Refining a search based on schedule items
First Claim
1. A method of operating a digital assistant, comprising:
- at a device having one or more processors and memory;
receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain;
identifying one or more schedule items relevant to the search request;
presenting the one or more schedule items to the user;
receiving a user input responsive to the presentation of the one or more schedule items, the user input specifying a search criterion associated with the one or more schedule items;
before performing the search request, augmenting the search request based on the search criterion specified in the user input and information contained in at least one of the identified schedule items, wherein the augmenting comprises modifying at least one of the one or more search criteria or adding an additional search criterion to the search request; and
presenting the augmented search request or search results retrieved based on the augmented search request to the user.
1 Assignment
0 Petitions

Accused Products

Abstract
A method includes receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain; identifying one or more schedule items relevant to the search request; augmenting the search request based on information contained in at least one of the identified schedule items; and presenting the augmented search request or search results retrieved based on the augmented search request to the user.
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50 Claims
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1. A method of operating a digital assistant, comprising:
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at a device having one or more processors and memory; receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain; identifying one or more schedule items relevant to the search request; presenting the one or more schedule items to the user; receiving a user input responsive to the presentation of the one or more schedule items, the user input specifying a search criterion associated with the one or more schedule items; before performing the search request, augmenting the search request based on the search criterion specified in the user input and information contained in at least one of the identified schedule items, wherein the augmenting comprises modifying at least one of the one or more search criteria or adding an additional search criterion to the search request; and presenting the augmented search request or search results retrieved based on the augmented search request to the user. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 29, 30, 31, 33, 35, 36, 37, 38)
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17. A method of operating a digital assistant, comprising:
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at a device having one or more processors and memory; receiving a speech input from a user; processing the speech input to infer a respective user intent, the user intent being embodied in one or more search criteria for a search request; based on the search request, generating a constrained selection task for selecting one or more selection items from a respective selection domain based on the one or more search criteria; identifying one or more schedule items relevant to the search request based on the respective selection domain and the one or more search criteria; presenting the one or more schedule items to the user; receiving a user input responsive to the presentation of the one or more schedule items, the user input specifying a search criterion associated with the one or more schedule items; before performing the search request, augmenting the search request based on the search criterion specified in the user input and information contained in at least one of the identified schedule items, wherein the augmenting comprises modifying at least one of the one or more search criteria or adding an additional search criterion to the search request; and presenting the augmented search request or search results retrieved based on the augmented search request to the user. - View Dependent Claims (18, 19, 20, 21, 22, 32, 34, 39, 40, 41, 42)
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23. A system, comprising:
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one or more processors; and memory having instructions stored thereon, the instructions, when executed by the one or more processors, cause the processors to perform; receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain; identifying one or more schedule items relevant to the search request; presenting the one or more schedule items to the user; receiving a user input responsive to the presentation of the one or more schedule items, the user input specifying a search criterion associated with the one or more schedule items; before performing the search request, augmenting the search request based on the search criterion specified in the user input and information contained in at least one of the identified schedule items, wherein the augmenting comprises modifying at least one of the one or more search criteria or adding an additional search criterion to the search request; and presenting the augmented search request or search results retrieved based on the augmented search request to the user. - View Dependent Claims (24, 25, 43, 44, 45, 46)
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26. A non-transitory computer readable medium having instructions stored thereon, the instructions, when executed by one or more processors, cause the processors to perform:
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receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain; identifying one or more schedule items relevant to the search request; presenting the one or more schedule items to the user; receiving a user input responsive to the presentation of the one or more schedule items, the user input specifying a search criterion associated with the one or more schedule items; before performing the search request, augmenting the search request based on the search criterion specified in the user input and information contained in at least one of the identified schedule items, wherein the augmenting comprises modifying at least one of the one or more search criteria or adding an additional search criterion to the search request; and presenting the augmented search request or search results retrieved based on the augmented search request to the user. - View Dependent Claims (27, 28, 47, 48, 49, 50)
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1 Specification
This application claims the benefit of U.S. Provisional Application No. 61/785,565, filed on Mar. 14, 2013, entitled REFINING A SEARCH BASED ON SCHEDULE ITEMS, which is hereby incorporated by reference in its entity for all purposes.
