Detecting visual attention during speech of user

Abstract

The invention relates to detecting visual attention during speech of a user. An example process includes concurrently receiving an audio stream and a video stream; determining whether visual attention of the user is directed to an electronic device when the user speaks based on a first portion of the audio stream received within a predetermined duration before a current time and a first portion of the video stream received within the predetermined duration before the current time; and in accordance with a determination that the visual attention of the user is directed to the electronic device when the user speaks: identifying a second portion of the audio stream as including a user speech intended for the electronic device; initiating, by a digital assistant operating on the electronic device, a task based on the second portion of the audio stream; and providing an output indicative of the initiated task.

Description

[0001] This application is a divisional application of invention patent application 202380042855.8, filed on May 22, 2023, and entitled “Detecting visual attention of user during speech”. Technical Field

[0002] The present disclosure generally relates to determining whether a user's visual attention is directed toward an electronic device while the user is speaking. Background Art

[0003] Intelligent automated assistants (or digital assistants) can provide a favorable interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in voice form and / or text form. For example, a user can provide a verbal input containing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from the verbal input and operationalize the user's intent into tasks. These tasks can then be performed by executing one or more services of the electronic device, and relevant output in response to the user's request can be returned to the user. Summary of the invention

[0004] An example method is disclosed herein. An example method includes, at an electronic device having one or more processors and memory: concurrently receiving an audio stream and a video stream; determining whether a user's visual attention is directed toward the electronic device when the user speaks based on a first portion of the audio stream received within a predetermined duration before a current time and a first portion of the video stream received within a predetermined duration before the current time; and based on determining that the user's visual attention is directed toward the electronic device when the user speaks: identifying a second portion of the audio stream as including user speech intended for the electronic device; initiating a task based on the second portion of the audio stream by a digital assistant operating on the electronic device; and providing an output indicating the initiated task.

[0005] The present invention discloses an example non-transitory computer-readable medium. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs include instructions that, when executed by one or more processors of an electronic device, cause the electronic device to: concurrently receive an audio stream and a video stream; determine whether the user's visual attention is directed to the electronic device when the user speaks based on a first portion of the audio stream received within a predetermined duration before the current time and a first portion of the video stream received within a predetermined duration before the current time; and based on determining that the user's visual attention is directed to the electronic device when the user speaks: identify the second portion of the audio stream as including user speech intended for the electronic device; initiate a task based on the second portion of the audio stream by a digital assistant operating on the electronic device; and provide an output indicating the initiated task.

[0006] An exemplary electronic device is disclosed herein. An exemplary electronic device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for the following operations: concurrently receiving an audio stream and a video stream; determining whether the user's visual attention is directed to the electronic device when the user speaks based on a first portion of the audio stream received within a predetermined duration before the current time and a first portion of the video stream received within a predetermined duration before the current time; and based on determining that the user's visual attention is directed to the electronic device when the user speaks: identifying a second portion of the audio stream as including user speech intended for the electronic device; initiating a task based on the second portion of the audio stream by a digital assistant operating on the electronic device; and providing an output indicating the initiated task.

[0007] An example electronic device includes components for: concurrently receiving an audio stream and a video stream; determining whether a user's visual attention is directed toward the electronic device when the user speaks based on a first portion of the audio stream received within a predetermined duration before a current time and a first portion of the video stream received within a predetermined duration before the current time; and based on determining that the user's visual attention is directed toward the electronic device when the user speaks: identifying a second portion of the audio stream as including user speech intended for the electronic device; initiating, by a digital assistant operating on the electronic device, a task based on the second portion of the audio stream; and providing output indicative of the initiated task.

[0008] Identifying the second portion of the audio stream based on determining that the user's visual attention is directed to the electronic device when the user speaks can allow the device to respond to speech input more accurately and effectively. For example, the user can simply look at the device (or part thereof) while speaking a user request for the device to provide a relevant response. In addition, in order to provide a relevant response, the device may not need to explicitly indicate that the speech input is intended for additional user inputs of the device, such as verbal trigger inputs, button selections, displayed affordances, etc. In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to operate the device, by reducing the user input required to prevent the device from incorrectly responding to speech inputs that are not intended for the device, by accurately responding to speech inputs intended for the device, by avoiding repeated speech inputs to the device), which additionally reduces power usage and extends the battery life of the device by enabling the user to use the device more quickly and effectively.

[0009] An example method is disclosed herein. An example method includes, at an electronic device having one or more processors and a memory: concurrently receiving an audio stream and a video stream; identifying a first portion of the video stream as including a first user; identifying a second portion of the video stream as including a second user; determining that the visual attention of the first user is directed to the electronic device when the first user speaks based on the first portion of the audio stream and the video stream: identifying the first portion of the audio stream as including the first user's intended speech for the electronic device; and providing a first output based on processing the first portion of the audio stream; and determining that the visual attention of the second user is directed to the electronic device when the second user speaks based on the second portion of the audio stream and the video stream: identifying the second portion of the audio stream as including the second user's intended speech for the electronic device; and providing a second output based on processing the second portion of the audio stream.

[0010] The present invention discloses an example non-transitory computer-readable medium. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs include instructions that, when executed by one or more processors of an electronic device, cause the electronic device to: concurrently receive an audio stream and a video stream; identify a first portion of the video stream as including a first user; identify a second portion of the video stream as including a second user; determine that the visual attention of the first user is directed to the electronic device when the first user speaks based on the first portion of the audio stream and the video stream: identify the first portion of the audio stream as including the first user's expected speech for the electronic device; and provide a first output based on processing the first portion of the audio stream; and determine that the visual attention of the second user is directed to the electronic device when the second user speaks based on the second portion of the audio stream and the video stream: identify the second portion of the audio stream as including the second user's expected speech for the electronic device; and provide a second output based on processing the second portion of the audio stream.

[0011] An example electronic device is disclosed herein. An exemplary electronic device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for the following operations: concurrently receiving an audio stream and a video stream; identifying a first portion of the video stream as including a first user; identifying a second portion of the video stream as including a second user; determining that the visual attention of the first user is directed to the electronic device when the first user speaks based on the first portion of the audio stream and the video stream: identifying the first portion of the audio stream as including the first user's intended speech for the electronic device; and providing a first output based on processing the first portion of the audio stream; and determining that the visual attention of the second user is directed to the electronic device when the second user speaks based on the second portion of the audio stream and the video stream: identifying the second portion of the audio stream as including the second user's intended speech for the electronic device; and providing a second output based on processing the second portion of the audio stream.

[0012] An example electronic device includes components for the following operations: concurrently receiving an audio stream and a video stream; identifying a first portion of the video stream as including a first user; identifying a second portion of the video stream as including a second user; determining that the first user's visual attention is directed toward the electronic device when the first user speaks based on the first portion of the audio stream and the video stream: identifying the first portion of the audio stream as including the first user's intended speech for the electronic device; and providing a first output based on processing the first portion of the audio stream; and determining that the second user's visual attention is directed toward the electronic device when the second user speaks based on the second portion of the audio stream and the video stream: identifying the second portion of the audio stream as including the second user's intended speech for the electronic device; and providing a second output based on processing the second portion of the audio stream.

[0013] Providing output based on the identified part of processing audio stream according to satisfying predetermined conditions can allow the device to accurately and effectively respond to the speech of the correct user in a multi-user environment.For example, in a multi-user environment, in order to respond to the speech of the user, the device determines that the same user is looking at the device when speaking.Therefore, the device can avoid mistakenly responding to the user speech that is not intended for the device.For example, if the first user looks at the device when not speaking and the second user speaks when not looking at the device, the device will not mistakenly respond to the speech of the second user.In this way, the user-device interface can be more effective and accurate (for example, by reducing the amount of user input required for operating the device, by reducing the user input required for preventing the device from responding to the speech of incorrect users, by accurately identifying the speech input from the correct user and responding to the speech input, by avoiding repeated speech input to the device), which can reduce power use and extend the battery life of the device by enabling the user to use the device more quickly and effectively.

[0014] An example method is disclosed herein. An example method includes, at an electronic device having one or more processors and a memory: concurrently receiving an audio stream and a video stream; determining a first type of confidence score indicating whether a user's visual attention is directed toward the electronic device when the user speaks based on the audio stream and the video stream; and after determining the first type of confidence score: determining a final confidence score indicating whether the user's visual attention is directed toward the electronic device when the user speaks based on the first type of confidence score and a second type of confidence score indicating whether the user's visual attention is directed toward the electronic device; and based on determining that the final confidence score exceeds a threshold: identifying a portion of the audio stream as including user speech intended for the electronic device; and providing an output based on processing the portion of the audio stream.

[0015] The present invention discloses an example non-transitory computer-readable medium. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs include instructions that, when executed by one or more processors of an electronic device, cause the electronic device to: concurrently receive an audio stream and a video stream; determine a first type of confidence score indicating whether the user's visual attention is directed to the electronic device when the user speaks based on the audio stream and the video stream; and after determining the first type of confidence score: determine a final confidence score indicating whether the user's visual attention is directed to the electronic device when the user speaks based on the first type of confidence score and a second type of confidence score indicating whether the user's visual attention is directed to the electronic device; and based on determining that the final confidence score exceeds a threshold: identify a portion of the audio stream as including user speech intended for the electronic device; and provide an output based on processing the portion of the audio stream.

[0016] An exemplary electronic device is disclosed herein. An exemplary electronic device includes one or more processors; a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs include instructions for the following operations: concurrently receiving an audio stream and a video stream; determining a first type of confidence score indicating whether a user's visual attention is directed to the electronic device when the user speaks based on the audio stream and the video stream; and after determining the first type of confidence score: determining a final confidence score indicating whether the user's visual attention is directed to the electronic device when the user speaks based on the first type of confidence score and a second type of confidence score indicating whether the user's visual attention is directed to the electronic device; and based on determining that the final confidence score exceeds a threshold: identifying a portion of the audio stream as including user speech intended for the electronic device; and providing an output based on processing the portion of the audio stream.

[0017] An example electronic device includes components for: concurrently receiving an audio stream and a video stream; determining a first type of confidence score indicating whether a user's visual attention is directed toward the electronic device when the user speaks based on the audio stream and the video stream; and after determining the first type of confidence score: determining a final confidence score indicating whether the user's visual attention is directed toward the electronic device when the user speaks based on the first type of confidence score and a second type of confidence score indicating whether the user's visual attention is directed toward the electronic device; and based on determining that the final confidence score exceeds a threshold: identifying a portion of the audio stream as including user speech intended for the electronic device; and providing an output based on processing the portion of the audio stream.

[0018] Identifying the portion of the audio stream based on determining that the final confidence score exceeds a threshold value can allow the device to respond to speech input more accurately and effectively. For example, determining the final confidence score allows the device to consider additional relevant factors (e.g., user posture, user gaze, relative motion between the user and the device, etc.) to more accurately determine that the user is looking at the device when speaking. As described, determining whether the user is looking at the device when speaking allows the device to respond to speech input effectively and accurately, such as without the need for additional user input. In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to operate the device, by reducing the user input required to prevent the device from incorrectly responding to speech input that is not intended for the device, by accurately responding to speech input intended for the device, by avoiding repeated speech input to the device), which additionally reduces power usage and extends the battery life of the device by enabling the user to use the device more quickly and effectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A block diagram is shown of a system and environment for implementing a digital assistant according to various examples.

[0020] Figure 2A A block diagram of a portable multifunction device that implements the client-side portion of a digital assistant according to various examples is shown.

[0021] Figure 2B is a block diagram illustrating exemplary components for event processing according to various examples.

[0022] Figure 3 A portable multifunction device implementing the client-side portion of a digital assistant according to various examples is shown.

[0023] Figure 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface according to various examples.

[0024] Figure 5A An exemplary user interface of a menu of applications on a portable multifunction device is shown according to various examples.

[0025] Figure 5B An exemplary user interface for a multifunction device having a touch-sensitive surface separate from the display is shown according to various examples.

[0026] Fig. 6A A personal electronic device according to various examples is shown.

[0027] Figure 6B A block diagram illustrating a personal electronic device according to various examples is shown.

[0028] Fig. 7A A block diagram of a digital assistant system or a server portion thereof according to various examples is shown.

[0029] Figure 7B According to various examples, Fig. 7A The capabilities of the digital assistant shown in .

[0030] Figure 7C Portions of an ontology according to various examples are shown.

[0031] Fig. 8A A system for determining whether a user's visual attention is directed toward an electronic device while the user is speaking according to various examples is shown.

[0032] Figure 8B Video frames stored in a memory buffer at a current time are shown according to various examples.

[0033] Fig. 9A device is shown that provides an output based on determining that a user's visual attention is directed toward the device while the user is speaking, according to various examples.

[0034] FIG. 10A to FIG. 10B Determining whether visual attention of a user is directed toward a device while the same user is speaking in a multi-user environment according to various examples is shown.

[0035] Fig.11 A process for determining whether a user's visual attention is directed toward an electronic device while the user is speaking is shown according to various examples.

[0036] Fig.12 A process for determining whether a user's visual attention is directed toward an electronic device while the user is speaking is shown according to various examples.

[0037] Fig.13 A process for determining whether a user's visual attention is directed toward an electronic device while the user is speaking is shown according to various examples. DETAILED DESCRIPTION

[0038] In the following description of the examples, reference will be made to the accompanying drawings, in which specific examples that may be implemented are shown by way of illustration. It should be understood that other examples may be used and structural changes may be made without departing from the scope of the various examples.

[0039] The present invention relates generally to determining whether a user's visual attention is directed toward an electronic device while the user is speaking. If the user's visual attention is directed toward the electronic device while the user is speaking, the electronic device may identify a portion of a received audio stream as including speech intended for the electronic device. The electronic device may then process the identified portion, such as using a digital assistant, to provide relevant output to the user.

[0040] Although the following description uses the terms "first," "second," etc. to describe various elements, these elements should not be limited by the terms. These terms are simply used to distinguish one element from another. For example, without departing from the scope of the various described examples, a first input may be referred to as a second input, and similarly, a second input may be referred to as a first input. Both the first input and the second input are inputs, and in some cases are independent and different inputs.

[0041] The terms used in the description of various examples described herein are only for the purpose of describing specific examples, and are not intended to be limiting. As used in the description of various examples described and in the appended claims, the singular forms "one", "a kind of" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" used herein refers to and encompasses any and all possible combinations of one or more items in the associated listed items. It will also be understood that the terms "include" and / or "comprising" when used in this specification specify the presence of stated features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or their grouping.

[0042] Depending on the context, the term "if" may be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting." Similarly, the phrases "if it is determined that" or "if [a stated condition or event] is detected" may be interpreted to mean "upon determining that" or "in response to determining that" or "upon detecting [a stated condition or event]" or "in response to detecting [a stated condition or event]," depending on the context.

[0043] 1. System and Environment

[0044] Figure 1 A block diagram of a system 100 according to various examples is shown. In some examples, the system 100 implements a digital assistant. The terms "digital assistant," "virtual assistant," "intelligent automated assistant," or "automatic digital assistant" 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 performs one or more of the following steps: identifying a task flow with steps and parameters designed to implement the inferred user intent, inputting specific requirements into the task flow according to the inferred user intent; executing the task flow by calling a program, method, service, API, etc.; and generating an output response to the user in an audible (e.g., verbal) and / or visual form.

[0045] Specifically, the digital assistant is capable of accepting user requests in the form of at least partially natural language commands, requests, statements, narrations and / or inquiries. Typically, the user requests to seek an informative answer or perform a task from the digital assistant. A satisfactory response to the user's request includes providing the requested informative answer, performing the requested task, or a combination of the two. For example, the user asks the digital assistant a question, such as "Where am I now?" Based on the user's current location, the digital assistant answers "You are near the West Gate of Central Park." The user also requests to perform a task, such as "Please invite my friends to my girlfriend's birthday party next week." In response, the digital assistant can confirm the request by saying "OK, right away", and then send a suitable calendar invitation to each of the user's friends listed in the user's electronic address book on behalf of the user. During the execution of the requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple information exchanges over a long period of time. There are many other methods of interacting with the digital assistant to request information or perform various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other video or audio forms, such as text, reminders, music, videos, animations, etc.

[0046] like Figure 1 As shown, in some examples, the digital assistant is implemented according to a client-server model. The digital assistant includes a client-side portion 102 (hereinafter referred to as "DA client 102") executed on a user device 104 and a server-side portion 106 (hereinafter referred to as "DA server 106") executed on a server system 108. The DA client 102 communicates with the DA server 106 via one or more networks 110. The DA client 102 provides client-side functionality, such as user-oriented input and output processing, and communication with the DA server 106. The DA server 106 provides server-side functionality for any number of DA clients 102, each located on a corresponding user device 104.

[0047] In some examples, the DA server 106 includes a client-facing I / O interface 112, one or more processing modules 114, data and models 116, and an I / O interface 118 to external services. The client-facing I / O interface 112 facilitates client-facing input and output processing of the DA server 106. One or more processing modules 114 utilize the data and models 116 to process verbal input and determine user intent based on natural language input. In addition, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, the DA server 106 communicates with external services 120 via one or more networks 110 to complete tasks or collect information. The I / O interface 118 to external services facilitates such communications.

[0048] User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multi-function device (e.g., Figure 2A The device 200 described above), a multifunctional device (eg, as described below with reference to Figure 4 The device 400) or a personal electronic device (eg, as described below) FIG. 6A to FIG. 6B The portable multifunction device is, for example, a mobile phone that also includes other functions such as a PDA and / or a music player. Specific examples of portable multifunction devices include the Apple iPod and Device. Other examples of portable multifunction devices include, but are not limited to, earbuds / headphones, speakers, and laptop computers or tablet computers. In addition, in some examples, user device 104 is a non-portable multifunction device. Specifically, user device 104 is a desktop computer, a game console, a speaker, a television, or a television set-top box. In some examples, user device 104 includes a touch-sensitive surface (e.g., a touch screen display and / or a touchpad). In addition, user device 104 optionally includes 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 multifunction devices are described in more detail below.

[0049] Examples of communication network 110 include local area networks (LANs) and wide area networks (WANs), such as the Internet. Communication network 110 is implemented using any known network protocol, including various wired or wireless protocols, such as 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.

[0050] The server system 108 is implemented on one or more stand-alone data processing devices or distributed computer networks. In some examples, the server system 108 also uses various virtual devices and / or services of third-party service providers (e.g., third-party cloud service providers) to provide potential computing resources and / or infrastructure resources of the server system 108.

[0051] In some examples, the user device 104 communicates with the DA server 106 via a second user device 122. The second user device 122 is similar or identical to the user device 104. For example, the second user device 122 is similar to the user device 104 described below with reference to Figure 2A , Figure 4 and FIG. 6A to FIG. 6B The device 200, 400 or 600 described. The user device 104 is configured to be communicatively coupled to the second user device 122 via a direct communication connection (such as Bluetooth, NFC, BTLE, etc.) or via a wired or wireless network (such as a local Wi-Fi network). In some examples, the second user device 122 is configured to act as a proxy between the user device 104 and the DA server 106. For example, the DA client 102 of the user device 104 is configured to transmit information (e.g., a user request received at the user device 104) to the DA server 106 via the second user device 122. The DA server 106 processes the information and returns relevant data (e.g., data content in response to the user request) to the user device 104 via the second user device 122.

[0052] In some examples, the user device 104 is configured to send an abbreviated request for data to a second user device 122 to reduce the amount of information transmitted from the user device 104. The second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request for transmission to the DA server 106. The system architecture can advantageously allow user devices 104 with limited communication capabilities and / or limited battery power (e.g., a watch or similar compact electronic device) to access services provided by the DA server 106 by using a second user device 122 (e.g., a mobile phone, laptop computer, tablet computer, etc.) with greater communication capabilities and / or battery power as a proxy to the DA server 106. Although Figure 1 Only two user devices 104 and 122 are shown, but it should be understood that in some examples, the system 100 may include any number and type of user devices configured to communicate with the DA server system 106 in this proxy configuration.

[0053] Although Figure 1 The digital assistant shown in includes both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), but in some examples, the functionality of the digital assistant is implemented as a stand-alone application installed on a user device. In addition, the functional division between the client portion and the server portion of the digital assistant may vary in different specific implementations. For example, in some examples, the DA client is a thin client that only provides user-oriented input and output processing functions and delegates all other functions of the digital assistant to the back-end server.

[0054] 2. Electronic devices

[0055] Attention now turns to embodiments of electronic devices for implementing the client-side portion of a digital assistant. Figure 2A2 is a block diagram showing a portable multifunction device 200 with a touch-sensitive display system 212 according to some embodiments. The touch-sensitive display 212 is sometimes referred to as a "touch screen" for convenience, and is sometimes referred to as or referred to as a "touch-sensitive display system". The device 200 includes a memory 202 (which optionally includes one or more computer-readable storage media), a memory controller 222, one or more processing units (CPUs) 220, a peripheral device interface 218, an RF circuit 208, an audio circuit 210, a speaker 211, a microphone 213, an input / output (I / O) subsystem 206, other input control devices 216, and an external port 224. The device 200 optionally includes one or more optical sensors 264. The device 200 optionally includes one or more contact force sensors 265 for detecting the intensity of contact on the device 200 (e.g., a touch-sensitive surface of the device 200 such as the touch-sensitive display system 212). Device 200 optionally includes one or more tactile output generators 267 for generating tactile output on device 200 (e.g., generating tactile output on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touch pad 455 of device 400). These components optionally communicate via one or more communication buses or signal lines 203.

[0056] As used in this specification and claims, the term "intensity" of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a surrogate (surrogate) of the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values ​​that includes at least four different values ​​and more typically includes hundreds of different values ​​(e.g., at least 256). The intensity of a contact is optionally determined (or measured) using various methods and various sensors or combinations of sensors. For example, one or more force sensors below or adjacent to the touch-sensitive surface are optionally used to measure the force at different points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., weighted average) to determine an estimated contact force. Similarly, the pressure-sensitive tip of a stylus is optionally used to determine the pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and / or its change, the capacitance of the touch-sensitive surface near the contact and / or its change, and / or the resistance of the touch-sensitive surface near the contact and / or its change are optionally used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some embodiments, the substitute measurement of the contact force or pressure is used directly to determine whether the intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurement). In some embodiments, the substitute measurement of the contact force or pressure is converted into an estimated force or pressure, and the estimated force or pressure is used to determine whether the intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of the contact as an attribute of the user input allows the user to access additional device functions that would otherwise be inaccessible to the user on a smaller device with limited real estate, which is used to display an enable indication (e.g., on a touch-sensitive display) and / or receive user input (e.g., via a touch-sensitive display, touch-sensitive surface, or physical / mechanical controls, such as knobs or buttons).

