Use text as avatar animation

Abstract

The present disclosure relates to using text as an avatar animation. The present invention provides a system and method for displaying an avatar in an animated manner. An exemplary method for displaying an avatar in an animated manner includes: at an electronic device having a memory and one or more processors, receiving text, determining an emotional state, and using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the determined emotional state.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This patent application claims priority to U.S. Provisional Application Serial No. 62 / 971,564, filed on February 7, 2020, entitled “USING TEXT FOR AVATARANIMATION,” Singapore Patent Application 10202001701Y, filed on February 26, 2020, entitled “USING TEXT FOR AVATAR ANIMATION,” and U.S. Application Serial No. 17 / 153,728, filed on January 20, 2021, entitled “USING TEXT FOR AVATAR ANIMATION,” which are hereby incorporated by reference in their entirety. Technical Field

[0003] The present disclosure relates generally to avatar animation, and more particularly to techniques for animating an avatar using text input. Background Art

[0004] An avatar is used to represent a user of an electronic device. The avatar may represent the user's physical appearance, or may represent an idealized or entirely fictitious image of the user. The avatar may then be associated with the user such that the avatar's appearance prompts others to associate or associate it with the user. Avatars may be created and edited for such purposes, including multimedia communications.

[0005] In some cases, an avatar may be created based on a representation of a user received by an electronic device. However, such creation may be limited to considering only specific representations or inputs, such as video inputs including the user. In addition, creating these avatars may require specific types of video inputs or video inputs including very specific features. Therefore, creating avatars in this manner may only work when specific circumstances are met, resulting in incomplete avatars, avatars that do not accurately represent the user, or no avatars being created. In addition, in some cases, when these types of inputs are not available, such as when the user may only provide text input, animated avatars may be desired. In addition, current avatars may not adequately provide the desired emotions because the range of actions that are considered and used to animate avatars does not include actions that humans use to convey certain emotions. Summary of the invention

[0006] Example methods are disclosed herein. An example method includes: at an electronic device having a memory and one or more processors, receiving text, determining an emotional state, and using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of an avatar based on the received text and the determined emotional state.

[0007] An example non-transitory computer-readable storage medium is disclosed herein. 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 text, determine an emotional state, and use a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of an avatar based on the received text and the determined emotional state.

[0008] An example electronic device is disclosed herein. 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, and the one or more programs include instructions for performing the following operations: receiving text, determining an emotional state, and using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of an avatar based on the received text and the determined emotional state.

[0009] An example electronic device includes means for receiving text, determining an emotional state, and using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of an avatar based on the received text and the determined emotional state.

[0010] Using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the determined emotional state allows for more accurate and flexible creation of an avatar representing the user. For example, in some cases, the user may not want or may not be able to provide video input to create an avatar as required in a conventional avatar creation method, but may be able to provide text input (e.g., at a meeting). By generating a speech data set and a set of parameters representing one or more actions of the avatar based on the received text, the current system can generate an avatar in these types of situations, thereby providing users with greater flexibility when generating avatars. In addition, by considering the emotional state determined during the generation of the speech data set and the set of parameters representing one or more actions of the avatar, the avatar may look more accurate and realistic to the recipient. For example, if the determined emotional state is sad, the generated avatar may include an action that conveys the user's sadness, thereby producing a more realistic avatar that more accurately conveys the desired message to the recipient. Therefore, generating a speech data set and a set of parameters representing one or more actions of the avatar in this way provides greater flexibility and accuracy, thereby bringing a more pleasant user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1A block diagram is shown of a system and environment for implementing a digital assistant according to various examples.

[0012] Figure 2A A block diagram of a portable multifunction device that implements the client-side portion of a digital assistant according to various examples is shown.

[0013] Figure 2B is a block diagram illustrating exemplary components for event processing according to various examples.

[0014] Figure 3 A portable multifunction device implementing the client-side portion of a digital assistant according to various examples is shown.

[0015] Figure 4 is a block diagram of an exemplary multifunction device with a display and a touch-sensitive surface according to various examples.

[0016] Figure 5A An exemplary user interface of a menu of applications on a portable multifunction device is shown according to various examples.

[0017] Figure 5B An exemplary user interface for a multifunction device having a touch-sensitive surface separate from the display is shown according to various examples.

[0018] Fig. 6A A personal electronic device according to various examples is shown.

[0019] Figure 6B A block diagram illustrating a personal electronic device according to various examples is shown.

