Determine if voice input is intended for a digital assistant

Abstract

This disclosure relates to determining whether a voice input is intended for use with a digital assistant. An example process includes: receiving voice input representing a user's utterance; determining a first score based on a text representation of the voice input, the first score corresponding to a type of the user's utterance; determining a second score based on the text representation of the voice input, the second score representing a correspondence between the user's utterance and a domain recognized by the digital assistant; determining whether the voice input is intended for use with the digital assistant based on the first score and the second score; and, upon determining that the voice input is intended for use with the digital assistant: initiating a task based on the voice input through the digital assistant; and providing an output indicating the initiated task.

Description

[0001] This application is a divisional application of invention patent application 202380038179.7, filed on May 12, 2023, entitled "Determining whether voice input is intended for use in a digital assistant". Technical Field

[0002] This article deals with intelligent automated assistants in general, and more specifically, with determining whether voice input is intended for use with intelligent automated assistants. Background Technology

[0003] Intelligent automated assistants (or digital assistants) provide a beneficial interface between human users and electronic devices. Such assistants allow users to interact with devices or systems using natural language in voice and / or text. For example, a user can provide voice input containing their request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from this voice input and act it out as a task. 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] This document discloses an example method. An example method includes, at an electronic device including one or more processors and memory: receiving voice input representing a user utterance; determining a first score based on a text representation of the voice input, the first score corresponding to a type of the user utterance; determining a second score based on the text representation of the voice input, the second score representing a correspondence between the user utterance and a domain recognized by a digital assistant operating on the electronic device; determining, based on the first score and the second score, whether the voice input is intended for use with the digital assistant; and, based on determining that the voice input is intended for use with the digital assistant: initiating a task based on the voice input via the digital assistant; and providing an output instructing the initiated task.

[0005] This document 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: receive voice input representing a user's utterance; determine a first score based on a text representation of the voice input, the first score corresponding to a type of the user's utterance; determine a second score based on the text representation of the voice input, the second score representing a correspondence between the user's utterance and a domain recognized by a digital assistant operating on the electronic device; determine, based on the first score and the second score, whether the voice input is intended for use with the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant, initiate a task based on the voice input through the digital assistant; and provide an output instructing the initiated task.

[0006] This document discloses an example electronic device. An example 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, the one or more programs including instructions for performing the following operations: receiving voice input representing a user utterance; determining a first score based on a text representation of the voice input, the first score corresponding to a type of the user utterance; determining a second score based on the text representation of the voice input, the second score representing a correspondence between the user utterance and a domain recognized by a digital assistant operating on the electronic device; determining, based on the first score and the second score, whether the voice input is intended for use with the digital assistant; and, based on determining that the voice input is intended for use with the digital assistant: initiating a task based on the voice input through the digital assistant; and providing an output instructing the initiated task.

[0007] An example electronic device includes components for performing the following operations: receiving voice input representing a user's utterance; determining a first score based on a text representation of the voice input, the first score corresponding to a type of the user's utterance; determining a second score based on the text representation of the voice input, the second score representing a correspondence between the user's utterance and a domain recognized by a digital assistant operating on the electronic device; determining, based on the first score and the second score, whether the voice input is intended for use with the digital assistant; and, based on determining that the voice input is intended for use with the digital assistant: initiating a task based on the voice input through the digital assistant; and providing an output indicating the initiated task.

[0008] Determining whether voice input is intended for use by a digital assistant based on a first and second score allows the digital assistant to respond to voice input more accurately and efficiently. For example, the digital assistant can determine whether voice input is intended for itself (and provide a relevant response to the voice input) without requiring additional input from the user explicitly indicating that the voice input is intended for use by the digital assistant (e.g., verbal trigger input, button selection, selection of displayed power indicators, etc.). Furthermore, the digital assistant can avoid responding to voice input that is not intended for use by the digital assistant (e.g., voice input intended for another person). In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to interact correctly with the digital assistant, by reducing the user input required to stop the digital assistant from providing incorrect responses to voice input that is not intended for use by the digital assistant, by accurately responding to voice input intended for use by the digital assistant, and by avoiding repetitive voice input to the digital assistant), which additionally reduces power consumption and improves device battery life by enabling users to use the device more quickly and efficiently.

[0009] This document discloses an example method. An example method includes, at an electronic device including one or more processors and memory: receiving a first voice input representing a first user utterance; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input, the second voice input representing a second user utterance; determining a first score based on a text representation of the second voice input, the first score representing a correspondence between the second user utterance and a domain recognized by the digital assistant; determining a second score based on the text representation of the second voice input, the second score representing contextual continuity between the first and second user utterances; determining, based on the first score and the second score, whether the second voice input is intended for use with the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: initiating a second task based on the second voice input via the digital assistant; and providing a second output indicating the initiated second task.

[0010] This document 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: receive a first voice input representing a first user utterance; initiate a first task based on the first voice input via a digital assistant operating on the electronic device; provide a first output indicating the initiated first task; and after providing the first output: receive a second voice input following the first voice input, the second voice input representing a second user utterance; determine a first score based on a text representation of the second voice input, the first score representing a correspondence between the second user utterance and a domain recognized by the digital assistant; determine a second score based on the text representation of the second voice input, the second score representing contextual continuity between the first and second user utterances; determine, based on the first score and the second score, whether the second voice input is intended for use with the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: initiate a second task based on the second voice input via the digital assistant; and provide a second output indicating the initiated second task.

[0011] This document discloses an example electronic device. An example 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, the one or more programs including instructions for performing the following operations: receiving a first voice input representing a first user utterance; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input, the second voice input representing a second user utterance; determining a first score based on a text representation of the second voice input, the first score representing a correspondence between the second user utterance and a domain recognized by the digital assistant; determining a second score based on the text representation of the second voice input, the second score representing contextual continuity between the first user utterance and the second user utterance; determining, based on the first score and the second score, whether the second voice input is intended for use with the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: initiating a second task based on the second voice input via the digital assistant; and providing a second output indicating the initiated second task.

[0012] An example electronic device includes components for performing the following operations: receiving a first voice input representing a first user utterance; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input, the second voice input representing a second user utterance; determining a first score based on a text representation of the second voice input, the first score representing a correspondence between the second user utterance and a domain recognized by the digital assistant; determining a second score based on the text representation of the second voice input, the second score representing a contextual continuity between the first user utterance and the second user utterance; determining, based on the first score and the second score, whether the second voice input is intended for use with the digital assistant; and, based on determining that the second voice input is intended for use with the digital assistant: initiating a second task based on the second voice input via the digital assistant; and providing a second output indicating the initiated second task.

[0013] Determining whether a second voice input is intended for the digital assistant allows the digital assistant to respond more accurately and efficiently to subsequent voice inputs (e.g., subsequent voice inputs) following the initial voice input. This, in turn, enables more accurate and efficient continuous interaction with the digital assistant. For example, the digital assistant can determine whether subsequent voice input is intended for itself (and provide a relevant response to the subsequent voice input) without requiring additional input from the user explicitly indicating that the subsequent voice input is intended for the digital assistant (e.g., verbal trigger input, button selection, selection of displayed power indicators, etc.). Furthermore, the digital assistant can avoid responding to subsequent voice inputs that are not intended for the digital assistant (e.g., subsequent voice inputs intended for another person). In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to interact correctly with the digital assistant, by reducing the user input required to stop the digital assistant from providing incorrect responses to subsequent voice inputs that are not intended for the digital assistant, by accurately responding to subsequent voice inputs intended for the digital assistant, and by avoiding repeated subsequent voice inputs to the digital assistant), which additionally reduces power consumption and improves device battery life by enabling users to use the device more quickly and efficiently.

[0014] This document discloses an example method. An example method includes, at an electronic device including one or more processors and memory: receiving voice input; determining a first score based on an acoustic representation of the voice input, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining a second score based on a text representation of the voice input, the second score indicating whether the voice input is intended for use with the digital assistant; determining whether the voice input is intended for use with the digital assistant based on the first score and the second score; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output indicating the initiated task.

[0015] This document 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: receive voice input; determine a first score based on an acoustic representation of the voice input, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determine a second score based on a text representation of the voice input, the second score indicating whether the voice input is intended for use with the digital assistant; determine whether the voice input is intended for use with the digital assistant based on the first score and the second score; initiate a task based on the voice input through the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant, provide an output indicating the initiated task.

[0016] This document discloses an example electronic device. An example 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, the one or more programs including instructions for performing: receiving voice input; determining a first score based on an acoustic representation of the voice input, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining a second score based on a text representation of the voice input, the second score indicating whether the voice input is intended for use with the digital assistant; determining whether the voice input is intended for use with the digital assistant based on the first score and the second score; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output indicating the initiated task.

[0017] An exemplary electronic device includes components for performing the following operations: receiving voice input; determining a first score based on an acoustic representation of the voice input, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining a second score based on a text representation of the voice input, the second score indicating whether the voice input is intended for use with the digital assistant; determining whether the voice input is intended for use with the digital assistant based on the first score and the second score; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output indicating the initiated task.

[0018] Determining whether voice input is intended for use by a digital assistant based on a first and second score allows the digital assistant to respond to voice input more accurately and efficiently. For example, the digital assistant can determine whether voice input is intended for itself (and provide a relevant response to the voice input) without requiring additional input from the user explicitly indicating that the voice input is intended for use by the digital assistant (e.g., verbal trigger input, button selection, selection of displayed power indicators, etc.). Furthermore, the digital assistant can avoid responding to voice input that is not intended for use by the digital assistant (e.g., voice input intended for another person). In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to interact correctly with the digital assistant, by reducing the user input required to stop the digital assistant from providing incorrect responses to voice input that is not intended for use by the digital assistant, by accurately responding to voice input intended for use by the digital assistant, and by avoiding repetitive voice input to the digital assistant), which additionally reduces power consumption and improves device battery life by enabling users to use the device more quickly and efficiently.

[0019] This document discloses an example method. An example method includes, at an electronic device including one or more processors and memory: receiving a first voice input; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input; determining a first score based on user gaze input and the second voice input, the first score indicating whether the second voice input is intended for use with the digital assistant; determining a second score based on an acoustic representation of the second voice input, the second score indicating whether the second voice input is intended for use with the digital assistant; determining whether the second voice input is intended for use with the digital assistant based on the first score and the second score; initiating a second task based on the second voice input via the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: providing a second output indicating the initiated second task.

[0020] This document 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: receive a first voice input; initiate a first task based on the first voice input via a digital assistant operating on the electronic device; provide a first output indicating the initiated first task; and after providing the first output: receive a second voice input following the first voice input; determine a first score based on user gaze input and the second voice input, the first score indicating whether the second voice input is intended for use with the digital assistant; determine a second score based on an acoustic representation of the second voice input, the second score indicating whether the second voice input is intended for use with the digital assistant; determine whether the second voice input is intended for use with the digital assistant based on the first score and the second score; initiate a second task based on the second voice input via the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: provide a second output indicating the initiated second task.

[0021] This document discloses an example electronic device. An example 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, the one or more programs including instructions for performing: receiving a first voice input; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input; determining a first score based on user gaze input and the second voice input, the first score indicating whether the second voice input is intended for use with the digital assistant; determining a second score based on an acoustic representation of the second voice input, the second score indicating whether the second voice input is intended for use with the digital assistant; determining whether the second voice input is intended for use with the digital assistant based on the first score and the second score; initiating a second task based on the second voice input via the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: providing a second output indicating the initiated second task.

[0022] An exemplary electronic device includes components for performing the following operations: receiving a first voice input; initiating a first task based on the first voice input via a digital assistant operating on the electronic device; providing a first output indicating the initiated first task; and after providing the first output: receiving a second voice input following the first voice input; determining a first score based on user gaze input and the second voice input, the first score indicating whether the second voice input is intended for use with the digital assistant; determining a second score based on an acoustic representation of the second voice input, the second score indicating whether the second voice input is intended for use with the digital assistant; determining whether the second voice input is intended for use with the digital assistant based on the first score and the second score; initiating a second task based on the second voice input via the digital assistant; and, upon determining that the second voice input is intended for use with the digital assistant: providing a second output indicating the initiated second task.

[0023] Determining whether a second voice input is intended for the digital assistant allows the digital assistant to respond more accurately and efficiently to subsequent voice inputs (e.g., subsequent voice inputs) following the initial voice input. This, in turn, enables more accurate and efficient continuous interaction with the digital assistant. For example, the digital assistant can determine whether subsequent voice input is intended for itself (and provide a relevant response to the subsequent voice input) without requiring additional input from the user explicitly indicating that the subsequent voice input is intended for the digital assistant (e.g., verbal trigger input, button selection, selection of displayed power indicators, etc.). Furthermore, the digital assistant can avoid responding to subsequent voice inputs that are not intended for the digital assistant (e.g., subsequent voice inputs intended for another person). In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to interact correctly with the digital assistant, by reducing the user input required to stop the digital assistant from providing incorrect responses to subsequent voice inputs that are not intended for the digital assistant, by accurately responding to subsequent voice inputs intended for the digital assistant, and by avoiding repeated subsequent voice inputs to the digital assistant), which additionally reduces power consumption and improves device battery life by enabling users to use the device more quickly and efficiently.

[0024] This document discloses an example method. An example method includes, at an electronic device including one or more processors and memory: receiving voice input; capturing a first dataset from a camera between a start time and an end time of the voice input; determining, based on the first dataset, a second dataset representing a user gaze pattern at the time the voice input is provided; determining, based on the voice input and the second dataset, a first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining, based on the first score, whether the voice input is intended for use with the digital assistant; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output indicating the initiated task.

[0025] This document 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: receive voice input; capture a first dataset from a camera between a start time and an end time of the voice input; determine, based on the first dataset, a second dataset representing a user gaze pattern at the time the voice input is provided; determine, based on the voice input and the second dataset, a first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determine, based on the first score, whether the voice input is intended for use with the digital assistant; initiate a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant, provide an output indicating the initiated task.

[0026] This document discloses an example electronic device. An example 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, the one or more programs including instructions for performing the following operations: receiving voice input; capturing a first dataset from a camera between a start time and an end time of the voice input; determining a second dataset representing a user gaze pattern when the voice input is provided, based on the first dataset; determining a first score based on the voice input and the second dataset, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining, based on the first score, whether the voice input is intended for use with the digital assistant; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output instructing the initiated task.

[0027] An exemplary electronic device includes components for performing the following operations: receiving voice input; capturing a first dataset from a camera between a start time and an end time of the voice input; determining a second dataset based on the first dataset representing a user gaze pattern when the voice input is provided; determining a first score based on the voice input and the second dataset, the first score indicating whether the voice input is intended for use with a digital assistant operating on the electronic device; determining, based on the first score, whether the voice input is intended for use with the digital assistant; initiating a task based on the voice input via the digital assistant; and, upon determining that the voice input is intended for use with the digital assistant: providing an output indicating the initiated task.

[0028] Determining whether voice input is intended for the digital assistant based on a first score allows the digital assistant to respond to voice input more accurately and efficiently. For example, the digital assistant can determine whether voice input is intended for itself (and provide a relevant response to the voice input) without requiring additional input from the user explicitly indicating that the voice input is intended for the digital assistant (e.g., verbal trigger input, button selection, selection of displayed power indicators, etc.). Alternatively, the digital assistant can rely on the user's gaze pattern to determine whether voice input is intended for itself. Furthermore, the digital assistant can avoid responding to voice input that is not intended for the digital assistant (e.g., voice input intended for another person). In this way, the user-device interface can be more efficient and accurate (e.g., by reducing the amount of user input required to interact correctly with the digital assistant, by reducing the user input required to stop the digital assistant from providing incorrect responses to voice input that is not intended for the digital assistant, by accurately responding to voice input intended for the digital assistant, and by avoiding repetitive voice input to the digital assistant), which additionally reduces power consumption and improves device battery life by enabling users to use the device more quickly and efficiently. Attached Figure Description

[0029] Figure 1 Block diagrams are shown for systems and environments used to implement digital assistants, based on various examples.

[0030] Figure 2A A block diagram is shown for a portable multi-functional device that implements the client-side portion of a digital assistant according to various examples.

[0031] Figure 2B A block diagram illustrating exemplary components for event handling, based on various examples.

[0032] Figure 3 Portable multi-functional devices are shown that implement the client-side portion of a digital assistant according to various examples.

[0033] Figure 4 A block diagram of an exemplary multifunctional device having a display and a touch-sensitive surface, according to various examples.

[0034] Figure 5A An exemplary user interface for the menu of an application on a portable multi-functional device, based on various examples, is shown.

[0035] Figure 5B Exemplary user interfaces of multifunctional devices with touch-sensitive surfaces separate from the display are shown according to various examples.

[0036] Figure 6A The images show personal electronic devices based on various examples.

[0037] Figure 6B A block diagram illustrating a personal electronic device based on various examples is provided.

[0038] Figure 7A A block diagram illustrating a digital assistant system or its server portion, based on various examples, is provided.

[0039] Figure 7B Examples are shown in Figure 7A The digital assistant functions shown.

[0040] Figure 7C A portion of the knowledge ontology is shown based on various examples.

[0041] Figure 8 The system shown illustrates, based on various examples, a system for determining whether a voice input is intended for use with a digital assistant.

[0042] Figures 9A to 9D The images show digital assistant user interfaces displayed by electronic devices, based on various examples.

[0043] Figure 10 The process for determining whether a voice input is intended for use with a digital assistant is illustrated, based on various examples.

[0044] Figure 11 The process for determining whether subsequent voice input is intended for use with a digital assistant is illustrated, based on various examples.

[0045] Figures 12A to 12B The process for determining whether a voice input is intended for use with a digital assistant is illustrated, based on various examples.

[0046] Figures 13A to 13B The process for determining whether a voice input is intended for use with a digital assistant is illustrated, based on various examples.

[0047] Figures 14A to 14C The process for determining whether subsequent voice input is intended for use with a digital assistant is illustrated, based on various examples. Detailed Implementation

[0048] The accompanying drawings will be referenced in the following description of the examples, which illustrate specific examples that can be implemented by way of example. It should be understood that other examples may be used and structural changes may be made without departing from the scope of the individual examples.

[0049] This typically involves determining whether the voice input is intended for use with a digital assistant. As described in this article, this involves determining the text representation of the voice input, the acoustic representation of the voice input, the user's gaze input, and / or various other inputs.

[0050] 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 only used to distinguish one element from another. For example, without departing from the scope of the various examples described, 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 and second inputs are inputs, and in some cases, they are independent and distinct inputs.

[0051] The terminology used in the description of the various examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and / or” as used herein refers to and covers any and all possible combinations of one or more of the associated listed items. It will also be understood that the term “comprising,” when used in this specification, specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052] Depending on the context, the term "if" can be interpreted as "when...", "at...", "in response to determination", or "in response to detection". Similarly, depending on the context, the phrases "if it is determined..." or "if [the stated condition or event] is detected" can be interpreted as "when it is determined...", "in response to determination...", "when [the stated condition or event] is detected", or "in response to detection of [the stated condition or event]".

[0053] 1. System and Environment

[0054] Figure 1 A block diagram of system 100 according to various examples is shown. In some examples, 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 the form of speech and / or text to infer user intent and performs actions based on the inferred user intent. For example, in order to act on an inferred user intent, the system performs one or more of the following steps: identifying a task flow having steps and parameters designed to achieve the inferred user intent; inputting a specific request into the task flow based on the inferred user intent; executing the task flow by invoking programs, methods, services, APIs, etc.; and generating an output response to the user in an audible (e.g., speech) and / or visual form.

[0055] Specifically, a digital assistant can accept user requests that are at least partially in the form of natural language commands, requests, statements, narration, and / or inquiries. Typically, user requests seek an informational response or task from the digital assistant. A satisfactory response to a user request includes providing the requested informational response, performing the requested task, or a combination of both. For example, a user asks a 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 entrance of Central Park.” The user also requests 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 “Okay, coming right away,” and then send the appropriate calendar invitations to each of the user’s friends listed in the user’s electronic address book. During the performance of the requested task, the digital assistant sometimes interacts with the user in a sustained conversation involving multiple exchanges of information over extended periods. Many other methods exist for interacting with a digital assistant to request information or perform various tasks. In addition to providing verbal responses and taking programmed actions, digital assistants also provide responses in other forms of video or audio, such as text, reminders, music, video, animation, etc.

[0056] 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") executing on user device 104 and a server-side portion 106 (hereinafter referred to as "DA server 106") executing on server system 108. DA client 102 communicates with DA server 106 via one or more networks 110. DA client 102 provides client-side functionality, such as user-oriented input and output processing, and communication with DA server 106. DA server 106 provides server-side functionality for any number of DA clients 102, each residing on a corresponding user device 104.

[0057] In some examples, 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 DA server 106. One or more processing modules 114 utilize data and models 116 to process voice input and determine user intent based on natural language input. Furthermore, one or more processing modules 114 perform task execution based on the inferred user intent. In some examples, 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 communication.

[0058] User equipment 104 can be any suitable electronic device. In some examples, user equipment 104 is a portable multi-functional device (e.g., see reference below). Figure 2A The aforementioned device 200), multifunctional device (for example, see below for reference) Figure 4 The device 400) or personal electronic device (e.g., referred to below) Figures 6A to 6B The device 600 is described above. A portable multifunction device is, for example, a mobile phone that also includes other functions, such as PDA and / or music player functionality. Specific examples of portable multifunction devices include Apple Inc. of Cupertino, California, USA. iPod and Devices. Other examples of portable multifunction devices include, but are not limited to, earbuds / headphones, speakers, and laptop or tablet computers. Additionally, in some examples, user device 104 is a non-portable multifunction device. Specifically, user device 104 is a desktop computer, game console, speaker, television, or set-top box. In some examples, user device 104 includes a touch-sensitive surface (e.g., a touchscreen display and / or touchpad). Furthermore, user device 104 optionally includes one or more other physical user interface devices, such as a physical keyboard, mouse, and / or joystick. Various examples of electronic devices such as multifunction devices are described in more detail below.

[0059] Examples of communication networks 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, for example, Ethernet, Universal Serial Bus (USB), FireWire, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi, Voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.

[0060] Server system 108 is implemented on one or more stand-alone data processing devices or distributed computer networks. In some examples, server system 108 also utilizes various virtual devices and / or services from third-party service providers (e.g., third-party cloud service providers) to provide potential computing and / or infrastructure resources for server system 108.

[0061] In some examples, user equipment 104 communicates with DA server 106 via a second user equipment 122. The second user equipment 122 is similar to or identical to user equipment 104. For example, the second user equipment 122 is similar to the one described below. Figure 2A , Figure 4 and Figures 6A to 6B The devices 200, 400, or 600 are described above. User equipment 104 is configured to be communicatively coupled to a second user equipment 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 equipment 122 is configured to act as a proxy between user equipment 104 and DA server 106. For example, a DA client 102 of user equipment 104 is configured to transmit information (e.g., a user request received at user equipment 104) to DA server 106 via the second user equipment 122. DA server 106 processes this information and returns relevant data (e.g., data content in response to the user request) to user equipment 104 via the second user equipment 122.

