Patent Grant
12087308
The present invention relates to intelligent systems, and more specifically for classes of applications for intelligent automated assistants.
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.
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.
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.
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.
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 recognition and dialog, multimodal input, personal information management, task flow management, orchestrating distributed services, and the like). Furthermore, to offer intelligent interfaces and useful functionality to users, the intelligent automated assistant of the present invention may, in at least some embodiments, coordinate these components and services. The conversation interface, and the ability to obtain information and perform follow-on task, are implemented, in at least some embodiments, by coordinating various components such as language components, dialog components, task management components, information management components and/or a plurality of external services.
According to various embodiments of the present invention, intelligent automated assistant systems may be configured, designed, and/or operable to provide various different types of operations, functionalities, and/or features, and/or to combine a plurality of features, operations, and applications of an electronic device on which it is installed. In some embodiments, the intelligent automated assistant systems of the present invention can perform 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 external services can be performed via APIs or by any other suitable mechanism. 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, 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.
In addition, in various embodiments, the assistant of the present invention provides a conversational interface that the user may find more intuitive 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 example speech, graphical user interfaces (buttons and links), text entry, and the like. The system can be implemented using any of a number of different platforms, such as device APIs, the web, email, and the like, or any combination thereof. Requests for additional input can be presented to the user in the context 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 information about the user.
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 applications on the device or on other devices. For example, the intelligent automated 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 automated assistant of the present invention can thereby interpret instructions such as “send him a text message”, wherein the “him” is interpreted according 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 available context data to determine which address book contact to use, which contact data to use, which telephone number to use for the contact, and the like, so that the user need not re-specify such information manually.
In various embodiments, the assistant can also take into account external events and respond accordingly, for example, to initiate action, initiate communication with the user, provide alerts, and/or modify previously initiated action in view of the external events. If input is required from the user, a conversational interface can again be used.
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. 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 intelligent automated assistant is implemented on a smartphone, personal digital 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.
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:
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.
In various embodiments, the intelligent automated assistant systems disclosed herein may be configured or designed to include functionality for 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 services, at least one intelligent automated assistant system embodiment disclosed herein may also enable the combined use of several sources of data and services at once. For ex-ample, it may combine information about products from several review sites, check prices and availability from multiple distributors, 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 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, performances, exhibits, shows and at-tractions); 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 dialog 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, 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; communicating 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, etc.). Further, at least one intelligent automated assistant system embodiment disclosed herein may be configured or designed to include functionality for identifying, generating, and/or providing 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 automated access to data and services.
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 operations 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.
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 recognize that the particular embodiments illustrated in the drawings are merely exemplary, and are not intended to limit the scope of the present invention.
Various techniques will now be described in detail with reference to a few example embodiments thereof as illustrated in the accompanying drawings. 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 reference herein may be practiced without some or all of 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.
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 be 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 more 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 disclosure, 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 invention(s) nor a listing of features of one or more of the invention(s) that must be present in all embodiments.
Headings 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 any way.
Devices that are in communication with each other need not be in continuous communication with each other, unless expressly specified otherwise. In addition, devices that are in communication with each other may communicate directly or indirectly through one or more intermediaries.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. To the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of one or more of the invention(s).
Further, although process steps, method steps, algorithms or the like may be described in a sequential order, such processes, methods and algorithms 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 being 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 exclusive 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 invention(s), and does not imply that the illustrated process is preferred.
When a single device or article is described, it will be readily apparent 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 will be readily apparent that a single device/article may be used in place of the more than one device or article.
The functionality and/or the features of a device may be alternatively 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.
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.
Although described within the context of intelligent automated assistant technology, it may be understood that the various aspects and techniques described herein (such as those associated with active ontologies, for example) may also be deployed and/or applied in other fields of technology involving human and/or computerized interaction with software.
Other aspects relating to intelligent automated assistant technology (e.g., 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:
Generally, the intelligent automated assistant techniques disclosed herein may be implemented on 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 applications, on a specially constructed machine, or on a network interface card. In a specific embodiment, the techniques disclosed herein may be implemented in software such as an operating system or in an application running on an operating system.
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 reconfigured by a computer program stored in memory. Such network devices may have multiple network interfaces which may be configured or designed to utilize different types of network communication protocols. A general architecture for some of these machines may appear from the descriptions disclosed herein. According to specific embodiments, at least some of the features and/or functionalities of the various intelligent automated assistant embodiments disclosed herein may be implemented on one or more general-purpose network host machines such as an end-user computer system, computer, network server or server system, mobile computing device (e.g., personal digital assistant, mobile phone, smartphone, laptop, tablet computer, or the like), consumer electronic device, music player, or any other suitable electronic device, router, switch, or the like, or any combination thereof. In at least some embodiments, at least some of the features and/or 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).
Referring now to
In one embodiment, computing device 60 includes central processing unit (CPU) 62, interfaces 68, and a bus 67 (such as a peripheral component inter-connect (PCI) bus). When acting under the control of appropriate software or firmware, CPU 62 may be responsible for implementing specific functions associated with the functions of a specifically configured computing device 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 intelligent automated assistant system utilizing CPU 62, memory 61, 65, and interface(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 any appropriate applications software, drivers, and the like.
CPU 62 may include one or more processor(s) 63 such as, for example, 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 circuits (ASICs), electrically erasable programmable read-only memories (EEPROMs), field-programmable gate arrays (FPGAs), and the like) for controlling the operations of computing device 60. In a specific embodiment, a memory 61 (such as non-volatile random access memory (RAM) and/or read-only memory (ROM)) also forms part of CPU 62. However, there are many different ways in which memory may be coupled to the system. Memory block 61 may be used for a variety of purposes such as, for example, caching and/or storing data, programming instructions, and the like.
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 application-specific integrated circuit, and any other programmable circuit.
In one embodiment, interfaces 68 are provided as interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over a computing network and sometimes support other peripherals used with computing device 60. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various types of interfaces may be provided such as, for example, universal serial bus (USB), Serial, Ethernet, Firewire, PCI, parallel, radio frequency (RF), Bluetooth™, near-field communications (e.g., using near-field magnetics), 802.11 (WiFi), frame relay, TCP/IP, ISDN, fast Ethernet interfaces, Gigabit Ethernet interfaces, asynchronous transfer mode (ATM) interfaces, high-speed serial interface (HSSI) interfaces, Point of Sale (POS) interfaces, fiber data distributed interfaces (FDDIs), and the like. Generally, such interfaces 68 may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile and/or nonvolatile memory (e.g., RAM).
Although the system shown in
Regardless of network device configuration, the system of the present invention may employ one or more memories or memory modules (such as, for example, memory block 65) configured to store data, program instructions 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 taxonomy information, advertisement information, user click and impression information, and/or other specific non-program information described herein.
Because such information and program instructions may be employed 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 media 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 devices that are specially configured to store and perform program instructions, such as read-only memory devices (ROM), flash memory, memristor memory, random access memory (RAM), and the like. Examples of program instructions include both machine code, such as produced by a compiler, and files containing higher level code that may be executed by the computer using an interpreter.
In one embodiment, the system of the present invention is implemented on a standalone computing system. Referring now to
In another embodiment, the system of the present invention is implemented on a distributed computing network, such as one having any number of clients and/or servers. Referring now to
In the arrangement shown in
In addition, in one embodiment, servers 1340 can call external services 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 various 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.
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 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 assistant 1002. 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 assistant 1002 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.
For example, the user may provide input to assistant 1002 such as “I need to wake tomorrow at 8 am”. Once assistant 1002 has determined the user's intent, using the techniques described herein, assistant 1002 can call external services 1360 to interface with an alarm clock function or application on the device. Assistant 1002 sets the alarm on behalf of the user. In this manner, the user can use assistant 1002 as a replacement for conventional mechanisms for setting the alarm or performing other functions on the device. If the user's requests are ambiguous or need further clarification, assistant 1002 can use the various techniques described herein, including active elicitation, paraphrasing, suggestions, and the like, to obtain the needed information so that the correct services 1360 are called and the intended action taken. In one embodiment, assistant 1002 may prompt the user for confirmation before calling a service 1360 to perform a function. In one embodiment, a user can selectively disable assistant's 1002 ability to call particular services 1360 or can disable all such service-calling if desired.
The system of the present invention can be implemented with many different types of clients 1304 and modes of operation. Referring now to
For messaging platforms including but not limited to email, instant messaging, discussion forums, group chat sessions, live help or customer support sessions and the like, assistant 1002 may act as a participant in the conversations. Assistant 1002 may monitor the conversation and reply to individuals or the group using one or more the techniques and methods described herein for one-to-one interactions.
In various embodiments, functionality for implementing the techniques of the present invention can be distributed among any number of client and/or server components. For example, various software modules can be implemented for performing various functions in connection with the present invention, and such modules can be variously implemented to run on server and/or client components. Referring now to
In the example of
In one embodiment, client 1304 maintains subsets and/or portions of these components locally, to improve responsiveness and reduce dependence on network communications. Such subsets and/or portions can be maintained and updated according to well-known cache management techniques. Such subsets and/or portions include, for example:
Additional components may be implemented as part of server 1340, including for example:
Each of these components will be described in more detail below. Server 1340 obtains additional information by interfacing with external services 1360 when needed.
Conceptual Architecture
Referring now to
For example, according to different embodiments, at least some intelligent automated assistant system(s) may be configured, 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 combinations thereof):
According to different embodiments, at least a portion of the various types of functions, operations, actions, and/or other features provided by intelligent automated assistant 1002 may be implemented at one or more client systems(s), at one or more server systems (s), and/or combinations thereof.
According to different embodiments, at least a portion of the various 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 example, with respect to
Additionally, various embodiments of assistant 1002 described herein may include or provide a number of different advantages and/or benefits over currently existing intelligent automated assistant technology such as, for example, one or more of the following (or combinations thereof):
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 operations. This may include, for example, input data/information and/or output data/information. For example, in at least one embodiment, intelligent automated assistant 1002 may be operable to access, process, and/or otherwise utilize information from one or more different types of sources, such as, for example, one or more local and/or remote memories, devices and/or systems. Additionally, in at least one embodiment, intelligent automated assistant 1002 may 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.
Examples of different types of input data/information which may be accessed and/or utilized by intelligent automated assistant 1002 may include, but are not limited to, one or more of the following (or combinations thereof):
The input to the embodiments described herein also includes the context of the user interaction history, including dialog and request history.
Examples of different types of output data/information which may be generated by intelligent automated assistant 1002 may include, but are not limited to, one or more of the following (or combinations thereof):
It may be appreciated that the intelligent automated assistant 1002 of
User Interaction
Referring now to
In one embodiment, information screen 101E shows information gathered and combined from a variety of services, including for example, any or all of the following:
In one embodiment, information screen 101E also includes some examples of services that assistant 1002 might offer on behalf of the user, including:
As shown in the example of
According to various embodiments, intelligent automated assistant 1002 may include a plurality of different types of components, devices, modules, processes, systems, and the like, which, for example, may be implemented and/or instantiated via the use of hardware and/or combinations of hardware and software. For example, as illustrated in the example embodiment of
As described in connection with
For purposes of illustration, at least a portion of the different types of components of a specific example embodiment of intelligent automated assistant 1002 will now be described in greater detail with reference to the example intelligent automated assistant 1002 embodiment of
Active Ontologies 1050
Active ontologies 1050 serve as a unifying infrastructure that integrates models, components, and/or data from other parts of embodiments of intelligent automated assistants 1002. In the field of computer and information science, ontologies provide structures for data and knowledge representation such as classes/types, relations, attributes/properties and their instantiation 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.
Within the context of the present invention, an “active ontology” 1050 may also serve as an execution environment, in which distinct processing 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 1002. Any number of active ontologies 1050 can be provided.
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):
In one embodiment, at least a portion of the functions, operations, actions, and/or other features of active ontologies 1050 described herein may be implemented, at least in part, using various methods and apparatuses described in U.S. patent application Ser. No. 11/518,292 for “Method and Apparatus for Building an Intelligent Automated Assistant,” filed Sep. 8, 2006.
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 memory devices. Examples of different types of data which may be accessed by active ontologies 1050 may include, but are not limited to, one or more of the following (or combinations thereof):
In this manner, active ontologies 1050 are used to unify elements of various components in intelligent automated assistant 1002. An active ontology 1050 allows an author, designer, or system builder to integrate components 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.
Referring now to
Active ontology 1050 in
As described above, active ontology 1050 allows the author, designer, or system builder to integrate components; thus, in the example of
Active ontologies 1050 may be embodied as, for example, configurations of models, databases, and components in which the relationships among models, databases, and components are any of:
For example, referring now to
Active Input Elicitation Component(s) 1094
In at least one embodiment, active input elicitation component(s) 1094 (which, as described above, may be implemented in a stand-alone configuration or in a configuration including both server and client components) 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):
By performing active input elicitation, assistant 1002 is able to disambiguate 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 performed. The results of such semantic interpretation can then be used to drive active input elicitation, which may offer the user alternative candidate words to choose among based on their degree of semantic fit as well as phonetic match.
In at least one embodiment, active input elicitation component(s) 1094 actively, automatically, and dynamically guide the user toward inputs that may be acted upon by one or more of the services offered by embodiments of assistant 1002. Referring now to
The procedure begins 20. In step 21, assistant 1002 may offer interfaces 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 interaction. 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.
In at least one embodiment, assistant 1002 offers default suggestions for the selected modality 23. 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 options naming domains of discourse (e.g., weather, sports, news, and the like)
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.
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 partial input is complete 22, which causes the procedure to return.
In 28, the latest input, whether selected or entered, is added to the cumulative input.
In 29, the system suggestions next possible inputs that are relevant given the current input and other sources of constraints on what constitutes relevant and/or meaningful input.
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 various 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 combinations thereof):
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 user and assistant communicate by making utterances back and forth in a conversational manner. Active input elicitation component(s) 1094 may be operable to perform and/or implement various types of conversational interfaces.