[0020] FIG. 6C to FIG. 6D Example components of a personal electronic device with a touch-sensitive display and an intensity sensor are shown according to some embodiments.

[0021] FIG. 6E to FIG. 6H Exemplary components and user interfaces of a personal electronic device according to some embodiments are shown.

[0022] Fig. 7A A block diagram of a digital assistant system or a server portion thereof according to various examples is shown.

[0023] Figure 7B According to various examples, Fig. 7A The capabilities of the digital assistant shown in .

[0024] Figure 7C A portion of an ontology according to various examples is shown.

[0025] Figure 8 A system for generating and animating an avatar according to some examples is shown.

[0026] 9A to 9C Avatars generated and animated according to various examples are shown.

[0027] FIG. 10A to FIG. 10C A process for generating and animating an avatar according to some examples is shown. DETAILED DESCRIPTION

[0028] In the following description of the examples, reference will be made to the accompanying drawings, in which specific examples that may be implemented are shown by way of illustration. It should be understood that other examples may be used and structural changes may be made without departing from the scope of the various examples.

[0029] In some examples, text is received and an emotional state is determined. A speech data set representing the received text and a set of parameters representing one or more actions are generated using a neural network based on the received text and the determined emotional state. Using a neural network to generate a speech data set representing the received text and a set of parameters representing one or more actions of an avatar based on the received text and the determined emotional state allows for more accurate and flexible creation of an avatar representing the user. Specifically, by generating these outputs based on text, a user can create an avatar in many different situations, including those where the user may not be allowed to provide video or audio input. In addition, by taking into account the determined emotional state, the generated avatar may appear more realistic and more accurately convey the desired message.

[0030] Although the following description uses the terms "first," "second," etc. to describe various elements, these elements should not be limited by the terms. These terms are simply used to distinguish one element from another. For example, without departing from the scope of the various described examples, a first input may be referred to as a second input, and similarly, a second input may be referred to as a first input. Both the first input and the second input are inputs, and in some cases are independent and different inputs.

[0031] The terms used in the description of various examples described herein are only for the purpose of describing specific examples and are not intended to be limiting. As used in the description of various examples described and in the appended claims, the singular forms "a", "an" and "the" are intended to also include plural forms, unless the context clearly indicates otherwise. It will also be understood that the term "and / or" used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will also be understood that the term "includes", "including", "comprises" and / or "comprising" when used in this specification specifies the presence of stated features, integers, steps, operations, elements and / or parts, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or their groupings.

[0032] Depending on the context, the term "if" may be interpreted to mean "when" or "upon" or "in response to determining" or "in response to detecting." Similarly, the phrases "if it is determined that ..." or "if [a stated condition or event] is detected" may be interpreted to mean "upon determining that ..." or "in response to determining that ..." or "upon detecting [a stated condition or event]" or "in response to detecting [a stated condition or event]," depending on the context.

[0033] 1. System and Environment

[0034] Figure 1 A block diagram of a system 100 according to various examples is shown. In some examples, the system 100 implements a digital assistant. The terms "digital assistant," "virtual assistant," "intelligent automated assistant," or "automatic digital assistant" refer to any information processing system that interprets natural language input in spoken and / or textual form to infer user intent and performs actions based on the inferred user intent. For example, to act on an inferred user intent, the system performs one or more of the following steps: identifying a task flow with steps and parameters designed to implement the inferred user intent, inputting specific requirements into the task flow according to the inferred user intent; executing the task flow by calling a program, method, service, API, etc.; and generating an output response to the user in an audible (e.g., voice) and / or visual form.

[0035] Specifically, the digital assistant is capable of accepting user requests in the form of at least partially natural language commands, requests, statements, narrations and / or inquiries. Typically, the user requests to seek an informative answer or perform a task from the digital assistant. A satisfactory response to the user's request includes providing the requested informative answer, performing the requested task, or a combination of the two. For example, the user asks the digital assistant a question, such as "Where am I now?" Based on the user's current location, the digital assistant answers "You are near the West Gate of Central Park." The user also requests to perform a task, such as "Please invite my friends to my girlfriend's birthday party next week." In response, the digital assistant can confirm the request by saying "OK, right away", and then send a suitable calendar invitation to each of the user's friends listed in the user's electronic address book on behalf of the user. During the execution of the requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple information exchanges over a long period of time. There are many other methods of interacting with the digital assistant to request information or perform various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other video or audio forms, such as text, alarms, music, videos, animations, etc.