[0062] In some examples, user equipment 104 is configured to send a shortened request for data to a second user equipment 122 to reduce the amount of information transmitted from user equipment 104. The second user equipment 122 is configured to determine supplementary information to add to the shortened request to generate a complete request to be transmitted to DA server 106. This system architecture can advantageously allow user equipment 104 (e.g., a watch or similar compact electronic device) with limited communication capabilities and / or limited battery power (e.g., a second user equipment 122 with strong communication capabilities and / or battery power, such as a mobile phone, laptop computer, tablet computer, etc.) acting as a proxy to DA server 106 to access the services provided by DA server 106 by using a second user equipment 122 with strong communication capabilities and / or battery power as a proxy to DA server 106. Although Figure 1 Only two user devices 104 and 122 are shown in this document, but it should be understood that in some examples, system 100 may include any number and type of user devices configured in this agent configuration to communicate with DA server system 106.

[0063] Although Figure 1 The digital assistant shown includes both a client-side component (e.g., DA client 102) and a server-side component (e.g., DA server 106), but in some examples, the digital assistant's functionality is implemented as a standalone application installed on the user's device. Furthermore, the functional division between the client and server components of the digital assistant can vary in different implementations. For example, in some examples, the DA client is a thin client that only provides user-facing input and output processing functions and delegates all other functions of the digital assistant to the backend server.

[0064] 2. Electronic equipment

[0065] Now let’s turn our attention to the implementation of electronic devices for the client-side portion of a digital assistant. Figure 2AA block diagram of a portable multi-functional device 200 with a touch-sensitive display system 212 according to some embodiments is shown. The touch-sensitive display 212 is sometimes referred to as a “touchscreen” for convenience, and is sometimes referred to as or called a “touch-sensitive display system.” 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 interface 218, an RF circuitry system 208, an audio circuitry system 210, a speaker 211, a microphone 213, an input / output (I / O) subsystem 206, other input control devices 216, and an external port 224. Device 200 optionally includes one or more optical sensors 264. Device 200 optionally includes one or more contact strength 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 haptic output generators 267 for generating haptic outputs on device 200 (e.g., generating haptic outputs on a touch-sensitive surface such as the touch-sensitive display system 212 of device 200 or the touchpad 455 of device 400). These components optionally communicate via one or more communication buses or signal lines 203.

[0066] As used in this specification and claims, the term "intensity" of contact on a tactile surface refers to the force or pressure (force per unit area) of a contact (e.g., finger contact) on a tactile surface, or to a substitute (alternative) for the force or pressure of a contact on a tactile surface. The intensity of contact has a range of values ​​that includes at least four different values ​​and more typically hundreds of different values ​​(e.g., at least 256). The intensity of 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 tactile surface are optionally used to measure the force at different points on the tactile surface. In some embodiments, force measurements from multiple force sensors are combined (e.g., weighted average) to determine the estimated contact force. Similarly, the pressure-sensitive tip of a stylus is optionally used to determine the pressure of the stylus on the tactile surface. Alternatively, the size and / or variation of the contact area detected on the touch-sensitive surface, the capacitance and / or variation of the touch-sensitive surface near the contact, and / or the resistance and / or variation of the touch-sensitive surface near the contact may optionally be used as substitutes 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 an intensity threshold (e.g., the intensity threshold is described in units corresponding to the substitute measurement) has been exceeded. 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 an intensity threshold (e.g., the intensity threshold is a pressure threshold measured in units of pressure) has been exceeded. Using the intensity of the contact as an attribute of user input allows the user to access additional device functions that would otherwise be inaccessible to the user on a smaller device with limited physical space, which is used (e.g., on a touch-sensitive display) to display an indication and / or receive user input (e.g., via a touch-sensitive display, touch-sensitive surface, or physical / mechanical controls, such as knobs or buttons).

[0067] As used in this specification and claims, the term "haptic output" refers to a physical displacement of the 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., the housing), or a displacement of a component relative to the center of mass of the device, which is detected by the user using the user's tactile sense. For example, when the device or a component of the device comes into contact with a touch-sensitive surface (e.g., a finger, palm, or other part of the user's hand), the haptic output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in the physical characteristics of the device or a component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or touchpad) may optionally be interpreted by the user as a "press-click" or "release-click" on a physically actuated button. In some cases, the user will feel a tactile sensation, such as a "press-click" or "release-click," even when a physically actuated button associated with the touch-sensitive surface, which has been physically pressed (e.g., displaced) by the user's movement, does not move. For example, even when the smoothness of the tactile surface remains unchanged, the movement of the tactile surface can optionally be interpreted or sensed by the user as the "roughness" of the tactile surface. While such interpretations of touch by users will be limited by the individualized sensory perceptions of the user, many sensory perceptions of touch are common to most users. Therefore, when a tactile output is described as corresponding to a specific 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 its components that will generate the sensory perception of a typical (or ordinary) user.

[0068] It should be understood that device 200 is merely an example of a portable multifunctional device, and device 200 may optionally have more or fewer components than shown, may optionally combine two or more components, or may optionally have different configurations or arrangements of these components. Figure 2A The various components shown are implemented in hardware, software, or a combination of both, including one or more signal processing and / or application-specific integrated circuits.

[0069] Memory 202 includes one or more computer-readable storage media. These computer-readable storage media are, for example, tangible and non-transitory. Memory 202 includes high-speed random access memory and also includes non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 222 controls other components of device 200 to access memory 202.

[0070] In some examples, the non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., aspects of the process described below) for use by or in conjunction with an instruction execution system, apparatus, or device (such as a computer-based system, a processor-containing system, or other system capable of fetching and executing instructions from and from the instruction execution system, apparatus, or device). In other examples, instructions (e.g., aspects of the process described below) are stored on a non-transitory computer-readable storage medium (not shown) of server system 108, or partitioned between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.

[0071] Peripheral interface 218 is used to couple the input and output peripherals of the device to CPU 220 and memory 202. One or more processors 220 run or execute various software programs and / or instruction sets stored in memory 202 to perform various functions of device 200 and process data. In some embodiments, peripheral interface 218, CPU 220, and memory controller 222 are implemented on a single chip (such as chip 204). In some other embodiments, they are implemented on separate chips.

[0072] RF (Radio Frequency) circuitry system 208 receives and transmits RF signals, also known as electromagnetic signals. RF circuitry system 208 converts electrical signals into electromagnetic signals and vice versa, and communicates with communication networks and other communication devices via electromagnetic signals. RF circuitry system 208 optionally includes well-known circuitry systems 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, Subscriber Identity Module (SIM) cards, memory, etc. RF circuitry system 208 optionally communicates with networks and other devices wirelessly, 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 circuitry system 208 optionally includes well-known circuitry systems for, for example, detecting near-field communication (NFC) fields via near-range communication radio. Wireless communication may optionally employ 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, Pure Data (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), and Wi-Fi (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n and / or IEEE 802.11ac), Voice over Internet Protocol (VoIP), Wi-MAX, email protocols (e.g., Internet Messaging 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 with Extended Utility (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 date of this document submission.

[0073] Audio circuitry system 210, speaker 211, and microphone 213 provide an audio interface between the user and device 200. Audio circuitry system 210 receives audio data from peripheral interface 218, converts the audio data into electrical signals, and transmits the electrical signals to speaker 211. Speaker 211 converts the electrical signals into sound waves that are audible to humans. Audio circuitry system 210 also receives electrical signals converted from sound waves by microphone 213. Audio circuitry system 210 converts the electrical signals into audio data and transmits the audio data to peripheral interface 218 for processing. Audio data is retrieved from and / or transmitted to memory 202 and / or RF circuitry system 208 via peripheral interface 218. In some embodiments, audio circuitry system 210 also includes a headset jack (e.g., ...). Figure 3 (312 in the text). The headset jack provides an interface between the audio circuitry system 210 and a removable audio input / output peripheral device, such as an output-only headphone or a headset with both output (e.g., a mono or binaural headphone) and input (e.g., a microphone).

[0074] I / O subsystem 206 couples input / output peripherals (such as touchscreen 212 and other input control devices 216) on device 200 to peripheral interface 218. I / O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. One or more input controllers 260 receive electrical signals from / send electrical signals to other input control devices 216. Other input control devices 216 optionally include physical buttons (e.g., push-buttons, rocker buttons, etc.), dial pads, slide switches, joysticks, click wheels, etc. In some alternative embodiments, input controllers 260 are optionally coupled to (or not coupled to) any of the following: keyboard, infrared port, USB port, and pointing device (such as mouse). One or more buttons (e.g., Figure 3 Optionally, 308) includes volume up / down buttons for volume control of speaker 211 and / or microphone 213. One or more buttons optionally include push-button buttons (e.g., Figure 3 (306 in the middle).

[0075] A rapid press of the down button disengages the touchscreen 212 from its lock or initiates a process of unlocking the device using gestures on the touchscreen, as described in U.S. Patent Application 11 / 322,549; U.S. Patent No. 7,657,849, filed December 23, 2005, entitled "Unlocking a Device by Performing Gestures on an Unlock Image," the entire contents of which are incorporated herein by reference. A longer press of the down button (e.g., 306) powers the device 200 on or off. The user can customize the function of one or more buttons. The touchscreen 212 is used to implement virtual buttons or soft buttons and one or more soft keyboards.

[0076] The touch-sensitive display 212 provides an input and output interface between the device and the user. The display controller 256 receives electrical signals from and / or sends electrical signals to the touchscreen 212. The touchscreen 212 displays visual output to the user. Visual output includes graphics, text, icons, video, and any combination thereof (collectively, "graphics"). In some embodiments, some or all of the visual output corresponds to user interface objects.

[0077] Touchscreen 212 has a touch-sensitive surface, sensor, or sensor array that accepts input from a user based on tactile and / or haptic contact. Touchscreen 212 and display controller 256 (along with any associated modules and / or instruction set in memory 202) detect contact on touchscreen 212 (and any movement or interruption of that contact) and translate the detected contact into interaction with user interface objects (e.g., one or more soft keys, icons, web pages, or images) displayed on touchscreen 212. In an exemplary embodiment, the contact point between touchscreen 212 and the user corresponds to the user's finger.

[0078] Touchscreen 212 uses LCD (Liquid Crystal Display) technology, LPD (Light Emitting Polymer Display) technology, or LED (Light Emitting Diode) technology, but other display technologies may be used in other embodiments. Touchscreen 212 and display controller 256 use any of a variety of touch sensing technologies currently known or to be developed thereafter, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touchscreen 212 to detect contact and any movement or interruption thereof. These various touch sensing technologies include, but are not limited to, capacitive, resistive, infrared, and surface acoustic wave technologies. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that used by Apple Inc. of Cupertino, California. and iPod The technology used.

[0079] In some embodiments, the touchscreen 212's touch-sensitive display is similar to the multi-touch panel described in the following 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 incorporated herein by reference in their entirety. However, the touchscreen 212 displays visual output from the device 200, while the touch-sensitive panel does not provide visual output.

[0080] The touch-sensitive display in some embodiments of the touchscreen 212 is described in the following applications: (1) U.S. Patent Application No. 11 / 381,313, filed May 2, 2006, “Multipoint Touch Surface Controller”; (2) U.S. Patent Application No. 10 / 840,862, filed May 6, 2004, “Multipoint Touchscreen”; (3) U.S. Patent Application No. 10 / 903,964, filed July 30, 2004, “Gestures For Touch Sensitive Input Devices”; (4) U.S. Patent Application No. 11 / 048,264, filed January 31, 2005, “Gestures For Touch Sensitive Input Devices”; and (5) U.S. Patent Application No. 11 / 038,590, filed January 18, 2005, “Mode-Based Graphical User Interfaces For Touch Sensitive Input”. (6) U.S. Patent Application No. 11 / 228,758, filed September 16, 2005, “Virtual Input Device Placement On A Touch Screen User Interface”; (7) U.S. Patent Application No. 11 / 228,700, filed September 16, 2005, “Operation Of A Computer With A Touch Screen Interface”; (8) U.S. Patent Application No. 11 / 228,737, filed September 16, 2005, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard”; and (9) U.S. Patent Application No. 11 / 367,749, filed March 3, 2006, “Multi-Functional Hand-Held Device”. The entire contents of all these applications are incorporated herein by reference.

[0081] Touchscreen 212 has a video resolution of over 100 dpi, for example. In some embodiments, the touchscreen has a video resolution of approximately 160 dpi. Users interact with touchscreen 212 using any suitable object or accessory such as a stylus, finger, etc. In some embodiments, the user interface is designed to function primarily through finger-based touch and gestures, which may be less precise than stylus-based input due to the larger contact area of ​​a finger on the touchscreen. In some embodiments, the device translates coarse finger-based input into precise pointer / cursor positioning or commands to perform the user-desired actions.

[0082] In some embodiments, in addition to the touchscreen, 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 touchscreen, does not display visual output. The touchpad is a touch-sensitive surface separate from the touchscreen 212, or an extension of the touch-sensitive surface formed by the touchscreen.

[0083] The device 200 also includes a power system 262 for supplying power to various components. The power system 262 includes a power management system, one or more power sources (e.g., a battery, alternating current (AC)), a recharging system, a power failure 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 the portable device.

[0084] The device 200 also includes one or more optical sensors 264. Figure 2A An optical sensor 264 is shown coupled to an optical sensor controller 258 in the I / O subsystem 206. The optical sensor 264 includes a charge-coupled device (CCD) or 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 an imaging module 243 (also called a camera module), the optical sensor 264 captures still images or video. In some embodiments, the optical sensor is located at the rear of the device 200, opposite to the touchscreen display 212 at the front of the device, such that the touchscreen display is used as a viewfinder for still image and / or video image acquisition. In some embodiments, the optical sensor is located at the front of the device, such that an image of the user is acquired for use in video conferencing while the user views other video conferencing participants on the touchscreen display. In some embodiments, the positioning of the optical sensor 264 can be changed by the user (e.g., by rotating the lenses and sensors within the device housing), such that a single optical sensor 264 is used in conjunction with the touchscreen display for both video conferencing and still image and / or video image acquisition.

[0085] The device 200 may optionally also include one or more contact strength sensors 265. Figure 2A A contact strength sensor 265 is shown coupled to a strength sensor controller 259 in I / O subsystem 206. The contact strength sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electro-force sensors, piezoelectric sensors, optical force sensors, capacitive touch-sensitive surfaces, or other strength sensors (e.g., sensors for measuring the force (or pressure) of contact on a touch-sensitive surface). The contact strength sensor 265 receives contact strength information (e.g., pressure information or a substitute for pressure information) from the environment. In some embodiments, at least one contact strength sensor is arranged juxtaposed with or adjacent to a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact strength sensor is located on the rear of device 200, opposite to the touchscreen display 212 located on the front of device 200.

[0086] The device 200 also includes one or more proximity sensors 266. Figure 2A A proximity sensor 266 coupled to a peripheral device interface 218 is shown. Alternatively, the proximity sensor 266 is coupled to an input controller 260 in an I / O subsystem 206. The proximity sensor 266 performs as described in the following U.S. patent applications: U.S. Patent Application 11 / 241,839, “Proximity Detector In Handheld Device”; U.S. Patent Application 11 / 240,788, “Proximity Detector In Handheld Device”; U.S. Patent Application 11 / 620,702, “Using Ambient Light Sensor To Augment Proximity Sensor Output”; U.S. Patent Application 11 / 586,862, “Automated Response To And Sensing Of User Activity In Portable Devices”; and U.S. Patent Application 11 / 638,251, “Methods And Systems For Automatic Configuration Of Peripherals”, the entire contents of which are incorporated herein by reference. In some implementations, the proximity sensor is turned off and the touchscreen 212 is disabled when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call).

[0087] The device 200 optionally also includes one or more tactile output generators 267. Figure 2AA haptic output generator coupled to a haptic feedback controller 261 in I / O subsystem 206 is shown. The haptic output generator 267 optionally includes one or more electroacoustic devices such as speakers or other audio components; and / or electromechanical devices for converting energy into linear motion such as motors, solenoids, electroactive polymerizers, piezoelectric actuators, electrostatic actuators, or other haptic output generating components (e.g., components for converting electrical signals into haptic outputs on the device). A contact intensity sensor 265 receives haptic feedback generation instructions from a haptic feedback module 233 and generates a haptic output on device 200 that can be felt by a user of device 200. In some embodiments, at least one haptic output generator is juxtaposed or adjacent to a haptic surface (e.g., haptic display system 212) and optionally generates the haptic output by moving the haptic surface vertically (e.g., in / outward from the surface of device 200) or laterally (e.g., backward and forward in the same plane as the surface of device 200). In some implementations, at least one haptic output generator sensor is located on the rear of the device 200, opposite to the touch screen display 212 located on the front of the device 200.

[0088] The device 200 also includes one or more accelerometers 268. Figure 2A An accelerometer 268 coupled to a peripheral device interface 218 is shown. Alternatively, the accelerometer 268 is coupled to an input controller 260 in an I / O subsystem 206. The accelerometer 268 performs as described in the following U.S. patent publications: U.S. Patent Publication 20050190059, “Acceleration-based Theft Detection System for Portable Electronic Devices” and U.S. Patent Publication 20060017692, “Methods and Apparatuses For Operating A Portable Device BasedOn An Accelerometer,” the entire contents of which are incorporated herein by reference. In some embodiments, information is displayed on a touchscreen display in portrait or landscape view based on analysis of data received from one or more accelerometers. Device 200 optionally includes, in addition to one or more accelerometers 268, a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) for obtaining information about the position and orientation (e.g., portrait or landscape) of device 200.

[0089] 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 program (or instruction set) 236. Furthermore, memory 202 stores data and models, such as user data and models 231. Additionally, in some embodiments, memory 202 ( Figure 2A ) or 470 ( Figure 4 Storage device / global internal state 257, such as Figure 2A and Figure 4 As shown in the figure. Device / global internal state 257 includes one or more of the following: active application state, which indicates which applications (if any) are currently active; display state, indicating what applications, views or other information occupy 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 relating to the device's position and / or orientation.

[0090] The operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or embedded operating systems such as VxWorks) includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitates communication between various hardware and software components.

[0091] The communication module 228 facilitates communication with other devices via one or more external ports 224 and includes various software components for processing data received by the RF circuitry system 208 and / or the external ports 224. The external ports 224 (e.g., Universal Serial Bus (USB), FireWire, etc.) are adapted to be directly coupled to other devices or indirectly coupled via a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is connected to… (Trademark of Apple Inc.) The same or similar and / or compatible multi-pin (e.g., 30-pin) connectors used in Apple Inc. devices.

[0092] The contact / motion module 230 optionally detects contact with the touchscreen 212 (in conjunction with the display controller 256) and other touch-sensitive devices (e.g., touchpads or physical click-based rotary dials). 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 intensity of contact (e.g., the force or pressure of the contact, or an alternative to force or pressure), determining whether there is movement of the contact and tracking 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 which is represented by a series of contact data. These operations are optionally applied to single-point contact (e.g., single-finger contact) or multi-point simultaneous contact (e.g., "multi-touch" / multiple-finger contact). In some implementations, the contact / motion module 230 and the display controller 256 detect contact on the touchpad.

[0093] In some implementations, 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., determining whether the user has “clicked” an icon). In some implementations, at least a subset of the intensity thresholds is determined based on software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of a specific physical actuator and can be adjusted without changing the physical hardware of the device 200). For example, the mouse “click” threshold for a touchpad or touchscreen can be set to any of a wide range of predefined thresholds without changing the touchpad or touchscreen display hardware. Additionally, in some implementations, the user of the device is provided with software settings for adjusting one or more intensity thresholds in a set (e.g., by adjusting the individual intensity thresholds and / or by adjusting multiple intensity thresholds at once using system-level clicks on the “intensity” parameter).

[0094] The touch / motion module 230 optionally detects the user's gesture input. Different gestures on a touch-sensitive surface have different contact patterns (e.g., different movements, timings, and / or intensities of the detected contact). Therefore, 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-off (lift-away) event at the same (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 subsequently detecting a finger lift-off (lift-away) event.

[0095] The graphics module 232 includes various known software components for rendering and displaying graphics on the touchscreen 212 or other displays, including components for altering the visual impact of the displayed graphics (e.g., brightness, transparency, saturation, contrast, or other visual characteristics). As used herein, the term "graphics" includes any object that can be displayed to a user, including but not limited to text, web pages, icons (such as user interface objects including soft keys), digital images, videos, animations, etc.

[0096] In some implementations, the graphics module 232 stores data representing the 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, and, if necessary, also receives coordinate data and other graphic attribute data, and then generates screen image data for output to the display controller 256.

[0097] The haptic feedback module 233 includes various software components for generating instructions that are used by one or more haptic output generators 267 to produce haptic output at one or more locations on the device 200 in response to user interaction with the device 200.

[0098] In some examples, the text input module 234, which is a component of the 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 that requires text input).

[0099] GPS module 235 determines the location of the device and provides that information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as image / video metadata; and to applications that provide location-based services, such as weather widgets, local yellow pages widgets, and map / navigation widgets).

[0100] The digital assistant client module 229 includes various client-side digital assistant commands to provide client-side functionality for the digital assistant. For example, the digital assistant client module 229 can accept voice input (e.g., voice 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 audio output (e.g., voice output), visual output, and / or tactile output through various output interfaces of the portable multifunction device 200 (e.g., speaker 211, touch-sensitive display system 212, one or more haptic output generators 267, etc.). For example, output can be provided as voice, sound, alerts, text messages, menus, graphics, video, animation, vibration, and / or combinations of both or more of these. During operation, the digital assistant client module 229 communicates with the DA server 106 using the RF circuitry system 208.

[0101] User data and models 231 include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciation, data from the user's electronic address book, to-do lists, shopping lists, etc.) to provide client-side functionality for the digital assistant. Furthermore, 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 ontology, task flow models, service models, etc.).

[0102] In some examples, the digital assistant client module 229 utilizes 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 contextual information, or a subset thereof, along with the user input to the DA server 106 to help infer the user's intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare output and deliver it to the user. This contextual information is referred to as contextual data.

[0103] In some examples, the contextual information accompanying user input includes sensor information such as lighting, ambient noise, ambient temperature, and images or videos of the surrounding environment. 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, and cellular signal strength. In some examples, information related to the software state of the DA server 106, such as the operation of the portable multifunction device 200, installed programs, past and current network activity, background services, error logs, and resource usage, is provided to the DA server 106 as contextual information associated with the user input.

[0104] In some examples, the digital assistant client module 229 selectively provides information (e.g., user data 231) stored on the portable multifunction device 200 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 natural language dialogue or other user interfaces when requested by the DA server 106. The digital assistant client module 229 transmits this additional input to the DA server 106 to assist the DA server 106 in intent inference and / or in realizing the user intent expressed in the user request.

