PROACTIVE ASSISTANT WITH MEMORY ASSISTANCE

A non-transitory computer-readable storage medium stores one or more programs including instructions, which when executed by an electronic device of a user, cause the electronic device to generate at least one experiential data structure accessible to a virtual assistant; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; store at least one experiential data structure; receive a natural-language user request for service from the virtual assistant, and output information responsive to the user request using at least one experiential data structure. The experiential data structure is a data structure that includes an organized set of data associated with at least one of the user and the electronic device at a particular point in time.

FIELD

The present disclosure relates generally to a virtual assistant, and more specifically use of a virtual assistant to remember user data and generate recommendations.

BACKGROUND

Intelligent automated assistants (or digital assistants) provide a beneficial interface between human users and electronic devices. Such assistants allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can access the services of an electronic device by providing a spoken user request to a digital assistant associated with the electronic device. The digital assistant can interpret the user's intent from the spoken user request and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more services of the electronic device and a relevant output can be returned to the user in natural language form.

A digital assistant can be helpful in remembering calendar events or other reminders that have been set specifically by a user. A digital assistant also can be helpful in generating a recommendation based on a user request and on third-party reviews that are publicly available. However, digital assistants have not been useful in remembering unstructured data, or in generating recommendations for a user based on the user's experience with or without an express user request for such a recommendation.

BRIEF SUMMARY

Some techniques for remembering user data and generating recommendations, however, are generally cumbersome and inefficient. For example, existing techniques use a complex and time-consuming user interface, which may include multiple key presses or keystrokes. Such a user interface may be impractical or impossible in certain circumstances, such as when the user is operating a motor vehicle or has his or her hands full. Existing techniques require more time than necessary, wasting user time and device energy. This latter consideration is particularly important in battery-operated devices.

Accordingly, there is a need for electronic devices with faster, more efficient methods and interfaces for remembering user data and generating recommendations. Such methods and interfaces optionally complement or replace other methods for remembering user data and generating recommendations based on a nonspecific, unstructured natural language request. Such methods and interfaces reduce the cognitive burden on a user and produce a more efficient human-machine interface. For battery-operated computing devices, such methods and interfaces conserve power and increase the time between battery charges.

Example non-transitory computer-readable storage media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs, the one or more programs comprising instructions, which when executed by an electronic device of a user, cause the electronic device to: generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store at least one experiential data structure; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; receive a natural-language user request for service from the virtual assistant; and output information responsive to the user request using at least one experiential data structure.

Example electronic devices are disclosed herein. An example electronic device comprises a memory and a processor coupled to the memory. In some examples, the processor is configured to generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time. In some examples, the processor is further configured to store at least one experiential data structure, modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant, receive a natural-language user request for service from the virtual assistant, and output information responsive to the user request using at least one experiential data structure.

An example electronic device comprises a memory and a processing unit coupled to the memory. The processing unit is configured to generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store at least one experiential data structure; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; receive a natural-language user request for service from the virtual assistant; and output information responsive to the user request using at least one experiential data structure.

Example methods are disclosed herein. An example method of using a virtual assistant comprises, at an electronic device configured to transmit and receive data: generating, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; storing at least one experiential data structure; modifying at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; receiving a natural-language user request for service from the virtual assistant, and outputting information responsive to the user request using at least one experiential data structure.

Example systems are disclosed herein. An example system utilizing an electronic device comprises means for generating, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; means for storing at least one experiential data structure; means for modifying at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; means for receiving a natural-language user request for service from the virtual assistant, and means for outputting information responsive to the user request using at least one experiential data structure.

An example non-transitory computer-readable storage medium stores one or more programs, the one or more programs comprising instructions, which when executed by an electronic device of a user, cause the electronic device to: generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store at least one experiential data structure; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; based on at least one of a user context and a device context, generate a request for a recommendation from the virtual assistant without a request from the user; analyze at least one stored experiential data structure based on the generated request; and output information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

An example electronic device comprises a memory, a microphone, and a processor coupled to the memory and the microphone. In some examples, the processor configured to: generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store at least one experiential data structure; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; based on at least one of a user context and a device context, generate a request for a recommendation from the virtual assistant without a request from the user; analyze at least one stored experiential data structure based on the generated request; and output information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

An example electronic device comprises a memory and a processing unit coupled to the memory. The processing unit is configured to generate, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store at least one experiential data structure; modify at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; based on at least one of a user context and a device context, generate a request for a recommendation from the virtual assistant without a request from the user; analyze at least one stored experiential data structure based on the generated request; and output information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

An example method of using a virtual assistant comprises, at an electronic device configured to transmit and receive data: generating, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; storing at least one experiential data structure; modifying at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; based on at least one of a user context and a device context, generating a request for a recommendation from the virtual assistant without a request from the user; analyzing at least one stored experiential data structure based on the generated request; and outputting information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

An example system using an electronic device comprises means for generating, in response to a trigger, at least one experiential data structure accessible to a virtual assistant, wherein the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; means for storing at least one experiential data structure; means for modifying at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; means for based on at least one of a user context and a device context, generating a request for a recommendation from the virtual assistant without a request from the user; means for analyzing at least one stored experiential data structure based on the generated request; and means for outputting information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

Thus, devices are provided with faster, more efficient methods and interfaces for remembering user data and generating recommendations, thereby increasing the effectiveness, efficiency, and user satisfaction with such devices. Such methods and interfaces may complement or replace other methods for remembering user data and generating recommendations.

DESCRIPTION OF EMBODIMENTS

There is a need for electronic devices that provide efficient methods and interfaces for remembering user data and generating recommendations. As described above, existing techniques are not as effective as they might be, such with unstructured requests. A digital assistant can reduce the cognitive burden on a user who utilizes a digital assistant to remember user data and generate recommendations, thereby enhancing productivity. Further, such techniques can reduce processor and battery power otherwise wasted on redundant user inputs.

Below,FIGS. 1, 2A-2B, 3, 4, 5A-5B and 6A-6Bprovide a description of exemplary devices for performing the techniques for remembering user data and generating recommendations.FIGS. 7A-7Care block diagrams illustrating a digital assistant system or a server portion thereof, and a portion of an ontology associated with the digital assistant system.FIGS. 8A-8JJillustrate exemplary user interfaces for remembering user data and generating recommendations.FIGS. 9A-9Gare flow diagrams illustrating methods of remembering user data and generating recommendations in accordance with some embodiments.FIGS. 10A-10Bare a functional block diagrams of an electronic device, according to various examples.

FIG. 1illustrates a block diagram of system100according to various examples. In some examples, system100can implement a digital assistant. The terms “digital assistant,” “virtual assistant,” “intelligent automated assistant,” or “automatic digital assistant” can refer to any information processing system that interprets natural language input in spoken and/or textual form to infer user intent, and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system can perform one or more of the following: identifying a task flow with steps and parameters designed to accomplish the inferred user intent, inputting specific requirements from the inferred user intent into the task flow; executing the task flow by invoking programs, methods, services, APIs, or the like; and generating output responses to the user in an audible (e.g., speech) and/or visual form.

Specifically, a digital assistant can be 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 can seek either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request can be a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user can ask the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant can answer, “You are in Central Park near the west gate.” The user can also request the performance of a task, for example, “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user's friends listed in the user's electronic address book. During performance of a requested task, the digital assistant can sometimes interact 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 can also provide responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.

As shown inFIG. 1, in some examples, a digital assistant can be implemented according to a client-server model. The digital assistant can include client-side portion102(hereafter “DA client102”) executed on user device104and server-side portion106(hereafter “DA server106”) executed on server system108. DA client102can communicate with DA server106through one or more networks110. DA client102can provide client-side functionalities such as user-facing input and output processing and communication with DA server106. DA server106can provide server-side functionalities for any number of DA clients102each residing on a respective user device104.

In some examples, DA server106can include client-facing I/O interface112, one or more processing modules114, data and models116, and I/O interface to external services118. The client-facing I/O interface112can facilitate the client-facing input and output processing for DA server106. One or more processing modules114can utilize data and models116to process speech input and determine the user's intent based on natural language input. Further, one or more processing modules114perform task execution based on inferred user intent. In some examples, DA server106can communicate with external services120through network(s)110for task completion or information acquisition. I/O interface to external services118can facilitate such communications.

User device104can be any suitable electronic device. For example, user devices can be a portable multifunctional device (e.g., device200, described below with reference toFIG. 2A), a multifunctional device (e.g., device400, described below with reference toFIG. 4), or a personal electronic device (e.g., device600, described below with reference toFIG. 6A-B.) A portable multifunctional device can be, for example, a mobile telephone that also contains other functions, such as PDA and/or music player functions. Specific examples of portable multifunction devices can include the iPhone®, iPod Touch®, and iPad® devices from Apple Inc. of Cupertino, Calif. Other examples of portable multifunction devices can include, without limitation, laptop or tablet computers. Further, in some examples, user device104can be a non-portable multifunctional device. In particular, user device104can be a desktop computer, a game console, a television, or a television set-top box. In some examples, user device104can include a touch-sensitive surface (e.g., touch screen displays and/or touchpads). Further, user device104can optionally include one or more other physical user-interface devices, such as a physical keyboard, a mouse, and/or a joystick. Various examples of electronic devices, such as multifunctional devices, are described below in greater detail.

Examples of communication network(s)110can include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s)110can be implemented using any known network protocol, including various wired or wireless protocols, such as, for example, 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.

Server system108can be implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system108can also employ 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 server system108.

In some examples, user device104can communicate with DA server106via second user device122. Second user device122can be similar or identical to user device104. For example, second user device122can be similar to devices200,400, or600described below with reference toFIGS. 2A, 4, and 6A-B. User device104can be configured to communicatively couple to second user device122via a direct communication connection, such as Bluetooth, NFC, BTLE, or the like, or via a wired or wireless network, such as a local Wi-Fi network. In some examples, second user device122can be configured to act as a proxy between user device104and DA server106. For example, DA client102of user device104can be configured to transmit information (e.g., a user request received at user device104) to DA server106via second user device122. DA server106can process the information and return relevant data (e.g., data content responsive to the user request) to user device104via second user device122.

In some examples, user device104can be configured to communicate abbreviated requests for data to second user device122to reduce the amount of information transmitted from user device104. Second user device122can be configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server106. This system architecture can advantageously allow user device104having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server106by using second user device122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server106. While only two user devices104and122are shown inFIG. 1, it should be appreciated that system100can include any number and type of user devices configured in this proxy configuration to communicate with DA server system106.

Although the digital assistant shown inFIG. 1can include both a client-side portion (e.g., DA client102) and a server-side portion (e.g., DA server106), in some examples, the functions of a digital assistant can be implemented as a standalone application installed on a user device. In addition, the divisions of functionalities between the client and server portions of the digital assistant can vary in different implementations. For instance, in some examples, the DA client can be a thin-client that provides only user-facing input and output processing functions, and delegates all other functionalities of the digital assistant to a backend server.

2. Electronic Devices

Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant.FIG. 2Ais a block diagram illustrating portable multifunction device200with touch-sensitive display system212in accordance with some embodiments. Touch-sensitive display212is sometimes called a “touch screen” for convenience and is sometimes known as or called a “touch-sensitive display system.” Device200includes memory202(which optionally includes one or more computer-readable storage mediums), memory controller222, one or more processing units (CPUs)220, peripherals interface218, RF circuitry208, audio circuitry210, speaker211, microphone213, input/output (I/O) subsystem206, other input control devices216, and external port224. Device200optionally includes one or more optical sensors264. Device200optionally includes one or more contact intensity sensors265for detecting intensity of contacts on device200(e.g., a touch-sensitive surface such as touch-sensitive display system212of device200). Device200optionally includes one or more tactile output generators267for generating tactile outputs on device200(e.g., generating tactile outputs on a touch-sensitive surface such as touch-sensitive display system212of device200or touchpad455of device400). These components optionally communicate over one or more communication buses or signal lines203.

It should be appreciated that device200is only one example of a portable multifunction device, and that device200optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. The various components shown inFIG. 2Aare implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application-specific integrated circuits.

Memory202optionally can include one or more computer-readable storage mediums. The computer-readable storage mediums optionally can be tangible and non-transitory. Memory202optionally can include high-speed random access memory and optionally also can include non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller222optionally can control access to memory202by other components of device200.

In some examples, a non-transitory computer-readable storage medium of memory202can be used to store instructions (e.g., for performing aspects of process900, described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of process900, described below) can be stored on a non-transitory computer-readable storage medium (not shown) of the server system108or can be divided between the non-transitory computer-readable storage medium of memory202and the non-transitory computer-readable storage medium of server system108. In the context of this document, a “non-transitory computer-readable storage medium” can be any medium that can contain or store the program for use by or in connection with the instruction execution system, apparatus, or device.

Peripherals interface218can be used to couple input and output peripherals of the device to CPU220and memory202. The one or more processors220run or execute various software programs and/or sets of instructions stored in memory202to perform various functions for device200and to process data. In some embodiments, peripherals interface218, CPU220, and memory controller222optionally can be implemented on a single chip, such as chip204. In some other embodiments, they optionally can be implemented on separate chips.

Audio circuitry210, speaker211, and microphone213provide an audio interface between a user and device200. Audio circuitry210receives audio data from peripherals interface218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker211. Speaker211converts the electrical signal to human-audible sound waves. Audio circuitry210also receives electrical signals converted by microphone213from sound waves. Audio circuitry210converts the electrical signal to audio data and transmits the audio data to peripherals interface218for processing. Audio data optionally can be retrieved from and/or transmitted to memory202and/or RF circuitry208by peripherals interface218. In some embodiments, audio circuitry210also includes a headset jack (e.g.,312,FIG. 3). The headset jack provides an interface between audio circuitry210and removable audio input/output peripherals, such as output-only headphones or a headset with both output (e.g., a headphone for one or both ears) and input (e.g., a microphone).

I/O subsystem206couples input/output peripherals on device200, such as touch screen212and other input control devices216, to peripherals interface218. I/O subsystem206optionally includes display controller256, optical sensor controller258, intensity sensor controller259, haptic feedback controller261, and one or more input controllers260for other input or control devices. The one or more input controllers260receive/send electrical signals from/to other input control devices216. The other input control devices216optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s)260are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g.,308,FIG. 3) optionally include an up/down button for volume control of speaker211and/or microphone213. The one or more buttons optionally include a push button (e.g.,306,FIG. 3).

A quick press of the push button optionally can disengage a lock of touch screen212or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g.,306) optionally can turn power to device200on or off. The user optionally can be able to customize a functionality of one or more of the buttons. Touch screen212is used to implement virtual or soft buttons and one or more soft keyboards.

Touch-sensitive display212provides an input interface and an output interface between the device and a user. Display controller256receives and/or sends electrical signals from/to touch screen212. Touch screen212displays visual output to the user. The visual output optionally can include graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output optionally can correspond to user-interface objects.

Touch screen212has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen212and display controller256(along with any associated modules and/or sets of instructions in memory202) detect contact (and any movement or breaking of the contact) on touch screen212and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen212. In an exemplary embodiment, a point of contact between touch screen212and the user corresponds to a finger of the user.

