereof);

= property quality ratings statically defining the expected quality of property values returned with the result object;

= an overall quality rating of the results the service may expect to re-turn.

[0284] For example, reasoning about the classes of parameters that service may Support, a Service model May state that services 1, 2, 3, and 4 may pro-vide restaurants that are near a particular location (a parameter), services -and . may filter or rank restaurants rnv quality (another parameter), services J, 4, and r may return reviews for restaurants (a data field returned), service o may list the food types served by restaurants (a data field returned), and ser-vice 7 may check availability of restaurants for particular time ranges a pa-rameter). Services S through 99 offer capabilities that are not required for this particular domain and task.

[0285] Using this declarative, qualitative metadata, the task, the task pa-rameters, and other information available from the runtime environment of the assistant, services orchestration component(s) 1082 determines 404 an op-timal set of service providers to invoke. The optimal set of service providers may support one or more task parameters (returning results that satisfy one or more parameters) and also considers the performance rating of at least one service provider and the overall quality rating of at least one service provider.
[0286] The result of step 404 is a dynamically generated list of services to call for this particular user and request.

[0287] In at least one embodiment, services orchestration component(s) 1082 considers the reliability of services as well as their ability to answer spe-cific information requests.

[0288] In at least one embodiment, services orchestration component(s) 1082 hedges against unreliability by calling overlapping or redundant ser-vices.

[0289] In at least one embodiment, services orchestration component(s) 1082 considers personal information about the user (from the short term per-sonal memory component) to select services. For example, the use,, may pre-fer some rating services over others.

[0290] in step 450, services orchestration component(s) 1082 dynamically and automatically invokes multiple services on behalf of a user. In at least one embodiment, these are called dynamically while responding to a user's request. According to specific embodiments, multiple instances or threads of the services may be concurrently called. In at least one embodiment, these are called over a network using APIs, or over a network using web service APIs, or over the Internet using web service APIs, or any combination thereof.
[0291] In at least one embodiment, the rate at which services are called is programmatically limited and or managed.

[0292] Referring now also to Fig. 38, there is shown an examvle of a se, -vice invocation procedure 450 according to one embodiment. Service invoca-tion is used, for example, to obtain additional information or to perform tasks by the use of external services. in one embodiment, request parameters are transformed as appropriate for the service's API. Once results are received from the service, the results are transformed to a results representation for presentation to the user within assistant 1002.

[0293] In at least one embodiment, services invoked by service invocation procedure 450 can be a web service, application running on the device, oper-ating system function, or the like.

[0294] Representation of request 390 is provided, including for example task parameters and the like. For at least one service available from service capability models 1088, service invocation procedure 450 performs transfor-mation 452, calling 454, and output-mapping 456 steps.

[0295] In transformation step 452, the current task parameters from re-quest representation 390 are transformed into a form that may be used by at least one service. Parameters to services, which may be offered as APIs or da-tabases, may differ from the data representation used in task requests, and also froi-n at least one other. Accordingly, the objective of step 452 is to map at least one task parameter in the one or more corresponding formats and values in at least one service being called.

[0296] For example, the names of businesses such as restaurants may vary across services that deal with such businesses. Accordingly, step 452 would involve transforming any names into forms that are best suited for at least one service.

[0297] As another example, locations are known at various levels of preci-sion and using various units and conventions across services. Service I might n,av require ZIP codes, service 2 GPS coordinates, and service J postal street addresses.

[0298] The service is called 454 over an API and its data gathered. In at least one embodiment, the results are cached. In at least one embodiment, the services that do not return within a specified level performance (e.g., as speci-fied in Service Level Agreement or SLA) are dropped.

[0299] In output mapping step 476, the data returned by a service is mapped back onto unified result representation 490. This step may include dealing with different formats, units, and so forth.

[0300] In step 412, results from multiple services are validated and merged. In one embodiment, if validated results are collected, an eauaiity nol-icy function--defined on a per-domain basis--is then called pair-wise across one or more results to determine which results represent identical concepts in the real world. When a pair of equal results is discovered, a set of property policy functions--also defined on a per-domain basis--are used to merge property values into a merged result. The property policy function may use the property quality ratings from the service capability models, the task pa-rameters, the domain context, and/or the long-term personal memory 1054 to decide the optimal merging strategy.

[0301] For example, lists of restaurants from different providers of restau-rants might be merged and duplicates removed. In at least one embodiment, the criteria for identifying duplicates may include fuzzy name matching, fuzzy location matching, fuzzy matching against multiple properties of do-main entities, such as name, location, phone number, and/Dr website address.
and/ or any combination thereof.

[0302] in step 414, the results are sorted and trimmed to return a result list of the desired length.

[03031 In at least one embodiment, a request relaxation loop is also ap-plied. If, in step 416, services orchestration component(s) 1082 determines that the current result list is not sufficient (e.r it has fewer than the desired b"
number of matching items), then task parameters may be relaxed 420 to allow for more results. For example, if the number o restaurants of the desired sort found within N miles of the target location is too small, then relaxation would run the request again, looking in an area larger than N miles away', and/or relaxing some other parameter of the search.

[0304) In at least one embodiment, the service orchestration method is ap-plied in a second pass to "annotate" results with auxiliary data that is useful to the task.

[03051 In step 418, services orchestration component(s) 1082 determines whether annotation is required. It may he required if, for example, if the task may require a plot of the results on a map, but the primary services did not return geo-coordinates required for mapping.

[0306] In 422, service capability models 1088 are consulted again to find services that may return the desired extra information. In one embodiment, the annotation process determines if additional or better data may be anno tated to a merged result. It does this by delegating to a property policy fun.-tion--defined on a per-domain basis--for at least one property of at least one merged result. The property policy function may use the merged property value and property quality rating, the property quality ratings of one or more other service providers, the domain context, and/or the user profile to decide if better data may be obtained. If it is determined that one or more service providers may annotate one or more properties for a merged result, a cost function is invoked to determine the optimal set of service providers to anno-tate.

[0307] At least one service provider in the optimal set of annotation service providers is then invoked 450 with the list of merged results, to obtain results 424. The changes made to at least one mer ged result by atlleast one service provider are tracked during this process, and the changes are then merged using the same property pohcv function process as was Used in step 412.

Their results are merged 426 into the ex sting result set.

[0308] The resulting data is sorted -28 and unified into a uniform repre-sentation 490.

[0309] It may be appreciated that one advantage of the methods and sys-tems described above with respect to services orchestration component(s) 1082 is that they may be advantageously applied and/ or utilized in various fields of techxiology other than those specifically relating to intelligent auto-mated assistants. Examples of such other areas of technolotties where aspects and/ or features of service orchestration procedures include, for example, one or more of the following:

Dynamic "mash ups" on websites and web-based applications and services;

Distributed database query optimization;

= Dynamic service oriented architecture configuration.
Service Capability Models Component(s) 1088 [0310] In at least one embodiment, service capability models component(s) 1088 may be operable to perform and/or implement various types of func-tions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof):

Provide machine readable information about the capabilities of ser-vices to perform certain classes of computation;

Provide machine readable information about the capabilities of ser-vices to answer certain classes of queries;

= Provide machine readable information about which classes of transactions are provided by various services;

Provide machine readable information about the parameters to APIs exposed by various services;

WO 2011/088053 PCT/1?S2011/020861 = Provide machine readable information about the parameters that may be used in database Queries on databases provided by various services.

Output Processor Component(s) 1090 [0311] In at least one embodiment, output processor component(s) 1090 may be operable to perform and/or implement various types of functions, operations, actions, and/o:- other features such as, for example, one or more of the following (or combinations thereof):

= Format output data that is represented in a uniform internal data structure into forms and layouts that render it appropriately on dif-ferent modalities. Output data may include, for example, communi-cation in natural language between the intelligent automated assis-tant and the user; data about domain entities, such as properties of restaurants, movies, products, and the like; domain specific data re-sults from information services, such as weather reports, flight status checks, prices, and the like; and/ or interactive links and but-tons that enable the user to respond by directly interacting with the output presentation.

= Render output data for modalities that may include, for example, any combination o'_=: b aphical user interfaces; text messages; email messages; sounds; animations; and/or speech output.

= Dynamically render data for different 2 rapfii.cal user interface dis-play engines based on the request. For example, use different out-put processing layouts and formats depending on which web browser and/ or device is being used.

= Render output data in different speech voices dynamically.

= Dynamically render to specified modalities based on user prefer-ences.

= Dynamically render output using user-specific "skins" that custom-ize the look and reell.
= Send a stream of output packages to a modality, showing interme-diate status, feedback:, or results throughout phases of interaction with assistant 1002.

x0312] According to specific embodiments, multipie instances or threads of output processor component(s) 1090 may be concurrently implemented and/or initiated via the use of one or more processor(s) 63 and/or other corn-binations of hardware and/or hardware and software. For example, in at least some embodiments, various aspects, features, and/ or functlonalities o"
output processor component(s) 1090 may be performed, implemented and/or initiated by one or more of the following types of systems, components, sys-tems, devices, procedures, processes, and the like (or combinations thereof):

= software modules within the client or server of an embodiment of an intelligent automated assistant;

= remotely callable services;

= using a mix of templates and procedural code.

[0313] Referring now to Fig. 39, there is shown a flow diagram depicting an example of a multiphase output procedure according to one embodiment.
The multiphase output procedure includes automated assistant 1002 process-ing steps 702 and multiphase output steps 704 [0314] In step 710, a speech input utterance is obtained and a speech-to-text component (such as component described in connection with Fig. 22) irn-terprets the speech to produce a set of candidate speech interpretations 712.
In one embodiment, speech-to-text component is implemented using, for ex-ample, Nuance Recognizer, available from Nuance Communications, Inc. of Burlington, MA. Candidate speech interpretations 712 may be shown to the use,- jr, 730, for example in paraphrased form. For example. the interface might show "did you say?" alternatives listing a few possible alternative tex-tual interpretations of the same speech sound sample.

[0315] In at least one embodiment, a user interface is provided to enable the user to interrupt and choose among the candidate speech interpretations.
[0316] In step: 714, the candidate speech interpretations 7i2 are sent to a Language interpreter 1070, ~,wwrach may produce representations of user intent 716 for at least one candidate speech interpretation 712. In step 732, para-phrases, of these representations of user intent 716 are generated and pre-sented to the user. (See related step 132 of procedure 120 in Fig. 22`

[03171 In at least one embodiment, the user interface enables the user to interrupt and choose among the paraphrases of natural language interpreta-tions 732.

[0318] In step 718, task and dialog analysis is performed. In step 7714, task and domain interpretations are presented to the user using an intent para-phrasing algorithm.

[0319] Referring now also to Fig. 40, there is shown a screen shot depicting an example of output processing according to one embodiment. Screen 4001 includes echo 4002 of the user's speech input, generated by step 730. Screen 4001 further includes paraphrase 4003 of the user's intent, generated by step 734. In one embodiment, as depicted in the example of Fig. 40, special format-ting/highlighting is used for key words such as `events", which may be used to facilitate training of the user for interaction with intelligent automated as-sistant 1002. For example, by visually observing the formatting of the dis-played text, the user may readily identify and interpret back the intelligent automated assistant recognizes keywords such as "events", "next Wednes-day", "San Francisco", and the like.

[0320] Returning to Fig. 39, as requests are dispatched 720 to services and results are dynamically gathered, intermediate results may be displayed in the form of real-time progress 736. For example, a list of restaurants may be returned and then their reviews may be populated dynamically as the results from the reviews services arrive. Services can include web-enabled services and/ or services that access information stored locally on the device and,/ or from any other source.

321] A uniform representation of response 721-2 is generates. and format-tea 724 for the appropriate output modality. After the final output format is completed, a different kind of paraphrase maybe offered in 738. In this phase, the entire result set ma v be analyzed and compared against the initial request. A summary of results or answer to a question may then be offered.
[0322] Referring also to Fig. 41, there is shown another example of output processing according to one embodiment. Screen 4101 depicts paraphrase 4102 of the text interpretation, generated by step 732, real-time progress generated by step 736, and paraphrased summary 7104 generated by step 738.
Also included are detailed results 4105.

[0323] In one embodiment, assistant 1002 is capable of generating output in multiple modes. Referring now to Fig. 42, there is shown a flow diagram depicting an example of multimodaI output processing according to one ern-bodiment.

[0324] The method begins 600. Output processor 1090 takes uniform rep-resentation of response 490 and formats 612 the response according to the de-vice and modality that is appropriate and applicable. Step 612 may include information from device and modality models 610 and/ or domain data mod-els 614.