[0105] Reference below Figures 7A to 7C A more detailed description of the digital assistant follows. It should be understood that the digital assistant client module 229 may include any number of sub-modules of the digital assistant module 726 described below.

[0106] Application 236 includes the following modules (or instruction sets) or subsets or supersets:

[0107] Contacts module 237 (sometimes called address book or contact list);

[0108] Telephone module 238;

[0109] ●Video conferencing module 239;

[0110] ●Email client module 240;

[0111] ●Instant Messaging (IM) module 241;

[0112] ● Fitness support module 242;

[0113] ● Camera module 243 for still images and / or video images;

[0114] ●Image Management Module 244;

[0115] ●Video player module;

[0116] ●Music player module;

[0117] ● Browser module 247;

[0118] ● Calendar module 248;

[0119] ● Module 249, which in some examples includes one or more of the following:

[0120] Weather widget 249-1, Stocks widget 249-2, Calculator widget 249-3, Alarm clock widget 249-4, Dictionary widget 249-5, and other widgets obtained by the user, as well as user-created widgets 249-6;

[0121] Module 250 is used to create user-created widgets 249-6;

[0122] ●Search module 251;

[0123] ● Video and music player module 252, which combines a video player module and a music player module;

[0124] ●Notepad module 253;

[0125] ●Map module 254; and / or

[0126] ● Online video module 255.

[0127] 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 access control, speech recognition, and speech duplication.

[0128] In conjunction with touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, and text input module 234, contact module 237 manages the address book or contact list (e.g., in the application internal state 292 of contact module 237 stored in memory 202 or memory 470), including: adding names to the address book; deleting names from the address book; associating phone numbers, email addresses, physical addresses, or other information with names; associating images with names; categorizing and classifying names; providing phone numbers or email addresses to initiate and / or facilitate communications via telephone 238, video conferencing module 239, email 240, or IM 241; and so on.

[0129] Combining the RF circuit system 208, audio circuit system 210, speaker 211, microphone 213, touchscreen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, the telephone module 238 is used to input character sequences corresponding to telephone numbers, access one or more telephone numbers in the contact module 237, modify already entered telephone numbers, dial corresponding telephone numbers, initiate conversations, and disconnect or hang up when a conversation is completed. As described above, wireless communication uses any of a variety of communication standards, protocols, and technologies.

[0130] Combining RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touchscreen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact / motion module 230, graphics module 232, text input module 234, contact module 237, and telephone module 238, video conferencing module 239 includes executable instructions to initiate, conduct, and terminate video conferences between the user and one or more other participants based on user instructions.

[0131] Incorporating the RF circuitry 208, touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, and text input module 234, the email client module 240 includes executable instructions for creating, sending, receiving, and managing emails in response to user commands. Combined with the image management module 244, the email client module 240 makes it very easy to create and send emails containing still images or video images captured by the camera module 243.

[0132] In conjunction with the RF circuitry 208, touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions for: inputting a character sequence corresponding to an instant message, modifying previously input characters, transmitting a corresponding instant message (e.g., using the Short Message Service (SMS) or Multimedia Messaging Service (MMS) protocol for telephone-based instant messaging or using XMPP, SIMPLE, or IMPS for internet-based instant messaging), receiving an instant message, and viewing a received instant message. In some embodiments, the transmitted and / or received instant messages include graphics, photographs, audio files, video files, and / or other attachments supported as in MMS and / or Enhanced Messaging Services (EMS). As used herein, "instant message" 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).

[0133] Incorporating the RF circuitry 208, touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, the fitness support module 242 includes executable instructions for: creating fitness activities (e.g., with time, distance, and / or calorie burning goals); communicating with fitness sensors (exercise equipment); receiving fitness sensor data; calibrating sensors used to monitor fitness; selecting and playing music for fitness activities; and displaying, storing, and transmitting fitness data.

[0134] In conjunction with the touchscreen 212, display controller 256, one or more optical sensors 264, optical sensor controller 258, contact / motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions for: capturing still images or videos (including video streams) and storing them in memory 202, modifying the characteristics of still images or videos, or deleting still images or videos from memory 202.

[0135] Incorporating touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions for arranging, modifying (e.g., editing), or otherwise manipulating, tagging, deleting, presenting (e.g., in a digital slideshow or album), and storing still images and / or video images.

[0136] Incorporating the RF circuitry 208, touchscreen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, the browser module 247 includes executable instructions for browsing the Internet according to user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as links to attachments and other files on the web pages.

[0137] Combining the RF circuit system 208, touch screen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, email client module 240, and browser module 247, the calendar module 248 includes executable instructions to create, display, modify, and store calendars and related data (e.g., calendar entries, to-dos, etc.) according to user instructions.

[0138] In conjunction with the RF circuitry 208, touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, and browser module 247, the widget module 249 is a micro-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 clock widget 249-4, and dictionary widget 249-5) or a user-created micro-application (e.g., user-created widget 249-6). In some embodiments, the widget includes HTML (Hypertext Markup Language) files, CSS (Cascading Style Sheets) files, and JavaScript files. In some embodiments, the widget includes XML (Extensible Markup Language) files and JavaScript files (e.g., Yahoo! widgets).

[0139] In conjunction with the RF circuit system 208, touch screen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 is used by the user to create widgets (e.g., to turn a user-specified part of a webpage into a widget).

[0140] In conjunction with the touchscreen 212, display controller 256, touch / 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) according to user instructions.

[0141] Combining touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, the video and music player module 252 includes executable instructions allowing users 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 touchscreen 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 (a trademark of Apple Inc.).

[0142] Combining the touchscreen 212, display controller 256, touch / 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 items, etc., according to user instructions.

[0143] Combining the RF circuit system 208, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, the map module 254 is used to receive, display, modify, and store maps and map-related data (e.g., driving directions, data related to shops and other points of interest at or near a specific location, and other location-based data) according to user instructions.

[0144] In conjunction with touchscreen 212, display controller 256, touch / motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, email client module 240, and browser module 247, online video module 255 includes instructions allowing users to access, browse, receive (e.g., via streaming and / or downloading), play back (e.g., on the touchscreen or on a connected external display via external port 224), send emails with links to specific online videos, and otherwise manage online videos in one or more file formats (such as H.264). In some embodiments, instant messaging module 241 is used instead of email client module 240 to send links to specific online videos. Further descriptions of online video applications can be found in U.S. Provisional Patent Application No. 60 / 936,562, filed 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 December 31, 2007, entitled “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos”, the contents of which are incorporated herein by reference in their entirety.

[0145] Each of the modules and applications described above corresponds to an executable set of instructions for performing one or more functions described above and the methods described in this patent application (e.g., computer-implemented methods and other information processing methods described herein). These modules (e.g., instruction sets) need not be implemented as standalone software programs, processes, or modules, and therefore various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, a video player module can be combined with a music player module into a single module (e.g., Figure 2A(e.g., video and music player module 252). In some embodiments, memory 202 stores a subset of the aforementioned modules and data structures. Additionally, memory 202 stores additional modules and data structures not described above.

[0146] In some implementations, device 200 is a device on which the operation of a predefined set of functions is performed solely via a touchscreen and / or touchpad. By using a touchscreen and / or touchpad as the primary input control device for the operation of device 200, the number of physical input control devices (such as push-buttons, dial pads, etc.) on device 200 is reduced.

[0147] A predefined set of functions, uniquely performed via a touchscreen and / or touchpad, optionally includes navigation between user interfaces. In some embodiments, the touchpad, when touched by a user, navigates device 200 from any user interface displayed on device 200 to the main menu, home menu, or root menu. In such embodiments, a "menu button" is implemented using a touchpad. In some other embodiments, the menu button is a physical push-button or other physical input control device, rather than a touchpad.

[0148] Figure 2B This is a block diagram illustrating exemplary components for event handling according to some embodiments. In some embodiments, memory 202 ( Figure 2A ) or memory 470 ( Figure 4 This includes an event classifier 270 (e.g., in operating system 226) and a corresponding application 236-1 (e.g., any one of the aforementioned applications 237-251, 255, 480-490).

[0149] Event classifier 270 receives event information and determines the application 236-1 to which the event information should be delivered and the application view 291 of application 236-1. Event classifier 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes an application internal state 292 that indicates the current application view displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device / global internal state 257 is used by event classifier 270 to determine which application(s) is currently active, and application internal state 292 is used by event classifier 270 to determine the application view 291 to which the event information should be delivered.

[0150] In some implementations, the application internal state 292 includes additional information such as one or more of the following: recovery information to be used when the application 236-1 resumes execution, user interface state information indicating that information is being displayed or ready to be displayed by the application 236-1, a state queue for enabling the user to return to the previous state or view of the application 236-1, and a repeat / undo queue for the user's previous actions.

[0151] Event monitor 271 receives event information from peripheral device interface 218. The event information includes information about sub-events (e.g., user touches on touch-sensitive display 212 as part of a multi-touch gesture). Peripheral device 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 (via audio circuitry 210). The information received by peripheral device interface 218 from I / O subsystem 206 includes information from touch-sensitive display 212 or touch-sensitive surfaces.

[0152] In some implementations, the event monitor 271 sends requests to the peripheral device interface 218 at predetermined intervals. In response, the peripheral device interface 218 transmits event information. In other implementations, the peripheral device interface 218 transmits event information only when a significant event occurs (e.g., receiving input above a predetermined noise threshold and / or receiving input for a predetermined duration).

[0153] In some implementations, the event classifier 270 also includes a hit view determination module 272 and / or an activity event recognizer determination module 273.

[0154] When the touch-sensitive display 212 displays more than one view, the hit view determination module 272 provides a software process for determining where a sub-event has occurred within one or more views. A view consists of controls and other elements that the user can see on the display.

[0155] 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 view (of the corresponding application) in which a touch is detected corresponds to a procedural level within the application's procedural hierarchy or view hierarchy. For example, the lowest-level view in which a touch is detected is called the hit view, and the set of events considered as correct input is determined at least in part based on the hit view of the initial touch that initiates the touch-based gesture.

[0156] 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 hierarchical structure, the hit view determination module 272 identifies the hit view as the lowest-level view in the hierarchical structure from which the sub-events should be processed. 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 forming an event or potential event) occurs. Once the 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 to which it was identified as the hit view.

[0157] The activity event recognizer determination module 273 determines which views(s) within the view hierarchy should receive a specific sub-event sequence. In some embodiments, the activity event recognizer determination module 273 determines that only the hit view should receive the specific sub-event sequence. In other embodiments, the activity event recognizer determination module 273 determines that all views including the physical location of the sub-event are actively participating views, and therefore determines that all actively participating views should receive the specific sub-event sequence. In other embodiments, even if the touch sub-event is entirely confined to the area associated with a particular view, the higher-level views in the hierarchy will still remain actively participating views.

[0158] Event assigner module 274 assigns event information to event identifiers (e.g., event identifier 280). In embodiments that include active event identifier determination module 273, event assigner module 274 delivers event information to the event identifier determined by active event identifier determination module 273. In some embodiments, event assigner module 274 stores event information in an event queue, which is retrieved by the corresponding event receiver 282.

[0159] 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 part of another module (such as contact / motion module 230) stored in memory 202.

[0160] In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events occurring within a corresponding view of the application's user interface. Each application view 291 of application 236-1 includes one or more event recognizers 280. Typically, a corresponding application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more event recognizers among the event recognizers 280 are part of a separate module, which is a higher-level object such as a user interface toolkit (not shown) from which application 236-1 inherits methods and other properties. In some embodiments, a corresponding event handler 290 includes one or more of the following: a data updater 276, an object updater 277, a GUI updater 278, and / or event data 279 received from an event classifier 270. The event handler 290 utilizes or invokes the data updater 276, the object updater 277, or the GUI updater 278 to update the application's internal state 292. Alternatively, one or more application views in application view 291 include one or more corresponding event handlers 290. Additionally, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in the corresponding application view 291.

[0161] The corresponding event identifier 280 receives event information (e.g., event data 279) from the event classifier 270 and identifies the event from the event information. The event identifier 280 includes an event receiver 282 and an event comparator 284. In some embodiments, the event identifier 280 also includes at least one subset of metadata 283 and event delivery instructions 288 (which includes sub-event delivery instructions).

[0162] Event receiver 282 receives event information from event classifier 270. The event information includes information about sub-events such as touch or touch movement. 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 touch movement, 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 (e.g., from a portrait orientation to a lateral orientation, or vice versa), and the event information includes corresponding information about the device's current orientation (also referred to as device pose).

[0163] Event comparator 284 compares event information with predefined event or sub-event definitions and, based on the comparison, determines the event or sub-event, or determines or updates the state of the event or sub-event. In some embodiments, event comparator 284 includes event definition 286. Event definition 286 contains definitions of events (e.g., predefined sequences of sub-events), such as event 1 (287-1), event 2 (287-2), and other events. In some embodiments, sub-events in event (287) include, for example, touch start, touch end, touch move, touch cancel, and multi-touch. In one example, event 1 (287-1) is defined as a double-click on a displayed object. For example, a double-click includes a first touch (touch start) of a predetermined duration on the displayed object, a first lift-off of a predetermined duration (touch end), a second touch (touch start) of a predetermined duration on the displayed object, and a second lift-off of a predetermined duration (touch end). In another example, event 2 (287-2) is defined as a drag on a displayed object. For example, dragging includes a touch (or contact) on the displayed object for a predetermined duration, movement of the touch on the touch-sensitive display 212, and lifting off the touch (end of touch). In some embodiments, the event also includes information for one or more associated event handlers 290.

[0164] In some implementations, event definition 287 includes definitions of events for corresponding user interface objects. In some implementations, event comparator 284 performs a hit test to determine which user interface object is associated with the 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 the event handler associated with the sub-event and the object that triggered the hit test.

[0165] In some implementations, the definition of the corresponding event (287) also includes a delay action that delays the delivery of event information until it has been determined whether the sub-event sequence actually corresponds to or does not correspond to the event type of the event recognizer.

[0166] When the corresponding event recognizer 280 determines that the sub-event sequence 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 based on touch gestures are ignored. In this case, other event recognizers (if any) that remain active in the hit view continue to track and process the ongoing sub-events based on touch gestures.

[0167] In some embodiments, the corresponding event recognizer 280 includes metadata 283 having configurable attributes, flags, and / or lists instructing how the event delivery system should perform sub-event delivery to actively participating event recognizers. In some embodiments, the metadata 283 includes configurable attributes, flags, and / or lists instructing how or how likely event recognizers can interact with each other. In some embodiments, the metadata 283 includes configurable attributes, flags, and / or lists instructing whether sub-events are delivered to different levels in a view or programmatic hierarchy.

[0168] In some implementations, when one or more specific sub-events of an event are identified, the corresponding event recognizer 280 activates the event handler 290 associated with the event. In some implementations, the corresponding event recognizer 280 delivers event information associated with the event to the event handler 290. Activating the event handler 290 is different from sending (and delaying) the sub-event to the corresponding hit view. In some implementations, the event recognizer 280 throws a tag associated with the identified event, and the event handler 290 associated with that tag retrieves the tag and executes a predefined procedure.

[0169] In some implementations, event delivery instruction 288 includes 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 the sub-event sequence or to an actively participating view. The event handler associated with the sub-event sequence or the actively participating view receives the event information and executes a predetermined process.

[0170] In some implementations, 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 implementations, 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 touch-sensitive display.

[0171] In some implementations, event handler 290 includes, or has access to, a data updater 276, an object updater 277, and a GUI updater 278. In some implementations, 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 implementations, they are included in two or more software modules.

[0172] It should be understood that the above discussion regarding event handling of user touch on a touch-sensitive display also applies to other forms of user input used to operate the multifunction device 200 using an input device, and not all user input is initiated on the touchscreen. For example, mouse movement and mouse button presses optionally in conjunction with single or multiple keyboard presses or holds; touch movements on the touchpad, such as taps, drags, scrolls, etc.; stylus input; device movement; verbal commands; detected eye movements; biometric input; and / or any combination thereof may optionally be used as input corresponding to sub-events that define the event to be identified.

[0173] Figure 3A portable multifunction device 200 with a touchscreen 212 is shown according to some embodiments. The touchscreen 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 graphics by gesturing over the graphics, for example, 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, selection of one or more graphics occurs when the user breaks contact with one or more graphics. 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 scrolling (from right to left, from left to right, up and / or down) of a finger already in contact with the device 200. In some specific embodiments or in some cases, unintentional contact with a graphic does not select the graphic. For example, a swipe gesture over an application icon optionally does not select the corresponding application when the gesture corresponding to selection is a tap.

[0174] Device 200 also includes one or more physical buttons, such as a "home" or menu button 304. As previously described, menu button 304 is used to navigate to any application 236 of a set of applications running on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touchscreen 212.

[0175] In some embodiments, device 200 includes a touchscreen 212, a menu button 304, a push-button 306 for powering on / off the device and locking the device, one or more volume control buttons 308, a SIM card slot 310, a headset jack 312, and a docking / charging external port 224. The push-button 306 is optionally used to power on / off the device by pressing the button and holding it in the pressed state for a predefined time interval; to lock the device by pressing the button and releasing it before the predefined time interval has elapsed; and / or to unlock the device or initiate an unlocking process. In another embodiment, device 200 also accepts verbal input via microphone 213 for activating or deactivating certain functions. Device 200 also optionally includes one or more contact strength sensors 265 for detecting the intensity of contact on the touchscreen 212, and / or one or more haptic output generators 267 for generating haptic outputs for the user of device 200.

[0176] Figure 4This is a block diagram of an exemplary multifunctional device having a display and a touch-sensitive surface according to some embodiments. Device 400 need not be portable. In some embodiments, device 400 is a laptop computer, desktop computer, tablet computer, multimedia player device, navigation device, educational device (such as a children's learning toy), gaming system, or control device (e.g., a home controller or industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communication interfaces 460, memory 470, and one or more communication buses 420 for interconnecting these components. Communication bus 420 optionally includes a circuit system (sometimes called a chipset) that interconnects system components and controls communication between system components. Device 400 includes an input / output (I / O) interface 430 including a display 440, which is typically a touchscreen display. I / O interface 430 also optionally includes a keyboard and / or mouse (or other pointing device) 450 and a touchpad 455, and a haptic output generator 457 for generating haptic output on device 400 (e.g., similar to the reference above). Figure 2A The one or more tactile output generators 267 and sensors 459 (e.g., optical sensors, accelerometers, proximity sensors, touch sensors, and / or contact intensity sensors similar to those mentioned above) Figure 2A The one or more contact strength sensors 265 mentioned above). Memory 470 includes high-speed random access memory, such as DRAM, SRAM, DDR RAM, or other random access solid-state memory devices; and optionally includes non-volatile memory, such as one or more disk storage devices, optical disk storage devices, flash memory devices, or other non-volatile solid-state storage devices. Memory 470 optionally includes one or more storage devices located remotely from CPU 410. In some embodiments, memory 470 stores data with portable multifunction device 200 (…). Figure 2A The memory 470 stores programs, modules, and data structures similar to those in the memory 202 of the portable multifunction device 200, or subsets thereof. Additionally, the memory 470 optionally stores additional programs, modules, and data structures not present in the memory 202 of the portable multifunction device 200. For example, the memory 470 of the 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 the portable multifunction device 200 ( Figure 2A The memory 202 optionally does not store these modules.

[0177] Figure 4Each of the aforementioned elements is stored in one or more of the previously mentioned memory devices in some examples. Each of the aforementioned modules corresponds to a set of instructions for performing the functions described above. The aforementioned modules or programs (e.g., instruction sets) need not be implemented as standalone software programs, processes, or modules; therefore, various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 stores a subset of the aforementioned modules and data structures. In addition, memory 470 stores additional modules and data structures not described above.

[0178] Now let’s turn our attention to implementations of user interfaces that can be implemented, for example, on a portable multi-functional device 200.

[0179] Figure 5A An exemplary user interface for an application menu on a portable multifunction device 200 according to some embodiments is shown. 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:

[0180] Signal strength indicator 502 for wireless communications such as cellular signals and Wi-Fi signals;

[0181] Time 504;

[0182] ●Bluetooth indicator 505;

[0183] ● Battery status indicator 506;

[0184] ● Tray 508 features icons for commonly used applications, such as:

[0185] The telephone module 238 has an icon 516 labeled "telephone", which optionally includes an indicator 514 indicating the number of missed calls or voicemails;

[0186] The email client module 240 has an icon 518 labeled "Mail", which optionally includes an indicator 510 for the number of unread emails;

[0187] The browser module 247 has an icon 520 labeled "Browser"; and

[0188] o The video and music player module 252 (also known as the iPod (Apple Inc. trademark) module 252) with an icon 522 labeled "iPod"; and

[0189] ● Icons of other applications, such as:

[0190] o IM module 241's icon 524 marked as "message";

[0191] The icon 526 labeled "Calendar" in the calendar module 248;

[0192] The image management module 244 has an icon 528 labeled "Photo";

[0193] The icon 530 of the camera module 243 is labeled "camera";

[0194] o The icon 532 of the online video module 255, which is labeled "online video";

[0195] The stock market widget 249-2 has an icon 534 labeled "Stock Market";

[0196] The icon 536 labeled "map" in the map module 254;

[0197] The weather widget 249-1 has icon 538 labeled "weather";

[0198] The alarm clock widget 249-4 has an icon 540 labeled "clock";

[0199] The icon 542 labeled "Fitness Support" in the fitness support module 242;

[0200] The notepad module 253 has an icon 544 labeled "Notepad"; and

[0201] o An icon 546 labeled "Settings" is used to set the settings of an application or module, which provides access to the settings of the device 200 and its various applications 236.

[0202] It should be noted that Figure 5A The illustrated icon labels are merely exemplary. For example, the icon 522 of the video and music player module 252 may optionally be labeled "Music" or "Music Player". Other labels may optionally be used for various application icons. In some embodiments, the label of a particular application icon includes the name of the application corresponding to that particular application icon. In some embodiments, the label of a particular application icon is different from the name of the application corresponding to that particular application icon.

[0203] Figure 5B A touch-sensitive surface 551 (e.g., separate from the display 550 (e.g., touchscreen display 212)) is shown. Figure 4 Devices (e.g., tablets or touchpads 455) Figure 4An exemplary user interface on device 400. Device 400 also optionally includes one or more contact intensity sensors (e.g., one or more sensors in sensor 459) for detecting the intensity of contact on tactile surface 551 and / or one or more tactile output generators 457 for generating tactile output for the user of device 400.