In at least one embodiment, active input elicitation component(s) 1094 may be operable to perform and/or implement various types of conversational 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 example, a series of steps required to elicit the information needed to perform a service.
In at least 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 input in real time as the user is inputting it.
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 Ser. No. 11/518,292 for “Method and Apparatus for Building an Intelligent Automated Assistant,” filed Sep. 8, 2006.
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 combinations of hardware and/or hardware and software.
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 satisfying one or more different types of minimum threshold criteria for triggering initiation of at least one instance of active input elicitation component(s) 1094. Various examples of conditions or events which may trigger initiation and/or implementation of one or more different threads or instances of active input elicitation component(s) 1094 may include, but are not limited to, one or more of the following (or combinations thereof):
In at least one embodiment, a given instance of active input elicitation 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 combinations thereof):
According to different embodiments, active input elicitation component(s) 1094 may apply active elicitation procedures to, for example, one or more of the following (or combinations thereof):
Referring now to
The method begins 110. Assistant 1002 receives 111 partial text input, for example via input device 1206. Partial text input may include, for example, 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 example, by pressing an Enter key. If not complete, a suggestion generator generates 114 candidate suggestions 116. These suggestions may be syntactic, semantic, and/or other kinds of suggestion based any of the sources of information or constraints described herein. If the suggestion is selected 118, the input is transformed 117 to include the selected suggestion.
In at least one embodiment, the suggestions may include extensions to the current input. For example, a suggestion for “rest” may be “restaurants”.
In at least one embodiment, the suggestions may include replacements of parts of the current input. For example, a suggestion for “rest” may be “places to eat”.
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 suggestion is chosen, the entire input may be rewritten as “find Italian restaurants”.
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 semantic concepts or entities underlying a string to be associated with the string when it is returned, which improves accuracy of subsequent language interpretation.
Referring now to
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In
In
In
In one screen 2101, and other displayed screens, are scrollable, allowing the user to scroll upwards to see screen 2001 or other previously presented screens, and to make changes to the query if desired.
Active Speech Input Elicitation
Referring now to
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 processor generates a set of candidate text interpretations 124 of the auditory signal. In one embodiment, speech-to-text service 122 is implemented using, for example, Nuance Recognizer, available from Nuance Communications, Inc. of Burlington, MA.
In one embodiment, assistant 1002 employs statistical language models to generate candidate text interpretations 124 of speech input 121.
In addition, in one embodiment, the statistical language models are tuned to look for words, names, and phrases that occur in the various models of assistant 1002 shown in
In one embodiment, the statistical language 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.
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 interpretations 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 associated 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 by semantic relevance 126 might rank “italian food for lunch” higher if it better matches the nodes assistant's 1002 active ontology 1050 (e.g., the words “italian”, “food” and “lunch” all match nodes in ontology 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).
In various embodiments, algorithms or procedures used by assistant 1002 for interpretation of text inputs, including any embodiment of the natural language processing procedure shown in
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-ranking interpretation may be automatically 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.
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
Whether input is automatically selected 130 or selected 136 by the user, the resulting input 138 is returned. In at least one embodiment, the returned input is annotated 138, 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, if “Italian food” was offered as one of the candidate interpretations 134 based on a semantic interpretation of Cuisine=ItalianFood, 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.
In at least one embodiment, candidate text interpretations 124 are generated based on speech interpretations received as output of speech-to-text service 122.
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 alternatives. For example, if speech-to-text service 122 indicates “place for meet”, the candidate interpretations presented to the user could be paraphrased as “place to meet (local businesses)” and “place for meat (restaurants)”.
In at least one embodiment, candidate text interpretations 124 include offers to correct substrings.
In at least one embodiment, candidate text interpretations 124 include offers to correct substrings of candidate interpretations using syntactic and semantic analysis as described herein.
In at least one embodiment, when the user selects a candidate interpretation, it is returned.
In at least one embodiment, the user is offered an interface to edit the interpretation before it is returned.
In at least one embodiment, the user is offered an interface to continue with more voice input before input is returned. This enables one to incrementally build up an input utterance, getting syntactic and semantic corrections, suggestions, and guidance at one iteration.
In at least one embodiment, the user is offered an interface to proceed directly from 136 to step 111 of a method of active typed input elicitation (described above in connection with
In at least one embodiment, the user is offered an interface to proceed directly from step 111 of an embodiment of active typed input elicitation to an embodiment of active speech input elicitation. This enables one to interleave typed and spoken input, getting syntactic and semantic corrections, suggestions, and guidance at one step.
Active GUI-Based Input Elicitation
Referring now to
The method begins 140. Assistant 1002 presents 141 graphical 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.
In at least one embodiment, some of the elements of the GUI are generated dynamically from the models of the active ontology, rather than written into a computer program. 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 the constraint and/or a value. For instance, the screen could have rows of a dynamically generated GUI layout with regions for the constraints Cuisine, Location, and Price Range. If the models of the active ontology change, the GUI screen would automatically change without reprogramming.
Active Dialog Suggestion Input Elicitation
In at least one embodiment, the suggestions offered in step 151 are offered as follow-up steps in a dialog and/or task flow.
In at least one embodiment, the suggestions 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 request.
In at least one embodiment, the suggestions offer options to choose among ambiguous alternative interpretations given by a language interpretation procedure or component.
In at least one embodiment, the suggestions offer options to choose among ambiguous alternative interpretations given by a language interpretation procedure or component.
In at least one embodiment, the suggestions offer options to choose among next steps in a workflow associated dialog flow model 1087. For example, dialog flow model 1087 may suggest that after gathering the constrained for one domain (e.g., restaurant dining), assistant 1002 should suggest other related domains (e.g., a movie nearby).
Active Monitoring for Relevant Events
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 may be provided as inputs to assistant 1002. Once interpreted, such events can be treated in a manner similar to any other input.
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 may continue the dialog by presenting alternative flights, making other suggestions, and the like, based on the detected event.
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 interested 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 devices, for example to tell the user when a coworker has returned from lunch (the coworker's device can signal such an event to the user's device, at which time assistant 1002 installed on the user's device responds accordingly).
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”.
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.
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 example, if a calendar event alert includes the name of a person invited to the event, that information can help disambiguate input which might match several people with the same or similar name.
Referring now to
Event data is converted 166 to a uniform input format, and returned.
In at least one embodiment, assistant 1002 may proactively offer services associated with events that were suggested for user attention. For example, if a flight status alert indicates a flight may be missed, assistant 1002 may suggest to the user a task flow for replanning the itinerary or booking a hotel.
Input Elicitation Component(s) Example:
The following example is intended to help illustrate some of the various types of functions, operations, actions, and/or other features which may be provided by active input elicitation component(s) 1094.
Example: Command Completion (What can the User Say to Assistant 1002)
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 input, any of several system responses are provided. Examples are shown below
Case: Null Input
Case: First Word Input
Case: Key Word Input
Remember to a thing, to do . . .
Case: Prompting for Arguments
Case: Suggesting Criteria
Case: Adding Criteria
Case: Adding Location or Other Constraints
Case: Starting from Constraint, Unknown Task or Domain
Example: Name Completion
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
Case: Name with Context
Example: Choosing Values from a Set
Here, the user is responding to a system request to enter a value for a specific parameter, such as location, time, 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
Example: Reusing Previous Commands
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 and there are no known constraints) or they may be suggested as relevant when in certain situations.
Case: Completion on Previous Queries
Italian restaurants (normal
Example: Retrieving Personal Memory Items
Assistant 1002 may remember certain events and/or entities in personal 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
Multimodal Active Input Elicitation
In at least one embodiment, active input elicitation component(s) 1094 may process input from a plurality of input modalities. At least one modality 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 may 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 triggers.
In at least one embodiment, for a single instance of intelligent automated assistant 1002, there may be support for one or more (or any combination of) typed input, speech input, GUI input, dialog input, and/or event input.
Referring now to
Offering active guidance for multiple modalities and levels enables constraint and guidance on the input beyond those available to isolated modalities. For example, the kinds of suggestions offered to choose among speech, text, and dialog steps are independent, so their combination is a significant improvement over adding active elicitation techniques to individual modalities or levels.
Combining multiple sources of constraints as described herein (syntactic/linguistic, vocabulary, entity databases, domain models, task models, service models, and the like) and multiple places where these constraints may be actively applied (speech, text, GUI, dialog, and asynchronous events) provides a new level of functionality for human-machine interaction.
Domain Models Component(s) 1056
Domain models 1056 component(s) include representations of the concepts, entities, relations, properties, and instances of a domain. For example, 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.
In at least one embodiment, domain models component(s) 1056 of assistant 1002 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):
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 combinations of hardware and/or hardware and software. For example, in at least some embodiments, various aspects, features, and/or functionalities of domain models component(s) 1056 may be performed, implemented and/or initiated by one or more of the following types of systems, components, systems, devices, procedures, processes, and the like (or combinations thereof):
According to various embodiments, one or more different threads or instances of domain models component(s) 1056 may 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 instance of domain models component(s) 1056. For example, trigger initiation and/or implementation of one or more different threads or instances of domain models component(s) 1056 may be triggered when domain model information is required, including during input elicitation, input interpretation, task and domain identification, natural language processing, service orchestration, and/or formatting output for users.
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 database information may be accessed via communication with one or more local and/or remote memory devices. For example, data from domain model component(s) 1056 may be associated with other model modeling components including vocabulary 1058, language pattern recognizers 1060, dialog flow models 1087, task flow models 1086, service capability models 1088, domain 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 components.
Domain Models Component(s) Example:
Referring now to
In at least one embodiment, domain models component(s) 1056 are the unifying data representation that enables the presentation of information shown in screens 103A and 103B about a restaurant, which combines data from several distinct data sources and services and which includes, for example: name, address, business categories, phone number, identifier for saving to long term personal memory, identifier for sharing over email, reviews from multiple sources, map coordinates, personal notes, and the like.
Language Interpreter Component(s) 1070
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 example, one or more of the following (or combinations thereof):
According to specific embodiments, multiple instances or threads of language interpreter component(s) 1070 may be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other combinations of hardware and/or hardware and software.
According to different embodiments, one or more different threads or instances of language interpreter component(s) 1070 may 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 instance of language interpreter component(s) 1070. Various examples of conditions or events which may trigger initiation and/or implementation of one or more different threads or instances of language interpreter component(s) 1070 may include, but are not limited to, one or more of the following (or combinations thereof):
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 data-base 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 the Language Interpreter component(s) may include, but are not limited to, one or more of the following (or combinations thereof):
Referring now also to
Referring now also to
The method begins 200. Language input 202 is received, such as the string “who is playing this weekend at the fillmore” in the example of
Language interpreter component(s) 1070 generate candidate syntactic 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 as games or with a category of event such as sporting events.
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 user, can be used, to improve performance, reduce ambiguity, and reinforce the conversational nature of the interaction. Data from active ontology 1050, domain models 1056, and task flow models 1086 can also be used, to implement evidential reasoning in determining valid candidate syntactic parses 212.
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 “Local Event At Venue” (part of a domain model 1056 represented by a cluster of nodes in active ontology 1050) and (2) “fillmore” (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 Name).
Semantic matching 220 may use data from, for example, active ontology 1050, short term personal memory 1052, and long term personal memory 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 1052) or personal favorite venues (from long term personal memory 1054).
A set of candidate, or potential, semantic parse results is generated 222.
In disambiguation step 230, language interpreter component(s) 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 “fillmore” as a Venue Name is a stronger match to a domain model than alternative combinations where, for instance, “playing” is associated with a domain model for sports but there is no association in the sports domain for “fillmore”.
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 embodiment, if three active ontology nodes are semantically matched and are all connected 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 Local 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.
In at least one embodiment, the connections between nodes in an active ontology that provide evidential support for disambiguating among candidate semantic parse results 222 are directed arcs, forming an inference lattice, in which matching nodes provide evidence for nodes to which they are connected by directed arcs.
In 232, language interpreter component(s) 1070 sort and select 232 the top semantic parses as the representation of user intent 290.
Domain Entity Database(s) 1072
In at least one embodiment, domain entity database(s) 1072 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):
According to specific embodiments, multiple instances or threads of domain entity database(s) 1072 may be concurrently implemented and/or initiated 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 embodiments, various aspects, features, and/or functionalities of domain entity database(s) 1072 may be performed, implemented and/or initiated by database software and/or hardware residing on client(s) 1304 and/or on server(s) 1340.
One example of a domain entity database 1072 that can be used in connection with the present invention according to one embodiment is a database of one or more businesses storing, for example, their names and locations. 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 many other arrangements and implementations are possible.
Vocabulary Component(s) 1058
In at least one embodiment, vocabulary component(s) 1058 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):
According to specific embodiments, multiple instances or threads of vocabulary component(s) 1058 may be concurrently implemented and/or initiated 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 embodiments, various aspects, features, and/or functionalities of vocabulary component(s) 1058 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 vocabulary component(s) 1058 may be implemented through direct APIs, network APIs, and/or database query interfaces. Creation and maintenance of vocabulary component(s) 1058 may be achieved via direct editing of files, database transactions, or through the use of domain model editing tools. Vocabulary component(s) 1058 may be implemented as part of or in association with active ontologies 1050. One skilled in the art will recognize that many other arrangements and implementations are possible.
According to different embodiments, one or more different threads or instances of vocabulary component(s) 1058 may 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 instance of vocabulary component(s) 1058. In one embodiment, vocabulary component(s) 1058 are accessed whenever vocabulary information is required, 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.
In at least one embodiment, a given instance of vocabulary component(s) 1058 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. In one embodiment, vocabulary component(s) 1058 may access data from external databases, for instance, from a data warehouse or dictionary.