[0325] Once response 490 has been formatted 612, any of a number of dif-ferent output mechanisms can be used, in any combination. Examples de-picted in Fig. 42 include:

Generating 620 text message output, which is sent 630 to a text mes-sage channel;

= Generating 622 email output, which is sent 632 as an email message;
Generating 624 GUI output, which is sent 634 to a device or web browser for rendering;;

WO 2011/0880.53 PCT/US2011/020861 = Generating 626 speech output, which is sent 636 to a speech genera-tion module.

[0326] One skilled in the a.-t will recognize that many other output n-iechs:-nisms can be used.

[0327] In one embodiment, the content of output messages generated by multiphase output procedure 700 is tailored to the mode of multimodal out-put processing 600. For example, if the output modality is speech 626, the language of used to paraphrase user input 730, text interpretations 732, task and domain interpretations 734, prog Tess 736, and/ or result summaries 738 may be more or less verbose or use sentences that are easier to comprehend in audible form than in written form. In one embodiment, the language is tai-lored in the steps of the multiphase output procedure 700; in other embodi-ments, the multiphase output procedure 700 produces an intermediate result that is further refined into specific language by multim.odal output processing 600.

Short Term Personal Memory Components) 1 052 [0328] In at least one embodiment, short term personal memory compo-nent(s) 1052 may be operable to perform and/or implement various types of functions, operations, actions, and/ or other features such as, for exampie, one or more of the following (or combinations thereof):

= Keep a history of the recent dialog between the embodiment of the assistant and the user, including the history of user inputs and their interpretations;

= Keep a history of recent selections by the user in the GUI, such as which items were opened or explored, which phone numbers were called, which items were mapped, which movie trailers where played, and the like;

= Store the history of the dialog and user interactions in a database on the client, the server in a user-specific session, or in client session state such as web browser cookies or RAM used by the client;

= Store the list of recent user requests;

= Store the sequence of results of recent user requests;

= Store the click-stream history of UI events, inciu ding button presses, taps, gestures, voice activated triggers, and or any other user input.

= Store device sensor data (such as location, time, positional orienta-tion, motion, light level, sound level, and the like) which might be correlated with interactions with the assistant.

[0329] According to specific embodiments, multiple instances or threads of short term personal memory component(s) 1052 may be concurrently imple-mented and/or initiated via the use of one or more processors 63 and/or other combinations of hardware and/or hardware and software.

[0330] According to different embodiments, one or more different threads or instances of short term personal memory component(s) 1052 may be initi-ated in response to detection of one or more conditions or events satisfying one or more different types of minimum threshold criteria for triggering ini-tiation of at least one instance of short term personal memory component(s) 1052. For example, short term personal memory component(s) 1052 may be invoked when there is a user session with the embodiment of assistant 1002, on at least one input form or action by the user or response by the system.
[03311 In at least one embodiment, a given instance of short term personal memory component(s) 1052 may access and/or utilize information from one or more associated databases. in at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/or remote memory devices. For example, short term personal memory component(s) 1052 may access data from Long-term personal mem-oi"v components(s) 1054 (for example, to obtain user identity and personal 204 _ preferences) and/or data from the local device about time and location, which may be included in short term memory entries.

[0332] Referring now to Figs. 43A and 433, there are shown screen shots depicting an example of the use of short term personal memory components s) 1052 to maintain dialog context while changing location, according to one embodiment. In this example, the user has asked about the local weather, then just says "in new york". Screen 4301 shows the initial response, includ-ing local weather. When the user says "in new york", assistant 1002 uses short term personal memory component(s) 10721 to access the dialog context and thereby determine that the current domain is weather, forecasts. This en-ables assistant 1002 to interpret the new utterance "in new York" to mean "what is the weather forecast in New York this coming Tuesday?". Screen 4302 shows the appropriate response, including weather forecasts for New York.

[0333] In the example of Figs. 43A and 433, what was stored in short term memory was not only the words of the input "is it going to rain the day after tomorrow?" but the system's semantic interpretation of the input as the weather domain and the time parameter set to the day after tomorrow.
Long-Term Personal Memory Component(s) 1054 [0334] In at least one embodiment, long-term personal memory compo-nent(s) 1054 may be operable to perform and/or implement various types of functions, operations, actions, and/or other features such as, for example, one or more of the following (or combinations thereof):

Tc> persistently store the personal information and data about a user, including for example his or her preferences, identities, au-thentication credentials, accounts, addresses, and the like;

= To store information that the user has collected by using the em-bodiment of assistant 1002, such as the equivalent of bookmarks, favorites, clippings, and the like;

= To persistently store saved lists of business entities including res-taurants, hotels, stores, theaters and other venues. In one embodi-ment, long-term personal memory comuonent(sl 10,54 saves more than just the names or URLs, but also saves the information suff_ cient to bring up a full listing or, the entities including phone num-bers, locations on z map, photos, and the like, = To persistently store saved movies, videos, music, shows, and other items of entertainment;

= To persistently store the user's personal calendar(s), to do list(s`), reminders and alerts, contact databases, social network lists, and the like;

= To persistently store shopping lists and wish lists for products and services, coupons and discount codes acquired, and the like;

= To persistently store the history and receipts for transactions in-cluding reservations, purchases, tickets to events, and the like.
[03351 According to specific embodiments, multiple instances or threads of long-term personal memory component(s) 10,54 may be concurrentI, imple-mented and/or initiated via the use of one or more processors 63 and/or other combinations of harden- are and/or hardware and software. For exam-ple, in at least some embodiments, various aspects, features, and/or function-alities of long-term personal. memory component(s) 1054 maybe performed, implemented and/or initiated using one or more databases and/or files on (or associated with) clients 1304 and/or servers 1340, and/or residing on stor-age devices.

[0336) According to different embodiments, one or more different threads or instances of long-term personal memory component(s) 1054 may be initi-ated in response to detection of one or more conditions or events satisfying one or more different types of minimum threshold criteria for triggering ini-tiation of at least one instance of long-term personal memory component(s) 1054. Various examples of conditions or events which may trigger initiation and/or implementation of one or more different threads or instances of Long-term personal memory component(s) 1054 may include, but are not limited to, one or more of the following (or combinations thereofi:

= Long term personal memory entries may be acquired as a side ef-fect of the user interacting with an embodiment of assistant 1002.
Any kind of interaction with the assistant may produce additions to the long term personal memory, including browsing, searching, finding, shopping, scheduling, purchasing, reserving, communicat-ing with other people via an assistant.

= Long term personal memory may also be accumulated as a conse-quence of users signing up for an account or service, enabling assis-tant 1002 access to accounts on other services, using an assistant 1002 service on a client device with access to other personal infor-mation databases such as calendars, to-do lists, contact lists, and the like.

[0337] In at least one embodiment, a given instance of long-term personal memory component(s) 1054 may access and/or utilize information from one or more associated databases. In at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/or remote memory devices, which may be located, for exam-ple, at client(s) 1304 and/ or server(s) 1340. Examples of different types of data which may be accessed by Long-term personal memory component(s) 1054 may include, but are not limited to data from other personal information da-tabases such as contact or friend lists, calendars, to-do lists, other list manag-ers, personal account and wallet managers provided by external services 1.360, and the like.

[0338] Referring now to Figs. 44A through 44C, there are shown screen shots depicting an example of the use of long term personal memory compo-nent(s) 1054, according to one embodiment. in the example, a feature is pro-vided (named "My. Stuff"), which includes access to saved entities such as res-taurants, movies, and businesses that are found Via interactive sessions with an embodiment of assistant 1002. in screen 4401 of Fig. 44A, the user has round a restaurant. The user taps on Save to My Stuff 4402, which saves in-formation about the restaurant in long-term personal memory component(s) 1054.

[0339] Screen 4403 of Fig. 44B depicts user access to My Stuff. In one em-bodiment, the user can select among categories to navigate to the desired item.

[0340] Screen 4404 of Fig. 44C depicts the My Restaurant categoryT. includ-ing items previously stored in Mr Stuff.

Automated Call and Response Procedure [0341] Referring now to Fig. 33, there is shown a flow diagram depicting an automatic call and response procedure, according to one embodiment. The procedure of Fig. 33 may be implemented in connection with one or more embodiments of intelligent automated assistant 1002. It may be appreciated that intelligent automated assistant 1002 as depicted in Fig. I is merely one example from a wide range of intelligent automated assistant system em-bodiments which may be implemented. Other embodiments of intelligent automated assistant systems (not shown) may include additional, fewer and / or different components,/ features than those illustrated, for example, in the example intelligent automated assistant 1002 depicted in Fig. 1.

[0342] In at least one embodiment, the automated call and response pro-cedure of Fig. 33 may be operable to perform and/or implement various types of functions, operations, actions, and/ or other features such as, for ex-ample, one or more of the following (or combinations thereof):

The automated call and response procedure of Fig. 33 may provide an interface control flow loop ofa conversational interface between the user and intelligent automated assistant 1002. At least one itera-tion of the automated call and response procedure may serve as a WO 2011/0880,53 PCT/US2011/020861 ply in the conversation. A conversational interface is an interface in which the user and assistant 1002 communicate by matting utter-ances back and forth in a conversational manner.

= The automated call and response procedure of Fig. 33 may provide the executive control flow for intelligent automated assistant 1002.
That is, the procedure controls the gathering of input, processing of input, generation of output, and presentation of output to the user.

= The automated call and response procedure of Fig. 33 may coordi-nate communications among components of intelligent automated assistant 1002. That is, it may direct where the output of one com-ponent feeds into another, and where the overall input from the en-vironment and action on the environment may occur.

[0343] In at least some embodiments, portions of the automated call and response procedure may also be implemented at other devices and/or sys-tems or a computer network.

[0344] According to specific embodiments, multiple instances or threads of the automated call and response procedure may be concurrently imple-mented and/or initiated via the use of one or more processors 63 and/or other combinations of hardware and/ or hardware and software. In at least one embodiment, one or more or selected portions of the automated call and response procedure may be implemented at one or more client(s) 1304, at one or more server(s) 1340, and/or combinations thereof.

[0345] For example, in at least some embodiments, various aspects, fea-tures, and/or functionaiities of the automated call and response procedure may he performed, implemented and; or initiated by software components, network services, databases, and/ or the like, or any combination thereof.
[0346] According to different embodiments, one or more different threads or instances of the automated call and response procedure maybe initiated in response to detection of one or more conditions or events satisfying one or more different types of criteria (such as, for example, minimum threshold cri-WO 2011/088053 PCT/IIa2011/020861 teria) for trigge-if ring initiation o at least one instance of automated -al, and response procedure. Examples of various types of conditions or events which mays trigger initiation and,/ or implementation of one or more different threads or instances of the automated call, and response procedure may in-clude, but are not limited to, one or more of the following (or combinations thereof):

= a user session with an instance of intelligent automated assistant 1002, such as, for example, but not limited to. one or more of:

o a mobile device application starting up, for instance, a mo-bile device application that is implementing an embodiment of intelligent automated assistant 1002;

o a computer application starting up, for instance, an applica-tion that is implementing an embodiment of intelligent automated assistant 1002;

o a dedicated button on a mobile device pressed, such as a "speech input button";

o a button on a peripheral device attached to a computer or mobile device, such as a headset, telephone handset or base station, a GPS navigation system, consumer appliance, re-mote control, or any other device with a button that might be associated with invoking assistance;

o a web session started from a web browser to a website im-plementing intelligent automated assistant 1002;

o an interaction started from within an existing web browser session to a website implementing intelligent automated as-sistant 1002, in which, for example, intelligent automated as-sistant 1002 service is requested;

c an email message sent to a modality server 1426 that is me-diating, communication with an embodiment of intelligent automated assistant 1002;

o a text message is sent to a modality server 1426 that is medh ating communication with an embodiment of intelligent automated assistant 100';

o a phone call is made to a modality serve.-1434 that is mediat-ing communication with an embodiment of intelligent auto-mated assistant 1002;

o an event such as an alert or notification is sent to an applica-tion that is providing an embodiment of intelligent auto-mated assistant 1002.

= when a device that provides intelligent automated assistant 1002 is turned on and/or started.

[0347] According to different embodiments, one or more different threads or instances of the automated call and response procedure may be initiated and/or implemented manually, automatically, statically, dynamically, con-currently, and/ or combinations thereof. Additionally, different instances and/or embodiments of the automated call and response procedure may be initiated at one or more different time intervals (e.g., during a specific time interval, at regular periodic intervals, at irregular periodic intervals, upon demand, and the like).

[0348] In at least one embodiment, a given instance of the automated call and response procedure may utilize and/ or generate various different types of data and/or other types of information when performing specific tasks and/or operations. This may include, for example, input data/ information and/or output data/ information. For example, in at least one embodiment, at least one instance of the automated call and response orocedure may access, process, and/ or otherwise utilize information from one or more different types of sources, such as, for example, one or more databases. In at least one embodiment, at least a portion of the database information may be accessed via communication with one or more local and/ or remote memory devices.
Additionally, at least one instance of the automated call and response proce-dure may generate one or more different types of output data/in ormLation., which, for example, may be stored in local memory and /or remote memory devices.