[0204] While some examples of input on a reference touchscreen display 212 (which combines a touch-sensitive surface and a display) are given in the following examples, in some implementations, the device detects input on a touch-sensitive surface separate from the display, such as... Figure 5B As shown in the diagram. In some embodiments, the touch-sensitive surface (e.g., Figure 5B 551) has a spindle (e.g., on the display (e.g., 550) that is aligned with the main axis on the display (e.g., Figure 5B The spindle corresponding to 553 in the figure (e.g., Figure 5B (552 in the example). According to these embodiments, the device detects the position corresponding to the corresponding position on the display (e.g., in the example). Figure 5B In the middle, 560 corresponds to 568 and 562 corresponds to 570) the contact with the touch-sensitive surface 551 at the location (e.g., Figure 5B (560 and 562 in the example). Thus, on touch-sensitive surfaces (e.g., Figure 5B 551 in the middle) and the display of a multi-functional device (e.g., Figure 5B When 550 is separated from 560, user input detected by the device on the touch-sensitive surface (e.g., contact with 560 and 562 and their movement) is used by the device to manipulate the user interface on the display. It should be understood that similar methods may be optionally used for other user interfaces described herein.

[0205] Additionally, while the examples below are given primarily with reference to finger input (e.g., finger touch, single-finger tap gesture, finger swipe gesture), it should be understood that in some implementations, one or more of these finger inputs may be replaced by input from another input device (e.g., mouse-based input or stylus input). For example, a swipe gesture may optionally be replaced by a mouse click (e.g., instead of a touch), followed by movement of the cursor along the swipe path (e.g., instead of movement of the touch). Similarly, a tap gesture may optionally be replaced by a mouse click while the cursor is over the location of the tap gesture (e.g., instead of detection of touch, followed by cessation of touch detection). Likewise, when multiple user inputs are detected simultaneously, it should be understood that multiple computer mice may optionally be used simultaneously, or mouse and finger touch may optionally be used simultaneously.

[0206] Figure 6AAn exemplary personal electronic device 600 is illustrated. Device 600 includes a body 602. In some embodiments, device 600 includes components relative to devices 200 and 400 (e.g., Figures 2A to 4 Some or all of the features described herein. In some embodiments, device 600 has a touch-sensitive display 604, hereinafter referred to as touchscreen 604. As an alternative to or complement to touchscreen 604, device 600 has a display and a touch-sensitive surface. Similar to devices 200 and 400, in some embodiments, touchscreen 604 (or touch-sensitive surface) has one or more intensity sensors for detecting the intensity of an applied contact (e.g., a touch). The one or more intensity sensors of touchscreen 604 (or touch-sensitive surface) provide output data representing the intensity of the touch. The user interface of device 600 responds to touches based on touch intensity, meaning that touches of different intensities may invoke different user interface operations on device 600.

[0207] Techniques for detecting and processing touch intensity may exist, for example, in the following related applications: International Patent Application Serial No. PCT / US2013 / 040061, filed May 8, 2013, entitled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application”, and International Patent Application Serial No. PCT / US2013 / 069483, filed November 11, 2013, entitled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships”, each of which is incorporated herein by reference in its entirety.

[0208] In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608 (if included) are physical in form. Examples of physical input mechanisms include push-buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms (if included) allow device 600 to be attached to, for example, hats, glasses, earrings, necklaces, shirts, jackets, bracelets, watch straps, bangles, trousers, belts, shoes, wallets, backpacks, etc. These attachment mechanisms allow a user to wear device 600.

[0209] Figure 6BAn exemplary personal electronic device 600 is illustrated. In some embodiments, device 600 includes information about... Figure 2A , Figure 2B and Figure 4 Some or all of the aforementioned components. Device 600 has a bus 612 that operatively couples I / O portion 614 to one or more computer processors 616 and memory 618. I / O portion 614 is connected to display 604, which may have touch-sensitive component 622 and optionally also has touch intensity-sensitive component 624. Furthermore, I / O portion 614 is connected to 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 pressable input device and a rotatable input device. In some examples, input mechanism 608 is a button.

[0210] In some examples, the input mechanism 608 is a microphone. The personal electronic device 600 includes, for example, various sensors such as a GPS sensor 632, an accelerometer 634, an orientation sensor 640 (e.g., a compass), a gyroscope 636, a motion sensor 638, and / or combinations thereof, all of which are operatively connected to the I / O section 614.

[0211] The memory 618 of the personal electronic device 600 is a non-transitory computer-readable storage medium for storing computer-executable instructions, which, when executed by one or more computer processors 616, cause the computer processors to perform the techniques and processes described above. 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, apparatus, or device, such as a computer-based system, a processor-containing system, or other system capable of retrieving and executing instructions from and from an instruction execution system, apparatus, or device. The personal electronic device 600 is not limited to... Figure 6B It can be the components and configurations, or it can include other components or additional components in a variety of configurations.

[0212] As used herein, the term "power indication" refers to the indication of power in devices 200, 400, 600, and / or 900. Figure 2A , Figure 4 , Figures 6A to 6B ,as well as Figures 9A to 9D A graphical user interface object displayed on a screen. For example, images (e.g., icons), buttons, and text (e.g., hyperlinks) each constitute a display representation.

[0213] As used herein, the term "focus selector" refers to an input element used to indicate the current portion of a user interface with which a user is interacting. In some specific implementations that include a cursor or other positional 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), the cursor is positioned on a touch-sensitive surface (e.g., a...). Figure 4 The touchpad 455 or Figure 5B When an input (e.g., a press input) is detected on the touch-sensitive surface 551 of the display, the specific user interface element is adjusted according to the detected input. This applies to touchscreen displays (e.g., those capable of direct interaction with user interface elements on a touchscreen display) that enable direct interaction with user interface elements on the touchscreen display. Figure 2A The touch-sensitive display system 212 or Figure 5A In some embodiments of the touchscreen 212, a touch detected on the touchscreen acts as a "focus selector," such that when input (e.g., a press input by touch) is detected at the location of a particular user interface element (e.g., a button, window, slider, or other user interface element) on the touchscreen display, that particular user interface element is adjusted according to the detected input. In some embodiments, focus moves from one area of ​​the user interface to another without corresponding movement of the cursor or movement of a touch on the touchscreen display (e.g., moving focus from one button to another using tab keys or arrow keys); in these embodiments, the focus selector moves according to the movement of focus between different areas of the user interface. Regardless of the specific form the focus selector takes, the focus selector is typically a user-controlled user interface element (or a touch on the touchscreen display) that delivers the user-expected interaction with the user interface (e.g., by indicating to the device the element of the user interface that the user expects to interact with). For example, when a press input is detected on a touch-sensitive surface (e.g., a touchpad or touchscreen), the position of the focus selector (e.g., a cursor, touch, or selection box) above the corresponding button will indicate to the user that they expect to activate the corresponding button (rather than other user interface elements shown on the device's display).

[0214] As used in the specification and claims, the term "characteristic intensity" of a contact refers to a characteristic of the 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 over 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, before or after contact begins to move, before contact ends, before or after contact intensity is detected to increase and / or before or after contact intensity decreases). The characteristic intensity of the 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 contact intensity over time). In some implementations, the feature intensity is compared to a set of one or more intensity thresholds to determine whether a user has performed an action. For example, the set of one or more intensity thresholds may include a first intensity threshold and a second intensity threshold. In this example, contact with a feature intensity not exceeding the first threshold results in a first action, contact with a feature intensity exceeding the first intensity threshold but not exceeding the second intensity threshold results in a second action, and contact with a feature intensity exceeding the second threshold results in a third action. In some implementations, a comparison between the feature intensity and one or more thresholds is used to determine whether to perform one or more actions (e.g., whether to perform the corresponding action or abort performing the corresponding action), rather than to determine whether to perform the first or second action.

[0215] In some implementations, a portion of the gesture is identified for determining the characteristic intensity. For example, a touch-sensitive surface receives a series of swipes that transition from a starting position to an ending position, where the intensity of the contact increases. In this example, the characteristic intensity of the contact at the ending position is based only on a portion of the series of swipes, rather than the entire swipe (e.g., the swipe contact is only the portion at the ending position). In some implementations, a smoothing algorithm is applied to the intensity of the swipe contact before determining its characteristic intensity. For example, the smoothing algorithm optionally includes one or more of the following: unweighted moving average smoothing algorithm, triangular smoothing algorithm, median filter smoothing algorithm, and / or exponential smoothing algorithm. In some cases, these smoothing algorithms eliminate narrow spikes or dips in the intensity of the swipe contact to achieve the purpose of determining the characteristic intensity.

[0216] 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 performs an operation typically associated with clicking a button on a physical mouse or touchpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device performs an operation different from the operation typically associated with clicking a button on a physical mouse or touchpad. In some embodiments, when a contact with a characteristic intensity lower than the light press intensity threshold (e.g., and higher than the nominal contact detection intensity threshold, where contacts lower than the nominal contact detection intensity threshold are no longer detected) is detected, the device will move the focus selector based on the movement of the contact on the touch-sensitive surface without performing the operation associated with the light press intensity threshold or the deep press intensity threshold. Generally, unless otherwise stated, these intensity thresholds are consistent across different groups of user interface figures.

[0217] An increase in contact intensity from 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 intensity from 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 intensity from 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 a contact on the touch surface. A decrease in contact intensity from above a contact detection intensity threshold to an intensity below the contact detection intensity threshold is sometimes referred to as detecting a contact being lifted off 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.

[0218] In some embodiments described herein, one or more operations are performed in response to detecting a gesture including 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 intensity of the contact (or multiple contacts) increases to above a press input intensity threshold. In some embodiments, the corresponding operation is performed in response to detecting that the intensity of the corresponding contact increases to above a press input intensity threshold (e.g., a "downward stroke" of the corresponding press input). In some embodiments, the press input includes the intensity of the corresponding contact increasing to above a press input intensity threshold and the intensity of the contact subsequently decreasing to below the press input intensity threshold, and the corresponding operation is performed in response to detecting that the intensity of the corresponding contact subsequently decreases to below the press input threshold (e.g., an "upward stroke" of the corresponding press input).

[0219] In some implementations, the device employs intensity hysteresis to avoid unintended inputs sometimes referred to as "jitter," wherein the device defines or selects a hysteresis intensity threshold that has a predefined relationship with 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 percentage of the press input intensity threshold). Therefore, in some implementations, a press input includes an increase in the intensity of the corresponding contact above the press input intensity threshold and a subsequent decrease in the intensity of that contact 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 below the hysteresis intensity threshold (e.g., an "upstroke" of the corresponding press input). Similarly, in some implementations, 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 the contact intensity subsequently decreases to an intensity equal to or below the hysteresis intensity threshold, and a corresponding operation is performed in response to detecting a press input (e.g., an increase or decrease in contact intensity depending on the environment).

[0220] For ease of explanation, optionally, the description of an operation triggered in response to a press input associated with a press input strength threshold or in response to a gesture including a press input is provided in response to detecting any of the following conditions: the contact strength increases to above the press input strength threshold, the contact strength increases from below a hysteresis strength threshold to above the press input strength threshold, the contact strength decreases to below the press input strength threshold, and / or the contact strength decreases to below the hysteresis strength threshold corresponding to the press input strength threshold. Additionally, in the example where the operation is described as being performed in response to detecting a decrease in contact strength below the press input strength threshold, the operation is optionally performed in response to detecting a decrease in contact strength below a hysteresis strength threshold corresponding to and less than the press input strength threshold.

[0221] 3. Digital Assistant System

[0222] Figure 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 standalone 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 server and client parts, wherein the client part resides on one or more user devices (e.g., device 104, device 122, device 200, device 400, device 600, or device 900) and communicates with the server part (e.g., server system 108) via one or more networks, for example, as... Figure 1 As shown in the image. In some examples, the digital assistant system 700 is... Figure 1 The specific implementation of the server system 108 (and / or DA server 106) shown is illustrated. It should be noted that the digital assistant system 700 is merely an example of a digital assistant system, and the digital assistant system 700 may have more or fewer components than shown, combine two or more components, or have different configurations or layouts of components. Figure 7A The various components shown 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.

[0223] The digital assistant system 700 includes a memory 702, one or more processors 704, an input / output (I / O) interface 706, and a network communication interface 708. These components can communicate with each other via one or more communication buses or signal lines 710.

[0224] 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).

[0225] In some examples, I / O interface 706 couples input / output devices 716 of digital assistant system 700, such as a display, keyboard, touchscreen, and microphone, to user interface module 722. I / O interface 706, together with user interface module 722, receives user input (e.g., voice input, keyboard input, touch input, etc.) and processes this input accordingly. In some examples, such as when the digital assistant is implemented on a standalone user device, digital assistant system 700 includes a user interface module 722. Figure 2A , Figure 4 , Figures 6A to 6B and Figures 9A to 9D The components and I / O communication interfaces described in devices 200, 400, 600, or 900, respectively. In some examples, digital assistant system 700 represents the server portion of a digital assistant implementation and can interact with the user through a client-side portion located on a user device (e.g., device 104, device 200, device 400, device 600, or device 900).

[0226] In some examples, the network communication interface 708 includes one or more wired communication ports 712 and / or wireless transmission and reception circuitry 714. The one or more wired communication ports receive and transmit communication signals via one or more wired interfaces such as Ethernet, Universal Serial Bus (USB), FireWire, etc. The wireless circuitry 714 receives RF signals and / or optical signals from the communication network and other communication devices, and transmits RF signals and / or optical signals to the 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 via networks (such as the Internet, intranets, and / or wireless networks, such as cellular telephone networks, wireless local area networks (LANs), and / or metropolitan area networks (MANs)).

[0227] In some examples, memory 702 or its computer-readable storage medium 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 application programs 724, and digital assistant module 726. Specifically, memory 702 or its computer-readable storage medium stores instructions for performing the above-described processes. One or more processors 704 execute these programs, modules, and instructions, and read data from or write data to data structures.

[0228] Operating systems 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or embedded operating systems such as VxWorks) include various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.) and facilitate communication between various hardware, firmware, and software components.

[0229] The communication module 720 facilitates communication between the digital assistant system 700 and other devices via the network communication interface 708. For example, the communication module 720 communicates with electronic devices (such as those in…) Figure 2A , Figure 4 , Figures 6A to 6B The device 200, 400, or 600 shown communicates with the RF circuitry system 208. The communication module 720 also includes various components for processing data received by the wireless circuitry system 714 and / or the wired communication port 712.

[0230] The user interface module 722 receives commands and / or input from the user (e.g., from a keyboard, touchscreen, 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., voice, sound, animation, text, icons, vibration, haptic feedback, lighting, etc.) and delivers it to the user via the I / O interface 706 (e.g., through a display, audio channel, speaker, touchpad, etc.).

[0231] Application 724 includes programs and / or modules configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, application 724 includes user applications such as games, calendar applications, navigation applications, or email applications. If the digital assistant system 700 is implemented on a server, application 724 includes, for example, resource management applications, diagnostic applications, or scheduling applications.

[0232] The memory 702 also stores the 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: input / output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis processing module 740. Each of these modules has access to one or more, or subsets or supersets of, the following systems or data and models of the digital assistant module 726: knowledge ontology 760, vocabulary index 744, user data 748, task flow model 754, service model 756, and ASR system 758.

[0233] In some examples, using the processing modules, data, and models implemented in the digital assistant module 726, the digital assistant can perform at least some of the following: converting voice input into text; identifying user intent expressed in natural language input received from the user; proactively eliciting and obtaining the information needed to fully infer the user intent (e.g., by disambiguating words, games, intents, etc.); determining a task flow to satisfy the inferred intent; and executing the task flow to satisfy the inferred intent.

[0234] In some examples, such as Figure 7B As shown, the I / O processing module 728 can... Figure 7A The I / O device 716 in the middle interacts with the user or through Figure 7AThe network communication interface 708 interacts with user equipment (e.g., device 104, device 200, device 400, or device 600) to receive user input (e.g., voice input) and provide a response to the user input (e.g., as voice output). The I / O processing module 728 optionally receives contextual information associated with the user input from the user equipment along with or shortly after receiving the user input. Contextual information includes user-specific data, vocabulary, and / or preferences associated with the user input. In some examples, this contextual information also includes the software and hardware states of the user equipment at the time the user request is received, and / or information related to the user's surrounding environment at the time 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 a user request is received by the I / O processing module 728 and the user request includes voice input, the I / O processing module 728 forwards the voice input to the STT processing module 730 (or a speech recognizer) for speech-to-text conversion.

[0235] STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process speech input received through I / O processing module 728 to produce recognition results. 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 spectral features characterizing the speech input as a sequence of representative multidimensional vectors. Additionally, 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 grammar language models, and other statistical models. Examples of speech recognition engines include engines based on Dynamic Time Warping (VTW) 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 representative features extracted by the front-end speech preprocessor to produce intermediate recognition results (e.g., phonemes, phoneme strings, and sub-words) and ultimately produce text recognition results (e.g., words, word strings, or symbol sequences). In some examples, the speech input is processed at least in part by a third-party service or on the user's device (e.g., device 104, device 200, device 400, or device 600) to produce the recognition results. Once the STT processing module 730 produces a recognition result containing a text string (e.g., words, or sequences of words, or sequences of symbols), the recognition result is passed to the natural language processing module 732 for intent inference. In some examples, the STT processing module 730 produces 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. In another example, the five highest-ranked (n=5) candidate text representations are delivered to the natural language processing module 732 for intent inference.

[0236] Further details regarding speech-to-text processing are described in U.S. Utility Model Patent Application Serial No. 13 / 236,942, entitled "Consolidating Speech Recognition Results," filed on September 20, 2011, the entire disclosure of which is incorporated herein by reference.

[0237] In some examples, the STT processing module 730 includes a vocabulary of recognizable words and / or accesses that vocabulary via the speech-to-letter conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of a word represented in the speech recognition alphabet. Specifically, the vocabulary of recognizable words includes words associated with multiple candidate pronunciations. For example, the vocabulary includes words associated with... and The candidate pronunciations are associated with the word "tomato". Additionally, vocabulary words are associated with custom candidate pronunciations based on previous speech input from the user. These custom candidate pronunciations are stored in the STT processing module 730 and associated with a specific user via a user profile on the device. In some examples, candidate pronunciations are determined based on the spelling of the word and one or more linguistic and / or phonetic rules. In some examples, candidate pronunciations are generated manually, for example, based on known standard pronunciations.

[0238] In some examples, candidate pronunciations are ranked based on their prevalence. For example, candidate pronunciations... The ranking is higher than This is because the former is a more commonly used pronunciation (e.g., among all users, for users in a specific geographic region, or for any other suitable subset of users). In some examples, candidate pronunciations are ranked based on whether they are custom candidate pronunciations associated with a user. For example, custom candidate pronunciations rank higher than standard candidate pronunciations. This can be used to identify proper nouns with unique pronunciations that deviate from the canonical pronunciation. In some examples, candidate pronunciations are associated with one or more phonological features such as geographic origin, country, or ethnicity. For example, candidate pronunciations... Associated with the United States, and candidate pronunciation The candidate pronunciations are associated with the United Kingdom. Furthermore, the ranking of candidate pronunciations is based on one or more characteristics of the user (e.g., geographic origin, country, ethnicity, etc.) stored in the user profile on the device. For example, it can be determined from the user profile that the user is associated with the United States. Based on the user's association with the United States, candidate pronunciations... (US-related) Comparable candidate pronunciations (Related to the UK) It ranks higher. In some examples, one of the ranked candidate pronunciations can be selected as the predicted pronunciation (e.g., the most likely pronunciation).

[0239] Upon receiving voice input, the STT processing module 730 is used (e.g., using an acoustic model) to determine the phonemes corresponding to the voice input, and then attempts (e.g., using a language model) to determine the words that match those phonemes. For example, if the STT processing module 730 first identifies a sequence of phonemes corresponding to a portion of the voice input... It can then determine, based on vocabulary index 744, that the sequence corresponds to the word "tomato".

[0240] In some examples, the STT processing module 730 uses fuzzy matching techniques to determine words in a utterance. Therefore, for example, the STT processing module 730 determines phoneme sequences. This corresponds to the word "tomato," even if the specific phoneme sequence is not a candidate phoneme sequence for that word.

[0241] The digital assistant's natural language processing module 732 ("natural language processor") acquires 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 intentions" recognized by the digital assistant. An "executable intention" (or "user intention") represents a task that can be performed by the digital assistant and may have an associated task flow implemented in the task flow model 754. An associated task flow is a series of programmed actions and steps taken by the digital assistant to perform the task. The capabilities of the digital assistant depend on the number and type of task flows implemented and stored in the task flow model 754, or in other words, on the number and type of "executable intentions" recognized by the digital assistant. However, the effectiveness of the digital assistant also depends on its ability to infer the correct "one or more executable intentions" from user requests expressed in natural language.

[0242] 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, for example, contextual information associated with the user request from the I / O processing module 728. The natural language processing module 732 optionally uses the contextual information to clarify, supplement, and / or further define the information contained in the candidate text representation received from the STT processing module 730. Contextual information includes, for example, user preferences, the hardware and / or software state of the user's device, sensor information collected before, during, or shortly after the user request, previous interactions (e.g., conversations) between the digital assistant and the user, and so on. As described herein, in some examples, the contextual information is dynamic and varies with the time, location, content, and other factors of the conversation.

[0243] In some examples, natural language processing is based on, for example, a knowledge ontology 760. Knowledge ontology 760 is a hierarchical structure containing many nodes, each node representing an "executable intent" or an "attribute" associated with one or more of the "executable intent" or other "attributes." As mentioned above, an "executable intent" represents a task that a digital assistant can perform; that is, the task is "executable" or can be done. An "attribute" represents a parameter associated with a sub-aspect of an executable intent or another attribute. The links between executable intent nodes and attribute nodes in knowledge ontology 760 define how the parameters represented by the attribute nodes are subordinate to the task represented by the executable intent nodes.

[0244] In some examples, the knowledge ontology 760 consists of executable intent nodes and attribute nodes. Within the knowledge ontology 760, each executable intent node is directly linked to or connected to one or more attribute nodes via one or more intermediate attribute nodes. Similarly, each attribute node is directly linked to or connected to one or more executable intent nodes via one or more intermediate attribute nodes. For example, as... Figure 7C As shown, knowledge ontology 760 includes a "Restaurant Reservation" node (i.e., an executable intent node). The attribute nodes "Restaurant", "Date / Time" (for reservations) and "Participant Size" are all directly linked to the executable intent node (i.e., the "Restaurant Reservation" node).

[0245] Furthermore, the attribute nodes "Cuisine," "Price Range," "Phone Number," and "Location" are child nodes of the attribute node "Restaurant," and all are linked to the "Restaurant Reservation" node (i.e., the executable intent node) through the intermediate attribute node "Restaurant." For example, ... Figure 7C As shown, knowledge ontology 760 also includes a "Set Reminder" node (i.e., another executable intent node). The attribute nodes "Date / Time" (for setting reminders) and "Topic" (for reminders) are both linked 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 linked to both the "Restaurant Reservation" node and the "Set Reminder" node in knowledge ontology 760.