Language Pattern Recognizer Component(s) 1060
In at least one embodiment, language pattern recognizer component(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 grammatical, idiomatic, and/or other composites of input tokens. These patterns correspond to, for example, one or more of the following (or combinations thereof): words, names, phrases, data, parameters, commands, and/or signals of speech acts.
According to specific embodiments, multiple instances or threads of pattern recognizer component(s) 1060 may be concurrently implemented and/or initiated 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 embodiments, various aspects, features, and/or functionalities of language pattern recognizer component(s) 1060 may be performed, implemented and/or initiated by one or more files, databases, and/or programs containing expressions in a pattern matching language. In at least one embodiment, 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. Examples of declarative representations may include, but are not limited to, one or more of the following (or combinations thereof): regular expressions, pattern matching rules, natural language grammars, parsers based on state machines and/or other parsing models.
One skilled in the art will recognize that other types of systems, components, systems, devices, procedures, processes, and the like (or combinations thereof) can be used for implementing language pattern recognizer component(s) 1060.
According to different embodiments, one or more different threads or instances of language pattern recognizer component(s) 1060 may 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 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 language pattern recognizer component(s) 1060 may include, but are not limited to, one or more of the following (or combinations thereof):
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 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 language pattern recognizer component(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 assistant 1002, which may include, but are not limited to, one or more of the following (or combinations thereof):
In one embodiment, access of data from other parts of embodiments of assistant 1002 may be coordinated by active ontologies 1050.
Referring again to
Dialog Flow Processor Component(s) 1080
In at least one embodiment, dialog flow processor component(s) 1080 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):
According to specific embodiments, multiple instances or threads of dialog flow processor component(s) 1080 may be concurrently implemented 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, a given instance of dialog flow processor component(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 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 dialog flow processor component(s) 1080 may include, but are not limited to, one or more of the following (or combinations thereof):
Referring now to
As shown in screen 3001, user requests a dinner reservation by providing speech or text input 3002 “book me a table for dinner”. Assistant 1002 generates a prompt 3003 asking the user to specify time and party size.
Once these parameters have been provided, screen 3101 is shown. Assistant 1002 outputs a dialog box 3102 indicating that results are being presented, and a prompt 3103 asking the user to click a time. Listings 3104 are also displayed.
In one embodiment, such a dialog is implemented as follows. Dialog flow processor component(s) 1080 are given a representation of user intent from language interpreter component 1070 and determine that the appropriate response is to ask the user for information required to perform the next step in a task flow. In this case, the domain is restaurants, the task is getting a reservation, and the dialog step is to ask the user for information required to accomplish the next step in the task flow. This dialog step is exemplified by prompt 3003 of screen 3001.
Referring now also to
The method begins 300. Representation of user intent 290 is received. As described in connection with
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 current 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 information from the user.
In 312, the dialog flow processor component(s) 1080 determine the preferred interpretation of the semantic parse with other information to determine the task to perform and its parameters. Information may be obtained, 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 reserve a table. Task parameters are the time constraint along with others that are inferred in step 312.
In 320, the task flow model is consulted to determine an appropriate next step. Information may be obtained, for example, from domain models 1056, task flow models 1086, and/or dialog flow models 1087, or any combination 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
As described above,
Dialog Flow Models Component(s) 1087
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 automated assistant 1002. For example, the dialog flow for the generic task of performing a transaction includes steps for getting the necessary data for the transaction and confirming the transaction parameters before committing it.
Task Flow Models Component(s) 1086
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 get a reservation for a specific time with the restaurant.
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 combinations 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 implemented as programs, state machines, or other ways of identifying an appropriate step in a flow graph.
In at least one embodiment, task flow models component(s) 1086 may use a task modeling framework called generic tasks. Generic tasks are abstractions that model the steps in a task and their required inputs and generated 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 transaction—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.
At least a portion of the functions, operations, actions, and/or other features associated with task flow models component(s) 1086 and/or procedure(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.
Additionally, at least a portion of the functions, operations, actions, and/or other features associated with task flow models component(s) 1086 and/or procedure(s) described herein may be implemented, at least in part, using concepts, features, components, processes, and/or other aspects relating to constrained selection tasks, as described herein. For example, one embodiment of generic tasks may be implemented using a constrained selection task model.
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 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 task flow models component(s) 1086 may include, but are not limited to, one or more of the following (or combinations thereof):
Referring now to
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 constraints. 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 user might be interested in Asian food and may want a place to eat near his or her office.
In step 353, assistant 1002 presents items that meet the stated criteria and constraints for the user to browse. In this example, it may be a list of restaurants and their properties which may be used to select among them.
In step 354, the user is given an opportunity to refine criteria and constraints. For example, the user might refine the request by saying “near my office”. The system would then present a new set of results in step 353.
Referring now also to
In step 355, the user can select among the matching items. Any of a number of follow-on tasks 359 may then be made available, such as for example 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, making a telephone call, sending an email or text message, setting an alarm, and the like).
In the example of
In various embodiments, the flow steps may be offered to the user in any of several input modalities, including but not limited to any combination of explicit dialog prompts and GUI links.
Services Component(s) 1084
Services component(s) 1084 represent the set of services that intelligent automated assistant 1002 might call on behalf of the user. Any service that can be called may be offered in a services component 1084.
In at least one embodiment, services component(s) 1084 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):
According to specific embodiments, multiple instances or threads of services component(s) 1084 may be concurrently implemented and/or initiated 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 embodiments, various aspects, features, and/or functionalities of services component(s) 1084 may be performed, implemented and/or initiated by one or more of the following types of systems, components, systems, devices, procedures, processes, and the like (or combinations thereof):
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.
As another example, services component(s) 1084 for an intelligent automated assistant 1002 that helps with restaurant selection and meal planning might include any or all of the following set of services which are available from third parties over the network:
Services orchestration component(s) 1082 of intelligent automated assistant 1002 executes a service orchestration procedure.
In at least one embodiment, services orchestration component(s) 1082 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):
For example, in some situations, there may be several ways to accomplish a particular task. For example, user input such as “remind me to leave for my meeting across town at 2 pm” specifies an action that can be accomplished in at least three ways: set alarm clock; create a calendar event; or call a to-do manager. In one embodiment, services orchestration component(s) 1082 makes the determination as to which way to best satisfy the request.
Services orchestration component(s) 1082 can also make determinations 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 determinations as to which services to call in order to perform such functions as looking up reviews, getting 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 invoke.
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 combinations of hardware and/or hardware and software.
In at least one embodiment, a given instance of services orchestration 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 of the following (or combinations thereof):
In at least one embodiment, a given instance of services orchestration 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):
Referring now to
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.
Consider the task of finding restaurants that are of high quality, are well reviewed, near a particular location, available for reservation at a particular time, and serve a particular kind of food. The method begins 400. These domain and task parameters are given as input 390.
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 answer 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 response may be handled without invoking any external services.
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):
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 provide restaurants that are near a particular location (a parameter), services 2 and 3 may filter or rank restaurants by quality (another parameter), services 3, 4, and 5 may return reviews for restaurants (a data field returned), service 6 may list the food types served by restaurants (a data field returned), and service 7 may check availability of restaurants for particular time ranges (a parameter). Services 8 through 99 offer capabilities that are not required for this particular domain and task.
Using this declarative, qualitative metadata, the task, the task parameters, and other information available from the runtime environment of the assistant, services orchestration component(s) 1082 determines 404 an optimal 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.
The result of step 404 is a dynamically generated list of services to call for this particular user and request.
In at least one embodiment, services orchestration component(s) 1082 considers the reliability of services as well as their ability to answer specific information requests.
In at least one embodiment, services orchestration component(s) 1082 hedges against unreliability by calling overlapping or redundant services.
In at least one embodiment, services orchestration component(s) 1082 considers personal information about the user (from the short term personal memory component) to select services. For example, the user may prefer some rating services over others.
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.
In at least one embodiment, the rate at which services are called is programmatically limited and/or managed.
Referring now also to
In at least one embodiment, services invoked by service invocation procedure 450 can be a web service, application running on the device, operating system function, or the like.
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 transformation 452, calling 454, and output-mapping 456 steps.
In transformation step 452, the current task parameters from request 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 databases, may differ from the data representation used in task requests, and also from 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.
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.
As another example, locations are known at various levels of precision and using various units and conventions across services. Service 1 might may require ZIP codes, service 2 GPS coordinates, and service 3 postal street addresses.
The service is called 454 over an API and its data gathered. In at least one embodiment, the results 410 are cached. In at least one embodiment, the services that do not return within a specified level performance (e.g., as specified in Service Level Agreement or SLA) are dropped.
In output mapping step 456, 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.
In step 412, results from multiple services are validated and merged. In one embodiment, if validated results are collected, an equality policy 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 parameters, the domain context, and/or the long-term personal memory 1054 to decide the optimal merging strategy.
For example, lists of restaurants from different providers of restaurants 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 domain entities, such as name, location, phone number, and/or website address, and/or any combination thereof
In step 414, the results are sorted and trimmed to return a result list of the desired length.
In at least one embodiment, a request relaxation loop is also applied. If, in step 416, services orchestration component(s) 1082 determines that the current result list is not sufficient (e.g., it has fewer than the desired number of matching items), then task parameters may be relaxed 420 to allow for more results. For example, if the number of 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.
In at least one embodiment, the service orchestration method is applied in a second pass to “annotate” results with auxiliary data that is useful to the task.
In step 418, services orchestration component(s) 1082 determines whether annotation is required. It may be required if, for example, if the task may require a plot of the results on a map, but the primary services did not return geocoordinates required for mapping.
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 annotated to a merged result. It does this by delegating to a property policy function—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 annotate.
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 merged result by at least one service provider are tracked during this process, and the changes are then merged using the same property policy function process as was used in step 412. Their results are merged 426 into the existing result set.
The resulting data is sorted 428 and unified into a uniform representation 490.
It may be appreciated that one advantage of the methods and systems described above with respect to services orchestration component(s) 1082 is that they may be advantageously applied and/or utilized in various fields of technology other than those specifically relating to intelligent automated assistants. Examples of such other areas of technologies where aspects and/or features of service orchestration procedures include, for example, one or more of the following:
In at least one embodiment, service capability models component(s) 1088 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):
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/or other features such as, for example, one or more of the following (or combinations thereof):
According to specific embodiments, multiple 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 combinations of hardware and/or hardware and software. For example, in at least some embodiments, various aspects, features, and/or functionalities of output processor component(s) 1090 may be performed, implemented and/or initiated by one or more of the following types of systems, components, systems, devices, procedures, processes, and the like (or combinations thereof):
Referring now to
In step 710, a speech input utterance is obtained and a speech-to-text component (such as component described in connection with
In at least one embodiment, a user interface is provided to enable the user to interrupt and choose among the candidate speech interpretations.
In step 714, the candidate speech interpretations 712 are sent to a language interpreter 1070, which may produce representations of user intent 716 for at least one candidate speech interpretation 712. In step 732, paraphrases of these representations of user intent 716 are generated and presented to the user. (See related step 132 of procedure 120 in
In at least one embodiment, the user interface enables the user to interrupt and choose among the paraphrases of natural language interpretations 732.
In step 718, task and dialog analysis is performed. In step 734, task and domain interpretations are presented to the user using an intent paraphrasing algorithm.
Referring now also to
Returning to
A uniform representation of response 722 is generated and formatted 724 for the appropriate output modality. After the final output format is completed, a different kind of paraphrase may be offered in 738. In this phase, the entire result set may be analyzed and compared against the initial request. A summary of results or answer to a question may then be offered.
Referring also to
In one embodiment, assistant 1002 is capable of generating output in multiple modes. Referring now to
The method begins 600. Output processor 1090 takes uniform representation of response 490 and formats 612 the response according to the device and modality that is appropriate and applicable. Step 612 may include information from device and modality models 610 and/or domain data models 614.
Once response 490 has been formatted 612, any of a number of different output mechanisms can be used, in any combination. Examples depicted in
One skilled in the art will recognize that many other output mechanisms can be used.
In one embodiment, the content of output messages generated by mul-tiphase output procedure 700 is tailored to the mode of multimodal output 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, progress 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 tailored in the steps of the multiphase output procedure 700; in other embodiments, the multiphase output procedure 700 produces an intermediate result that is further refined into specific language by multimodal output processing 600.
Short Term Personal Memory Component(s) 1052
In at least one embodiment, short term personal memory component(s) 1052 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):
According to specific embodiments, multiple instances or threads of short term personal memory component(s) 1052 may be concurrently implemented and/or initiated via the use of one or more processors 63 and/or other combinations of hardware and/or hardware and software.
According to different embodiments, one or more different threads or instances of short term personal memory component(s) 1052 may 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 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.
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 memory components(s) 1054 (for example, to obtain user identity and personal preferences) and/or data from the local device about time and location, which may be included in short term memory entries.
Referring now to
In the example of
Long-Term Personal Memory Component(s) 1054
In at least one embodiment, long-term personal memory component(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):
According to specific embodiments, multiple instances or threads of long-term personal memory component(s) 1054 may be concurrently implemented and/or initiated 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 embodiments, various aspects, features, and/or functionalities of long-term personal memory component(s) 1054 may be 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 storage devices.
According to different embodiments, one or more different threads or instances of long-term personal memory component(s) 1054 may 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 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 thereof):
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 example, 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 databases such as contact or friend lists, calendars, to-do lists, other list managers, personal account and wallet managers provided by external services 1360, and the like.
Referring now to
Screen 4403 of
Screen 4404 of
Automated Call and Response Procedure
Referring now to
In at least one embodiment, the automated call and response procedure of
In at least some embodiments, portions of the automated call and response procedure may also be implemented at other devices and/or systems of a computer network.
According to specific embodiments, multiple instances or threads of the automated call and response procedure may be concurrently implemented 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.
For example, in at least some embodiments, various aspects, features, and/or functionalities of the automated call and response procedure may be performed, implemented and/or initiated by software components, network services, databases, and/or the like, or any combination thereof.