[0057] As used in this specification and claims, the term "tactile output" refers to a physical displacement of a device relative to a previous position of the device, a physical displacement of a component of the device (e.g., a touch-sensitive surface) relative to another component of the device (e.g., a housing), or a displacement of a component relative to the center of mass of the device, which will be detected by a user using the user's sense of touch. For example, in the case where a device or a component of the device is in contact with a user's touch-sensitive surface (e.g., a finger, palm, or other part of the user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in a physical property of the device or a component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or a touchpad) is optionally interpreted by the user as a "press click" or "release click" to a physical actuation button. In some cases, the user will feel a tactile sensation, such as a "press click" or "release click", even when the physical actuation button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movement does not move. As another example, even when there is no change in the smoothness of the touch-sensitive surface, movement of the touch-sensitive surface may optionally be interpreted or sensed by the user as "roughness" of the touch-sensitive surface. Although such a user's interpretation of touch will be limited by the user's individualized sensory perceptions, many sensory perceptions of touch are common to most users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., "press click," "release click," "roughness"), unless otherwise stated, the generated tactile output corresponds to a physical displacement of the device or a component thereof that would generate that sensory perception for a typical (or average) user.

[0058] It should be understood that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. Figure 2A The various components shown are implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0059] The memory 202 includes one or more computer-readable storage media. These computer-readable storage media are, for example, tangible and non-transitory. The memory 202 includes a high-speed random access memory and also includes a non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. The memory controller 222 controls other components of the device 200 to access the memory 202.

[0060] In some examples, the non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing various aspects of the processes described below) for use by or in conjunction with an instruction execution system, device, or apparatus, such as a computer-based system, a system containing a processor, or other system that can fetch instructions from an instruction execution system, device, or apparatus and execute the instructions. In other examples, the instructions (e.g., for performing various aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of server system 108, or are divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.

[0061] The peripheral device interface 218 is used to couple the input and output peripheral devices of the device to the CPU 220 and the memory 202. The one or more processors 220 run or execute various software programs and / or instruction sets stored in the memory 202 to perform various functions of the device 200 and process data. In some embodiments, the peripheral device interface 218, the CPU 220, and the memory controller 222 are implemented on a single chip such as the chip 204. In some other embodiments, they are implemented on separate chips.

[0062] RF (radio frequency) circuit 208 receives and sends RF signals, also known as electromagnetic signals. RF circuit 208 converts electrical signals into / converts electromagnetic signals into electrical signals, and communicates with communication networks and other communication devices via electromagnetic signals. RF circuit 208 optionally includes well-known circuits for performing these functions, including but not limited to antenna systems, RF transceivers, one or more amplifiers, tuners, one or more oscillators, digital signal processors, codec chipsets, user identity modules (SIM) cards, memory, etc. RF circuit 208 optionally communicates with networks and other devices through wireless communications, such as the Internet (also known as the World Wide Web (WWW)), intranets, and / or wireless networks (such as cellular telephone networks, wireless local area networks (LANs), and / or metropolitan area networks (MANs)). RF circuit 208 optionally includes well-known circuits for detecting near field communication (NFC) fields, such as through short-range communication radio components. Wireless communication optionally uses any of a variety of communication standards, protocols and technologies, including, but not limited to, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), High Speed ​​Downlink Packet Access (HSDPA), High Speed ​​Uplink Packet Access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual Cell HSPA (DC-HSPDA), Long Term Evolution (LTE), Near Field Communication (NFC), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11d), IEEE 802.11e, IEEE 802.11f, IEEE 802.11g, IEEE 802.11g). 802.11n and / or IEEE 802.11ac), Voice over Internet Protocol (VoIP), Wi-MAX, email protocols (e.g., Internet Message Access Protocol (IMAP) and / or Post Office Protocol (POP)), instant messaging (e.g., Extensible Messaging and Presence Protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Utilizing Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)) and / or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols that have not yet been developed as of the filing date of this document.

[0063] The audio circuit 210, speaker 211, and microphone 213 provide an audio interface between a user and the device 200. The audio circuit 210 receives audio data from the peripheral device interface 218, converts the audio data into electrical signals, and transmits the electrical signals to the speaker 211. The speaker 211 converts the electrical signals into sound waves audible to humans. The audio circuit 210 also receives electrical signals converted from sound waves by the microphone 213. The audio circuit 210 converts the electrical signals into audio data and transmits the audio data to the peripheral device interface 218 for processing. The audio data is retrieved from and / or transmitted to the memory 202 and / or the RF circuit 208 via the peripheral device interface 218. In some embodiments, the audio circuit 210 also includes a headset jack (e.g., Figure 3 The headset jack provides an interface between the audio circuit 210 and a removable audio input / output peripheral device, such as an output-only headset or a headset with both output (e.g., a single-ear headset or a dual-ear headset) and input (e.g., a microphone).

[0064] The I / O subsystem 206 couples input / output peripherals on the device 200, such as a touch screen 212 and other input control devices 216, to a peripheral device interface 218. The I / O subsystem 206 optionally includes a display controller 256, an optical sensor controller 258, an intensity sensor controller 259, a tactile feedback controller 261, and one or more input controllers 260 for other input or control devices. One or more input controllers 260 receive / send electrical signals from other input control devices 216. Other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slide switches, joysticks, click wheels, etc. In some alternative embodiments, the input controller 260 is optionally coupled to any of the following (or none of the following): a keyboard, an infrared port, a USB port, and a pointing device such as a mouse. One or more buttons (e.g., Figure 3 308 in ) optionally includes an up / down button for volume control of the speaker 211 and / or the microphone 213. The one or more buttons optionally include a push button (e.g., Figure 3 306 in ).

[0065] A quick press of the push button disengages the lock of the touch screen 212 or begins the process of unlocking the device using gestures on the touch screen, as described in U.S. Patent Application 11 / 322,549, filed December 23, 2005, entitled "Unlocking a Device by Performing Gestures on an Unlock Image"; U.S. Patent No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) powers the device 200 on or off. The user can customize the function of one or more buttons. The touch screen 212 is used to implement virtual buttons or soft buttons and one or more soft keyboards.

[0066] The touch-sensitive display 212 provides an input interface and an output interface between the device and the user. The display controller 256 receives electrical signals from the touch screen 212 and / or sends electrical signals to the touch screen 212. The touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, videos, and any combination thereof (collectively referred to as "graphics"). In some embodiments, some or all of the visual output corresponds to user interface objects.

[0067] The touch screen 212 has a touch-sensitive surface, sensor, or sensor group that accepts input from a user based on tactile and / or haptic contact. The touch screen 212 and display controller 256 (together with any associated modules and / or instruction sets in memory 202) detect contact (and any movement or interruption of the contact) on the touch screen 212 and convert the detected contact into interaction with a user interface object (e.g., one or more soft keys, icons, web pages, or images) displayed on the touch screen 212. In an exemplary embodiment, the point of contact between the touch screen 212 and the user corresponds to the user's finger.

[0068] The touch screen 212 uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. The touch screen 212 and display controller 256 use any of a variety of touch sensing technologies now known or later developed, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen 212 to detect contact and any movement or interruption thereof, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as in the Apple ® from Apple Inc. (Cupertino, California). and iPod The technology used in

[0069] In some embodiments, the touch-sensitive display of the touch screen 212 is similar to the multi-touch-sensitive touch pads described in U.S. Patents 6,323,846 (Westerman et al.), 6,570,557 (Westerman et al.), and / or 6,677,932 (Westerman), and / or U.S. Patent Publication 2002 / 0015024A1, all of which are hereby incorporated by reference in their entirety. However, the touch screen 212 displays visual output from the device 200, whereas the touch-sensitive touch pad does not provide visual output.

[0070] The touch-sensitive display in some embodiments of the touch screen 212 is described in the following applications: (1) U.S. patent application No. 11 / 381,313, filed on May 2, 2006, "Multipoint Touch Surface Controller"; (2) U.S. patent application No. 10 / 840,862, filed on May 6, 2004, "Multipoint Touchscreen"; (3) U.S. patent application No. 10 / 903,964, filed on July 30, 2004, "Gestures For Touch Sensitive Input Devices"; (4) U.S. patent application No. 11 / 048,264, filed on January 31, 2005, "Gestures For Touch Sensitive Input Devices"; (5) U.S. patent application No. 11 / 038,590, filed on January 18, 2005, "Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices”; (6) U.S. patent application Ser. No. 11 / 228,758, filed on September 16, 2005, “Virtual Input Device Placement On A Touch Screen User Interface”; (7) U.S. patent application Ser. No. 11 / 228,700, filed on September 16, 2005, “Operation Of A Computer With A Touch Screen Interface”; (8) U.S. patent application Ser. No. 11 / 228,737, filed on September 16, 2005, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard”; and (9) U.S. patent application Ser. No. 11 / 367,749, filed on March 3, 2006, “Multi-Functional Hand-Held Device”. All of these applications are incorporated herein by reference in their entirety.

[0071] The touch screen 212 has, for example, a video resolution of more than 100 dpi. In some embodiments, the touch screen has a video resolution of about 160 dpi. The user contacts the touch screen 212 using any suitable object or appendage, such as a stylus, a finger, etc. In some embodiments, the user interface is designed to work primarily through finger-based contacts and gestures, which may not be as precise as stylus-based input due to the larger contact area of ​​the finger on the touch screen. In some embodiments, the device converts rough finger-based input into precise pointer / cursor positioning or commands for performing the actions desired by the user.

[0072] In some embodiments, in addition to the touch screen, the device 200 also includes a touchpad (not shown) for activating or deactivating specific functions. In some embodiments, the touchpad is a touch-sensitive area of ​​the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from the touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.

[0073] The device 200 also includes a power system 262 for powering the various components. The power system 262 includes a power management system, one or more power sources (e.g., batteries, alternating current (AC)), a recharging system, a power fault detection circuit, a power converter or inverter, a power status indicator (e.g., a light emitting diode (LED)), and any other components associated with the generation, management, and distribution of power in a portable device.

[0074] The device 200 also includes one or more optical sensors 264 . Figure 2A An optical sensor coupled to an optical sensor controller 258 in the I / O subsystem 206 is shown. The optical sensor 264 includes a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) phototransistor. The optical sensor 264 receives light projected through one or more lenses from the environment and converts the light into data representing an image. In conjunction with the imaging module 243 (also called a camera module), the optical sensor 264 captures a static image or video. In some embodiments, the optical sensor is located at the rear of the device 200, opposite to the touch screen display 212 at the front of the device, so that the touch screen display is used as a viewfinder for static images and / or video image acquisition. In some embodiments, the optical sensor is located at the front of the device so that the user's image is obtained for video conferencing while the user views other video conference participants on the touch screen display. In some embodiments, the positioning of the optical sensor 264 can be changed by the user (e.g., by rotating the lens and sensor in the device housing) so that a single optical sensor 264 is used with the touch screen display for both video conferencing and static image and / or video image acquisition.

[0075] Device 200 optionally also includes one or more contact intensity sensors 265 . Figure 2A A contact force sensor is shown coupled to a force sensor controller 259 in the I / O subsystem 206. The contact force sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electrical force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other force sensors (e.g., sensors for measuring the force (or pressure) of contact on a touch-sensitive surface). The contact force sensor 265 receives contact force information (e.g., pressure information or a surrogate for pressure information) from the environment. In some embodiments, at least one contact force sensor is juxtaposed or adjacent to a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact force sensor is located on the rear of the device 200, opposite the touch screen display 212 located on the front of the device 200.

[0076] Device 200 also includes one or more proximity sensors 266 . Figure 2A A proximity sensor 266 is shown coupled to the peripherals interface 218. Alternatively, the proximity sensor 266 is coupled to the input controller 260 in the I / O subsystem 206. The proximity sensor 266 is implemented as described in the following U.S. patent applications: Nos. 11 / 241,839, entitled "Proximity Detector In Handheld Device"; 11 / 240,788, entitled "Proximity Detector In Handheld Device"; 11 / 620,702, entitled "Using Ambient Light Sensor To Augment Proximity Sensor Output"; 11 / 586,862, entitled "Automated Response To And Sensing Of User Activity In Portable Devices"; and 11 / 638,251, entitled "Methods And Systems For Automatic Configuration Of Peripherals", which are hereby incorporated by reference in their entirety. In some embodiments, when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call), the proximity sensor turns off and the touch screen 212 is disabled.

[0077] Device 200 optionally also includes one or more tactile output generators 267 . Figure 2AA tactile output generator coupled to a tactile feedback controller 261 in the I / O subsystem 206 is shown. The tactile output generator 267 optionally includes one or more electroacoustic devices such as a speaker or other audio component; and / or an electromechanical device for converting 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 for converting an electrical signal into a tactile output on the device). The contact force sensor 265 receives tactile feedback generation instructions from the tactile feedback module 233 and generates a tactile output on the device 200 that can be felt by a user of the device 200. In some embodiments, at least one tactile output generator is arranged in juxtaposition or proximity to a touch-sensitive surface (e.g., a touch-sensitive display system 212) and optionally generates tactile output by moving the touch-sensitive surface vertically (e.g., inward / outward of the surface of the device 200) or laterally (e.g., backward and forward in the same plane as the surface of the device 200). In some embodiments, at least one tactile output generator sensor is located on the back of the device 200, opposite the touch screen display 212 located on the front of the device 200.

[0078] Device 200 also includes one or more accelerometers 268 . Figure 2A An accelerometer 268 coupled to the peripheral device interface 218 is shown. Alternatively, the accelerometer 268 is coupled to the input controller 260 in the I / O subsystem 206. The accelerometer 268 performs as described in the following U.S. patent publications: U.S. Patent Publication No. 20050190059, "Acceleration-based Theft Detection System for Portable Electronic Devices" and U.S. Patent Publication No. 20060017692, "Methods And Apparatuses For Operating A Portable Device Based OnAn Accelerometer", both of which are incorporated herein by reference in their entirety. In some embodiments, information is displayed in a portrait view or a landscape view on the touch screen display based on analysis of data received from one or more accelerometers. The device 200 optionally includes a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) in addition to one or more accelerometers 268 for obtaining information about the position and orientation (e.g., portrait or landscape) of the device 200.

[0079] In some embodiments, the software components stored in memory 202 include an operating system 226, a communication module (or instruction set) 228, a contact / motion module (or instruction set) 230, a graphics module (or instruction set) 232, a text input module (or instruction set) 234, a global positioning system (GPS) module (or instruction set) 235, a digital assistant client module 229, and an application (or instruction set) 236. In addition, memory 202 stores data and models, such as user data and models 231. In addition, in some embodiments, memory 202 ( Figure 2A ) or 470( Figure 4 ) storage device / global internal state 257, such as Figure 2A and Figure 4 . The device / global internal state 257 includes one or more of the following: active application state, which indicates which application (if any) is currently active; display state, which indicates what application, view or other information occupies various areas of the touch screen display 212; sensor state, including information obtained from the device's various sensors and input control devices 216; and position information related to the device's position and / or posture.

[0080] The operating system 226 (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 facilitating communication between various hardware components and software components.

[0081] The communication module 228 facilitates communication with other devices through one or more external ports 224, and also includes various software components for processing data received by the RF circuit 208 and / or the external port 224. The external port 224 (e.g., Universal Serial Bus (USB), FireWire, etc.) is suitable for coupling directly to other devices, or indirectly through a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is connected to (trademark of Apple Inc.) devices.

[0082] The contact / motion module 230 optionally detects contact with the touch screen 212 (in conjunction with the display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). The contact / motion module 230 includes various software components for performing various operations related to contact detection, such as determining whether contact has occurred (e.g., detecting a finger press event), determining the contact strength (e.g., the force or pressure of the contact, or a substitute for the force or pressure of the contact), determining whether there is movement of the contact and tracking the movement on the touch-sensitive surface (e.g., detecting one or more finger drag events), and determining whether the contact has stopped (e.g., detecting a finger lift event or contact disconnection). The contact / motion module 230 receives contact data from the touch-sensitive surface. Determining the movement of the contact point optionally includes determining the rate (magnitude), velocity (magnitude and direction) and / or acceleration (change in magnitude and / or direction) of the contact point, the movement of the contact point being represented by a series of contact data. These operations are optionally applied to a single point of contact (e.g., a single finger contact) or multiple points of simultaneous contact (e.g., "multi-touch" / multiple finger contacts). In some embodiments, the contact / motion module 230 and display controller 256 detect contact on the touch pad.

[0083] In some embodiments, the contact / motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has "clicked" an icon). In some embodiments, at least a subset of the intensity thresholds are determined based on software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of specific physical actuators and can be adjusted without changing the physical hardware of the device 200). For example, without changing the touchpad or touchscreen display hardware, the mouse "click" threshold of a touchpad or touchscreen can be set to any one of a large range of predefined thresholds. Additionally, in some specific implementations, a software setting is provided to the user of the device for adjusting one or more intensity thresholds in a set of intensity thresholds (e.g., by adjusting individual intensity thresholds and / or by utilizing a system-level click on an "intensity" parameter to adjust multiple intensity thresholds at once).

[0084] The contact / motion module 230 optionally detects gesture input from a user. Different gestures on a touch-sensitive surface have different contact patterns (e.g., different motions, timings, and / or intensities of the detected contacts). Thus, gestures are optionally detected by detecting specific contact patterns. For example, detecting a finger tap gesture includes detecting a finger press event, and then detecting a finger lift (lift-off) event at the same location (or substantially the same location) as the finger press event (e.g., at the location of an icon). As another example, detecting a finger swipe gesture on a touch-sensitive surface includes detecting a finger press event, then detecting one or more finger drag events, and then detecting a finger lift (lift-off) event.

[0085] The graphics module 232 includes various known software components for rendering and displaying graphics on the touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual characteristics) of the displayed graphics. As used herein, the term "graphics" includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user interface objects including soft keys), digital images, videos, animations, etc.

[0086] In some embodiments, the graphics module 232 stores data representing graphics to be used. Each graphic is optionally assigned a corresponding code. The graphics module 232 receives one or more codes specifying the graphics to be displayed from an application or the like, along with coordinate data and other graphic attribute data if necessary, and then generates screen image data to be output to the display controller 256.

[0087] The tactile feedback module 233 includes various software components for generating instructions used by the one or more tactile output generators 267 to produce tactile output at one or more locations on the device 200 in response to user interaction with the device 200 .

[0088] Text input module 234, which in some examples is a component of graphics module 232, provides a soft keyboard for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application requiring text input).

[0089] The GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to the phone 238 for use in location-based dialing; to the camera 243 as picture / video metadata; and to applications that provide location-based services, such as weather widgets, local yellow pages widgets, and map / navigation widgets).

[0090] The digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functions of the digital assistant. For example, the digital assistant client module 229 can accept voice input (e.g., speech input), text input, touch input and / or gesture input through various user interfaces of the portable multifunction device 200 (e.g., microphone 213, one or more accelerometers 268, touch-sensitive display system 212, one or more optical sensors 264, other input control devices 216, etc.). The digital assistant client module 229 can also provide output in the form of audio (e.g., speech output), output in the form of visual output and / or output in the form of tactile output through various output interfaces of the portable multifunction device 200 (e.g., speaker 211, touch-sensitive display system 212, one or more tactile output generators 267, etc.). For example, the output is provided as voice, sound, reminder, text message, menu, graphic, video, animation, vibration and / or a combination of two or more of the above. During operation, the digital assistant client module 229 communicates with the DA server 106 using the RF circuit 208.

[0091] User data and models 231 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 items, shopping lists, etc.) to provide client-side functionality of the digital assistant. In addition, user data and models 231 include various models for processing user input and determining user intent (e.g., speech recognition models, statistical language models, natural language processing models, knowledge ontologies, task flow models, service models, etc.).

[0092] In some examples, the digital assistant client module 229 utilizes the various sensors, subsystems, and peripherals of the portable multifunction device 200 to collect additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with the user, the current user interaction, and / or the current user input. In some examples, the digital assistant client module 229 provides the context information, or a subset thereof, to the DA server 106 along with the user input to help infer the user's intent. In some examples, the digital assistant also uses the context information to determine how to prepare and transmit output to the user. The context information is referred to as context data.

[0093] In some examples, contextual information accompanying the user input includes sensor information, such as lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information may also include the physical state of the device, such as device orientation, device location, device temperature, power level, speed, acceleration, motion pattern, cellular signal strength, etc. In some examples, information related to the software state of the DA server 106, such as the running process of the portable multifunction device 200, installed programs, past and current network activities, background services, error logs, resource usage, etc., is provided to the DA server 106 as contextual information associated with the user input.

[0094] In some examples, the digital assistant client module 229 selectively provides information stored on the portable multifunction device 200 (e.g., user data 231) in response to a request from the DA server 106. In some examples, the digital assistant client module 229 also elicits additional input from the user via a natural language dialog or other user interface upon request by the DA server 106. The digital assistant client module 229 transmits the additional input to the DA server 106 to assist the DA server 106 in inferring intent and / or implementing the user intent expressed in the user request.

[0095] Reference below 7A to 7C A more detailed description of the digital assistant is provided. It should be appreciated that the digital assistant client module 229 may include any number of sub-modules of the digital assistant module 726 described below.

[0096] Application 236 includes the following modules (or instruction sets) or a subset or superset thereof:

[0097] Contacts module 237 (sometimes referred to as address book or contact list);

[0098] ● Telephone module 238;

[0099] ● Video conferencing module 239;

[0100] ● Email client module 240;

[0101] ●Instant messaging (IM) module 241;

[0102] ●Fitness support module 242;

[0103] ● Camera module 243 for still images and / or video images;

[0104] ● Image management module 244;

[0105] ●Video player module;

[0106] ●Music player module;

[0107] ●Browser module 247;

[0108] ●Calendar module 248;

[0109] ● Widget module 249, which in some examples includes one or more of the following: weather widget 249-1, stock widget 249-2, calculator widget 249-3, alarm clock widget 249-4, dictionary widget 249-5, and other user-acquired widgets and user-created widgets 249-6;

[0110] A widget creator module 250 for forming a user-created widget 249-6;

[0111] ●Search module 251;

[0112] ● Video and music player module 252, which merges the video player module and the music player module;

[0113] ●Notepad module 253;

[0114] ● Map module 254; and / or

[0115] ●Online video module 255.