[0246] An executable intent node, along with its linked attribute nodes, is described as a "domain." 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 knowledge ontology 760 shown includes examples of a restaurant reservation domain 762 and a reminder domain 764 within the knowledge ontology 760. The restaurant reservation domain includes an actionable intent node “Restaurant Reservation”, attribute nodes “Restaurant”, “Date / Time”, and “Participant Size”, and sub-attribute nodes “Cuisine”, “Price Range”, “Phone Number”, and “Location”. The reminder domain 764 includes an actionable intent node “Set Reminder” and attribute nodes “Topic” and “Date / Time”. In some examples, the knowledge 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 associated with many different domains (e.g., itinerary domain, travel booking domain, movie ticket domain, etc.).

[0247] although Figure 7C Two example fields within knowledge ontology 760 are shown, but other fields include, for example, "Find a movie," "Initiate a phone call," "Find directions," "Schedule a meeting," "Send a message," and "Provide answers to questions," "Reading lists," "Provide navigation instructions," "Provide instructions for a task," etc. The "Send a message" field is associated with the "Send a 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 sub-attribute nodes such as "Recipient name" and "Message address."

[0248] In some examples, knowledge ontology 760 includes all domains (and thus executable intents) that a digital assistant can understand and act upon. In some examples, knowledge ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying the relationships between nodes within knowledge ontology 760.

[0249] In some examples, nodes associated with multiple related executable intents are clustered under a “superdomain” in knowledge ontology 760. For example, the “Travel” superdomain includes clusters of travel-related attribute nodes and executable intent nodes. Travel-related executable intent nodes include “flight booking,” “hotel booking,” “car rental,” “route planning,” “finding points of interest,” and so on. Executable intent nodes under the same superdomain (e.g., the “Travel” superdomain) have multiple shared attribute nodes. For example, executable intent nodes for “flight booking,” “hotel booking,” “car rental,” “get route,” and “finding points of interest” share one or more of the attribute nodes “starting location,” “destination,” “departure date / time,” “arrival date / time,” and “party size.”

[0250] 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 called the "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, returning... Figure 7B The vocabulary associated with nodes of the "restaurant" attribute includes words such as "food," "drinks," "cuisine," "hunger," "eat," "pizza," "fast food," and "meals." Similarly, the vocabulary associated with nodes of the "initiate a phone call" action includes words and phrases such as "call," "make a phone call," "dial," "talk to," "call this number," and "make a phone call." The vocabulary index 744 optionally includes words and phrases from different languages.

[0251] Natural Language Processing (NLP) module 732 receives candidate text representations (e.g., one or more text strings or one or more sequences of symbols) from STT processing module 730 and, for each candidate representation, determines which nodes the words in the candidate text representation relate to. In some examples, if a word or phrase in a candidate text representation is found to be associated with one or more nodes in knowledge ontology 760 (via lexical index 744), the word or phrase "triggers" or "activates" those nodes. Based on the number and / or relative importance of the activated nodes, NLP module 732 selects one executable intent as the task 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 individual 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, such as whether the digital assistant has previously correctly interpreted similar requests from the user, are also considered in the node selection process.

[0252] 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- or long-term information for each user. In some examples, the natural language processing module 732 uses user-specific information to supplement the information contained in the user input to further refine the user's intent. For example, in response to a user request "Invite my friends to my birthday party," the natural language processing module 732 can access user data 748 to determine who the "friends" are and when and where the "birthday party" will be held, without requiring the user to explicitly provide such information in their request.

[0253] It should be recognized that, in some examples, the natural language processing module 732 is implemented using one or more machine learning agencies (e.g., neural networks). Specifically, the one or more machine learning agencies are configured to receive candidate text representations and contextual information associated with the candidate text representations. Based on the candidate text representations and the associated contextual information, the one or more machine learning agencies are configured to determine an intent confidence score based on a set of candidate executable intents. The natural language processing module 732 can select one or more candidate executable intents from the set of candidate executable intents based on the determined intent confidence score. In some examples, a knowledge ontology (e.g., knowledge ontology 760) is also utilized to select one or more candidate executable intents from the set of candidate executable intents.

[0254] Further details regarding the symbol string-based search of knowledge ontology are described in U.S. Utility Model Patent Application Serial No. 12 / 341,743, entitled “Method and Apparatus for Searching Using An Active Ontology,” filed on December 22, 2008, the entire disclosure of which is incorporated herein by reference.

[0255] In some examples, once the natural language processing module 732 identifies an executable intent (or domain) based on a user request, it 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 these parameters are populated with specific information and requirements specified in the user request. For example, a user says, “Reserve a table at a sushi restaurant for 7 pm.” 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 for the “restaurant reservation” domain includes parameters such as {cuisine}, {time}, {date}, {number of people}, etc. In some examples, based on voice input and text derived from the voice input using the STT processing module 730, the natural language processing module 732 generates a partially structured query for the restaurant reservation domain, where the partially structured query includes the parameters {cuisine = “sushi”} and {time = “7 pm”}. However, in this example, the user’s utterance contains insufficient information to complete a structured query associated with the domain. Therefore, based on the currently available information, no other necessary parameters such as {number of people at the party} and {date} are specified in the structured query. In some examples, the natural language processing module 732 uses the received context information to populate some parameters of the structured query. For example, in some examples, if a user requests a "nearby" sushi restaurant, the natural language processing module 732 uses GPS coordinates from the user's device to populate the {location} parameter in the structured query.

[0256] In some examples, the Natural Language Processing (NLP) module 732 identifies multiple candidate executable intents for each candidate text representation received from the STT processing module 730. Additionally, in some examples, a corresponding structured query (partially or entirely) is generated for each identified candidate executable intent. The NLP module 732 determines an intent confidence score for each candidate executable intent and ranks the candidate executable intents based on the intent confidence scores. In some examples, the NLP module 732 transmits one or more generated 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 intents 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 intents, along with corresponding one or more candidate text representations, are provided to the task flow processing module 736.

[0257] Further details regarding the inference of user intent based on multiple candidate executable intents determined from multiple candidate text representations of speech input are described in U.S. Utility Model Patent Application Serial No. 14 / 298,725, filed June 6, 2014, entitled “System and Method for Inferring UserIntent From Speech Inputs,” the entire disclosure of which is incorporated herein by reference.

[0258] Task flow processing module 736 is configured to receive one or more structured queries from 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 task flow model 754. In some examples, task flow model 754 includes processes for obtaining additional information from the user, and task flows for performing actions associated with the executable intent.

[0259] As described above, to complete a structured query, task flow processing module 736 needs to initiate additional dialogue with the user to obtain additional information and / or clarify potentially ambiguous statements. When such interaction is necessary, task flow processing module 736 invokes dialogue flow processing module 734 to participate in the dialogue with the user. In some examples, dialogue flow processing module 734 determines how (and / or when) to request additional information from the user and receives and processes user responses. Questions are presented to the user and answers are received from the user via I / O processing module 728. In some examples, dialogue flow processing module 734 presents dialogue output to the user via audible and / or visual output and receives input from the user via voice or physical (e.g., click) responses. Continuing with the above example, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the "party size" and "date" information for a structured query associated with the domain "restaurant reservation," dialogue flow processing module 734 generates questions such as "How many people in a row?" and "Which day to book?" and presents them to the user. Once a response is received from the user, the dialogue flow processing module 734 either fills 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.

[0260] Once the task flow processing module 736 has completed a structured query for the executable intent, it begins executing 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 based on 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 the restaurant and actually requesting a reservation for a specific number of people at a specific time for a specific party. For example, using a structured query such as: {restaurant reservation, restaurant = ABC Cafe, date = 3 / 12 / 2012, time = 7 pm, number of people = 5}, the task flow processing module 736 can perform the following steps: (1) log in to ABC Cafe's server or such The restaurant reservation system, (2) inputs date, time and party number information on the website, (3) submits the form, and (4) creates a calendar entry for the reservation in the user's calendar.

[0261] In some examples, task flow processing module 736, with the assistance of service processing module 738 (“service processing module”), completes the task requested in the user input or provides the informational answer requested in the user input. For example, service processing module 738 may, on behalf of task flow processing module 736, initiate a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user's device, or invoke or interact with third-party services (e.g., restaurant reservation portals, social networking sites, bank portals, etc.). In some examples, the protocols and application programming interfaces (APIs) required for each service are specified through the corresponding service model in service model 756. Service processing module 738 accesses the appropriate service model for a service and, based on the service model, generates a request for that service according to the protocols and APIs required by that service.

[0262] For example, if a restaurant has enabled an online reservation service, it submits a service model that specifies the necessary parameters for making a reservation and the values ​​of those parameters to be sent to the online reservation service's API. When requested by the task flow processing module 736, the service processing module 738 uses the web address stored in the service model to establish a network connection with the online reservation service and sends the necessary reservation parameters (e.g., time, date, number of party members) to the online reservation interface in a format appropriate to the online reservation service's API.

[0263] In some examples, the natural language processing module 732, the dialogue flow processing module 734, and the task flow processing module 736 are used together and repeatedly to infer and define the user's intent, obtain information to further clarify and refine the user's intent, and ultimately generate a response (i.e., output to the user, or complete the task) to satisfy the user's intent. The generated response is a dialogue response to the voice input that at least partially satisfies the user's intent. Additionally, in some examples, the generated response is output as voice output. In these examples, the generated response is sent to the speech synthesis processing module 740 (e.g., a speech synthesizer), which processes the generated response to synthesize the dialogue response in speech form. In other examples, the generated response is data content related to satisfying the user's request in the voice input.

[0264] 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 a first structured query of the received structured queries to attempt to complete the first structured query and / or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest-ranking executable intent. In other examples, the first structured query is selected from structured queries received based on a 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 necessary parameters), the task flow processing module 736 may continue to select and process a second structured query from the received structured queries that corresponds to a lower-ranking executable intent. For example, the second structured query may be selected based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate executable intent, missing necessary parameters in the first structured query, or any combination thereof.

[0265] The speech synthesis processing module 740 is configured to synthesize speech output for presentation to a user. The speech synthesis processing module 740 synthesizes speech output based on text provided by a 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 audible speech output. The speech synthesis processing module 740 uses any appropriate speech synthesis techniques to generate speech output from text, including but not limited to: concatenation synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulation synthesis, Hidden Markov Model (HMM) based synthesis, 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 those words. For example, phoneme strings are associated with words in the generated dialogue response. The phoneme strings are stored in metadata associated with the words. The speech synthesis processing module 740 is configured to directly process the phoneme strings in the metadata to synthesize words in speech form.

[0266] In some examples, instead of using a speech synthesis processing module 740 (or other alternatives), speech synthesis is performed on a remote device (e.g., server system 108), and the synthesized speech is sent to a user device for output to the user. For example, this could occur in some implementations where the output of a digital assistant is generated at the server system. And since server systems typically have greater processing power or more resources than user devices, they are likely to achieve higher quality speech output than client-side synthesis would achieve.

[0267] Additional details regarding digital assistants can be found in U.S. Utility Model Patent Application No. 12 / 987,982, entitled “Intelligent Automated Assistant,” filed January 10, 2011, and U.S. Utility Model Patent Application No. 13 / 251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed September 30, 2011, the entire disclosure of which is incorporated herein by reference.

[0268] 4. Determine if voice input is intended for use with a digital assistant.

[0269] Figure 8 A system 800 for determining whether voice input is intended for use with a digital assistant is illustrated, according to various examples. In some examples, system 800 is implemented on a standalone computer system (e.g., device 104, 122, 200, 400, 600, or 900). In some examples, system 800 is distributed across multiple computers. For example, some of the components and functions of system 800 are divided into server and client portions, wherein the client portion resides 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, e.g., as... Figure 1 As shown in the image.

[0270] System 800 is implemented using hardware, software, or a combination of hardware and software to perform the principles discussed herein. In some examples, components and functions of System 900 are implemented within Digital Assistant Module 726, as described above relative to... Figures 7A to 7C As discussed. For example, each component of system 800 is implemented as a set of computer-executable instructions stored in memory 702.

[0271] System 800 is exemplary, and therefore system 800 may have more or fewer components than those shown, may combine two or more components, or may have different component configurations or arrangements. Furthermore, 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 may be performed at more than one component of system 800.

[0272] System 800 receives voice input (e.g., a user's speech) and determines whether the voice input is intended for use with a digital assistant, based on the techniques discussed below. In some examples, based on determining that the voice input is intended for use with a digital assistant, system 800 initiates a digital assistant session, or if a digital assistant session has already been initiated, causes the digital assistant to provide output in response to the voice input.

[0273] In some examples, system 800 determines whether voice input is intended for use with a digital assistant without detecting a verbal trigger for initiating a digital assistant session (e.g., "Hey Siri", "Siri", "Hey Assistant", etc.). In some examples, system 800 determines whether voice input is intended for use with a digital assistant without detecting a verbal trigger for initiating a digital assistant session (e.g., "Hey Siri", "Siri", "Hey Assistant", etc.). Figures 9A to 9D The system determines whether the voice input is intended for the digital assistant if the selection of the digital assistant (904) is not detected and no selection of a button on the electronic device is detected. Therefore, the system 800 can determine whether the voice input is intended for the digital assistant without relying on any other input that explicitly indicates that the voice input is intended for the digital assistant.

[0274] In some examples, system 800 determines whether voice input is intended for use by the digital assistant based on whether the digital assistant is waiting for a response to a generated prompt for further user input (e.g., a prompt eliciting further information about the user's request). For example, after the digital assistant outputs a prompt (e.g., "To whom?" in response to the natural language input "Send a message"), the digital assistant enters a listening state for a predetermined duration to wait for a user response. The digital assistant can therefore assume that voice input received during the predetermined duration is intended for use by the digital assistant and thus abandon the use of system 800 to process the voice input.

[0275] System 800 includes a text analysis module 802. The text analysis module 802 is configured to determine one or more scores based on the text representation of the voice input to determine whether the voice input is intended for use with a digital assistant.

[0276] In some examples, the text analysis module 802 includes an automatic speech recognition (ASR) module 804. The ASR module 804 is configured to perform speech recognition on the speech input to determine candidate text representations of the speech input. For example, the ASR module 804 implements the STT processing module 730. In some examples, the text representation of the speech input includes candidate text representations.

[0277] In some examples, the ASR module 804 is configured to process candidate text representations to obtain a text representation of the speech input. For example, the ASR module 804 processes the highest-ranked candidate text representation, or processes each of the n best candidate text representations. In some examples, the ASR module 804 implements a bidirectional encoder representation (BERT) model from the transformer to process the candidate text representations to obtain the corresponding embedding (e.g., a vector) representing the speech input.

[0278] In some examples, the text analysis module 802 includes a discourse classifier 806. The discourse classifier 806 is configured to determine a score (discourse type score) corresponding to the type of a user discourse based on a text representation of the speech input (e.g., an embedding). In some examples, determining the discourse type score involves determining the probability that a user discourse corresponds to each of a plurality of user discourse types. Example user discourse types include:

[0279] The first type of command, such as commands to people, includes phrases like "think about it" or "sit down";

[0280] The second type of command, such as commands to electronic devices, includes phrases like "play music" or...

[0281] "Try searching on Google";

[0282] ● Type 1 questions, such as yes or no questions, like "Do you have any other suggestions?"

[0283] Or, "Are you afraid of any animals?"

[0284] ●The second type of question, such as those addressing opinions, includes questions like, "What is your favorite movie?" or "Which brand of smartphone do you prefer?";

[0285] ●The third type of question, such as questions requiring a true answer, includes questions like "What is your name?" or "When does the game start?";

[0286] ● Type 1 responses, such as affirmative responses, like "yes," "yes, I do," "I think," or "definitely";

[0287] ●The second type of answer, such as negative answers, including "no," "never," etc.

[0288] "None", "Not entirely", or "I don't have";

[0289] ● The third type of answer, such as answers that differ from affirmative and negative answers, for example

[0290] "I don't know," "5 p.m."

[0291] ● Opinions or comments, such as "It's good to have..." or "That's weird";

[0292] ●Statements, such as "The store opens at 8 a.m." or "I am reading."

[0293] ● The first type of social function, such as expressions of gratitude, like "thank you" or "thank you";

[0294] ●The second type of social function, such as apology, for example, "I'm sorry," "I apologize."

[0295] Or "Excuse me";

[0296] ● The third type of social function, such as a response to an apology, for example, "No problem" or

[0297] "It doesn't matter";

[0298] ● The fourth type of social function, such as responding to expressions of gratitude, for example, "You're welcome" or "It's my pleasure";

[0299] ● The fifth type of social function, such as expressions of appreciation, for example, "amazing,"

[0300] "Great job" or "Well done"

[0301] ● Initial phrases in a digital assistant conversation, such as phrases associated with verbal triggers and / or other inputs (e.g., gaze input, gesture input) used to initiate the digital assistant conversation, such as, “Siri, what’s the weather like today?”, “Hey Siri, turn on the lights”, or

[0302] "How many spoonfuls should I put in a glass?"; and

[0303] A utterance following the initial utterance in the same digital assistant session (e.g., received later), such as "How's Paris?" following the initial utterance "Siri, how's the weather today?" or "How many teaspoons?" following the initial utterance "How many spoons?".

[0304] It should be understood that the above-described discourse types are merely exemplary, and the discourse classifier 806 can determine the probability that a user's discourse corresponds to other discourse types. Furthermore, the discourse classifier 806 can determine the probability that a user's discourse corresponds to only some of the above discourse types, for example, without determining the probability that the user's discourse corresponds to other discourse types. In some examples, the determined probabilities are normalized to a sum of one.

[0305] In some examples, determining the discourse type score involves selecting a subset of the determined corresponding probabilities. Each probability in the subset corresponds to a pre-defined discourse type, such as a discourse type more likely to be intended for use with a digital assistant. In some examples, the pre-defined discourse types include at least some of the following: a second type of command (e.g., a command to an electronic device), a first type of question (e.g., a yes or no question), a third type of question (e.g., a question to answer a fact), the initial discourse of a digital assistant session, and discourse following the initial discourse in the same digital assistant session.

[0306] In some examples, the discourse classifier 806 determines a discourse type score based on a selected subset of the corresponding probabilities. For example, the discourse classifier 806 sums the corresponding probabilities to obtain a first score. As a specific example, suppose the discourse classifier 806 receives a text representation of the voice input “How’s the weather today?” The discourse classifier 806 determines the corresponding probability of “How’s the weather today?” for each of a variety of discourse types, for example, a probability of 0.1 for a first-type command, a probability of 0.3 for a second-type command, a probability of 0.08 for a first-type question, a probability of 0.02 for a second-type question, a probability of 0.45 for a third-type question, a probability of 0.03 for an opinion or comment, and a probability of 0.02 for a statement. The discourse classifier 806 then determines the discourse type score of “How’s the weather today?” to be 0.83, which is the sum of the corresponding probabilities of the second-type command, the first-type question, and the third-type question.

[0307] In some examples, the discourse classifier 806 implementation is configured as a machine learning model (e.g., a neural network) to determine discourse type scores. For example, the neural network is trained in a multi-task manner to determine the probability that a discourse corresponds to each of a variety of discourse types. In some examples, the training data includes a set of text representations of corresponding different user discourses, where each text representation is labeled with the correct corresponding user discourse type.

[0308] In some examples, the text analysis module 802 includes a domain correspondence detector 808. The domain correspondence detector 808 is configured to determine a score (domain correspondence score) representing the correspondence between a user's utterance and a domain recognized by the digital assistant, based on the text representation of the speech input. In some examples, determining the domain correspondence score includes determining whether the user's utterance corresponds to multiple user utterances recognized by the digital assistant, such as utterances corresponding to tasks that the digital assistant can fulfill. In some examples, determining the domain correspondence score includes determining whether the user's utterance corresponds to vocabulary associated with a domain recognized by the digital assistant. For example, as discussed above, vocabulary associated with the restaurant reservation domain includes words such as "food," "drinks," "cuisine," "hunger," "eat," "pizza," "fast food," "meal," etc. Therefore, the domain correspondence score can indicate whether the vocabulary and / or structure (e.g., grammar) of the user's utterance corresponds to the vocabulary and / or structure of utterances intended for use by the digital assistant.

[0309] In some examples, the domain correspondence detector 808 is implemented as a binary classification neural network configured to determine domain correspondence scores (e.g., binary scores). For example, a domain correspondence score of 1 indicates that a user utterance corresponds to a domain recognized by the digital assistant, and a domain correspondence score of 0 indicates that a user utterance does not correspond to any domain recognized by the digital assistant. In some examples, the binary classification neural network is trained using a dataset that includes text representations of corresponding different user utterances, where each text representation is labeled (e.g., with a label of 0 or 1) to indicate whether the corresponding user utterance corresponds to a domain recognized by the digital assistant.

[0310] In some examples, the text analysis module 802 includes a context continuity detector 810. The context continuity detector 810 is configured to determine a score (context continuity score) representing the context continuity between a user utterance and a previous user utterance determined to be intended for use with the digital assistant, based on the text representation of the voice input. In some examples, the system 800 determines that the previous user utterance (e.g., included in the previous voice input) is intended for use with the digital assistant. In some examples, the digital assistant determines the previous utterance intended for itself based on the detection of a verbal trigger (e.g., “Hey Siri”). For example, if the previous user utterance was “Hey Siri, what’s the weather like today?” and the (current) user utterance is “What’s Paris like?”, then the context continuity detector 810 determines a score representing the context continuity between the two utterances.

[0311] In this example, the context continuity detector 810 implements a binary classification neural network configured to determine a context continuity score (e.g., a binary score). For example, the binary classification neural network takes as input a text representation of the current user utterance and a text representation (e.g., an embedding) of a previous user utterance. The text representation of the previous user utterance is determined similarly to the text representation of the current user utterance. In this example, the context continuity detector 810 may determine a high context continuity score (e.g., 1) for the current user utterance “How is Paris?” because “How is Paris?” is likely context-continuous with the previous user utterance “Hey Siri, what’s the weather like today?”. For example, a digital assistant could use the previous user utterance to interpret “How is Paris?” as a question about the weather in Paris, France. As another example, the context continuity detector 810 may determine a low context continuity score (e.g., 0) for the current utterance “Let’s go out to eat” because “Let’s go out to eat” is unlikely to be context-continuous with “Hey Siri, what’s the weather like today?”. For example, the previous utterance “Hey Siri, what’s the weather like today?” might not help the digital assistant interpret “Let’s go out to eat.”

[0312] In some examples, a binary classification neural network is trained using a dataset that includes pairs of user utterances (e.g., each user utterance includes a text representation of a previous user utterance and a text representation of the current user utterance). Each pair of user utterances is labeled (e.g., with a binary score of 0 or 1), for example, to indicate whether the previous user utterance is contextually continuous with the current user utterance.

[0313] In some examples, the current voice input (e.g., the current user utterance) is consecutive to the previous voice input (e.g., the previous user utterance). For example, a device implementing system 800 does not receive any other voice input between the current voice input and the previous voice input, and / or does not receive any other voice input determined to be intended for use with a digital assistant between receiving the current voice input and the previous voice input. As another example, the current voice input and the previous voice input are received for a predetermined duration (e.g., 15 seconds, 30 seconds, 1 minute, 1 hour, etc.) of each other.