The disclosed embodiments relate generally to digital assistants, and more specifically to digital assistants that intelligently perform searches in one or more selection domains based on a user request.
Just like human personal assistants, digital assistants or virtual assistants can perform requested tasks and provide requested advice, information, or services. An assistant'"'"'s ability to fulfill a user'"'"'s request is dependent on the assistant'"'"'s correct comprehension of the request or instruction. Recent advances in natural language processing have enabled users to interact with digital assistants using natural language, in spoken or textual forms, rather than employing a conventional user interface (e.g., menus or programmed commands). Such digital assistants can interpret the user'"'"'s input to infer the user'"'"'s intent; translate the inferred intent into actionable tasks and parameters; execute operations or deploy services to perform the tasks; and produce outputs that are intelligible to the user. Ideally, the outputs produced by a digital assistant should fulfill the user'"'"'s intent expressed during the natural language interaction between the user and the digital assistant. A digital assistant can perform searches in a selection domain (e.g., a restaurant domain, etc.) and present qualifying selection items (e.g., restaurants) in response to a search request received from a user.
The ability of a digital assistant system to produce satisfactory responses to user requests depends on the natural language processing, knowledge base, and artificial intelligence implemented by the system. A well-designed response procedure can improve a user'"'"'s experience in interacting with the system and promote the user'"'"'s confidence in the system'"'"'s services and capabilities.
A conventional digital assistant system responds to a user'"'"'s search request based on the search criteria provided in the user'"'"'s direct input, and does not take into account of the user'"'"'s schedule items that may be relevant to the search request. The embodiments disclosed herein provide methods, systems, computer readable storage medium and user interfaces for a digital assistant to intelligently identify schedule items relevant to a search request received from a user, and use additional information obtained from these relevant schedule items to further refine the search request received from the user. Various techniques for how to identify the relevant schedule items, and how to utilize the information in the identified schedule items can be used to improve the responsiveness and efficiency of the digital assistant, making the service provided by the digital assistant more user friendly, efficient, and intuitive.
Accordingly, some embodiments provide a method for operating a digital assistant, the method including, at a device including one or more processors and memory storing one or more programs: receiving a search request from a user, the search request specifying one or more search criteria for selecting one or more selection items from a respective selection domain; identifying one or more schedule items relevant to the search request; augmenting the search request based on information contained in at least one of the identified schedule items; and presenting the augmented search request or search results retrieved based on the augmented search request to the user.
In some embodiments, the relevant schedule items include one or more calendar items from a calendar associated with the user.
In some embodiments, the relevant schedule items further include one or more calendar items from a calendar that has been shared with the user by another user.
In some embodiments, the relevant schedule items further include one or more reminder items in the user'"'"'s electronic schedule.
In some embodiments, the relevant schedule items further include or one or more to-dos in the user'"'"'s electronic schedule.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying one or more schedule items that are relevant to the search request in at least one of location, time, associated people, and subject matter.
In some embodiments, augmenting the search request based on the information contained in the at least one of the identified schedule items includes: establishing, based on the information contained in the at least one of the identified schedule items, a refinement on an existing criterion specified in the search request; and modifying the search request based on the refinement on the existing criterion.
In some embodiments, augmenting the search request based on the information contained in the at least one of the identified schedule items includes: establishing, based on the information contained in the at least one of the identified schedule items, a new search criterion that has not been specified in the search request; and modifying the search request to include the new search criterion.
In some embodiments, augmenting the search request based on the information contained in the at least one of the identified schedule items includes: establishing, based on the information contained in the at least one of the identified schedule items, a new preference order that has not been specified in the search request; and modifying the search request to include the new preference order.
In some embodiments, augmenting the search request further includes: adding a new time criterion or modifying an existing time criterion based on an event time associated with the at least one of the identified schedule items.
In some embodiments, adding the new time criterion or modifying the existing time criterion is further based on an event location associated with at least one of the identified schedule items.
In some embodiments, augmenting the search request further includes: adding a new location criterion or modifying an existing location criterion based on a respective location associated with at least one of the identified schedule items.
In some embodiments, adding the new location criterion or modifying the existing location criterion is further based on an event time associated with at least one of the identified schedule items.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying a respective group of relevant schedule items, wherein each of the respective group of relevant schedule items is associated with a respective time window overlapping with a present time window of a given duration.