[0349] In at least one embodiment, initial configuration of a given instance of the automated call and response procedure may be performed using one or more different types of initialization parameters. in at least one embodiment, at least a portion of the initialization parameters max be accessed via commu-nication with one or more local and/or remote memory devices. In at least one embodiment, at least a portion of the initialization parameters provided to an instance of the automated call and response procedure may correspond to and/or may be derived from the input data/information.

[0350] In the particular example of Fig. 33, it is assumed that a single user is accessing an instance of intelligent automated assistant 1002 over a network from a client application with speech input capabilities. The user is interested in finding a good place for dinner at a restaurant, and is engaging intelligent automated assistant 1002 in a conversation to help provide this service.

[0351] In step 100, the user is prompted to enter a request. The user inter-face of the client offers several modes of input, as described in connection with Fib. 26. These max include, for example:

= an interface for typed input, which may invoke an active typed-input elicitation procedure as illustrated in Fig. 11;

= an interface for speech input, which may invoke an active speech input elicitation procedure as illustrated in Fig. 22.

= an interface for selecting inputs from a menu, which may invoke ac-tive GUI-based input elicitation as illustrated in FL-. 23.

[0352] One skilled in the art will recognize that other input modes maybe provided.

[03531 In one embodiment, step 100 may include presenting options re-maining from a previous conversation with assistant 1002, for example using the techniques described in the active dialog suggestion input elicitation pro-cedure described in connection with Fi`. 24.

[0354] For example, by one of the methods of active input elicitation in step 100, the user might say to assistant 1002, "where may I get some good 117 - Italian around here?" For example, the user might have spoken this into a speech input component. An embodiment of an active input elicitation com-ponent 1094 calls a speech-to-text service, asks the user for confirmation, and then represents the confirmed user input as a uniform annotated input format 2690.

[0355] An embodiment of language interpreter component 1070 is then called in step 200, as described in connection with Fig. 29. Language inter-preter component 1070 parses the text input and generates a list of possible interpretations of the user's intent 290. In one parse, the word "italian" is as-sociated with restaurants of style Italian; "good" is associated with the reconn-inencation propertu of restaurants; and "around here" is associated with a loca-tion parameter describing a distance from a global sensor reading (for exam-0 pie, the user's location as given by GPS on a mobile device).

[0356] In step 300, the representation of the user's intent 290 is passed to dialog flow processor 1080, which implements an embodiment of a dialog and flow analysis procedure as described in connection with Fib. 32. Dialog flow processor 1080 determines which interpretation of intent is most likely, maps this interpretation to instances of domain models and parameters of a task model, and determines the next flow step in a dialog flow. In the current ex-ample, a restaurant domain model is instantiated with a constrained selection task to find a restaurant by constraints (the cuisine style, recommendation level, and proximity constraints). The dialog flow model indicates that the next step is to get some examples of restaurants meeting these constraints and present them to the user.

[0357] In step 400, an embodiment of the flow and service orchestration procedure 400 is invoked, via services orchestration component 1082. It in-WO 2011/0880.53 PCT/US2011/020861 yokes a set of services 1084 on behalf of the user's request to find a restaurant.
In one embodiment, these services 1084 contribute some data to a common result. Their data are merged and the resuidng List of restaurants is repre-sented in a uniform, service-independent form.

[0358] In step 500, output processor 1092 generates a dialog summary of the results, such as, "I found some recommended Italian restaurants near here." Output processor 1092 combines this summary with the output result data, and then sends the combination to a module that formats the output for the user's particular mobile device in step 600.

[0359] In step 700, this device-specific output package is sent to the mobile device, and the client software on the device renders it on the screen (or other output device) of the mobile device.

[0360] The user browses this presentation, and decides to explore different options. If the user is done 790, the method ends. If the user is not done 490, another iteration of the loop is initiated by returning to step 100.

[0361] The automatic call and response procedure may be applied, for ex-ample to a user's query "how about mexican food?". Such input may be elic-ited in step 100. In step 200, the input is interpreted as restaurants of stair Mexican, and combined with the other state (held in short term personal memory 1052) to support the interpretation of the same intent as the last time, with one change in the restaurant style parameter. In step 300, this updated intent produces a refinement of the request, which is given to service orches-tration component(s) 1082 in step 400.

[0362] in step 400 the updated request is dispatched to multiple services 1084, resulting in a new set of restaurants which are summarized in dialog in 500, formatted for the device in 600, and sent over the network to show new information on the user's mobile device in step 700.

[0363] In this case, the user finds a restaurant of his or her liking, shows it on a map, and sends directions to a friend.

WO 2011/0880-53 PCT/US2011/020861' [0364] One skilled in the art will recognize that different embodiments of the automated call and response procedure (not shown) may include addi-tional features and/or operations than those illustrrated in the specific em-bodiment of Fig. 33, and/ or may omit at least a portion of the features and/or operations of automated call and response procedure illustrated in the spe-cific embodiment of Fig. 33.

Constrained Selection [0365] In one embodiment, intelligent automated assistant 1002 uses con-strained selection in its interactions with the user, so as to more effectively identify and present items that are likely to be of interest to the user.

[0366] Constrained selection is a kind of generic task. Generic tasks are abstractions that characterize the kinds of domain objects, inputs, outputs, and control flow that are common among a class of tasks. A constrained se-lection task is performed by selecting items from a choice set of domain objects (such as restaurants) based on selection constraints (such as a desired cuisine or location). In one embodiment, assistant 1002 helps the user explore the space of possible choices, eliciting the user's constraints and preferences, presenting choices, and offering actions to perform on those choices such as to reserve, buy, remember, or share them. The task is complete when the user selects one or more items on which to perform the action.

[0367] Constrained selection is useful in many contexts: for example, pick-ing a movie to see, a restaurant for dinner, a hotel for the night, a place to buy a book, or the like. In general, constrained selection is useful when one knows the category and needs to select an instance of the category with some desired properties.

[0368] One conventional approach to constrained selection is a directory service. The user picks a category and the system offers a list of choices. In a local directory, one may constrain the director- to a location, such as a city.
For instance, in a "yellow pages" service, users select the book for a city and then look up the category, and the boot: shows one or more items for that category. The main problem with a directory service is that the number of possibly relevant choices is large (e.., restaurants in given city;.

[0369] Another conventional approach is a database application, which provides a way to generate a choice set by eliciting a query from the user, re- matching items, and presenting the items in some way that highlights salient features. The user browses tine rows and columns of the result set, possibly sorting the results or changing the query until he or she finds some suitable candidates. The problem with the database service is that it may re-quire the user to operationalize their human need as a formal query and to use the abstract machinery of sort, filter, and browse to explore the resulting data. These are difficult for most people to do, even with graphical user inter-faces.

[0370] A third conventional approach is open-ended search, such as "local.
search". Search is easy to do, but there are several problems with search ser-vices that make them difficult for people to accomplish the task of constrained selection. Specifically:

= As with directory search, the user may not just enter a category and look at one or more possible choice, but must narrow down the list.
= If the user can narrow the selection by constraints, it is not obvious what constraints may be used (e.g., may I search for places that are within walking distance or are open late?) = It is not clear how to state constraints (e.g., is it called cuisine or res-taurant type, and what are the possible values?) = Multiple preferences conflict; there is usually no objectively "best"
answer to a given situation (e.g., I want a place that is close by and cheap serving gourmet food with excellent service and which is open until midnight).

= Preferences are relative, and they depend on what is available. For example, if the user may get a table at a highly rated restaurant, he L, or she might choose it even though it is expensive. In general, though, the user would prefer less expensive options.

[3371] in various embodiments, assistant 1002 of the present invention helps streamline the task of constrained selection. in various embodiments, assistant 1002 employs database and search services, as well as other func-tionality, to reduce the effort, on the part of the user, of stating what he or she is looking for, considering what is available, and deciding on a satisfactory solution.

[0372] In various embodiments, assistant 1002 helps to make constrained selection simpler for humans in any of a number o different was.

[0373] For example, in one embodiment, assistant 1002 may operationalize properties into constraints. The user states what he or she wants in terms of properties of tine desired outcome. Assistant '1002 operationalizes this input into formal constraints. For example, instead of saving "find one or more restau-rants less than 2 miles from the center of Palo Alto whose cuisine includes Italian food" the user may just say "Italian restaurants in Palo alto".
Assistant 11 1002 may also operationalize qualities requested by the user that are not pa-rameters to a database. For example, if the user requests romantic restau-rants, the system may operationalize this as a text search or tag matching, con-straint. In this manner, assistant 1002 helps overcome some of the problems users may otherwise have with constrained selection. It is easier, for a user, to imagine and describe a satisfactory solution than to describe conditions that would distinguish suitable from unsuitable solutions.

[0374] In one embodiment, assistant 1002 may suggest useful selection cri-teria, and the user need only say which criteria are important at the moment.
For example, assistant 1002 may ask "which of these matter: price (cheaper is better), location (closer is better), rating (higher rated is better)?"
Assistant 1002 may also suggest criteria that may require specific values; for example, "you can say what kind of cuisine you wouid like or a food item you wouid like".

[0375] In one embodiment, assistant 1002 may help the user make a deci-sion among choices that differ on a number of competing criteria (for e)" am-pie, price, quality, availability, and convenience).

[0376] By providing such guidance, assistant 100-1 may help users in mak-ing multiparametric decisions in any of several_ ways:

= One is to reduce the difnensionaiiti1 of the space, combining raw data such as ratings from multiple sources into a composite "recom-mendation" score. The composite score may take into account do-main knowledge about the sources of data (e.g., Zagat ratings may be more predictive of quality than Yelp).

= Another approach is to focus on a subset of criteria, turning a problem of "what are all the possible criteria to consider and how to they combine?" into a selection of the most important criteria in a giver, situation (e.g., "which is more important, price or proximity?").

= Another way to simply the decision making is to assume dcjaullt val-ues and preference orders (e.g., all things being equal, higher rated and closer and cheaper are better). The system may also remember users' previous responses that indicate their default values and preferences.

= Fourth, the system may offer salient properties of items in the choice set that were not mentioned in the original request. For ex-ample, the user may have asked for local Italian food. The system may offer a choice set of restaurants, and with them, a list of popu-lar tags used by reviewers or a tag line from a guide book (e.g., "a nice spot for a date" "great pasta"). This could let people pick out a specific item and complete the task. Research shows that most people make decisions by evaluating specific instances rather than deciding on criteria and rationally accepting the one that pops to the top. It also shows that people learn about features from con-crete cases. For example, when choosing among cars, buyers may not care about navigation systems until they see that some of the cars have there (and then the navigation system may become an important criterion). Assistant 1002 may present salient properties of listed items that help people pick a winner or that suggest a di-mension along which to optimize.

Conceptual Data Model [0377] In one embodiment, assistant 1002 offers assistance with the con-strained selection task by simplifying the conceptual data model. The concep-tual data model is the abstraction presented to users in the interlace of assis-tant 1002. To overcome the psychological problems described above, in one embodiment assistant 1002 provides a model that allows users to describe what they want in terms of a few easily recognized and recalled properties of suitable choices rather than constraint expressions. in this manner, properties can be made easy to compose in natural language requests (e.g., adjectives modifying keyword markers) and be recognizable in prompts ("you may also favor recommended restaurants..."). In one embodiment, a data model is used that allows assistant 1002 to determine the domain of interest (e.g., restaurants versus hotels) and a general approach to guidance that may be instantiated with domain-specific properties.

[0378] In one embodiment, the conceptual data model used by assistant 1002 includes a selection class. This is a representation of the space of things from which to choose. For example, in the find-a-restaurant application, the selection class is the class of restaurants. The selection class may be abstract and have subclasses, such as "things to do while in a destination". In one embodiment, the conceptual data model assumes that, in a given problem solving situation, the user is interested in choosing from a single selection class. This assumption simplifies the interaction and also allows assistant 1002 to declare its boundaries of competence ("1 know about restaurants, ho-tels, and movies" as opposed to "I know about life in the city").

[0379] Given a selection class, in one embodiment the data model pre-seated to the user for the constrained selection task includes, for examnie:
items; item features; selection. criteria; an d constraints.

[0380] Items are instances of the selection class.

[0381] Item features are properties, attributes, or computed values that may be presented and/ or associated with at least one item. For example, the name and phone number of a restaurant are item features. Features mar be intrinsic (the name or cuisine of a restaurant) or relational (e.g., the distance from one's current location of interest). They may be static (e.g., restaurant name) or dynamic (rating). They may be composite values computed from other data (e.g., a "value for money" score). Item features are abstractions for the user made by the domain modeler; they do not need to correspond to u.n-deriving data from back-end services.