[0116] Examples of other applications 236 stored in memory 202 include other word processing applications, other image editing applications, drawing applications, rendering applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.

[0117] In combination with the touch screen 212, display controller 256, touch / motion module 230, graphics module 232, and text input module 234, the contact module 237 is used to manage an address book or contact list (for example, stored in the application internal state 292 of the contact module 237 in memory 202 or memory 470), including: adding a name to the address book; deleting a name from the address book; associating a phone number, email address, physical address or other information with a name; associating an image with a name; categorizing and classifying names; providing a phone number or email address to initiate and / or facilitate communications via telephone 238, video conferencing module 239, email 240 or IM 241; and the like.

[0118] In conjunction with RF circuit 208, audio circuit 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, phone module 238 is used to enter a character sequence corresponding to a phone number, access one or more phone numbers in contact module 237, modify an already entered phone number, dial a corresponding phone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As described above, wireless communications use any of a variety of communication standards, protocols, and technologies.

[0119] In combination with the RF circuit 208, the audio circuit 210, the speaker 211, the microphone 213, the touch screen 212, the display controller 256, the optical sensor 264, the optical sensor controller 258, the contact / motion module 230, the graphics module 232, the text input module 234, the contact module 237 and the telephone module 238, the video conferencing module 239 includes executable instructions for initiating, conducting and terminating a video conference between a user and one or more other participants in accordance with user instructions.

[0120] In conjunction with RF circuit 208, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, email client module 240 includes executable instructions for creating, sending, receiving, and managing emails in response to user instructions. In conjunction with image management module 244, email client module 240 makes it very easy to create and send emails with still images or video images captured by camera module 243.

[0121] In conjunction with the RF circuit 208, the touch screen 212, the display controller 256, the contact / motion module 230, the graphics module 232, and the text input module 234, the instant messaging module 241 includes executable instructions for the following operations: inputting a character sequence corresponding to an instant message, modifying previously input characters, transmitting a corresponding instant message (e.g., using a short message service (SMS) or multimedia message service (MMS) protocol for telephone-based instant messaging or using XMPP, SIMPLE, or IMPS for Internet-based instant messaging), receiving instant messages, and viewing received instant messages. In some embodiments, the transmitted and / or received instant messages include graphics, photos, audio files, video files, and / or other attachments such as those supported in MMS and / or enhanced messaging services (EMS). As used herein, "instant messaging" refers to both telephone-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).

[0122] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the contact / motion module 230, the graphics module 232, the text input module 234, the GPS module 235, the map module 254, and the music player module, the fitness support module 242 includes executable instructions for: creating a workout (e.g., with time, distance, and / or calorie burn goals); communicating with fitness sensors (sports equipment); receiving fitness sensor data; calibrating sensors for monitoring fitness; selecting and playing music for fitness; and displaying, storing, and transmitting fitness data.

[0123] In combination with the touch screen 212, the display controller 256, one or more optical sensors 264, the optical sensor controller 258, the touch / motion module 230, the graphics module 232, and the image management module 244, the camera module 243 includes executable instructions for the following operations: capturing still images or videos (including video streams) and storing them in the memory 202, modifying the characteristics of the still images or videos, or deleting the still images or videos from the memory 202.

[0124] In conjunction with the touch screen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, and camera module 243, the image management module 244 includes executable instructions for arranging, modifying (e.g., editing), or otherwise manipulating, labeling, deleting, presenting (e.g., in a digital slide show or album), and storing still images and / or video images.

[0125] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232 and the text input module 234, the browser module 247 includes executable instructions for browsing 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.

[0126] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234, the email client module 240 and the browser module 247, the calendar module 248 includes executable instructions for creating, displaying, modifying and storing calendars and data associated with the calendar (e.g., calendar entries, to-do items, etc.) in accordance with user instructions.

[0127] In conjunction with RF circuit 208, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, text input module 234, and browser module 247, widget module 249 is a mini-application that can be downloaded and used by a user (e.g., weather widget 249-1, stock market widget 249-2, calculator widget 249-3, alarm widget 249-4, and dictionary widget 249-5) or a mini-application created by a user (e.g., user-created widget 249-6). In some embodiments, the widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, the widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! widget).

[0128] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234 and the browser module 247, the widget creator module 250 is used by users to create widgets (e.g., to turn a user-specified portion of a web page into a widget).

[0129] In combination with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232 and text input module 234, the search module 251 includes executable instructions for searching the memory 202 for text, music, sound, images, videos and / or other files that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

[0130] In conjunction with touch screen 212, display controller 256, contact / motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow a 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), as well as executable instructions for displaying, presenting, or otherwise playing back video (e.g., on touch screen 212 or on an external display connected via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player such as an iPod (trademark of Apple Inc.).

[0131] In conjunction with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232 and text input module 234, the notepad module 253 includes executable instructions for creating and managing notes, to-do lists, etc. according to user instructions.

[0132] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234, the GPS module 235 and the browser module 247, the map module 254 is used to receive, display, modify and store maps and data associated with the maps (e.g., driving directions, data related to stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

[0133] In conjunction with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232, audio circuit 210, speaker 211, RF circuit 208, text input module 234, email client module 240, and browser module 247, the online video module 255 includes instructions that allow a user to access, browse, receive (e.g., by streaming and / or downloading), play back (e.g., on the touch screen or on a connected external display via external port 224), send an email with a link to a specific online video, and otherwise manage online videos in one or more file formats (such as H.264). In some embodiments, the instant messaging module 241 is used instead of the email client module 240 to send a link to a specific online video. Further descriptions of online video applications can be found in U.S. Provisional Patent Application No. 60 / 936,562, filed on June 20, 2007, entitled “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” and U.S. Patent Application No. 11 / 968,067, filed on December 31, 2007, entitled “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” the contents of which are hereby incorporated by reference in their entirety.

[0134] Each of the modules and applications described above corresponds to an executable instruction set for performing one or more of the functions described above and the methods described in this patent application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., instruction sets) do not have to be implemented as independent software programs, processes, or modules, and therefore various subsets of these modules may be combined or otherwise rearranged in various embodiments. For example, a video player module may be combined with a music player module into a single module (e.g., Figure 2AIn some embodiments, the memory 202 stores a subset of the above modules and data structures. In addition, the memory 202 stores additional modules and data structures not described above.

[0135] In some embodiments, the device 200 is a device on which the operation of a predefined set of functions is performed exclusively through a touch screen and / or a touch pad. By using a touch screen and / or a touch pad as the primary input control device for the operation of the device 200, the number of physical input control devices (such as push buttons, dials, etc.) on the device 200 is reduced.

[0136] A predefined set of functions that are performed exclusively through a touch screen and / or a touch pad optionally includes navigation between user interfaces. In some embodiments, the touch pad, when touched by a user, navigates the device 200 to a main menu, a home menu, or a root menu from any user interface displayed on the device 200. In such embodiments, a touch pad is used to implement a "menu button." In some other embodiments, the menu button is a physical push button or other physical input control device, rather than a touch pad.

[0137] Figure 2B is a block diagram illustrating exemplary components for event processing according to some embodiments. In some embodiments, memory 202 ( Figure 2A ) or memory 470( Figure 4 ) includes an event classifier 270 (e.g., in the operating system 226) and a corresponding application 236-1 (e.g., any one of the aforementioned applications 237-251, 255, 480-490).

[0138] The event classifier 270 receives event information and determines the application 236-1 and the application view 291 of the application 236-1 to which the event information is to be delivered. The event classifier 270 includes an event monitor 271 and an event distributor module 274. In some embodiments, the application 236-1 includes an application internal state 292 that indicates the current application view displayed on the touch-sensitive display 212 when the application is active or executing. In some embodiments, the device / global internal state 257 is used by the event classifier 270 to determine which application(s) is currently active, and the application internal state 292 is used by the event classifier 270 to determine the application view 291 to which the event information is to be delivered.

[0139] In some embodiments, the application internal state 292 includes additional information, such as one or more of the following: resumption information to be used when application 236-1 resumes execution, user interface state information indicating that information is being displayed or is ready to be displayed by application 236-1, a state queue for enabling a user to return to a previous state or view of application 236-1, and a repeat / undo queue of previous actions taken by the user.

[0140] Event monitor 271 receives event information from peripherals interface 218. Event information includes information about sub-events (e.g., user touches on touch-sensitive display 212 as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I / O subsystem 206 or sensors such as proximity sensor 266, one or more accelerometers 268, and / or microphone 213 (through audio circuit 210). The information that peripherals interface 218 receives from I / O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.

[0141] In some embodiments, event monitor 271 sends requests to peripheral device interface 218 at predetermined intervals. In response, peripheral device interface 218 transmits event information. In other embodiments, peripheral device interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and / or receiving an input for more than a predetermined duration).

[0142] In some embodiments, the event classifier 270 also includes a hit view determination module 272 and / or an active event identifier determination module 273.

[0143] When the touch-sensitive display 212 displays more than one view, the hit view determination module 272 provides software procedures for determining where within one or more views a sub-event has occurred. A view consists of controls and other elements that a user can see on the display.

[0144] Another aspect of the user interface associated with an application is a set of views, sometimes referred to herein as application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of the respective application) in which a touch is detected correspond to a programmatic hierarchy of the application or a programmatic level within the view hierarchy. For example, the lowest level view in which a touch is detected is called a hit view, and the set of events that are considered to be correct input is determined at least in part based on the hit view of the initial touch that started the touch-based gesture.

[0145] The hit view determination module 272 receives information related to sub-events of touch-based gestures. When an application has multiple views organized in a hierarchy, the hit view determination module 272 identifies the hit view as the lowest view in the hierarchy that should handle the sub-events. In most cases, the hit view is the lowest level view in which the initiating sub-event (e.g., the first sub-event in a sequence of sub-events that form an event or potential event) occurs. Once a hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it is identified as the hit view.

[0146] Active event recognizer determination module 273 determines which view or views within the view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of the sub-event are actively participating views, and therefore determines that all actively participating views should receive a particular sequence of sub-events. In other embodiments, even if a touch sub-event is completely confined to an area associated with one particular view, higher views in the hierarchy will still remain as actively participating views.

[0147] Event distributor module 274 distributes event information to event identifiers (e.g., event identifier 280). In embodiments including active event identifier determination module 273, event distributor module 274 delivers the event information to the event identifier determined by active event identifier determination module 273. In some embodiments, event distributor module 274 stores the event information in an event queue, which is retrieved by corresponding event receiver 282.

[0148] In some embodiments, operating system 226 includes event classifier 270. Alternatively, application 236-1 includes event classifier 270. In yet another embodiment, event classifier 270 is a standalone module or is part of another module stored in memory 202, such as contact / motion module 230.

[0149] In some embodiments, application 236-1 includes multiple event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events occurring in a corresponding view of the user interface of the application. Each application view 291 of application 236-1 includes one or more event identifiers 280. Typically, the corresponding application view 291 includes multiple event identifiers 280. In other embodiments, one or more event identifiers in event identifiers 280 are part of an independent module, which is a higher-level object such as a user interface toolkit (not shown) or application 236-1 from which methods and other properties are inherited. In some embodiments, the corresponding event handler 290 includes one or more of the following: data updater 276, object updater 277, GUI updater 278, and / or event data 279 received from event classifier 270. Event handler 290 utilizes or calls data updater 276, object updater 277 or GUI updater 278 to update application internal state 292. Alternatively, one or more of the application views 291 include one or more corresponding event handlers 290. In addition, in some embodiments, one or more of the data updater 276, the object updater 277, and the GUI updater 278 are included in the corresponding application view 291.

[0150] A corresponding event identifier 280 receives event information (e.g., event data 279) from event classifier 270 and identifies an event from the event information. Event identifier 280 includes an event receiver 282 and an event comparator 284. In some embodiments, event identifier 280 also includes metadata 283 and at least a subset of event delivery instructions 288 (which includes sub-event delivery instructions).

[0151] The event receiver 282 receives event information from the event classifier 270. The event information includes information about sub-events such as touches or touch movements. Depending on the sub-event, the event information also includes additional information, such as the location of the sub-event. When the sub-event involves the movement of a touch, the event information also includes the rate and direction of the sub-event. In some embodiments, the event includes the device rotating from one orientation to another orientation (e.g., from a longitudinal orientation to a transverse orientation, or vice versa), and the event information includes corresponding information about the current orientation of the device (also referred to as the device posture).

[0152] Event comparator 284 compares event information with 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 comparator 284 includes event definition 286. Event definition 286 includes the definition of an event (e.g., a predefined sub-event sequence), such as event 1 (287-1), event 2 (287-2), and other events. In some embodiments, sub-events in an event (e.g., event 1 (287-1) or event 2 (287-2)) include, for example, touch start, touch end, touch move, touch cancel, and multi-touch. In one example, the definition of event 1 (287-1) is a double-click on a displayed object. For example, a double-click includes a first touch (touch start) of a predetermined duration on a displayed object, a first lift-off (touch end) of a predetermined duration, a second touch (touch start) of a predetermined duration on a displayed object, and a second lift-off (touch end) of a predetermined duration. In another example, the definition of event 2 (287-2) is a drag on a displayed object. For example, dragging includes a touch (or contact) of a predetermined duration on a displayed object, movement of the touch on the touch-sensitive display 212, and lifting of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.

[0153] In some embodiments, event definition 286 includes a definition of an event for a corresponding user interface object. In some embodiments, event comparator 284 performs a hit test to determine which user interface object is associated with a sub-event. For example, in an application view displaying three user interface objects on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs 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 corresponding event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with a sub-event and the object that triggered the hit test.

[0154] In some embodiments, the definition of a corresponding event (e.g., event 1 (287-1) or event 2 (287-2)) also includes a delay action that delays delivery of the event information until it has been determined that the sub-event sequence does or does not correspond to the event type of the event identifier.

[0155] When a corresponding event recognizer 280 determines that a sequence of sub-events does not match any event in event definition 286, the corresponding event recognizer 280 enters an event impossible, event failed, or event ended state, after which subsequent sub-events of the touch-based gesture are ignored. In this case, other event recognizers (if any) that remain active for the hit view continue to track and process sub-events of the ongoing touch-based gesture.

[0156] In some embodiments, the corresponding event identifier 280 includes metadata 283 with configurable properties, flags, and / or lists that indicate how the event delivery system should perform sub-event delivery to actively participating event identifiers. In some embodiments, metadata 283 includes configurable properties, flags, and / or lists that indicate how event identifiers interact or can interact with each other. In some embodiments, metadata 283 includes configurable properties, flags, and / or lists that indicate whether sub-events are delivered to different levels in a view or programmatic hierarchy.

[0157] In some embodiments, when one or more specific sub-events of an event are identified, the corresponding event identifier 280 activates the event handler 290 associated with the event. In some embodiments, the corresponding event identifier 280 delivers the event information associated with the event to the event handler 290. Activating the event handler 290 is different from sending (and deferred sending) the sub-events to the corresponding hit view. In some embodiments, the event identifier 280 throws a tag associated with the identified event, and the event handler 290 associated with the tag obtains the tag and performs a predefined process.

[0158] In some embodiments, the event delivery instructions 288 include a sub-event delivery instruction that delivers event information about a sub-event without activating an event handler. Instead, the sub-event delivery instruction delivers the event information to an event handler associated with a sub-event sequence or to an actively participating view. The event handler associated with the sub-event sequence or with an actively participating view receives the event information and executes a predetermined process.

[0159] In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates phone numbers used in contact module 237 or stores video files used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates new user interface objects or updates the positioning of user interface objects. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends the display information to graphics module 232 for display on a touch-sensitive display.

[0160] In some embodiments, event handler 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of the corresponding application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.

[0161] It should be understood that the above discussion of event processing for user touches on a touch-sensitive display also applies to other forms of user input that utilize input devices to operate the multifunction device 200, and not all user input is initiated on the touch screen. For example, mouse movement and mouse button presses, optionally in conjunction with single or multiple keyboard presses or holddowns; contact movement on a touch pad, such as tapping, dragging, scrolling, etc.; stylus input; movement of the device; verbal commands; detected eye movement; biometric input; and / or any combination thereof are optionally used as input corresponding to sub-events defining the event to be distinguished.

[0162] Figure 3A portable multifunction device 200 with a touch screen 212 according to some embodiments is shown. The touch screen optionally displays one or more graphics within a user interface (UI) 300. In this embodiment and other embodiments described below, a user can select one or more of these graphics by, for example, making gestures on the graphics using one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, when the user interrupts contact with one or more graphics, selection of one or more graphics will occur. In some embodiments, gestures optionally include one or more taps, one or more swipes (from left to right, from right to left, up and / or down) and / or rolling of fingers that have been in contact with the device 200 (from right to left, from left to right, up and / or down). In some specific implementations or in some cases, inadvertent contact with a graphic will not select the graphic. For example, when the gesture corresponding to the selection is a tap, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application.

[0163] The device 200 also includes one or more physical buttons, such as a "home" or menu button 304. As previously described, the menu button 304 is used to navigate to any application 236 in a set of applications executing on the device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on the touch screen 212.

[0164] In some embodiments, the device 200 includes a touch screen 212, a menu button 304, a push button 306 for powering the device on / off and for locking the device, one or more volume adjustment buttons 308, a user identity module (SIM) card slot 310, an earphone jack 312, and a docking / charging external port 224. The push button 306 is optionally used to turn the device on / off by pressing the button and keeping the button in a pressed state for a predefined time interval; lock the device by pressing the button and releasing the button before the predefined time interval passes; and / or unlock the device or initiate an unlocking process. In an alternative embodiment, the device 200 also accepts speech input for activating or deactivating certain functions through a microphone 213. The device 200 also optionally includes one or more contact strength sensors 265 for detecting the strength of contact on the touch screen 212, and / or one or more tactile output generators 267 for generating tactile output for a user of the device 200.

[0165] Figure 44 is a block diagram of an exemplary multi-function device with a display and a touch-sensitive surface according to some embodiments. Device 400 does not have to be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a children's learning toy), a game system, or a control device (for example, a home controller or an industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communication interfaces 460, a memory 470, and one or more communication buses 420 for interconnecting these components. Communication bus 420 optionally includes circuits (sometimes referred to as chipsets) that interconnect system components and control communications between system components. Device 400 includes an input / output (I / O) interface 430 with a display 440, which is typically a touch screen display. I / O interface 430 also optionally includes a keyboard and / or mouse (or other pointing device) 450 and a touchpad 455, a tactile output generator 457 for generating tactile output on device 400 (for example, similar to the above reference Figure 2A The one or more tactile output generators 267 described above), sensors 459 (e.g., optical sensors, acceleration sensors, proximity sensors, touch sensors, and / or contact intensity sensors (similar to those described above with reference to Figure 2A The memory 470 may include one or more storage devices located away from the CPU 410. In some embodiments, the memory 470 stores information related to the portable multifunction device 200 ( Figure 2A ) or a subset thereof. In addition, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores a drawing module 480, a rendering module 482, a word processing module 484, a website creation module 486, a disk editing module 488, and / or a spreadsheet module 490, while portable multifunction device 200( Figure 2A )'s memory 202 optionally does not store these modules.

[0166] Figure 4Each of the above-mentioned elements in some examples is stored in one or more previously mentioned memory devices. Each module in the above-mentioned modules corresponds to an instruction set for performing the functions described above. The above-mentioned modules or programs (e.g., instruction sets) do not have to be implemented as independent software programs, processes or modules, so the various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 stores a subset of the above-mentioned modules and data structures. In addition, memory 470 stores additional modules and data structures not described above.

[0167] Attention is now turned to embodiments of a user interface that may be implemented on, for example, portable multifunction device 200.

[0168] Figure 5A An exemplary user interface of an application menu on portable multifunction device 200 is shown according to some embodiments. A similar user interface is implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:

[0169] Signal strength indicators 502 for wireless communications such as cellular signals and Wi-Fi signals;

[0170] ●Time 504;

[0171] ●Bluetooth indicator 505;

[0172] ●Battery status indicator 506;

[0173] A tray 508 with icons of commonly used applications such as:

[0174] o an icon 516 labeled “Phone” of the phone module 238, which optionally includes an indicator 514 of the number of missed calls or voice messages;

[0175] o an icon 518 labeled “Mail” of the email client module 240 , which optionally includes an indicator 510 of the number of unread emails;

[0176] o an icon 520 labeled "Browser" of the browser module 247; and

[0177] o an icon 522 labeled “iPod” of the video and music player module 252 (also referred to as the iPod (trademark of Apple Inc.) module 252); and

[0178] Icons for other apps, such as:

[0179] o Icon 524 labeled "Messages" of IM module 241;

[0180] o Icon 526 labeled “Calendar” of calendar module 248;

[0181] o Icon 528 labeled "Photos" of the image management module 244;

[0182] o Icon 530 labeled “Camera” of camera module 243;

[0183] o Icon 532 labeled “Online Video” of the online video module 255;

[0184] o Icon 534 labeled “Stock Market” of the Stock Market widget 249-2;

[0185] o Icon 536 labeled “Map” of the map module 254;

[0186] o Icon 538 labeled “Weather” of the Weather widget 249-1;

[0187] o Icon 540 labeled “Clock” of the Alarm Clock widget 249-4;

[0188] o An icon 542 labeled “Fitness Support” of the fitness support module 242;

[0189] o Icon 544 labeled "Notepad" of the Notepad module 253; and

[0190] o An icon 546 labeled “Settings” for a settings application or module that provides access to settings for the device 200 and its various applications 236 .

[0191] It should be noted that Figure 5A The icon labels shown are merely exemplary. For example, the icon 522 of the video and music player module 252 is optionally labeled "Music" or "Music Player". Other labels are optionally used for various application icons. In some embodiments, the label of the corresponding application icon includes the name of the application corresponding to the corresponding application icon. In some embodiments, the label of a particular application icon is different from the name of the application corresponding to the particular application icon.