[0314] In some examples, the current voice input is received within the same digital assistant session as the previous voice input. For example, when the same digital assistant session is initiated, both the current voice input and the previous voice input are received. In some examples, when the digital assistant user interface (e.g., the following) Figures 9A to 9D The user interface (906) is continuously displayed when and / or when certain calculation processes are being executed (e.g., calculation processes associated with displaying the digital assistant user interface, and operations). Figure 7BThe digital assistant session is initiated by the computational process associated with one or more of the components shown in the diagram. In some examples, stopping the display of the digital assistant user interface and / or stopping the execution of certain computational processes defines the end of the digital assistant session (e.g., termination).

[0315] In some examples, system 800 includes an acoustic analysis module 812. The acoustic analysis module 812 is configured to determine a score (acoustic score) indicating whether the voice input is intended for use with a digital assistant based on an acoustic representation of the voice input. In some examples, the acoustic representation includes a signal representing the voice input (e.g., a signal sampled by one or more microphones). In some examples, the acoustic representation includes one or more embeddings (e.g., vectors) representing the voice input. For example, the acoustic analysis module 812 is configured to determine a machine learning model (e.g., a neural network) of the embedding. Each embedding representation is associated with acoustic features (e.g., the presence or absence of human speech, background noise level, audio energy level within a predetermined frequency band, etc.) relevant to determining whether the voice input is intended for use with a digital assistant. Example techniques for determining embeddings are described in U.S. Patent Application 17 / 123,428, filed December 16, 2020, entitled “REDUCING DEVICEPROCESSING OF UNINTENDED AUDIO,” the entire contents of which are incorporated herein by reference.

[0316] In some examples, the acoustic analysis module 812 implements a machine learning model (e.g., a neural network) configured to determine an acoustic score. For example, the neural network is configured to accept an acoustic representation (e.g., an embedding) as input and output an acoustic score. In some examples, the neural network is configured to determine multiple scores (e.g., binary scores) as the device receives voice input, each score indicating whether the voice input is intended for use with a digital assistant. For example, each of the multiple scores corresponds to a corresponding frame of the voice input (e.g., a 10-millisecond frame). Therefore, the acoustic analysis module 812 can determine the score indicating whether the corresponding frame of the voice input is intended for use with a digital assistant in real time (e.g., when voice input is received). In some examples, the multiple scores include an acoustic score. For example, the acoustic score is the last of the multiple scores, such as the score corresponding to the last frame of the voice input (e.g., the last 10 milliseconds). In some examples, the acoustic analysis module 812 alternatively aggregates (e.g., averages) each of the multiple scores to obtain the acoustic score. Example techniques (e.g., example descriptions of neural networks) for determining acoustic scores are described in U.S. Patent Application No. 17 / 123,428, filed on December 16, 2020, entitled “REDUCING DEVICE PROCESSING OF UNINTENDED AUDIO”.

[0317] In this way, the acoustic analysis module 812 can determine whether the acoustic features of the speech input are consistent with the typical acoustic features of speech intended for use in a digital assistant. In some examples, the acoustic analysis module 812 does not perform ASR on the speech input to determine an acoustic score.

[0318] In some examples, system 800 includes an ASR analysis module 814. The ASR analysis module 814 is configured to determine a score (ASR score) indicating whether the voice input is intended for use with a digital assistant based on a text representation of the voice input. In some examples, the text representation of the voice input includes an ASR word graph, i.e., a graph representing one or more ASR hypotheses of the voice input, where each path of the graph represents a different ASR hypothesis. In some examples, the ASR analysis module 814 implements speech recognition techniques known in the art to determine the ASR word graph. In some examples, the ASR analysis module 814 further determines an embedding (e.g., a vector) representing the ASR word graph according to techniques known in the art.

[0319] In some examples, the ASR analysis module 814 implements a neural network (e.g., a word graph RNN model) to determine the ASR score. The word graph RNN is configured to accept an ASR word graph (or its embedding) as input and output an ASR score (e.g., a bigram score) indicating whether the voice input was intended for use with a digital assistant.

[0320] In some examples, the ASR analysis module 814 uses different types of text representations (e.g., different from ASR word graphs) to determine the ASR score. For example, the ASR analysis module 814 implements a second neural network (e.g., a Listening, Attention, and Spelling (LAS) model) configured to perform speech recognition to determine different types of text representations (e.g., character sequences). In some examples, the ASR analysis module 814 implements another neural network configured to accept a character sequence as input, determine an embedding representing the character sequence, and determine an ASR score (e.g., a binary score) based on that embedding.

[0321] Therefore, as described above, system 800 can use different types of text representations of the voice input to determine various scores indicating whether the voice input is intended for use with a digital assistant. For example, text analysis module 802 uses a first type of text representation (e.g., embeddings obtained according to BERT technology) to determine utterance type scores, domain correspondence scores, and contextual continuity scores. Conversely, ASR analysis module 814 uses different types of text representations (e.g., ASR word graphs and / or character sequences) to determine ASR scores.

[0322] In some examples, system 800 includes a speaker identification module 816. Speaker identification module 816 is configured to determine a speaker identification score based on speaker recognition (e.g., speech recognition) performed on the voice input. For example, the speaker identification score indicates the confidence level that the voice input matches the voice of an authorized user of an electronic device (e.g., a user whose electronic device is configured to recognize their credentials (e.g., password, biometric authentication credentials (e.g., face, fingerprint, retina, etc.))). As described below, score combiner 824 can use the speaker identification score to determine whether the voice input is intended for use with a digital assistant.

[0323] In some examples, system 800 includes a gesture identification module 818. The gesture identification module 818 is configured to process detected gesture input to determine a gesture score. Example sensors used to detect gesture input include accelerometers, gyroscopes, cameras, biometric sensors (e.g., sensors configured to detect photoplethysmography (e.g., pulse oximeters), sensors for electroencephalography, other biometric sensors that can be used to detect hand or arm gestures), touchscreen displays, or combinations or sub-combinations thereof. The gesture score indicates, for example, the confidence that the gesture input corresponds to a predetermined type of gesture (e.g., a gesture to raise a device from a lowered position, a gesture to move a device closer to the user's mouth, a pointing gesture, a predetermined type of finger movement). In some examples, the gesture identification module 818 determines the gesture score based on determining that the gesture input was received within a predetermined time window around the voice input (e.g., for a predetermined duration before the start time of the voice input, during the voice input, and / or for a predetermined duration after the end time of the voice input). As described below, the scoring combiner 824 can use the gesture score to determine whether the voice input is intended for use with a digital assistant.

[0324] In some examples, system 800 includes a verbal trigger detector 820. The verbal trigger detector 820 is configured to detect verbal triggers (e.g., pre-defined words or phrases such as “Siri,” “Hey Siri,” “Hey Assistant,” etc.) used to initiate a digital assistant session. For example, the verbal trigger detector 820 is configured to compare voice input with one or more reference representations of the verbal trigger. In some examples, the electronic device generates the reference representations via a registration process, where the device instructs the user to speak the verbal trigger once or more. In some examples, the verbal trigger detector 820 determines a trigger score that indicates the degree of matching between the voice input and the verbal trigger (e.g., between the voice input and the reference representation). In some examples, the verbal trigger detector 820 detects a verbal trigger if the trigger score is greater than a threshold.

[0325] In some examples, system 800 determines whether the voice input is intended for use with a digital assistant based on the detection of a verbal trigger (e.g., determining a score other than the trigger score discussed herein). Thus, the detection of a verbal trigger can initiate the processing described with respect to system 800. In other examples, system 800 determines whether the voice input is intended for use with a digital assistant regardless of whether a verbal trigger is detected. For example, score combiner 824 uses the verbal trigger score to determine whether the voice input is intended for use with a digital assistant.

[0326] Including a verbal trigger in voice input does not explicitly indicate whether the voice input is intended for use with a digital assistant. For example, if the verbal trigger is "Siri," some voice inputs that include a verbal trigger may be intended for use with a digital assistant (e.g., "Siri, what's the weather like?" or "Siri, help me navigate home"), while other voice inputs may not be intended for use with a digital assistant (e.g., "Let's ask Siri" or "Siri is the best"). Therefore, System 800 can help determine whether such voice input is intended for use with a digital assistant.

[0327] In some examples, the electronic device captures the first dataset from a camera (e.g., an RGB camera and / or an infrared (IR) camera). In some examples, the electronic device captures the first dataset between the start and end times of voice input. For example, the camera begins capturing data at a defined start time of voice input and stops capturing data at a defined end time of voice input. In some examples, the start time is when a digital assistant session is initiated, when the device detects a verbal trigger, when the device detects a selection of a hardware button (e.g., to initiate a digital assistant session), or when the device detects a power indication to the digital assistant (e.g., ...). Figures 9A to 9D When selecting the digital assistant (904) in the device. In some examples, the device uses voice activity detection (VAD) technology known in this art to determine the start time. In some examples, the electronic device determines the end time based on voice end point technology known in this art.

[0328] In some examples, the camera captures data based on predetermined conditions being met (e.g., when). Example predetermined conditions include the device's display being turned on (e.g., a screen), the device being unlocked, a digital assistant session being initiated, the digital assistant session being recently terminated (e.g., within a predetermined duration prior to the current time), the digital assistant not waiting for a prompt for further user input (e.g., a prompt generated by the digital assistant), voice input following (e.g., consecutively with) another voice input received within the same digital assistant session, and the digital assistant having output a response to the initial voice input in the digital assistant session (e.g., an audio response, a display response). In some examples, the first dataset includes camera data captured between a predetermined duration (e.g., 0.5 seconds, 1 second) prior to the start time of the voice input and a second predetermined duration (e.g., 0.5 seconds, 1 second) after the end time of the voice input. For example, such camera data is included in the first dataset if it is available (e.g., due to the satisfaction of the predetermined conditions).

[0329] In some examples, system 800 includes a gaze module 822. The gaze module 822 is configured to determine a second dataset (gaze pattern data) representing a user gaze pattern based on a first dataset when voice input is provided. In some examples, when voice input is provided, the user gaze pattern indicates multiple user gaze locations (e.g., a series of (x,y) coordinates or a heatmap indicating the relative frequency of the user gaze at each location). In some examples, the user gaze locations are each on the display of an electronic device. In some examples, the user gaze pattern also indicates a corresponding time for each gaze location (e.g., a series of (x,y,t) coordinates). In some examples, the gaze module 822 uses gaze tracking techniques to process the first dataset to determine the gaze location and corresponding time. In some examples, if the determined gaze location is not on the display, the gaze module 822 does not include that gaze location in the gaze pattern data.

[0330] In some examples, gaze module 822 determines whether a first dataset represents the user's eyes and / or face, and determines gaze pattern data based on the determination that the first dataset represents the user's eyes and / or face. For example, gaze module 822 uses face detection and / or gaze tracking techniques to process the first dataset to attempt to detect the user's eyes and / or face. If gaze module 822 cannot detect the user's eyes and / or face (e.g., no part of the first dataset represents the user's eyes and / or face), then gaze module 822 abandons the determination of gaze pattern data.

[0331] As discussed in detail below, gaze module 822 is configured to determine a gaze score indicating whether the voice input is intended for use with a digital assistant. In some examples, if gaze module 822 abandons determining gaze pattern data, it determines a relatively low gaze score (e.g., 0) or a gaze score indicating that it is unaware whether the voice input is intended for use with a digital assistant. In some examples, gaze module 822 determines the gaze score without performing ASR on the voice input. For example, as discussed below, gaze module 822 alternatively considers the acoustic characteristics of the voice input and the gaze pattern to determine the gaze score.

[0332] In some examples, the gaze module 822 is configured to determine a third dataset (saccade data) representing user saccades based on gaze pattern data. For example, the gaze module 822 determines the corresponding portions of the gaze pattern corresponding to saccades and gaze fixations by analyzing the timing between user gaze positions. For example, if the corresponding gaze positions are relatively far apart from each other during short durations (e.g., 20ms, 50ms, 100ms, 200ms, 500ms), the gaze position may indicate a saccade. Conversely, if the corresponding gaze positions are relatively close together during short durations, the gaze position may indicate a gaze fixation. The saccade data thus indicates, for example, the corresponding portions of the gaze pattern corresponding to saccades and gaze fixations and / or the percentage of the gaze pattern corresponding to saccades.

[0333] In some examples, the gaze module 822 is configured to determine a fourth dataset (acoustic feature data) based on the speech input, representing features of the speech input (e.g., acoustic features). Example acoustic features include pitch, tone, frequency, amplitude, energy level, entropy, etc. In some examples, the gaze module 822 implements one or more signal processing techniques (e.g., Fourier transform, application of filters, etc.) to determine the values ​​of the acoustic features. Therefore, the acoustic feature data indicates how the values ​​of the acoustic features change throughout the speech input.

[0334] In some examples, gaze module 822 is configured to determine a fifth dataset (correlation data) representing the correlation between features (e.g., acoustic features) representing voice input and multiple user gaze locations. For example, gaze module 822 processes both voice input and gaze pattern data simultaneously to determine corresponding values ​​for the acoustic features for each gaze location. Thus, the correlation data can indicate how the acoustic features change when the user gazes around (e.g., at the device's display).

[0335] In some examples, the gaze module 822 is configured to determine a gaze score based on at least some of gaze pattern data, saccade data, acoustic feature data, and correlation data. In some examples, the gaze module 822 implements rule-based techniques to determine the gaze score. For example, the gaze module 822 compares the above data with one or more corresponding predetermined datasets and / or determines whether the data meets one or more criteria, as discussed below.

[0336] In some examples, gaze module 822 compares gaze pattern data with one or more predetermined datasets, each representing one or more predetermined gaze patterns. For example, a predetermined gaze pattern (e.g., a set of (x,y,t) coordinates or a heatmap indicating gaze location) represents a typical user gaze pattern when the user's voice is intended for use with a digital assistant. For example, a predetermined gaze pattern indicates that the gaze location is concentrated within a predetermined portion of the display. Example predetermined portions of the display include the top percentage of the display (e.g., 10%, 20%, 30%), the bottom percentage of the display (e.g., 10%, 20%, 30%), and the display area of ​​the responsive display 902 (as shown below). Figures 9A to 9D (in the middle) and the display area of ​​the digital assistant indicator 904 (in the following Figures 9A to 9D (In the middle). As an example, the gaze position of the predetermined gaze pattern indicating a threshold amount (e.g., 50%, 75%, 80%, 90%, 95%) falls within the top 30% or bottom 30% of the display. As another example, the gaze position of the predetermined gaze pattern indicating a threshold amount falls within the area occupied by the response indication 902 or the digital assistant indication 904.

[0337] Figures 9A to 9D A digital assistant user interface 906 displayed by an electronic device 900 is shown according to various examples. The electronic device 900 is implemented as device 200, 400, or 600. In some examples, the digital assistant user interface 906 includes a responsive power indicator 902 and a digital assistant power indicator 904. Figure 9A In this context, device 900 displays the digital assistant user interface 906 in response to the voice input "Hey Siri, what's the weather like today?". Because users can associate enable indicators 902 and 904 with the digital assistant, when providing voice input intended for use with the digital assistant, the user may gaze at the area (and / or surrounding area) occupied by enable indicators 902 and / or 904. Therefore, a predetermined gaze pattern can instruct the user to focus their gaze on this area (and / or nearby area).

[0338] In some examples, if the gaze pattern data matches at least some predetermined gaze patterns to a predetermined degree (e.g., 50% match, 75% match, 95% match), the gaze module 922 determines a relatively high gaze score (or increases the gaze score). In some examples, if the gaze pattern data does not match at least some of the predetermined gaze patterns to a predetermined degree, the gaze module 922 determines a relatively low gaze score (or decreases the gaze score). For example, if the voice input is not intended for use with a digital assistant, the gaze pattern data may indicate a relatively uniform distribution of gaze positions on the display (e.g., a heatmap indicating that gaze positions are concentrated near the center of the display). Such gaze pattern data may not match any predetermined gaze patterns sufficiently, resulting in a low gaze score.

[0339] In some examples, gaze module 822 determines whether a user gaze pattern meets one or more criteria. In some examples, determining that a user gaze pattern meets one or more criteria includes determining that the user gaze is guided within a predetermined portion of the display. Example predetermined portions of the display include the top percentage of the display (e.g., 10%, 20%, 30%), the bottom percentage of the display (e.g., 10%, 20%, 30%), the display area of ​​the responsive display 902, and the display area of ​​the digital assistant display 904, or combinations or sub-combinations thereof. In some examples, determining that the user gaze is guided within a predetermined portion includes determining that the user gaze is guided within a predetermined portion of a threshold percentage (e.g., 10%, 20%, 30%, 40%, 50%, 60%) of the voice input, such as determining that at least a threshold percentage of the (x, y) coordinates determined upon receiving voice input falls within the predetermined portion. In some examples, determining that the user gaze is guided within a predetermined portion includes determining that a threshold percentage of the total gaze position falls within the predetermined portion.

[0340] In some examples, determining that a user gaze is guided within a predetermined portion includes determining that the user gaze is guided within the predetermined portion for a predetermined duration, such as determining that the gaze position (or a threshold percentage thereof) falls within the predetermined portion during the predetermined duration. As an example, gaze module 822 determines that the user gaze is guided within a first predetermined portion of the display for a first predetermined duration (e.g., 0.5 seconds, 1 second, 2 seconds, 3 seconds) after the start time of voice input. As another example, gaze module 822 determines that the user gaze is guided within a second predetermined portion of the display for a second predetermined duration (e.g., 0.5 seconds, 1 second, 2 seconds, 3 seconds) before the end time of voice input. The first and second predetermined portions of the display may be the same portion of the display or different portions of the display. For example, gaze module 822 determines whether the user gaze is pointing to the top 30% or bottom 30% of the display during 2 seconds after the start time, and determines whether the user gaze is pointing to the top 30% or bottom 30% of the display during 2 seconds before the end time.

[0341] In some examples, if the gaze module 822 determines that the gaze pattern meets one or more criteria, then the gaze module 822 determines a relatively high gaze score (or increases the gaze score). In some examples, if the gaze module 822 determines that the gaze pattern does not meet one or more criteria (e.g., if the gaze module 822 determines that the user's gaze is not directed within a predetermined portion of the display), then the gaze module 822 determines a relatively low gaze score (or decreases the gaze score).

[0342] While the above description describes determining gaze scores based on gaze pattern data, in some examples, the gaze module 822 determines gaze scores in a similar manner based on other data (e.g., saccade data, acoustic feature data, correlation data). For example, the gaze module 822 compares the other data with a corresponding predetermined dataset and / or determines whether the other data meets one or more criteria. If the other data matches at least some of the corresponding predetermined dataset to a predetermined degree (e.g., 50% match, 75% match, 90% match), the gaze module 822 determines a relatively high gaze score (or increases the gaze score). If the other data does not match at least some of the corresponding predetermined dataset to a predetermined degree, the gaze module 822 determines a relatively low gaze score (or decreases the gaze score). Similarly, if the other data meets one or more criteria, the gaze module 822 determines a relatively high gaze score (or increases the gaze score). If the other data does not meet one or more criteria, the gaze module 822 determines a relatively low gaze score (or decreases the gaze score).

[0343] As an example, gaze module 822 compares the correlation data with predetermined datasets representing predetermined correlations between acoustic features and multiple gaze locations. For example, when the voice input is intended for use with a digital assistant, the predetermined correlation is the typical correlation between acoustic features and gaze locations. As another example, gaze module 822 determines whether the saccade data indicates that at least a threshold percentage (e.g., 25%, 50%, 75%) of the gaze pattern corresponds to a saccade. If so, the saccade data does not meet the criteria, for example, because a high percentage of saccades during speech could indicate that the speech is not intended for use with a digital assistant.

[0344] In some examples, gaze module 822 implements machine learning techniques to determine a gaze score. For example, gaze module 822 implements a neural network configured to accept at least some of the following as input: voice input, gaze pattern data, saccade data, acoustic feature data, and related data. In some examples, gaze module 822 preprocesses at least some of the aforementioned data to determine a corresponding embedding (e.g., a vector) representing that data. In some examples, the neural network alternatively accepts embeddings as input. For example, gaze module 822 preprocesses gaze pattern data to determine an embedding representing the gaze pattern data and / or a second embedding indicating whether a threshold percentage indicating the gaze location falls within a predetermined portion of the display (e.g., the top 30% or the bottom 30%).

[0345] In some examples, the neural network determines a gaze score based on the inputs described above. In some examples, the neural network is trained using a dataset that includes at least some of the following: reference speech input, reference gaze pattern data, reference acoustic feature data, and reference relevance data. For example, each reference data is labeled to indicate whether the corresponding speech input is intended for use with a digital assistant. In this way, the gaze module 822 can learn to implicitly make determinations and / or perform the comparisons discussed above regarding rule-based techniques used for the gaze module 822.

[0346] In some examples, the digital assistant initiates a task based on voice input, processing the voice input, for example, as discussed with respect to the natural language processing module 732 and the task flow processing module 736. In some examples, the digital assistant determines a task flow score corresponding to the initiated task. In some examples, the task flow score quantifies the success of the task. For example, the task flow score is based on whether the digital assistant can determine one or more parameters of the task. Further description of determining a task flow score is provided in U.S. Patent No. 10,789,945, entitled “LOW-LATENCY INTELLIGENT AUTOMATED ASSISTANT,” published September 29, 2020, the contents of which are incorporated herein by reference in their entirety.

[0347] System 800 includes a scoring combiner 824. The scoring combiner 824 is configured to determine whether the voice input is intended for use with a digital assistant based on at least some of the following: utterance type score, domain correspondence score, context continuity score, acoustic score, ASR score, speaker identification score, gesture score, verbal triggering score, gaze score, task flow score, speech recognition confidence score (e.g., determined based on the voice input by STT processing module 730), and intent confidence score (e.g., determined based on the voice input by natural language processing module 732).

[0348] In some examples, the score combiner 824 implements a neural network to determine whether the voice input is intended for use with a digital assistant. For example, the neural network is configured to accept at least some of the scores as input and determine a final score indicating whether the voice input is intended for use with a digital assistant. In some examples, the neural network is configured to obtain the final score, for example, by weighting the scores according to a linear combination, by applying logistic regression techniques, etc. In some examples, the neural network is trained (e.g., learns weights) based on a training dataset including scores, where each score is labeled to indicate whether the corresponding input from which the score is determined (e.g., voice input, camera data, gesture input) corresponds to a voice input intended for use with a digital assistant.

[0349] In some examples, the final score is binary. Therefore, the score combiner 824 determines that the voice input is intended for use with a digital assistant if the final score is 1, and determines that the voice input is not intended for use with a digital assistant if the final score is 0. In some examples, the final score falls within a predetermined range, such as 0 to 1. In such examples, the score combiner 824 compares the final score to a threshold (e.g., 0.5), and determines that the voice input is intended for use with a digital assistant if the final score is above the threshold, and determines that the voice input is not intended for use with a digital assistant if the final score is below the threshold.