According to different embodiments, one or more different threads or instances of the automated call and response procedure may be 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 criteria) for triggering initiation of at least one instance of automated call and response procedure. Examples of various types of conditions or events which may trigger initiation and/or implementation of one or more different threads or instances of the automated call and response procedure may include, but are not limited to, one or more of the following (or combinations thereof):
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, concurrently, 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).
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 procedure 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 procedure may generate one or more different types of output data/information, which, for example, may be stored in local memory and/or remote memory devices.
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 may be accessed via communication 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.
In the particular example of
In step 100, the user is prompted to enter a request. The user interface of the client offers several modes of input, as described in connection with
One skilled in the art will recognize that other input modes may be provided.
In one embodiment, step 100 may include presenting options remaining from a previous conversation with assistant 1002, for example using the techniques described in the active dialog suggestion input elicitation procedure described in connection with
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 Italian around here?” For example, the user might have spoken this into a speech input component. An embodiment of an active input elicitation component 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.
An embodiment of language interpreter component 1070 is then called in step 200, as described in connection with
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
In step 400, an embodiment of the flow and service orchestration procedure 400 is invoked, via services orchestration component 1082. It invokes 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 resulting list of restaurants is represented in a uniform, service-independent form.
In step 500, output processor 1092 generates a dialog summary of the results, such as, “1 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.
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.
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.
The automatic call and response procedure may be applied, for example to a user's query “how about mexican food?”. Such input may be elicited in step 100. In step 200, the input is interpreted as restaurants of style 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 orchestration component(s) 1082 in step 400.
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.
In this case, the user finds a restaurant of his or her liking, shows it on a map, and sends directions to a friend.
One skilled in the art will recognize that different embodiments of the automated call and response procedure (not shown) may include additional features and/or operations than those illustrated in the specific embodiment of
Constrained Selection
In one embodiment, intelligent automated assistant 1002 uses constrained 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.
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 selection 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.
Constrained selection is useful in many contexts: for example, picking 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.
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 directory 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 book 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.g., restaurants in a given city).
Another conventional approach is a database application, which provides a way to generate a choice set by eliciting a query from the user, retrieving matching items, and presenting the items in some way that highlights salient features. The user browses the 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 require 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 interfaces.
A third conventional approach is open-ended search, such as “local search”. Search is easy to do, but there are several problems with search services that make them difficult for people to accomplish the task of constrained selection. Specifically:
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 functionality, 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.
In various embodiments, assistant 1002 helps to make constrained selection simpler for humans in any of a number of different ways.
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 the desired outcome. Assistant 1002 operationalizes this input into formal constraints. For example, instead of saying “find one or more restaurants 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 1002 may also operationalize qualities requested by the user that are not parameters to a database. For example, if the user requests romantic restaurants, the system may operationalize this as a text search or tag matching constraint. 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.
In one embodiment, assistant 1002 may suggest useful selection criteria, 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 would like or a food item you would like”.
In one embodiment, assistant 1002 may help the user make a decision among choices that differ on a number of competing criteria (for example, price, quality, availability, and convenience).
By providing such guidance, assistant 1002 may help users in making multi-parametric decisions in any of several ways:
In one embodiment, assistant 1002 offers assistance with the constrained selection task by simplifying the conceptual data model. The conceptual data model is the abstraction presented to users in the interface of assistant 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.
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, hotels, and movies” as opposed to “I know about life in the city”).
Given a selection class, in one embodiment the data model presented to the user for the constrained selection task includes, for example: items; item features; selection criteria; and constraints.
Items are instances of the selection class.
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 may 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 underlying data from back-end services.
Selection criteria are item features that may be used to compare the value or relevance of items. That is, they are ways to say which items are preferred. 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. Location 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 interest.
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.
One or more selection criteria are also item features; they are those features related to choosing among possible items. However, item features are not necessarily related to a preference (e.g., the names and phone numbers of restaurants are usually irrelevant to choosing among them).
In at least one embodiment, constraints are restrictions on the desired values of the selection criteria. Formally, constraints might be represented as set membership (e.g., cuisine type includes Italian), pattern matches (e.g., restaurant review text includes “romantic”), fuzzy inequalities (e.g., distance less than a few miles), qualitative thresholds (e.g., 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 constraints to symbolic values that may be matched as words. Time and distance may be 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 represented as a symbolic value such as “Italian” or “Chinese”. A constraint on rating is “recommended” (a binary choice). For time and distance, in one embodiment assistant 1002 uses proprietary representations that handle a range of inputs and constraint values. For example, distance might be “walking distance” and time might be “tonight”; in one embodiment, assistant 1002 uses special processing to match such input to more precise data.
In at least one embodiment, some constraints may be required constraints. 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 desired location, even if one knows the name.
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.
Often, multiple criteria may compete and constraints may match partially. The data model reduces the selection problem from an optimization (finding the best solution) to a matching problem (finding items that do well on a set of specified criteria and match a set of symbolic constraints). The algorithms for selecting criteria and constraints and determining an ordering are described in the next section.
Methodology for Constrained Selection
In one embodiment, assistant 1002 performs constrained selection by taking as input an ordered list of criteria, with implicit or explicit constraints 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 selection criteria, then specify constraints (the boundaries of acceptable solutions), and search through instances in order of best fit to find acceptable items.
Referring now to
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 governed via a dialog between assistant 1002 and the user which operates by other procedures, such as dialog and task flow models. Constrained selection offers a framework for building dialog and task flow models at this level of abstraction (that is, suitable for constrained selection tasks regardless of domain).
Referring now to
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 (e.g., constrained selection task 4602) and domain.
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 4604 what is needed, receives input. If there is enough information to constrain the choice set to a reasonable size, then assistant 1002 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 1002 reasons about the constraints already stated, and uses domain-specific knowledge to suggest other criteria that might help, soliciting constraints on these criteria as well. For example, assistant 1002 may reason that, when recommending restaurants within walking distance of a hotel, the useful criteria to solicit would be cuisine and table availability.
The constrained selection task is complete when the user selects 4607 an instance of the selection class. In one embodiment, additional follow-on tasks 4609 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 options might be to put the restaurant on a calendar or send in invitation with directions to friends.
Referring now to
The method begins 4701. Input is received 4702 from the user, according 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.
In step 4717, assistant 1002 determines whether the user provides additional input. If so, assistant 1002 returns to step 4702. Otherwise the method ends 4799.
If, in step 4703, the task is known, assistant 1002 determines 4704 whether the task is constrained selection. If not, assistant 1002 proceeds 4706 to the specified task flow.
If, in step 4704, the task is constrained selection, assistant 1002 determines 4707 whether the selection class can be determined. If not, assistant 1002 offers 4708 a choice of known selection classes, and returns to step 4717.
If, in step 4707, the selection class can be determined, assistant 1002 determines 4709 whether all required constraints can be determined. If not, assistant 1002 prompts 4710 for required information, and returns to step 4717.
If, in step 4709, all required constants can be determined, assistant 1002 determines 4711 whether any result items can be found, given the constraints. 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 algorithm. In one embodiment, if there are items that meet some of the constraints, then assistant 1002 may paraphrase the situation (outputting, for example, “I could not find Recommended Greek restaurants that deliver on Sundays in San Carlos. However, I found 3 Greek restaurants and 7 Recommend restaurants in San Carlos.”). In one embodiment, if there are no items that match any constraints, then assistant 1002 may paraphrase this situation and prompt for different constraints (outputting, for example, “Sony, I could not find any restaurants in Anytown, Texas. You may pick a different location.”). Assistant 1002 returns to step 4717.
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=yes, cuisine=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 away” meets the “within walking distance of an address” constraint. In one embodiment, assistant 1002 allows the user to drill down for more detail on an item, which results in display of more item features.
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.
If, in step 4714, the user does not select an item, assistant 1002 offers 4716 the user ways to select other criteria and constraints and returns to step 4717. For example, given the currently specified criteria and constraints, assistant 1002 may offer criteria that 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 previously determined constraints when steps 4703 to 4713 are repeated.
Since one or more criteria may have an inherent preference value, selecting the criteria may add information to the request. For example, allowing the user to indicate that positive reviews are valued allows assistant 1002 to sort by this criterion. Such information can be taken into account when steps 4703 to 4713 are repeated.
In one embodiment, assistant 1002 allows the user to raise the importance 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 repeated.
In one embodiment, the user can provide additional input at any point while the method of
In one embodiment, when outputting an item or list of items, assistant 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 restaurants, such item features for distance and cuisine may be shown in the presentation of the item. This may include highlighting matches, as well as listing selection criteria that were involved in the presentation of an item.
Example Domains
Filtering and Sorting Results
In one embodiment, when presenting items that meet currently specified criteria and constraints, a filter/sort methodology can be employed. In one embodiment selection constraints may serve as both filter and sort parameters 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 might be sorted higher in the list.
In one embodiment, selection constraints and associated filtering and sorting are at discrete “levels”, which are functions of both the underlying 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 station” 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 important for sorting operations, so that multiple criteria may participate in determining the best-first ordering.
In one embodiment, the item list—those items that are considered “matching” or “good enough”—may be shorter or 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 pagination 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.
In one embodiment, there is a precedence ordering among selection criteria. That is, some criteria may matter more than others in the filter and sort. In one embodiment, those criteria selected by the user are given higher precedence than others, and there is a default ordering over one or more criteria. This allows for a general lexicographic sort. The assumption is that there is a meaningful a priori precedence. For example, unless the user states otherwise, it may be more important for a restaurant to be close than to be inexpensive. In one embodiment, the a priori precedence ordering is domain-specific. The model allows for user-specific preferences to override the domain defaults, if that is desired.
Since the values of constraints can represent several internal data types, there are different ways for constraints to match, and they may be specific to the constraint. For example, in one embodiment:
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 sorting. For example, the affordability criterion might be modeled as a roughly binary constraint, where affordable restaurants are any under some threshold price range. When the data justify multiple discrete levels for selection, constraints 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.
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 expression if it is handled specially (such as time and location).
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 features. In many cases, however, the problem is over- or under-constrained. When it is over-constrained, there are few or no items that meet the constraints. When it is under-constrained, there are so many candidates that examining 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
In one embodiment, the following method is used for filtering and sorting results:
The techniques described herein allow assistant 1002 to be extremely 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.
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.
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.
In one embodiment, the user may override the default criteria ordering 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, proximity, recommendation, and food item, and there were no fully matching items, the user could say that food item was more important than recommendation level and change the mix so the desired food item matches were sorted to the top.
In one embodiment, when precedence order is determined, user-specified constraints take precedence over others. For example, in one embodiment, proximity is a required constraint and so is always specified, and further has precedence over other unselected constraints. Therefore it does not have to be the highest precedence constraint in order to be fairly dominant. 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 important to them, and otherwise is not relevant to sorting items.
For example, the following is a candidate precedence sorting paradigm for the restaurant domain:
The following is an example of a design rationale for the above sorting paradigm:
It may be desirable to distinguish between the data that are available for computation by assistant 1002 and the data used for making selections. 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 distinguish 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 discovered by some query (such as whether it matches a text pattern or is available at a given 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 various criteria into features of the items. Assistant 1002 provides a user-facing domain data model and maps it to data found in web services.
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.
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 references to “Chinese food” or “Szechuan” to semantically match one or more of these nodes, with confidence levels reflecting the degree of match.
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, assistant 1002 uses a deep structural representation of locations and times that may be mapped to different surface data values.
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”.
In at least one embodiment, constraints may be mapped to operational preference orderings. That is, given the name of a selection criterion and its constraint value, assistant 1002 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:
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 conversational dialog model of the present invention, the paraphrase is what assistant 1002 outputs after the user's input, as a preface (for example, paraphrase 4003 in
The prompt is a suggestion to the user about what else they can do to refine their request or explore the selection space along some dimensions.
In one embodiment, the purposes of paraphrase and prompt text include, for example:
For example, the following paraphrase and prompt illustrates several of these goals:
In one embodiment, assistant 1002 responds to user input relatively quickly with the paraphrase. The paraphrase is then updated after results are known. For example, an initial response may be “Looking for Indonesian restaurants 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.
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.
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:
In one embodiment, the paraphrase algorithm accounts for the query, domain model 1056, and the service results. Domain model 1056 contains classes and features including metadata that is used to decide how to generate text. Examples of such metadata for paraphrase generation include:
For example, a parse might contain these elements:
In one embodiment, assistant 1002 is able to handle unmatched input. To handle such input, domain model 1056 can provide for nodes of type GuessedQualifier for each selection class, and rules that match otherwise unmatched words if they are in the right grammatical context. That is, GuessedQualifiers 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 SearchQualifiers is that the latter are matched to vocabulary in the ontology. This distinction allows us to paraphrase that assistant 1002 identified the intent solidly on the SearchQualifiers and can be more hesitant when echoing back the GuessedQualifiers.
In one embodiment, assistant 1002 performs the following steps when generating paraphrase text:
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 a grammatical position, based on its type. For example, in one embodiment, assistant 1002 may construct a paraphrase such as:
Table 1 provides some examples of paraphrases that may be provided in response to the first input for a task, according to one embodiment.
restaurants, hotels, and other local businesses, flights,
weather, and buying things online. What are you looking
Alto.
food.
Palo Alto.
Palo Alto.
Palo Alto.
Palo Alto.
osso buco and are described as “quiet”.
Refining a Query about Places to Eat
Table 2 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.
Table 3 provides some examples of results summaries that are provided once results are obtained.
Alto. To reserve a table, click on available
Park. To reserve a table, click on available
cuisines or locations.
Menlo Park.
hotels, or local businesses?
events, and things to do in San Francisco.
events, and things to do in San Francisco.
Menlo Park. Any other names for it?
CA. Want to reserve a table? When and for
6pm to 8pm tonight for a party of two in Palo
6pm to 8pm tonight for a party of two in Palo
Alto, CA
2.
romantic italian restaurants near Menlo Park
romantic italian restaurants near Menlo Park
Menlo Park. Do you prefer any particular
Park.