In some embodiments, identifying the respective group of relevant schedule items is performed in accordance with a determination that the search request includes neither any distant time criterion nor any distant location criterion.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying an additional group of relevant schedule items, wherein each of the additional group of relevant schedule items is associated with (1) a respective time window that does not overlap with the present time window, and (2) a respective local geographic region that meets a location criterion specified in the search request.
In some embodiments, identifying the additional group of relevant schedule items is performed in accordance with a determination that the search request includes a local location criterion and does not include any time criterion.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying an additional group of relevant schedule items, wherein each of the additional group of relevant schedule items is associated with (1) a respective time window that does not overlap with the present time window, and (2) a respective local geographic region that matches a current location of the user.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying an additional group of relevant schedule items, wherein each of the additional group of relevant schedule items is associated with (1) a respective time window that does not overlap with the present time window, and (2) a respective group of attendees that overlaps with a group of relevant people specified by a respective person criterion in the search request.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying an additional group of relevant schedule items, wherein each of the additional group of relevant schedule items is associated with (1) a respective time window that does not overlap with the present time window, and (2) a respective subject matter that relates to a relevant subject matter specified by a respective selection domain criterion in the search request.
In some embodiments, identifying the additional group of relevant schedule items is performed in accordance with a determination that the search request does not include any location or time criterion.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying a respective group of relevant schedule items based on a respective distant time criterion specified in the search request, wherein each of the respective group of relevant schedule items is associated with a respective time window overlapping with a relevant time range specified by the respective distant time criterion.
In some embodiments, identifying the respective group of relevant schedule items is performed in accordance with a determination that the search request includes the respective distant time criterion and does not include any distant location criterion.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying a respective group of relevant schedule items based on a respective distant location criterion specified in the search request, wherein each of the respective group of relevant schedule items is associated with a respective geographic location overlapping with a relevant geographic location specified by the respective distant location criterion.
In some embodiments, identifying the respective group of relevant schedule items is performed in accordance with a determination that the search request includes the respective distant location criterion and does not include any distant time criterion.
In some embodiments, identifying the one or more schedule items relevant to the search request further includes: identifying a respective group of relevant schedule items based on the respective distant time criterion and the respective distant location criterion of the search request, wherein each of the respective group of relevant schedule items is associated with (1) a respective time window overlapping with the relevant time range specified by the respective distant time criterion, and (2) a respective geographic location overlapping with the relevant geographic location specified by the respective distant location criterion.
In some embodiments, identifying the respective group of relevant schedule items is performed in accordance with a determination that the search request includes the respective distant time criterion and the respective distant location criterion.
In some embodiments, identifying the one or more schedule items relevant to the respective selection domain or the one or more selection criteria further includes: determining whether the one or more search criteria include any distant time criterion or any distant location criterion, wherein a distant time criterion specifies a relevant time range outside of a present time window of a given duration, and a distant location criterion specifies a relevant geographic region outside of a local geographic region of the user; and based on an outcome of the determination, selecting a respective set of rules for identifying an initial set of potentially relevant schedule items from which the one or more relevant schedule items are identified for the search request.
In some embodiments, at a device having one or more processors and memory, a method of operating a digital assistant includes: receiving an input (e.g., a speech input) from a user; processing the speech input to infer a respective user intent, the user intent being embodied in one or more search criteria for a search request; based on the search request, generating a constrained selection task for selecting one or more selection items from a respective selection domain based on the one or more search criteria; identifying one or more schedule items relevant to the search request based on the respective selection domain and the one or more search criteria; augmenting the search request based on information contained in at least one of the identified schedule items; and presenting the augmented search request or search results retrieved based on the augmented search request to the user.
In some embodiments, the method further implements features of any combination of the methods described above and in the remainder of this specification.