A touch-sensitive display in some embodiments of touch screen212optionally can be analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen212displays visual output from device200, whereas touch-sensitive touchpads do not provide visual output.

Device200optionally also can include one or more optical sensors264.FIG. 2Ashows an optical sensor coupled to optical sensor controller258in I/O subsystem206. Optical sensor264optionally can include charge-coupled device (CCD) or complementary metal-oxide semiconductor (CMOS) phototransistors. Optical sensor264receives light from the environment, projected through one or more lenses, and converts the light to data representing an image. In conjunction with imaging module243(also called a camera module), optical sensor264optionally can capture still images or video. In some embodiments, an optical sensor is located on the back of device200, opposite touch screen display212on the front of the device so that the touch screen display optionally can be used as a viewfinder for still and/or video image acquisition. In some embodiments, an optical sensor is located on the front of the device so that the user's image optionally can be obtained for video conferencing while the user views the other video conference participants on the touch screen display. In some embodiments, the position of optical sensor264can be changed by the user (e.g., by rotating the lens and the sensor in the device housing) so that a single optical sensor264optionally can be used along with the touch screen display for both video conferencing and still and/or video image acquisition.

Device200optionally also includes one or more contact intensity sensors265.FIG. 2Ashows a contact intensity sensor coupled to intensity sensor controller259in I/O subsystem206. Contact intensity sensor265optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electric force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other intensity sensors (e.g., sensors used to measure the force (or pressure) of a contact on a touch-sensitive surface). Contact intensity sensor265receives contact intensity information (e.g., pressure information or a proxy for pressure information) from the environment. In some embodiments, at least one contact intensity sensor is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system212). In some embodiments, at least one contact intensity sensor is located on the back of device200, opposite touch screen display212, which is located on the front of device200.

Device200optionally also can include one or more proximity sensors266.FIG. 2Ashows proximity sensor266coupled to peripherals interface218. Alternately, proximity sensor266optionally can be coupled to input controller260in I/O subsystem206. Proximity sensor266optionally can perform as described in U.S. patent application Ser. No. 11/241,839, “Proximity Detector In Handheld Device”; Ser. No. 11/240,788, “Proximity Detector In Handheld Device”; Ser. No. 11/620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; Ser. No. 11/586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and Ser. No. 11/638,251, “Methods And Systems For Automatic Configuration Of Peripherals,” which are hereby incorporated by reference in their entirety. In some embodiments, the proximity sensor turns off and disables touch screen212when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).

Device200optionally also includes one or more tactile output generators267.FIG. 2Ashows a tactile output generator coupled to haptic feedback controller261in I/O subsystem206. Tactile output generator267optionally includes one or more electroacoustic devices such as speakers or other audio components and/or electromechanical devices that convert energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component that converts electrical signals into tactile outputs on the device). Contact intensity sensor265receives tactile feedback generation instructions from haptic feedback module233and generates tactile outputs on device200that are capable of being sensed by a user of device200. In some embodiments, at least one tactile output generator is collocated with, or proximate to, a touch-sensitive surface (e.g., touch-sensitive display system212) and, optionally, generates a tactile output by moving the touch-sensitive surface vertically (e.g., in/out of a surface of device200) or laterally (e.g., back and forth in the same plane as a surface of device200). In some embodiments, at least one tactile output generator sensor is located on the back of device200, opposite touch screen display212, which is located on the front of device200.

In some embodiments, the software components stored in memory202include operating system226, communication module (or set of instructions)228, contact/motion module (or set of instructions)230, graphics module (or set of instructions)232, text input module (or set of instructions)234, Global Positioning System (GPS) module (or set of instructions)235, Digital Assistant Client Module229, and applications (or sets of instructions)236. Further, memory202can store data and models, such as user data and models231. Furthermore, in some embodiments, memory202(FIG. 2A) or470(FIG. 4) stores device/global internal state257, as shown inFIGS. 2A and 4. Device/global internal state257includes one or more of: active application state, indicating which applications, if any, are currently active; display state, indicating what applications, views or other information occupy various regions of touch screen display212; sensor state, including information obtained from the device's various sensors and input control devices216; and location information concerning the device's location and/or attitude.

Operating system226(e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, 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 communication between various hardware and software components.

Communication module228facilitates communication with other devices over one or more external ports224and also includes various software components for handling data received by RF circuitry208and/or external port224. External port224(e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.

Contact/motion module230optionally detects contact with touch screen212(in conjunction with display controller256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module230includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module230receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module230and display controller256detect contact on a touchpad.

In some embodiments, graphics module232stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module232receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller256.

Haptic feedback module233includes various software components for generating instructions used by tactile output generator(s)267to produce tactile outputs at one or more locations on device200in response to user interactions with device200.

Text input module234, which optionally can be a component of graphics module232, provides soft keyboards for entering text in various applications (e.g., contacts237, e mail240, IM241, browser247, and any other application that needs text input).

GPS module235determines the location of the device and provides this information for use in various applications (e.g., to telephone238for use in location-based dialing; to camera243as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).

Digital assistant client module229can include various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module229can be capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone213, accelerometer(s)268, touch-sensitive display system212, optical sensor(s)229, other input control devices216, etc.) of portable multifunction device200. Digital assistant client module229can also be capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker211, touch-sensitive display system212, tactile output generator(s)267, etc.) of portable multifunction device200. 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, digital assistant client module229can communicate with DA server106using RF circuitry208.

User data and models231can include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user's electronic address book, to-do lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models231can includes various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.

In some examples, digital assistant client module229can utilize the various sensors, subsystems, and peripheral devices of portable multifunction device200to gather additional information from the surrounding environment of the portable multifunction device200to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module229can provide the contextual information or a subset thereof with the user input to DA server106to help infer the user's intent. In some examples, the digital assistant can also use the contextual information to determine how to prepare and deliver outputs to the user. Contextual information can be referred to as context data.

In some examples, the contextual information that accompanies the user input can include sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also include 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 examples, information related to the software state of DA server106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device200can be provided to DA server106as contextual information associated with a user input.

In some examples, the digital assistant client module229can selectively provide information (e.g., user data231) stored on the portable multifunction device200in response to requests from DA server106. In some examples, digital assistant client module229can also elicit additional input from the user via a natural language dialogue or other user interfaces upon request by DA server106. Digital assistant client module229can pass the additional input to DA server106to help DA server106in intent deduction and/or fulfillment of the user's intent expressed in the user request.

A more detailed description of a digital assistant is described below with reference toFIGS. 7A-C. It should be recognized that digital assistant client module229can include any number of the sub-modules of digital assistant module726described below.

Applications236optionally can include the following modules (or sets of instructions), or a subset or superset thereof:Contacts module237(sometimes called an address book or contact list);Telephone module238;Video conference module239;E-mail client module240;Instant messaging (IM) module241;Workout support module242;Camera module243for still and/or video images;Image management module244;Video player module;Music player module;Browser module247;Calendar module248;Widget modules249, which optionally can include one or more of: weather widget249-1, stocks widget249-2, calculator widget249-3, alarm clock widget249-4, dictionary widget249-5, and other widgets obtained by the user, as well as user-created widgets249-6;Widget creator module250for making user-created widgets249-6;Search module251;Video and music player module252, which merges video player module and music player module;Notes module253;Map module254; and/orOnline video module255.

Examples of other applications236that optionally can be stored in memory202include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, contacts module237optionally can be used to manage an address book or contact list (e.g., stored in application internal state292of contacts module237in memory202or memory470), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone238, video conference module239, e-mail240, or IM241; and so forth.

In conjunction with RF circuitry208, audio circuitry210, speaker211, microphone213, touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, telephone module238optionally can be used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication optionally can use any of a plurality of communications standards, protocols, and technologies.

In conjunction with RF circuitry208, audio circuitry210, speaker211, microphone213, touch screen212, display controller256, optical sensor264, optical sensor controller258, contact/motion module230, graphics module232, text input module234, contacts module237, and telephone module238, video conference module239includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, e-mail client module240includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module244, e-mail client module240makes it very easy to create and send e-mails with still or video images taken with camera module243.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, GPS module235, map module254, and music player module, workout support module242includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.

In conjunction with touch screen212, display controller256, optical sensor(s)264, optical sensor controller258, contact/motion module230, graphics module232, and image management module244, camera module243includes executable instructions to capture still images or video (including a video stream) and store them into memory202, modify characteristics of a still image or video, or delete a still image or video from memory202.

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, and camera module243, image management module244includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, browser module247includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, e-mail client module240, and browser module247, calendar module248includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, and browser module247, widget modules249are mini-applications that optionally can be downloaded and used by a user (e.g., weather widget249-1, stocks widget249-2, calculator widget249-3, alarm clock widget249-4, and dictionary widget249-5) or created by the user (e.g., user-created widget249-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, and browser module247, the widget creator module250optionally can be used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, search module251includes executable instructions to search for text, music, sound, image, video, and/or other files in memory202that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, audio circuitry210, speaker211, RF circuitry208, and browser module247, video and music player module252includes executable instructions that allow the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen212or on an external, connected display via external port224). In some embodiments, device200optionally includes the functionality of an MP3 player, such as an iPod (trademark of Apple Inc.).

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, and text input module234, notes module253includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.

In conjunction with RF circuitry208, touch screen212, display controller256, contact/motion module230, graphics module232, text input module234, GPS module235, and browser module247, map module254optionally can be used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

In conjunction with touch screen212, display controller256, contact/motion module230, graphics module232, audio circuitry210, speaker211, RF circuitry208, text input module234, e-mail client module240, and browser module247, online video module255includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port224), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module241, rather than e-mail client module240, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.

Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules optionally can be combined or otherwise rearranged in various embodiments. For example, video player module optionally can be combined with music player module into a single module (e.g., video and music player module252,FIG. 2A). In some embodiments, memory202optionally can store a subset of the modules and data structures identified above. Furthermore, memory202optionally can store additional modules and data structures not described above.

In some embodiments, device200is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device200, the number of physical input control devices (such as push buttons, dials, and the like) on device200optionally can be reduced.

The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device200to a main, home, or root menu from any user interface that is displayed on device200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.

FIG. 2Bis a block diagram illustrating exemplary components for event handling in accordance with some embodiments. In some embodiments, memory202(FIG. 2A) or470(FIG. 4) includes event sorter270(e.g., in operating system226) and a respective application236-1(e.g., any of the aforementioned applications237-251,255,480-490).

Event sorter270receives event information and determines the application236-1and application view291of application236-1to which to deliver the event information. Event sorter270includes event monitor271and event dispatcher module274. In some embodiments, application236-1includes application internal state292, which indicates the current application view(s) displayed on touch-sensitive display212when the application is active or executing. In some embodiments, device/global internal state257is used by event sorter270to determine which application(s) is (are) currently active, and application internal state292is used by event sorter270to determine application views291to which to deliver event information.

In some embodiments, application internal state292includes additional information, such as one or more of: resume information to be used when application236-1resumes execution, user interface state information that indicates information being displayed or that is ready for display by application236-1, a state queue for enabling the user to go back to a prior state or view of application236-1, and a redo/undo queue of previous actions taken by the user.

Event monitor271receives event information from peripherals interface218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display212, as part of a multi-touch gesture). Peripherals interface218transmits information it receives from I/O subsystem206or a sensor, such as proximity sensor266, accelerometer(s)268, and/or microphone213(through audio circuitry210). Information that peripherals interface218receives from I/O subsystem206includes information from touch-sensitive display212or a touch-sensitive surface.

In some embodiments, event monitor271sends requests to the peripherals interface218at predetermined intervals. In response, peripherals interface218transmits event information. In other embodiments, peripherals interface218transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).

In some embodiments, event sorter270also includes a hit view determination module272and/or an active event recognizer determination module273.

Hit view determination module272provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display212displays more than one view. Views are made up of controls and other elements that a user can see on the display.

Hit view determination module272receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module272identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination module272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.

Event dispatcher module274dispatches the event information to an event recognizer (e.g., event recognizer280). In embodiments including active event recognizer determination module273, event dispatcher module274delivers the event information to an event recognizer determined by active event recognizer determination module273. In some embodiments, event dispatcher module274stores in an event queue the event information, which is retrieved by a respective event receiver282.

In some embodiments, operating system226includes event sorter270. Alternatively, application236-1includes event sorter270. In yet other embodiments, event sorter270is a stand-alone module, or a part of another module stored in memory202, such as contact/motion module230.

In some embodiments, application236-1includes a plurality of event handlers290and one or more application views291, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view291of the application236-1includes one or more event recognizers280. Typically, a respective application view291includes a plurality of event recognizers280. In other embodiments, one or more of event recognizers280are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application236-1inherits methods and other properties. In some embodiments, a respective event handler290includes one or more of: data updater276, object updater277, GUI updater278, and/or event data279received from event sorter270. Event handler290optionally can utilize or call data updater276, object updater277, or GUI updater278to update the application internal state292. Alternatively, one or more of the application views291include one or more respective event handlers290. Also, in some embodiments, one or more of data updater276, object updater277, and GUI updater278are included in a respective application view291.

A respective event recognizer280receives event information (e.g., event data279) from event sorter270and identifies an event from the event information. Event recognizer280includes event receiver282and event comparator284. In some embodiments, event recognizer280also includes at least a subset of: metadata283, and event delivery instructions288(which optionally can include sub-event delivery instructions).

Event comparator284compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator284includes event definitions286. Event definitions286contain definitions of events (e.g., predefined sequences of sub-events), for example, event1(287-1), event2(287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event1(287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event2(287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers290.

In some embodiments, event definition287includes a definition of an event for a respective user-interface object. In some embodiments, event comparator284performs a hit test to determine which user-interface object is associated with a sub-event. For example, in an application view in which three user-interface objects are displayed on touch-sensitive display212, when a touch is detected on touch-sensitive display212, event comparator284performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective event handler290, the event comparator uses the result of the hit test to determine which event handler290should be activated. For example, event comparator284selects an event handler associated with the sub-event and the object triggering the hit test.

In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.

When a respective event recognizer280determines that the series of sub-events do not match any of the events in event definitions286, the respective event recognizer280enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.

In some embodiments, a respective event recognizer280includes metadata283with configurable properties, flags, and/or lists that indicate how the event delivery system should perform sub-event delivery to actively involved event recognizers. In some embodiments, metadata283includes configurable properties, flags, and/or lists that indicate how event recognizers optionally can interact, or are enabled to interact, with one another. In some embodiments, metadata283includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.

In some embodiments, a respective event recognizer280activates event handler290associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer280delivers event information associated with the event to event handler290. Activating an event handler290is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer280throws a flag associated with the recognized event, and event handler290associated with the flag catches the flag and performs a predefined process.

In some embodiments, data updater276creates and updates data used in application236-1. For example, data updater276updates the telephone number used in contacts module237, or stores a video file used in video player module. In some embodiments, object updater277creates and updates objects used in application236-1. For example, object updater277creates a new user-interface object or updates the position of a user-interface object. GUI updater278updates the GUI. For example, GUI updater278prepares display information and sends it to graphics module232for display on a touch-sensitive display.