[0350] In some examples, the digital assistant initiates a task based on determining that the voice input is intended for use by the digital assistant. In some examples, the digital assistant also provides output indicating the initiated task (e.g., audio output and / or display output). For example, if the scoring combiner 824 determines that the voice input “Hey Siri, what’s the weather like today?” is intended for use by the digital assistant, the digital assistant initiates a task to retrieve weather information and provides the retrieved weather information, such as… Figure 9A As shown in the example. In some examples, the digital assistant abandons initiating the task based on the determination that the voice input was not intended for use by the digital assistant.

[0351] In some examples, the scoring combiner 824 is configured to determine an initial score indicating whether the voice input is intended for use with the digital assistant. For example, similar to determining the final score, the scoring combiner determines the initial score based on scores other than the task flow score and the intent confidence score. If the scoring combiner 824 determines that the initial score indicates the voice input is intended for use with the digital assistant, the scoring combiner 824 causes the digital assistant to initiate a task, thereby determining the intent confidence score and / or the task flow score. Based on the intent confidence score and / or the task flow score (and based on other scores), the scoring combiner 824 determines the final score. Thereafter, based on the determination that the voice input is intended for use with the digital assistant (e.g., based on the final score), the digital assistant provides an output indicating the initiated task. Based on the determination that the voice input is not intended for use with the digital assistant (e.g., based on the final score), the digital assistant abandons providing output.

[0352] In this way, the digital assistant can initiate a task before it is finally determined whether the voice input is intended for use with the digital assistant (e.g., before the final score is determined). Therefore, task initiation refines the determination of whether the voice input is intended for use with the digital assistant. For example, if the initial score indicates that the voice input is intended for use with the digital assistant, but the intent confidence score and / or task flow score are low, the initiated task may correspond to an error. Such an error may indicate that the voice input is not intended for use with the digital assistant. Therefore, the final score may indicate that the voice input is not intended for use with the digital assistant. In some examples, based on the final determination that the voice input is not intended for use with the digital assistant, the score combiner 822 causes the digital assistant to stop initiating the task.

[0353] While the functionality of the respective components of system 800 has been described individually above, in other examples, system 800 implements a single machine learning model (e.g., as one or more neural networks) configured to perform combined functions of one or more components. For example, the neural network is configured to accept at least some of voice input, gesture input, and camera input as input and determine a final score indicating whether the voice input is intended for use with a digital assistant. Therefore, instead of using a score combiner 824 to determine and aggregate scores separately, the neural network alternatively aggregates the input data (e.g., into the determined embedding) and determines the final score without determining the other scores. For example, the neural network can be trained to implicitly make the determinations discussed above regarding the various components of system 800, such that the final score represents the combined result of the implicit determination. For example, the neural network can be trained using a dataset including reference voice input, reference gesture input, and reference camera input, where each reference input is labeled to indicate whether the corresponding voice input is intended for use with a digital assistant.

[0354] 5. Determine if subsequent voice input is intended for use with a digital assistant.

[0355] Return to Figure 9A In some examples, the corresponding voice input (e.g., "Hey Siri, what's the weather like today?") is the first voice input representing the first user's utterance. In some examples, the first voice input is the initial voice input of the initiated digital assistant session.

[0356] In some examples, the digital assistant initiates a first task based on a first voice input (e.g., retrieving weather information). The digital assistant also provides a first output indicating the initiated first task. For example, the digital assistant displays a response enable display 902 and / or audibly outputs the requested weather information.

[0357] In some examples, device 900 determines whether the first voice input is intended for use with a digital assistant, and provides a first output based on the determination that the first voice input is intended for use with a digital assistant. In some examples, device 900 uses system 800 to make the determination, as described above. In some examples, device 900 makes the determination based on the detection of a verbal trigger (e.g., “Hey Siri”) and / or based on the detection of other inputs that explicitly indicate the voice input is intended for use with a digital assistant (e.g., selection of a button on device 900, selection of a digital assistant enablement display 904). For example, device 900 determines whether the first voice input is intended for use with a digital assistant without using system 800.

[0358] Turning Figure 9B In some examples, after providing the first output, device 900 enables the display mode of digital assistant power indicator 904 to change in response to user gaze input. For example, after initially displaying response power indicator 902 and / or after providing audio output, device 900 begins capturing gaze input, such as as camera data. Based on determining that the user gaze is guided to a predetermined position (e.g., digital assistant power indicator 904, device 900, or response power indicator 902), device 900 modifies the display mode of digital assistant power indicator 904. For example, in Figure 9B In this process, device 900 modifies (e.g., enlarges) the size of display representation 904. Other example modifications to the display representation 904 include color modification, modification of animations displayed within display representation 904, and modification of the display position of display representation 904. In this way, device 900 can instruct itself to capture gaze input and can use the gaze input to determine whether subsequent voice input (discussed below) is intended for use with a digital assistant.

[0359] In some examples, after providing a first output, device 900 receives a second voice input (subsequent voice input) following the first voice input, which represents a second user utterance. In some examples, the subsequent voice input is consecutive to the first voice input. In some examples, device 900 receives both the first and subsequent voice inputs within the same digital assistant session, for example, when the same digital assistant session is initiated. In some examples, device 900 receives the subsequent voice input without detecting a verbal trigger and / or other inputs that explicitly indicate the subsequent voice input is intended for use with the digital assistant (e.g., selection of a hardware button, selection of the digital assistant enable display 904) after providing the first output. Therefore, device 900 can determine whether the subsequent voice input is intended for use with the digital assistant without relying on explicit indication. Figure 9C In the middle, device 900 receives subsequent voice input: "How is Paris?"

[0360] In some examples, device 900 determines whether subsequent voice input is intended for use by a digital assistant using system 800. For example, the techniques described above regarding processing voice input using system 800 also apply to processing subsequent voice input.

[0361] In some examples, system 800 uses different components and / or technologies to process the first voice input and subsequent voice input. As an example, system 800 does not use gaze module 822 to process the first voice input (e.g., the gaze score of the first voice input is uncertain) and uses gaze module 822 to process subsequent voice input. Therefore, system 800 can determine whether subsequent voice input is intended for use with a digital assistant by considering only the gaze input.

[0362] As another example, different instances of the gaze module 822 (e.g., corresponding to different instances of a neural network) determine whether a first voice input and subsequent voice input are intended for use with a digital assistant. For example, a first instance of the neural network determines a first gaze score based on the gaze input and the first voice input. A second instance of the neural network determines a second gaze score based on the gaze input and subsequent voice input. The first and second instances of the neural network are trained differently, for example, to more accurately determine whether the corresponding voice input is intended for use with a digital assistant. For example, because the user gaze pattern may differ when the initial voice input is provided and when subsequent voice input is provided, the corresponding instances of the neural network can be trained to account for such differences. As yet another example, different instances of the acoustic analysis module 812 (e.g., corresponding to different instances of a neural network) determine whether a first voice input is intended for use with a digital assistant and whether subsequent voice input is intended for use with a digital assistant. For example, a first instance of the neural network determines a first acoustic score based on the acoustic representation of the first voice input. A second instance of the neural network determines a second acoustic score based on the acoustic representation of the subsequent voice input. For example, since the acoustic features of subsequent voice input intended for use in a digital assistant can differ from the acoustic features of the initial voice input intended for use in a digital assistant, corresponding instances of a neural network can be trained to take such differences into account.

[0363] In some examples, the digital assistant initiates a second task based on subsequent voice input. In some examples, the digital assistant initiates the second task based on determining that the subsequent voice input is intended for use by the digital assistant. In some examples, based on determining that the subsequent voice input is intended for use by the digital assistant, the digital assistant provides a second output instructing the initiated second task. For example, in Figure 9C In this example, device 900 initiates a task to retrieve weather information for Paris, France. Device 900 also displays a response capability representation 908 indicating the weather information and / or audibly outputs the requested weather information. In some examples, the digital assistant abandons providing a second output based on the determination that subsequent voice input is not intended for use by the digital assistant.

[0364] Turning Figure 9DIn some examples, the second output includes audio output, and the digital assistant determines whether the second output indicates an error. For example, the digital assistant determines whether the second audio output corresponds to a response such as "Sorry, I don't know what you mean," "I can't do it," "I don't understand," or "I'm having difficulty processing your request." In some examples, based on the determination that the second output indicates an error, the digital assistant abandons providing the second output (e.g., abandons providing any audio output and / or abandons displaying the response indication 908) and instead displays the indication 910. In some examples, the digital assistant provides the second output based on the determination that it does not indicate an error. In this way, if the response to subsequent voice input indicates an error, the digital assistant can reduce disruption to the user experience, for example, by not providing audio output (and / or not providing text output) and by displaying a relatively visually non-intrusive indication 910. For example, the indication 910 may be relatively small compared to other indications displayed by the digital assistant, may not include any text, and / or may have increased transparency compared to other displayed indications.

[0365] 6. The process for determining whether voice input is intended for use with a digital assistant.

[0366] Figure 10 A process 1000 for determining whether a voice input is intended for use with a digital assistant is illustrated, according to various examples. For example, process 1000 may be performed using one or more electronic devices implementing a digital assistant. In some examples, process 1000 may be performed using a client-server system (e.g., system 100), and the boxes of process 1000 may be divided in any way between a server (e.g., DA server 106) and client devices. In other examples, the boxes of process 1000 may be divided between a server and multiple client devices (e.g., mobile phones and smartwatches). Thus, while portions of process 1000 are described herein as being performed by a specific device of the client-server system, it should be understood that process 1000 is not limited thereto. In other examples, process 1000 may be performed using only client devices (e.g., user device 104 or device 900) or only multiple client devices. In process 1000, some boxes may be optionally combined, the order of some boxes may be optionally changed, and some boxes may be optionally omitted. In some examples, additional steps may be performed in conjunction with process 1000.

[0367] At box 1002, voice input representing a user's speech is received. In some examples, the voice input is consecutive to a previous voice input received within the same digital assistant session. In some examples, the digital assistant determines that the previous voice input is intended for use with the digital assistant based on the detection of a verbal trigger used to initiate a digital assistant session.

[0368] At box 1004, a first score (e.g., a speech type score) corresponding to the type of user speech is determined based on the text representation of the speech input (e.g., via speech classifier 806). In some examples, determining the first score includes: determining a corresponding probability that the user speech corresponds to each of a plurality of user speech types; selecting a subset of the corresponding probabilities, wherein each probability in the subset of the corresponding probabilities corresponds to a corresponding predetermined user speech type among the plurality of user speech types; and determining the first score based on the subset of the corresponding probabilities. In some examples, the plurality of user speech types include a first type of command; a second type of command; a first type of question; a second type of question; a first type of answer; a second type of answer; an opinion; and a statement. In some examples, the corresponding predetermined user speech types include: a second type of command; and a first type of question.

[0369] At box 1006, a second score (e.g., domain correspondence score) representing the correspondence between a user's utterance and a domain recognized by a digital assistant operating on an electronic device is determined based on the text representation of the speech input (e.g., via a domain correspondence detector 808). In some examples, determining the second score includes determining whether the user's utterance corresponds to multiple utterances recognized by the digital assistant. In some examples, determining the second score includes determining whether the user's utterance corresponds to a word associated with a domain recognized by the digital assistant. In some examples, determining the second score includes using a binary classification neural network to determine the second score.

[0370] In some examples, determining the first score and determining the second score are each performed based on determining that the digital assistant is not waiting for a response to a prompt generated by the digital assistant for further user input.

[0371] At box 1008, a third score (e.g., a context continuity score) representing the contextual continuity between a user utterance and a previous user utterance determined as intended for use in a digital assistant is determined based on the text representation of the speech input (e.g., via a context continuity detector 810). In some examples, the user utterance is continuous with the previous user utterance. In some examples, determining the third score involves determining the third score based on the text representation of the previous user utterance. In some examples, determining the third score involves using a second binary classification neural network to determine the third score.

[0372] At box 1010, it is determined (e.g., via score combiner 824) whether the voice input is intended for use with a digital assistant based on a first score and a second score. In some examples, the determination of whether the voice input is intended for use with a digital assistant is also based on a third score. In some examples, the determination of whether the voice input is intended for use with a digital assistant is performed without detecting a verbal trigger for initiating a digital assistant session. In some examples, the determination of whether the voice input is intended for use with a digital assistant is performed without detecting a selection of a displayed power indicator (e.g., digital assistant power indicator 904) and without detecting a selection of a button on the electronic device.

[0373] In some examples, determining whether a voice input is intended for use with a digital assistant includes: weighting a first score, a second score, and a third score to obtain a final score indicating whether the voice input is intended for use with a digital assistant; comparing the final score to a threshold; determining that the voice input is intended for use with a digital assistant based on the determination that the final score exceeds the threshold; and determining that the voice input is not intended for use with a digital assistant based on the determination that the first score is below the threshold.

[0374] At box 1012, based on the determination that voice input is not intended for use with a digital assistant: abandon the initiation of a voice input-based task.

[0375] At box 1014, based on the determination that the voice input is intended for use by the digital assistant: initiate a voice-based task through the digital assistant.

[0376] In box 1016, output indicating the initiated task is provided.

[0377] The above is for reference only. Figure 10 The described operation can optionally be... Figures 1 to 4 , Figures 6A to 6B , Figures 7A to 7C and Figure 8 The components depicted are used to implement this. For example, the operation of process 1000 can be implemented via digital assistant module 726 and system 800. Those skilled in the art will clearly understand how to implement this based on the components depicted in the diagram. Figures 1 to 4 , Figures 6A to 6B and Figures 7A to 7C The components described herein are used to perform other processes.

[0378] Figure 11A process 1100 for determining whether subsequent voice input is intended for use with a digital assistant is illustrated, based on various examples. For example, process 1100 may be performed using one or more electronic devices implementing a digital assistant. In some examples, process 1100 may be performed using a client-server system (e.g., system 100), and the boxes of process 1100 may be divided in any way between a server (e.g., DA server 106) and client devices. In other examples, the boxes of process 1100 may be divided between a server and multiple client devices (e.g., mobile phones and smartwatches). Therefore, while portions of process 1100 are described herein as being performed by a specific device of the client-server system, it should be understood that process 1100 is not limited thereto. In other examples, process 1100 may be performed using only client devices (e.g., user device 104 or device 900) or only multiple client devices. In process 1100, some boxes may be optionally combined, the order of some boxes may be optionally changed, and some boxes may be optionally omitted. In some examples, additional steps may be performed in conjunction with process 1100.

[0379] At box 1102, a first voice input representing a first user's speech is received.

[0380] At box 1104, a first task is initiated by a digital assistant operating on an electronic device based on a first voice input.

[0381] At box 1106, the first output indicating the first task initiated is provided.

[0382] At box 1108, after providing the first output, a second voice input following the first voice input is received, the second voice input representing a second user utterance. In some examples, the first and second voice inputs are received within the same digital assistant session.

[0383] At box 1110, a first score (e.g., domain correspondence score) representing the correspondence between the second user utterance and the domains recognized by the digital assistant is determined based on the text representation of the second speech input (e.g., via a domain correspondence detector 808). In some examples, determining the first score includes determining whether the second user utterance corresponds to multiple utterances recognized by the digital assistant. In some examples, determining the first score includes determining whether the second user utterance corresponds to a word associated with a domain recognized by the digital assistant. In some examples, determining the first score includes using a binary classification neural network to determine the first score.

[0384] At box 1112, a second score (e.g., a context continuity score) representing the contextual continuity between the first user utterance and the second user utterance is determined based on the text representation of the second speech input (e.g., via a context continuity detector 810). In some examples, determining the second score includes determining the second score based on the text representation of the first user utterance. In some examples, determining the second score includes determining the second score using a second binary classification neural network.

[0385] At box 1114, a third score (e.g., a speech type score) corresponding to the type of the second user speech is determined based on the text representation of the second speech input (e.g., via speech classifier 806). In some examples, determining the third score includes: determining a corresponding probability that the second user speech corresponds to each of a plurality of user speech types; selecting a subset of the corresponding probabilities, wherein each probability in the subset of the corresponding probabilities corresponds to a corresponding predetermined user speech type among the plurality of user speech types; and determining the third score based on the subset of the corresponding probabilities. In some examples, the plurality of user speech types include: a first type of command; a second type of command; a first type of question; a second type of question; a first type of answer; a second type of answer; an opinion; and a statement. In some examples, the corresponding predetermined user speech types include a first type of command; and a first type of question.

[0386] At box 1116, a second voice input is determined (e.g., via score combiner 824) whether it is intended for use with a digital assistant based on a first score and a second score. In some examples, determining whether the second voice input is intended for use with a digital assistant is also based on a third score. In some examples, determining whether the second voice input is intended for use with a digital assistant includes: weighting the first score, the second score, and the third score to obtain a final score indicating whether the second voice input is intended for use with a digital assistant; comparing the final score to a threshold; determining that the second voice input is intended for use with a digital assistant if the final score exceeds the threshold; and determining that the second voice input is not intended for use with a digital assistant if the first score is below the threshold.

[0387] In some examples, based on the detection of a verbal trigger for initiating a digital assistant session, it is determined that a first voice input is intended for use with the digital assistant, wherein: determining that the first voice input is intended for use with the digital assistant is performed to initiate a first task; and in the absence of a detected verbal trigger, determining whether a second voice input is intended for use with the digital assistant is performed. In some examples, determining whether a second voice input is intended for use with the digital assistant is performed without detecting a selection of a displayed capability indicator or a button selection on the electronic device.

[0388] At box 1118, based on the determination that the second voice input is not intended for use with a digital assistant: abandon the initiation of a second task based on the second voice input.

[0389] At box 1120, it is determined that the second voice input is intended for use with the digital assistant: initiate a second task based on the second voice input through the digital assistant.

[0390] At box 1122, a second output indicating the second task initiated is provided.

[0391] The above is for reference only. Figure 11 The described operation may optionally be performed by Figures 1 to 4 , Figures 6A to 6B , Figures 7A to 7C and Figure 8 The components depicted herein are used to implement this. For example, the operation of process 1100 can be implemented by digital assistant system 726 and via system 800. Those skilled in the art will clearly understand how to implement this based on the components depicted herein. Figures 1 to 4 , Figures 6A to 6B and Figures 7A to 7C The components described herein are used to perform other processes.

[0392] Figures 12A to 12B A process 1200 for determining whether a voice input is intended for use with a digital assistant is illustrated, based on various examples. For example, process 1200 may be performed using one or more electronic devices implementing a digital assistant. In some examples, process 1200 may be performed using a client-server system (e.g., system 100), and the boxes of process 1200 may be divided in any way between a server (e.g., DA server 106) and client devices. In other examples, the boxes of process 1200 may be divided between a server and multiple client devices (e.g., mobile phones and smartwatches). Therefore, while portions of process 1200 are described herein as being performed by a specific device of the client-server system, it should be understood that process 1200 is not limited thereto. In other examples, process 1200 may be performed using only client devices (e.g., user device 104 or device 900) or only multiple client devices. In process 1200, some boxes may be optionally combined, the order of some boxes may be optionally changed, and some boxes may be optionally omitted. In some examples, additional steps may be performed in conjunction with process 1200.

[0393] At box 1202, a verbal trigger for initiating a digital assistant session is detected (e.g., via verbal trigger detector 820).

[0394] Voice input is received at box 1204.

[0395] At box 1206, an acoustic representation based on the voice input is determined (e.g., via acoustic analysis module 812) by a first score (e.g., an acoustic score) indicating whether the voice input is intended for use with a digital assistant operating on an electronic device. In some examples, determining the first score includes using a second neural network to determine the first score, wherein the second neural network is configured to determine multiple scores, each indicating whether the voice input is intended for use with a digital assistant, when voice input is received, including the first score.

[0396] At box 1208, a second score (e.g., an ASR score) indicating whether the voice input is intended for use with a digital assistant is determined based on the text representation of the voice input (e.g., via ASR analysis module 814). In some examples, the text representation of the voice input includes an automatic speech recognition (ASR) word map; and determining the second score includes using a third neural network configured to accept an ASR word map as input.

[0397] At box 1210, a third score (e.g., discourse type score) corresponding to the type of user discourse represented by the speech input is determined based on the second text representation of the speech input (e.g., by discourse classifier 806).

[0398] At box 1212, a fourth score (e.g., domain correspondence score) representing the correspondence between a user's utterance and a domain identified by the digital assistant is determined based on the second text representation (e.g., via the domain correspondence detector 808).

[0399] At box 1214, a fifth score (e.g., context continuity score) representing the contextual continuity between a user utterance and a previous user utterance determined as intended for use in a digital assistant is determined based on a second text representation (e.g., via a context continuity detector 810).

[0400] At box 1216, (e.g., via speaker identification module 816) a sixth score (e.g., speaker identification score) is determined based on speaker identification performed on the voice input.

[0401] At box 1218, detect gesture input.

[0402] At box 1220, a seventh score (e.g., gesture score) is determined based on gesture input (e.g., via gesture identification module 818).

[0403] At box 1222, initiate a voice-based task via a digital assistant.

[0404] At box 1224, determine the task flow score corresponding to the initiated task. In some examples, the task flow score is based on whether the digital assistant can determine the parameters of the task.

[0405] In some examples, determining a first score (e.g., block 1206), a second score (e.g., block 1208), a third score (e.g., block 1210), a fourth score (e.g., block 1212), a fifth score (e.g., block 1214), a sixth score (e.g., block 1216), a seventh score (e.g., block 1220), a task flow score (e.g., block 1224), or a combination or sub-combination thereof is performed based on the detection of a verbal trigger (e.g., at block 1202).

[0406] At box 1226, whether the voice input is intended for use with a digital assistant is determined (e.g., by score combiner 824) based on at least some of the following: a first score, a second score, a third score, a fourth score, a fifth score, a sixth score, a seventh score, and a task flow score. In some examples, determining whether the voice input is intended for use with a digital assistant includes determining at least some of the following: a first score, a second score, a third score, a fourth score, a fifth score, a sixth score, a seventh score, and a task flow score, for example, at boxes 1206, 1208, 1210, 1212, 1214, 1216, 1220, and 1224, respectively. In some examples, determining whether the voice input is intended for use with a digital assistant includes using a first neural network and determining a final score indicating whether the voice input is intended for use with a digital assistant based on at least some of the following: a first score, a second score, a third score, a fourth score, a fifth score, a sixth score, a seventh score, and a task flow score.

[0407] In some examples, it is determined whether the voice input is intended for use by the digital assistant without detecting a selection of a displayed capability representation or a selection of a button on the electronic device. In some examples, at least some of the following are each determined based on the determination that the digital assistant does not wait for a response to a prompt generated by the digital assistant for further user input: a first score, a second score, a third score, a fourth score, a fifth score, a sixth score, a seventh score, and a task flow score.