The above embodiments, and other embodiments described in this specification may help realize one or more of the following advantages. In some embodiments, user intent may be clarified during the interaction between the user and the digital assistant when the relevant schedule items are presented to the user. In some embodiments, scheduling conflicts may be identified for the user, such that the user can alter the search request or rearrange his or her schedule based on the information presented by the digital assistant. In some embodiments, the digital assistant identifies schedule items that may create some synergy (e.g., convenience, efficiency, etc.) when performed in conjunction with the action hind the intent of the search request, and making the user'"'"'s actions more rewarding or more pleasant. In some embodiments, the particular manners in which the digital assistant identifies the relevant schedule items, presenting the relevant schedule items, and/or making suggestions based on the relevant schedule items are designed such that the interaction between the user and the digital assistant is smooth, efficient, and less cumbersome.
The details of one or more embodiments of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims.
Like reference numerals refer to corresponding parts throughout the drawings.
Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request is either provision of the requested informational answer, performance of the requested task, or a combination of the two. For example, a user may ask the digital assistant a question, such as “Where am I right now?” Based on the user'"'"'s current location, the digital assistant may answer, “You are in Central Park.” The user may also request the performance of a task, for example, “Please remind me to call mom at 4 pm today.” In response, the digital assistant may acknowledge the request and then creates an appropriate reminder item in the user'"'"'s electronic schedule. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.
An example of a digital assistant is described in Applicant'"'"'s U.S. Utility application Ser. No. 12/987,982 for “Intelligent Automated Assistant,” filed Jan. 10, 2011, the entire disclosure of which is incorporated herein by reference.
As shown in
In some embodiments, the DA server 106 includes a client-facing I/O interface 112, one or more processing modules 114, data and models 116, and an I/O interface to external services 118. The client-facing I/O interface facilitates the client-facing input and output processing for the digital assistant server 106. The one or more processing modules 114 utilize the data and models 116 to determine the user'"'"'s intent based on natural language input and perform task execution based on inferred user intent. In some embodiments, the DA-server 106 communicates with external services 120 through the network(s) 110 for task completion or information acquisition. The I/O interface to external services 118 facilitates such communications.
Examples of the user device 104 include, but are not limited to, a handheld computer, a personal digital assistant (PDA), a tablet computer, a laptop computer, a desktop computer, a cellular telephone, a smart phone, an enhanced general packet radio service (EGPRS) mobile phone, a media player, a navigation device, a game console, a television, a remote control, or a combination of any two or more of these data processing devices or other data processing devices. More details on the user device 104 are provided in reference to an exemplary user device 104 shown in
Examples of the communication network(s) 110 include local area networks (“LAN”) and wide area networks (“WAN”), e.g., the Internet. The communication network(s) 110 are, optionally, implemented using any known network protocol, including various wired or wireless protocols, such as e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.
The server system 108 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some embodiments, the server system 108 also employs various virtual devices and/or services of third party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of the server system 108.
Although the digital assistant shown in
For example, a motion sensor 210, a light sensor 212, and a proximity sensor 214 are coupled to the peripherals interface 206 to facilitate orientation, light, and proximity sensing functions. One or more other sensors 216, such as a positioning system (e.g., GPS receiver), a temperature sensor, a biometric sensor, a gyro, a compass, an accelerometer, and the like, are also connected to the peripherals interface 206, to facilitate related functionalities.
In some embodiments, a camera subsystem 220 and an optical sensor 222 are utilized to facilitate camera functions, such as taking photographs and recording video clips. Communication functions are facilitated through one or more wired and/or wireless communication subsystems 224, which can include various communication ports, radio frequency receivers and transmitters, and/or optical (e.g., infrared) receivers and transmitters. An audio subsystem 226 is coupled to speakers 228 and a microphone 230 to facilitate voice-enabled functions, such as voice recognition, voice replication, digital recording, and telephony functions.
In some embodiments, an I/O subsystem 240 is also coupled to the peripheral interface 206. The I/O subsystem 240 includes a touch screen controller 242 and/or other input controller(s) 244. The touch-screen controller 242 is coupled to a touch screen 246. The touch screen 246 and the touch screen controller 242 can, for example, detect contact and movement or break thereof using any of a plurality of touch sensitivity technologies, such as capacitive, resistive, infrared, surface acoustic wave technologies, proximity sensor arrays, and the like. The other input controller(s) 244 can be coupled to other input/control devices 248, such as one or more buttons, rocker switches, thumb-wheel, infrared port, USB port, and/or a pointer device such as a stylus.