[0382] Selection criteria are item features that maybe used to compare the value or relevance of items. That is, they are ways to say which items are pre-ferred. Selection criteria are modeled as features of the items themselves, whether they are intrinsic properties or computed. For example, proximity (defined as distance from the location of interest) is a selection criterion.
Loca-tion in space-time is a property, not a. selection criterion, and it is used along with the location of interest to compute the distance from the location of in-terest.

[0383] Selection criteria may have an inherent preference order. That is, the values of any particular criterion may be used to line up items in a best first order. For example, the proximity criterion has an inherent preference that closer is better. Location, on the other hand, has no inherent preference value. This restriction allows the system to make default assumptions and guide the selection if the user only mentions the criterion. For example, the user interface might offer to "sort by rating" and assume that higher rated is better.

[0384] One or more selection criteria are also item features; they are those features related to choosing among possible items. However, item features are not necessari c related to a preference the names and phone numbers of restaurants are usually irrelevant to choosing among them).

[0385] In at least one embodiment, constraints are restrictions on the de-sired values of the selection criteria. Formally-, constraints might be repre-sented as set membership (e.g., cuisine type includes Italian), pattern matches (e.g., restaurant review text includes "romantic"), fuzzy inequalities (e.(r., dis-tance less than a few miles), qualitative thresholds (e.(-., highly rated), or more complex functions (e.g., a good value for money). To make things simple enough for normal humans, this data model reduces at least one or more con-straints to symbolic values that may be matched as words. Time and distance maybe excluded from this reduction. In one embodiment, the operators and threshold values used for implementing constraints are hidden from the user.
For example, a constraint on the selection criteria called "cuisine" may be rep-resented as a symbolic value such as "Italian" or "Chinese". A constraint on rating is "recommended" (a binary choice). For time and distance, in one em-bodiment assistant 1002 uses proprietary representations that handle a range of inputs and constraint values. For example, distance might be "walking dis-tance" and time might be "tonight"; in one embodiment, assistant 1002 uses special processing to match such input to more precise data.

[0386] In at least one embodiment, some constraints may be required con-straints. This means that the task simply cannot be completed without this data. For example, it is hard to pick a restaurant without some notion of de-sired location, even if one knows the name.

[0387] To summarize, a domain is modeled as selection classes with item features that are important to users. Some of the features are used to select and order items offered to the user - these features are called selection criteria.
Constraints are symbolic limits on the selection criteria that narrow the set of items to those that match.

[0388] Often, multiple criteria may compete and constraints may match nartia liv. The data model reduces the selection problem from an optimization (finding the best solution) to a matching problem (finding items that do v,-ell on a set of specified criteria and match a set of symbolic constraints). The al-gorithms for selecting criteria and constraints and determining an ordering are described in the next section.

Methodology for Constrained Selection [0389] In one embodiment, assistant 1002 performs constrained selection by taking as input an ordered list of criteria, with implicit of explicit con straints on at least one, and generating a set of candidate items with salient features. Computationally, the selection task may, be characterized as a nested search: first, identify a selection class, then identify the important se-lection criteria, then specify constraints (the boundaries of acceptable solu-tions), and search through instances in order of best-fit to find acceptable items.

[0390] Referring now to Fig. 45, there is shown an example of an abstract model 4500 for a constrained selection task as a nested search. In the example assistant 1002 identifies 4505 a selection call among all local search types 4501.
The identified class is restaurant. Within the set of all restaurants 4502, assis-tant 1002 selects 4506 criteria. In the example, the criterion is identified as dis-tance. Within the set of restaurants in PA 4503, assistant 1002 specifies 4507 constraints for the search. In the example, the identified constraint is "Italian cuisine"). Within the set of Italian restaurants in PA 4504, assistant 4508 se-lects items for presentation to the user.

[0391] In one embodiment, such a nested. search is what assistant 1002 does once it has the relevant input data, rather than the flow for eliciting the data and presenting results. In one embodiment, such control flow is gov-erned Vic a dialog between assistant 1002 and irLe user which operates b~-other procedures, such as dialog and task flow models. Constrained selection -~22-offers a framework for building dialog and task flow models at this level of abstraction (that is, suitable for constrained selection tasks regardless of do-main;.

[0392] Referring now to Fig. 46, there is shown an example of a dialog 4600 to help guide the user through a search process, so that the relevant input data can be obtained.

[0393] In the example dialog 4600, the first step is for the user to state the kind of thing; they are looking for, which is the selection class. For example, the user might do this by saying "dining in Palo alto". This allows assistant 1002 to infer 4601 the task and domain.

[0394] Once assistant 1002 has understood the task and domain binding (selection class = restaurants), the next step is to understand which selection criteria are important to this user, for example by soliciting 4603 criteria and/or constraints. In the example above, "in palo alto" indicates a location of interest. In the context of restaurants, the system may interpret a location as a proximity constraint (technically, a constraint on the proximity criterion).
Assistant 1002 explains what is needed, receives input. If there is enough in-formation to constrain the choice set to a reasonable size, then assistant paraphrases the input and presents 4605 one or more restaurants that meet the proximity constraint, sorted in some useful order. The user can then select 4607 from this list, or refine 4606 the criteria and constraints. Assistant reasons about the constraints already stated, and uses domain-specific knowl-edge to suggest other criteria that might help, soliciting constraints on these criteria as well. For example, assistant 1002 may reason that, when recom-mending restaurants within walking distance of a hotel, the useful criteria to solicit would be cuisine and table availability.

[0395] The constrained selection task is complete when the user selects 4607 an instance of the selection class. In one embodiment, additional follow-on tasks 4602 are enabled by assistant 1002. Thus, assistant 1002 can offer services that indicate selection while providing some other value. Examples 4608 booking a restaurant, setting a reminder on a calendar, and/ or sharing the selection with others by sending an invitation. For example, booking a restaurant certainly indicates that it was selected; other option-, might be to put the restaurant on a calendar or send in invitation with directions to friends.

[0396] Referring now to Fig. 47, there is shown a flow diagram depicting a method of constrained selection according to one embodiment. In one err:-bodiment, assistant 1002 operates in an opportunistic and mixed-initiative manner, permitting the user to jump to the inner loop, for instance, bar stating task, domain, criteria, and constraints one or more at once in the input.
[0397] The method begins 4701. Input is received 4702 from the user, ac-cording to any of the modes described herein. If, based on the input, the task not known, assistant 1002 requests 4705 clarifying input from the user.

[0398] In step 4717, assistant 1002 determines wheiher the user provides additional input. If so, assistant 1002 returns to step 4702. Otherwise the method ends 4799.

[0399] If, in stet 4703, the task is known, assistant 1002 determines 4704 whether the task is constrained selection. If not, assistant 1002 proceeds to the specified task flow.

[0400] If, in step 4704, the task is constrained selection, assistant 1002 de-termines 4707 whether the selection class can be determine-'. If not, assistant 1002 offers 4708 a choice of known selection classes, and returns to step 4717.
[0401] If,, in step 4707, the selection class can be determined, assistant determines 4709 whether all required constraints can be determined. If not, assistant 1002 prompts 4710 for required information, and returns to step 4717.

[0402] If, in step 4709, all required constants can be determined, assistant 1002 determines 4711 whether any result items can be found, given the con straints. If there are no items that meet the constraints, assistant 1002 offers 4712 ways to relax the constraints. For example, assistant 1002 may relax the constraints from lowest to highest precedence, using a filter/sort al(orithm.

In one embodiment, if there are items teat mee: some of the constraints, then t,e T
fo, amylP., "I could assistant 10021 may naraphras~e the situation (oatputting~, e,.

not find Recommended Greek restaurants that deliver on Sundaas in San Car-los. However, I found 3 Greek restaurants and 7 Recommend restaurants in San Carlos."). in one embodiment, if there are no items that match any con-straints, then assistant 1002 may paraphrase this situation and prompt for dif-ferent constraints (outputting, for example, "Sorry, I could not find any res taurants in Anytown, Texas. You may pick a different location."). Assistant 1002 returns to step 4717.

[0403] If, in step 4711, result items can be found, assistant 1002 offers 4713 a list of items. In one embodiment, assistant 1002 paraphrases the currently specified criteria and constraints (outputting, for example, "Here are some recommended Italian restaurants in San Jose." (recommended=ves, cu;-sine=Italian, proximity=<in San Jose>)). In one embodiment, assistant 1002 presents a sorted, paginated list of items that meet the known constraints. If an item only shows some of the constraints, such a condition can be shown as part of the item display. In one embodiment, assistant 1002 offers the user ways to select an item, for example by initiating another task on that item such as booking, remembering, scheduling, or sharing. In one embodiment, on any given item, assistant 1002 presents item features that are salient for picking instances of the selection class. In one embodiment, assistant 1002 shows how the item meets a constraint; for example, Zagat rating of 5 meets the Recommended=yes constraint, and "1 mile awaymeets the "within walking distance of an address" constraint. In one embodiment, assistant 1002 allows the use,. to drill down for more detail on an item, which results in display of more item features.

[0404] Assistant 1002 determines 4714 whether the user has selected an item. If the user selects an item, the task is complete. Any follow-on task is performed 4715, if there is one, and the method ends 4799.

[0405] If, in step 4714, the user does not select an item, assistant 1002 offers 4756 the user ways to select other criteria arnct constraints anct returns to step 417. Foi example, given the currenth' Speclned criteria and cons--raints, sistant 1002 may otter criteria trim are most likely to constrain the choice Set to a desired size. If the user selects a constraint value, that constraint value is added to the previoushy determined constraints when steps 4703 to 4713 are repeated.

[0406] Since one or more criteria may have an inherent preference value, selecting the criteria may add information to the request. For example, allow-ing the user to indicate that positive reviews are valued allows assistant to sort by this criterion. Such information can be taken into account when steps 4703 to 4713 are repeated.

[0407] In one embodiment, assistant 1002 allows the user to raise the im-portance of a criterion that is already, specified, so that it would be higher in the precedence order. For example, if the user asked for fast, cheap, highly recommended restaurants within one block of their location, assistant 1002 may request that the user chooses which of these criteria are more important.
Such information can be taken into account when steps 4703 to 4713 are re-peated.

[0408] In one embodiment, the user can provide additional input at any-point while the method of Fig. 47 is being performed. In one embodiment, assistant 1002 checks periodically or continuously for such input, and, in re-sponse, loops back to step 4703 to process it.

[0409] In one embodiment, when outputting an item or list of items, assis-tant 1002 indicates, in the presentation of items, the features that were used to select and order them. For example, if the user asked for nearby Italian restau-rants, such item features for distance and cuisine may be shown in the presen-tation of the item. This may include highlighting matches, as well as listing selection criteria that were involved in the presentation of an item.

WO 2011/088053 PCT/ ,tS2011/020861 Example Domains [0410] T able 1 Provides an example of constrained selection domains that may be handled by assistant 1002 according to various embodiments.
Select a based on nese criteria Location rice Availability i ype j ua sty game Services special genera;
I
search search Restaurant proximity a for - open tables cuisine rating by restaurant delivery menu Keywords ability guide, re- name items f view Hotel proximity price available motel, ho- rating by notes name amenities Keywords range rooms tel, B&B, ... guide, re-view Movie neatre snow times genre rating by movie i ie actors, proximity review etc oca usi- proximity business rating by business Keywords ness category review name Local event venue by date even itie Keywords proximity concert venue oy tour music band name Dana Keywords proximity schedule genre members oo price on ine, in download, popularity album or artist, Keywords DVD to buy range store, etc physical song name title; etc.

Table 1 Filtering and Sorting Results 10411] in one embodiment, when presenting items that meet currently specified criteria and constraints, a filter/ sort methodology can ee employed.
In one embodiment selection constraints may serve as both filter- and sort pa-rameters to the underlying services. Thus, any selection criterion can be used to determine which items are in the list, and to compute the order in which to paginate and show them. Sort order for this task is akin to relevance rank in search. For example, proximity is a criterion with symbolic constraint values such as "within driving distance" and a general notion of sorting by-distance.
The "driving distance" constraint might be used to select a group of candidate items. Within that group, closer items .night be sorted higher in the list.

[04121 In one embodiment, selection constraints and associated filtering and sorting are at discrete "levels", which are functions of both the underly-ing data and the input from the user. For example, proximity is grouped into levels such as "walking distance", "taxi distance", "driving distance". When sorting, one or more items within walking distance are treated as if they were the same distance. The input from the user may come into play in the way he or she specifies a constraint. If the user enters "in palo alto", for example, then one or more items within the Palo Alto city limits are perfect matches and are equivalent. If the user enters, "near the university Avenue train sta-tion" then the match would depend on a distance from that address, with the degree of match dependent on the selection class (e.g., near for restaurants is different than near for hotels). Even within a constraint that may be specified with a continuous value, a discretization may be applied. This may, be impor-tant for sorting operations, so that multiple criteria may participate in deter-mining the best-first ordering.