Park.
Table 4 provides some examples of prompts that are provided when users click on active links.
Prompt when Users Click on Active Links
Prompt when Users Click on Active Links
Suggesting Possible Responses in a Dialog
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 embodiment, the most salient suggestions may be offered in line in the dialog, an extended 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 autocomplete options. One skilled in the art will recognize that other mechanisms may be used for providing suggestions.
In various embodiments, different types of suggestions may be provided. Examples of suggestion types include:
In various embodiments, the context that determines the most relevant suggestions may be derived from, for example:
In various embodiments, suggestions may be generated by any mechanism, such as for example:
According to one embodiment, suggestions are generated as operations on commands in some state of completion. Commands are explicit, canonical 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.
In one embodiment, each command is an imperative sentence having some combination of a
These parts of a command (verb, domain, constraints) correspond to nodes in the ontology.
A suggestion, then, may 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:
A suggestion may also involve some combination of the above. For example:
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.
In one embodiment, assistant 1002 determines which constraints to modify by considering any or all of the following factors:
In one embodiment, assistant 1002 determines an output format for the suggestion. Examples of output formats include:
In one embodiment, assistant 1002 attempts to resolve ambiguities via suggestions. For example, if the set of current interpretations of user intent 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 intent 290 and presents the differences among them to the user. For example, if the user input includes the word “café” 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 ‘café’ or ‘café restaurants’?”
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 information available in active ontology 1050, short term memory 1052, and/or other sources of information available to assistant 1002. For example:
In cases where the assistant 1002 infers constraint values, it may also offer these assumptions as suggestions for the user to overrule. For example, it might tell the user “I assumed you meant around here. Would you like to look at a different location?”
As described earlier in the specification, when the digital assistant is active, the digital assistant optionally maintains a dialogue interface to facilitate real-time, two-way communications between the digital assistant and the user. The dialogue interface of the digital assistant is capable of receiving inputs from and providing outputs to the user through multiple modalities (e.g., via speech, text, touch, gestures, and other audio, visual, and tactile means). In some embodiments, the dialogue interface is a speech-only (e.g., an eyes-free and hands-free interface) input and output interface, and the user and the digital assistant communicate entirely through speech and sounds. In some embodiments, the dialogue interface provides a conversation screen in addition to a speech-based input and output interface. In some embodiments, the conversation screen presents textual paraphrases of the user's speech input (e.g., a search request) and/or presents information and search results in textual and graphical forms. In some embodiments, the conversation screen allows the user to enter text, select search results, and/or interact with various user interface elements presented on the conversation screen. In some embodiments, the conversation screen is a single frame displayed on the user device containing textual representations of the dialogue between the user and the digital assistant. In some embodiments, the textual representations include a series of text boxes, text bubbles, or other visual renditions of the original text input from the user, text derived from user speech input (e.g., original words or paraphrases), and/or text output provided the digital assistant.
In the process 4800, in response to receiving the search request, the digital assistant presents a plurality of search results to the user through the dialogue interface, each search result corresponding to a respective reservable item identified in accordance with the search request (4820). A reservable item refers to an item that can be reserved and/or purchase according to a reservation or booking process prescribed by the provider of the item. Examples of a reservable item include a table at a restaurant, a ticket or seating at an event, concert, movie, sports game, performance, talk, or the like, an item on sale at an auction, and the like. A search result corresponds to a respective reservable item identified in accordance with the search request, and provides information (e.g., name, location, show time, price, rating, etc.) about the reservable item or a third-party provider of the reservable item. For example, a search result for a restaurant search request provides information (e.g., name, cuisine, and location) on a restaurant that has open tables for reservation. A search result for an entertainment search request provides information (e.g., name, time, location, and price) on an entertainment event (e.g., a movie or a concert) that has tickets for sale. Search criteria for identifying reservable items include a search class or selection class (e.g., restaurants, entertainment events, etc.), and various constraints (e.g., location, time, price, review, genre, cuisine, etc.) for filtering and ranking the search results.
In some embodiments, the digital assistant presents a list of search results on the conversation screen. In some embodiments, the digital assistant reads through the list of search results on the conversation screen to the user. In an eyes-free mode, the digital assistant optionally reads the list of search results to the user without presenting them on a conversation screen. In some embodiments, the digital assistant provides a speech output summarizing the search results, and presents more detailed information regarding the search results on the conversation screen.
In some embodiments, the search request is a restaurant search request, and search results that are presented to the user correspond to restaurants that are identified in accordance the restaurant search request. For example, when a digital assistant is active, the digital assistant monitors various input channels for user input (e.g., listens to capture the user's speech input and/or captures text or gesture inputs on the conversation screen). When the digital assistant captures a speech input from the user requesting a search for restaurants (e.g., a speech input “Find me a romantic Italian restaurant in Menlo Park.”), the digital assistant parses the speech input to determine the search criteria specified in the speech input (e.g., Task={Search or ConstrainedSelection}, SelectionClass=Restaurant, SearchConstraints={RestaurantCuisine=“Italian”, RestaurantQualifiers=“romantic”, RestaurantLocation=“Menlo Park” }). In some embodiments, one or more of the search criteria are based on context information such as other inputs from the user or saved preferences of the user.
After the search criteria of the search are determined, the digital assistant performs a search for restaurants according to the search criteria specified in the speech input and the context information. Then the digital assistant presents the search results to the user through the dialogue interface of the digital assistant.
In some embodiments, before the search is performed or before the search results are presented, the digital assistant provides a speech output through the dialogue interface, the speech output paraphrasing the search request received from the user. For example, when the user says “Find me a place to eat in Palo Alto.” The digital assistant can respond by saying “OK, searching for restaurants in Palo Alto.” For another example, when the user says “I want to have Italian for dinner today.” If the current time is close to dinner time, the digital assistant can respond by saying “OK, searching for Italian restaurants near your current location.”
In some embodiments, the digital assistant provides a speech output with the search results, where the speech output announces the one or more search criteria used to identify the search results. For example, after the digital assistant has performed a search for Italian restaurants in Palo Alto according to the user's request, the digital assistant announces the search results by saying “Here are five Italian restaurants I found in Palo Alto: . . . ” For another example, after the digital assistant has performed a search for restaurants near the user's current location, the digital assistant announces the search results by saying “I found a fast food restaurant and a French restaurant nearby: . . . ”
In some embodiments, after announcing the search results through a speech output, the digital assistant optionally read through the list of search results to the user. In some embodiments, both the announcement and the search results are presented on a conversation screen on the user device. In some embodiments, text representing the search criteria (e.g., restaurant, Italian, Palo Alto, nearby) used to identify the search results are highlighted in the announcement and/or the search results.
In some embodiments, the announcement for the search results can indicate changes (e.g., relaxation and/or variation) in the search criteria used to obtain to the search results. For example, when the user had requested to find a Malaysian restaurant in Palo Alto, and no Malaysian restaurants were found in Palo Alto, the digital assistant can expand the search area to include the nearby cities (e.g., Menlo Park). When the digital assistant presents the search results obtained from the expanded search, the digital assistant announces the search results by saying, “No Malaysian restaurants were found in Palo Alto, but I found one Malaysian restaurant in Menlo Park which borders Palo Alto.”
In some embodiments, the search request is an entertainment search request, and the search results correspond to a plurality of entertainment events identified in accordance the entertainment search request. Entertainment events refer to public activities and events related to entertainment and fun that generally occur at predetermined times and typically require a reservation or ticket for attendance. Examples of entertainment events include movies, shows, concerts, performances, sports games, tours, etc.
For example, when the digital assistant captures a speech input from the user requesting a search for a particular type of entertainment events (e.g., a speech input “Who's playing at the Fillmore next week?”), the digital assistant processes the speech input and determines that “the Fillmore” is the name of a music venue, and that the user is searching for concerts that are playing at the Fillmore next week. Based on the speech processing, the digital assistant obtains the search criteria specified in the speech input (e.g., Task=(Search or ConstrainedSelection, SelectionClass=Entertainment Event, Sub-class=Concerts, SearchConstraints={Venue=“Fillmore,” Time=“next week” }). In some embodiments, one or more of the search criteria can be based on context information associated with the speech input, such as other inputs from the user or saved preferences of the user. After the search criteria of the entertainment event search are determined, the digital assistant performs a search for entertainment events (e.g., upcoming concerts) according to the search criteria specified in the speech input (e.g., Venue=Fillmore, and Time=next week) and the context information (e.g., the current date). Once the entertainment events meeting the search criteria have been identified, the digital assistant generates and presents the search results to the user through the dialogue interface of the digital assistant.
In some embodiments, before the search is performed or before the search results are presented, the digital assistant provides a speech output through the dialogue interface, the speech output paraphrasing the search request received from the user. For example, when the user's speech input was, “Who's playing at the Fillmore next week?” The digital assistant determines the search criteria based on the speech input, and paraphrases the search request by saying, “Searching for concerts at the Fillmore Concert Hall next week.”
In some embodiments, when paraphrasing the search request, the digital assistant provides both explicit search criteria and implicit search criteria derived from the context information in the paraphrase. For example, if the digital assistant had just booked a table for two at an Italian restaurant for Saturday evening at the user's request, and subsequently detected a speech input saying, “I want to catch a movie before the dinner. What's on?” Based on the speech input, the digital assistant can determine explicit search criteria such as Task=Search, SelectionClass=Entertainment Events, Sub-class=Movies, and implicit search criteria such as Date=“Saturday evening”=(e.g., Jun. 9, 2012), Time=“before dinner”=(e.g., between 4:00 PM-6:00 PM), Location=“near restaurant”=(e.g., within 15 miles of Palo Alto). Therefore, when paraphrasing the search request, the digital assistant speaks or presents on the conversation screen, “Searching for movies playing between 4:00 PM to 6:00 PM on Saturday.”
In some embodiments, the digital assistant provides a speech output with the search results to announce one or more search criteria used to identify the search results. For example, when presenting the search results for the movie search in the above example, the digital assistant can provide a speech output saying “Here are movies that are showing before your dinner on Saturday: . . . ” or “I found 6 movies with show times between 4:00 PM and 6:00 PM on Saturday: . . . ” After announcing the search results through a speech output, the digital assistant optionally read through the list of search results to the user. In some embodiments, both the announcement and the search results are presented on a conversation screen on the user device. In some embodiments, text representing the search criteria (e.g., “movies”, “Saturday”, “before your dinner”, “between 4:00 PM and 6:00 PM”) used to identify the search results are highlighted in the announcement and/or the search results.
As shown in the examples above, in some embodiments, a search request includes context information associated with the speech input received from the user. In some embodiments, the digital assistant maintaining the context information based on one or more prior user interactions between the user and the digital assistant through the dialogue interface. More details on how context information is utilized have been provided in earlier sections of the specification. Examples of context information include personal interaction history (e.g., dialog history, previous selections from results, and the like), personal physical context (e.g., user's current location and the current time), and personal information gathered in the context of interaction (e.g., name, email addresses, physical addresses, phone numbers, account numbers, calendar information, contacts, preferences, and the like).
In some embodiments, the digital assistant determines at least some of the search criteria in the search request based at least in part on the context information associated with the speech input. After the search criteria are determined from the speech input and the associated context information, the digital assistant identifies reservable items in accordance with the search criteria that have been determined, and generate the search results based on the identified reservable items.
Below is another example in which context information is used to derive a search criterion of the search request. While the digital assistant is executing the reservation procedure at the user's request to purchase two tickets for a selected concert at the Fillmore for Saturday, or shortly after the reservation was made, the user provides a speech input through the dialogue interface saying, “Where can we eat before the concert?” In response to the speech input, and based on the context information already available to the digital assistant (e.g., the location and time for the concert), the digital assistant executes a restaurant search for places near the Fillmore that are open before the concert time on Saturday. In some embodiments, the digital assistant also infers a party-size requirement for the restaurant search request from the context information (e.g., the number of concert tickets that is being purchased), and presents only restaurants that have open tables meeting the party-size requirement as search results.
In some embodiments, after a search request is received from the user, the digital assistant determines based on the speech input and the associated context information that additional input from the user is needed to further constrain the search, or to sort the search results already obtained using the search criteria initially provided by the user. For example, when the user says “What's playing at the Fillmore?” The digital assistant will be able to determine that the user is interested in searching for events occurring at the Fillmore. However, without earlier user inputs or context information indicating a relevant time period for the search, the digital assistant will not be able to provide the most relevant search results. In such a scenario, the digital assistant determines that additional input from the user is needed or desirable to further constrain the search.
In some embodiments, upon determining that additional user input specifying one or more additional search criteria is needed or desirable, the digital assistant generates a speech output through the dialogue interface of the digital assistant to request the additional search criteria. For example, the digital assistant may provide a speech output saying, “OK, are you interested in all performances of this week? Or just the weekend?” In response to providing the speech output, the digital assistant receives additional inputs from the user through the dialogue interface, and supplements the search request with the additional inputs. For example, if the user says “Just this weekend.” Then, the digital assistant derives an additional search criterion (Date=“this weekend”=actual date range of the upcoming weekend (e.g., “Jun. 9th-10th, 2012”)) and supplements the original search request with this additional search criterion. If the user instead says, “Fine me performances for this Saturday only.” The digital assistant derives an additional search criterion {Date=“this Saturday”=actual date of the upcoming Saturday (e.g., Jun. 9, 2012)} and supplements the original search request with this additional search criterion. In some embodiments, the digital assistant also paraphrases the augmented search request to the user, by saying “OK, searching for performances at the Fillmore for this weekend” or “OK, searching for performances at the Fillmore for this Saturday.”