[0192] Figure 5B A touch-sensitive surface 551 (eg, touch screen display 212) is shown having a touch-sensitive surface 551 (eg, Figure 4 A device (e.g., a tablet or touch pad 455) Figure 4Device 400 also optionally includes one or more contact intensity sensors (e.g., one or more of sensors 459) for detecting intensity of contacts on touch-sensitive surface 551 and / or one or more tactile output generators 457 for generating tactile output for a user of device 400.

[0193] Although some of the examples that follow will be given with reference to input on a touch screen display 212 (where a touch-sensitive surface and a display are combined), in some embodiments, the device detects input on a touch-sensitive surface that is separate from the display, such as Figure 5B In some embodiments, the touch-sensitive surface (e.g., Figure 5B 551) has a main axis (e.g., Figure 5B 553) corresponding to the principal axis (for example, Figure 5B According to these embodiments, the device detects a position corresponding to a corresponding position on the display (e.g., Figure 5B , 560 corresponds to 568 and 562 corresponds to 570) at a contact with touch-sensitive surface 551 (eg, Figure 5B Thus, on a touch-sensitive surface (e.g., Figure 5B 551) and a display of a multi-function device (e.g., Figure 5B When the user input detected by the device on the touch-sensitive surface (e.g., contacts 560 and 562 and their movement) is separated, the device is used to manipulate the user interface on the display. It should be understood that similar methods are optionally used for other user interfaces described herein.

[0194] Additionally, although the following examples are primarily given with reference to finger inputs (e.g., finger contacts, single-finger tap gestures, finger swipe gestures), it should be understood that in some embodiments, one or more of these finger inputs are replaced by input from another input device (e.g., mouse-based input or stylus input). For example, a swipe gesture is optionally replaced by a mouse click (e.g., instead of contact), followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is optionally replaced by a mouse click when the cursor is over the location of the tap gesture (e.g., instead of detecting contact, followed by ceasing to detect contact). Similarly, when multiple user inputs are detected simultaneously, it should be understood that multiple computer mice are optionally used simultaneously, or a mouse and finger contact are optionally used simultaneously.

[0195] Fig. 6AAn exemplary personal electronic device 600 is shown. Device 600 includes a body 602. In some embodiments, device 600 includes a body 602 relative to devices 200 and 400 (e.g., Figures 2A to 4 ) or some or all of the features described in the foregoing. In some embodiments, device 600 has a touch-sensitive display screen 604, referred to hereinafter as touch screen 604. As an alternative or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or touch-sensitive surface) has one or more intensity sensors for detecting the intensity of contact (e.g., touch) being applied. One or more intensity sensors of touch screen 604 (or touch-sensitive surface) provide output data representing the intensity of the touch. The user interface of device 600 responds to touch based on the intensity of the touch, which means that touches of different intensities can invoke different user interface operations on device 600.

[0196] Technology for detecting and processing touch intensity may be found, for example, in related applications: International patent application serial number PCT / US2013 / 040061, filed on May 8, 2013, entitled “Device, Method, and Graphical User Interface for Displaying UserInterface Objects Corresponding to an Application,” and International patent application serial number PCT / US2013 / 069483, filed on November 11, 2013, entitled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” each of which is hereby incorporated by reference in its entirety.

[0197] In some embodiments, the device 600 has one or more input mechanisms 606 and 608. The input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, the device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, may allow the device 600 to be attached to, for example, hats, glasses, earrings, necklaces, shirts, jackets, bracelets, watchbands, bracelets, pants, belts, shoes, wallets, backpacks, etc. These attachment mechanisms allow the user to wear the device 600.

[0198] Figure 6BAn exemplary personal electronic device 600 is shown. In some embodiments, the device 600 includes Figure 2A , Figure 2B and Figure 4 Some or all of the components described. Device 600 has a bus 612 that operatively couples an I / O portion 614 to one or more computer processors 616 and a memory 618. I / O portion 614 is connected to a display 604, which may have a touch-sensitive component 622 and, optionally, also a touch-intensity sensitive component 624. In addition, I / O portion 614 is connected to a communication unit 630 for receiving application and operating system data using Wi-Fi, Bluetooth, near field communication (NFC), cellular, and / or other wireless communication technologies. Device 600 includes input mechanisms 606 and / or 608. For example, input mechanism 606 is a rotatable input device or a depressible input device and a rotatable input device. In some examples, input mechanism 608 is a button.

[0199] In some examples, input mechanism 608 is a microphone. Personal electronic device 600 includes, for example, various sensors such as GPS sensor 632, accelerometer 634, orientation sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and / or combinations thereof, all of which are operably connected to I / O portion 614.

[0200] The memory 618 of the personal electronic device 600 is a non-transitory computer-readable storage medium for storing computer-executable instructions that, when executed by one or more computer processors 616, for example, cause the computer processors to perform the above-described techniques and processes. The computer-executable instructions are also stored and / or transmitted, for example, in any non-transitory computer-readable storage medium for use by or in conjunction with an instruction execution system, device, or apparatus, such as a computer-based system, a system containing a processor, or other system that can obtain instructions from an instruction execution system, device, or apparatus and execute the instructions. The personal electronic device 600 is not limited to Figure 6B components and configurations, but may include other components or additional components in a variety of configurations.

[0201] As used herein, the term "indicative representation" refers to, for example, a display in device 200, 400, 600, and / or 900 ( Figure 2A , Figure 4 , FIG. 6A to FIG. 6B , Fig. 9 as well as FIG. 10A to FIG. 10B ) is a user-interactive graphical user interface object displayed on a display screen of a computer. For example, an image (e.g., an icon), a button, and text (e.g., a hyperlink) each constitute an affordance.

[0202] As used herein, the term "focus selector" refers to an input element used to indicate the current portion of a user interface that a user is interacting with. In some implementations that include a cursor or other position marker, the cursor acts as a "focus selector" such that when the cursor is over a particular user interface element (e.g., a button, window, slider, or other user interface element), a focus selector is displayed on a touch-sensitive surface (e.g., Figure 4 Touchpad 455 or Figure 5B In the event that an input (e.g., a press input) is detected on the touch-sensitive surface 551 in FIG. 5A , the particular user interface element is adjusted according to the detected input. In the case that a touch screen display (e.g., Figure 2A A touch sensitive display system 212 or Figure 5A In some implementations of the touch screen 212 in FIG. 1 , a contact detected on the touch screen acts as a “focus selector” such that when an input (e.g., a press input by the contact) is detected at the location of a particular user interface element (e.g., a button, window, slider, or other user interface element) on the touch screen display, the particular user interface element is adjusted in accordance with the detected input. In some implementations, the focus moves from one area of ​​the user interface to another area of ​​the user interface without corresponding movement of a cursor or movement of a contact on the touch screen display (e.g., by using a tab key or arrow keys to move the focus from one button to another); in these implementations, the focus selector moves in accordance with the movement of the focus between different areas of the user interface. Regardless of the specific form the focus selector takes, the focus selector is typically a user interface element (or contact on the touch screen display) that is controlled by the user to deliver the user's intended interaction with the user interface (e.g., by indicating to the device the element of the user interface with which the user desires to interact). For example, when a press input is detected on a touch-sensitive surface (e.g., a touchpad or touchscreen), the position of a focus selector (e.g., a cursor, contact, or selection box) over a corresponding button will indicate that the user intends to activate the corresponding button (rather than other user interface elements shown on the device display).

[0203] As used in the specification and claims, the term "characteristic intensity" of a contact refers to a characteristic of a contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is optionally based on a predefined number of intensity samples or a set of intensity samples collected during a predetermined time period (e.g., 0.05 seconds, 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, 2 seconds, 5 seconds, 10 seconds) relative to a predefined event (e.g., after contact is detected, before contact is detected to be lifted off, before or after contact starts to move, before contact ends, before or after contact intensity is detected to increase, and / or before or after contact intensity is detected to decrease). The characteristic intensity of a contact is optionally based on one or more of the following: the maximum value of the contact intensity, the mean value of the contact intensity, the average value of the contact intensity, the value at the top 10% of the contact intensity, the half-maximum value of the contact intensity, the 90% maximum value of the contact intensity, etc. In some embodiments, the duration of the contact is used when determining the characteristic intensity (e.g., when the characteristic intensity is the average value of the intensity of the contact over time). In some embodiments, the feature strength is compared to a set of one or more strength thresholds to determine whether the user has performed an operation. For example, the set of one or more strength thresholds includes a first strength threshold and a second strength threshold. In this example, a contact whose feature strength does not exceed the first threshold results in a first operation, a contact whose feature strength exceeds the first strength threshold but does not exceed the second strength threshold results in a second operation, and a contact whose feature strength exceeds the second threshold results in a third operation. In some embodiments, a comparison between the feature strength and one or more thresholds is used to determine whether to perform one or more operations (e.g., whether to perform the corresponding operation or give up performing the corresponding operation), rather than to determine whether to perform the first operation or the second operation.

[0204] In some embodiments, portions of a gesture are identified for determining characteristic strength. For example, a touch-sensitive surface receives a continuous swipe contact that transitions from a starting position and reaches an end position, where the strength of the contact increases. In this example, the characteristic strength of the contact at the end position is based only on the portion of the continuous swipe contact, rather than the entire swipe contact (e.g., the portion of the swipe contact that is only located at the end position). In some embodiments, a smoothing algorithm is applied to the strength of the swipe contact before determining the characteristic strength of the contact. For example, a smoothing algorithm optionally includes one or more of the following: an unweighted sliding average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and / or an exponential smoothing algorithm. In some cases, these smoothing algorithms eliminate narrow peaks or depressions in the strength of the swipe contact to achieve the purpose of determining the characteristic strength.

[0205] The intensity of a contact on a touch-sensitive surface is characterized relative to one or more intensity thresholds such as a contact detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and / or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform an operation typically associated with clicking a button of a physical mouse or trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform an operation different from the operation typically associated with clicking a button of a physical mouse or trackpad. In some embodiments, when a contact having a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact detection intensity threshold, contacts lower than the nominal contact detection intensity threshold are no longer detected) is detected, the device will move the focus selector according to the movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally speaking, unless otherwise stated, these intensity thresholds are consistent between different groups of user interface illustrations.

[0206] An increase in contact feature intensity from an intensity below a light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a "light press" input. An increase in contact feature intensity from an intensity below a deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a "deep press" input. An increase in contact feature intensity from an intensity below a contact detection intensity threshold to an intensity between the contact detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting contact on the touch surface. A decrease in contact feature intensity from an intensity above the contact detection intensity threshold to an intensity below the contact detection intensity threshold is sometimes referred to as detecting contact lifted from the touch surface. In some embodiments, the contact detection intensity threshold is zero. In some embodiments, the contact detection intensity threshold is greater than zero.

[0207] In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a corresponding press input or in response to detecting a corresponding press input performed using a corresponding contact (or multiple contacts), wherein the corresponding press input is detected at least in part based on detecting that the strength of the contact (or multiple contacts) increases to above a press input strength threshold. In some embodiments, the corresponding operation is performed in response to detecting that the strength of the corresponding contact increases to above the press input strength threshold (e.g., a "down stroke" of the corresponding press input). In some embodiments, the press input includes an increase in the strength of the corresponding contact to above the press input strength threshold and the strength of the contact subsequently decreases to below the press input strength threshold, and the corresponding operation is performed in response to detecting that the strength of the corresponding contact subsequently decreases to below the press input threshold (e.g., an "up stroke" of the corresponding press input).

[0208] In some embodiments, the device employs intensity hysteresis to avoid unexpected inputs sometimes referred to as "jitter," where the device defines or selects a hysteresis intensity threshold that has a predefined relationship to a press input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press input intensity threshold, or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press input intensity threshold). Thus, in some embodiments, a press input includes an increase in the intensity of the corresponding contact to above the press input intensity threshold and a subsequent decrease in the intensity of the contact to below the hysteresis intensity threshold corresponding to the press input intensity threshold, and a corresponding operation is performed in response to detecting that the intensity of the corresponding contact subsequently decreases to below the hysteresis intensity threshold (e.g., an "upward stroke" of the corresponding press input). Similarly, in some embodiments, a press input is detected only when the device detects that the contact intensity increases from an intensity equal to or below the hysteresis intensity threshold to an intensity equal to or above the press input intensity threshold and, optionally, that the contact intensity subsequently decreases to an intensity equal to or below the hysteresis intensity, and a corresponding operation is performed in response to detecting the press input (e.g., an increase in contact intensity or a decrease in contact intensity, depending on the circumstances).

[0209] For ease of explanation, optionally, a description of an operation performed in response to a press input associated with a press input intensity threshold or in response to a gesture including a press input is triggered in response to detecting any of the following: contact intensity increasing to above the press input intensity threshold, contact intensity increasing from an intensity below a hysteresis intensity threshold to an intensity above the press input intensity threshold, contact intensity decreasing to below the press input intensity threshold, and / or contact intensity decreasing to below a hysteresis intensity threshold corresponding to the press input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting that the intensity of the contact decreases below the press input intensity threshold, the operation is optionally performed in response to detecting that the intensity of the contact decreases below a hysteresis intensity threshold that corresponds to and is less than the press input intensity threshold.

[0210] 3. Digital Assistant System

[0211] Fig. 7A A block diagram of a digital assistant system 700 according to various examples is shown. In some examples, the digital assistant system 700 is implemented on a stand-alone computer system. In some examples, the digital assistant system 700 is distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant are divided into a server portion and a client portion, where the client portion is located on one or more user devices (e.g., devices 104, 122, 200, 400, 600, or 900) and communicates with the server portion (e.g., server system 108) via one or more networks, such as Figure 1 In some examples, the digital assistant system 700 is Figure 1Specific implementation of the server system 108 (and / or DA server 106) shown in . It should be noted that the digital assistant system 700 is only one example of a digital assistant system, and the digital assistant system 700 has more or fewer components than shown, combines two or more components, or may have a different configuration or layout of components. Fig. 7A The various components shown in the drawings are implemented in hardware, software instructions for execution by one or more processors, firmware (including one or more signal processing integrated circuits and / or application specific integrated circuits), or a combination thereof.

[0212] The digital assistant system 700 includes a memory 702, an input / output (I / O) interface 706, a network communication interface 708, and one or more processors 704. These components can communicate with each other via one or more communication buses or signal lines 710.

[0213] In some examples, memory 702 includes non-transitory computer-readable media, such as high-speed random access memory and / or non-volatile computer-readable storage media (e.g., one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).

[0214] In some examples, the I / O interface 706 couples the input / output devices 716 of the digital assistant system 700, such as a display, keyboard, touch screen, and microphone, to the user interface module 722. The I / O interface 706 and the user interface module 722 receive user input (e.g., voice input, keyboard input, touch input, etc.) and process the input accordingly. In some examples, for example, when the digital assistant is implemented on a stand-alone user device, the digital assistant system 700 includes a plurality of components related to the user interface module 722. Figure 2A , Figure 4 , FIG. 6A to FIG. 6B , Fig. 9 and FIG. 10A to FIG. 10B In some examples, digital assistant system 700 represents a server portion of a specific implementation of a digital assistant, and can interact with a user through a client-side portion located on a user device (e.g., device 104, 200, 400, 600, or 900).

[0215] In some examples, the network communication interface 708 includes one or more wired communication ports 712 and / or wireless transmission and reception circuits 714. One or more wired communication ports receive and send communication signals via one or more wired interfaces such as Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuit 714 receives RF signals and / or optical signals from a communication network and other communication devices and sends RF signals and / or optical signals to a communication network and other communication devices. Wireless communication uses any of a variety of communication standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. The network communication interface 708 enables the digital assistant system 700 to communicate with other devices through a network, 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).

[0216] In some examples, the memory 702 or the computer-readable storage medium of the memory 702 stores programs, modules, instructions, and data structures, including all or a subset of the following: operating system 718, communication module 720, user interface module 722, one or more applications 724, and digital assistant module 726. Specifically, the memory 702 or the computer-readable storage medium of the memory 702 stores instructions for performing the above processes. One or more processors 704 execute these programs, modules, and instructions, and read data from or write data to data structures.

[0217] The operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.), and facilitates communication between various hardware, firmware, and software components.

[0218] The communication module 720 facilitates communication between the digital assistant system 700 and other devices through the network communication interface 708. For example, the communication module 720 and the electronic devices (such as Figure 2A , Figure 4 and FIG. 6A to FIG. 6B The communication module 720 also includes various components for processing data received by the wireless circuit 714 and / or the wired communication port 712.

[0219] The user interface module 722 receives commands and / or input from the user (e.g., from a keyboard, touch screen, pointing device, controller, and / or microphone) via the I / O interface 706 and generates user interface objects on the display. The user interface module 722 also prepares output (e.g., speech, sound, animation, text, icon, vibration, tactile feedback, lighting, etc.) and transmits it to the user via the I / O interface 706 (e.g., through a display, audio channel, speaker, touchpad, etc.).

[0220] Applications 724 include programs and / or modules configured to be executed by the one or more processors 704. For example, if the digital assistant system is implemented on a stand-alone user device, applications 724 include user applications such as games, calendar applications, navigation applications, or email applications. If the digital assistant system 700 is implemented on a server, applications 724 include, for example, resource management applications, diagnostic applications, or scheduling applications.

[0221] The memory 702 also stores a digital assistant module 726 (or the server portion of the digital assistant). In some examples, the digital assistant module 726 includes the following submodules or subsets or supersets thereof: an input / output processing module 728, a speech-to-text (STT) processing module 730, a natural language processing module 732, a dialog flow processing module 734, a task flow processing module 736, a service processing module 738, and a speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726 or a subset or superset thereof: a knowledge ontology 760, a vocabulary index 744, user data 748, a task flow model 754, a service model 756, and an ASR system 758.

[0222] In some examples, using the processing modules, data, and models implemented in the digital assistant module 726, the digital assistant may perform at least some of the following: converting speech input into text; identifying user intent expressed in natural language input received from a user; proactively eliciting and obtaining information needed to fully infer user intent (e.g., by disambiguating words, games, intents, etc.); determining a task flow for satisfying the inferred intent; and executing the task flow to satisfy the inferred intent.

[0223] In some examples, such as Figure 7B As shown in FIG. , the I / O processing module 728 can be Fig. 7A The I / O device 716 interacts with the user or through Fig. 7AThe network communication interface 708 in interacts with a user device (e.g., device 104, 200, 400, or 600) to obtain user input (e.g., speech input) and provide a response to the user input (e.g., as speech output). The I / O processing module 728 optionally obtains context information associated with the user input from the user device along with or shortly after receiving the user input. The context information includes user-specific data, vocabulary, and / or preferences related to the user input. In some examples, the context information also includes the software state and hardware state of the user device when the user request is received, and / or information related to the user's surrounding environment when the user request is received. In some examples, the I / O processing module 728 also sends follow-up questions related to the user request to the user and receives answers from the user. When the user request is received by the I / O processing module 728 and the user request includes speech input, the I / O processing module 728 forwards the speech input to the STT processing module 730 (or speech recognizer) for speech-to-text conversion.

[0224] The STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input received by the I / O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech preprocessor. The front-end speech preprocessor extracts representative features from the speech input. For example, the front-end speech preprocessor performs a Fourier transform on the speech input to extract the spectral features that characterize the speech input as a sequence of representative multidimensional vectors. In addition, each ASR system 758 includes one or more speech recognition models (e.g., acoustic models and / or language models) and implements one or more speech recognition engines. Examples of speech recognition models include hidden Markov models, Gaussian mixture models, deep neural network models, n-gram language models, and other statistical models. Examples of speech recognition engines include engines based on dynamic time warping and engines based on weighted finite state transformers (WFST). One or more speech recognition models and one or more speech recognition engines are used to process the extracted representative features of the front-end speech preprocessor to produce intermediate recognition results (e.g., phonemes, phoneme strings, and sub-words), and finally produce text recognition results (e.g., words, word strings, or symbol sequences). In some examples, the speech input is at least partially processed by a third-party service or processed on the user's device (e.g., device 104, 200, 400, or 600) to produce recognition results. Once the STT processing module 730 generates a recognition result containing a text string (e.g., a word, or a sequence of words, or a sequence of symbols), the recognition result is transmitted to the natural language processing module 732 for intention inference. In some examples, the STT processing module 730 generates multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or symbols corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence score, the STT processing module 730 ranks the candidate text representations and provides the n best (e.g., the n highest ranked) candidate text representations to the natural language processing module 732 for intent inference, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is delivered to the natural language processing module 732 for intent inference. For another example, the 5 highest ranked (n=5) candidate text representations are delivered to the natural language processing module 732 for intent inference.

[0225] Further details regarding speech-to-text processing are described in U.S. Utility Patent Application Serial No. 13 / 236,942, filed on September 20, 2011, entitled “Consolidating Speech Recognition Results,” the entire disclosure of which is incorporated herein by reference.

[0226] In some examples, the STT processing module 730 includes a vocabulary of recognizable words and / or accesses the vocabulary via the phonetic alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in the speech recognition phonetic alphabet. Specifically, the vocabulary of recognizable words includes words associated with multiple candidate pronunciations. For example, the vocabulary includes words associated with and In some examples, the candidate pronunciations for the words are determined based on the spelling of the words and one or more linguistic and / or phonetic rules. In some examples, the candidate pronunciations are manually generated, for example, based on a known standard pronunciation.

[0227] In some examples, candidate pronunciations are ranked based on their prevalence. Ranked higher than Because the former is a more common pronunciation (e.g., among all users, for users in a particular geographic region, or for any other suitable subset of users). In some examples, the candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, a custom candidate pronunciation is ranked higher than a standard candidate pronunciation. This can be used to identify proper nouns with unique pronunciations that deviate from the standard pronunciation. In some examples, the candidate pronunciation is associated with one or more speech features such as geographic origin, country, or ethnicity. For example, the candidate pronunciation associated with the United States, while the candidate pronunciation associated with the United Kingdom. In addition, the ranking of the candidate pronunciations is based on one or more characteristics of the user (e.g., geographic origin, country, ethnicity, etc.) in a user profile stored on the device. For example, it may be determined from the user profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciations are ranked (associated with the United States) Comparable candidate pronunciations (associated with the United Kingdom) is ranked higher. In some examples, one of the ranked candidate pronunciations may be selected as the predicted pronunciation (eg, the most likely pronunciation).