[0413] In one embodiment, the item list -- those items that are considered "matching" or good enough" -- may be shorter oI" longer than the number of items shown on one "page" of the output. Generally, items in the first page are given the most attention, but conceptually there is a longer list, and pagi-nation is simply a function of the form factor of the output medium. This means, for instance, that if the user is offered a way to sort or browse the items by some criterion, then it is the entire set of items (more than one page worth) that is sorted or browsed.

WO 201110880.53 PCT/US2011/020861 [0414] In one embodiment, there is a precedence ordering among selection criteria. Ti Lat is, some criteria may matte more than others in the filter an"' so:-. In one embodiment, those Criteria selected 'Jt' the user are given nigher"

precedence than others, and there is a default ordering over one or more crite-ria. This allows for a general lexicographic sort. The assumption is that there is a meaningful a priori precedence. For example, unless the user states oth-erwise, it may be more important for a restaurant to be close than to be inex-pensive. In one embodiment, the a priori precedence ordering is domain-specific. The model allows for user-specific preferences to override the do-main defaults, if that is desired.

[0415] Since the values of constraints can represent several internal data types, there are different ways for constraints to match, and they may be spe-cific to the constraint. For example, in one embodiment:

= Binary constraints match one or more or none. For example, whether a restaurant is "Fast" might be either true or not.

= Set membership constraints match one or more or none based on a property value. For example, cuisine=Greek means the set of cui-sines for a restaurant includes Greek.

= Enumeration constraints match at a threshold. For example, a rat-ing criterion might have constraint values rated, highly-rated, or top-rated. Constraining to hi-E] ,-rated would also match top rated.

= Numeric constraints match at a threshold that may be criterion spe-cific. For example, "open late" might be a criterion, and the user might ask for places open after 10:00,Dm. This kind of constraint may be slightly out of scope for the constrained selection task, since it is not a symbolic constraint value. However, in one embodiment, assistant 1002 recognizes some cases of numeric constraints like this, and maps them to threshold values with symbolic constraints (e.g., restaurants in Palo alto Open now" -> "here are 2 restaurants in palo alto that are open late").

vocation and time are Handled specially. A constraint on proximity might be a location of interest specified at some level of granularity, and that determines the match. If the user specifies a city, then city-level matching is appropriate; a ZIP code may allow for a radius.
Assistant 1002 may also understand locations that are "near" other locations of interest, also based on special processing. Time is rele-vant as a constraint value of criteria that have threshold value based on a service call, such as table availability or flights within a given time range.

[0416] In one embodiment, constraints can be modeled so that there is a single threshold value for selection and a small set of discrete values for sort-ing. For example, the affordability criterion might be modeled as a roughly bi-nary constraint, where affordable restaurants are any under some threshold price range. When the data justify multiple discrete levels for selection, con-straints can be modeled using a gradient of matching. In one embodiment two levels of matching (such as strong and weak matching) may be provided;
however, one skilled in the art will recognize that in other embodiments, any number of levels of matching can be provided. For example, proximity may be matched with a fuzzy boundary, so that things that are near the location of interest may match weakly. The operational consequence of a strong or weak match is in the filter/sort algorithm as described below.

[0417] For at least one criterion, an approach to matching and default thresholds can be established, if relevant. The user may be able to say just the name of the constraint, a symbolic constraint value, or a precise constraint ex-pression if it is handled specially (such as time and location).

[0418] An ideal situation for constrained selection occurs when the user states constraints that result in a short list of candidates, one or more of which meet the constraints. The user then chooses among winners based on item reatures. In manv cases, however, the problem is over- or under-constrained.
When it is over-constrained, there are few or no items that meet the con-straints. When it is under-constrained, there are so manv candidates that ex amining the list is not expedient. In one embodiment, the general constrained selection model of the present invention is able to handle multiple constraints with robust matching and usually produce something to choose from. Then the user may elect to refine their criteria and constraints or just complete the task with a "good enough" solution.

Method [0419] In one embodiment, the following method is used for filtering and sorting results:

1. Given an ordered list of selection criteria selected by the user, deter-mine constraints on at least one.

a. If the user specified a constraint value, use it. For example, if the user said "g reek food" the constraint is cuisine=Greek. If the user said "san Francisco" the constraint is In the City of San Fran-cisco. If the user said "south of market" then the constraint is in the Neighborhood of SoMa.

b. Otherwise use a domain- and criteria-specific default. For ex ample, if the user said "a table at some thai place" he or she is indicating that the availability criterion is relevant, but he or she did not specify a constraint value. The default constraint values for availability might be some range of date tunes such as tonight and a default party size of 2.

2. Select a i niniinum of N results bz% specified constraints.
a. Try to get N results at strong match.

b. If that fails, try to relax constraints, in reverse precedence order.
That is, match at strong level for one or more of the criteria ex-cept the last, which may match at a weak level. If there is no weak match for that constraint, then try weak matches un the line from lowest to highest precedence.

Then repeat the loop allowing failure to match on constraints, from lowest to highest precedence.

3. After getting, a minimum choice set, sort lexicographically over one or more criteria (which may include user-specified criteria as well as other criteria) in precedence order.

a. Consider the set of user-specified criteria as highest precedence, then one or more remaining criteria in their a priori precedence.
For example, if the a priori precedence is (availability, cuisine, proximity, rating), and the user gives constraints on proximity and cuisine, then the sort precedence is (cuisine, proximity, availability, rating).

b. Sort on criteria using discrete match levels (strong, weak, none), using the same approach as in relaxing constraints, this time ap-plied the full criteria list.

i. If a choice set was obtained without relaxing constraints, then one or more of the choice set may "tie" in the sort because they one or more match at strong levels. Then, the next criteria in the precedence list may kick in to sort them. For example, if the user says cuisine=] taiian, prox-irnity=ln San Francisco, and the sort precedence is (cui-sine, proximity, availability, rating), then one or more the places on the list have equal match values for cuisine and proximity. So the list would be sorted on availability (places with tables available bubble to the top). Within the available places, the highest rated ones would be at the top.

ii. If the choice set was obtained by relaxing constraints, then one or more of the fully matching items are at the WO 2011/088053 PCT[US2011/020861 top of the list, then the partially matching items. Within the matching group, they are sorted by the remaining cri-teria, and the same for the partially matching group. For example, if there were only two Italian restaurants in San Francisco, then the available one would be shown first, then the unavailable one. Then the rest of the restaurants in San Francisco would be shown, sorted by availability and rating.

Precedence Ordering [0420] The techniques described herein allow assistant 1002 to be ex-tremely robust in the face of partially specified constraints and incomplete data. In one embodiment, assistant 1002 uses these techniques to generate a user list of items in best-first order, i.e. according to relevance.

[0421] In one embodiment, such relevance sorting is based on an a priori precedence ordering. That is, of the things that matter about a domain, a set of criteria is chosen and placed in order of importance. One or more things being equal, criteria higher in the precedence order may be more relevant to a constrained selection among items than those lower in the order. Assistant 1002 may operate on any number of criteria. In addition, criteria may be modified over time without breaking existing behaviors.

[0422] In one embodiment, the precedence order among criteria may be tuned with domain-specific parameters, since the way criteria interact may depend on the selection class. For example, when selecting among hotels, availability and price may be dominant constraints, whereas for restaurants, cuisine and proximity may be more important.

[0423] in one embodiment, the user may override the default criteria or-dering in the dialog. This allows the system to guide the user when searches are over-constrained, by using the ordering to determine which constraints should be relaxed. For example, if the user gave constraints on cuisine, prox-imity, recommendation, and food item, and there were no fully matching items, the user could say that food item was more important than recommen-dation level and change the mix so the desired food item matches were sorted to the top.

[0424] In one embodiment, when precedence order is determined, user-specified constraints take precedence over others. For example. in one em-bodiment, proximity is a required constraint and so is always specified, and further has precedence over other unelected constraints. Therefore it does not have to be the highest precedence constraint in order to be fairly domi-nant Also, many criteria may not match at one or more unless a constraint is given by the user, and so the precedence of these criteria only matters within user-selected criteria. For example, when the user specifies a cuisine it is im-portant to them, and otherwise is not relevant to sorting items.

[0425] For example, the following is a candidate precedence sorting para-digm for the restaurant domain:

1. cuisine' (not sortable unless a constraint value is given) 2. availability* (sortable using a default constraint value, e.g., time) 3. recommended 4. proximity" (a constraint value is always given) 5. affordability 6. may deliver i. food item (not sortable unless a constraint value, e.g., a keyword, is given) 8. keywords (not sortable unless a constraint value, e.g., a keyword, is given) 9. restaurant name [0426] The following is an exam-pie of a design rationale for the above sort-ing paradigm:

= If a user specifies a cuisine, he or she wants it to stick.

= One or more things being equal, sort by rating level (it is the highest precedence among criteria than nay ne used to sort without a con-str aint ).

= In at !east one embodiment, proximity maybe more important than most things. However, since it matches at discrete levels (in a city, within a radius for walking and the like', and it is always specified, then most of the time most matching items may "tie" on proximity.

= Availability (as determined by a search on a website such as open-table.com, for instance) is a valuable sort criterion, and may be based on a default value for sorting when not specified. If the user indicates a time for booking, then only available places may be in the list and the sort may be based on recommendation.

= If the user says they want highly recommended places, then it may sort above proximity and availability, and these criteria may be re-laxed before recommendation. The assumption is that if someone is looking for nice place, they may be willing to drive a bit farther and it is more important than a default table availability. If a specific time for availability is specified, and the user requests recom-mended places, then places that are both recommended and avail-able may come first, and recommendation may relax to a weak match before availability fails to match at one or more.

= The remaining constraints except for name are one or more based on incomplete data or matching. So they are weak sort heuristics by default, and when they are specified the match one or more-or-none.

= Name may be used as a constraint to handle the case where some-one mentions the restaurant by name, e.g., find one or more Ho-bee's restaurants near Palo Alto. In this case, one or more items may match the name, and may be sorted by proximity (the other specified constraint in this example).

-i35-Domain Modeling: Mapping Selection Criteria to underlying data [0427] It may be desirable to distinguish between the data that are avail-able for computation by assistant 1002 and the data used for making selec-tions. In one embodiment, assistant 1002 uses a data model that reduces the complexity for the user by folding one or more kinds of data used to distin-guish among items into a simple selection criteria model. Internally, these data may take several forms. Instances of the selection class can have intrinsic properties and attributes (such as cuisine of a restaurant), may be compared along dimensions (such as the distance from some location), and may be dis-covered by some query (such as whether it matches a text pattern or is avail-able at a oven time). They may also be computed from other data which are not exposed to the user as selection criteria (e.g., weighted combinations of ratings from multiple sources). These data are one or more relevant to the task, but the distinctions among these three kinds of data are not relevant to the user. Since the user thinks in terms of features of the desired choice rather than in properties and dimensions, assistant 1002 operationalizes these vari-ous criteria into features of the items. Assistant 1002 provides a user-facing domain data model and maps it to data found in web services.

[0428] One type of mapping is an isomorphism from underlying data to user-facing criteria. For example, the availability of tables for reservations as seen by the user could be exactly what an online reservation website, such as opentable.com, offers, using the same granularity for time and party size.
[0429] Another type of mapping is a normalization of data from one or more services to a common value set, possibly with a unification of equivalent values. For example, cuisines of one or more restaurants may be represented as a single ontology in assistant 1002, and mapped to various vocabularies used in different services. That ontology might be hierarchical, and have leaf nodes pointing to specific values from at least one service. For example, one service might have a cuisine value for "Chinese", another for "Szechuan ", and a third for "Asian". The ontology used by assistant 1002 would cause refer-ences to "Chinese food" or "Szechuan" to semantically match one or more of these nodes, with confidence levels reflecting the degree of match.

[0430] Normalization might also be involved when resolving differences in precision. For example, the location of a restaurant may be given to the street level in one service but only to city in another. In one embodiment, as-sistant 1002 uses a deep structural representation of locations and times that may be mapped to different surface data values.

[0431 ] In one embodiment, assistant 1002 uses a special kind of mapping for open-ended qualifiers (e.g., romantic, quiet) which may be mapped to matches in full text search, tags, or other open-textured features. The name of the selection constraint in this case would be something like "is described as".
[0432] In at least one embodiment, constraints may be mapped to opera-tional preference orderings. That is, given the name of a selection criterion and its constraint value, assistant 1.002 is able to interpret the criterion as an ordering over possible items. There are several technical issues to address in such a mapping. For example:

Preference orderings may conflict. The ordering given by one con-straint may be inconsistent or even inversely correlated with the ordering given by another. For example, price and quality tend to be in opposition. In one embodiment, assistant 1002 interprets con-straints chosen by the user in a weighted or otherwise combined ordering that reflects the user's desires but is true to the data. For example, the user may ask for "cheap fast food French restaurants within walking distance rated highly". In many locations, there may not be any such restaurant. However, in one embodiment, as-sistant 1002 may show a list of items that tries to optimize for at least one constraint, and explain why at least one is listed. For ex-ample, item one might be "highly rated French cuisine" and an-other "cheap fast food within walking distance".