[0036] like Figure 1 As shown, in some examples, the digital assistant is implemented according to a client-server model. The digital assistant includes a client-side portion 102 (hereinafter referred to as "DA client 102") executed on a user device 104 and a server-side portion 106 (hereinafter referred to as "DA server 106") executed on a server system 108. The DA client 102 communicates with the DA server 106 via one or more networks 110. The DA client 102 provides client-side functionality, such as user-oriented input and output processing, and communication with the DA server 106. The DA server 106 provides server-side functionality for any number of DA clients 102, each located on a corresponding user device 104.

[0037] In some examples, the DA server 106 includes a client-facing I / O interface 112, one or more processing modules 114, data and models 116, and an I / O interface 118 to external services. The client-facing I / O interface 112 facilitates client-facing input and output processing of the DA server 106. One or more processing modules 114 utilize the data and models 116 to process voice input and determine user intent based on natural language input. In addition, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, the DA server 106 communicates with external services 120 via one or more networks 110 to complete tasks or collect information. The I / O interface 118 to external services facilitates such communications.

[0038] User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multi-function device (e.g., Figure 2A The device 200 described above), a multifunctional device (eg, as described below with reference to Figure 4 The device 400) or a personal electronic device (eg, as described below) FIG. 6A to FIG. 6B The portable multifunction device is, for example, a mobile phone that also includes other functions (such as a PDA and / or a music player function). Specific examples of portable multifunction devices include the Apple iPod and Device. Other examples of portable multifunction devices include, but are not limited to, earbuds / headphones, speakers, and laptops or tablets. In addition, in some examples, user device 104 is a non-portable multifunction device. Specifically, user device 104 is a desktop computer, a game console, a speaker, a television, or a television set-top box. In some examples, user device 104 includes a touch-sensitive surface (e.g., a touch screen display and / or a trackpad). In addition, user device 104 optionally includes one or more other physical user interface devices, such as a physical keyboard, a mouse, and / or a joystick. Various examples of electronic devices such as multifunction devices are described in more detail below.

[0039] Examples of one or more communication networks 110 include local area networks (LANs) and wide area networks (WANs), such as the Internet. One or more communication networks 110 are implemented using any known network protocols, including various wired or wireless protocols, such as Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Wi-Fi, Voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.

[0040] The server system 108 is implemented on one or more stand-alone data processing devices or distributed computer networks. In some examples, the server system 108 also uses various virtual devices and / or services of third-party service providers (e.g., third-party cloud service providers) to provide the potential computing resources and / or infrastructure resources of the server system 108.

[0041] In some examples, the user device 104 communicates with the DA server 106 via a second user device 122. The second user device 122 is similar or identical to the user device 104. For example, the second user device 122 is similar to the user device 104 described below with reference to Figure 2A , Figure 4 as well as FIG. 6A to FIG. 6B The device 200, 400 or 600 described. The user device 104 is configured to be communicatively coupled to the second user device 122 via a direct communication connection (such as, Bluetooth, NFC, BTLE, etc.) or via a wired or wireless network (such as, a local Wi-Fi network). In some examples, the second user device 122 is configured to act as a proxy between the user device 104 and the DA server 106. For example, the DA client 102 of the user device 104 is configured to transmit information (e.g., a user request received at the user device 104) to the DA server 106 via the second user device 122. The DA server 106 processes the information and returns relevant data (e.g., data content in response to the user request) to the user device 104 via the second user device 122.

[0042] In some examples, the user device 104 is configured to send an abbreviated request for data to a second user device 122 to reduce the amount of information transmitted from the user device 104. The second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request for transmission to the DA server 106. The system architecture can advantageously allow user devices 104 with limited communication capabilities and / or limited battery power (e.g., a watch or similar compact electronic device) to access services provided by the DA server 106 by using a second user device 122 (e.g., a mobile phone, laptop, tablet, etc.) with greater communication capabilities and / or battery power as a proxy to the DA server 106. Although Figure 1 Only two user devices 104 and 122 are shown, but it should be understood that in some examples, the system 100 may include any number and type of user devices configured to communicate with the DA server system 106 in this proxy configuration.