In some embodiments, the memory interface 202 is coupled to memory 250. The memory 250 can include high-speed random access memory and/or non-volatile memory, such as one or more magnetic disk storage devices, one or more optical storage devices, and/or flash memory (e.g., NAND, NOR).
In some embodiments, the memory 250 stores an operating system 252, a communication module 254, a graphical user interface module 256, a sensor processing module 258, a phone module 260, and applications 262. The operating system 252 includes instructions for handling basic system services and for performing hardware dependent tasks. The communication module 254 facilitates communicating with one or more additional devices, one or more computers and/or one or more servers. The graphical user interface module 256 facilitates graphic user interface processing. The sensor processing module 258 facilitates sensor-related processing and functions. The phone module 260 facilitates phone-related processes and functions. The application module 262 facilitates various functionalities of user applications, such as electronic-messaging, web browsing, media processing, Navigation, imaging and/or other processes and functions.
As described in this specification, the memory 250 also stores client-side digital assistant instructions (e.g., in a digital assistant client module 264) and various user data 266 (e.g., user-specific vocabulary data, preference data, and/or other data such as the user'"'"'s electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant.
In various embodiments, the digital assistant client module 264 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., the I/O subsystem 244) of the user device 104. The digital assistant client module 264 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms. For example, output can be provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, the digital assistant client module 264 communicates with the digital assistant server using the communication subsystems 224.
In some embodiments, the digital assistant client module 264 utilizes the various sensors, subsystems and peripheral devices to gather additional information from the surrounding environment of the user device 104 to establish a context associated with a user, the current user interaction, and/or the current user input. In some embodiments, the digital assistant client module 264 provides the context information or a subset thereof with the user input to the digital assistant server to help infer the user'"'"'s intent. In some embodiments, the digital assistant also uses the context information to determine how to prepare and delivery outputs to the user.
In some embodiments, the context information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some embodiments, the context information also includes the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some embodiments, information related to the software state of the user device 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., of the user device 104 are provided to the digital assistant server as context information associated with a user input.
In some embodiments, the DA client module 264 selectively provides information (e.g., user data 266) stored on the user device 104 in response to requests from the digital assistant server. In some embodiments, the digital assistant client module 264 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by the digital assistant server 106. The digital assistant client module 264 passes the additional input to the digital assistant server 106 to help the digital assistant server 106 in intent inference and/or fulfillment of the user'"'"'s intent expressed in the user request.
In various embodiments, the memory 250 includes additional instructions or fewer instructions. Furthermore, various functions of the user device 104 may be implemented in hardware and/or in firmware, including in one or more signal processing and/or application specific integrated circuits.
The digital assistant system 300 includes memory 302, one or more processors 304, an input/output (I/O) interface 306, and a network communications interface 308. These components communicate with one another over one or more communication buses or signal lines 310.
In some embodiments, the memory 302 includes a non-transitory computer readable medium, such as high-speed random access memory and/or a non-volatile computer readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).
In some embodiments, the I/O interface 306 couples input/output devices 316 of the digital assistant system 300, such as displays, a keyboards, touch screens, and microphones, to the user interface module 322. The I/O interface 306, in conjunction with the user interface module 322, receive user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and process them accordingly. In some embodiments, e.g., when the digital assistant is implemented on a standalone user device, the digital assistant system 300 includes any of the components and I/O and communication interfaces described with respect to the user device 104 in
In some embodiments, the network communications interface 308 includes wired communication port(s) 312 and/or wireless transmission and reception circuitry 314. The wired communication port(s) receive and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 314 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications, optionally, use any of a plurality of communications standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. The network communications interface 308 enables communication between the digital assistant system 300 with networks, such as the Internet, an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices.
In some embodiments, memory 302, or the computer readable storage media of memory 302, stores programs, modules, instructions, and data structures including all or a subset of: an operating system 318, a communications module 320, a user interface module 322, one or more applications 324, and a digital assistant module 326. The one or more processors 304 execute these programs, modules, and instructions, and reads/writes from/to the data structures.
The operating system 318 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and/or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communications between various hardware, firmware, and software components.