-13~ -= Data may be used a.; either hard or soft constr aints. For example, the nrice range of a restaurant ma v be important to choosing one, but it may 'o-- difficult to state a threshold value for price up-front.
Even seemingly hard constraints like cuisine may be, in practice, soft constraints because of partial matching. Since, in one embodi-ment, assistant 1002 using a data modeling strategz that seeks to flatten one or more criteria into symbolic values (such as "cheap" or "close"), these constraints may be mapped into a function that gets the criteria and order right, without being strict about matching spe-cific threshold values. For symbolic criteria with clear objective truth values, assistant 1002 may weight the objective criteria higher than other criteria, and make it clear in the explanation that it knows that some of the items do not strictly match the requested criteria.

= Items may match some but not one or more constraints, and the "best fitting" items may he shown.

= In general, assistant 1002 determines which item features are salient for a domain, and which may serve as selection criteria, and for a;

least one criteria, possible constraint values. Such information can be provided, for example, via operational data and API calls.
Paraphrase and Prompt Text [0433] As described above, in one embodiment assistant 1002 provides feedback to show it understands the user's intent and is working toward the user's goal by producing paraphrases of its current understanding. In the 17 conversational dialog model of the present invention, the paraphrase is what assistant 1002 outputs after the user's input, as a preface (for example, para-phrase 4003 in Fig. 40) or summary of the results to follow (for example, list 3502 in Fig. 35). The prompt is a suggestion to the user about what else they -13~-can do to refine their reauest or explore the selection space along some d-mensions.

[0434] In one embodiment, the purposes of paraphrase and prompt text include, for example:

= to show that assistant 1002 understands the concepts in the user's input, not just the text;

= to indicate the boundaries of assistant's 1002 understanding;

= to guide the user to enter text that is required for the assumed task;
= to help the user explore the space of possibilities in constrained se-lection;

= to explain the current results obtained from services in terms of the user's stated criteria and assistant's 1002 assumptions (for example, to explain the results of under- and over-constrained requests).

[0435] For example, the following paraphrase and prompt illustrates sev-eral of these goals:

User input indonesian food in menlo park System interpretation:
Task=constrainedSelection Selection Class =restaurant Constraints:
Location=Menlo Park, CA
Cuisine=Indonesian (known in ontology) Results from Services: no strong matches Paraphrase: Sorry, I can't find any Indonesian restaurants near Menlo Park.

Prompt: You could try other cuisines or locations.
Prompt under hypertext links:

Indonesian: You can trv other food categories such as Chinese, or a fa-vorite food item such as steak.

Menlo Park: Enter a location such as a city, neighborhood, street ad-dress, or "near" followed by a landmark.

Cuisines: Enter a food category such as Chinese or Pizza.

Locations: Enter a location: a city, zip code, or "near" foIIowed by the name of a place.

[0436] In one embodiment, assistant 1002 responds to user input relative!)-quickly with the paraphrase. The paraphrase is then updated after results are known. For example, an initial response may be "Looking for Indonesian res-taurants near Menlo Park..." Once results are obtained, assistant 1002 would update the text to read, "Sorry, I can't find any Indonesian restaurants near Menlo Park. You could try- other cuisines or locations." Note that certain items are highlighted (indicated here by underline), indicating that those items represent constraints that can be relaxed or changed.

[0437] In one embodiment, special formatting/highlighting is used for key words in the paraphrase. This can be helpful to facilitate training of the user for interaction with intelligent automated assistant 1002, by.indicating to the user which words are most important to, and more likely to be recognized by, assistant 1002. User may then be more likely to use such words in the future.
[0438] In one embodiment, paraphrase and prompt are generated using any relevant context data. For example, any of the following data items can be used, alone or in combination:

= The parse - a tree of ontology nodes bound to their matching input tokens, with annotations and exceptions. For each node in the parse, this may include the node's metadata and/or any tokens in the input that provide evidence for the node's value.

= The task, if known = The selection class.

= The location constraint, independent of selection class.

= Which requires parameters are unknown for the given selection class (e.g., location is a recuired constraint on restaurants).

= The name of a named entity in the parse that is an instance of the selection class, if there is one (e.g., a specific restaurant or movie name.) = is this a follow-up refinement or the beginning of a conversation.?
(Reset starts a new conversation.) = Which constraints in the parse are bound to values in the input that changed their values? In other words, which constraints were just changed by the latest input?

= Is the selection class inferred or directly stated?
= Sorted by quality, relevance, or proximity?

= For each constraint specified, how well was it matched?
= Was refinement entered as text or clicking?

[0439] In one embodiment, the paraphrase algorithm accounts for the query, domain model 1056, and the service results. Domain model 1056 con-tains classes and features including metadata that is used to decide how to generate text. Examples of such metadata for paraphrase generation include:
= IsConstraint={true I false}

= IsMultiValued={true I false}

= ConstraintTvpe={EntityNam.e, Location, Time, CategoryConstraint, AvailabilitvConstraint, BinaryConstraint, SearchQualifier, Guess edQualifier}

= DisplavName=string = DisplavTernplateSingular=string = Display T emplatePlural=string = Grammatical-Roie={AdjectiveBeforeNoun,Noun,ThatClauseModifer}
[0440] For example, a parse might contain these elements:

Class: Restaurant IsConstraint=wise DisplayT empiateSin-uia:-="restaurant"
DisplayTemplatePlural="restaurants"
Gr ammaticalRole=Noun Feature: RestaurantName (example: "II Fornaio") IsConstraint=true IsMultiValued=false ConstraintType=EntityN ame Displa.vTempiateSingular="named $1"
Display TemplatePlural="named S1"
GrammaticalRoie=Noun Feature: RestaurantCuisine (example: "Chinese") IsConstraint=true IsMultiValued=false ConstralntType= Category Constraint GrammaticalRole=AdjectiveBeforeNoun Feature: RestaurantSubtype (example: "cafe") IsConstraint=tru e IsMultiValued=false ConstraintType= Category Constraint Di splavTemplateSingular="$1 "
DisplayTemplatePlural="S1s"
GrammaticalRoie=Noun Feature: RestaurantQualifiers (example: romantic") IsConstraint=true IsMultiValued=true ConstraintType=SearchQuaiifier DisnlavTemplateSingular="is described as Si"
DisnlayTemplatePluraI=" are described as S1"
DisplayTemplateCompact='' matching Si"
GrammaticalRole=Noun Feature: FoodType (example: "burritos") Is Constraint= true IsMulti Valued =false ConstraintType= SearchQualifier DisplayTemplateSingular="serves $1"
DisplayTemplatePlural="serve $1"
DisplayTemplateCompact="serving $1"
GrammaticalRole=ThatCl auseModifer Feature: IsRecommended (example: true) IsConstraint=true IsMultiValued=false ConstraintType= BinarvConstraint DisplayTempl ateSingular="recommended"
DisplayTemplatePlural=" recommended"
GrammaticalRole=AdjectiveBeforeNoun Feature: RestaurantGuessedQualifiers (example: "spectacular") IsConstraint=true IsMultiValued=false ConstraintType= GuessedQuaiifier DisplayTemplateSingular="matches S1 in reviews"
DisplayTemplatePlural="match S1 in reviews"

DisplavTemolateComnact= "matching $1"
GrarnmaticalRoie=ThatCla'jseMociife.r !0441] In one embodiment, assistant 1002 is able to handle unmatched in-put. To handle such input, domain model 1055 can provide for nodes of type GuessedQualifier for each selection class, and rules that match otherwise un-matched words it they are in the right grammatical context. That is, Guess-edQualifiers are treated as miscellaneous nodes in the parse which match when there are words that are not found in the ontology- but which are in the right context to indicate that that are probably qualifiers of the selection class.
The difference between GuessedQualifiers and SearchQuaiifiers is that the lat-ter are matched to vocabulary in the ontology. This distinction allows us to paraphrase that assistant 1002 identified the intent solidly on the SearchQuali-fiers and can be more hesitant when echoing back the GuessedQualifiers.
[0442] In one embodiment, assistant 1002 performs the following steps when generating paraphrase text:

-. If the task is unknown, explain what assistant 1002 can do and prompt for more input.

2. If the task is a constrained selection task and the location is known, then explain the domains that assistant 1002 knows and prompt for the selection class.

3. If the selection class is known but a required constraint is missing, then prompt for that constraint. (for example, location is required for con-strained selection on restaurants) 4. If the input contains an EntitvName of the selection class, then output "looking up" <name> in <location>.

5. If this is the initial request in a conversation, then output "looking for"
followed by the complex noun phrase that describes the constraints.
6. If this is a follow-up refinement step in the dialog, a. If the user just completed a required input, then output "thanks"
and then paraphrase normally. (This happens when there is a required constraint that is mapped to the user input.) b. If the user is changing a constraint, acl:nowledae this and then paraphrase normally.

c. If the user tuned in the proper name of an instance of the selec-tion class, handle this specially .

d. If the user just added an unrecognized phrase, then indicate how it will be folded in as search. If appropriate, the input may be dispatched to a search service.

e. If the user is just adding a normal constraint, then output "OK", and paraphrase normally.

To explain results, use the same approach for paraphrase. However, when the results are surprising or unexpected, then explain the results using knowiedge about the data and service. Also, when the query is over- or underconstrained, prompt for more input.

Graininar for Constructing Comdex Noun Phrases [0443] In one embodiment, when paraphrasing 734 a constrained selection task query, the foundation is a complex noun phrase around the selection class that refers to the current constraints. '-Each constraint has c grammatical position, based on its type. For example, in one embodiment, assistant 100-1 may construct a paraphrase such as:

recommended romantic Italian restaurants near Menlo Park with open tables for? that serve osso burn and are described as "quiet"
A grammar to construct this is -1In-<paraphraseNounClause> :__ <binaryConstraint> <searchQualifier> <cate-goryConstraint> <itemNoun> <1 ocation Constraint> <avaiiabiltvCon-straint> <adjectivalClauses>

<binarvConstraint> :_= single adjective that indicates the presence or absence of a Binary Constraint (e.g., recommended (best), affordable (cheap)) It is possible to list more than one in the sane quenj.

<searchQualifier> :_= a word or words that match the ontology for a qualifier of the selection class, which would be passed into a search engine ser-vice. (e.g., romantic restaurants, funny movies).

Use when ConstraintType= SearchQuali f ier.

<categorvConstraini> :_= an adjective that identifies the genre, cuisine, or category of the selection class (e.g., Chinese restaurant or R-rated file).
It is the last prefix adjective because it is the most intrinsic. Use for fea-tures of type CategorvConstraint and Grarnmatical-Role=AdjectiveBeforeNoun.
<itemNoun> == <namedEntityPhrase> I <selectionClass> I <selection-ClassSubType>
find the most specific way to display the noun. NamedEntity < SubType <
Class <selectionClass > :_= a noun that is the generic name for the selection class (e.g., restaurant, movie, place) <selectionClassSubType> :_= a noun phrase that is the subtype of the selec-tion class if it is known (e.g., diner, museum, store, bar for the selection class local business). Use for features in which ConstraintType =Categon~lConstraint and Grar~zrnaticalRoie=AdjeciiveBeforeNoun.

<namedEntityPhrase> :=_ <entityName>

"the" (<selectionClass> I <selectionClassSubType>) <entityName> == the proper name of an instance of the selection class (e.g., "II Fornaio" "Animal House" "Harry's Bar") <IocationConstraint> :_= <locationPreposition> <locationName>
<IocationPreposition> :__ "in", "near", "at", and the like <locationName> :_= city, street address, landmark, or something for GPS like your current location"

<availabilityConstraint> :_= the availability constraint expressed as a preposi-tional phrase that comes after the noun (e.g., "with open tables", "with seats available", "available online"). It comes right after the noun to give it salience.

<adjectivalClauses> :__ <modiferVerbPhrase> ~ "that"
<modiferV erbPhrase> "and" <m odiferVerbPhrase>
<modiferVerbPhrase> := a verb phrase that expresses a search-keyword style constraint on the selection class (e.g., restaurants that "are described as quiet", "serve meat after 11", "match 'tragically hip' in reviews"; mov-ies that "contain violence", "star Billy Bob Thornton"). Include all con-straints in the parse whose GrammaticalRole=ThatClauseModifer, using the "that... and" variant if there are more than one. Use the Display-TemplatePlural to generate the "that" clauses, putting the Guessed-Qualifier last. If there is only one such constraint, use the Displav-TemplateCompact variant.