[0043] Although Figure 1 The digital assistant shown in includes both a client-side portion (e.g., DA client 102) and a server-side portion (e.g., DA server 106), but in some examples, the functionality of the digital assistant is implemented as a stand-alone application installed on a user device. In addition, the division of functionality between the client and server portions of the digital assistant may vary in different specific implementations. For example, in some examples, the DA client is a thin client that only provides user-facing input and output processing functions and delegates all other functions of the digital assistant to a backend server.

[0044] 2. Electronic devices

[0045] Attention now turns to embodiments of electronic devices for implementing the client-side portion of a digital assistant. Figure 2A is a block diagram showing a portable multifunction device 200 with a touch-sensitive display system 212 according to some embodiments. The touch-sensitive display system 212 is sometimes referred to as a "touch screen 212" for convenience, and is sometimes referred to as or referred to as a "touch-sensitive display system." The device 200 includes a memory 202 (which optionally includes one or more computer-readable storage media), a memory controller 222, one or more processing units (CPUs) 220, a peripheral device interface 218, an RF circuit 208, an audio circuit 210, a speaker 211, a microphone 213, an input / output (I / O) subsystem 206, other input control devices 216, and an external port 224. The device 200 optionally includes one or more optical sensors 264. The device 200 optionally includes one or more contact force sensors 265 for detecting the intensity of contact on the device 200 (e.g., a touch-sensitive surface of the device 200 such as the touch-sensitive display system 212). Device 200 optionally includes one or more tactile output generators 267 for generating tactile output on device 200 (e.g., generating tactile output on a touch-sensitive surface such as touch-sensitive display system 212 of device 200 or touch pad 455 of device 400). These components optionally communicate via one or more communication buses or signal lines 203.

[0046] As used in this specification and claims, the term "intensity" of a contact on a touch-sensitive surface refers to the force or pressure (force per unit area) of a contact (e.g., a finger contact) on the touch-sensitive surface, or to a surrogate (surrogate) of the force or pressure of a contact on the touch-sensitive surface. The intensity of a contact has a range of values ​​that includes at least four different values ​​and more typically includes hundreds of different values ​​(e.g., at least 256). The intensity of a contact is optionally determined (or measured) using various methods and various sensors or combinations of sensors. For example, one or more force sensors below or adjacent to the touch-sensitive surface are optionally used to measure the force at different points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., weighted averaged) to determine an estimated contact force. Similarly, the pressure-sensitive tip of a stylus is optionally used to determine the pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and / or its change, the capacitance of the touch-sensitive surface near the contact and / or its change, and / or the resistance of the touch-sensitive surface near the contact and / or its change are optionally used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some embodiments, the substitute measurement of the contact force or pressure is used directly to determine whether the intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurement). In some embodiments, the substitute measurement of the contact force or pressure is converted into an estimated force or pressure, and the estimated force or pressure is used to determine whether the intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of the contact as an attribute of the user input allows the user to access additional device functions that would otherwise be inaccessible to the user on a smaller device with limited real estate, which is used to display an enable indication (e.g., on a touch-sensitive display) and / or receive user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical control / mechanical control, such as a knob or button).

[0047] As used in this specification and claims, the term "tactile output" refers to a physical displacement of a device relative to a previous position of the device, a physical displacement of a component of the device (e.g., a touch-sensitive surface) relative to another component of the device (e.g., a housing), or a displacement of a component relative to the center of mass of the device, which will be detected by a user using the user's sense of touch. For example, in the case where a device or a component of the device is in contact with a user's touch-sensitive surface (e.g., a finger, palm, or other part of the user's hand), the tactile output generated by the physical displacement will be interpreted by the user as a tactile sensation corresponding to a perceived change in a physical characteristic of the device or component of the device. For example, movement of a touch-sensitive surface (e.g., a touch-sensitive display or trackpad) is optionally interpreted by the user as a "press click" or "release click" to a physical actuation button. In some cases, the user will feel a tactile sensation, such as a "press click" or "release click", even when the physical actuation button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movement does not move. As another example, even when there is no change in the smoothness of the touch-sensitive surface, movement of the touch-sensitive surface may optionally be interpreted or sensed by the user as "roughness" of the touch-sensitive surface. Although such a user's interpretation of touch will be limited by the user's individualized sensory perceptions, many sensory perceptions of touch are common to most users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., "press click," "release click," "roughness"), unless otherwise stated, the generated tactile output corresponds to a physical displacement of the device or a component thereof that would generate that sensory perception for a typical (or average) user.