The communications module 320 facilitates communications between the digital assistant system 300 with other devices over the network communications interface 308. For example, the communication module 320, optionally, communicates with the communication interface 254 of the device 104 shown in
The user interface module 322 receives commands and/or inputs from a user via the I/O interface 306 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generates user interface objects on a display. The user interface module 322 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, and light, etc.) to the user via the I/O interface 306 (e.g., through displays, audio channels, speakers, and touch-pads, etc.).
The applications 324 include programs and/or modules that are configured to be executed by the one or more processors 304. For example, if the digital assistant system is implemented on a standalone user device, the applications 324, optionally, include user applications, such as games, a calendar application, a navigation application, or an email application. If the digital assistant system 300 is implemented on a server farm, the applications 324, optionally, include resource management applications, diagnostic applications, or scheduling applications, for example.
The memory 302 also stores the digital assistant module (or the server portion of a digital assistant) 326. In some embodiments, the digital assistant module 326 includes the following sub-modules, or a subset or superset thereof: an input/output processing module 328, a speech-to-text (STT) processing module 330, a natural language processing module 332, a dialogue flow processing module 334, a task flow processing module 336, a service processing module 338, and a search augmentation module 340. Each of these modules has access to one or more of the following data and models of the digital assistant 326, or a subset or superset thereof: ontology 360, vocabulary index 344, user data 348, task flow models 354, service models 356, and user schedule data 358.
In some embodiments, using the processing modules, data, and models implemented in the digital assistant module 326, the digital assistant performs at least some of the following: identifying a user'"'"'s intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user'"'"'s intent (e.g., by disambiguating words, names, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent. In this specifications, more details regarding the search augmentation module 340 and its use of the user schedule data 358 are provided later in
In some embodiments, as shown in
The speech-to-text processing module 330 receives speech input (e.g., a user utterance captured in a voice recording) through the I/O processing module 328. In some embodiments, the speech-to-text processing module 330 uses various acoustic and language models to recognize the speech input as a sequence of phonemes, and ultimately, a sequence of words or tokens written in one or more languages. The speech-to-text processing module 330 can be implemented using any suitable speech recognition techniques, acoustic models, and language models, such as Hidden Markov Models, Dynamic Time Warping (DTW)-based speech recognition, and other statistical and/or analytical techniques. In some embodiments, the speech-to-text processing can be performed at least partially by a third party service or on the user'"'"'s device. Once the speech-to-text processing module 330 obtains the result of the speech-to-text processing, e.g., a sequence of words or tokens, it passes the result to the natural language processing module 332 for intent inference.
More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.
The natural language processing module 332 (“natural language processor”) of the digital assistant takes the sequence of words or tokens (“token sequence”) generated by the speech-to-text processing module 330, and attempts to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” represents a task that can be performed by the digital assistant, and has an associated task flow implemented in the task flow models 354. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant'"'"'s capabilities is dependent on the number and variety of task flows that have been implemented and stored in the task flow models 354, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, is also dependent on the assistant'"'"'s ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.
In some embodiments, in addition to the sequence of words or tokens obtained from the speech-to-text processing module 330, the natural language processor 332 also receives context information associated with the user request, e.g., from the I/O processing module 328. The natural language processor 332, optionally, uses the context information to clarify, supplement, and/or further define the information contained in the token sequence received from the speech-to-text processing module 330. The context information includes, for example, user preferences, hardware and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like.
In some embodiments, the natural language processing is based on ontology 360. The ontology 360 is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent, a domain concept or entity, or a sub-aspect of another property. A linkage between an actionable intent node and a property node in the ontology 360 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.
In some embodiments, the ontology 360 is made up of actionable intent nodes and property nodes. Within the ontology 360, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in
An actionable intent node, along with its linked concept nodes, may be described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships therebetween) associated with the particular actionable intent. For example, the ontology 360 shown in
While
In some embodiments, the ontology 360 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some embodiments, the ontology 360 is optionally modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 360.