[0444] Table 2 provides some examples of paraphrases that may be pro-vided in response to the first input for a task, according to one embodiment.

input initial Paraphrase l tie nouse Sorry; I con's understand "tie nouse". i Know aeut restaurants, li hotels, and other local businesses, fllahrs weather, and buvinc thlnas online. What are you lookina for. and where?
naic alto i can neip you fino restaurants, businesses events, and things to do in Palo Alto. What are you looking for?
food Looking for a place to eat? Where? (examples%
Italian tooo ; I can tell you about Italian restaurants in your area. Wnere would you like to eat?
burrito i can help you find places tna serve burritos in your area.
Where would you like to eat?
best mexican food l I can help you find good Mexican restaurants in your area.
Where are you?
restaurants palo alto Looking for restaurants near Paio Alto.
Italian palo alto Looking for Italian restaurants near Palo Alto.
thai food in menlo park Looking for Thai restaurants near Menlo Park indonesian food in menio Looking for Indonesian restaurants near Menlo Park park best mexican food in Palo Looking for recommended Mexican restaurants near Palo Alto.
Alto fast food memo park Looking for fast food restaurants near Menlo nark healthy food in memo park 1 Looking for places near Menlo Park that serve healthy food.
dive bar in menlo park Looking for dive bars near Menlo Park.
pool room in menio park Looking for pooi rooms near Menlo Park.
hookah room in menio park Looking for places near Menlo Park aescribed as "hookah room".
spicy food in at Looking for places near Menlo Park that serve snicy food.
burgers in Menlo park Looking for places near Menlo Park that serve burgers.
belly dancing place in sf Looking for places near San Francisco aescribed as "belly dancing'.
belly dancing place in menlo Looking for places near Menlo Park aescribed as "belly danc-park inc".
juke joint in sf Looking for joints near San rancisco described as "juke tie house Menlo park Looking for places near Menlo lark described as "tie house'.
tnai house pale alto Looking up Thai House in Palo Alto it fornaio menio park Looking up II Hornaio in Palo Alto it fornao in Menlo park Looking for places near Palo Alto described as "il fornao".
cliff house in Menlo park Looking up Cliff House in Pan Alto romantic restaurant in pale Looking for romantic restaurants in Palo Alto alto romantic restauran; in paic Looking for romanti; restaurants in Pain Alto alto quiet romantic restaurant in Looking for romantic restaurants in Paic Alto described as Palo alto ouiet quiet romantic restaurant in Looking for romantic restaurants in Palo Alto described as Palo alto "quiet"
tragically hip restaurant in Looking for restaurants in I`alo Alto descrined as "tragically hip' pale alto tragically hip restaurant in Looking for restaurants in Palo Alto descrined as "tragically nip palo alto table for two tonight in palo Checking availability for tonight at restaurants near Palc Alto.
alto table for two itaiian food to- Checking availability for tonight at Italian restaurants near Palo night in paid alto Alto.
it fornaio palo alto 6pm for 2 Checking availability for tables tonight at li Fornaio in Daio Alto.
table for 2 at trial house in PA Checking availability for tables tonight at Thai House in Palo Alto.
pizza delivery in menlo park I want the best quiet romantic OK, looking for recommended romantic italian restaurants near italian restaurant for osso Menlo Park that serve osso buco and are described as 'ouiet buco in or near menlo park Book me a table for 2 at the OK, looking for recommended romantic Italian restaurants near best quiet romantic Italian Menlo Park with open tables for 2 that serve osso buco and are restaurant for osso buco in or described as 'quiet'.
near menlo park Table 2: Paraphrases in response to first input Refining a query about places to eat [0445] Table J provides some examples of paraphrases in response to a situation where user's intent to find a place to eat is known, but they have not selected a particular place to eat. They are facing a list of restaurants, and they provide refinements.

Refinements menlo park (was un-known' Thanks, now looking for restaurants in Menlo Park. -150-cneapest OK, looking for affordable restaurants near Menlo Park.
OK, changing cuisine to Cninese. Looking fo"-, C ninese restaurants near chinese (was Italian) Menlo Park.
pizza (adding food item) OK, looking for Italian restaurants near Menlo Park that serve o17za.
I san francisco was OK, chancing location to San Francisco. Looking for Itai an restaurants menlo park) near San rancisco tnat serve pizza.
tragically nip OK., I'll search reviews for "tr agicaliy hip'.
quiet romantic OK, looking for romantic restaurants in Palo Alto described as . ,quit cliff house Cliff House sounds familiar. I'll iook it uc in Palo Altc.
Table 3: Paraphrases in response to refinements [0446] Table 4 provides some examples of results summaries that are pro-vided once results are obtained.
Input 1 # Results Results Summary tie house N/A N/A (no call to services) Palo alto N/A N/A (no call to services) food N/A N/A (no call to services) Italian food N/A N/A (no call to services) burrito N/A N/A (no call to services) best mexican food N/A N/A (no call to services) restaurants pain alto many Here are some recommences restaurants near Palo Altc. Do you prefer any particular food type? Would you like a reserva-tion?
Itaiian Palo alto some Here are some Italian restaurants near Pair) Alto, To reserve a table, click on available times.
tnai food in menlo restaurants near Menic Dark. To reserve Apark some ., Here are some Thai ra table, click on available times.

Indonesian food in menio 0 Sorry, I can't find any Indonesian restaurants near Menlo Park.
park You could try other cuisines or locations.
best mexican food in some Here are some recommended Mexican restaurants near Paio Palo Alto Alto.
fast food men-lo park some OK, here are some fast food restaurants near Menlo Parr.
healthy food in menlo some Here are some places near Menlo Park that might serve park healthy food, based on reviews.
dive bar in menio park some Here are some bars near Menlo Park. i don't see anything specific to "dive bar". Are there other ways of saying it?

pool room in menio park some Here are some places in Menlo Darr that are cescrlced as pool rooms. You can add other words to describe what you want.
hookan room in menic 0 I can't find places Menlo Park matching "hookah room inier-park ested in restaurants, hotels, or local businesses?
Spry food in sf weak Here are some places to eat in San Francisco. I done know match which serve spicy food. Could it go by another name?
burgers in Menlo park some You might find burners at these places to eat near Menlo Park, belly dancing place in sf some Here are some places in Menlo Park that are described as "belly dancing". You can add other words to describe what you want.

belly dancing place in 0 I can help you find restaurants, businesses, events, and things menlo park to do in San Francisco. What are some other words for "beliy dancing place"?
juke joint in sf 0 I can help you find restaurants. businesses, events, and things to do in San Francisco. What are some other words for "juke joint"?
tie house Menlo park 0 Sorry, 1 don't know about "tie house" in Menlo ParK.
Any other names for it?
trial house palo alto 1 Got it: Thai House restaurant in Palo Alto CA. Want to re-serve a table? When and for how many9 it fornaio menio park weak There is a II Fornaio restaurant in Palo Alto, which is near match Menlo Park.
it fornao in Menlo park spell Did you mean II Fornaio in alo Alto? If not, try different match words.
cliff house in Menlo park 0 I couidn't find Miff House in Paid Alto. Could it go by a differ-ent name or be in a different city?
romantic restaurant in some Here are some restaurants near Palo Alto that are described in palo alto reviews as romantic.
romantic restaurant in 0 Here are restaurants near Paio Alto. Searcning reviews did not palo alto find any described as romantic. Any other preferences?
i quiet romantic restaurant some Here are some restaurants near Pain Alto that matcn "quiet in paid alto romantic" in reviews.
quiet romantic restaurant 0 Here are some restaurants near Palo Alto.
Searching reviews in palo alto did not find any matching "quiet romantic". Any other prefer-ences?
Hragically nip restaurant some Here are some restaurants near Palo Alto that match "rags in palo alto dally hip" in reviews.

WO 2011/088053 PCT/ti'S2011/020861 tragioalrv hip restaurant 0 Here are some restaurants near Paio Alto.
Searching reviews in Paio alto aid not find any matching "tragically hip". Any other prefer-ences?
table tar two tonight in some Here are restaurants with open tables from 62m to. 8am ronion_.
palo alto for a party Of two in Pain Alto CA.
table for two Italian food some Here are restaurants with open rabies from Sam to Sam tonion-tonight in pain alto for a party of two in Palo Alto. CA.
it fornaio Palo alto 6pm 1 Here are table options at II Tornaic restaurant in Palo Aito_ CA.
for 2 at 6pm today for a party of 2.
table for 2 at thai house 1 Here is the information for Thai douse in Paio Alto. CA. You in PA can call them at the phone number listed.
pizza delivery in menlo Here are places tnat serve pizza in menlo park. I do not nave park delivery options at this time. Click on restaurant phone num-bers to call for delivery.

I want the best quiet ro- weak Here are the best matches for recommended romantic itaiian mantic Italian restaurant match, restaurants near Menlo Park that serve osso buco and are for osso buco in or near sorted by described as "quiet'. Click on available times to reserve a ta-I menlo park availability bie.
Book me a table for 2 at weak Here are the best matcnes for recommended romantic itaiian the best quiet romantic match, restaurants near Manic Park with open tables for 2 that serve Italian restaurant for sorted by osso buco and are described as 'quiet". Click on available osso buco in or near availability times to reserve a table.
memo park refinements menlo park (was un- some Here are some recommended restaurants near Menic Park.
known) Do you prefer any particular food type?
cheapest some I found 4 affordable restaurants near Menio Park.
chinese (was Italian) found 4 Chinese restaurants near Manic Park.
pizza (adding food item) some found 4 Italian restaurants near Menlo Park that serve pizza.
san francisco (was some I found 4 Italian restaurants nea, San Francisoo_ menlo park) tragically hip some i found 4 restaurants near Palo, Alto that match "tragicaiiy nip' in reviews.
quiet romantic some Here are some restaurants near Paio Alto that match 'quiet romantic" in reviews.

WO 2011/088053 PCT/L?S2011/020861 cliff house 0 1 couldn't find Ciif-'House in Dale Alto. Couid it go by a differ-ent name or be in a different city?
Table Results Summaries x0447] Table 5 provides some examples o prompts. that are provided when users click: on active links.

Prompts when users click on active finks Anchor Text Prompt Text Notes Location. Enter a location: a city, zip code, or This prompt might ne used wnen the where "near" followed by the name of a place. user has not specified a location yet.
Palo Alto Enter a location such as a city, neian- T his prompt might be usec when the bornood, street address, or "near" toi- user is changing locations.
lowed by a landmark.
food type Enter a food category such as Cninese Merge food type and cuisine can be or Pizza. merged Italian You can try other food categories sucn User already said Italian.
Assistant as Chinese, or a favorite food item such 1002 is helping the user explore alter as steak. natives. If it is a food item, it dominates over cuisine.
reservation Enter the day and time to reserve a Prompting for a reservation table, such as "tomorrow at F.
healthy food You can also enter menu items or cui- Known food type sines spicy food You can also enter menu items or cui- Unknown food type sines restaurants What kind of restaurant? (e.g., On:!- Clicking on the restaurants iinK snouic nese, Pizza) insert the word "restaurant" ' on the end of the text input.
businesses You can find local fiorists, ATMs, doo- Clicking on the businesses ink snoulo tors, drug stores, and the like What add to the machine readable tag that kind of business are you looking for?
this is a local search events You can discover upcoming converts, shows, and the like What interests you?
tninas to co Music, art, tneater, sports, and the like What kind of thing would you like to do in this area?

hotels can neip you Tinc an availabie note!
room Any preferences for amenities or ocation?
weather Enter a city, and I'll tell you what the if location is known, just show the weather is like there. weather data ouying things 1 can help you find music movies;
nooks, eiectronics, toys, and more --and buy it from Amazon. What are you iookino Tor?
Table 5: Prompts when users click on active links Suggesting Possible Responses in a Dialog [04481 In one embodiment, assistant 1002 provides contextual suggestions.
Suggestions a way for assistant 1002 to offer the user options to move forward from his or her current situation in the dialog. The set of suggestions offered by assistant 1002 depends on context, and the number of suggestions offered may depend on the medium and form factor. For example, in one embodi-ment, the most salient suggestions may be offered in line in the dialog, an ex-tended list of suggestions ("more") may be offered in a scrollable menu, and even more suggestions are reachable by typing a few characters and picking from autocompiete options. One skilled in the art will recognize that other mechanisms may he used for providing suggestions.

[0449] In various embodiments, different types of suggestions may be pro-vided. Examples of suggestion types include:

= options to refine a query, including adding or removing or chang-ing constraint values;

= options to repair or recover from bad situations, such as "not what I
mean" or "start over" or "search the web";

= options to disambiguate among;;
= interpretations of speech;

= interpretations of text, including spell correction and seman-tic ambiguity,,-= context-specific commands, such as "show these on a map" or "send directions to my date" or "explain these results";

= suggested cross-selling offers, Sikh a s next steps in meal or event planning scenarios;

= options to reuse previous commands, or parts of them.