In some embodiments, the digital assistant can perform a search based on multiple search criteria specified in the original search request, and obtains some preliminary search results that do not meet all search criteria equally well. In such a scenario, in some embodiments, the digital assistant generates a speech input requesting additional input regarding the relative importance of the search criteria to the user. For example, if the original search request is for “romantic Italian restaurants in Palo Alto” and the digital assistant has identified non-Italian restaurants in Palo Alto that are considered romantic, romantic Italian restaurants that are just outside of Palo Alto, and Italian restaurants in Palo Alto that may or may not be considered romantic. Based on the mix of preliminary search results, the digital assistant generates an output requesting additional input regarding the relative importance of the three search criteria “RestaurantQualifiers=romantic,” “RestaurantCuisine=Italian,” and “RestaurantLocation=Palo Alto.” For example, the digital assistant can ask the user, “Which is more important, atmosphere, location, or cuisine?” Based on the user's subsequent input (e.g., a speech input saying “Cuisine and atmosphere.”), the digital assistant derives the additional constraint, and filters and/or ranks the search results according to the additional constraint (e.g., give more weight to the Cuisine and Atmosphere criteria).
In some embodiments, for each search result, the digital assistant presents one or more properties of the reservable item corresponding to the search result, where each property corresponds to one of the search criteria used in identifying the search results. For example, if the search request was for a place to eat before the concert, the properties shown along with the name of each restaurant in the search results can include the distance between the restaurant and the Fillmore (i.e., the location of the concert). For another example, if the search was for romantic Italian restaurants in Menlo Park, the digital assistant will present a snippet for each restaurant in the search results, and in the snippets, keywords such as “Italian” “Italy” “romantic” “nice atmosphere,” “Tuscany,” etc. are highlighted in the restaurant name, cuisine, and/or customer reviews.
In some embodiments, when presenting the search results, the digital assistant concurrently presenting one or more user interface elements in the dialogue interface (e.g., in the conversation screen) along with the plurality of search results. The user interface elements include, for example, links, buttons, and other interactive elements. In some embodiments, selection of one of the user interface elements causes presentation of additional information related to one or more of the search results in the dialogue interface. For example, a reviews user interface element causes more customer reviews for a selected search result to be presented through the dialogue interface. Alternatively, a reviews user interface element causes the interface of a restaurant review site to be loaded on the user device, and customer reviews for a selected search result are shown in their original format at the review site (i.e., selection of the reviews user interface element caused the review site to punch out of the dialogue interface of the digital assistant). Other examples of the user interface elements include links and buttons that cause presentation of a review, a map, a trailer, an availability summary, and a rating associated with one or more of the plurality of search results.
In some embodiments, when presenting the search results, the digital assistant also concurrently present one or more user interface elements in the dialogue interface (e.g., on a conversation screen) for invoking various follow-on actions regarding the search results. Selection of such a user interface element causes a follow-on action associated with one or more of the search results to be executed by the digital assistant. For example, in some embodiments, the digital assistant presents a call button for a search result. When the call button is invoked, it causes the digital assistant to initiates a telephone call to a telephone number associate with the search result (e.g., a telephone number associated with a restaurant or ticketing office). In some embodiments, the digital assistant presents a reviews button for each search result. When the reviews button is invoked, it causes the digital assistant to present reviews associated with the reservable item (e.g., restaurant, movies, concerts, etc.) corresponding to the search result. In some embodiments, the reviews are sourced from multiple third-party websites or services. In some embodiments, the digital assistant presents a directions button for each search result. When the directions button is invoked, it causes the digital assistant to generate a directions request for an address associated with the search result (e.g., the address of a restaurant identified by the search result). In response to the directions request, a directions interface can be presented with the address associated with the search result as the destination. Alternatively, a route between the user's current location and the address associated with the search result can be presented. In some embodiments, the directions interface or the route can be presented through the dialogue interface of the digital assistant, and the user can continue to interact with the digital assistant while reviewing the directions interface or the route. In some embodiments, the digital assistant presents one or more reservation or purchase user interface element (e.g., a reservation or buy button) for each search result. When one of the reservation or purchase user interface elements is invoked, the digital assistant initiates a reservation procedure for reserving or purchasing the reservable item (e.g., a table at a restaurant, tickets for an entertainment event) associated with the search result. In some embodiments, the digital assistant also presents user interface elements for sharing a link or saving a link to a search result.
As shown in
In the process 4800, in response to receiving the reservation request, the digital assistant executes a reservation procedure to reserve a respective reservable item corresponding to the particular search result while maintaining the dialogue interface to receive subsequent user input (4840). In some embodiments, the reservation procedure executed by the digital assistant reserves a table at a respective restaurant corresponding to the particular search result for which the reservation request was submitted. In some embodiments, the reservation procedure purchases or reserves one or more tickets for a respective entertainment events corresponding to the particular search result for which the reservation request was submitted.
In some embodiments, the digital assistant executes a reservation procedure to reserve a respective reservable item (e.g., a restaurant table or seating at an event) by invoking a task flow associated with the reservation task. In some embodiments, the task flow invokes third-party services (e.g., an online reservation service for restaurants or an online ticketing service for movies and other entertainment events) for making the requested reservation. In some embodiments, while executing the reservation procedure, the digital assistant and the user continue to communicate through the dialogue interface regarding the reservation process, and related transactions. For example, while executing the reservation procedure, the digital assistant can ask the user whether the user has any additional requirements for the reservation (e.g., dietary restrictions, party-size, wine preference, etc. for restaurant reservation, and seating preferences for event reservations).
In some embodiments, the digital assistant performs the requested reservation in the background, while the user and the digital assistant continue to interact regarding other search results, and/or other unrelated tasks and requests through the dialogue interface. For example, while the digital assistant executes the reservation procedure, the user can review the menu of the restaurant that is being reserved, or ask the digital assistant to search for a flower shop near the restaurant through the dialogue interface of the digital assistant.
In some embodiments, the task flow of the reservation task further includes generating and storing a reminder or calendar entry in accordance with the reservation. For example, after a reservation has been made successfully at a restaurant or after tickets have been purchase for an event by the digital assistant, the digital assistant generates a reminder or calendar entry for the reservation according to the date, time, and/or location of the reservation. In some embodiments, the digital assistant has access to the user's electronic calendars, and inserts the reminder or calendar entry directly into the user's calendars. In some embodiments, the digital assistant sends a request to a third-party service, e.g., an online calendar service, to insert the reminder or calendar entry in the user's calendar. In some embodiments, the digital assistant requests the user's permission through the dialogue interface before the reminder or calendar entry is created for the user.
In some embodiments, the task flow of the reservation task further includes generating an invitation for another user in accordance with the reservation. For example, in some embodiments, if the reservation is for more than one person (e.g., reservation for a table for two or more people at a restaurant, or purchasing of two or more tickets for an event), the digital assistant determines the identities of the people who will be invited to join the meal at the restaurant or to attend the entertainment event with the user. Once the identities of these other people have been determined, the digital assistant generates an invitation to those people.
In some embodiments, the digital assistant determines the identities of the invitees to the meal or event based on the context information collected during the dialogue session in which the reservation was made. For example, if the user had said “Make a reservation for me and my mom at this restaurant.” The digital assistant can determined the identity and contact information of the user's mom based on the user's contacts in the user's address book. In some embodiments, the digital assistant can offer to generate the invitation and prompt the user to provide the necessary contact information of the invitee(s).
The features provided above with respect to
For example, suppose that the user has bought a ticket for a particular movie through the digital assistant during a particular dialogue session. Afterwards, the user asks the digital assistant to find a restaurant near the movie theater by providing a speech input “Where can we eat after the movie?” In such a scenario, the digital assistant uses the context provided by the prior user interactions (e.g., purchasing the movie ticket) to determine which movie the user was referring to in the speech input. After determining that the user was referring to the particular movie for which the user has just purchased a ticket, the digital assistant is able to determine the date, time, and location associated with that movie. The digital assistant then uses the determined date, time, and location as the search criteria for the newly received search request (e.g., the restaurant search request). In other words, without having to require the user to explicitly repeat the name, date, time, and location of the movie, the digital assistant processed the user's speech input, and determined the appropriate search criteria for the newly received restaurant search request using context information, including prior user interactions between the digital assistant and the user.
In some embodiments, the digital assistant processes the speech input from the user along with the associated context information to determine whether additional input is required to clarify the search request, to further constraint a search, and/or to rank the relative importance of various search criteria specified in the search request. If the digital assistant determines that such additional input is needed or desirable, the digital assistant generates a speech output or a textual prompt to request such additional input.
In some embodiments, the digital assistant provides a speech output through the dialogue interface requesting at least one additional search criterion for the restaurant search request. In response to providing the speech output, the digital assistant receives additional input (e.g., a speech input responding to the digital assistant's request) from the user through the dialogue interface. The digital assistant processes the additional input and supplements the restaurant search request with the additional user input.
In some embodiments, the digital assistant also provides a speech input requesting the user to specify the relative importance among multiple search criteria. Once the digital assistant receives the additional user input specifying the relative importance among the multiple search criteria, the digital assistant ranks and/or filters the search results based on the additional user input.
In some embodiments, the digital assistant receives the speech input from the user, and processes the speech input and associated context information to identify a party-size requirement for the restaurant search request. For example, when the user says, “I want to invite my parents to a nice dinner on Saturday night, please find me a good restaurant.” Based on this speech input, the digital assistant can infer a party-size requirement of three (3), i.e., the user and the user's parents, for the restaurant search request.
In the above example, the digital assistant infers the party-size requirement from the user's speech input directly. In another example, if the user has just purchased two movie tickets through the digital assistant, and then asks the digital assistant, “Where can we eat before the movie′?” Based on the context information associated with the speech input (i.e., the purchasing of the movie tickets), the digital assistant infers that the movie referred to in the speech input is the same movie for which the tickets have been purchased. In addition, based on the fact that two movie tickets have been purchased, the digital assistant infers a party-size requirement of two (2) (i.e., the two people who are going to the movie) for the restaurant search request. In some embodiments, the digital assistant generates a speech output requesting the user to provide a party-size requirement for the restaurant search, and obtains the party-size requirement from the user's additional input provided in response to the digital assistant's request.
In some embodiments, after the digital assistant determines the party-size requirement for the restaurant search request, the digital assistant identifies one or more restaurants each having at least one open table meeting the party-size requirement. In some embodiments, the digital assistant submits the party-size requirement along with one or more other search criteria of the restaurant search request to one or more third-party information sources (e.g., online restaurant search and reservation services), and requests that only restaurants meeting the party-size requirement be provided by the third-party information sources. In some embodiments, the digital assistant requests the availability information for restaurants that satisfy at least a minimum set of the search criteria, and select only restaurants that have at least one open table meeting the party-size requirement as potential search results.
In some embodiments, after the restaurants meeting the party-size requirement (as well as the other search criteria) have been identified, the digital assistant generates the search results based on the identified restaurants. In some embodiments, each search result presents information about a respective restaurant, and also availability information about the restaurant. For example, if the user's restaurant search request did not specify a date and time for the restaurant search, the search results optionally presents the date(s) and time slot(s) that have availability (i.e., open tables) meeting the party-size requirement of the restaurant search request.
In some embodiments, before presenting the search results to the user, the digital assistant ranks the search results based on information collected from multiple third-party information services. For example, in some embodiments, the digital assistant compiles the reviews of candidate restaurants from multiple online sources, and reconciles the differences among the reviews and ratings from the different online sources. In some embodiments, the digital assistant also removes duplicate information, and/or integrates different facets of information regarding the same candidate restaurant that are available from the different online sources. Then, after reconciling and integrating the information from the multiple online sources regarding the candidate restaurants, the digital assistant generates and ranks the search results based on the integrated and reconciled information. More details regarding how information from multiple sources are integrated and reconciled is provided in earlier sections of this specification.
In some embodiments, the digital assistant identifies multiple restaurants in accordance with the restaurant search request, and places a hold on a respective open reservation slot for each of the multiple restaurants. For example, if the user has specified a party-size requirement in the restaurant search request, or if the digital assistant has inferred the party-size requirement based on context information (e.g., prior user input, prior user interactions, or default user preferences, etc.), the digital assistant places a hold on a restaurant reservation slot (or an open table) satisfying the party-size requirement. In some embodiments, the hold is placed through an application program interface (API) of a third-party online restaurant reservation service. In some embodiments, the hold is automatically placed on each restaurant that has been determined to satisfy all of the search criteria of the restaurant search request. In some embodiments, a hold is not placed on restaurants that only satisfy some of the search criteria of the restaurant search request until an affirmative instruction to do so is provided by the user (e.g., through real-time input or through previously saved user preferences). In some embodiments, the hold is automatically released after a predetermined time period if no subsequent reservation procedure is performed to convert the hold to an actual reservation.
In some embodiments, after the search results are presented to the user through the dialogue interface of the digital assistant, the digital assistant receives a reservation request for one of the search results from the user through the dialogue interface of the digital assistant. In response to receiving the reservation request, the digital assistant executes a reservation procedure to make a reservation for a respective restaurant corresponding to the search result. More details on how the digital assistant receives the reservation request and executes the reservation procedure are provided with respect to
In some embodiments, the digital assistant receives a reservation request for one of the search results from the user through the dialogue interface of the digital assistant. In response to receiving the reservation request, if a hold has been placed for the restaurant corresponding to the search result, the digital assistant converts the hold placed for the respective restaurant to a reservation. After successfully converting the hold to the reservation, the digital assistant releases the holds placed on other restaurants that are not reserved. In some embodiments, the digital assistant requests a user confirmation before actually making the reservation. In some embodiments, the hold is placed without a party-size requirement, and the digital assistant requests the party-size requirement from the user when the user asks the digital assistant to convert a hold to an actual reservation.
In some embodiments, when presenting the search results to the user, the digital assistant concurrently presenting a predetermined number of search results through the dialogue interface each time. For example, the digital assistant provides no more than five results (e.g., 3-5 results) concurrently to the user. In some embodiments, more results are only presented to the user when the user requests to see more results. By limiting the number of concurrently presented search results to a small number (e.g., 3-5), the digital assistant can reduce the cognitive burden placed on the user when the user reviews the results.