[0228] When speech input is received, the STT processing module 730 is used to determine (e.g., using an acoustic model) a phoneme corresponding to the speech input and then attempts to determine (e.g., using a language model) a word that matches the phoneme. For example, if the STT processing module 730 first identifies a phoneme sequence corresponding to a portion of the speech input It can then determine based on the lexical index 744 that the sequence corresponds to the word "tomato".

[0229] In some examples, the STT processing module 730 uses fuzzy matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines the phoneme sequence corresponds to the word "tomato", even though this particular phoneme sequence is not a candidate phoneme sequence for that word.

[0230] The natural language processing module 732 ("natural language processor") of the digital assistant obtains the n best candidate text representations ("word sequences" or "symbol sequences") generated by the STT processing module 730, and attempts to associate each candidate text representation with one or more "executable intents" recognized by the digital assistant. An "executable intent" (or "user intent") represents a task that can be executed by the digital assistant and can have an associated task flow implemented in the task flow model 754. The associated task flow is a series of programmed actions and steps taken by the digital assistant to perform the task. The scope of the digital assistant's capabilities depends on the number and variety of task flows that have been implemented and stored in the task flow model 754, or in other words, on the number and variety of "executable intents" recognized by the digital assistant. However, the effectiveness of the digital assistant also depends on the assistant's ability to infer the correct "one or more executable intents" from user requests expressed in natural language.

[0231] In some examples, in addition to the sequence of words or symbols obtained from the STT processing module 730, the natural language processing module 732 also receives context information associated with the user request, for example, from the I / O processing module 728. The natural language processing module 732 optionally uses the context information to clarify, supplement, and / or further qualify the information contained in the candidate text representation received from the STT processing module 730. The context information includes, for example, user preferences, hardware and / or software state of the user's device, sensor information collected before, during, or shortly after the user's request, previous interactions (e.g., conversations) between the digital assistant and the user, and the like. As described herein, in some examples, the context information is dynamic and changes with the time, location, content, and other factors of the conversation.

[0232] In some examples, natural language processing is based on, for example, knowledge ontology 760. Knowledge ontology 760 is a hierarchical structure containing many nodes, each node representing an "executable intent" or an "attribute" related to one or more of the "executable intent" or other "attributes". As described above, an "executable intent" represents a task that the digital assistant can perform, that is, the task is "executable" or can be performed. "Attributes" represent parameters associated with sub-aspects of an executable intent or another attribute. The connection between executable intent nodes and attribute nodes in knowledge ontology 760 defines how the parameters represented by the attribute nodes are subordinate to the tasks represented by the executable intent nodes.

[0233] In some examples, the knowledge ontology 760 is composed of executable intent nodes and attribute nodes. In the knowledge ontology 760, each executable intent node is directly connected to or connected to one or more attribute nodes through one or more intermediate attribute nodes. Similarly, each attribute node is directly connected to or connected to one or more executable intent nodes through one or more intermediate attribute nodes. For example, Figure 7C As shown, ontology 760 includes a "restaurant reservation" node (i.e., an executable intent node). The attribute nodes "restaurant", "date / time" (for reservations), and "party size" are all directly connected to the executable intent node (i.e., the "restaurant reservation" node).

[0234] In addition, the attribute nodes "cuisine", "price range", "telephone number", and "location" are child nodes of the attribute node "restaurant", and are all linked to the "restaurant reservation" node (i.e., executable intent node) through the intermediate attribute node "restaurant". Figure 7C As shown, ontology 760 also includes a "set reminder" node (i.e., another executable intent node). The attribute nodes "date / time" (for setting reminders) and "subject" (for reminders) are both connected to the "set reminder" node. Since the attribute "date / time" is related to both the task of making a restaurant reservation and the task of setting a reminder, the attribute node "date / time" is connected to both the "restaurant reservation" node and the "set reminder" node in ontology 760.

[0235] Executable intent nodes, together with their linked attribute nodes, are described as "domains". In this discussion, each domain is associated with a corresponding executable intent and refers to a set of nodes (and the relationships between these nodes) associated with a particular executable intent. For example, Figure 7CThe ontology 760 shown in includes an example of a restaurant reservation domain 762 and an example of a reminder domain 764 within the ontology 760. The restaurant reservation domain includes an executable intent node "restaurant reservation", attribute nodes "restaurant", "date / time" and "party size", and sub-attribute nodes "cuisine", "price range", "phone number" and "location". The reminder domain 764 includes an executable intent node "set reminder" and attribute nodes "subject" and "date / time". In some examples, the ontology 760 consists of multiple domains. Each domain shares one or more attribute nodes with one or more other domains. For example, in addition to the restaurant reservation domain 762 and the reminder domain 764, the "date / time" attribute node is also associated with many different domains (e.g., itinerary scheduling domain, travel booking domain, movie ticket domain, etc.).

[0236] although Figure 7C Two example domains within ontology 760 are shown, but other domains include, for example, "find a movie," "make a phone call," "find directions," "schedule a meeting," "send a message," and "provide answers to questions," "read a list," "provide navigation instructions," "provide instructions for a task," etc. The "send message" domain is associated with the "send message" executable intent node, and further includes attribute nodes such as "one or more recipients," "message type," and "message body." The attribute node "recipients" is further defined, for example, by child attribute nodes such as "recipient name" and "message address."

[0237] In some examples, ontology 760 includes all domains (and thus executable intents) that a digital assistant can understand and act upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying the relationships between nodes within ontology 760.

[0238] In some examples, nodes associated with multiple related executable intents are clustered under a "superdomain" in the knowledge ontology 760. For example, the "travel" superdomain includes a cluster of attribute nodes and executable intent nodes related to travel. Executable intent nodes related to travel include "ticket booking", "hotel booking", "car rental", "route planning", "find points of interest", etc. Executable intent nodes under the same superdomain (e.g., the "travel" superdomain) have multiple shared attribute nodes. For example, the executable intent nodes for "ticket booking", "hotel booking", "car rental", "get directions", and "find points of interest" share one or more of the attribute nodes "starting location", "destination", "departure date / time", "arrival date / time", and "party size".

[0239] In some examples, each node in the knowledge ontology 760 is associated with a set of words and / or phrases related to the attribute or executable intent represented by the node. The corresponding set of words and / or phrases associated with each node is a so-called "vocabulary" associated with the node. The corresponding set of words and / or phrases associated with each node is stored in the vocabulary index 744 associated with the attribute or executable intent represented by the node. For example, return Figure 7B , the vocabulary associated with the node of the "restaurant" attribute includes words such as "food", "drinks", "cuisine", "hunger", "eat", "pizza", "fast food", "meals", etc. For another example, the vocabulary associated with the node of the "make phone call" executable intent includes words and phrases such as "call", "make a phone call", "dial", "talk to...", "call the number", "call", etc. The vocabulary index 744 optionally includes words and phrases in different languages.

[0240] The natural language processing module 732 receives candidate text representations (e.g., one or more text strings or one or more symbol sequences) from the STT processing module 730, and for each candidate representation, determines which nodes the words in the candidate text representation involve. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in the knowledge ontology 760 (via the vocabulary index 744), the word or phrase "triggers" or "activates" those nodes. Based on the number and / or relative importance of the activated nodes, the natural language processing module 732 selects one of the executable intents as the task that the user intends the digital assistant to perform. In some examples, the domain with the most "triggered" nodes is selected. In some examples, the domain with the highest confidence (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and importance of the triggered nodes. In some examples, additional factors are also considered in the process of selecting nodes, such as whether the digital assistant has previously correctly interpreted similar requests from the user.

[0241] User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default second language, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses user-specific information to supplement the information contained in the user input to further qualify the user's intent. For example, for a user request to "invite my friends to my birthday party," natural language processing module 732 can access user data 748 to determine who "friends" are and when and where the "birthday party" will be held, without requiring the user to explicitly provide such information in their request.

[0242] It should be recognized that in some examples, the natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). Specifically, the one or more machine learning mechanisms are configured to receive candidate text representations and context information associated with the candidate text representations. Based on the candidate text representations and the associated context information, the one or more machine learning mechanisms are configured to determine an intent confidence score based on a set of candidate executable intents. The natural language processing module 732 may select one or more candidate executable intents from a set of candidate executable intents based on the determined intent confidence scores. In some examples, a knowledge ontology (e.g., knowledge ontology 760) is also utilized to select one or more candidate executable intents from a set of candidate executable intents.

[0243] Additional details of searching knowledge ontologies based on symbolic strings are described in U.S. Utility Patent Application Serial No. 12 / 341,743, filed on December 22, 2008, entitled “Method and Apparatus for Searching Using An Active Ontology,” the entire disclosure of which is incorporated herein by reference.

[0244] In some examples, once the natural language processing module 732 identifies an executable intent (or domain) based on the user request, the natural language processing module 732 generates a structured query to represent the identified executable intent. In some examples, the structured query includes parameters for one or more nodes within the domain of the executable intent, and at least some of the parameters are filled with specific information and requirements specified in the user request. For example, the user says "help me reserve a seat at 7pm at the sushi restaurant." In this case, the natural language processing module 732 is able to correctly identify the executable intent as "restaurant reservation" based on the user input. According to the knowledge ontology, the structured query of the "restaurant reservation" domain includes parameters such as {cuisine}, {time}, {date}, {party number}, etc. In some examples, based on the speech input and the text derived from the speech input using the STT processing module 730, the natural language processing module 732 generates a partial structured query for the restaurant reservation domain, wherein the partial structured query includes parameters {cuisine = "sushi class"} and {time = "7pm"}. However, in this example, the user utterance contains insufficient information to complete the structured query associated with the domain. Therefore, based on the currently available information, other necessary parameters such as {number of party members} and {date} are not specified in the structured query. In some examples, the natural language processing module 732 populates some parameters of the structured query with the received contextual information. For example, in some examples, if the user requests a sushi restaurant "nearby", the natural language processing module 732 populates the {location} parameter in the structured query with the GPS coordinates from the user's device.

[0245] In some examples, the natural language processing module 732 identifies multiple candidate executable intentions for each candidate text representation received from the STT processing module 730. In addition, in some examples, a corresponding structured query is generated (partially or entirely) for each identified candidate executable intention. The natural language processing module 732 determines the intent confidence score for each candidate executable intention and ranks the candidate executable intention based on the intent confidence score. In some examples, the natural language processing module 732 transmits the generated one or more structured queries (including any completed parameters) to the task flow processing module 736 ("task flow processor"). In some examples, one or more structured queries for the m best (e.g., the m highest ranked) candidate executable intentions are provided to the task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, one or more structured queries for the m best candidate executable intentions are provided to the task flow processing module 736 together with the corresponding one or more candidate text representations.

[0246] Additional details for inferring user intent based on multiple candidate executable intents determined based on multiple candidate textual representations of speech input are described in U.S. utility model patent application serial number 14 / 298,725, entitled “System and Method for Inferring User Intent From Speech Inputs,” filed on June 6, 2014, the entire disclosure of which is incorporated herein by reference.

[0247] The task flow processing module 736 is configured to receive one or more structured queries from the natural language processing module 732, complete the structured queries (if necessary), and perform the actions required to "complete" the user's final request. In some examples, the various processes necessary to complete these tasks are provided in the task flow model 754. In some examples, the task flow model 754 includes a process for obtaining additional information from the user, and a task flow for performing actions associated with an executable intent.

[0248] As described above, in order to complete the structured query, the task flow processing module 736 needs to initiate an additional dialogue with the user in order to obtain additional information and / or clarify the utterances that may be ambiguous. When it is necessary to conduct such an interaction, the task flow processing module 736 calls the dialogue flow processing module 734 to participate in the dialogue with the user. In some examples, the dialogue flow processing module 734 determines how (and / or when) to request additional information from the user, and receives and processes the user response. The question is provided to the user and the answer is received from the user through the I / O processing module 728. In some examples, the dialogue flow processing module 734 presents the dialogue output to the user via an audible output and / or a visual output, and receives input from the user via a verbal or physical (e.g., click) response. Continuing the above example, when the task flow processing module 736 calls the dialogue flow processing module 734 to determine the "party number" and "date" information for the structured query associated with the domain "restaurant reservation", the dialogue flow processing module 734 generates questions such as "how many people in a row?" and "which day to book?" and passes them to the user. Once the answer is received from the user, the dialog flow processing module 734 fills in the structured query with the missing information, or passes the information to the task flow processing module 736 to complete the missing information based on the structured query.

[0249] Once the task flow processing module 736 has completed the structured query for the executable intent, the task flow processing module 736 begins to execute the final task associated with the executable intent. Therefore, the task flow processing module 736 executes 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 executable intent "restaurant reservation" includes steps and instructions for contacting a restaurant and actually requesting a reservation for a specific party size at a specific time. For example, using a structured query such as: {restaurant reservation, restaurant = ABC Cafe, date = 3 / 12 / 2012, time = 7pm, party size = 5}, the task flow processing module 736 can perform the following steps: (1) Log in to the server of ABC Cafe or such A restaurant reservation system that (2) enters date, time, and party size information into a form on the website, (3) submits the form, and (4) creates a calendar entry for the reservation in the user's calendar.

[0250] In some examples, the task flow processing module 736 completes the task requested in the user input or provides the informational answer requested in the user input with the assistance of the service processing module 738 ("service processing module"). For example, the service processing module 738 initiates a phone call, sets a calendar entry, invokes a map search, invokes or interacts with other user applications installed on the user device, and invokes or interacts with third-party services (e.g., restaurant reservation portals, social networking sites, bank portals, etc.) on behalf of the task flow processing module 736. In some examples, the protocols and application programming interfaces (APIs) required for each service are specified by the corresponding service models in the service model 756. The service processing module 738 accesses the appropriate service model for the service and generates a request for the service according to the protocol and API required by the service based on the service model.

[0251] For example, if the restaurant has enabled an online reservation service, the restaurant submits a service model that specifies the necessary parameters for making a reservation and transmits the values ​​of the necessary parameters to the API of the online reservation service. When requested by the task flow processing module 736, the service processing module 738 can use the web address stored in the service model to establish a network connection with the online reservation service and send the necessary parameters for 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.

[0252] In some examples, the natural language processing module 732, the dialog flow processing module 734, and the task flow processing module 736 are used together and repeatedly to infer and define the user's intention, obtain information to further clarify and refine the user's intention, and ultimately generate a response (i.e., output to the user, or complete the task) to meet the user's intention. The generated response is a dialogue response to the speech input that at least partially meets the user's intention. In addition, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to a speech synthesis processing module 740 (e.g., a speech synthesizer), where the generated response can be processed to synthesize the dialogue response in speech form. In other examples, the generated response is data content related to satisfying the user request in the speech input.

[0253] In an example where the task flow processing module 736 receives multiple structured queries from the natural language processing module 732, the task flow processing module 736 first processes the first structured query of the received structured query in an attempt to complete the first structured query and / or perform one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest-ranked executable intent. In other examples, the first structured query is selected from the structured query received based on the combination of the corresponding speech recognition confidence score and the corresponding intent confidence score. In some examples, if the task flow processing module 736 encounters an error during the processing of the first structured query (e.g., due to the inability to determine the necessary parameters), the task flow processing module 736 may continue to select and process the second structured query corresponding to the executable intent with a lower ranking in the received structured query. For example, the second structured query is selected based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate executable intent, the missing necessary parameters in the first structured query, or any combination thereof.

[0254] The speech synthesis processing module 740 is configured to synthesize speech output for presentation to the user. The speech synthesis processing module 740 synthesizes speech output based on the text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. The speech synthesis processing module 740 converts the text string into an audible speech output. The speech synthesis processing module 740 uses any appropriate speech synthesis technology to generate speech output from the text, including but not limited to: concatenation synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, pronunciation synthesis, synthesis based on hidden Markov model (HMM) and sine wave synthesis. In some examples, the speech synthesis processing module 740 is configured to synthesize individual words based on phoneme strings corresponding to these words. For example, the phoneme string is associated with the words in the generated dialogue response. The phoneme string is stored in the metadata associated with the word. The speech synthesis processing module 740 is configured to directly process the phoneme string in the metadata to synthesize the words in speech form.

[0255] In some examples, instead of (or in addition to) using the speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this may occur in some implementations where the output of the digital assistant is generated at the server system. And because the server system typically has more processing power or more resources than the user device, it is possible to obtain higher quality speech output than client-side synthesis would achieve.

[0256] Additional details about digital assistants can be found in U.S. utility patent application Ser. No. 12 / 987,982, filed on Jan. 10, 2011, and entitled “Intelligent Automated Assistant,” and U.S. utility patent application Ser. No. 13 / 251,088, filed on Sept. 30, 2011, and entitled “Generating and Processing Task Items That Represent Tasks to Perform,” the disclosures of which are incorporated herein by reference in their entirety.

[0257] 4. Determine whether the user's visual attention is directed toward the electronic device when the user is speaking

[0258] Fig. 8A A system 800 for determining whether a user's visual attention is directed toward an electronic device (e.g., device 104, 122, 200, 400, 600, or 900) when the user speaks is shown according to various examples. In some examples, the system 800 is implemented on a separate computer system (e.g., device 104, 122, 200, 400, 600, or 900). In some examples, the system 800 is distributed across multiple computers. For example, some of the components and functions of the system 800 are divided into a server portion and a client portion, where the client portion is located on one or more user devices (e.g., device 104, 122, 200, 400, 600, or 900) and communicates with the server portion (e.g., server system 108) via one or more networks, such as Figure 1 shown.

[0259] System 800 is implemented using hardware, software, or a combination of hardware and software to perform the principles discussed herein. In some examples, the components and functions of system 800 are implemented within digital assistant module 726, as described above with respect to 7A to 7C For example, each component of system 800 is implemented as a set of computer-executable instructions stored in memory 702.

[0260] System 800 is exemplary, and thus system 800 may have more or fewer components than those shown, may combine two or more components, or may have a different configuration or arrangement of components. In addition, although the following discussion describes functions performed at a single component of system 800, it should be understood that these functions may be performed at other components of system 800, and these functions may be performed at more than one component of system 800.

[0261] System 800 receives an audio stream and a video stream concurrently. In some examples, one or more cameras (e.g., an RGB camera, an infrared (IR) camera, a depth camera, etc.) of a device implementing system 800 captures a video stream. In some examples, a camera external to a device implementing system 800 captures a video stream. Similarly, in some examples, one or more audio sensors (e.g., a microphone) of a device implementing system 800 samples an audio stream. In some examples, an audio sensor external to a device implementing system 800 samples an audio stream. In some examples, an audio stream includes audio sampled by an audio sensor of the device and audio sampled by an audio sensor external to the device.

[0262] In some examples, the system 800 includes a preprocessing module 802. The preprocessing module 802 is configured to process an audio stream and / or a video stream. As an example, the preprocessing module 802 uses speech enhancement technology and / or noise reduction technology to process the audio stream. As another example, the preprocessing module 802 identifies a corresponding portion of the video stream (e.g., a cropped portion) as including different users (e.g., including the user's head). For example, the preprocessing module 802 tracks the head position of the user in the video stream. In some examples, identifying the corresponding portion of the video stream as including different users includes: cropping the video stream to obtain the corresponding portion, for example, so that the first portion of the video stream includes the head (e.g., face) of the first user, the second portion of the video stream includes the head (e.g., face) of the second user, and so on.

[0263] In some examples, the system 800 includes a memory buffer 804. The memory buffer 804 is configured to store an (optionally preprocessed) audio stream and an (optionally preprocessed) video stream. In an example where corresponding portions (e.g., cropped portions) of a video data stream are identified as including different users, the system 800 includes a separate instance of the memory buffer 804 for each user. For example, a first instance of the memory buffer 804 includes an audio stream and a first portion of a video stream including a head (e.g., face) of a first user, and a second instance of the memory buffer 804 includes an audio stream and a second portion of a video stream including a head (e.g., face) of a second user. As described below, the audiovisual call module 806 may separately process the stored contents of each instance of the memory buffer 804 to determine, for each user, whether the user's visual attention is directed toward the electronic device when the user is speaking.

[0264] In some examples, memory buffer 804 stores only a portion (e.g., a time portion) of an audio stream received within a predetermined duration (e.g., 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, or 2 seconds) before a current time, and stores only a portion (e.g., a time portion) of a video stream received within a predetermined duration before a current time. For example, memory buffer 804 is a first-in, first-out (FIFO) buffer (e.g., a ring buffer) that includes the last x seconds of an audio stream and the last x seconds of a video stream, where x is the predetermined duration before the current time.

[0265] The system 800 includes an audio-visual invocation module 806. The audio-visual invocation module 806 is configured to determine whether the user's visual attention is directed toward the electronic device when the user speaks based on the audio stream and the video stream. In some examples, the audio-visual invocation module 806 makes this determination by processing the stored contents of the memory buffer 804, for example, based on portions of the audio stream and the video stream that were each received within a predetermined duration before the current time.

[0266] In some examples, determining whether the user's visual attention is directed toward the electronic device while the user is speaking includes determining whether the user's visual attention is directed toward a display of the electronic device (e.g., Fig. 9 and FIG. 10A to FIG. 10B In some examples, determining that the user's visual attention is directed toward the electronic device while the user is speaking includes determining whether the user's visual attention is directed toward an affordance displayed by the electronic device (e.g., an affordance representing a digital assistant). In some examples, determining that the user's visual attention is directed toward the electronic device while the user is speaking includes determining that the user's gaze is directed toward the electronic device while the user is speaking. In some examples, determining that the user's visual attention is directed toward the electronic device while the user is speaking includes determining that the user (e.g., the user's posture (e.g., head posture)) is facing the electronic device while the user is speaking.