In some embodiments, event handler(s)290includes or has access to data updater276, object updater277, and GUI updater278. In some embodiments, data updater276, object updater277, and GUI updater278are included in a single module of a respective application236-1or application view291. In other embodiments, they are included in two or more software modules.

FIG. 3illustrates a portable multifunction device200having a touch screen212in accordance with some embodiments. The touch screen optionally displays one or more graphics within user interface (UI)300. In this embodiment, as well as others described below, a user is enabled to select one or more of the graphics by making a gesture on the graphics, for example, with one or more fingers302(not drawn to scale in the figure) or one or more styluses303(not drawn to scale in the figure). In some embodiments, selection of one or more graphics occurs when the user breaks contact with the one or more graphics. In some embodiments, the gesture optionally includes one or more taps, one or more swipes (from left to right, right to left, upward and/or downward), and/or a rolling of a finger (from right to left, left to right, upward and/or downward) that has made contact with device200. In some implementations or circumstances, inadvertent contact with a graphic does not select the graphic. For example, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.

Device200optionally also can include one or more physical buttons, such as “home” or menu button304. As described previously, menu button304optionally can be used to navigate to any application236in a set of applications that optionally can be executed on device200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen212.

In one embodiment, device200includes touch screen212, menu button304, push button306for powering the device on/off and locking the device, volume adjustment button(s)308, subscriber identity module (SIM) card slot310, headset jack312, and docking/charging external port224. Push button306is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device200also accepts verbal input for activation or deactivation of some functions through microphone213. Device200also, optionally, includes one or more contact intensity sensors265for detecting intensity of contacts on touch screen212and/or one or more tactile output generators267for generating tactile outputs for a user of device200.

FIG. 4is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface in accordance with some embodiments. Device400need not be portable. In some embodiments, device400is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a child's learning toy), a gaming system, or a control device (e.g., a home or industrial controller). Device400typically includes one or more processing units (CPUs)410, one or more network or other communications interfaces460, memory470, and one or more communication buses420for interconnecting these components. Communication buses420optionally include circuitry (sometimes called a chipset) that interconnects and controls communications between system components. Device400includes input/output (I/O) interface430comprising display440, which is typically a touch screen display. I/O interface430also optionally includes a keyboard and/or mouse (or other pointing device)450and touchpad455, tactile output generator457for generating tactile outputs on device400(e.g., similar to tactile output generator(s)267described above with reference toFIG. 2A), sensors459(e.g., optical, acceleration, proximity, touch-sensitive, and/or contact intensity sensors similar to contact intensity sensor(s)265described above with reference toFIG. 2A). Memory470includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid state memory devices; and optionally includes non-volatile memory, such as one or more magnetic disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid state storage devices. Memory470optionally includes one or more storage devices remotely located from CPU(s)410. In some embodiments, memory470stores programs, modules, and data structures analogous to the programs, modules, and data structures stored in memory202of portable multifunction device200(FIG. 2A), or a subset thereof. Furthermore, memory470optionally stores additional programs, modules, and data structures not present in memory202of portable multifunction device200. For example, memory470of device400optionally stores drawing module480, presentation module482, word processing module484, website creation module486, disk authoring module488, and/or spreadsheet module490, while memory202of portable multifunction device200(FIG. 2A) optionally does not store these modules.

Each of the above-identified elements inFIG. 4optionally can be stored in one or more of the previously mentioned memory devices. Each of the above-identified modules corresponds to a set of instructions for performing a function described above. The above-identified modules or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules optionally can be combined or otherwise rearranged in various embodiments. In some embodiments, memory470optionally can store a subset of the modules and data structures identified above. Furthermore, memory470optionally can store additional modules and data structures not described above.

Attention is now directed towards embodiments of user interfaces that optionally can be implemented on, for example, portable multifunction device200.

FIG. 5Aillustrates an exemplary user interface for a menu of applications on portable multifunction device200in accordance with some embodiments. Similar user interfaces optionally can be implemented on device400. In some embodiments, user interface500includes the following elements, or a subset or superset thereof:

Signal strength indicator(s)502for wireless communication(s), such as cellular and Wi-Fi signals;Time504;Bluetooth indicator505;Battery status indicator506;Tray508with icons for frequently used applications, such as:Icon516for telephone module238, labeled “Phone,” which optionally includes an indicator514of the number of missed calls or voicemail messages;Icon518for e-mail client module240, labeled “Mail,” which optionally includes an indicator510of the number of unread e-mails;Icon520for browser module247, labeled “Browser;” andIcon522for video and music player module252, also referred to as iPod (trademark of Apple Inc.) module252, labeled “iPod;” andIcons for other applications, such as:Icon524for IM module241, labeled “Messages;”Icon526for calendar module248, labeled “Calendar;”Icon528for image management module244, labeled “Photos;”Icon530for camera module243, labeled “Camera;”Icon532for online video module255, labeled “Online Video;”Icon534for stocks widget249-2, labeled “Stocks;”Icon536for map module254, labeled “Maps;”Icon538for weather widget249-1, labeled “Weather;”Icon540for alarm clock widget249-4, labeled “Clock;”Icon542for workout support module242, labeled “Workout Support;”Icon544for notes module253, labeled “Notes;” andIcon546for a settings application or module, labeled “Settings,” which provides access to settings for device200and its various applications236.

FIG. 5Billustrates an exemplary user interface on a device (e.g., device400,FIG. 4) with a touch-sensitive surface551(e.g., a tablet or touchpad455,FIG. 4) that is separate from the display550(e.g., touch screen display212). Device400also, optionally, includes one or more contact intensity sensors (e.g., one or more of sensors457) for detecting intensity of contacts on touch-sensitive surface551and/or one or more tactile output generators459for generating tactile outputs for a user of device400.

Although some of the examples which follow will be given with reference to inputs on touch screen display212(where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown inFIG. 5B. In some embodiments, the touch-sensitive surface (e.g.,551inFIG. 5B) has a primary axis (e.g.,552inFIG. 5B) that corresponds to a primary axis (e.g.,553inFIG. 5B) on the display (e.g.,550). In accordance with these embodiments, the device detects contacts (e.g.,560and562inFIG. 5B) with the touch-sensitive surface551at locations that correspond to respective locations on the display (e.g., inFIG. 5B, 560corresponds to568and562corresponds to570). In this way, user inputs (e.g., contacts560and562, and movements thereof) detected by the device on the touch-sensitive surface (e.g.,551inFIG. 5B) are used by the device to manipulate the user interface on the display (e.g.,550inFIG. 5B) of the multifunction device when the touch-sensitive surface is separate from the display. It should be understood that similar methods are, optionally, used for other user interfaces described herein.

FIG. 6Aillustrates exemplary personal electronic device600. Device600includes body602. In some embodiments, device600can include some or all of the features described with respect to devices200and400(e.g.,FIGS. 2A-4B). In some embodiments, device600has touch-sensitive display screen604, hereafter touch screen604. Alternatively, or in addition to touch screen604, device600has a display and a touch-sensitive surface. As with devices200and400, in some embodiments, touch screen604(or the touch-sensitive surface) optionally can have one or more intensity sensors for detecting intensity of contacts (e.g., touches) being applied. The one or more intensity sensors of touch screen604(or the touch-sensitive surface) can provide output data that represents the intensity of touches. The user interface of device600can respond to touches based on their intensity, meaning that touches of different intensities can invoke different user interface operations on device600.

Techniques for detecting and processing touch intensity can be found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety.

In some embodiments, device600has one or more input mechanisms606and608. Input mechanisms606and608, if included, can be physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device600has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device600with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms optionally can permit device600to be worn by a user.

FIG. 6Bdepicts exemplary personal electronic device600. In some embodiments, device600can include some or all of the components described with respect toFIGS. 2A, 2B, and4. Device600has bus612that operatively couples I/O section614with one or more computer processors616and memory618. I/O section614can be connected to display604, which can have touch-sensitive component622and, optionally, touch-intensity sensitive component624. In addition, I/O section614can be connected with communication unit630for receiving application and operating system data, using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and/or other wireless communication techniques. Device600can include input mechanisms606and/or608. Input mechanism606optionally can be a rotatable input device or a depressible and rotatable input device, for example. Input mechanism608optionally can be a button, in some examples.

Input mechanism608optionally can be a microphone, in some examples. Personal electronic device600can include various sensors, such as GPS sensor632, accelerometer634, directional sensor640(e.g., compass), gyroscope636, motion sensor638, and/or a combination thereof, all of which can be operatively connected to I/O section614.

Memory618of personal electronic device600can be a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors616, for example, can cause the computer processors to perform the techniques described below, including process900(FIGS. 8A-D). The computer-executable instructions can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For purposes of this document, a “non-transitory computer-readable storage medium” can be any medium that can tangibly contain or store computer-executable instructions for use by or in connection with the instruction execution system, apparatus, or device. The non-transitory computer-readable storage medium can include, but is not limited to, magnetic, optical, and/or semiconductor storages. Examples of such storage include magnetic disks, optical discs based on CD, DVD, or Blu-ray technologies, as well as persistent solid-state memory such as flash, solid-state drives, and the like. Personal electronic device600is not limited to the components and configuration ofFIG. 6B, but can include other or additional components in multiple configurations.

As used here, the term “affordance” refers to a user-interactive graphical user interface object that optionally can be displayed on the display screen of devices200,400, and/or600(FIGS. 2, 4, and 6). For example, an image (e.g., icon), a button, and text (e.g., hyperlink) optionally can each constitute an affordance.

3. Digital Assistant System

FIG. 7Aillustrates a block diagram of digital assistant system700in accordance with various examples. In some examples, digital assistant system700can be implemented on a standalone computer system. In some examples, digital assistant system700can be distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant can be divided into a server portion and a client portion, where the client portion resides on one or more user devices (e.g., devices104,122,200,400, or600) and communicates with the server portion (e.g., server system108) through one or more networks, e.g., as shown inFIG. 1. In some examples, digital assistant system700can be an implementation of server system108(and/or DA server106) shown inFIG. 1. It should be noted that digital assistant system700is only one example of a digital assistant system, and that digital assistant system700can have more or fewer components than shown, optionally can combine two or more components, or optionally can have a different configuration or arrangement of the components. The various components shown inFIG. 7Acan be implemented in hardware, software instructions for execution by one or more processors, firmware, including one or more signal processing and/or application specific integrated circuits, or a combination thereof.

Digital assistant system700can include memory702, one or more processors704, input/output (I/O) interface706, and network communications interface708. These components can communicate with one another over one or more communication buses or signal lines710.

In some examples, memory702can include 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 examples, I/O interface706can couple input/output devices716of digital assistant system700, such as displays, keyboards, touch screens, and microphones, to user interface module722. I/O interface706, in conjunction with user interface module722, can receive user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system700can include any of the components and I/O communication interfaces described with respect to devices200,400, or600inFIGS. 2A, 4, 6A-B, respectively. In some examples, digital assistant system700can represent the server portion of a digital assistant implementation, and can interact with the user through a client-side portion residing on a user device (e.g., devices104,200,400, or600).

In some examples, the network communications interface708can include wired communication port(s)712and/or wireless transmission and reception circuitry714. The wired communication port(s) can receive and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry714can receive and send RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications can 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. Network communications interface708can enable communication between digital assistant system700with 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 examples, memory702, or the computer-readable storage media of memory702, can store programs, modules, instructions, and data structures including all or a subset of: operating system718, communications module720, user interface module722, one or more applications724, and digital assistant module726. In particular, memory702, or the computer-readable storage media of memory702, can store instructions for performing process900, described below. One or more processors704can execute these programs, modules, and instructions, and reads/writes from/to the data structures.

Operating system718(e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) can include 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.

Communications module720can facilitate communications between digital assistant system700with other devices over network communications interface708. For example, communications module720can communicate with RF circuitry208of electronic devices such as devices200,400, and600shown inFIG. 2A, 4, 6A-B, respectively. Communications module720can also include various components for handling data received by wireless circuitry714and/or wired communications port712.

User interface module722can receive commands and/or inputs from a user via I/O interface706(e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module722can also prepare and deliver outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface706(e.g., through displays, audio channels, speakers, touch-pads, etc.).

Applications724can include programs and/or modules that are configured to be executed by one or more processors704. For example, if the digital assistant system is implemented on a standalone user device, applications724can include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system700is implemented on a server, applications724can include resource management applications, diagnostic applications, or scheduling applications, for example.

Memory702can also store digital assistant module726(or the server portion of a digital assistant). In some examples, digital assistant module726can include the following sub-modules, or a subset or superset thereof: input/output processing module728, speech-to-text (STT) processing module730, natural language processing module732, dialogue flow processing module734, task flow processing module736, service processing module738, and speech synthesis module740. Each of these modules can have access to one or more of the following systems or data and models of the digital assistant module726, or a subset or superset thereof: ontology760, vocabulary index744, user data748, task flow models754, service models756, and ASR systems.

In some examples, using the processing modules, data, and models implemented in digital assistant module726, the digital assistant can perform at least some of the following: converting speech input into text; 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, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.

In some examples, as shown inFIG. 7B, I/O processing module728can interact with the user through I/O devices716inFIG. 7Aor with a user device (e.g., devices104,200,400, or600) through network communications interface708inFIG. 7Ato obtain user input (e.g., a speech input) and to provide responses (e.g., as speech outputs) to the user input. I/O processing module728can optionally obtain contextual information associated with the user input from the user device, along with or shortly after the receipt of the user input. The contextual information can include user-specific data, vocabulary, and/or preferences relevant to the user input. In some examples, the contextual information also includes software and hardware states of the user device at the time the user request is received, and/or information related to the surrounding environment of the user at the time that the user request was received. In some examples, I/O processing module728can also send follow-up questions to, and receive answers from, the user regarding the user request. When a user request is received by I/O processing module728and the user request can include speech input, I/O processing module728can forward the speech input to STT processing module730(or speech recognizer) for speech-to-text conversions.

STT processing module730can include one or more ASR systems. The one or more ASR systems can process the speech input that is received through I/O processing module728to produce a recognition result. Each ASR system can include a front-end speech pre-processor. The front-end speech pre-processor can extract representative features from the speech input. For example, the front-end speech pre-processor can perform a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system can include one or more speech recognition models (e.g., acoustic models and/or language models) and can implement one or more speech recognition engines. Examples of speech recognition models can include Hidden Markov Models, Gaussian-Mixture Models, Deep Neural Network Models, n-gram language models, and other statistical models. Examples of speech recognition engines can include the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines can be used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input can be processed at least partially by a third-party service or on the user's device (e.g., device104,200,400, or600) to produce the recognition result. Once STT processing module730produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result can be passed to natural language processing module732for intent deduction.

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.

In some examples, STT processing module730can include and/or access a vocabulary of recognizable words via phonetic alphabet conversion module731. Each vocabulary word can be associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words can include a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary optionally can include the word “tomato” that is associated with the candidate pronunciations of ///. Further, vocabulary words can be associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations can be stored in STT processing module730and can be associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words can be determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations can be manually generated, e.g., based on known canonical pronunciations.