In some embodiments, for each of the search results, the digital assistant presents one or more properties of the respective restaurant corresponding to the search result, where each property corresponds to a respective restaurant search criterion used in identifying the plurality of search results. For example, if distance from a particular location was used as a search criterion for the restaurant search, then, for each search result, the distance between that particular location and a respective restaurant corresponding to the search result can be presented for the search result. Other properties such as cuisine (e.g., French, Italian, pizza, vegetarian, American, burgers, Californian, etc.), atmosphere (e.g., romantic, nice, trendy, cool, clean, good, etc.), price (e.g., expensive, $$$, cheap, etc.), popularity (e.g., number of reviews, years in existence, number of good reviews, number of bad reviews, etc.) can also be presented for each search result when they are used as the restaurant search criteria.
In some embodiments, the digital assistant concurrently presents one or more user interface elements (e.g., a reviews tab, a maps button, a ratings link, etc.) in the dialogue interface (e.g., on the conversation screen) along with the plurality of search results, where selection of the user interface elements causes presentation of a review, a map, or a rating associated with a respective restaurant corresponding to a selected search result.
In some embodiments, the digital assistant concurrently presents one or more user interface elements (e.g., a call button, a review submission button or link, a link-saving button, a link-sharing button, a direction request button, a reservation button, etc.) in the dialogue interface (e.g., on the conversation screen) along with the plurality of search results. Selection of the user interface elements causes follow-on actions of: initiating a telephonic call to a third party, submitting a review, saving a link, sharing a link, requesting directions, or making a reservation, with regard to a respective restaurant corresponding to a selected search result.
In some embodiments, supplementing the speech input includes determining a search criterion that is not specified or referred to in the speech input. For example, the context information may be used to provide a location search criterion, while the speech input makes no mention of any location. In some embodiments, disambiguating the speech input includes determining that the speech input includes a term that has multiple reasonable interpretations, selecting one of the multiple reasonable interpretations based on the context information associated with the speech input, and generating a search criterion based on the selected interpretation.
In some embodiments, the context information associated with the speech input includes at least one prior interaction between the user and the digital assistant through the dialogue interface. In some embodiments, the context information includes information stored in one or more applications, such as a calendar application, an e-mail application, an instant messaging application, and an address book application. For example, if the user mentions a name or scheduled appointment in the speech input, the digital assistant can obtain more information about the person(s) associated with that name or information about the scheduled appointment by accessing the user's address book or calendar through the appropriate applications.
In some embodiments, the context information includes one or more of a current location, a current time, and current or forecasted weather information. In some embodiments, the context information includes information extracted from one or more speech inputs previously received through the dialogue interface. In some embodiments, the context information includes information extracted from one or more non-verbal input previously received through the dialogue interface. For example, the non-verbal input includes text input, pointer input, and gesture input received from the user through various input devices coupled to the user device.
In some embodiments, the digital assistant concurrently presents one or more user interface elements in the dialogue interface along with the search results. In some embodiments, one of the user interface elements is for downloading and playing a media file associated with a corresponding entertainment event search result. For example, among the search results for a movie search, a respective trailers button can be presented for each movie search result. Selection of the trailers button will cause a movie trailer to be downloaded and played on the user device. In some embodiments, user interface elements for presenting reviews, show times, and making reservations are also provided for each search result.
In some embodiments, after the digital assistant has provided the search results to the user through the dialogue interface, the digital assistant continue to communicate with the user through the dialogue interface. For example, the digital assistant can receive a follow-up speech input from the user requesting additional information. The requested additional information can be in regard to one or more of the search results previously presented through the dialogue interface (e.g., through speech outputs by the digital assistant, and/or presented on a conversation screen) or one or more search results that are currently presented on the conversation screen. For example, after the digital assistant presented a number of movie search results based on the user's initial search request through the dialogue interface, the user can provide a follow-up speech input requesting additional information regarding one or more of the movie search results by saying, “Are there other movies on this topic?” or “Show me some other movies by the same director.” or “Show me only movies that have a B+ rating.” For another example, after the user has viewed the trailer for one of the movie search results, the user can provide a follow-up speech input by saying, “Are there other movies played by the same actress?”
In some embodiments, in response to receiving the follow-up speech input, the digital assistant disambiguates at least part of the follow-up speech input to generate a search query for retrieving the requested additional information, where the disambiguating is based at least in part on one or more properties related to respective entertainment events corresponding to the search results. For example, suppose that the user selects one of the movie search results, and provides a follow-up speech input, “Are there other movies by the same director?” In response to the follow-up speech input, the digital assistant identifies the movie that corresponds to the selected search results, and determines a relevant property associated with movie, namely, the director for the movie. Based on the relevant property (e.g., the identity of the director for the movie), the digital assistant disambiguates the user's follow-up speech input, and generates a search query for retrieving movies that were directed by the identified director.
In some embodiments, the digital assistant retrieves the requested additional information based on the search query, and presents the information to the user through the dialogue interface of the digital assistant. Continuing with the above example, once the digital assistant has retrieved the search results for movies that were directed by the identified director, the digital assistant presents the newly retrieved search results to the user through the dialogue interface. The user and the digital assistant can continue to interact with each other through the dialogue interface with respect to the newly retrieved search results, the search results presented earlier, and/or a new search request.
In some embodiments, during a dialogue session in which a speech input was received, the digital assistant maintains a dialogue context based on user inputs received through the dialogue interface and information provided to the user through the dialogue interface. A dialogue session includes the exchange of speech input and output between the user and the digital assistant. In some embodiments, the dialogue session also includes requests and responses exchanged through other modalities, such as textual prompts provided by the digital assistant and textual response provided by the user, as well as notifications or alerts provided by the digital assistant and user inputs to address the notifications and alerts.
A dialogue session can be defined in various ways. In some embodiments, a dialogue session is defined by a predetermined duration, e.g., a period of several minutes, tens of minutes, one hour, one day, several days, or even longer. It is advantageous to keep the dialogue session within a reasonable length, e.g., less than half an hour, so that maintaining the context information does not take up too much computation resources. In addition, the user is more likely to refer back to an item mentioned recently in the dialogue, and therefore, it is not particularly necessary to define a very long dialogue session.
In some embodiments, a dialogue session is defined as continuous or intermittent interactions between the digital assistant and the user with no interruptions longer than a predetermined duration (e.g., 2 minutes or 5 minutes). For example, a user can interact with the digital assistant continuously for a while, punch out of the digital assistant's conversation screen to access another application, and then resumes interaction with the digital assistant afterwards. In this example, as long as the user resumes the interaction with the digital assistant before the predetermined duration required for maintaining the dialogue session expires, the dialogue session persists, and the dialogue context associated with the dialogue session is continuously maintained.
In some embodiments, a dialogue session is defined by a starting trigger event and an ending trigger event. For example, the user can press a button or speak a trigger phrase to invoke the active listening of the digital assistant and start the dialogue session. After interacting with the digital assistant during the dialogue session, the user can press the button or speak another trigger phrase to end the dialogue session. Other ways of defining a dialogue session is possible.
In some embodiments, during the dialogue session in which an initial speech input was received, the digital assistant receives one or more additional speech inputs from the user through the dialogue interface. The digital assistant processes each of the one or more additional speech inputs based on the dialogue context maintained by the digital assistant to determine a user intent expressed in the additional speech input. In some embodiments, the processing of the additional speech input includes supplementing and/or disambiguating the additional speech input based on the dialogue context. In some embodiments, the digital assistant provides additional information in response to each of the one or more additional speech inputs based on the processing.
In some embodiments, when the digital assistant has presented the entertainment search results (e.g., movie search results or concert search results), the digital assistant receives a reservation request for one or more of the search results from the user through the dialogue interface of the digital assistant. In response to receiving the reservation request, the digital assistant executes a reservation procedure to purchase one or more tickets for a respective entertainment event corresponding to the one or more search results.
In some embodiments, the digital assistant performs the reservation procedure in the background while the digital assistant and the user continue to interact through the dialogue interface. In some embodiments, during the reservation procedure, the digital assistant communicates with the user regarding various preferences (e.g.,) and additional information (e.g., credit card information, payment authorization, etc.) regarding the reservation through the dialogue interface of the digital assistant. In some embodiments, the digital assistant loads the interface of a third-party online reservation service, and allows the user to make the reservation directly through the interface of the third-party online reservation service.
In some embodiments, during the dialogue session in which an initial speech input (e.g., the speech input initiating the entertainment search request) was received, the digital assistant receives a reservation request through the dialogue interface of the digital assistant after presentation of the search results has been replaced with other information in response to subsequent user inputs. In response to the reservation request, the digital assistant determines a target entertainment event of the reservation request based on the dialogue context, where the target entertainment event corresponds to one of the search results previously presented. Once the target entertainment event has been determined, the digital assistant executes a reservation procedure to purchase one or more tickets for the determined target entertainment event. The example below illustrates this process:
Suppose that the user had requested a movie search using an initial speech input “What movies are on this Saturday?” After the digital assistant had presented the list of movies that were showing this Saturday through the dialogue interface, the user asked to see some trailers for some of the movie results. In response, the digital assistant showed the trailers to the user, and as a result, the list of movies previously presented on the screen was replaced with the trailers. While the digital assistant interacts with the user regarding the movie search, the movie search results, and the trailers, the digital assistant maintained the dialogue context related to the current dialogue session. After the movie trailers were shown, the user submits a reservation request to the digital assistant, for example, using a speech input “Ok, get me a ticket for the dinosaur movie.” Based on the speech input and the dialogue context of the current dialogue session, the digital assistant identifies the trailers that have been reviewed by the user, and determines the movies that correspond to those trailers. The digital assistant then identifies the particular movie among those movies that relates to dinosaurs. Once that particular movie related to dinosaurs has been identified, the digital assistant initiates the reservation procedure for the target entertainment event (i.e., the particular dinosaur movie that has been identified). In some embodiments, the digital assistant announces the name of the movie before proceeding to make the reservation or purchase the ticket for the movie.
For another example, a movie domain includes vocabulary, domain entity data, and language patterns associated with movies, actors, directors, movie theatres, movie histories, etc. The movie domain also includes task flows, dialogue flows, and service capability models associated with movie/actor/director searches, movie ratings, downloading and playing movie trailers, reserving or buying movie tickets, and other questions and answers related to movies.
Similarly, in some embodiments, the digital assistant also implements a sports domain. The sports domain includes vocabulary, domain entities, language patterns, task flows, dialogue flows, and service capability models associated with searching for information on sports, performing tasks related to sporting events, sending short messages related to sports, trading sports-related memorabilia, and the like.
In some embodiments, the digital assistant implements a music domain. The music domain includes vocabulary, domain entities, language patterns, task flows, dialogue flows, and service capability models associated with searching for information on music and music-related people, events, and products, performing tasks related to music (e.g., purchasing CDs, albums, merchandises and/or downloading media files), and buying tickets to events (e.g., concerts, premieres, album signings).
In some embodiments, the digital assistant implements an entertainment domain. The entertainment domain includes vocabulary, domain entities, language patterns, task flows, dialogue flows, and service capability models associated with searching for information on entertainment-related people and events.
In some embodiments, the digital assistant implements a shopping domain. The shopping domain includes vocabulary, domain entities, language patterns, task flows, dialogue flows, and service models associated with searching for products and shopping.
In some embodiments, the digital assistant implements a gaming domain. The shopping domain includes vocabulary, domain entities, language patterns, task flows, dialogue flows, and service models associated with searching for information on games, gaming, gaming products, walkthroughs, making gaming-related purchases, and trade gaming-related virtual and real goods, equipments, and properties.
In some embodiments, each domain includes multiple sub-domains. For example, in some embodiments, the entertainment domain includes the movies domain, the sports domain, and the music domain. In some embodiments, the sports domain includes sub-domains for different sports. In some embodiments, the music domain includes sub-domains for different music genres. In some embodiments, the sub-domains may further include their own sub-domains, and the sub-domains may overlap.
In some embodiments, the digital assistant selectively provides information related to a domain of information in response to natural language requests from the user. For example, based on the vocabulary (e.g., particular keywords), domain entities (e.g., well-known people and entities), and/or language patterns (e.g., a particular query template or request pattern) commonly associated with requests for each information domain implemented by the digital assistant, the digital assistant is able to determine that the user's request is likely to be directed to a particular domain of information. Once the digital assistant has determined the likely domain of interest, natural language processing of the user's current and subsequent requests can be biased toward that particular domain to disambiguate the names of people, locations, entities, and user's intent expressed in the natural language request.
For example, vocabulary associated with the sports domain includes proper names and nicknames of different sports, athletes, sports teams, sporting events, and well-known sponsors of sporting events, brands for sporting goods, venues of sporting events and games, sport history, terms of art and jargons of different sports and games, and the like. The language patterns associated with the sports domain include keywords and phrases such as “what's the score for . . . ”, “who is playing against . . . ?” “which team won the . . . ”, “what's the game stats for . . . ”, and so on. Based on the sports-related vocabulary and language patterns, the digital assistant can infer whether a user's request is related to the sports domain.
For another example, for the music domain, user often ask questions related to music, genres, billboard rankings of songs and albums, lyrics, albums and tours for particular artists, news about famous artists, the latest hits, and the like. The vocabulary associated with the music domain would include proper names and nicknames of musicians, singers, songs, genres, music groups, etc. The language patterns associated with the music domain include keywords and phrases such as “show me the lyrics for . . . ” “What's the name of the song that “Who sang the song “What's the latest song by . . . ”, and so on. Based on the music-related vocabulary and language patterns, the digital assistant can infer whether a user's request is related to the music domain.