[0408] At box 1228, based on the determination that the voice input is not intended for use with a digital assistant: abandon providing output indicating the task initiated.

[0409] At box 1230, based on the determination that the voice input is intended for use by a digital assistant, output is provided to indicate the initiated task.

[0410] The above text is about Figures 12A to 12B The described operation is optionally provided by Figures 1 to 4 , Figures 6A to 6B , Figures 7A to 7C and Figure 8The components depicted are used to implement this. For example, the operation of process 1200 can be implemented by digital assistant module 726 and system 800. Those skilled in the art will clearly understand how to implement this based on... Figures 1 to 4 , Figures 6A to 6B and Figures 7A to 7C The components described herein are used to perform other processes.

[0411] Figures 13A to 13B The diagram illustrates a process 1300 for determining whether a voice input is intended for use with a digital assistant, based on various examples. For instance, process 1300 may be performed using one or more electronic devices implementing a digital assistant. In some examples, process 1300 may be performed using a client-server system (e.g., system 100), and the boxes of process 1300 may be divided in any way between a server (e.g., DA server 106) and client devices. In other examples, the boxes of process 1300 may be divided between a server and multiple client devices (e.g., mobile phones and smartwatches). Therefore, while portions of process 1300 are described herein as being performed by a specific device of the client-server system, it should be understood that process 1300 is not limited thereto. In other examples, process 1300 may be performed using only client devices (e.g., user device 104 or device 900) or only multiple client devices. In process 1300, some boxes may be optionally combined, the order of some boxes may be optionally changed, and some boxes may be optionally omitted. In some examples, additional steps may be performed in conjunction with process 1300.

[0412] Voice input is received at box 1302.

[0413] At box 1304, a first dataset from the camera of the electronic device is captured between the start and end times of the voice input. In some examples, the camera includes at least one of the following: an RGB camera; and an infrared (IR) camera. In some examples, the first dataset is captured based on determining that the digital assistant is not waiting for a response to a prompt generated by the digital assistant for further user input. In some examples, the first dataset is captured based on determining that the voice input follows a second voice input received within the same digital assistant session.

[0414] At box 1306, stop capturing the first dataset at the end time.

[0415] At box 1308, determine (e.g., via gaze module 822) whether the first dataset represents a user's face.

[0416] At box 1310, a second dataset representing the user's gaze pattern when the second input is provided is determined based on the first dataset (e.g., via gaze module 822). In some examples, the second dataset is determined based on determining that the first dataset represents the user's face. In some examples, the user gaze pattern indicates multiple user gaze locations when voice input is provided. In some examples, the user gaze pattern indicates a corresponding time for each of the multiple user gaze locations.

[0417] At box 1312, a first score (e.g., gaze score) indicating whether the voice input is intended for use by a digital assistant operating on an electronic device is determined based on the voice input and a second dataset (e.g., via gaze module 822). In some examples, the first score is determined without performing automatic speech recognition (ASR) on the voice input.

[0418] In some examples, determining a first score includes determining (e.g., via gaze module 822) a third dataset representing the correlation between features of the voice input and multiple user gaze locations, and determining a first score based on the third dataset, as shown in box 1314. In some examples, determining a first score based on the third dataset includes comparing the third dataset with a predetermined dataset representing the predetermined correlation between features and predetermined multiple gaze locations, as shown in box 1316.

[0419] In some examples, determining the first score includes determining a fourth dataset representing a second feature of the voice input based on the voice input (e.g., by gaze module 822), and determining the first score based on the fourth dataset, as shown in box 1318.

[0420] In some examples, determining the first score involves using a first neural network to determine the first score, wherein the first neural network is configured to accept a second dataset and voice input as corresponding inputs.

[0421] In some examples, determining the first score involves comparing a second dataset with a second predetermined dataset representing a predetermined gaze pattern (e.g., using gaze module 822), as shown in box 1320.

[0422] In some examples, determining a first score includes determining whether a user gaze pattern meets one or more criteria, as shown in box 1322, based on determining (e.g., via gaze module 822). In some examples, determining that a user gaze pattern meets one or more criteria includes determining, based on a second dataset, that the user gaze is directed within a predetermined portion of the display of the electronic device. In some examples, determining that a user gaze pattern meets one or more criteria includes at least one of the following: determining, based on the second dataset, that the user gaze is directed within the predetermined portion for a first predetermined duration after a start time; and determining, based on the second dataset, that the user gaze is directed within a second predetermined portion of the display for a second predetermined duration before an end time. In some examples, the predetermined portion of the display displays a digital assistant response indication (e.g., response indication 902 or 908), and the second predetermined portion of the display displays a digital assistant capability indication (e.g., digital assistant capability indication 904). In some examples, the predetermined portion of the display displays a digital assistant capability indication; and the second predetermined portion of the display displays a digital assistant response indication.

[0423] At box 1324, a voice input-based task is initiated by the digital assistant.

[0424] At box 1326, a determination is made (e.g., via score combiner 824) based on a first score to determine whether the voice input is intended for use with a digital assistant. In some examples, determining whether the voice input is intended for use with a digital assistant involves performing actions without detecting selections of displayed capabilities or buttons on the electronic device.

[0425] At box 1328, based on the determination that the voice input is not intended for use with a digital assistant, the output of instructions for the initiated task is abandoned.

[0426] At box 1330, based on the determination that the voice input is intended for use by a digital assistant, output is provided to indicate the initiated task.

[0427] The above text is about Figures 13A to 13B The described operation is optionally provided by Figures 1 to 4 , Figures 6A to 6B , Figures 7A to 7C and Figure 8 The components depicted are used to implement this. For example, the operation of process 1300 can be implemented by digital assistant module 726 and system 800. Those skilled in the art will clearly understand how to implement this based on... Figures 1 to 4 , Figures 6A to 6B and Figures 7A to 7C The components described herein are used to perform other processes.

[0428] Figures 14A to 14CA process 1400 for determining whether subsequent voice input is intended for use with a digital assistant is illustrated, based on various examples. For example, process 1400 may be performed using one or more electronic devices implementing a digital assistant. In some examples, a client-server system (e.g., system 100) is used to perform process 1400, and the boxes of process 1400 may be divided in any way between a server (e.g., DA server 106) and client devices. In other examples, the boxes of process 1400 may be divided between a server and multiple client devices (e.g., mobile phones and smartwatches). Therefore, while portions of process 1400 are described herein as being performed by a specific device of the client-server system, it should be understood that process 1400 is not limited thereto. In other examples, process 1400 may be performed using only client devices (e.g., user device 104 or device 900) or only multiple client devices. In process 1400, some boxes may be optionally combined, the order of some boxes may be optionally changed, and some boxes may be optionally omitted. In some examples, additional steps may be performed in conjunction with process 1400.

[0429] At box 1402, the first voice input is received.

[0430] At box 1404, it is determined (e.g., via system 800) that a first voice input is intended for use with a digital assistant. In some examples, determining that the first voice input is intended for use with a digital assistant is based on detecting a verbal trigger for initiating a digital assistant session. In some examples, determining that the first voice input is intended for use with a digital assistant includes: determining (e.g., via gaze module 822) a third score (e.g., gaze score) indicating whether the first voice input is intended for use with a digital assistant based on a second user gaze input and the first voice input and using a first instance of a first neural network. In some examples, determining that the first voice input is intended for use with a digital assistant includes: determining (e.g., via acoustic analysis module 812) a fourth score (e.g., acoustic score) indicating whether the first voice input is intended for use with a digital assistant based on an acoustic representation of the first voice input and using a first instance of a second neural network.

[0431] At box 1406, a first task is initiated by a digital assistant operating on an electronic device based on a first voice input.

[0432] At box 1408, a first output indicating the initiation of the first task is provided. In some examples, the first output is provided based on determining that the first voice input is intended for use with a digital assistant.

[0433] At box 1410, after providing the first output, a second voice input is received following the first voice input. In some examples, the first and second voice inputs are received within the same digital assistant session.

[0434] At box 1412, the first dataset from the camera of the electronic device is captured between the start and end times of the second voice input.

[0435] At box 1414, a second dataset representing the user's gaze pattern when the second voice input is provided is determined based on the first dataset (e.g., via gaze module 822). In some examples, the user gaze pattern indicates multiple user gaze locations when the second voice input is provided. In some examples, the user gaze pattern indicates a corresponding time for each of the multiple user gaze locations.

[0436] At box 1416, a first score (e.g., a gaze score) indicating whether the second voice input is intended for use with the digital assistant is determined based on the user's gaze input and the second voice input (e.g., via gaze module 822). In some examples, determining the first score includes using a second instance of the first neural network to determine the first score. In some examples, determining the first score includes determining the first score based on a second dataset, as shown in box 1418.

[0437] In some examples, determining the first score includes determining (e.g., via gaze module 822) a third dataset representing the correlation between features of the second speech input and multiple user gaze locations, and determining the first score based on the third dataset, as shown in box 1420. In some examples, determining the first score based on the third dataset includes comparing the third dataset with a predetermined dataset representing the predetermined correlation between features and predetermined multiple gaze locations (e.g., via gaze module 822), as shown in box 1422.

[0438] In some examples, determining the first score involves comparing the second dataset with a second predetermined dataset representing a predetermined gaze pattern (e.g., via gaze module 822), as shown in box 1424.

[0439] In some examples, determining a first score includes determining whether a user gaze pattern meets one or more criteria, as shown in box 1426, based on determining (e.g., via gaze module 822). In some examples, determining that a user gaze pattern meets one or more criteria includes determining, based on a second dataset, that the user gaze is directed within a predetermined portion of the display of the electronic device. In some examples, determining that a user gaze pattern meets one or more criteria includes at least one of the following: determining, based on the second dataset, that the user gaze is directed within the predetermined portion for a first predetermined duration after a start time; and determining, based on the second dataset, that the user gaze is directed within a second predetermined portion of the display for a second predetermined duration before an end time. In some examples, the predetermined portion of the display displays a digital assistant response indication (e.g., response indication 902 or 908), and the second predetermined portion of the display displays a digital assistant indication (e.g., digital assistant indication 904). In some examples, the predetermined portion of the display displays a digital assistant indication, and the second predetermined portion of the display displays a digital assistant response indication.

[0440] At box 1428, a second score (e.g., an acoustic score) indicating whether the second voice input is intended for use with a digital assistant is determined based on the acoustic representation of the second voice input (e.g., via acoustic analysis module 812). In some examples, determining the second score involves using a second instance of a second neural network to determine the second score.

[0441] At box 1430, the text representation of the second voice input is used to determine (e.g., via ASR analysis module 814) whether the second voice input is intended for a fifth score (e.g., ASR score) of the digital assistant.

[0442] At box 1432, a sixth score (e.g., context continuity score) representing the context continuity between the first user utterance represented by the first voice input and the second user utterance represented by the second voice input is determined (e.g., by context continuity detector 810) based on the second text representation of the second voice input.

[0443] At box 1434, initiate the second task based on the second voice input.

[0444] At box 1436, determine the task flow score corresponding to the initiated second task.

[0445] At box 1438, whether the second voice input is intended for use with a digital assistant is determined (e.g., via score combiner 824) based on at least some of the following: a first score, a second score, a fifth score, a sixth score, and a task flow score (e.g., boxes 1416, 1428, 1430, 1432, and 1436, respectively). In some examples, determining whether the second voice input is intended for use with a digital assistant includes determining at least some of the following: a first score, a second score, a fifth score, a sixth score, and a task flow score. In some examples, determining whether the second voice input is intended for use with a digital assistant also includes: using a third neural network to determine a final score indicating whether the second voice input is intended for use with a digital assistant based on at least some of the following: a first score, a second score, a fifth score, a sixth score, and a task flow score.

[0446] In some examples, determining whether the second voice input is intended for use with the digital assistant is performed without detecting a verbal trigger. In some examples, determining whether the second voice input is intended for use with the digital assistant is performed without detecting a selection of a displayed ability indicator or a selection of a button on the electronic device.

[0447] At box 1440, based on the determination that the second voice input is not intended for use with a digital assistant, the second output that provides instructions for the second task initiated is abandoned.

[0448] At box 1442, based on the determination that the second voice input is intended for use with a digital assistant, a second output is provided indicating the initiated second task. In some examples, the second output includes audio output.

[0449] At box 1444, determine whether the second output indicates an error.

[0450] At box 1446, based on the determination that the second output indicates an error, the provision of the second output is abandoned.

[0451] At box 1448, based on determining that the second output indicates an error, an indication of error (e.g., indication 910) is displayed, wherein the second output is provided based on determining that the second output does not indicate an error.

[0452] The above text is about Figures 14A to 14C The operation is optionally performed by Figures 1 to 4 , Figures 6A to 6B , Figures 7A to 7C and Figure 8 The components depicted herein will be used to implement this. For example, the operation of process 1400 can be implemented by digital assistant module 726 and system 800. Those skilled in the art will clearly understand how to implement this based on... Figures 1 to 4 , Figures 6A to 6B and Figures 7A to 7C The components described herein are used to perform other processes.

[0453] According to some specific embodiments, a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) is provided that stores one or more programs executable 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.

[0454] According to some specific embodiments, an electronic device (e.g., a portable electronic device) is provided, which includes components for performing any of the methods or processes described herein.

[0455] According to some specific embodiments, an electronic device (e.g., a portable electronic device) is provided, the electronic device including a processing unit configured to perform any of the methods and processes described herein.

[0456] According to some specific embodiments, an electronic device (e.g., a portable electronic device) is provided, the electronic device including 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.

[0457] For purposes of explanation, the foregoing description has been given by reference to specific embodiments. However, the illustrative discussion above is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible based on the teachings above. These embodiments were chosen and described in order to best explain the principles of these techniques and their practical application. Others skilled in the art will thus be able to best utilize these techniques and the various embodiments with various modifications suitable for the particular intended use.

[0458] While this 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 this disclosure and examples as defined by the claims.

[0459] As described above, one aspect of the present invention involves collecting and using data available from various sources to determine the intended use of voice input for a digital assistant. This disclosure contemplates that, in some instances, the collected data may include personal information that uniquely identifies or can be used to contact or locate a specific person. Such personal information may include demographic data, location-based data, telephone numbers, email addresses, Twitter IDs, home addresses, data or records relating to a user's health or fitness level (e.g., vital sign measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

[0460] This disclosure recognizes that the use of such personal information data in the techniques of this invention can benefit users. For example, personal information data can be used to determine whether voice input is intended for use with a digital assistant. Therefore, the use of personal information data can help provide efficient and accurate user-digital assistant interaction. Furthermore, this disclosure also anticipates other uses of personal information data that benefit users. For example, health and fitness data can be used to provide insights into a user's overall health status or as positive feedback to individuals using the technology to pursue health goals.

[0461] This disclosure anticipates that entities responsible for the collection, analysis, disclosure, transmission, storage, or other use of such personal information data will comply with robust privacy policies and / or privacy measures. Specifically, such entities should implement and adhere to privacy policies and practices recognized as meeting or exceeding industry or governmental requirements for maintaining the privacy and security of personal information data. Such policies should be easily accessible to users and should be updated as the collection and / or use of data changes. Personal information from users should be collected for legitimate and reasonable entity purposes and should not be shared or sold outside of these legitimate purposes. Furthermore, such collection / sharing should be conducted only after receiving informed consent from users. Additionally, such entities should consider taking any necessary steps to protect and safeguard access to such personal information data and ensure that other entities authorized to access such personal information data comply with their privacy policies and procedures. Furthermore, such entities may be subject to third-party assessments to demonstrate their compliance with widely accepted privacy policies and practices. Additionally, policies and practices should be adapted to the specific types of personal information data collected and / or accessed, and to applicable laws and standards, including considerations of specific 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 in other countries, health data 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.

[0462] Regardless of the foregoing, this disclosure also contemplates implementation schemes for users to selectively block the use or access to personal information data. That is, this disclosure contemplates hardware and / or software components to prevent or block access to such personal information data. For example, in cases where personal information data is collected to determine whether voice input is intended for use with a digital assistant, this technology can be configured to allow a user to opt-in or opt-out at any time during or after service registration. In another example, a user may choose not to provide personal information data used to determine whether voice input is intended for use with a digital assistant. As another example, a user may choose to limit the length of time personal information data is retained. In addition to providing opt-in and opt-out options, this disclosure also contemplates providing notifications related to access to or use of personal information. For example, a user may be notified when downloading an application that their personal information data will be accessed, and then reminded again just before the application accesses the personal information data.

[0463] Furthermore, the intent of this disclosure is that personal information data should be managed and processed in a manner that minimizes the risk of unintentional or unauthorized access or use. Once data is no longer needed, this risk can be minimized by restricting data collection and data deletion. Additionally, and where applicable, including in certain health-related applications, data deidentification can be used to protect user privacy. Where appropriate, deidentification 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 address level), controlling how data is stored (e.g., aggregating data among users), and / or other methods.

[0464] Therefore, while this disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, it is also contemplated that various embodiments can be implemented without access to such personal information data. That is, various embodiments of the present invention will not be rendered inoperable due to the absence of all or part of such personal information data. For example, a digital assistant can determine whether voice input is intended for use with the digital assistant based on non-personal information data or an absolute minimum amount of personal information (such as content requested by the user's associated device, other non-personal information applicable to the digital assistant, or publicly available information).

Claims

1. A non-transitory computer-readable storage medium storing one or more programs, said one or more programs comprising instructions that, when executed by one or more processors of an electronic device, cause the electronic device to: Receive first voice input representing the first user's speech; A first task is initiated based on the first voice input by a digital assistant operating on the electronic device; Provides the first output indicating the first task initiated; as well as After providing the first output: Receive a second voice input following the first voice input, the second voice input representing a second user speech; A first score is determined based on the text representation of the second voice input, the first score representing the correspondence between the second user utterance and the domain recognized by the digital assistant; A second score is determined based on the text representation of the second voice input, the second score representing the contextual continuity between the first user utterance and the second user utterance; Based on the first score and the second score, determine whether the second voice input is intended for use with the digital assistant; as well as Based on the determination that the second voice input is intended for use with the digital assistant: Initiate a second task based on the second voice input via the digital assistant; and Provides a second output indicating the second task initiated.

2. The non-transitory computer-readable storage medium of claim 1, wherein the one or more programs further comprises instructions that, when executed by the one or more processors, cause the electronic device to: Based on the determination that the second voice input was not intended for use with the digital assistant: Abandon initiating the second task.

3. The non-transitory computer-readable storage medium of claim 1, wherein the first voice input and the second voice input are received within the same digital assistant session.

4. The non-transitory computer-readable storage medium of claim 1, wherein the one or more programs further comprises instructions that, when executed by the one or more processors, cause the electronic device to: The first voice input is determined to be intended for the digital assistant based on the detection of a verbal trigger for initiating a digital assistant session, wherein: The first task is initiated based on determining that the first voice input is intended for use with the digital assistant; and Determining whether the second voice input is intended for use by the digital assistant is performed without detecting the verbal trigger.

5. The non-transitory computer-readable storage medium of claim 1, wherein determining whether the second voice input is intended for the digital assistant is performed without detecting the selection of the displayed enable representation and without detecting the selection of a button on the electronic device.

6. The non-transitory computer-readable storage medium of claim 1, wherein determining the first score includes determining whether the second user utterance corresponds to a plurality of utterances recognized by the digital assistant.

7. The non-transitory computer-readable storage medium of claim 1, wherein determining the first score includes determining whether the second user utterance corresponds to a word associated with the domain identified by the digital assistant.

8. The non-transitory computer-readable storage medium of claim 1, wherein determining the first score comprises using a binary classification neural network to determine the first score.

9. The non-transitory computer-readable storage medium of claim 1, wherein determining the second score includes determining the second score based on the text representation of the first user utterance.

10. The non-transitory computer-readable storage medium of claim 1, wherein determining the second score comprises using a second binary classification neural network to determine the second score.

11. The non-transitory computer-readable storage medium of claim 1, wherein the one or more programs further comprises instructions that, when executed by the one or more processors, cause the electronic device to: A third score is determined based on the text representation of the second voice input, the third score corresponding to the type of the second user utterance, wherein determining whether the second voice input is intended for use with the digital assistant is also based on the third score.

12. The non-transitory computer-readable storage medium of claim 11, wherein determining the third score comprises: Determine the probability that the second user utterance corresponds to each of the multiple user utterance types; Select a subset of the corresponding probabilities, wherein each probability in the subset of the corresponding probabilities corresponds to a corresponding predetermined user utterance type among the plurality of user utterance types; and The third score is determined based on the subset of the corresponding probabilities.

13. The non-transitory computer-readable storage medium of claim 12, wherein the plurality of user speech types include: Commands of type 1; Second type of command; Type 1 questions; The second type of problem; Type 1 response; The second type of answer; Opinions; and statement.

14. The non-transitory computer-readable storage medium of claim 13, wherein the corresponding predetermined user speech type includes: Commands of the first type; and The first type of problem.

15. The non-transitory computer-readable storage medium of claim 11, wherein determining whether the second voice input is intended for use with the digital assistant comprises: The first score, the second score, and the third score are weighted to obtain a final score that indicates whether the second voice input is intended for use with the digital assistant; The final score is compared with a threshold. Based on the determination that the final score is higher than the threshold: Determine that the second voice input is intended for use with the digital assistant; and Based on the determination that the final score is lower than the threshold: It was determined that the second voice input was not intended for use with the digital assistant.

16. 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 including instructions for the following operations: Receive first voice input representing the first user's speech; A first task is initiated based on the first voice input by a digital assistant operating on the electronic device; Provides the first output indicating the first task initiated; as well as After providing the first output: Receive a second voice input following the first voice input, the second voice input representing a second user speech; A first score is determined based on the text representation of the second voice input, the first score representing the correspondence between the second user utterance and the domain recognized by the digital assistant; A second score is determined based on the text representation of the second voice input, the second score representing the contextual continuity between the first user utterance and the second user utterance; Based on the first score and the second score, determine whether the second voice input is intended for use with the digital assistant; as well as Based on the determination that the second voice input is intended for use with the digital assistant: Initiate a second task based on the second voice input via the digital assistant; and Provides a second output indicating the second task initiated.

17. A method for interpreting user intent in voice input, comprising: In electronic devices having one or more processors and memory: Receive first voice input representing the first user's speech; A first task is initiated by a digital assistant operating on the electronic device based on the first voice input; Provides the first output indicating the first task initiated; as well as After providing the first output: Receive a second voice input following the first voice input, the second voice input representing a second user speech; A first score is determined based on the text representation of the second voice input, the first score representing the correspondence between the second user utterance and the domain recognized by the digital assistant; A second score is determined based on the text representation of the second voice input, the second score representing the contextual continuity between the first user utterance and the second user utterance; Based on the first score and the second score, determine whether the second voice input is intended for use with the digital assistant; as well as Based on the determination that the second voice input is intended for use with the digital assistant: The digital assistant initiates a second task based on the second voice input; as well as Provides a second output indicating the second task initiated.

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