In some examples, the candidate pronunciations can be ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation // can be ranked higher than //, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations can be ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations can be ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations can be associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation // can be associated with the United States, whereas the candidate pronunciation // can be associated with Great Britain. Further, the rank of the candidate pronunciation can be based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user's profile on the device. For example, it can be determined from the user's profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation // (associated with the United States) can be ranked higher than the candidate pronunciation // (associated with Great Britain). In some examples, one of the ranked candidate pronunciations can be selected as a predicted pronunciation (e.g., the most likely pronunciation).

When a speech input is received, STT processing module730can be used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module730can first identify the sequence of phonemes // corresponding to a portion of the speech input, it can then determine, based on vocabulary index744, that this sequence corresponds to the word “tomato.”

In some examples, STT processing module730can use approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module730can determine that the sequence of phonemes // corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.

In some examples, natural language processing module732can be configured to receive metadata associated with the speech input. The metadata can indicate whether to perform natural language processing on the speech input (or the sequence of words or tokens corresponding to the speech input). If the metadata indicates that natural language processing is to be performed, then the natural language processing module can receive the sequence of words or tokens from the STT processing module to perform natural language processing. However, if the metadata indicates that natural language process is not to be performed, then the natural language processing module can be disabled and the sequence of words or tokens (e.g., text string) from the STT processing module can be outputted from the digital assistant. In some examples, the metadata can further identify one or more domains corresponding to the user request. Based on the one or more domains, the natural language processor can disable domains in ontology760other than the one or more domains. In this way, natural language processing is constrained to the one or more domains in ontology760. In particular, the structure query (described below) can be generated using the one or more domains and not the other domains in the ontology.

Natural language processing module732(“natural language processor”) of the digital assistant can take the sequence of words or tokens (“token sequence”) generated by STT processing module730, and attempt to associate the token sequence with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” can represent a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models754. The associated task flow can be 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 can be dependent on the number and variety of task flows that have been implemented and stored in task flow models754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, can also be dependent on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.

In some examples, in addition to the sequence of words or tokens obtained from STT processing module730, natural language processing module732can also receive contextual information associated with the user request, e.g., from I/O processing module728. The natural language processing module732can optionally use the contextual information to clarify, supplement, and/or further define the information contained in the token sequence received from STT processing module730. The contextual information can include, 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. As described herein, contextual information can be dynamic, and can change with time, location, content of the dialogue, and other factors.

In some examples, the natural language processing can be based on, e.g., ontology760. Ontology760can be 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” can represent a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” can represent a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology760can define how a parameter represented by the property node pertains to the task represented by the actionable intent node.

In some examples, ontology760can be made up of actionable intent nodes and property nodes. Within ontology760, each actionable intent node can be linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node can be linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown inFIG. 7C, ontology760can include a “restaurant reservation” node (i.e., an actionable intent node). Property nodes “restaurant,” “date/time” (for the reservation), and “party size” can each be directly linked to the actionable intent node (i.e., the “restaurant reservation” node).

In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” can be sub-nodes of the property node “restaurant,” and can each be linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown inFIG. 7C, ontology760can also include a “set reminder” node (i.e., another actionable intent node). Property nodes “date/time” (for setting the reminder) and “subject” (for the reminder) can each be linked to the “set reminder” node. Since the property “date/time” can be relevant to both the task of making a restaurant reservation and the task of setting a reminder, the property node “date/time” can be linked to both the “restaurant reservation” node and the “set reminder” node in ontology760.

An actionable intent node, along with its linked concept nodes, can be described as a “domain.” In the present discussion, each domain can be associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology760shown inFIG. 7Ccan include an example of restaurant reservation domain762and an example of reminder domain764within ontology760. The restaurant reservation domain includes the actionable intent node “restaurant reservation,” property nodes “restaurant,” “date/time,” and “party size,” and sub-property nodes “cuisine,” “price range,” “phone number,” and “location.” Reminder domain764can include the actionable intent node “set reminder,” and property nodes “subject” and “date/time.” In some examples, ontology760can be made up of many domains. Each domain can share one or more property nodes with one or more other domains. For example, the “date/time” property node can be associated with many different domains (e.g., a scheduling domain, a travel reservation domain, a movie ticket domain, etc.), in addition to restaurant reservation domain762and reminder domain764.

WhileFIG. 7Cillustrates two example domains within ontology760, other domains can include, for example, “find a movie,” “initiate a phone call,” “find directions,” “schedule a meeting,” “send a message,” and “provide an answer to a question,” “read a list,” “providing navigation instructions,” “provide instructions for a task” and so on. A “send a message” domain can be associated with a “send a message” actionable intent node, and optionally can further include property nodes such as “recipient(s),” “message type,” and “message body.” The property node “recipient” can be further defined, for example, by the sub-property nodes such as “recipient name” and “message address.”

In some examples, ontology760can include all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology760can be modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology760.

In some examples, nodes associated with multiple related actionable intents can be clustered under a “super domain” in ontology760. For example, a “travel” super-domain can include a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel can include “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 “travel” super domain) can have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” can share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.”

In some examples, each node in ontology760can be 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 can be the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node can be stored in vocabulary index744in association with the property or actionable intent represented by the node. For example, returning toFIG. 7B, the vocabulary associated with the node for the property of “restaurant” can include words such as “food,” “drinks,” “cuisine,” “hungry,” “eat,” “pizza,” “fast food,” “meal,” and so on. For another example, the vocabulary associated with the node for the actionable intent of “initiate a phone call” can include words and phrases such as “call,” “phone,” “dial,” “ring,” “call this number,” “make a call to,” and so on. The vocabulary index744can optionally include words and phrases in different languages.

Natural language processing module732can receive the token sequence (e.g., a text string) from STT processing module730, and determine what nodes are implicated by the words in the token sequence. In some examples, if a word or phrase in the token sequence is found to be associated with one or more nodes in ontology760(via vocabulary index744), the word or phrase can “trigger” or “activate” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module732can select one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes can be selected. In some examples, the domain having the highest confidence value (e.g., based on the relative importance of its various triggered nodes) can be selected. In some examples, the domain can be selected based on a combination of the number and the importance of the triggered nodes. In some examples, 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.

User data748can include 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 examples, natural language processing module732can use 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,” natural language processing module732can be able to access user data748to 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 examples, once natural language processing module732identifies an actionable intent (or domain) based on the user request, natural language processing module732can generate a structured query to represent the identified actionable intent. In some examples, the structured query can include 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 7.” In this case, natural language processing module732can 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 can include parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module730, natural language processing module732can generate 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} optionally cannot be specified in the structured query based on the information currently available. In some examples, natural language processing module732can populate some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module732can populate a {location} parameter in the structured query with GPS coordinates from the user device.

In some examples, natural language processing module732can pass the generated structured query (including any completed parameters) to task flow processing module736(“task flow processor”). Task flow processing module736can be configured to receive the structured query from natural language processing module732, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some examples, the various procedures necessary to complete these tasks can be provided in task flow models754. In some examples, task flow models754can 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, task flow processing module736optionally can need to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module736can invoke dialogue flow processing module734to engage in a dialogue with the user. In some examples, dialogue flow processing module734can determine how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions can be provided to and answers can be received from the users through I/O processing module728. In some examples, dialogue flow processing module734can present 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 task flow processing module736invokes dialogue flow processing module734to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module734can generate questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, dialogue flow processing module734can then populate the structured query with the missing information, or pass the information to task flow processing module736to complete the missing information from the structured query.

Once task flow processing module736has completed the structured query for an actionable intent, task flow processing module736can proceed to perform the ultimate task associated with the actionable intent. Accordingly, task flow processing module736can execute the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the actionable intent of “restaurant reservation” can include steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flow processing module736can perform the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.

In some examples, task flow processing module736can employ the assistance of service processing module738(“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module738can act on behalf of task flow processing module736to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service can be specified by a respective service model among service models756. Service processing module738can access the appropriate service model for a service and generate requests for the service in accordance with the protocols and APIs required by the service according to the service model.

For example, if a restaurant has enabled an online reservation service, the restaurant can submit a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module736, service processing module738can establish a network connection with the online reservation service using the web address stored in the service model, and send the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.

In some examples, natural language processing module732, dialogue flow processing module734, and task flow processing module736can be used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent. The generated response can be a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response can be output as a speech output. In these examples, the generated response can be sent to speech synthesis module740(e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response can be data content relevant to satisfying a user request in the speech input.

Speech synthesis module740can be configured to synthesize speech outputs for presentation to the user. Speech synthesis module740synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response can be in the form of a text string. Speech synthesis module740can convert the text string to an audible speech output. Speech synthesis module740can use any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis module740can be configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string can be associated with a word in the generated dialogue response. The phonemic string can be stored in metadata associated with the word. Speech synthesis model740can be configured to directly process the phonemic string in the metadata to synthesize the word in speech form.

In some examples, instead of (or in addition to) using speech synthesis module740, speech synthesis can be performed on a remote device (e.g., the server system108), and the synthesized speech can be sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it can be possible to obtain higher quality speech outputs than would be practical with client-side synthesis.

Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference.

FIGS. 8A-8JJillustrate exemplary user interfaces for remembering user data and generating recommendations, in accordance with some embodiments. The user interfaces in these figures are used to illustrate the processes described below, including the exemplary processes inFIGS. 9A-9G.

Referring toFIG. 8A, an electronic device200includes a display212and a microphone213in accordance with some embodiments. A digital assistant, as described above, is accessed by a user, who utters unstructured natural language user input that is acquired via the microphone213. The timing of the user utterance is under the control of the user. The user input is converted from speech to text, and, in accordance with some embodiments, the textual user input1040is displayed on the display212. By displaying the textual user input1040, in accordance with some embodiments, the user can verify that the digital assistant has received correctly the request to remember user data. In other embodiments, such as but not limited to embodiments in which the digital assistant is operable in a hands-free mode, the textual user input1040is not displayed.

As illustrated in the example ofFIG. 8A, the user requests that the digital assistant remember user data: in this case, a particular wine that the user likes. The digital assistant generates at least one experiential data structure, as described below in greater detail relative toFIGS. 9A-9G. The experiential data structure is a data structure that includes an organized set of data associated with at least one of the user and the electronic device200at a particular point in time. The data is associated with items that a user wishes to remember, as well as data that has utility in generating recommendations to the user. According to some embodiments, the kinds of data that the user finds significant to remember, and has utility in generating recommendations to the user, are referred to as dimensions of the experiential data structure. The dimensions are flexible and optionally can change over time or with context, according to some embodiments. In the example ofFIG. 8A, the primary dimension is the user's daily activity dimension, which includes reminders. As described in greater detail below with regard to block906, according to some embodiments there are six primary dimensions: a social dimension, a location dimension, a media dimension, a content dimension, a photographic dimension, and a daily activity dimension. As is seen below, there can be overlap between dimensions, and a given data item can be assigned to any suitable dimension consistent with the method900. As used in this document, the six primary dimensions also are referred to interchangeably as social information, location information, media information, content information, photographic information, and daily activity information.

Next, the virtual assistant tags the experiential data structure with one or more annotations, as described below in greater detail relative toFIGS. 9A-9G. The annotations include at least one of user context and device context, as described below in greater detail relative toFIGS. 9A-9G. A number of examples are provided below, illustrating the operation of the method900ofFIGS. 9A-9Gin remembering user data and generating recommendations. In the example ofFIG. 8A, the device context includes the location of the device (e.g., map coordinates, the name of a restaurant or wine retailer). The virtual assistant tags the experiential data structure, then stores the experiential data structure. The experiential data structure is stored at the DA server106or server system108according to some embodiments, in order to reduce the load on memory storage of the electronic device200. This is particularly useful where the electronic device200is portable (e.g., a smartphone, a smart watch). According to other embodiments, at least one experiential data structure or part of an experiential data structure is stored at the electronic device200. This is particularly useful where the electronic device200has ample memory, and/or where the experiential data structure is particularly important to the user or is expected to be utilized in the near future.

As illustrated in the example ofFIG. 8B, the user requests that the digital assistant remember user data: in this case, that the user likes the carnitas tacos here. The digital assistant generates at least one experiential data structure, similar to the manner described above with regard toFIG. 8A. In the example ofFIG. 8B, one dimension of the experiential data structure is the user's daily activity dimension, which includes reminders, and another dimension of the experiential data structure is the location, which in this example is “here.” “Here” is a word that, standing alone, does not denote a particular, unique location. The digital assistant recognizes that “here” is a word with insufficient clarity to allow for the creation of an experiential data structure. However, the word “here” refers to a particular geographical location at the time of its utterance, and the virtual assistant utilizes the GPS module235and/or the map module254of the electronic device250to determine where “here” is, according to some embodiments. In some embodiments, the digital assistant generates an experiential data structure that includes the map coordinates of the location where the request to remember user data were uttered. In some embodiments, the digital assistant uses the map coordinates in conjunction with the map module to determine that the map coordinates are associated with a restaurant (e.g., Heinrich's Taqueria), and generates an experiential data structure that includes the restaurant as well as, or instead of, the map coordinates. The digital assistant tags the experiential data structure with user or device context, such as time, and stores the experiential data structure.

As illustrated inFIG. 8C, the user requests that the digital assistant remember user data: in this case, that the user likes the ham sandwich here. The digital assistant generates at least one experiential data structure in response. As described above with regard toFIG. 8B, the word “here” is ambiguous, but in the example ofFIG. 8B, was disambiguated by the use of location modules that are part of the electronic device200. In the example ofFIG. 8C, the user is sitting inside, which can cause a loss of resolution of a GPS or other locator signal, and the user is sitting at a table against the wall separating two restaurants. In this example, the digital assistant thus cannot on its own disambiguate the location between the two restaurants. Continuing the example, the digital assistant then requests additional information from the user, as seen inFIG. 8D. The electronic device200could be located at one of two restaurants, so the digital assistant makes a request1042of the user: “Where are you? You are near Sandwich Shop and Jimbo's Indian Buffet.” In response, referring toFIG. 8E, the user responds “Sandwich Shop.” The digital assistant now has enough information to generate and store an experiential data structure remembering that the user likes the ham sandwich at Sandwich Shop. Optionally, referring toFIG. 8F, the digital assistant confirms1044with a message on the display that it received the user input and has generated an experiential data structure associated with the user request.

According to some embodiments, the virtual assistant is configured to allow the user to annotate any virtual object—a photo, a song, a website, a news article, a calendar event, an electronic mail message, and/or any content that is viewable or listenable via the electronic device. Such annotations provide for a richer set of data that is usable by the virtual assistant to satisfy user requests. As one example, referring toFIG. 8FF, the display212displays a photo1088to the user, such as through a photo application. The user likes the photo, and invokes the virtual assistant and states1089to the virtual assistant that “This is a great photo of Marcus!”. In response, the virtual assistant generates an experiential data structure, and includes information (e.g., the photo dimension) associated with the fact that Marcus is in the photo. Optionally, the virtual assistant searches the user's contact list for a person named Marcus. If one is found, the virtual assistant automatically includes that information in the experiential data structure, or alternately requests the user to confirm the identity of the person (i.e., disambiguate to ensure the correct “Marcus”).