[0267] In some examples, the audio-visual call module 806 implements a machine learning model that is trained to determine whether the user's visual attention is directed to the electronic device when the user speaks. The machine learning model is implemented, for example, as a neural network (e.g., a recursive neural network, a convolutional neural network, a feedforward neural network, etc.) and is configured to accept the contents of the memory buffer 804 (e.g., portions of an audio stream and a video stream) as input. The neural network is trained using a labeled training data set that includes different audio-visual training data (e.g., audio data and video data received concurrently). Each element of the audio-visual training data is labeled to indicate:

[0268] The user's visual attention is directed toward the electronic device (looking at and speaking) while the user is speaking;

[0269] The user's visual attention is not directed toward the electronic device while the user is speaking (not looking and speaking);

[0270] The user's visual attention is directed toward the electronic device when the user is not speaking (looking and not speaking);

[0271] The user's visual attention is not directed toward the electronic device when the user is not speaking (not looking and not speaking); or

[0272] ● No user is visible in the audiovisual data (eg, regardless of whether the audio stream includes user speech).

[0273] In this way, the audiovisual invocation module 806 is trained to classify audio and video streams as representing (1) looking and talking, (2) not looking and talking, (3) looking and not talking, (4) not looking and not talking, or (5) no user visible.

[0274] In addition, such training can enable the audio-visual invocation module 806 to implicitly learn to detect features in the audio stream and the video stream that indicate that the user is looking at the electronic device and speaking, such as the correlation between the user's mouth movement and the speech input in the audio stream. For example, the audio-visual invocation module 806 uses the parameters (e.g., learned weights) of the machine learning model representing the correlation between the user's mouth movement and the speech input to process the representation of the portion of the audio stream and the portion of the video stream (each received within a predetermined duration before the current time).

[0275] The following discusses the technology used by the audio-visual invocation module 806 to determine whether the user's visual attention is directed to the electronic device when the user is speaking (for the sake of brevity, referred to as "whether the user is looking and speaking").

[0276] Figure 8B Video frames stored in memory buffer 804 at a current time are shown according to various examples. As shown, the portion of the video stream received within a predetermined duration before the current time includes a plurality of video frames 852, 854, 856, 858, 860, 862, and 864. Although the present example shows that memory buffer 804 includes seven video frames, the number of video frames stored in memory buffer 804 may vary based on a predetermined duration (e.g., 1 second or 1.5 seconds), a camera frame rate, and / or an amount of memory allocated to memory buffer 804. Video frames 852, 854, 856, 858, 860, 862, and 864 correspond to times T1, T2, T3, T4, T5, T6, and T7, respectively, e.g., the times at which the video frames were captured.

[0277] In some examples, determining whether the user is looking and talking includes determining a corresponding confidence score for each of the video frames 852 to 864 to obtain a plurality of corresponding confidence scores S1 to S7. For a corresponding video frame, each corresponding confidence score indicates whether the user is looking and talking. For example, if the confidence score exceeds a threshold, the confidence score indicates that the user is looking and talking. If the confidence score does not exceed the threshold, the confidence score indicates that the user is not looking and talking (or looking and not talking, or not looking and not talking, or no user is visible). As another example, the value of the confidence score indicates a confidence level (e.g., high, medium, or low) that the user is looking and talking.

[0278] In some examples, for each video frame 852, 854, 856, 858, 860, 862, and 864, the audio-visual invocation module 806 determines other confidence scores indicating whether (1) the user is not looking and talking, (2) the user is looking and not talking, (3) the user is not looking and not talking, and (4) no user is visible, respectively. In some examples, the audio-visual invocation module 806 normalizes the confidence scores for each video frame so that the sum is one.

[0279] In some examples, determining a confidence score (e.g., S4) for a video frame (e.g., video frame 858) includes processing the video frame based on, for example, using a machine learning model to determine an initial confidence score for the video frame (e.g., ). In some examples, determining the initial confidence score for the video frame includes processing one or more earlier video frames, e.g., video frames corresponding to a time before T4. For example, at an earlier time, when video frame 858 is the most recent video frame in memory buffer 804, the initial confidence score Based on processing the earlier six video frames in the memory buffer 804 and processing the audio content stored in the memory buffer 804 at an earlier time. In other words, the machine learning model uses the earlier contextual information obtained by processing the earlier video and audio data to determine the initial confidence score

[0280] In some examples, determining the confidence score for the video frame also includes adjusting the initial confidence score based on processing one or more later video frames (e.g., corresponding to time after T4) to determine the confidence score. Figure 8B At time T7, the machine learning model uses contextual information obtained by processing later video frames 860, 862, and 864 and by processing the audio content stored in memory buffer 804 at time T7 to calculate the initial confidence score of video frame 858. Adjusted to S4=0.81. Therefore, by determining the confidence score of the video frame based on processing earlier and / or later video frames, the audio-visual call module 806 can consider the relationship between the video frames (e.g., how the user's face and / or mouth changes relative to the speech in the audio stream) to determine whether the user is looking and talking. This approach can provide increased accuracy compared to techniques for determining whether the user is looking and talking based on considering static video frames (e.g., based on determining the confidence score of the video frame based on the video frame itself). It should be understood that the audio-visual call module 806 can determine other confidence scores for each video frame in a similar manner (indicating whether (1) the user is not looking and talking, (2) the user is looking and not talking, (3) the user is not looking and not talking, and (4) no user is visible).

[0281] As the initial confidence score of the adjusted video frame 858 For example, consider that the audio-visual call module 806 determines that the user is looking and talking at time T4, as indicated by the relatively high (adjusted) confidence score S4 of video frame 858. At an earlier time T4 (e.g., earlier than T7 and when video frame 858 is the most recent video frame in memory buffer 804), the initial confidence score S4 is Specifically, at time T4, as shown, the user's mouth begins to open while the user is looking at the device. Based on processing earlier video frames at time T4, the audio-visual call module 806 cannot confidently determine whether this initial mouth opening indicates that the user is looking and speaking, resulting in a relatively low initial confidence score. However, based on processing the later video frames 860, 862, and 864 at time T7, the audiovisual call module 806 sets the initial confidence score Adjusted to a relatively high confidence score S4 = 0.81. For example, because later video frames show that the user's mouth continues to move while the user is looking at the device, by processing such later video frames concurrently with the audio stream indicating the corresponding user's speech, the audio-visual call module 806 determines with greater confidence that the user is looking and speaking at time T4.

[0282] In some examples, the audiovisual call module 806 determines that the user is looking and talking by determining that the confidence score of any video frame in the memory buffer 804 exceeds a threshold or by determining that the average confidence score of the video frames in the memory buffer 804 exceeds a threshold.

[0283] In some examples, the audio-visual call module 806 determines that the user is looking and talking by determining that the confidence score of a particular video frame exceeds a threshold. In some examples, the particular video frame occupies a pre-selected position in the memory buffer 804, for example, the earliest position (currently occupied by video frame 852), the middle position (currently occupied by video frame 858), or the most recent position (currently occupied by video frame 864). It should be understood that determining whether the confidence score of the most recent video frame 864 exceeds the threshold can determine whether the user is looking and talking relatively quickly, but the determination may be less accurate, for example, because the determination depends on the initial confidence score of the video frame. In contrast, determining whether the confidence score of the earliest video frame 852 exceeds the threshold can more accurately determine whether the user is looking and talking (for example, because the confidence score is based on processing a later video frame), but the determination may be relatively slow. Therefore, the video frame position pre-selected for determining whether the user is looking and talking can be changed to balance accuracy and speed considerations suitable for a specific implementation of the system 800. In some examples, the machine learning model is configured to optimize (e.g., minimize) a loss function associated with a pre-selected video frame position, for example, minimizing a loss function associated with intermediate video frame 858.

[0284] The system 800 includes a post-processing module 808. Based on determining that the user is looking and speaking, the post-processing module 808 is configured to identify a portion of the audio stream as including user speech expected for the electronic device (device expected speech). In some examples, identifying the portion of the audio stream as including device expected speech includes: based on determining that a confidence score of a video frame (e.g., a video frame occupying a pre-selected position) exceeds a threshold, determining that a time corresponding to the video frame (e.g., T4) is a start time for the portion of the audio stream. In some examples, determining the start time also includes determining that a confidence score of a previous (e.g., the immediately previous) video frame does not exceed the threshold. Therefore, the post-processing module 808 can determine the start time by determining that the confidence score corresponding to the pre-selected video frame position rises above the threshold.

[0285] In some examples, identifying the portion of the audio stream as including speech expected by the device includes: determining that the confidence score of the first video frame is above a threshold and determining that the confidence score of a second video frame (e.g., a video frame occupying a pre-selected position) that is continuous with the first video frame is below a threshold. Based on this determination, the post-processing module 808 determines that the time corresponding to the second video frame is the end time of the portion of the audio stream. Therefore, the post-processing module 808 can determine the end time by determining that the confidence score corresponding to the pre-selected video frame position drops below a threshold. In some examples, the post-processing module 808 additionally or alternatively implements speech endpoint techniques known in the art to determine the end time. The confidence score discussed with respect to identifying the start time and the end time can be an initial confidence score for a video frame, an adjusted confidence score for a video frame, or a final confidence score for a video frame (discussed in detail below).

[0286] Those of ordinary skill in the art will appreciate that the post-processing module 808 may implement various other techniques to determine the start time and / or end time using the confidence score (e.g., initial confidence score, adjusted confidence score, final confidence score). As one example, based on determining that each video frame in the memory buffer 804 has a corresponding confidence score above a threshold, the post-processing module 808 determines the start time as a time corresponding to the earliest video frame in the memory buffer 804 (or corresponding to an intermediate video frame or corresponding to a most recent video frame). Similarly, based on determining that each video frame in the memory buffer 804 has a corresponding confidence score below a threshold, the post-processing module 808 determines the end time as a time corresponding to the earliest video frame in the memory buffer 804 (or corresponding to an intermediate video frame or corresponding to a most recent video frame). As another example, based on determining that the corresponding confidence scores of a consecutive number (e.g., 2, 3, or 4) of video frames in the memory buffer 804 each exceed a threshold, the post-processing module 808 determines the start time as a time corresponding to the earliest video frame in the consecutive video frames. Similarly, based on determining that the corresponding confidence scores of the consecutive number (e.g., 2, 3, or 4) of video frames in the memory buffer 804 are each below a threshold, the post-processing module 808 determines the end time as a time corresponding to the earliest video frame in the consecutive video frames. As another example, based on determining that the average confidence score of the consecutive number (e.g., 2, 3, or 4) of video frames in the memory buffer 804 exceeds a threshold, the post-processing module 808 determines the start time as a time corresponding to the earliest video frame in the consecutive video frames. Similarly, based on determining that the average confidence score of the consecutive number (e.g., 2, 3, or 4) of video frames in the memory buffer 804 is below a threshold, the post-processing module 808 determines the end time as a time corresponding to the earliest video frame in the consecutive video frames. Those of ordinary skill in the art will appreciate that other variations for determining the start time and / or end time using confidence scores are possible and fall within the scope of the present disclosure.

[0287] The post-processing module 808 is further configured to cause a digital assistant operating on the device 900 to initiate a task based on the identified portion of the audio stream. For example, the post-processing module 808 causes the digital assistant to process the identified portion of the audio stream, such as with respect to 7A to 7C In some examples, device 900 provides an output indicative of the initiated task.

[0288] Fig. 9A device 900 is shown that provides output based on determining that the visual attention of a user 904 is directed toward the device 900 when the user 904 speaks according to various examples. The device 900 is implemented as the device 104, 122, 200, 400, or 600 and includes one or more cameras 902. The device 900 also at least partially implements the digital assistant system 700, such as with respect to 7A to 7C described. Fig. 9 and FIG. 10A to FIG. 10B Device 900 is shown as being implemented as a particular type of device, but device 900 may be implemented as another type of device, such as a smart phone, a laptop computer, a desktop computer, a smart watch, a television, a smart speaker, a head-mounted device, a smart home appliance, a tablet device, or a vehicle head unit.

[0289] exist Fig. 9 In the example of FIG. 1 , according to the techniques discussed above, system 800 identifies user 904's speech input "read my messages" as the device expected speech. Therefore, the digital assistant initiates the task of reading user 904's messages and provides the output (e.g., in audio and / or display format) "OK, Max's first message to you is 'Hello.'"

[0290] In this way, device 900 identifies portions of the audio stream as including device intended speech without detecting a verbal trigger for initiating a digital assistant session (e.g., "Hey, Siri," "Siri," "Hey, Assistant," "OK, computer," etc.) and without relying on other explicit indications of speech intended by device 900 (e.g., user selection of a hardware button, user selection of a displayed affordance). This may provide for more natural and efficient interaction with device 900.

[0291] In some examples, the system 800 includes an identification module 810. In some examples, the identification module 810 is configured to receive an audio stream and identify the user 904 based on the audio stream (e.g., according to voice identification techniques known in the art). In some examples, the identification module 810 is configured to receive a video stream and identify the user 904 based on the video stream (e.g., according to image / face identification techniques known in the art). In some examples, the output (e.g., "OK, the first message Max sent you is 'Hello'") is based on the identified user. For example, the output is personalized for the identified user by reading the identified user's messages.

[0292] In some examples, the identification module 810 identifies the user 904 based on the digital assistant determining that satisfying the user intent includes retrieving or modifying the personal data of the user 904. Example personal data includes messages, emails, photos, videos, calendar information, notes, financial information, health information, home security information (e.g., whether the device is locked, whether the appliance is turned on, the settings of the home appliance), voice recordings, and any other potentially sensitive information that the user 904 may not want to expose to others. In some examples, the identification module 810 combines the confidence associated with the voice identification of the user 904 and the confidence associated with the facial identification of the user 904 to identify the user 904. For example, if the confidence score associated with the voice identification and the confidence score associated with the facial identification are each insufficient to confidently identify the user 904, the combination of these scores can more confidently identify the user 904, for example, if the voice identification and the facial identification each indicate a medium confidence of the same user 904. In some examples, based on identifying the user 904, the identification module 810 causes the digital assistant to provide output personalized for the identified user 904.

[0293] In some examples, based on determining that the user's visual attention is not directed toward the electronic device when the user speaks (the user is not looking and speaking), the post-processing module 808 abandons identifying the portion of the audio stream as including device expected speech. For example, the audio-visual invocation module 806 determines that the user is not looking and speaking by determining that a confidence score indicating whether the user is not looking and speaking (e.g., for a video frame occupying a pre-selected position) exceeds a threshold.

[0294] In some examples, based on determining that the user's visual attention is directed toward the electronic device when the user is not speaking (the user is looking and not speaking), the post-processing module 808 abandons identifying the portion of the audio stream as including device expected speech. For example, the audio-visual invocation module 806 determines that the user is looking and not speaking by determining that a confidence score indicating whether the user is looking and not speaking (e.g., for a video frame occupying a pre-selected position) exceeds a threshold.

[0295] In some examples, based on determining that the user's visual attention is not directed toward the electronic device when the user is not speaking (the user is not looking and not speaking), the post-processing module 808 abandons identifying the portion of the audio stream as including device expected speech. For example, the audio-visual invocation module 806 determines that the user is not looking and not speaking by determining that a confidence score indicating whether the user is not looking and not speaking (e.g., for a video frame occupying a pre-selected position) exceeds a threshold.

[0296] In some examples, based on determining that the user is not visible in the first portion of the video stream: the post-processing module 808 abandons identifying the portion of the audio stream as including device expected speech. For example, the audio-visual invocation module 806 determines that no user is visible by determining that a confidence score indicating whether no user is visible (e.g., for a video frame occupying a pre-selected position) exceeds a threshold.

[0297] FIG. 10A to FIG. 10B Determining whether visual attention of a user is directed toward device 900 while the same user is speaking in a multi-user environment is shown according to various examples.

[0298] exist FIG. 10A to FIG. 10B In the embodiment, the device 900 (eg, the system 800) concurrently receives the audio stream and the video stream, for example, as described above with respect to Fig. 8A As discussed. In some examples, the audio stream includes speech of the first user 1000 and speech of the second user 1002. In some examples, the video stream includes a video of the first user 1000 and a video of the second user 1002. For example, in a multi-user environment, the device 900 may detect multiple users (e.g., via a camera) and speech of multiple users (e.g., via a microphone). As described below, the device 900 may determine whether the visual attention of the same user is directed toward the device 900 when the user speaks, in order to provide relevant responses to the user and avoid incorrectly responding to speech that is not intended for the device 900.

[0299] In some examples, the device 900 identifies a first portion (e.g., a cropped portion) of the video stream as including a first user 1000, and identifies a second portion (e.g., a cropped portion) of the video stream as including a second user 1002, for example, as discussed above with respect to the pre-processing module 802.

[0300] Based on the first part of the audio stream and the video stream, the device 900 can be used, for example, according to the above description with respect to FIG. 8A to FIG. 8B The discussed techniques determine whether the first user 1000 is looking and talking. Similarly, based on the second part of the audio stream and the video stream, the device 900, for example, according to the above with respect to FIG. 8A to FIG. 8BThe discussed technology determines whether the second user 1002 is looking and talking. For example, the device 900 stores a portion of the audio stream received within a predetermined duration before the current time and a first portion of the video stream received within a predetermined duration before the current time (e.g., the last x seconds of the cropped video stream) in a first instance of the memory buffer 804. The device 900 also stores the portion of the audio stream and the second portion of the video stream received within the predetermined duration before the current time in a second instance of the memory buffer 804. The audio-visual call module 806 then processes the stored contents of each instance of the memory buffer 804 separately as described above, thereby determining a confidence score for each user indicating whether the user is looking and talking. In some examples, processing the stored contents of each instance of the memory buffer 804 also includes determining other confidence scores for each user indicating whether (1) the user is not looking and talking, (2) the user is looking and not talking, (3) the user is not looking and not talking, and (4) no user is visible video frames.

[0301] Based on a determination based on the first portion of the audio stream and the video stream that the first user 1000 is looking and speaking, the device 900 identifies the first portion of the audio stream as including speech of the first user 1000 intended for the device 900. Based on a determination based on the second portion of the audio stream and the video stream that the second user 1002 is looking and speaking, the device 900 identifies the second portion of the audio stream as including speech of the second user 1002 intended for the device 900. For example, in accordance with the techniques discussed above, the post-processing module 808 identifies a start time and / or an end time of the first portion of the audio stream (and / or the second portion of the audio stream).

[0302] exist Fig. 10A In the example, the device 900 further provides a first output based on processing a first portion of the audio stream identified as including device-expected speech from the user 1000. In some examples, providing the first output includes processing the first portion of the audio stream using a digital assistant. For example, because the first user 1000 is determined to be looking at the device 900 and speaking, the digital assistant processes the first user's 1000 speech input "read my messages" to provide an output "OK, the first message Max sent you is 'Hello.'" Fig. 10A In the example of , the second user 1002 is determined to be looking at the device 900 and not speaking. Therefore, the device 900 abandons identifying any second portion of the audio stream and does not provide any output for the second user 1002.

[0303] In an example where device 900 identifies the second portion of the audio stream as including speech intended for device 900 by second user 1002, device 900 similarly provides a second output based on processing the second portion of the audio stream. In some examples, providing the second output includes processing the second portion of the audio stream using a digital assistant.

[0304] In some examples, the identification module 810 identifies the first user 1000 based on a first portion (e.g., a cropped portion) of the audio stream and / or the video stream. In some examples, the first output is based on the identified first user 1000. For example, in Fig. 10A , the first output “OK, Max’s first message to you is Hello” is personalized for the identified first user 1000. In some examples, the identification module 810 identifies the second user 1002 based on a second portion (e.g., a cropped portion) of the audio stream and / or the video stream. In some examples, the second output is based on the identified second user 1002, for example, personalized for the identified second user 1002.

[0305] In some examples, based on a determination based on the first portion of the audio stream and the video stream that the first user 1000 is looking and not speaking, the device 900 abandons identifying the first portion of the audio stream as including speech intended for the device 900 by the first user 1000. Similarly, in some examples, based on a determination based on the second portion of the audio stream and the video stream that the second user 1002 is looking and not speaking, the device 900 abandons identifying the second portion of the audio stream as including speech intended for the device 900 by the second user 1002. Similarly, the device 900 abandons identifying the first (or second) portion of the audio stream based on a determination based on the first (or second) portion of the audio stream and the video stream that the first user 1000 (or the second user 1002) (1) is not looking and speaking, (2) is not looking and not speaking, or (3) is not visible.

[0306] Go to Fig. 10B, the device 900 determines that the visual attention of the first user 1000 is directed to the device 900 when the second user 1002 is speaking. For example, the post-processing module 808 determines that the first video frame indicates that the first user 1000 is looking and not speaking, and the second video frame indicates that the second user 1002 is not looking and speaking, wherein the first video frame and the second video frame correspond to the same time. According to this determination, the device 900 abandons identifying any part of the audio stream as including the device expected speech and therefore abandons providing any response to the speech input. For example, the device 900 will not mistakenly interpret the speech of the second user 1002 "What's for dinner?" as intended for the device 900, although the visual attention of the first user 1000 is directed to the device 900 when the device 900 receives the speech input. Similarly, according to determining that the visual attention of the second user 1002 is directed to the device 900 when the first user 1000 is speaking (for example, the second user 1002 is looking and not speaking, while the first user 1000 is speaking and not looking), the device 900 abandons identifying any part of the audio stream as including the device expected speech. In this way, to provide a response to a speech input, the device 900 determines whether the same user is looking and speaking. This prevents the device 900 from erroneously providing a response, such as in response to a Fig. 10B in the question "What's for dinner?" instead of correctly outputting restaurant search results.

[0307] In some examples, the confidence score indicating whether the user is looking and speaking (e.g., for a video frame) is a first type of confidence score (an audio-visual score). In some examples, after determining the audio-visual score, the post-processing module 808 determines a final confidence score indicating whether the user is looking and speaking based on one or more other types of confidence scores indicating whether the user's visual attention is directed toward the device 900 and the audio-visual score. In some examples, the final confidence score is for a video frame of the same video stream as the audio-visual score. For example, see Figure 8B The post-processing module 808 determines a final confidence score for the video frame 858 based on the audiovisual score S4 and one or more other types of confidence scores.

[0308] Now discuss example other types of confidence scores. Determining a final confidence score based on other types of confidence scores can more accurately determine whether the user is looking and speaking by refining the audiovisual score based on potentially relevant factors (e.g., user gaze, user posture, relative motion between the user and the device 900, user gestures, etc.), as discussed below.