[04501 In various embodiments, the context that determines the most rele-vant suggestions may be derived from, for example:

= dialog state = user state, including, for example:

o static properties (name, home address, etc) o dynamic properties (location, time, network speed) = interaction history, including, for example:

o query history o results history o the text that has been entered so far into autocomplete.
[0451] In various embodiments, suggestions may be generated by any mechanism, such as for example:

= paraphrasing a domain, task, or constraint based on the ontology model;

= prompting in autocomplete based on the current domain and con-straints;

= paraphrasing ambiguous alternative interpretations;
= alternative interpretations of speech-to-text;

= hand authoring, based on special dialog conditions.

[04521 According to one embodiment, suggestions are generated as opera-tions on commands in some state of completion. Commands are explicit, ca-nonical representations of requests, including assumptions and inferences, based on attempted interpretations on user input. In situations where the user input is incomplete or ambiguous, suggestions are an attempt to help the user adjust the input to clarify the command.

[0453] In one embodiment, each cointarand is an imperative sentence hav-ing some combination of a = command verb (imperative such as "find" or "where is");
= domain (selection class such as "restaurants");

= constraint(s) such as location=Palo Alto and cuisine=Italian.
[0454] These parts of a command (verb, domain, constraints) correspond to nodes in the ontolob [04;,5] A suggestion, then, I1 ay be thought of as operations on a command, such as setting it, changing it, or declaring that it is relevant or not relevant.
Examples include:

= setting a command verb or domain ("find restaurants" ) = changing a command verb ("book it", "map it", "save it") = changing a domain ("looking for a restaurant, not a local business") = stating that a constraint is relevant ("try refining by cuisine") = choosing a value for a constraint ("Italian", "French", and the like) = choosing a constraint and value together ("near here", "tables for = stating that a constraint value is wrong ("not that Boston") = stating that a constraint is not relevant ("ignore the expense") = stating the intent to change a constraint value ("try a different loca-tion") = changing a constraint value ("Italian, not Chinese") = adding- to a constraint value and with a pool, too") = snapping a value to grid ("Los Angeles, not los angelos") = initiating a new command, reusing context ([after movies] "find nearby restaurants", "send directions to my friend") = initiating a command that is "meta" to context ("explain these re-sults") = initiating a new command, resetting or ignoring context ( "start over", "help with speech") WO 2011/0880,53 PCT/US2011/020861 [0456] A suggestion may also involve some combination of the above. For example:

= "the movie Milk not [restaurants serving] the food item milk"
= "restaurants serving pizza, not lust pizza joints' = "The place called Costco in Mountain View, I don't care whether you think it is a restaurant or local business"

= "Chinese in mountain view" [a recent query]

[0457] In one embodiment, assistant 1002 includes a general mechanism to maintain a list of suggestions, ordered by relevance. The format in which a suggestion is offered may differ depending on current context, mode, and form factor of the device.

[0458] In one embodiment, assistant 1002 determines which constraints to modify by considering any or all of the following factors:

= Consider whether the constraint has a value;

= Consider whether the constraint was inferred or explicitly stated;

= Consider its salience (suggestionIndex).

[0459] In one embodiment, assistant 1002 determines an output format for the suggestion. Examples of output formats include:

= change domain:

= if autocomplete option "find restaurants", then "try some-thing different"

= else [was inferred] "not looking for restaurants"
= change name constraint:

= if name was inferred, offer alterative ambiguous interpreta-tion"

= stuff into autocomplete the entity- names from current results = different name = consider that it wasn't a name lookup (remove constraint) -maybe offer category in place of it = "not named"

= "not in Berkeley"
= some other day = not that sense of (use ambiguity alternatives) = inferred date: "any day, I don't need a reservation"

[0460] In one embodiment, assistant 1002 attempts to resolve ambiguities via suggestions. For example, if the set of current interpretations of user in-tent is too ambiguous 310, then suggestions are one way to prompt for more information 322. in one embodiment, for constrained selection tasks, assistant 1002 factors out common constraints among ambiguous interpretations of in-tent 290 and presents the differences among them to the user. For example, if the user input includes the word "cafe" and this word could match the name of a restaurant or the type of restaurant, then assistant 102 can ask "did you mean restaurants named 'cafe' or 'cafe restaurants'?"

[0461] In one embodiment, assistant 1002 infers constraints under certain situations. That is, for constrained selection tasks, not all constraints need be mentioned explicitly in the user input; some can be inferred from other in-formation available in active ontology 1050, short term memory 1052, and/or other sources of information available to assistant 1002. For example:

= Inferring domain or location = Default assumption, like location = Weakly matched constraint (fuzzy, low salience location, etc) = Ambiguous criteria (match to constraint value without prefix (name vs.
category, often ambiguous) [0462] in cases where the assistant 1002 infers constraint values, it may also offer these assumptions as suggestions for the user to overrule. For ex-ample, it might tell the use,. "I assumed you meant around here. Would you like to look at a different location?"

[0463] The present invention has been described in particular detail with respect to possible embodiments. Those of si-,ill in the art will appreciate that the invention may be practiced in other" embodiments. First, the particular naming of the components, capitalization of terms, the attributes, data struc-tures o anti other proms amming or structural aspect is not mandatory or sig-nificant, and the mechanisms that implement the invention or its features may have different names, formats, or protocols. Further, the system maybe im-plemented via a combination of hardware and software, as described, or en-tirely in hardware element, or entirely in software elements. Also, the par-ticular division of functionality between the various system components de-scribed herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple compo-nents, and functions performed by multiple components may instead be per-formed by a single component.

[0464] In various embodiments, the present invention can be implemented as a system or a method for performing the above-described techniques, ei-ther singly or in any combination. In another embodiment, the present inven-tion can be implemented as a computer program product comprising a non-transitory computer-readable storage medium and computer program code, encoded on the medium, for causing a processor in a computing device or other electronic device to perform the above-described techniques.

[0465] Reference in the specification to "one embodiment" or to "an em-bodiment" means that a particular feature, structure, or characteristic de-scribed in connection with the embodiments is included in at least one em-bodiment of the invention. The appearances of the phrase "in one embodi-ment" in various places in the specification are not necessarily all referring to the same embodiment.

[0466] Some portions of the above are presented in terms of algorithms and symbolic representations of operations on data bits within a memory of a computing device. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generalh.-, conceived to be a self-consistent sequence of steps (in-structions] leading to a desired result. The steps are those requiring physical manipulations of nhvsicai quantities. ,sualiy, though no necessa.-ily, these Quantities take the form of electrical, magnetic or optical signals capable of being stored, transferred, combined, compared and otherwise manipulated.

It is convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. Furthermore, it is also convenient at times, to refer to certain ar-rangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.

[0467] It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the following discussion, it is appreciated that throughout the description, discussions utilizing terms such as "process-ing" or "computing" or "calculating" or "displaying" or "determining" or the like, refer to the action and processes of a computer system, or similar elec-tronic computing module and/or device, that manipulates and transforms data represented as physical (electronic) quantities within the computer sys-tem memories or registers or other such information storage, transmission or display devices.

[0468] Certain aspects of the present invention include process steps and instructions described herein in the form of an algorithm. It should be noted that the process steps and instructions of the present invention can be embod-ied in software, firmware and/or hardware, and when embodied in software, can be downloaded to reside on and be operated from different platforms used by a variety of operating systems.

[0469] The present invention also relates to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computing device. Such a computer urouram may be stored in a compute_-readable storage medium, such as, but is not limited to, any tv pe of disk in ciuding floppy disks, optical disks, CD-ROMs, magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, application specific integrated circuits (ASICs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Further, the computing devices re-ferred to herein may include a single processor or may be architectures em-plowing multiple processor designs for increased computing capability.

[0470] The algorithms and displays presented herein are not inherently related to any particular computing device, virtuaiized system, or other appa-ratus. Various general-purpose systems may also be used with programs in accordance with the teachings herein, or it may prove convenient to construct more specialized apparatus to perform the required method steps. The re-quired structure for a variety of these systems will be apparent from the de-scription provided herein. In addition, the present invention is not described with reference to any particular programming language. It will be appreci-ated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any references above to specific languages are provided for disclosure of enabiement and best mode of the present invention.

[04711 Accordingly, in various embodiments, the present invention can be implemented as software, hardware, and/or other elements for controlling a computer system, computing device, or other electronic device, or any combi-nation or plurality thereof. Such an electronic device can include, for exam-ple, a processor, an input device (such as a keyboard, mouse, touchpad, trackpad, joystick, trackball, microphone, and/or any combination thereof), an output device (such as a screen, speaker, and/or the like), memory, long-term storage (such as magnetic storage, optical storage, and/or the like), W0 2011/088053 PCT[US2011/020861 and 'or network connectivity, according to techniques that are well known in the art. Such an electronic device may be portable or nonportable. Examples of electronic devices that may be used for implementing the invention iro-dude: a mobile phone, personal digital assistant, smartphone, kiosk, desktop computer, laptop computer, tablet computer, consumer electronic device, consumer entertainment device; music player; camera; television; sei-top box;
electronic gaming unit; or the like. An electronic device for implementing the present invention may use any operating system such as, for example, iOS or MacOS, available from Apple Inc. of Cupertino, California, or any other oper-ating system that is adapted for use on the device.

[0472] While the invention has been described with respect to a limited number of embodiments, those skilled in the art, having benefit of the above description, will appreciate that other embodiments may be devised which do not depart from the scope of the present invention as described herein. In ad-dition, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subiect matter.
Accordingly, the disclosure of the present invention is intended to be illustra-tive, but not limiting, of the scope of the invention, which is set forth in the claims.

Claims (18)

1. A method for operating an automated assistant, comprising:
at an electronic device comprising a processor and memory storing instructions for execution by the processor:
performing a first task using a first parameter;
obtaining a text string from a speech input received from a user;
identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter; and performing the first task using the second parameter or performing the second task using the first parameter.
2. The method of claim 1, further comprising, prior to performing the first task using the first parameter:
obtaining an initial text string from an initial speech input from the user:
identifying, based at least in part on the initial text string, the first task and the first parameter.
3. The method of claim 2, further comprising, interpreting the first text string to derive a first representation of user intent, wherein the identification of the first task and the first parameter is based at least in part on the first representation of user intent.
4. The method of any one of claims 1-3, further comprising:
prior to obtaining the text string, producing an output for presentation to the user based on the performing of the first task using the first parameter.
5. The method of any one of claims 1-4, wherein the performing comprises performing the second task using the first parameter.
6. The method of any one of claims 1-4, wherein the performing comprises performing the first task using the second parameter.
7. The method of any one of claims 1-5, wherein:
the first task is a telephone call;
the first parameter is a participant of the telephone call;
the identifying comprises identifying that the second task is sending a text message; and the performing the second task using the first parameter comprises sending the text message to the participant of the telephone call.
8. The method of any one of claims 1-4 and 6, wherein:
the first task is a weather forecast task;
the first parameter is a first location associated with the weather forecast;
the identifying comprises identifying that the second parameter is a second location; and the performing the first task using the second parameter comprises presenting a second weather forecast associated with the second location to the user.
9. The method of any one of claims 1-6, wherein the first parameter is a first geographic location, and wherein the second parameter is a second geographic location.
10. The method of any one of claims 1-6, wherein the first parameter is a first time, and wherein the second parameter is a second time.
11. The method of any one of claims 1-6 and 9-10, wherein the first task is a weather forecast task.
12. The method of any one of claims 1-6 and 9-10, wherein the first task is an event planning task.
13. The method of any of claims 1-6 and 9-10, wherein the first task is a restaurant reservation task.
14. The method of any of claims 1-6 and 9-10, wherein the first task is a constrained selection task.
15. A system for operating an intelligent automated assistant, comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the processors to perform operations comprising:
performing a first task using a first parameter;
obtaining a text string from a speech input received from a user;
identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter; and performing the first task using the second parameter or performing the second task using the first parameter.
16. A system for operating an intelligent automated assistant, comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the processors to perform the methods of any one of claims 1-14.
17. A computer readable storage medium storing instructions that, when executed by an electronic device with one or more processors, cause the processors to perform operations comprising:
performing a first task using a first parameter;
obtaining a text string from a speech input received from a user;
identifying, based at least partially on the text string, a second task different from the first task or a second parameter different from the first parameter;
and performing the first task using the second parameter or performing the second task using the first parameter.
18. A computer readable storage medium storing instructions that, when executed by an electronic device with one or more processors, cause the processors to perform the methods of any one of claims 1-14.
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CA3000109A Active CA3000109C (en) 2010-01-18 2011-01-11 Intelligent automated assistant
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