As another example, referring toFIG. 8GG, the user takes a photograph1090of a plate of spaghetti at dinner at a restaurant. The user invokes the virtual assistant and states1091that “The spaghetti at this restaurant is really good!” The virtual assistant recognizes that this statement is associated with a desire to remember the information. For example, the invocation of the virtual assistant coupled with the utterance “really good” allows the virtual assistant to infer that the user wishes to remember something. In response, the virtual assistant generates an experiential data structure, and includes information relating to spaghetti, and that the user really likes it. As set forth above, the virtual assistant optionally utilizes global positioning system information or other information to determine the location of the device at the time of creation of the experiential data structure, and uses that information to determine the name of the particular restaurant at which the user is located. For example, the restaurant is Italiano Ristorante in Santa Clara, Calif. The virtual assistant then includes that information in the experiential data structure, such as in the location dimension of that experiential data structure.

As illustrated in the examples ofFIGS. 8G-8J, the virtual assistant automatically generates experiential data structures, such at intervals of time (regular or irregular), or upon changes to dimension or context associated with the user or device. This automatic generation of experiential data structures occurs without an express user request to generate an experiential data structure. For example, referring toFIG. 8G, the user receives a message1046from Aaron asking if she is going to the meeting at 5:00. The user responds1048“yes.” The messages optionally can be SMS messages, messages in the iMessage® software feature of Apple, Inc., Cupertino, Calif., or any other kind of message. The digital assistant determines that an interaction has occurred in the social dimension and/or daily activity dimension, and generates an experiential data structure accordingly, including relevant content from the messages1046,1048. In this example, the relevant content includes the time of the meeting, the time of the communication, the person with whom messages were exchanged (Aaron), and the content of the communication.

As another example, referring toFIG. 8H, the electronic device has moved to 425 Market Street, San Francisco, Calif., from a different location. The digital assistant determines that the electronic device200has moved, such that the location dimension has changed, and generates an experiential data structure accordingly, for example including the address, the time (4:54 p.m., as seen inFIG. 8H), and the date.

As another example, referring toFIG. 8G, the user has requested electronic device200to play back media1050, such as Bach's Brandenburg Concerto #1 in F Major. Upon commencing playback, the digital assistant determines that media playback has begun, such that the media dimension has changed, and generates an experiential data structure accordingly, for example including one or more identifiers of the media, and the time and date at which media playback occurred.

Referring toFIG. 8K, as one example, the virtual assistant receives a natural-language request1052for service. In the example ofFIG. 8K, the user asks “who is in my 5 pm meeting?” In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with experiential data structures that include information associated with a 5:00 p.m. meeting on today's date. After identifying those experiential data structure(s), the virtual assistant identifies information associated with those experiential data structures (whether as dimensions, contexts, or tags) that includes names of attendees of the meeting. Referring toFIG. 8L, the virtual assistant displays that information on the display212, indicating to the user that Aaron, Marie, and Ian are attending the meeting. Referring back toFIG. 8G, in that example, an experiential data structure was generated indicating that Aaron planned to attend the 5:00 p.m. meeting; at least that experiential data structure was utilized to determine that Aaron is in the 5:00 p.m. meeting.

Referring toFIG. 8M, as one example, the virtual assistant receives a natural-language request1052for service. In the example ofFIG. 8M, the user asks “when is the last time I was in Denver?” In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with experiential data structures that include a location dimension within the Denver city limits. After identifying those experiential data structure(s), the virtual assistant identifies date and time information associated with those experiential data structures. The user may have visited Denver during Oct. 6-8 of 2014. The virtual assistant determines that the most recent experiential data structure associated with the city of Denver that was tagged with a date was tagged on Oct. 8, 2014. As used herein, the term “tagged” refers to the addition or placement of data in an experiential data structure. The virtual assistant also determines that experiential data structures associated with the city of Denver were generated on October 6 and October 7. Because the user's electronic device200was in Denver on three contiguous days, the virtual assistant infers (in a manner, for example, as previously described) that the time span between October6and October8was the last time the user was in Denver, and presents1058that information on display212as shown inFIG. 8N.

Referring toFIG. 8P, as one example, the virtual assistant receives a natural-language request1060for service. In the example ofFIG. 8P, the user asks “where was I last Monday morning?” In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with identifying the date of the previous Monday (e.g., Sep. 28, 2015) and then searches for experiential data structures tagged with a time between 12:01 am and noon on Sep. 28, 2015. After identifying those experiential data structure(s), the virtual assistant identifies location information associated with those experiential data structures. Based on those experiential data structures, the virtual assistant determines the user was at the location of his home until 8 am, was in motion until approximately 8:30 am, was at a location associated with the Breakfast Diner from 8:30 until 10:00 am, was in motion after that, and was then at a location associated with “work.” Further, the virtual assistant determines from the stored experiential data structures that the experiential data structures tagged with the location of Breakfast Diner also include information about a meeting with Bob at Breakfast Diner. The location associated with “work” may be so associated as a result of a previously-stored user input identifying a particular location as a work location, or may be automatically tagged as “work” by the virtual assistant due to the amount of time spent there and the content of communications transmitted and received there. The virtual assistant presents1062that information on display212as shown inFIG. 8N.

Referring toFIG. 8R, as one example, the virtual assistant receives a natural-language request1062for service. In the example ofFIG. 8P, the user asks “what is that Thai restaurant I like in Cupertino?” In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with searching for experiential data structures tagged with the location “Cupertino, Calif.” After identifying those experiential data structure(s), the virtual assistant identifies location information associated with those experiential data structures. The virtual assistant then determines which of those experiential data structures are tagged with or associated with data indicating a location of a restaurant, and then out of those experiential data structures, determines which are tagged with or associated with data indicating Thai cuisine. As another exemplary search strategy, the virtual assistant starts with search for experiential data structures tagged with or associated with information including a restaurant. After identifying those experiential data structure(s), the virtual assistant identifies location information of Cupertino, Calif. associated with those experiential data structures, and also determines which experiential data structures are tagged with or associated with data indicating Thai cuisine. The virtual assistant then determines which of those restaurants were tagged by the user as a restaurant that he or she liked. Alternately, in some embodiments the virtual assistant starts off looking for experiential data structures tagged with an indication that the user liked something, and then narrows the search for a Thai restaurant in Cupertino, Calif. The virtual assistant determines that the user has tagged Thai Plus Plus as a restaurant that she likes, and the virtual assistant presents1064that information on display212as shown inFIG. 8S.

Referring toFIG. 8T, as one example, the virtual assistant receives a natural-language request1068for service. In the example ofFIG. 8T, the user asks “where was I when I heard that Massive Attack song?” In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with identifying the date and time that that a song or album from Massive Attack was last played on the electronic device200, and then searches for experiential data structures tagged with that date and time. After identifying those experiential data structure(s), the virtual assistant identifies location information associated with those experiential data structures. Based on those experiential data structures, the virtual assistant determines the user was at home the last time that Massive Attack was played. The location associated with “home” may be so associated as a result of a previously-stored user input identifying a particular location as a home location, or may be automatically tagged as “home” by the virtual assistant due to the amount of time spent there and the content of communications transmitted and received there. The virtual assistant presents1070that information on display212as shown inFIG. 8U.

Referring toFIG. 8V, as one example, the virtual assistant receives a natural-language request1072for service. In the example ofFIG. 8V, the user asks “Where are my keys?” This request is at a higher level of abstraction. In response to the receipt of this request for service, the virtual assistant utilizes at least one stored experiential data structure. For example, the virtual assistant, which includes at least one of the DA client102and DA server106, searches experiential data structures stored at the electronic device200and the server system108, respectively. An exemplary search strategy starts with searching for experiential data structures tagged with the word “keys.” According to some embodiments, the search optionally can start with, or be limited to, experiential data structures generated in response to receipt of a user request. After identifying those experiential data structure(s), the virtual assistant identifies location information associated with those experiential data structures. For example, the user may have misplaced keys before, found them in a location, and requested the virtual assistant to remember the location. Depending on the overall forgetfulness of the user, the user may do so multiple times in different locations. Based on those experiential data structures, the virtual assistant determines the keys are most likely on the kitchen counter next to the toaster. In this example, the user noted3times in the past that the keys were there, and noted once that the keys were on the ottoman in the living room. By applying statistical analysis, including the dates and times the4inputs were made in the past, the virtual assistant determines that the keys are most likely on the kitchen counter next to the toaster. The virtual assistant presents1074that information on display212as shown inFIG. 8W. According to some embodiments, when the user asks the virtual assistant to remember the location of his or her keys, the virtual assistant disambiguates the location, in a manner such as that described with reference toFIG. 8D. The virtual assistant, in some embodiments, recognizes that a request to remember the location of a small item, such as “keys,” requires a finer level of location discernment than may be available to the electronic device. As a result, the virtual assistant asks the user, “where exactly are you leaving your keys?” Upon receiving a response from the user, the virtual assistant stores that information in an experiential data structure, which includes the location (e.g., “next to the toaster”), time of request (e.g., 11:45 p.m.) and item to be remembered (e.g., “keys”).

With reference toFIG. 8V, in a different example the user asks “Where are my keys?” In this example, the user's keys are attached or associated with a tracking unit, such as a Bluetooth® wireless device, radio-frequency identification device (RFID), or other tracking device. Such a tracking device is attached to a key ring in some embodiments, and/or is included within one or more keys in some embodiments. The electronic device200periodically receives a signal from the tracking device, whether in response to a request for a signal from the tracking device, or by listening periodically (e.g., once every minute, once every 10 minutes) for the signal from the tracking device. In response to such a receipt of a signal from the tracking device, the virtual assistant generates an experiential data structure that includes a location dimension associated with the location of the tracking device, and therefore the keys. When the user asks “where are my keys?”, the virtual assistant detects a signal from the tracking device, and responds based on that signal. In other embodiments, the virtual assistant identifies experiential data structures that include location dimension information associated with the tracking device. The virtual assistant determines which of those experiential data structures is most recent in time, and uses the location dimension information associated with the most recent experiential data structure to determine that the keys are most likely on the kitchen counter next to the toaster. The virtual assistant presents1074that information on display212as shown inFIG. 8W.

As with the example above, the stored experiential data structures provide an archive of locations where the keys have been located across a span of time. The virtual assistant, in some embodiments, applies statistical analysis to that data just as described above, to determine the user's most common places to leave his keys. In response, the virtual assistant provides information to the user associated with the most likely location of the keys.

Further, the electronic device (e.g., device104,200,400,600) is configured to recognize multiple tracking devices, such as RFID tags, according to some embodiments. One or more tracking devices recognizable to the electronic device104,200,400,600is associated with a particular person, in some embodiments. For example, one or more tracking devices may be associated with the user, and may be attached to or associated with the user's keys, the user's wallet, the user's glasses, and/or other objects important to the user. One or more other tracking devices may be associated with the user's spouse or significant other, and similarly may be attached to or associated with that person's keys, wallet, glasses, and/or other objects. In this way, if the user's spouse has lost his wallet, the user requests the virtual assistant to “find Jim's wallet.” As set forth above, the virtual assistant determines the location of Jim's wallet, in the same or similar manner as the virtual assistant would do for the user.

Additionally, by associating a particular person with particular RFID tags or tracking devices, the virtual assistant is able to add information about who the user is with when generating experiential data structures. By way of example, the user may be at dinner with her spouse at a restaurant. As the virtual assistant generates one or more experiential data structures associated with the dinner, it adds the name of the spouse (e.g., as determined through proximity to a tracking device on the spouse's keys, wallet, or other object) to the social dimension of that experiential data structure. At a later time, if the user forgets the name of the restaurant, she can ask “What was that restaurant I went to with Kate?” In a similar manner as described above with regard toFIGS. 8R-8S, the virtual assistant searches stored experiential data structures to determine which restaurants the user has patronized within a recent period of time, then searches those experiential data structures for information associated with Kate being at the restaurant at the same time. The virtual assistant then presents a list of one or more restaurants, optionally including information such as addresses, links to reviews, and photo thumbnails, to the user on display212.

Referring toFIG. 8HH, the user requests1092a recommendation from the virtual assistant of “Where should I eat spaghetti?” Referring back toFIG. 8GG, the user previously had taken a photograph1090of a plate of spaghetti and requested the virtual assistant to remember that it is really good. The virtual assistant generated and stored an experiential data structure that includes the photo1090, information that the user liked the spaghetti, and the name of the restaurant. Upon receiving the request1092of “where should I eat spaghetti?”, the virtual assistant searches stored experiential data structures for “spaghetti,” and upon finding them, determines for each experiential data structure whether a restaurant is associated with the term “spaghetti”. By searching for restaurants, not locations, locations such as “Grandma's house” that are not relevant to the request are not included. In other embodiments, the virtual assistant first searches stored experiential data structures for restaurants, then searches for the term “spaghetti.” In still further embodiments, the virtual assistant searches stored experiential data structures for both the term “spaghetti” and for restaurants, in order to maximize speed. Upon completion of the faster search, the other search is abandoned, and the results of the faster search are then searched further in order to respond to the user request. In the example ofFIG. 8HH, the virtual assistant finds the stored experiential data structure that includes the photo1090, information that the user liked the spaghetti, and the name of the restaurant, then displays the photo1090to the user and indicates1093that the user likes the spaghetti at Italiano Ristorante in Santa Clara.

Referring toFIG. 8X, as one example, the virtual assistant makes a recommendation without input from the user. In the context of this example, the user is at the Delhi Palacio and has the electronic device200in her possession. The electronic device200recognizes that it is at the Delhi Palacio, and passes this information to the virtual assistant. The virtual assistant searches stored experiential data structures for the location Delhi Palacio, and determines if any of those experiential data structures include stored user information relating to the user's preferences. The virtual assistant determines that one stored experiential data structure includes information that the user likes the chicken tikka masala, and that another stored experiential data structure includes information that the user likes the lentil soup. Without receiving a user request for a recommendation, the virtual assistant presents1076information on the display212as shown inFIG. 8X, reminding the user that she likes the chicken tikka masala and the lentil soup at the Delhi Palacio.

Referring toFIG. 8Y, as another example, the virtual assistant makes a recommendation without input from the user. In the context of this example, the user's calendar includes an entry for a birthday party. The virtual assistant periodically checks the calendar for upcoming events, and recognizes that the birthday party is tonight. It also recognizes the location of that birthday party (“Anne's house”). The virtual assistant includes one or more task templates, and the presence of the words “birthday party” in association with the event trigger performance of a task to remind the user to purchase a gift. The virtual assistant determines that none of the stored experiential data structures includes information that the user has purchased a birthday gift. Without receiving a user request for a recommendation, the virtual assistant presents1078information on the display212as shown inFIG. 8Y, reminding the user that he is scheduled to go to a birthday party tonight, and asking the user if he purchased a gift.