In some embodiments, the digital assistant stores a collection of common query types representing the type of questions that user frequently ask in a particular information domain. For example, for the sports domain, users often ask questions related to game scores and player statistics, game times, team rosters, athlete salaries, player and/or team history, and current sports news. For the music domain, users often ask questions related to artists, genre, lyrics, the latest hits, classics, famous songs, well-known lines in particular songs, and so on.
In some embodiments, the digital assistant also implements sub-domains of common query types related to famous personalities and entities in the domain. For example, in some embodiments, in the sports domain, the digital assistant also implements sub-domains of common query types revolved around famous athletes, sports teams, and sports events, such as “Kobe-related questions,” “Giants-related questions”, “Super Bowl related questions, and the like. In some embodiments, for the music domain, the digital assistant can implement sub-domains of common query types related to famous singers, songs, and genres, such as “Elvis-related questions” “Blues-related questions”, “Hip-Pop-related questions, and the like.
As shown in
In the process 5100, the digital assistant receives a speech input from a user through a dialogue interface of a digital assistant (5104). The digital assistant determines context information associated with the speech input (5106). The digital assistant processes the speech input to determine one or more search criteria, where the processing includes supplementing and/or disambiguating the speech input using the context information and the vocabulary associated with the predetermined domain of information (5108).
For example, if the user's speech input include sports-related vocabulary, once the digital assistant detects the sports-related vocabulary, (e.g., keywords or language patterns associated with a predetermined query type stored in the sports domain), the digital assistant supplements and/or disambiguate the speech input using context information and the vocabulary associated with the sports domain. For a more detailed example, suppose the user says, “Who is playing the Lakers tonight?” From this speech input, the digital assistant recognizes the sports-related vocabulary “the Lakers” and the sports-related language pattern “Who is playing [a sports team] . . . ” which have both been stored in the sports domain of the digital assistant. As a result, the digital assistant determines that the user is interested in sports-related information. The digital assistant also uses the context information (e.g., the current date) to determine which date the user is referring to by the word “tonight” in the speech input. After the digital assistant has fully disambiguated the user's speech input, the digital assistant proceeds to perform a search to retrieve the requested information. Specifically, the digital assistant retrieves the name of the team that is playing against the Lakers in the evening of the current date.
For another more detailed example, suppose the user asks, “How did Lebron do last night?” According to the vocabulary (e.g., including proper and nicknames of basketball players) and domain entities (e.g., data records on players and sports teams) stored under the sports domain, the digital assistant recognizes that “Lebron” is the name of a famous basketball player “LeBron James”, and a current member of an NBA team called the “Miami Heat”. Based on the language pattern “How did [player name] do . . . ” stored under the sports domain, the digital assistant also recognizes that the user is asking a question related to player game statistics (i.e., a predetermined query type stored under the sports domain). After the digital assistant recognizes that the user is interested in knowing the game statistics for a basketball player of a particular team, the digital assistant uses the context information (e.g., the current date) to determine which game the Miami Heat played the day before, and proceeds the retrieve the game statistics for LeBron James for that game.
As shown in the example above, in the process 5100, the digital assistant retrieves information related to the predetermined domain of information (e.g., the sports domain) in accordance with the one or more search criteria (e.g., {query type=player's game statistics, player name=LeBron James, Team=Miami Heat, game date=yesterday=e.g., Jun. 8, 2012)) (5110). After the information is retrieved based on the search criteria, the digital assistant presents the retrieved information to the user through the dialogue interface of the digital assistant (5112). For example, the digital assistant can produce a speech output to the user saying, “LeBron did well last night, 30 points, 8 rebounds, and 2 assists.”
In some embodiments, after the digital assistant has established that the user is interested in a particular domain of information (e.g., after the digital assistant has satisfactorily answered a user's question related to the sport domain), the digital assistant can continue the conversation with the user within the sports domain. In some embodiments, the digital assistant carries on the conversation (e.g., continued questions and answers) by maintaining a dialogue context of the conversation. The dialogue context keeps track of what information have been asked and received by the user, at which time, and/or in which order.
For example, in some embodiments, during a dialogue session in which a speech input (e.g., an initial user question related to a particular domain of information) was received, the digital assistant maintains a dialogue context based on user inputs received through the dialogue interface and information provided to the user through the dialogue interface. Then, during the same dialogue session, the digital assistant receives one or more additional speech inputs from the user through the dialogue interface. The digital assistant processes each of the one or more additional speech inputs based on the dialogue context to determine the user intent expressed in the additional speech input. In some embodiments, the processing includes supplementing and/or disambiguating the additional speech inputs based on the dialogue context. Then, based on the processing, the digital assistant provides additional information in response to each of the one or more additional speech inputs.
Continuing with the above sport-related example, during the dialogue session in which the user asked about LeBron James' game statistics, the digital assistant maintains the dialogue context and keeps track of what the user has asked about, and what information has been provided to the user, at which times, and/or in which order. Suppose that, after hearing LeBron's game statistics, the user asks another question through an additional speech input “What about Kobe?” Based on this speech input, the digital assistant is able to identify that “Kobe” also refers to a basketball player. Based on the dialogue context stored by the digital assistant, the digital assistant determines that the user has just asked and received answers about the game statistics for another basketball player “LeBron James.” Therefore, the digital assistant interprets the user's question “What about Kobe” as a request for game statistics for Kobe Bryant. In some implementations, the digital assistant performs the task flow for retrieving player game statistics as previously performed, except for replacing the player name “LeBron James” with a different player name “Kobe Bryant.” Once the digital assistant retrieves the player game statistics for Kobe Bryant for last night, and the digital assistant presents the game statistics to the user through the dialogue interface through a speech output, “Kobe also did well last night, 30 points, 4 rebounds, and 4 assists.”
In some embodiments, for some query types, the digital assistants implements dialogue flows that offer to provide the user with additional information related to the user's current questions. For example, after the digital assistant provided the game statistics information about particular players, the digital assistant can offer to show a video clip of the game in which the players had scored. For example, after the digital assistant has provided the user with Kobe's game statistics last night, the digital assistant can ask the user “Would you like to see a video clip of the game?” If the user accepts, the digital assistant proceeds to play the video clip for the game last night in which Kobe has scored.
Suppose that after the digital assistant has provided Kobe's game statistics to the user, or after the video clips are shown to the user, the conversation between the digital assistant and the user continues. For example, the user then asks, “So, who makes more money?” The digital assistant identifies that the user's input matches a language pattern (e.g., “makes . . . money”) associated with a query type “player salaries” stored under the sports domain. Based on the dialogue context stored for the current dialogue session, the digital assistant determines that two basketball players (e.g., LeBron James and Kobe Bryant) have been mentioned in the most recent exchange between the user and the digital assistant. Based on the dialogue context, the digital assistant disambiguates the user's input and interprets the user's input as a question about the salaries of the two basketball players, and specifically, a comparison between the salaries of the two basketball players. After the digital assistant determines the user's intent, the digital assistant proceeds to retrieve the salary information about the two players, and formulate an output comparing the salaries of the two players. For example, the digital assistant can provide a speech output saying, “Based on my sources, Kobe made X dollars in 2011 in salaries, and LeBron made Y dollars in 2011 in salaries.”
In some embodiments, the digital assistant implements a dialogue flow to provide additional related information that may be of interest to the user. For example, in the dialogue flow associated with the player salary related queries, the digital assistant can provide information on product sponsorship by the players, and deals on the sponsored products. For example, the based on the dialogue flow, instead of a plain answer to the user's question regarding player salaries, the digital assistant provides a speech output saying, “Based on my sources, Kobe made X dollars in 2011 in salaries, and LeBron made Y dollars in 2011 in salaries. Both Kobe and LeBron contribute to charities. Would you like to join them in supporting the “After-School All-Stars” a national children's charity that provides after-school programs to inner-city kids?” If the user answers in the affirmative, the digital assistant invokes an appropriate task flow to initiate a donation procedure for the user.
In some embodiments, the digital assistant implements a dialogue flow to provide related information that may be of interest to the user if the digital assistant cannot provide a satisfactory answer to the user's original question. For example, if the user asks the digital assistant “Who is a better player?” The digital assistant determines that it is not able to provide a satisfactory answer to this complex question. As a result, the digital assistant provides an output that says “That's a touch question. But did you know that Kobe and LeBron both likes to tweet? But LeBron has four times more followers on Twitter™?”
Although the above examples are provided in the context of the sports domain, vocabulary, task flows, dialogue flows for other domains can be implemented in an analogous manner. For example, the digital assistant can also implement an entertainment domain to answer users' questions about celebrities, and the entertainment industry; a travels domain to answer users' questions about traveling, sight-seeing, and local and foreign cultures; a gaming domain to answer users' questions about games, gaming gadgets, walkthroughs, competitions, etc. Other domains are also possible.
The present invention has been described in particular detail with respect to possible embodiments. Those of skill 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 structures, or any other programming or structural aspect is not mandatory or significant, and the mechanisms that implement the invention or its features may have different names, formats, or protocols. Further, the system may be implemented via a combination of hardware and software, as described, or entirely in hardware elements, or entirely in software elements. Also, the particular division of functionality between the various system components described herein is merely exemplary, and not mandatory; functions performed by a single system component may instead be performed by multiple components, and functions performed by multiple components may instead be performed by a single component.
In various embodiments, the present invention can be implemented as a system or a method for performing the above-described techniques, either singly or in any combination. In another embodiment, the present invention can be implemented as a computer program product comprising a nontransitory 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.
Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
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 generally, conceived to be a self-consistent sequence of steps (instructions) leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, 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 arrangements of steps requiring physical manipulations of physical quantities as modules or code devices, without loss of generality.
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 “processing” or “computing” or “calculating” or “displaying” or “determining” or the like, refer to the action and processes of a computer system, or similar electronic computing module and/or device, that manipulates and transforms data represented as physical (electronic) quantities within the computer system memories or registers or other such information storage, transmission or display devices.
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 embodied 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.
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 program may be stored in a computer readable storage medium, such as, but is not limited to, any type of disk including 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 (AS1Cs), or any type of media suitable for storing electronic instructions, and each coupled to a computer system bus. Further, the computing devices referred to herein may include a single processor or may be architectures employing multiple processor designs for increased computing capability.
The algorithms and displays presented herein are not inherently related to any particular computing device, virtualized system, or other apparatus. 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 required structure for a variety of these systems will be apparent from the description provided herein. In addition, the present invention is not described with reference to any particular programming language. It will be appreciated 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 enablement and best mode of the present invention.
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 combination or plurality thereof. Such an electronic device can include, for example, a processor, an input device (such as a keyboard, mouse, touchpad, trackpad, joy-stick, 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), 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 include: a mobile phone, personal digital assistant, smartphone, kiosk, desktop computer, laptop computer, tablet computer, consumer electronic device, consumer entertainment device; music player; camera; television; set-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 operating system that is adapted for use on the device.
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 addition, 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 subject matter. Accordingly, the disclosure of the present invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the claims.
This application is a continuation of U.S. patent application Ser. No. 16/352,410, entitled “Intelligent Automated Assistant,” filed Mar. 13, 2019, which is a continuation application of U.S. patent application Ser. No. 13/913,336 filed Jun. 7, 2013, which claims the benefit of U.S. Provisional Application Ser. No. 61/657,736, entitled “Intelligent Automated Assistant,” filed Jun. 8, 2012, which is a continuation-in-part of U.S. patent application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, which claims the benefit of U.S. Provisional Application Ser. No. 61/295,774, filed Jan. 18, 2010. All of the above-mentioned applications are incorporated herein by reference in their entirety. This application is further related to (1) U.S. application Ser. No. 11/518,292, filed Sep. 8, 2006, entitled “Method and Apparatus for Building an Intelligent Automated Assistant;” (2) U.S. Provisional Application Ser. No. 61/186,414 filed Jun. 12, 2009, entitled “System and Method for Semantic Auto-Completion;” (3) U.S. application Ser. No. 13/725,512, filed Dec. 21, 2012, entitled “Active Input Elicitation by Intelligent Automated Assistant,”; (4) U.S. application Ser. No. 13/725,550, filed Dec. 21, 2012, entitled “Determining User Intent Based on Ontologies of Domains,”; (5) U.S. application Ser. No. 13/725,616, filed Dec. 21, 2012, entitled “Service Orchestration for Intelligent Automated Assistant,”; (6) U.S. application Ser. No. 13/725,656, filed Dec. 21, 2012, entitled “Prioritizing Selection Criteria by Automated Assistant,”; (7) U.S. application Ser. No. 13/725,713, filed Dec. 21, 2012, entitled “Disambiguation Based on Active Input Elicitation by Intelligent Automated Assistant,”; (8) U.S. application Ser. No. 13/784,694, filed Mar. 4, 2013, entitled “Paraphrasing of User Request by Automated Digital Assistant,”; (9) U.S. application Ser. No. 13/784,707, filed Mar. 4, 2013, entitled “Maintaining Context Information Between User Interactions with a Voice Assistant,”; (10) U.S. application Ser. No. 13/725,742, filed Dec. 21, 2012, entitled “Intent Deduction Based on Previous User Interactions with a Voice Assistant,”; and (11) U.S. application Ser. No. 13/725,761, filed Dec. 21, 2012, entitled “Using Event Alert Text as Input to an Automated Assistant,”, all of which are incorporated herein by reference in their entirety. This application is related to U.S. Provisional Patent Application Ser. No. 61/186,414 for “System and Method for Semantic Auto-Completion,” filed Jun. 12, 2009, which is incorporated herein by reference.
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| Number | Date | Country | |
|---|---|---|---|
| 20200327895 A1 | Oct 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| 61657736 | Jun 2012 | US | |
| 61295774 | Jan 2010 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 16352410 | Mar 2019 | US |
| Child | 16912278 | US | |
| Parent | 13913336 | Jun 2013 | US |
| Child | 16352410 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 12987982 | Jan 2011 | US |
| Child | 13913336 | US |