In some embodiments, nodes associated with multiple related actionable intents are optionally clustered under a “super domain” in the ontology 360. For example, a “travel” super-domain optionally includes a cluster of property nodes and actionable intent nodes related to travels. The actionable intent nodes related to travels optionally includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travels” super domain) sometimes have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest” sometimes share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”
In some embodiments, each node in the ontology 360 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node is the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node can be stored in the vocabulary index 344 in association with the property or actionable intent represented by the node. For example, returning to
The natural language processor 332 receives the token sequence (e.g., a text string) from the speech-to-text processing module 330, and determines what nodes are implicated by the words in the token sequence. In some embodiments, if a word or phrase in the token sequence is found to be associated with one or more nodes in the ontology 360 (via the vocabulary index 344), the word or phrase will “trigger” or “activate” those nodes. Based on the quantity and/or relative importance of the activated nodes, the natural language processor 332 will select one of the actionable intents as the task that the user intended the digital assistant to perform. In some embodiments, the domain that has the most “triggered” nodes is selected. In some embodiments, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) is selected. In some embodiments, the domain is selected based on a combination of the number and the importance of the triggered nodes. In some embodiments, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.
In some embodiments, the digital assistant also stores names of specific entities in the vocabulary index 344, so that when one of these names is detected in the user request, the natural language processor 332 will be able to recognize that the name refers to a specific instance of a property or sub-property in the ontology. In some embodiments, the names of specific entities are names of businesses, restaurants, people, movies, and the like. In some embodiments, the digital assistant searches and identifies specific entity names from other data sources, such as the user'"'"'s address book, a movies database, a musicians database, and/or a restaurant database. In some embodiments, when the natural language processor 332 identifies that a word in the token sequence is a name of a specific entity (such as a name in the user'"'"'s address book), that word is given additional significance in selecting the actionable intent within the ontology for the user request.
For example, when the words “Mr. Santo” are recognized from the user request, and the last name “Santo” is found in the vocabulary index 344 as one of the contacts in the user'"'"'s contact list, then it is likely that the user request corresponds to a “send a message” or “initiate a phone call” domain. For another example, when the words “ABC Café” are found in the user request, and the term “ABC Café” is found in the vocabulary index 344 as the name of a particular restaurant in the user'"'"'s city, then it is likely that the user request corresponds to a “restaurant reservation” domain.
User data 348 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user'"'"'s default and secondary languages, user'"'"'s contact list, and other short-term or long-term information for each user. In some embodiments, the natural language processor 332 uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” the natural language processor 332 is able to access user data 348 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.
Other details of searching an ontology based on a token string is described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.
In some embodiments, once the natural language processor 332 identifies an actionable intent (or domain) based on the user request, the natural language processor 332 generates a structured query to represent the identified actionable intent. In some embodiments, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user may say “Make me a dinner reservation at a sushi place at seven o'"'"'clock.” In this case, the natural language processor 332 may be able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain optionally includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some embodiments, based on the information contained in the user'"'"'s utterance, the natural language processor 332 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in this example, the user'"'"'s utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some embodiments, the natural language processor 332 populates some parameters of the structured query with received context information. For example, in some embodiments, if the user requested a sushi restaurant “near me,” the natural language processor 332 populates a {location} parameter in the structured query with GPS coordinates from the user device 104.
In some embodiments, the natural language processor 332 passes the structured query (including any completed parameters) to the task flow processing module 336 (“task flow processor”). The task flow processor 336 is configured to receive the structured query from the natural language processor 332, complete the structured query, if necessary, and perform the actions required to “complete” the user'"'"'s ultimate request. In some embodiments, the various procedures necessary to complete these tasks are provided in task flow models 354. In some embodiments, the task flow models include procedures for obtaining additional information from the user, and task flows for performing actions associated with the actionable intent.
As described above, in order to complete a structured query, the task flow processor 336 sometimes needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, the task flow processor 336 invokes the dialogue processing module 334 (“dialogue processor 334”) to engage in a dialogue with the user. In some embodiments, the dialogue processor 334 determines how (and/or when) to ask the user for the additional information, and receives and processes the user responses. The questions are provided to and answers are received from the users through the I/O processing module 328. In some embodiments, the dialogue processor 334 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when the task flow processor 336 invokes the dialogue flow processor 334 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” the dialogue flow processor 335 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, the dialogue flow processor 334 can then populate the structured query with the missing information, or pass the information to the task flow processor 336 to complete the missing information from the structured query.
In some cases, the task flow processor 336 receives a structured query that has one or more ambiguous properties, e.g., when a structured query for the “send a message” domain indicate