With reference toFIG. 8Z, the user asks for a recommendation from Sandwich Shop restaurant. Referring back toFIGS. 8C-8F, the user had previously generated experiential data structures tagged with the user's liking of the ham sandwich at Sandwich Shop. The virtual assistant analyzes at least one stored experiential data structure based on the user request. For example, the virtual assistant locates the at least one stored experiential data structure including a location dimension of Sandwich Shop, where that experiential data structure includes the user's like of the ham sandwich. According to some embodiments, the virtual assistant satisfies the user request based on analysis of at least one stored experiential data structure, and inFIG. 8AAreminds the user that he likes the ham sandwich.

With reference toFIG. 8BB, the user asks the virtual assistant to “Remember that I parked here.” Remembering the location of a vehicle can be challenging in a parking garage, at a shopping mall, or other location with a large capacity for vehicles. As described above with regard toFIGS. 8B and 8C, the virtual assistant first disambiguates the term “here,” meaning that the virtual assistant determines the particular location associated with the term “here.” In this example, the user (and thus the electronic device200) are located in a parking garage, where the structure interferes with the ability of the electronic device200to receive global positioning system (GPS) signals. Consequently, the virtual assistant is unable to determine the particular location associated with the term “here.” The virtual assistant then requests additional information from the user, as illustrated inFIG. 8CC, and makes a request1085of the user: “I'm sorry, but I can't determine where you are. Can you say the name of a location?” In response, referring toFIG. 8DD, the user responds “I'm in the parking garage at my dentist.” In other embodiments, the user takes a picture of the location, where the picture is stored an experiential data structure. According to some embodiments, the virtual assistant is able to disambiguate the term “here”, at least partially, based on the motion of the user prior to arriving at the parking garage. For example, the electronic device200is able to receive a GPS signal up to a time shortly before the user parks. In this example, the last location at which the GPS signal is received by the electronic device200is used to infer that the current location is in proximity to (e.g., under, or inside) the location at which the signal was last received. A BLUETOOTH® wireless pairing is used to disambiguate location, at least in part, according to other embodiments. Such a wireless pairing allows the electronic device200to use positioning information from another device, such as the user's car, that is connected to the electronic device via BLUETOOTH® wireless connectivity. According to some embodiments, after receiving location information from the user, the virtual assistant generates and stores an experiential data structure, including a location that is specified by the phrase “parking garage at my dentist, Pillar 2-B.” This information provides adequate specificity to allow the user to find his or her vehicle upon receiving that information from the virtual assistant. Referring toFIG. 8EE, in some embodiments, the virtual assistant indicates1087on the display212, and/or via audio, that the information has been received. Further, according to some embodiments, the virtual assistant searches the user's contacts, and determines that an entry is associated with the term “dentist.” The virtual assistant then acquires address information from the that contact, and populates the location dimension of the experiential data structure with that address. As illustrated inFIG. 8EE, that address may be shown to the user to confirm that the virtual assistant has acquired that information.

As illustrated above byFIGS. 8A-8JJ, and described in the text associated with those figures and further described below, a user utilizes the virtual assistant to search any dimension of the stored experiential data structures in order to satisfy a user request. As one example, a user may find music by time and/or place. As another example, a user may find a place by asking about an meeting with a person (i.e., “where was I when I met Paul?”) In this way, the virtual assistant performs actions that may be similar to the workings of human memory. Human memory is associational, and creates interconnections between things that happen at the same time. The virtual assistant uses the experiential data structures to store information across dimensions in discrete data structures separated in time. Retrieving information from those experiential data structures then occurs, in some embodiments, by using a portion of that information. That is, the virtual assistant stores and indexes data that can be retrieved using a subset of that data. Further, the virtual assistant can group experiential data structures based on any dimension and/or one or more tags. For example, if a user is visiting San Francisco for a weekend, that user may request information about food, about restaurants, photos from previous visits, and music from previous visits. To fulfill this request, the location dimension is dominant, and the time and date of the experiential data structures is less important or unimportant. For example, the user may ask the virtual assistant to show all photos she has taken in the city of San Francisco, in order to remember previous trips with friends and family. In response to that request, the virtual assistant searches stored experiential data structures that include the location of San Francisco, then searches the photo dimension of those experiential data structures to determine which photos were taken in San Francisco (or vice versa), then displays those photos to the user such as on the display212of the electronic device200. As is apparent from this example, the virtual assistant can proceed along several paths in satisfying a user request, and end at the same data set regardless of the path. In this way, retrieval of information from the experiential data structures may be referred to as path-independent.

As illustrated above byFIGS. 8A-8JJ, and described in the text associated with those figures and further described below, a user utilizes the virtual assistant to search any dimension of the stored experiential data structures in order to satisfy a user request. According to some embodiments, a recommendation made by the virtual assistant to satisfy a user request (e.g., the name and location of a restaurant, such as inFIG. 8S) is one item of several presented to the user. For example, where the user requests “Where should I eat spaghetti?”, such as inFIG. 8HH, the result ofFIG. 8JJ(Italiano Ristorante in Santa Clara) is presented in a list (at the top, or at another location) of recommendations that include recommendations from other sources, such as reviews in social media, and/or a simple list of Italian restaurants in physical proximity to the user's location (i.e., the location of the electronic device200) regardless of their reviews.

FIGS. 9A-9Gillustrate a process900for operating a digital assistant according to various examples. More specifically, process900can be implemented to remember user data and generate recommendations using a digital assistant. The process900can be performed using one or more electronic devices implementing a digital assistant. In some examples, the process900can be performed using a client-server system (e.g., system100) implementing a digital assistant. The individual blocks of the process900optionally can be distributed in any appropriate manner among one or more computers, systems, or electronic devices. For instances, in some examples, process900can be performed entirely on an electronic device (e.g., devices104,200,400, or600). References in this document to any one particular electronic device (104,200,400, or600) shall be understood to encompass all of the electronic devices (104,200,400, or600) unless one or more of those electronic devices (104,200,400or600) is excluded by the plain meaning of the text. For example, the electronic device (104,200,400or600) utilized in several examples is a smartphone. However, the process900is not limited to use with a smartphone; the process900optionally can be implemented on any other suitable electronic device, such as a tablet, a desktop computer, a laptop, or a smart watch. Electronic devices with greater computing power and greater battery life optionally can perform more of the blocks of the process900. The distribution of blocks of the process900need not be fixed, and optionally can vary depending upon network connection bandwidth, network connection quality, server load, availability of computer power and battery power at the electronic device (e.g.,104,200,400,600), and/or other factors. Further, while the following discussion describes process900as being performed by a digital assistant system (e.g., system100and/or digital assistant system700), it should be recognized that the process or any particular part of the process is not limited to performance by any particular device, combination of devices, or implementation. The description of the process is further illustrated and exemplified byFIGS. 8A-8JJ, and the description above related to those figures.

FIGS. 9A-9Fare a flow diagram900illustrating a method for remembering user data and generating recommendations using a digital assistant and an electronic device (104,200,400, or600) in accordance with some embodiments. Some operations in process900optionally can be combined, the order of some operations optionally can be changed, and some operations optionally can be omitted. In particular, optional operations indicated with dashed-line shapes inFIGS. 9A-9Foptionally can be performed in any suitable order, if at all, and need not be performed in the order set forth inFIGS. 9A-9F.

As described below, method900provides an intuitive way for remembering user data and generating recommendations using a digital assistant. The method reduces the cognitive burden on a user for remembering user data and generating recommendations using a digital assistant, thereby creating a more efficient human-machine interface. For battery-operated computing devices, enabling a user to remember user data and generate recommendations based on a nonspecific, unstructured natural-language request using a digital assistant more accurately and more efficiently conserves power and increases the time between battery charges.

At the beginning of process900, in block902, the digital assistant generates at least one experiential data structure and/or the electronic device104,200,400,600generates at least one experiential data structure accessible to the digital assistant. The experiential data structure is a data structure that includes an organized set of data associated with the user and/or the electronic device200at a particular point in time. The data is associated with items that a user wishes to remember, and data that has utility in generating recommendations to the user.

Optionally, in block904, the digital assistant and/or electronic device104,200,400,600generate a plurality of experiential data structures separated by time intervals. According to some embodiments, the time intervals are substantially regular. For example, the digital assistant and/or electronic device104,200,400,600generate a new experiential data structure every second, every thirty seconds, every minute, every five minutes, or at any other suitable interval. According to some embodiments, the user selects the time interval. More experiential data structures provide a greater resolution with regard to items to be remembered, but require more memory space. According to other embodiments, the time interval is set by the digital assistant or the electronic device104,200,400,600. According to other embodiments, the time interval is variable. For example, late at night when the electronic device104,200,400,600is stationary at home, and little to no use is made of the electronic device104,200,400,600, the digital assistant infers that the user is asleep and generate a new experiential data structure once per hour, or less. When the user wakes, the digital assistant and/or electronic device104,200,400,600begin to generate experiential data structures more frequently, and that frequency of generation increases when the user begins his or her work day.

Optionally, instead of (or in addition to) generating a new experiential data structure after an interval of time since the previous one, the digital assistant and/or electronic device104,200,400,600generate a new experiential data structure in block906when at least one dimension of the experiential data structure changes, when the device context changes, or when the user context changes. As described above with regard toFIGS. 8A-8JJ, according to some embodiments, the kinds of data that the user finds significant to remember, and has utility in generating recommendations to the user, are referred to as dimensions of the experiential data structure. The dimensions optionally can overlap with the device context and/or user context. Generally speaking, the dimension(s) of the experiential data structure are the elements of information likely to be more important in terms of remembering user data and generating recommendations. By generating a new experiential data structure when a dimension of the experiential data structure or a context changes, the digital assistant captures those changes, and can reduce the generation of experiential data structures that have negligible value in remembering user data and generating recommendations.

According to some embodiments, there are six primary dimensions: a social dimension, a location dimension, a media dimension, a content dimension, a photographic dimension, and a daily activity dimension. As is seen below, there can be overlap between dimensions, and a given data item can be assigned to any suitable dimension consistent with the method900.

According to some embodiments, the social dimension includes information associated with at least one person other than the user, such as, communications and social links between people. In some embodiments, the social dimension includes the content of email accessible by the digital assistant, such as sender information, recipient information, time sent, and message content. In some embodiments, the social dimension includes the content of text messages accessible by the digital assistant, such as sender information, recipient information, time sent, and message content. The text messages optionally can be SMS messages, messages in the iMessage® software feature of Apple, Inc., Cupertino, Calif., or any other kind of message. In some embodiments, the social dimension includes the characteristics of calendar events (for example, meetings and events) accessible by the digital assistant. The characteristics of the calendar events include the identity of the participants, the time of the calendar event, and the time of the calendar event. In some embodiments, the social dimension includes information associated with contacts accessible to the virtual assistant, such as name, address, phone number, email address, and social media connections, as well as information associated with the creation of contacts. In some embodiments, the social dimension includes notes about people that are accessible by the virtual assistant. Such notes optionally can include information about the contact's family, preferences of food, birthdays, and any other information relevant to the user and the contact.

According to some embodiments, the location dimension includes information relating to the location of the electronic device104,200,400,600, and by extension the location of the user. In some embodiments, the location dimension includes information associated with a period of time during which the electronic device104,200,400,600is generally stationary at a location, such as a restaurant, a classroom, or a church. In some embodiments, the location dimension includes information associated with a period of time during which the electronic device104,200,400,600is generally in motion. In some embodiments, the location dimension includes information associated with the frequency with which the electronic device104,200,400,600is at a particular location, such as the ice cream shop or the gym. In some embodiments, the location dimension includes information associated with a user-identified location. In some embodiments, location information includes a location of an object associated with the electronic device, such as a tracking device (e.g., an RFID tag). The location of the electronic device104,200,400,600is determined in any suitable manner. In some embodiments, the location is determined at least in part via a GPS; the virtual assistant utilizes the GPS module235and/or the map module254to determine location. In some embodiments, the location of the electronic device104,200,400,600is determined at least in part via nearby communications towers, such as cell phone signal towers, by comparing the relative signal strength from multiple towers at the electronic device104,200,400,600. In some embodiments, the location of the electronic device104,200,400,600is determined at least in part via nearby wireless communication access points compliant with the IEEE 802.11x standard. In some embodiments, the electronic device104,200,400,600is configured to receive signals from a wireless location transmitter or transmitters other than GPS, such as a Bluetooth® wireless location transmitter, or an iBeacon™ location and proximity detector of Apple, Inc., Cupertino, Calif.; the virtual assistant is configured to determine location information based on the receipt of such transmissions. In some embodiments, the location of the electronic device104,200,400,600is determined by its proximity to the electronic devices of other users, and/or by communications received from the electronic devices of other users.

According to some embodiments, the media dimension includes information relating to user media stored on the electronic device104,200,400,600or accessible to the digital assistant. The data associated with media (such as music, videos, and books) stored on the electronic device104,200,400,600includes, in some embodiments, the presence of that media, bibliographic information of that media (e.g., title, album, release date), information relating to the playback history of that media (e.g., number of times the media has been played back, date the media was last played back, date the media was added to the electronic device), and metadata relating to that media. In some embodiments, the media dimension includes information associated with a podcast (such as the podcast title, podcaster, and production date) played via the electronic device104,200,400,600. In some embodiments, the media dimension includes information associated with an electronic book (such as the title, author, and publication date) played via the electronic device104,200,400,600. In accordance with some embodiments, the user context includes media associated with the user, regardless of the storage location of the media. Such media optionally can be stored in the cloud, or optionally can be associated with a streaming music service accessible to the user, such as Apple Music or iTunes Radio' (services of Apple, Inc. of Cupertino, Calif.).

According to some embodiments, the content dimension includes information relating to one of the content and/or application streams stored on the electronic device104,200,400,600or accessible to the digital assistant. In some embodiments, the content dimension includes the browsing stream, which refers to the Internet browsing history of the user via the electronic device104,200,400,600, and the content accessed by the user via that browsing history. In some embodiments, the content dimension includes the written stream, which refers to user-generated notes and documents produced with or through the electronic device104,200,400,600. In some embodiments, the content dimension includes the application history usage stream, which includes the history of use of apps and applications at the electronic device104,200,400,600.

According to some embodiments, the photographic dimension includes information relating to photographs taken by and stored on the electronic device104,200,400,600or other location accessible to the digital assistant. In accordance with some embodiments, the photographic dimension includes metadata associated with the photograph, such as the date taken and the location taken.

According to some embodiments, the daily activity dimension includes information relating to personal day-to-day activities of the user. In accordance with some embodiments, the daily activity dimension includes reminders, such as those set by the user, stored at the electronic device104,200,400,600and/or otherwise accessible to the digital assistant. In accordance with some embodiments, the daily activity dimension includes at least one of diet and exercise information stored at the electronic device104,200,400,600and/or otherwise accessible to the digital assistant. For example, the electronic device104,200,400,600optionally can be coupled to an Apple Watch® wrist wearable device of Apple, Inc. of Cupertino, Calif., which acquires exercise information associated with a user's daily activity. In accordance with some embodiments, the daily activity dimension includes a user journal or blog stored at the electronic device104,200,400,600and/or otherwise accessible to the digital assistant.