[0309] In some examples, the second type of confidence score (gaze score) indicates whether the user's gaze is directed to the device 900. For example, a gaze model (e.g., a machine learning model) implemented in the post-processing module 808 or implemented on a device external to the device 900 processes the video stream to determine the gaze score. In some examples, the gaze model is similar to the machine learning model discussed above with respect to the audio-visual call module 806. For example, the gaze model determines a gaze score for a video frame (or for a specific time). In some examples, a gaze score above a threshold indicates that the user's gaze is directed to the device 900, while a gaze score below a threshold indicates that the user's gaze is not directed to the device 900.

[0310] In some examples, if the gaze score indicates that the user's gaze is directed toward the device 900, the post-processing module 808 increases the final confidence score relative to the audio-visual score. Similarly, in some examples, if the gaze score indicates that the user's gaze is not directed toward the device 900, the post-processing module 808 decreases (or does not change) the final confidence score relative to the audio-visual score. In some examples, the gaze score corresponds to the same time as the audio-visual score. For example, if Figure 8B If the gaze score at time T4 in FIG. 8 indicates that the user is gazing at the pointing device 900 , the post-processing module 808 increases the final confidence score of the video frame 858 .

[0311] In some examples, a third type of confidence score (posture score) indicates whether the user's posture (e.g., head posture) is facing the device 900. For example, a posture model (e.g., a machine learning model) implemented in the post-processing module 808 or implemented on a device external to the device 900 processes the video stream to determine the posture score. In some examples, the posture model is similar to the machine learning model discussed above with respect to the audio-visual call module 806. For example, the posture model determines a posture score for a video frame (or for a specific time). In some examples, a posture score above a threshold indicates that the user's posture is facing (or starting to face) the device 900, while a posture score below a threshold indicates that the user's posture is not facing (or starting to face) the device 900.

[0312] In some examples, if the posture score indicates that the user's posture is facing (or beginning to face) the device 900, the post-processing module 808 increases the final confidence score relative to the audio-visual score. Similarly, in some examples, if the posture score indicates that the user's posture is not facing (or not beginning to face) the device 900, the post-processing module 808 decreases (or does not change) the final confidence score relative to the audio-visual confidence score. In some examples, the posture score corresponds to the same time as the audio-visual score. For example, if Figure 8B If the posture score at time T4 in FIG. 8 indicates that the user's posture is not facing the device 900 , the post-processing module 808 reduces the final confidence score of the video frame 858 .

[0313] In some examples, a fourth type of confidence score (gesture score) indicates whether a predetermined type of user gesture is detected. Example predetermined types of user gestures include hand gesture types (e.g., waving gestures, pointing gestures, raising hand gestures, grabbing gestures, pushing gestures, etc.) and finger gesture types (e.g., fingers moving in a specific way). For example, a gesture model (e.g., a machine learning model) implemented in the post-processing module 808 or implemented on a device external to the device 900 processes the video stream to determine a gesture score. In some examples, the gesture model is similar to the machine learning model discussed above with respect to the audio-visual call module 806. For example, the gesture model determines a gesture score for a video frame (or for a specific time). In some examples, a gesture score above a threshold indicates that a gesture of a predetermined type is detected, while a gesture score below a threshold indicates that a gesture of a predetermined type is not detected.

[0314] In some examples, if the gesture score indicates that a gesture of a predetermined type was detected, then the post-processing module 808 increases the final confidence score relative to the audio-visual confidence score. Similarly, in some examples, if the gesture score indicates that a gesture of a predetermined type was not detected, then the post-processing module 808 decreases (or does not change) the final confidence score relative to the audio-visual score. In some examples, the gesture score corresponds to the same time as the audio-visual score. For example, if Figure 8B If the gesture score at time T4 in the video frame 858 indicates that a predetermined type of user gesture is detected (eg, the user is performing a gesture at time T4), the post-processing module 808 increases the final confidence score of the video frame 858.

[0315] In some examples, a fifth type of confidence score (relative motion score) indicates relative motion between the user and the device 900, e.g., whether the user is moving toward the device 900. For example, a relative motion model (e.g., a machine learning model) implemented in the post-processing module 808 or implemented on a device external to the device 900 processes the video stream and / or the audio stream to determine the relative motion score. In some examples, the relative motion model is similar to the machine learning model discussed above with respect to the audio-visual call module 806. For example, the relative motion model determines a relative motion score for a video frame (or for a particular time) indicating whether the user is moving toward the device 900. In some examples, a relative motion score having a relative motion score above a threshold indicates that the user is moving toward the device 900, while a relative motion score having a relative motion score below a threshold indicates that the user is not moving toward the device 900.

[0316] In some examples, if the relative motion score indicates that the user is moving toward device 900, then post-processing module 808 increases the final confidence score relative to the audio-visual score. Similarly, in some examples, if the relative motion score indicates that the user is not moving toward device 900, then post-processing module 808 decreases (or does not change) the final confidence score relative to the audio-visual score. In some examples, the relative motion score corresponds to the same time as the audio-visual score. For example, if Figure 8B If the relative motion score at time T4 in video frame 858 indicates that the user is moving toward device 900 (eg, the user moves toward device 900 at time T4), post-processing module 808 increases the final confidence score of video frame 858.

[0317] In some examples, determining the final confidence score includes determining whether the content of the audio stream (e.g., spoken content) corresponds to a digital assistant request, for example, whether the audio stream includes a request that is typically spoken to a digital assistant. In some examples, the post-processing module 808 determines whether the content corresponds to a digital assistant request based on determining the type of the content (e.g., by using the natural language processing capabilities of the digital assistant). For example, if the content is of a first type (e.g., a command or a question), the post-processing module 808 determines that the content corresponds to a digital assistant request and / or increases the probability that the content corresponds to a digital assistant request. If the content is of a second type (e.g., a statement), the post-processing module 808 determines that the content does not correspond to a digital assistant request and / or reduces the probability that the content corresponds to a digital assistant request. In some examples, the post-processing module 808 determines whether the content corresponds to a digital assistant request based on determining whether the content corresponds to a vocabulary associated with the domain of the digital assistant (e.g., by using the natural language processing capabilities of the digital assistant). For example, if the content includes at least some (e.g., at least a threshold number) of the words in the vocabulary (e.g., the words "food," "drinks," "cuisine," "hunger," "eat," "pizza," "fast food," and "meal" for the restaurant reservation domain 762), the post-processing module 808 determines that the content corresponds to a digital assistant request and / or increases the probability that the content corresponds to a digital assistant request. If the content does not include at least some (e.g., includes less than a threshold number) of the words in the vocabulary, the post-processing module 808 determines that the content does not correspond to a digital assistant request and / or decreases the probability that the content corresponds to a digital assistant request.

[0318] In some examples, post-processing module 808 increases the final confidence score relative to the audio-visual score based on determining that the content corresponds to a digital assistant request. In some examples, post-processing module 808 decreases (or does not change) the final confidence score relative to the audio-visual score based on determining that the content does not correspond to a digital assistant request. In some examples, the content corresponds to the same time as the audio-visual score. For example, device 900 receives the content within a predetermined time window (e.g., 1, 2, 3, 4, or 5 seconds) around the time corresponding to the audio-visual score. For example, if post-processing module 808 determines that device 900 receives the content within a predetermined time window (e.g., 1, 2, 3, 4, or 5 seconds) around the time corresponding to the audio-visual score. Figure 8B If content corresponding to a digital assistant request is received around time T4 in the video frame 858, the post-processing module 808 increases the final confidence score of the video frame 858. In this way, if the audio-visual score indicates that the user is looking and speaking with insufficient confidence (e.g., the user is only partially looking at the device 900 when speaking), but the content of the user's speech corresponds to a typical digital assistant request, the device 900 can still correctly identify the user's device-expected speech and respond to it.

[0319] In some examples, determining the final confidence score includes determining an action currently performed by the device 900, such as an action performed by the device 900 when determining the final confidence score. In some examples, based on determining that the action is of a first type, the post-processing module 808 increases the final confidence score. In some examples, based on determining that the action is of a second type different from the first type, the post-processing module 808 decreases the final confidence score. The first type of action may indicate an increased likelihood of the user interacting with the digital assistant. Example actions of the first type include displaying a notification (e.g., a new message notification, a system notification, a notification that a timer has expired, an incoming phone call notification), actively executing the digital assistant (e.g., displaying a digital assistant user interface), and displaying a user interface of a predetermined type (e.g., a home screen user interface, a user interface of a predetermined application). The second type of action may indicate a reduced likelihood of the user interacting with the digital assistant. Example actions of the second type include outputting media content (e.g., a movie, a song, an audiobook) and actively executing a communication application (e.g., a phone call application, a video call application).

[0320] In some examples, the post-processing module 808 determines the final confidence score based on data representing the user's interaction with the digital assistant. In some examples, the data indicates an increased likelihood that the user is interacting with the digital assistant, for example, the device 900 is actively executing the digital assistant (e.g., displaying a digital assistant user interface), the digital assistant is frequently initiated on the device 900 (e.g., initiated more than a threshold number of times within a predetermined time period), the digital assistant recently (e.g., within a predetermined duration before the current time) outputs a response to the user, and / or the digital assistant has recently been cancelled (e.g., the device 900 recently stopped displaying the digital assistant user interface). In some examples, the data indicates a reduced likelihood that the user is interacting with the digital assistant, for example, the device 900 is not currently displaying the digital assistant user interface, the digital assistant is not frequently initiated on the device 900 (e.g., initiated less than a threshold number of times within a predetermined time period), the digital assistant did not provide output within a predetermined duration before the current time (e.g., within the past hour, the past day), and / or the digital assistant was last initiated more than a predetermined duration before the current time (e.g., more than 1 month ago). In some examples, based on determining that the data indicates an increased likelihood that the user is interacting with the digital assistant, the post-processing module 808 increases the final confidence score. In some examples, based on determining that the data indicates a decreased likelihood that the user is interacting with the digital assistant, the post-processing module 808 decreases the final confidence score.

[0321] In some examples, the post-processing module 808 determines whether the final confidence score exceeds a threshold, such as whether the user is looking and speaking. For example, the post-processing module 808 determines whether the final confidence score of the video frame occupying a pre-selected position (e.g., the earliest position, the middle position, or the latest position) in the memory buffer 804 exceeds the threshold.

[0322] In some examples, post-processing module 808 adjusts the threshold and compares the final confidence score to the adjusted threshold. As described below, adjusting the threshold makes it more or less likely that system 800 determines whether the user is looking and speaking, which in turn can increase system accuracy.

[0323] In some examples, based on determining that the action currently being performed by the device 900 is of the first type, the post-processing module 808 reduces the threshold, and based on determining that the action is of the second type, the post-processing module 808 increases the threshold. As another example, the post-processing module 808 adjusts the threshold based on data representing the user's interaction with the digital assistant (e.g., if the data corresponds to increasing the final confidence score, then the threshold is reduced, and if the data corresponds to decreasing the final confidence score, then the threshold is increased). As another example, the post-processing module 808 adjusts the threshold based on at least some of the other types of confidence scores (e.g., gaze scores, posture scores, gesture scores, relative motion scores). For example, if another type of confidence score corresponds to increasing (or decreasing) the final confidence score, the post-processing module 808 reduces (or increases) the threshold, respectively.

[0324] In some examples, the post-processing module 808 reduces the threshold based on determining that the user is alone in a physical environment associated with the device 900. The physical environment is, for example, a room in which the device 900 is located, a predetermined volume around the device 900 (e.g., within 10, 15, 20, or 25 feet of the device 900), or a vehicle interior (e.g., if the device 900 is in a vehicle). In some examples, the post-processing module 808 determines that the user is alone in the physical environment based on analyzing an audio stream (e.g., by detecting sounds corresponding to a single user (e.g., footsteps)), based on detecting user devices (e.g., only detecting user devices belonging to a single user in the physical environment), based on analyzing a video stream (e.g., determining that the video stream includes a single user), or based on a combination or sub-combination thereof. In this way, the system 800 may be more likely to interpret speech spoken by a user when alone as intended for the device 900.

[0325] In some examples, based on determining that the final confidence score exceeds an (optionally adjusted) threshold (e.g., the user is looking and speaking), the post-processing module 808 identifies the portion of the audio stream as including device expected speech. For example, rather than using the audio-visual score to determine the start time and / or end time, the post-processing module 808 uses the final confidence score to determine the start time and / or end time in a similar manner.

[0326] In some examples, identifying the portion of the audio stream as including the device expected speech includes: determining an initial start time for the portion of the audio stream to be a time corresponding to the audiovisual score or the final confidence score. For example, the post-processing module 808 determines the initial start time to be a time (e.g., T4) corresponding to a video frame (e.g., video frame 858) having an audiovisual score and / or a final confidence score that is determined to exceed a (optionally adjusted) threshold. In some examples, the post-processing module 808 adjusts the initial start time based on a third type of confidence score (gesture score). For example, assuming that the post-processing module 808 determines that the gesture score is for Figure 8B The time T1 in has a relatively high value, for example, indicating that the user begins to turn around and face the device at time T1. In addition, if the audio-visual invocation module 806 determines that the user speaks at time T1 (for example, indicating whether the user is not looking at time T1 and the confidence score of speaking exceeds a threshold), the post-processing module 808 adjusts the start time to T1. In this way, if the user begins to provide device-expected speech when starting to turn around and face the device 900 (but not yet facing the device 900), the system 800 can identify the correct start time of the device-expected speech. For example, relying on the audio-visual score (rather than the posture score) to determine the start time may incorrectly result in the start time being determined as time T4, for example, in the middle of the user's device-expected speech.

[0327] In some examples, device 900 provides output based on processing a portion of the audio stream identified as including speech expected by the device, such as Fig. 9 shown.

[0328] In some examples, based on determining that the final confidence score does not exceed the threshold, the post-processing module 808 abandons identifying the portion of the audio stream as including device expected speech.

[0329] Although the above describes determining the final confidence score for one user and adjusting the threshold, it should be understood that such technology is equally applicable to determining the final confidence score for multiple users and adjusting the threshold. For example, the audio-visual call module 806 processes the storage contents of different instances (corresponding to different users) of the memory buffer 804 separately to determine the audio-visual scores of different users. Then, for each user, the post-processing module 808 determines the final confidence score separately, optionally adjusts the corresponding threshold separately, and compares the final confidence score with the (optionally adjusted) corresponding threshold separately to determine whether each user in the different users is looking and speaking. Based on determining that the user is looking and speaking, the post-processing module 808 identifies the portion of the audio stream as including the device expected speech. Then the device 900 provides an output based on the identified portion of the processed audio stream, as described.

[0330] 5. A process for determining whether the user's visual attention is directed toward the electronic device while the user is speaking

[0331] Fig.11 A process 1100 for determining whether the user's visual attention is directed to an electronic device when the user speaks according to various examples is shown. For example, process 1100 is performed using one or more electronic devices that implement a digital assistant. In some examples, process 1100 is performed using a client-server system (e.g., system 100), and the boxes of process 1100 are divided in any way between a server (e.g., DA server 106) and a client device (e.g., device 900). In other examples, the boxes of process 1100 are divided between a server and multiple client devices (e.g., mobile phones and smart watches). Therefore, although part of process 1100 is described herein as being performed by a specific device of a client-server system, it should be understood that process 1100 is not limited thereto. In other examples, process 1100 is performed using only a client device (e.g., user device 104 or 900) or only multiple client devices. In process 1100, some boxes are optionally combined, the order of some boxes is optionally changed, and some boxes are optionally omitted. In some examples, additional steps may be performed in conjunction with process 1100 .

[0332] At block 1102 , an audio stream and a video stream are received concurrently, for example by device 900 .

[0333] At box 1104, it is determined (e.g., by audio-visual call module 806) whether the user's visual attention is directed toward the electronic device (e.g., device 900) while the user (e.g., user 904, 1000, or 1002) is speaking based on a first portion of an audio stream received within a predetermined duration before the current time and a first portion of a video stream received within a predetermined duration before the current time.

[0334] In some examples, determining whether the user's visual attention is directed toward the electronic device while the user is speaking includes determining whether the user's visual attention is directed toward a display of the electronic device. In some examples, determining whether the user's visual attention is directed toward the electronic device while the user is speaking includes determining whether the user's visual attention is directed toward an affordance displayed by the electronic device.

[0335] In some examples, determining that the user's visual attention is directed toward the electronic device when the user speaks includes determining that the user's gaze is directed toward the electronic device when the user speaks. In some examples, determining that the user's visual attention is directed toward the electronic device when the user speaks includes determining that the user is facing the electronic device when the user speaks.

[0336] In some examples, a first portion of the audio stream and a first portion of the video stream are stored in a memory buffer (e.g., memory buffer 804), where determining whether the user's visual attention is directed toward the electronic device while the user is speaking includes processing the stored contents of the memory buffer.

[0337] In some examples, the first portion of the video stream includes multiple video frames (e.g., video frames 852, 854, 856, 858, 860, 862, and 864). In some examples, determining whether the user's visual attention is directed toward the electronic device when the user speaks includes: determining a corresponding confidence score for each of the multiple video frames to obtain a plurality of corresponding confidence scores (e.g., confidence scores S1, S2, S3, S4, S5, S6, and S7), wherein the corresponding confidence score indicates, for the corresponding video frame, whether the user's visual attention is directed toward the electronic device when the user speaks.

[0338] In some examples, the plurality of corresponding confidence scores include a first confidence score for a first video frame in the plurality of video frames (e.g., confidence score S4 for video frame 858), and the first video frame corresponds to a first time (e.g., time T4). In some examples, determining the first confidence score includes determining an initial first confidence score based on the first video frame (e.g., initial confidence score S4). ); and adjusting the initial first confidence score based on processing a second video frame (e.g., video frames 860, 862, and / or 864) among the plurality of video frames to obtain a first confidence score, wherein the second video frame corresponds to a second time after the first time (e.g., time T5, T6, and / or T7).

[0339] In some examples, determining an initial first confidence score includes processing a third video frame (e.g., video frames 852, 854, and / or 856) among the plurality of video frames, where the third video frame corresponds to a third time before the first time (e.g., time T1, T2, and / or T3).

[0340] In some examples, determining whether a user's visual attention is directed toward an electronic device while the user is speaking includes: using a machine learning model to determine whether the user's visual attention is directed toward the electronic device while the user is speaking, including: using parameters of the machine learning model that represent a correlation between the user's mouth movements and speech input to process representations of a first portion of an audio stream and a first portion of a video stream.

[0341] At block 1106 , based on determining that the user's visual attention was directed toward the electronic device while the user was speaking, a second portion of the audio stream is identified (eg, by post-processing module 808 ) as including user speech intended for the electronic device.

[0342] In some examples, the plurality of corresponding confidence scores include a fourth confidence score for a fourth video frame in the plurality of video frames (e.g., confidence score S4 for video frame 858). In some examples, identifying the second portion of the audio stream as including user speech expected for the electronic device includes: based on determining that the fourth confidence score exceeds a threshold: determining that a fourth time corresponding to the fourth video frame (e.g., time T4) is a start time of the second portion of the audio stream.

[0343] In some examples, the plurality of video frames include a fifth video frame and a sixth video frame that is continuous with the fifth video frame. In some examples, identifying the second portion of the audio stream as including user speech expected for the electronic device includes: based on determining that a fifth confidence score of the fifth video frame is above a second threshold and a sixth confidence score of the sixth video frame is below the second threshold: determining that a sixth time corresponding to the sixth video frame is an end time of the second portion of the audio stream.

[0344] In some examples, a second portion of the audio stream is identified as including user speech intended for the electronic device without detecting a verbal trigger for initiating a digital assistant session.

[0345] At box 1108, a task based on the second portion of the audio stream is initiated by a digital assistant operating on the electronic device (e.g., by the digital assistant system 700).

[0346] At block 1110 , an output is provided, such as by device 900 , indicative of the initiated task.

[0347] In some examples, a user is identified based on an audio stream (e.g., by identification module 810), wherein the output is based on the identified user. In some examples, a user is identified based on a video stream (e.g., by identification module 810), wherein the output is based on the identified user.

[0348] At block 1112 , based on determining that the user's visual attention was not directed toward the electronic device while the user was speaking: abandoning identification of the second portion of the audio stream as including user speech intended for the electronic device.

[0349] In some examples, based on determining that the user's visual attention is directed toward the electronic device when the user is not speaking: abandoning identifying the second portion of the audio stream as including user speech intended for the electronic device.

[0350] In some examples, based on determining that the user's visual attention is not directed toward the electronic device when the user is not speaking: abandoning identifying the second portion of the audio stream as including user speech intended for the electronic device.

[0351] In some examples, based on determining that the user is not visible in the first portion of the video stream: foregoing identifying the second portion of the audio stream as including user speech intended for the electronic device.

[0352] References Fig.11 The described operations are optionally performed by Figures 1 to 4 , FIG. 6A to FIG. 6B , 7A to 7C and FIG. 8A to FIG. 8B For example, the operations of process 1100 may be implemented by digital assistant system 700 and system 800. A person skilled in the art would clearly know how to implement the process 1100 based on the components depicted in FIG. Figures 1 to 4 , FIG. 6A to FIG. 6B and 7A to 7C The components depicted in the figure are used to implement other processes.

[0353] Fig.12 A process 1200 for determining whether the user's visual attention is directed to an electronic device when the user speaks according to various examples is shown. For example, process 1200 is performed using one or more electronic devices that implement a digital assistant. In some examples, process 1200 is performed using a client-server system (e.g., system 100), and the boxes of process 1200 are divided in any way between a server (e.g., DA server 106) and a client device (e.g., device 900). In other examples, the boxes of process 1200 are divided between a server and multiple client devices (e.g., mobile phones and smart watches). Therefore, although part of process 1200 is described herein as being performed by a specific device of a client-server system, it should be understood that process 1200 is not limited thereto. In other examples, process 1200 is performed using only a client device (e.g., user device 104 or 900) or only multiple client devices. In process 1200, some boxes are optionally combined, the order of some boxes is optionally changed, and some boxes are optionally omitted. In some examples, additional steps may be performed in conjunction with process 1200 .

[0354] At block 1202, an audio stream and a video stream are received concurrently, for example, by device 900. In some examples, the audio stream includes speech by a first user and speech by a second user.

[0355] At block 1204, a first portion of the video stream is identified (e.g., by pre-processing module 802) as including a first user, such as user 1000. In some examples, identifying the first portion of the video stream as including the first user includes tracking a head position of the first user. In some examples, identifying the first portion of the video stream includes (e.g., by pre-processing module 802) cropping the video stream to obtain the first portion of the video stream.