According to some embodiments, device context includes information associated with the electronic device200itself. In some embodiments, the device context includes the location of the electronic device200. A GPS system or other system optionally can be used to localize the electronic device200, and optionally can be able to determine whether the user is moving, where the user is located (e.g., home, school, work, park, gym), and other information. In accordance with some embodiments, the electronic device200is configured to receive signals from a wireless location transmitter other than GPS, such as a Bluetooth® wireless location transmitter, or an iBeacon™ location and proximity detector of Apple, Inc., Cupertino, Calif. As one example, the digital assistant determines that the electronic device200, and thus the user, is moving at a rate of speed consistent with automobile travel. In accordance with some embodiments, the device context includes audio input from the microphone other than user speech, such as sound in the vicinity of the electronic device200. The electronic device, according to some embodiments, generates an acoustic fingerprint from that sound. An acoustic fingerprint is a condensed digital summary, generated from that sound, that can be used to identify that sound by comparing that acoustic fingerprint to a database. The electronic device, in other embodiments, also or instead converts that sound to text, where that sound includes recognizable speech. According to some embodiments, device context includes proximity of the electronic device104,200,400,600to a second electronic device, which in some embodiments is a smart watch such as the Apple Watch® wrist wearable device of Apple, Inc. of Cupertino, Calif.; the Apple TV® digital media extender of Apple, Inc. of Cupertino, Calif.; a home automation device;or other electronic device. According to some embodiments, the device context includes the connectivity status of one or more wireless networks at the electronic device104,200,400,600.

User context includes information associated with the user of the electronic device200. In accordance with some embodiments, user context includes demographic information about the user, such as the user's age, gender, or the like. In accordance with some embodiments, the user context includes specific locations associated with the user, such as “home,” “work,” “Mom's house,” and/or other locations that are defined by their association with the user in addition to their physical address and/or map coordinates.

Returning to method900, optionally at block908the electronic device104,200,400,600and/or digital assistant receive a user request to generate at least one experiential data structure. Such a request corresponds to, for example,FIGS. 8A-8C, in which the user expressly requests that the digital assistant remember information. In some embodiments, the user request optionally can be implied rather than express. In response to receipt of that user request, the electronic device104,200,400,600and/or the digital assistant generates at least one experiential data structure in block910.

Next, at block922, at least one experiential data structure is stored. As described above, experiential data structures are stored at the electronic device104,200,400,600and/or server system108, or any other location accessible to the digital assistant that includes the client-side DA client102or the server-side DA server106. Optionally, referring to block924, at least one experiential data structure is stored for a fixed period of time, such as 1 month, 1 year, or 10 years. Different experiential data structures optionally are stored for different amounts of time, depending on their contents, according to some embodiments. Referring to block926, optionally the fixed period of time of block924is set independent of the user. For example, the virtual assistant controls the amount of time the stored experiential data structures are retained, based on data it requires to satisfy user requests, and the frequency of certain types of user requests (e.g., requests referring to or requiring data from the far past), according to some embodiments. Alternately, according to some embodiments, optionally the virtual assistant receives in block928a period of time selected by the user, and in block930sets the fixed period of time of block930in accordance with the selection received from the user. For example, for privacy reasons, the user may desire that personal data contained in the experiential data structure is deleted sooner than a default time setting provided by the virtual assistant. The storing operations of block922optionally can be performed at any time in the method900, and/or repeated at any suitable time or location. The storage is short term storage, long term storage, or any other suitable storage that effectuates the performance of the method900.

Next, referring toFIG. 8B, at block912, utilizing the virtual assistant, at least one experiential data structure is modified with one or more annotations associated with the experiential data structure. Where the contents of the experiential data structure are sufficient to describe fully the information needed to remember user data and/or generate recommendations, annotations need not be associated with the experiential data structure. Further, the modifying of block912optionally can be performed as part of the generating of block902. The annotations may have any content associated with the experiential data structure, and one or more annotations optionally can be made to an experiential data structure as needed to describe fully a particular experiential data structure. For example, optionally at least one experiential data structure is tagged based on at least one device context (as described above) in block914. In some embodiments, optionally modifying is performed automatically. A change in device context is detected in block915. For example, the GPS coordinates of the electronic device104,200,400,600change by a non-trivial amount, which is detected in block915. In block916, in response to detection of the change in device context in block915, at least one experiential data structure is modified based on that changed device context. As another example, optionally at least one experiential data structure is tagged based on at least one user context (as described above) in block918. In some embodiments, optionally modifying is performed automatically. A change in user context is detected in block935. For example, personal information about the user changes. In block936, in response to detection of the change in user context in block935, at least one experiential data structure is modified based on that changed user context. Optionally, at least one experiential data structure is tagged based on express user input, in block938. Optionally, at block939, the virtual assistant analyzes the content of the express user input. Such analysis is a standard semantic analysis in the context of natural language processing, according to some embodiments. Other or additional analysis is performed on the content of the express user input at block939, according to some embodiments. At block940, the virtual assistant determines, based on the analysis of block939, whether that express user input is ambiguous. If the express user input is not ambiguous, then the method continues to block942, in which the electronic device104,200,400,600and/or digital assistant perform the action to modify at least one experiential data structure, after which the method continues to block912. If the express user input is ambiguous, the method continues to block944, where the virtual assistant requests additional information from the user to disambiguate the user input. For example, as seen inFIG. 8C, the user wishes to annotate an experiential data structure with a liking of a ham sandwich. However, as described in the examples above, disambiguation is required, so the virtual assistant performs block944as seen inFIG. 8D. Additional information is received from the user in block945. Next, in block946, based at least in part on the additional information received from the user (such as seen inFIG. 8E, for example), the virtual assistant performs the action to modify at least one experiential data structure, as confirmed by the virtual assistant inFIG. 8F, after which the method continues to block912.

In block932, the digital assistant receives from the user a natural-language request for service. Optionally, the method900proceeds to block965, referring also toFIG. 9E. At block939, the virtual assistant analyzes the content of the natural-language request for service. Such analysis is a standard semantic analysis in the context of natural language processing, according to some embodiments. Other or additional analysis is performed on the content of the natural-language request for service at block939, according to some embodiments. At block966, the virtual assistant determines, based on the analysis of block965, whether that natural-language request for service is ambiguous. If the natural-language request for service is not ambiguous, then the method continues to block968, where the virtual assistant then proceeds to output information responsive to the natural-language request for service. If the express user input is ambiguous, the method continues to block970, where the virtual assistant requests additional information from the user to disambiguate the user input. For example, in the example ofFIG. 8R, the user may have tagged no Thai restaurants in Cupertino. In that case, the virtual assistant searches for experiential data structures including a user annotation associated with a Thai restaurant the user liked, and finds two in nearby cities, one in Sunnyvale and one in San Jose. The virtual assistant then, in block970, informs the user that there are no Thai restaurants in Cupertino that the user indicated she liked, but there are Thai restaurants she liked in Sunnyvale and San Jose, and requests that the user select one. The user, in response, selects Sunnyvale, and that information is received in block972. Based in part on that additional information received in block972, optionally in block974the virtual assistant satisfies the user request by displaying the name, address, and other information associated with the Thai restaurant in Sunnyvale that the user likes.

Referring also toFIG. 9D, next, the method900outputs information responsive to the user request of block932at block948, using at least one stored experiential data structure. Such information may be output in any format, such as but not limited to visually on the display212, or as audio output through the speaker211. As used in this document and as is commonly understood by those skilled in the art, the terms “satisfy” and “fulfill” a user request are synonymous with output of information responsive to the user request. Optionally, the digital assistant analyzes at least one experiential data structure based on the user request at block950. In some embodiments, this analysis optionally can include matching the user request directly to one or more stored experiential data structures in block952. The direct matching of block952means that the one or more stored experiential data structures include all of the information responsive to the user request. For example, the request ofFIG. 8Kis met by finding and analyzing experiential data structure(s) associated with the 4:00 p.m. meeting that include the names of the other attendees. According to other embodiments, the virtual assistant utilizes at least one element of the user request to infer at least one additional element in block956. For example, with respect toFIG. 8T, as described above, no experiential data structures expressly include the answer to the user request. The virtual assistant uses the band name “Massive Attack” to search experiential data structures that include a media dimension associated with Massive Attack, then infers the user request relating to a “Massive Attack” song refers to the last time such a song was played. Referring to block958, the virtual assistant repeats the generation, and determines which experiential data structure that includes a media dimension associated with Massive Attack is the latest in time, then finds the location dimension in that experiential data structure. That is, the virtual assistant optionally repeats the instructions to generate at least one further additional element, one or more additional times in order to find the data in the stored experiential data structures that is capable of satisfying the user request. Optionally, the Optionally, referring to block960, the virtual assistant in some embodiments performs a statistical analysis on a plurality of stored experiential data structures based on at least one element of the user request. As a simple example, referring toFIG. 8Vand the discussion of that figure above, the user noted three times in the past that the keys were there, and noted once that the keys were on the ottoman in the living room. By applying statistical analysis, including the dates and times the four inputs were made in the past, the virtual assistant determines that the keys are most likely on the kitchen counter next to the toaster. As another example, referring to the second example making user ofFIG. 8Vin which an experiential data structure was generated periodically that included the location of a tracking device associated with the keys, the virtual assistant applies statistical analysis to those stored experiential data structures to determine the location or locations at which the user generally leaves the keys, without requiring that the user expressly input the location of the keys. A tracking unit, such as a Bluetooth® wireless tracking unit, has the advantage of providing finer location resolution than possible with GPS or similar location systems alone. Greater degrees of sophistication can be applied to larger sets of experiential data structures as needed to satisfy a user request.

Optionally, in some embodiments, in block976the virtual assistant receives a user request for a recommendation. For example, referring toFIG. 8Z, the user asks for a recommendation from Sandwich Shop restaurant. Referring back toFIGS. 8C-8F, the user had previously generated experiential data structures tagged with the user's liking of the ham sandwich at Sandwich Shop. In block978, the virtual assistant analyzes at least one stored experiential data structure based on the user request. For example, the virtual assistant locates the at least one stored experiential data structure including a location dimension of Sandwich Shop, where that experiential data structure includes the user's preference for the ham sandwich. The virtual assistant, in block984, optionally satisfies the user request based on analysis of at least one stored experiential data structure, and inFIG. 8JJreminds the user that he likes the ham sandwich. Optionally, in block980, the virtual assistant accesses tags associated with anonymized stored experiential data structures of other users, and analyzes those anonymized stored experiential data structures based on the user request in block982. Access to a large number of anonymized stored experiential data structures of other users is helpful in generating recommendations whether or not the user has expressed a liking of an item at that location in the past. For example, 96% of all diners at Sandwich Shop generated experiential data structures to remind them that they love the hot pastrami sandwich with piquillo peppers and bacalao. The user may not have considered this sandwich, and receiving a recommendation from the virtual assistant based on the stored experiential data structures of other users helps the user not to miss out on a delicious lunch. As another example, the user may want a recommendation of a dish at a restaurant where she has never eaten, and receiving a recommendation from the virtual assistant based on the stored experiential data structures of other users helps the user make a decision in the absence of any expressed previous user preference. Where blocks980and982are implemented, they end in block984, where at least one stored experiential data structure was anonymized and from someone other than the user.

Optionally, in some embodiments, the virtual assistant anonymizes at least one experiential data structure of the user in block986, then transmits at least one anonymized tagged experiential data structure from the electronic device104,200,400,600in block988. In this way, just as anonymized stored experiential data structures of other users were used in optional blocks980and982to satisfy a user request, the anonymized store experiential data structure(s) of the user can be aggregated with those of a wider user population in order to satisfy the requests of other users.

In accordance with some embodiments,FIG. 10Ashows an exemplary functional block diagram of an electronic device1100configured in accordance with the principles of the various described embodiments. In accordance with some embodiments, the functional blocks of electronic device1100are configured to perform the techniques described above. The functional blocks of the device1100are, optionally, implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described examples. It is understood by persons of skill in the art that the functional blocks described inFIG. 10Aare, optionally, combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein.

As shown inFIG. 11, an electronic device1100optionally includes a display unit1102configured to display a graphic user interface; optionally, a touch-sensitive surface unit1104configured to receive contacts; optionally, a microphone unit1106configured to receive audio signals; and a processing unit1108coupled optionally to one or more of the display unit1102, the touch-sensitive surface unit1104, and microphone unit1106. In some embodiments, the processing unit1108includes a generating unit1110, a modifying unit1112, a storing unit1114, a receiving unit1116, an outputting unit1118, and optionally, a determining unit1120, a requesting unit1122, an analyzing unit1124, a matching unit1126, a detecting unit1128, an accessing unit1130, an anonymizing unit1132, a transmitting unit1134, and a setting unit1136.

The processing unit1108is configured to generate (e.g., with generating unit1110) at least one experiential data structure accessible to a virtual assistant, where the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store (e.g., with the storing unit1114) at least one experiential data structure; modify (e.g., with the modifying unit1112) at least one experiential data structure with one or more annotations associated with the experiential data structure utilizing the virtual assistant; receive (e.g., with the receiving unit1116), a natural-language user request for service from the virtual assistant; and output (e.g., with the outputting unit1118) information responsive to the user request using at least one experiential data structure.

In some embodiments, the processing unit1108is further configured to generate (e.g., with the generating unit1110) an experiential data structure upon the passage of each time interval, where the trigger is the passage of a time interval.

In some embodiments, the processing unit1108is further configured to modify (e.g., with the modifying unit1112) at least one experiential data structure based on at least one device context.

In some embodiments, the processing unit1108is further configured to detect (e.g., with the detecting unit1128) a change in device context and, in response to detection of a change in device context, modify (e.g., with the modifying unit1112) at least one experiential data structure based on at least one changed device context.

In some embodiments, the device context includes a location of the device.

In some embodiments, the device context includes motion of the device.

In some embodiments, the device context includes proximity to a second electronic device.

In some embodiments, the processing unit1108is further configured to modify (e.g., with the modifying unit1112) at least one experiential data structure based on at least one user context.

In some embodiments, the processing unit1108is further configured to detect (e.g., with the detecting unit1128) a change in user context and, in response to detection of a change in user context, modify (e.g., with the modifying unit1112) at least one experiential data structure based on at least one changed user context.

In some embodiments, the user context includes personal information associated with the user.

In some embodiments, the user context includes locations associated with the user.

In some embodiments, the processing unit1108is further configured to receive (e.g., with the receiving unit1116) an express user request to generate at least one experiential data structure and, in response to receipt of the express user request, generate (e.g., with the generating unit1110) at least one experiential data structure, where the trigger is a user request.

In some embodiments, the processing unit1108is further configured to modify (e.g., with the modifying unit1112) the at least one experiential data structure based on express user input.

In some embodiments, the processing unit1108is further configured to analyze (e.g., with the analyzing unit1124) the content of the express user input; based on the analysis of the content of the express user input, determine (e.g., with the determining unit1120) whether the user request is ambiguous; in accordance with a determination that the user request is other than ambiguous, perform the action to modify at least one experiential data structure; and in accordance with a determination that the user request is ambiguous: request (e.g., with the requesting unit1122) additional information from the user to disambiguate; receive (e.g., with the receiving unit1116) the additional information from the user; and based in part on the additional information from the user, perform the action to modify at least one experiential data structure.

In some embodiments, at least one experiential data structure includes social information comprising information associated with at least one person other than the user.

In some embodiments, the social information includes the content of email accessible to the virtual assistant.

In some embodiments, the social information includes the content of text messages accessible by the virtual assistant.

In some embodiments, the social information includes the characteristics of calendar events accessible by the virtual assistant.

In some embodiments, the social information includes contacts accessible by the virtual assistant.

In some embodiments, the social information includes notes about people accessible by the virtual assistant.

In some embodiments, at least one experiential data structure includes location information.

In some embodiments, the location information includes information associated with a period of time during which the electronic device is generally stationary at a location.

In some embodiments, the location information includes information associated with a period of time during which the electronic device is generally in motion.

In some embodiments, the location information includes information associated with the frequency with which the electronic device is at a particular location.

In some embodiments, the location information includes information associated with a user-identified location.

In some embodiments, the location information includes a location of an object associated with the electronic device.

In some embodiments, at least one experiential data structure includes media information.

In some embodiments, the media information includes information associated with a podcast played via the electronic device.

In some embodiments, the media information includes information associated with music played via the electronic device.

In some embodiments, the media information includes information associated with video played via the electronic device.

In some embodiments, at least one experiential data structure includes content information.

In some embodiments, the content information includes a browser history of the electronic device.

In some embodiments, the content information includes content received through a browser at the electronic device.

In some embodiments, the content information includes documents generated by the user with the electronic device.

In some embodiments, the content information includes a history of application usage at the electronic device.

In some embodiments, at least one experiential data structure includes photographic information.

In some embodiments, at least one experiential data structure includes daily activity information.

In some embodiments, the daily activity information includes reminders accessible to the virtual assistant.

In some embodiments, the daily activity information includes at least one of diet and exercise information accessible to the virtual assistant.

In some embodiments, the daily activity information includes user journal information accessible to the virtual assistant.

In some embodiments, the processing unit1108is further configured to generate (e.g., with the generating unit1110) at least one new experiential data structure when at least one of the items of information of the experiential data structure, the device context, and the user context changes.

In some embodiments, the processing unit1108is further configured to receive (e.g., with the receiving unit1116) a user request for service from the virtual assistant associated with at least one stored experiential data structure, analyze (e.g., with the analyzing unit1124) at least one stored experiential data structure based on at least one element of the user request, and output (e.g., with the outputting unit1118) information responsive to the user request based on the analysis of at least stored one experiential data structure.

In some embodiments, the processing unit1108is further configured to match (e.g., with the matching unit1126) the user request directly to one or more stored experiential data structures.

In some embodiments, the processing unit1108is further configured to generate (e.g., with the generating unit1110) at least one additional element based on at least one element of the user request and match (e.g., with the matching unit1126the generated element to at least one stored experiential data structure.

In some embodiments, the processing unit1108is further configured to generate (e.g., with the generating unit1110) at least one further additional element, based on the at least one additional element and repeat the instruction to generate at least one further additional element, based on the at least one additional element, at least one additional time.

In some embodiments, analyzing at least one stored experiential data structure based on the user request includes analyzing (e.g., with the analyzing unit1124) statistically a plurality of experiential data structures based on at least one element of the user request.

In some embodiments, the processing unit1108is further configured to analyze (e.g., with the analyzing unit1124) the content of the user request; based on the analysis of the user request, determine (e.g., with the determining unit1120) whether the user request is ambiguous; in accordance with a determination that the user request is other than ambiguous, proceed to output information responsive to the user request; and in accordance with a determination that the user request is ambiguous: request (e.g., with the requesting unit1122) additional information from the user to disambiguate; receive (e.g., with the receiving unit1116) the additional information from the user; and based in part on the additional information from the user, proceed to output information responsive to the user request.

In some embodiments, the processing unit1108is further configured to receive (e.g., with the receiving unit1116) a user request for a recommendation from the virtual assistant, analyze (e.g., with the analyzing unit1124) at least one stored experiential data structure based on the user request, and output (e.g., with the outputting unit1118) information responsive to the user request based on the analysis of the at least one stored experiential data structure.

In some embodiments, analyzing at least one stored experiential data structure based on the user request, includes accessing (e.g., with the accessing unit1130), using the virtual assistant, tags associated with anonymized stored experiential data structures of other users and analyzing (e.g., with the analyzing unit1124), using the virtual assistant, the anonymized stored experiential data structures of other users based on the user request.

In some embodiments, the processing unit1108is further configured to anonymize (e.g., with the anonymizing unit1132) at least one experiential data structure and transmit (e.g., with the transmitting unit1134) at least one anonymized experiential data structure from the electronic device.

In some embodiments, the processing unit1108is further configured to store (e.g., with the storing unit1114) at least one experiential data structure for a fixed period of time.

In some embodiments, the processing unit1108is further configured to set (e.g., with the setting unit1136) the fixed period of time independent of the user.

In some embodiments, the processing unit1108is further configured to receive (e.g., with the receiving unit1116) a period of time selected by the user and set (e.g., with the setting unit1136) the fixed period of time in accordance with the selection received from the user.

The operations described above with reference toFIGS. 9A-9Fare, optionally, implemented by components depicted inFIGS. 1A-7CorFIG. 10A. It would be clear to a person having ordinary skill in the art how processes can be implemented based on the components depicted inFIGS. 1A-7CorFIG. 10A.

It is understood by persons of skill in the art that the functional blocks described inFIG. 11are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. For example, processing unit1108can have an associated “controller” unit that is operatively coupled with processing unit1108to enable operation. This controller unit is not separately illustrated inFIG. 11but is understood to be within the grasp of one of ordinary skill in the art who is designing a device having a processing unit1108, such as device1100. As another example, one or more units, such as the generating unit1110, may be hardware units outside of processing unit1108in some embodiments. The description herein thus optionally supports combination, separation, and/or further definition of the functional blocks described herein.

FIG. 9Gis a flow diagram1000illustrating a method for remembering user data and generating recommendations using a digital assistant and an electronic device (104,200,400, or600) in accordance with some embodiments. Some operations in process1000optionally can be combined, the order of some operations optionally can be changed, and some operations optionally can be omitted. In particular, optional operations indicated with dashed-line shapes inFIG. 9Goptionally can be performed in any suitable order, if at all, and need not be performed in the order set forth inFIG. 9G.

As described below, method1000provides an intuitive way for remembering user data and generating recommendations using a digital assistant. The method reduces the cognitive burden on a user for remembering user data and generating recommendations using a digital assistant, thereby creating a more efficient human-machine interface. For battery-operated computing devices, enabling a user to remember user data and generate recommendations based on a nonspecific, unstructured natural-language request using a digital assistant more accurately and more efficiently conserves power and increases the time between battery charges.

At the beginning of process1000, in block1002, the digital assistant generates at least one experiential data structure and/or the electronic device104,200,400,600generates at least one experiential data structure accessible to the digital assistant. The experiential data structure is a data structure that includes an organized set of data associated with the user and/or the electronic device200at a particular point in time. The data is associated with items that a user wishes to remember, and data that has utility in generating recommendations to the user. The at least one experiential data structure in block1002is generated in a similar manner as in block902, according to some embodiments. The optional generation of a plurality of experiential data structures separated by time intervals in block1004is performed in a similar manner as in block904, according to some embodiments. The optional generation of at least one experiential data structure when at least one dimension of the experiential data structure, a device context, and a user context changes is performed in a similar manner as in block906, according to some embodiments.

Next, in block1014, at least one tagged experiential data structure is stored in a similar manner as in block922, according to some embodiments.

Next, in block1008, the virtual assistant tags at least one experiential data structure with one or more annotations associated with the experiential data structure in a similar manner as in block912, according to some embodiments. Optionally, a change in device context is detected in block1009. For example, the GPS coordinates of the electronic device104,200,400,600change by a non-trivial amount, which is detected in block1009. In block1010, in response to detection of the change in device context in block1009, at least one experiential data structure is modified based on that changed device context. Optionally, a change in user context is detected in block1011. In block1012, in response to detection of the change in user context in block1009, at least one experiential data structure is modified based on that changed user context. The optional modifying of at least one experiential data structure based on at least one device context in block1010is performed in a similar manner as in block914, according to some embodiments. The optional modifying of at least one experiential data structure based on at least one user context in block1012is performed in a similar manner as in block918, according to some embodiments.

In block1016, based on at least one of a user context and a device context, the virtual assistant generates a request for the recommendation without input from the user. For example, referring toFIG. 8Xand the description ofFIG. 8Xabove, the user is at the Delhi Palacio and has the electronic device102,200,400,600in her possession, such that the device context includes the location of the Delhi Palacio. The electronic device102,200,400,600recognizes that it is at the Delhi Palacio, and generates a request for a recommendation from the virtual assistant for a food item that the user would like. The virtual assistant searches stored experiential data structures for the location Delhi Palacio, and determines if any of those experiential data structures include stored user information relating to the user's preferences. The virtual assistant determines that one stored experiential data structure includes information that the user likes the chicken tikka masala, and that another stored experiential data structure includes information that the user likes the lentil soup. Without receiving a user request for a recommendation, the virtual assistant presents1076information on the display212as shown inFIG. 8X, reminding the user that she likes the chicken tikka masala and the lentil soup at the Delhi Palacio. In this way, even if the user has forgotten that she generated an experiential data structure to remind her that she likes those items, the virtual assistance can provide her with the benefit of her previous request to remember her likes. As another example, upon recognizing that the electronic device102,200,400,600is located at the Delhi Palacio, the virtual assistant searches stored experiential data structures for the location Delhi Palacio, and determines if any of those experiential data structures include stored user information relating to the user's preferences. Some stored experiential data structures, in this example, include a content dimension that includes the content of one or more reviews of the Delhi Palacio written by the user, and others, in this example, include a content dimension that includes data associated with the contents of previous orders by the user, such as through an app for food delivery. Based on those experiential data structures, the virtual assistant determines that “lamb korma” appears in a review, and also appears in several previous orders by the user. Without receiving a user request for a recommendation, the virtual assistant presents1076information to the user that she may like the lamb korma here, and optionally informs the user that she has ordered it several times in the past.

Referring toFIG. 8Y, as another example, the user's calendar includes an entry for a birthday party. The virtual assistant periodically checks the calendar for upcoming events, and recognizes a user context that a birthday party to which the user has been invited is tonight. The virtual assistant also recognizes the location of that birthday party (“Anne's house”). The virtual assistant optionally includes one or more task templates, and the presence of the words “birthday party” in association with the event trigger performance of a task to remind the user to purchase a gift. The virtual assistant determines that none of the stored experiential data structures includes information that the user has purchased a birthday gift. Without receiving a user request for a recommendation, the virtual assistant presents1078information on the display212as shown inFIG. 8Y, reminding the user that he is scheduled to go to a birthday party tonight, and gently reminding the user to purchase a gift if he has not yet done so.

Next, the analysis of at least one stored experiential data structure based on the generated request of block1018is performed in a similar manner as in block950, according to some embodiments. The satisfaction of the user request based on the analysis of the least one stored experiential data structure is performed in a similar manner as in block948.

In accordance with some embodiments,FIG. 10Bshows an exemplary functional block diagram of an electronic device1200configured in accordance with the principles of the various described embodiments. In accordance with some embodiments, the functional blocks of electronic device1200are configured to perform the techniques described above. The functional blocks of the device1200are, optionally, implemented by hardware, software, or a combination of hardware and software to carry out the principles of the various described examples. It is understood by persons skilled in the art that the functional blocks described inFIG. 10Bare, optionally, combined or separated into sub-blocks to implement the principles of the various described examples. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein.

As shown inFIG. 10B, an electronic device1200optionally includes a display unit1202configured to display a graphic user interface; optionally, a touch-sensitive surface unit1204configured to receive contacts; optionally, a microphone unit1206configured to receive audio signals; and a processing unit1208coupled optionally to one or more of the display unit1202, the touch-sensitive surface unit1204and microphone unit1206. In some embodiments, the processing unit1208includes a generating unit1210, a modifying unit1212, a storing unit1214, an analyzing unit1216, and an outputting unit1218.

The processing unit1208is configured to generate (e.g., with the generating unit1210), in response to a trigger, at least one experiential data structure accessible to a virtual assistant, where the experiential data structure comprises an organized set of data associated with at least one of the user and the electronic device at a particular point in time; store (e.g., with the storing unit1214) at least one experiential data structure; modify (e.g., with the modifying unit1212) at least one experiential data structure with one or more annotations associated with the experiential data structure, utilizing the virtual assistant; based on at least one of a user context and a device context, generate (e.g., with the generating unit1210) a request for a recommendation from the virtual assistant without a request from the user; analyze (e.g., with the analyzing unit1216) at least one stored experiential data structure based on the generated request; and output (e.g., with the outputting unit1118) information responsive to the generated request based on the analysis of the at least one stored experiential data structure.

In some embodiments, the processing unit1208is further configured to generate (e.g., with the generating unit1210) a plurality of experiential data structures separated by time intervals.

In some embodiments, the processing unit1208is further configured to modify (e.g., with the modifying unit1212) at least one experiential data structure based on at least one device context.

In some embodiments, the processing unit1208is further configured to modify (e.g., with the modifying unit1212) at least one experiential data structure based on at least one user context.

In some embodiments, at least one experiential data structure includes social information.

In some embodiments, at least one experiential data structure includes location information

In some embodiments, at least one experiential data structure includes media information.

In some embodiments, at least one experiential data structure includes content information.

In some embodiments, at least one experiential data structure includes photographic information.

In some embodiments, at least one experiential data structure includes daily activity information.

In some embodiments, the processing unit1208is further configured to generate (e.g., with the generating unit1210) at least one new experiential data structure when at least one of the items of information of the experiential data structure, the device context, and the user context changes.

The operations described above with reference toFIG. 9Gare, optionally, implemented by components depicted inFIGS. 1A-7CorFIG. 10B. It would be clear to a person having ordinary skill in the art how processes can be implemented based on the components depicted inFIGS. 1A-7CorFIG. 10B.

It is understood by persons of skill in the art that the functional blocks described inFIG. 12are, optionally, combined or separated into sub-blocks to implement the principles of the various described embodiments. Therefore, the description herein optionally supports any possible combination or separation or further definition of the functional blocks described herein. For example, processing unit1208can have an associated “controller” unit that is operatively coupled with processing unit1208to enable operation. This controller unit is not separately illustrated inFIG. 12but is understood to be within the grasp of one of ordinary skill in the art who is designing a device having a processing unit1208, such as device1200. As another example, one or more units, such as the generating unit1210, may be hardware units outside of processing unit1208in some embodiments. The description herein thus optionally supports combination, separation, and/or further definition of the functional blocks described herein.

As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve the delivery to users of content that may be of interest to them. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, home addresses, or any other identifying information.

The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to deliver targeted content that is of greater interest to the user. Accordingly, use of such personal information data enables calculated control of the delivered content. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure.

Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of advertisement delivery services, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services. In another example, users can select not to provide location information for targeted content delivery services. In yet another example, users can select to not provide precise location information, but permit the transfer of location zone information.