[0356] At block 1206, a second portion of the video stream is identified (e.g., by pre-processing module 802) as including a second user, such as user 1002. In some examples, identifying the second portion of the video stream as including the second user includes tracking a head position of the second user. In some examples, identifying the second portion of the video stream includes (e.g., by pre-processing module 802) cropping the video stream to obtain the second portion of the video stream.

[0357] At block 1208, based on determining (e.g., by audiovisual invocation module 806) based on the first portion of the audio stream and the video stream that the first user's visual attention was directed toward the electronic device (e.g., device 900) when the first user spoke, the first portion of the audio stream is identified (e.g., by post-processing module 808) as including speech by the first user intended for the electronic device. In some examples, the first portion of the audio stream is identified as including speech by the first user intended for the electronic device without detecting a verbal trigger for initiating a digital assistant session.

[0358] In some examples, a third portion of the audio stream received within a predetermined duration before the current time is stored in a memory buffer (e.g., memory buffer 804). In some examples, a first portion of the video stream is stored in a memory buffer, wherein the first portion of the video stream is received within a predetermined duration before the current time, and wherein determining that the visual attention of the first user is directed toward the electronic device when the first user speaks includes processing the stored contents of the memory buffer.

[0359] In some examples, the first portion of the video stream includes multiple video frames (e.g., video frames 852, 854, 856, 858, 860, 862, and 864). In some examples, determining that the visual attention of the first user is directed toward the electronic device when the first user is speaking includes: determining a corresponding confidence score for each of the multiple video frames to obtain a plurality of corresponding confidence scores (e.g., confidence scores S1, S2, S3, S4, S5, S6, and S7), wherein the corresponding confidence score indicates, for the corresponding video frame, whether the visual attention of the first user is directed toward the electronic device when the first user is speaking.

[0360] In some examples, the plurality of corresponding confidence scores include a first confidence score (e.g., confidence score S4) of a first video frame (e.g., video frame 858) in the plurality of video frames, and the first video frame corresponds to a first time (e.g., time T4). In some examples, determining the first confidence score includes: determining an initial first confidence score (e.g., initial confidence score S4) based on the first video frame ); and adjusting the initial first confidence score based on processing a second video frame (e.g., video frames 860, 862, and / or 864) among the plurality of video frames to obtain a first confidence score, wherein the second video frame corresponds to a second time after the first time (e.g., time T5, T6, and / or T7).

[0361] In some examples, determining an initial first confidence score includes processing a third video frame (e.g., video frames 852, 854, and / or 856) among the plurality of video frames, where the third video frame corresponds to a third time before the first time (e.g., time T1, T2, and / or T3).

[0362] In some examples, the plurality of corresponding confidence scores include a fourth confidence score (e.g., S4) of a fourth video frame (e.g., video frame 858) in the plurality of video frames. In some examples, identifying the first portion of the audio stream as including speech intended for the electronic device by the first user includes: based on determining that the fourth confidence score exceeds a threshold: determining that a fourth time (e.g., time T4) corresponding to the fourth video frame is a start time of the first portion of the audio stream.

[0363] In some examples, the plurality of video frames include a fifth video frame and a sixth video frame that is continuous with the fifth video frame. In some examples, identifying the first portion of the audio stream as including speech that is expected to be used by the first user for the electronic device includes: based on determining that a fifth confidence score of the fifth video frame is above a second threshold and a sixth confidence score of the sixth video frame is below the second threshold: determining that a sixth time corresponding to the sixth video frame is an end time of the first portion of the audio stream.

[0364] At block 1210 , a first output is provided (eg, by device 900 ) based on processing a first portion of an audio stream.

[0365] In some examples, a first user is identified based on at least one of a first portion of the video stream and an audio stream (e.g., by identification module 810), wherein the first output is based on the identified first user. In some examples, providing the first output includes processing the first portion of the audio stream using a digital assistant (e.g., digital assistant system 700) operating on the electronic device.

[0366] At block 1212, based on determining (e.g., by audiovisual invocation module 806) based on the second portion of the audio stream and the video stream that the second user's visual attention was directed toward the electronic device when the second user spoke, (e.g., by post-processing module 808) the second portion of the audio stream is identified as including the second user's intended speech for the electronic device. In some examples, the second portion of the audio stream is identified as including the second user's intended speech for the electronic device without detecting a verbal trigger.

[0367] At block 1214, a second output is provided (eg, by device 900) based on processing the second portion of the audio stream.

[0368] In some examples, a second user is identified based on at least one of the second portion of the video stream and the audio stream (e.g., by identification module 810), wherein the second output is based on the identified second user. In some examples, providing the second output includes processing the second portion of the audio stream using a digital assistant.

[0369] In some examples, based on determining (for example, by the audiovisual calling module 806) that the visual attention of the first user is directed toward the electronic device when the first user is not speaking based on the first part of the audio stream and the video stream: abandoning identifying the first part of the audio stream as including the first user's intended speech for the electronic device. In some examples, based on determining (for example, by the audiovisual calling module 806) that the visual attention of the second user is directed toward the electronic device when the second user is not speaking based on the second part of the audio stream and the video stream: abandoning identifying the second part of the audio stream as including the second user's intended speech for the electronic device.

[0370] In some examples, based on determining (for example, by the audiovisual call module 806) based on a first portion of the audio stream and the video stream that the visual attention of the first user is not directed toward the electronic device when the first user is speaking: abandoning identifying the first portion of the audio stream as including the first user's intended speech for the electronic device. In some examples, based on determining (for example, by the audiovisual call module 806) based on a second portion of the audio stream and the video stream that the visual attention of the second user is not directed toward the electronic device when the second user is speaking: abandoning identifying the second portion of the audio stream as including the second user's intended speech for the electronic device.

[0371] In some examples, based on determining (for example, by the audiovisual calling module 806) that the visual attention of the first user is directed toward the electronic device when the second user is speaking based on the first portion of the audio stream and the video stream: abandoning identifying the first portion of the audio stream as including the first user's intended speech for the electronic device. In some examples, based on determining (for example, by the audiovisual calling module 806) that the visual attention of the second user is directed toward the electronic device when the first user is speaking based on the second portion of the audio stream and the video stream: abandoning identifying the second portion of the audio stream as including the second user's intended speech for the electronic device.

[0372] References Fig.12 The described operations are optionally performed by Figures 1 to 4 , FIG. 6A to FIG. 6B , 7A to 7C and FIG. 8A to FIG. 8BFor example, the operations of process 1200 may be implemented by digital assistant system 700 and system 800. A person skilled in the art would clearly know how to implement the process 1200 based on the components depicted in FIG. Figures 1 to 4 , FIG. 6A to FIG. 6B and 7A to 7C The components depicted in the figure are used to implement other processes.

[0373] Fig.13 A process 1300 for determining whether the user's visual attention is directed to an electronic device when the user speaks according to various examples is shown. For example, process 1300 is performed using one or more electronic devices that implement a digital assistant. In some examples, process 1300 is performed using a client-server system (e.g., system 100), and the boxes of process 1300 are divided in any way between a server (e.g., DA server 106) and a client device (e.g., device 900). In other examples, the boxes of process 1300 are divided between a server and multiple client devices (e.g., mobile phones and smart watches). Therefore, although part of process 1300 is described herein as being performed by a specific device of a client-server system, it should be understood that process 1300 is not limited thereto. In other examples, process 1300 is performed using only a client device (e.g., user device 104 or 900) or only multiple client devices. In process 1300, some boxes are optionally combined, the order of some boxes is optionally changed, and some boxes are optionally omitted. In some examples, additional steps may be performed in conjunction with process 1300 .

[0374] At block 1302 , an audio stream and a video stream are received concurrently, for example by device 900 .

[0375] At box 1304, a first type of confidence score (e.g., an audiovisual score) is determined based on the audio stream and the video stream (e.g., by the audiovisual call module 806) to indicate whether the visual attention of a user (e.g., user 904, 1000, or 1002) is directed toward the electronic device (e.g., device 900) when the user speaks.

[0376] In some examples, determining the first type of confidence score includes determining the first type of confidence score based on a first portion of the audio stream received within a predetermined duration before the current time and a first portion of the video stream received within a predetermined duration before the current time.

[0377] In some examples, the first portion of the video stream includes multiple video frames (e.g., video frames 852, 854, 856, 858, 860, 862, and 864), and the first type of confidence score is for a first video frame in the multiple video frames (e.g., video frame 858). In some examples, the final confidence score is for the first video frame.

[0378] At block 1306, after determining the first type of confidence score, a final confidence score indicating whether the user's visual attention was directed toward the electronic device when the user was speaking is determined based on the first type of confidence score and a second type of confidence score indicating whether the user's visual attention was directed toward the electronic device (e.g., by post-processing module 808). In some examples, the second type of confidence score (e.g., a gaze score) indicates whether the user's gaze is directed toward the electronic device.

[0379] In some examples, determining the final confidence score includes determining the final confidence score based on a third type of confidence score (e.g., a gesture score) indicating whether the user's gesture is facing the electronic device.

[0380] In some examples, determining the final confidence score includes determining the final confidence score based on a fourth type of confidence score (e.g., a gesture score) indicating whether a predetermined type of user gesture was detected.

[0381] In some examples, determining the final confidence score includes determining the final confidence score based on a fifth type of confidence score (e.g., a relative motion score) indicating relative motion between the user and the electronic device.

[0382] In some examples, determining a final confidence score includes: determining an action currently performed by the electronic device; based on determining that the action is of a first type: increasing the final confidence score; and based on determining that the action is of a second type different from the first type: decreasing the final confidence score.

[0383] In some examples, determining a final confidence score includes determining the final confidence score based on data representing user interactions with a digital assistant operating on the electronic device.

[0384] At box 1308, it is determined (e.g., by post-processing module 808) whether the final confidence score exceeds a threshold. In some examples, the threshold is reduced (e.g., by post-processing module 808) based on determining (e.g., by post-processing module 808) that the user is alone in a physical environment associated with the electronic device. In some examples, based on determining (e.g., by post-processing module 808) that the action currently being performed by the electronic device is of a first type: reduce the threshold. In some examples, based on determining (e.g., by post-processing module 808) that the action currently being performed by the electronic device is of a second type different from the type: increase the threshold. In some examples, the threshold is adjusted (e.g., by post-processing module 808) based on data representing the user's interaction with a digital assistant operating on the electronic device.

[0385] At block 1310, based on determining that the final confidence score exceeds a threshold: (e.g., by post-processing module 808) identifying a portion of the audio stream as including user speech intended for the electronic device. In some examples, identifying the portion of the audio stream as including user speech intended for the electronic device includes determining that a first time (e.g., time T4) corresponding to a first video frame (e.g., video frame 858) is a start time for the portion of the audio stream. In some examples, identifying the portion of the audio stream as including user speech intended for the electronic device without detecting a verbal trigger for initiating a digital assistant session.

[0386] In some examples, identifying the portion of the audio stream as including user speech expected for the electronic device includes: determining an initial start time of the portion of the audio stream to be a second time corresponding to a first type of confidence score (e.g., an audiovisual score); and adjusting the initial start time based on a third type of confidence score (e.g., a gesture score).

[0387] At block 1312, an output is provided based on processing the portion of the audio stream (e.g., by device 900). In some examples, providing the output includes processing the portion of the audio stream using a digital assistant (e.g., digital assistant system 700) operating on the electronic device.

[0388] At block 1314 , based on determining that the final confidence score does not exceed the threshold, discard identifying the portion of the audio stream as including user speech intended for the electronic device.

[0389] References Fig.13 The described operations are optionally performed by Figures 1 to 4 , FIG. 6A to FIG. 6B , 7A to 7C and FIG. 8A to FIG. 8B For example, the operations of process 1300 may be implemented by digital assistant system 700 and system 800. A person skilled in the art would clearly know how to implement the process 1300 based on the components depicted in FIG. Figures 1 to 4 , FIG. 6A to FIG. 6B and 7A to 7C The components depicted in the figure are used to implement other processes.

[0390] According to some specific implementations, a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) is provided, which stores one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.

[0391] According to some implementations, an electronic device (eg, a portable electronic device) is provided that includes means for performing any of the methods or processes described herein.

[0392] According to some implementations, an electronic device (eg, a portable electronic device) is provided that includes a processing unit configured to perform any of the methods and processes described herein.

[0393] According to some specific implementations, an electronic device (e.g., a portable electronic device) is provided, which includes one or more processors and a memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.

[0394] For the purpose of explanation, the preceding description is described by reference to specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or limit the invention to the disclosed precise form. According to the above teachings, many modifications and variations are possible. These embodiments are selected and described in order to best explain the principles of these technologies and their practical applications. Others skilled in the art can thus best utilize these technologies and various embodiments with various modifications suitable for the intended specific use.

[0395] Although the disclosure and examples have been fully described with reference to the accompanying drawings, it should be noted that various changes and modifications will become apparent to those skilled in the art. It should be understood that such changes and modifications are considered to be included within the scope of the disclosure and examples defined by the claims.

[0396] As described above, one aspect of the technology of the present invention is to collect and use data available from various sources to determine whether the user's visual attention is directed to the electronic device when the user speaks. The present disclosure contemplates that, in some instances, the collected data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data may include demographic data, location-based data, phone numbers, email addresses, Twitter IDs, home addresses, data or records related to the user's health or fitness level (e.g., vital sign measurements, medication information, exercise information), date of birth, or any other identification or personal information.

[0397] The present disclosure recognizes that the use of such personal information data in the present technology can be used to benefit the user. For example, the personal information data can be used to provide relevant responses to user requests to a digital assistant. Therefore, the use of such personal information data can enable the effective and correct use of electronic devices. In addition, the present disclosure also anticipates other uses of personal information data that benefit the user. For example, health and fitness data can be used to provide insights into the user's overall health status, or can be used as positive feedback to individuals who use technology to pursue health goals.

[0398] The disclosure anticipates that entities responsible for the collection, analysis, disclosure, transmission, storage or other purposes of such personal information data will comply with sound privacy policies and / or privacy measures. Specifically, such entities should implement and adhere to the use of privacy policies and practices that are recognized as meeting or exceeding the industry or government requirements for maintaining the privacy and security of personal information data. Such policies should be easy for users to access and should be updated as changes in the collection and / or use of data occur. Personal information from users should be collected for legal and reasonable entity purposes and should not be shared or sold outside these legal purposes. In addition, such collection / sharing should be carried out after receiving the informed consent of the user. In addition, such entities should consider taking any necessary steps to protect and safeguard access to such personal information data, and ensure that other entities with access to personal information data comply with their privacy policies and procedures. In addition, such entities can subject themselves to third-party assessments to prove that they comply with widely accepted privacy policies and practices. In addition, policies and practices should be adapted to specific types of personal information data collected and / or accessed, and to applicable laws and standards including considerations of special jurisdictions. For example, in the United States, the collection or acquisition of certain health data may be governed by federal and / or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); while health data in other countries may be subject to other regulations and policies and should be handled accordingly. Therefore, different privacy measures should be advocated for different types of personal data in each country.

[0399] Regardless of the foregoing, the present disclosure also contemplates implementation schemes in which users selectively block use or access to personal information data. That is, the present disclosure contemplates hardware elements and / or software elements to prevent or block access to such personal information data. For example, with respect to determining whether a user is looking and speaking, the technology of the present invention may be configured to allow a user to choose to "opt in" or "opt out" of participating in the collection of personal information data at any time during or after registration for a service. In another example, a user may choose not to allow the use of personal information data to determine whether they are looking and speaking. In yet another example, a user may choose to limit the amount of time that personal information data is collected to determine whether they are looking and speaking.

[0400] In addition to providing "opt-in" and "opt-out" options, the present disclosure also contemplates providing notifications related to access or use of personal information. For example, a user may be notified that their personal information data will be accessed when downloading an application, and then be reminded again just before their personal information data is accessed by the application.

[0401] In addition, it is the intention of the present disclosure that personal information data should be managed and processed in a manner that minimizes the risk of unintentional or unauthorized access or use. Once the data is no longer needed, the risk can be minimized by limiting data collection and data deletion. In addition, and when applicable, including in certain health-related applications, data de-identification can be used to protect the privacy of users. Where appropriate, de-identification can be facilitated by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or characteristics of stored data (e.g., collecting location data at the city level rather than the address level), controlling how the data is stored (e.g., aggregating data between users), and / or other methods.

[0402] Thus, while the present disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, the present disclosure also contemplates that various embodiments may also be implemented without access to such personal information data. That is, various embodiments of the present technology will not fail to function properly due to the lack of all or part of such personal information data. For example, a device may respond to speech input based on non-personal information data or an absolute minimum amount of personal information (such as content requested by a device associated with a user, other non-personal information available to the device, or publicly available information).

Claims

1. A method comprising: At an electronic device having one or more processors and a memory: Concurrently receiving audio stream and video stream; determining whether visual attention of the user is directed toward the electronic device while the user is speaking based on a first portion of the audio stream received within a predetermined duration prior to a current time and a first portion of the video stream received within the predetermined duration prior to a current time; as well as Based on determining that the visual attention of the user is directed toward the electronic device when the user speaks: identifying a second portion of the audio stream as including user speech intended for the electronic device; initiating, by a digital assistant operating on the electronic device, a task based on the second portion of the audio stream; as well as Provides output indicating the task initiated.

2. The method of claim 1, wherein determining whether the visual attention of the user is directed toward the electronic device when the user speaks comprises: Determining whether the visual attention of the user is directed toward a display of the electronic device.

3. The method of claim 1 , wherein determining whether the visual attention of the user is directed toward the electronic device when the user speaks comprises: Determine whether the user's visual attention is directed toward an affordance displayed by the electronic device.

4. A method according to any one of claims 1 to 2, wherein the second portion of the audio stream is identified as including user speech intended for the electronic device in the absence of detecting a verbal trigger for initiating a digital assistant session.

5. The method according to any one of claims 1 to 2, further comprising: The first portion of the audio stream and the first portion of the video stream are stored in a memory buffer, wherein determining whether the visual attention of the user is directed toward the electronic device while the user is speaking includes processing stored contents of the memory buffer.

6. The method according to claim 1, wherein: The first portion of the video stream includes a plurality of video frames; and Determining whether the visual attention of the user is directed toward the electronic device when the user speaks includes: A respective confidence score is determined for each of the plurality of video frames to obtain a plurality of respective confidence scores, wherein the respective confidence scores indicate, for respective video frames, whether the visual attention of the user is directed toward the electronic device when the user speaks.

7. The method according to claim 6, wherein: The plurality of corresponding confidence scores comprises a first confidence score for a first video frame in the plurality of video frames; The first video frame corresponds to a first time; and Determining the first confidence score includes: determining an initial first confidence score based on the first video frame; as well as The initial first confidence score is adjusted based on processing a second video frame of the plurality of video frames to obtain the first confidence score, wherein the second video frame corresponds to a second time after the first time. 8 . The method of claim 7 , wherein determining the initial first confidence score comprises processing a third video frame of the plurality of video frames, wherein the third video frame corresponds to a third time prior to the first time.

9. The method according to claim 6, wherein: The plurality of corresponding confidence scores include a fourth confidence score for a fourth video frame in the plurality of video frames; and Identifying the second portion of the audio stream as including user speech intended for the electronic device comprises: According to determining that the fourth confidence score exceeds a threshold: A fourth time corresponding to the fourth video frame is determined to be a start time of the second portion of the audio stream.

10. The method according to claim 6, wherein: The plurality of video frames include a fifth video frame and a sixth video frame continuous with the fifth video frame; and Identifying the second portion of the audio stream as including user speech intended for the electronic device comprises: Based on determining that the fifth confidence score of the fifth video frame is above a second threshold and the sixth confidence score of the sixth video frame is below the second threshold: A sixth time corresponding to the sixth video frame is determined to be an end time of the second portion of the audio stream.

11. The method according to any one of claims 1 to 2, wherein determining whether the visual attention of the user is directed toward the electronic device when the user speaks comprises: Using a machine learning model to determine whether the visual attention of the user is directed toward the electronic device when the user speaks, comprising: Representations of the first portion of the audio stream and the first portion of the video stream are processed using parameters of the machine learning model representing correlations between mouth movements of the user and speech input.

12. The method according to any one of claims 1 to 2, wherein determining that the visual attention of the user is directed toward the electronic device when the user speaks comprises: It is determined that the user's gaze is directed toward the electronic device while the user is speaking.

13. The method according to any one of claims 1 to 2, wherein determining that the visual attention of the user is directed toward the electronic device when the user speaks comprises: It is determined that the user is facing the electronic device when the user speaks.

14. The method according to any one of claims 1 to 2, further comprising: The user is identified based on the audio stream, wherein the output is based on the identified user.

15. The method according to any one of claims 1 to 2, further comprising: The user is identified based on the video stream, wherein the output is based on the identified user.

16. The method according to any one of claims 1 to 2, further comprising: Based on determining that the visual attention of the user is not directed toward the electronic device when the user is speaking: Identifying the second portion of the audio stream as including user speech intended for the electronic device is abandoned.

17. The method according to any one of claims 1 to 2, further comprising: Based on determining that the visual attention of the user is directed toward the electronic device when the user is silent: Identifying the second portion of the audio stream as including user speech intended for the electronic device is abandoned.

18. The method according to any one of claims 1 to 2, further comprising: Based on determining that the visual attention of the user is not directed toward the electronic device when the user is not speaking: Identifying the second portion of the audio stream as including user speech intended for the electronic device is abandoned.

19. The method according to any one of claims 1 to 2, further comprising: Based on determining that the user is not visible in the first portion of the video stream: Identifying the second portion of the audio stream as including user speech intended for the electronic device is abandoned.

20. An electronic device, comprising: one or more processors; Memory; and One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for executing the method according to any one of claims 1 to 19.

21. A non-transitory computer-readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by one or more processors of an electronic device, cause the electronic device to perform the method according to any one of claims 1 to 19.

22. An electronic device, comprising: Device for carrying out the method according to any one of claims 1 to 19.

23. A computer program product, comprising one or more programs configured to be executed by one or more processors of an electronic device, the one or more programs comprising instructions for executing the method according to any one of claims 1 to 19.

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