[0048] It should be understood that device 200 is only one example of a portable multifunction device, and that device 200 optionally has more or fewer components than shown, optionally combines two or more components, or optionally has a different configuration or arrangement of the components. Figure 2A The various components shown in the EMBODIMENTS 2000 are implemented in hardware, software, or a combination of hardware and software, including one or more signal processing and / or application specific integrated circuits.

[0049] The memory 202 includes one or more computer-readable storage media. These computer-readable storage media are, for example, tangible and non-transitory. The memory 202 includes a high-speed random access memory and also includes a non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. The memory controller 222 controls other components of the device 200 to access the memory 202.

[0050] In some examples, the non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing various aspects of the processes described below) for use by or in conjunction with an instruction execution system, device, or apparatus, such as a computer-based system, a system containing a processor, or other system that can fetch instructions from an instruction execution system, device, or apparatus and execute the instructions. In other examples, the instructions (e.g., for performing various aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of server system 108, or are divided between the non-transitory computer-readable storage medium of memory 202 and the non-transitory computer-readable storage medium of server system 108.

[0051] The peripheral device interface 218 is used to couple the input and output peripheral devices of the device to the CPU 220 and the memory 202. The one or more processors 220 run or execute various software programs and / or instruction sets stored in the memory 202 to perform various functions of the device 200 and process data. In some embodiments, the peripheral device interface 218, the CPU 220, and the memory controller 222 are implemented on a single chip such as the chip 204. In some other embodiments, they are implemented on separate chips.

[0052] RF (radio frequency) circuit 208 receives and sends RF signals, also known as electromagnetic signals. RF circuit 208 converts electrical signals into / converts electromagnetic signals into electrical signals, and communicates with communication networks and other communication devices via electromagnetic signals. RF circuit 208 optionally includes well-known circuits for performing these functions, including but not limited to antenna systems, RF transceivers, one or more amplifiers, tuners, one or more oscillators, digital signal processors, codec chipsets, user identity modules (SIM) cards, memory, etc. RF circuit 208 optionally communicates with networks and other devices through wireless communications, such as the Internet (also known as the World Wide Web (WWW)), intranets, and / or wireless networks (such as cellular telephone networks, wireless local area networks (LANs), and / or metropolitan area networks (MANs)). RF circuit 208 optionally includes well-known circuits for detecting near field communication (NFC) fields, such as through short-range communication radio components. Wireless communication optionally uses any of a variety of communication standards, protocols, and technologies, including, but not limited to, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), High Speed ​​Downlink Packet Access (HSDPA), High Speed ​​Uplink Packet Access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual Cell HSPA (DC-HSPDA), Long Term Evolution (LTE), Near Field Communication (NFC), Wideband Code Division Multiple Access (W-CDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE802.11g, IEEE 802.11n and / or IEEE 802.11ac), Voice over Internet Protocol (VoIP), Wi-MAX, email protocols (e.g., Internet Message Access Protocol (IMAP) and / or Post Office Protocol (POP)), instant messaging (e.g., Extensible Messaging and Presence Protocol (XMPP), Session Initiation Protocol for Instant Messaging and Presence Utilizing Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)) and / or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols that have not yet been developed as of the filing date of this document.

[0053] The audio circuit 210, speaker 211, and microphone 213 provide an audio interface between a user and the device 200. The audio circuit 210 receives audio data from the peripheral device interface 218, converts the audio data into electrical signals, and transmits the electrical signals to the speaker 211. The speaker 211 converts the electrical signals into sound waves audible to humans. The audio circuit 210 also receives electrical signals converted from sound waves by the microphone 213. The audio circuit 210 converts the electrical signals into audio data and transmits the audio data to the peripheral device interface 218 for processing. The audio data is retrieved from and / or transmitted to the memory 202 and / or the RF circuit 208 via the peripheral device interface 218. In some embodiments, the audio circuit 210 also includes a headset jack (e.g., Figure 3 The headset jack provides an interface between the audio circuit 210 and a removable audio input / output peripheral device, such as an output-only headset or a headset with both output (e.g., a single-ear headset or a dual-ear headset) and input (e.g., a microphone).

[0054] The I / O subsystem 206 couples input / output peripherals on the device 200, such as a touch screen 212 and other input control devices 216, to a peripheral device interface 218. The I / O subsystem 206 optionally includes a display controller 256, an optical sensor controller 258, an intensity sensor controller 259, a tactile feedback controller 261, and one or more input controllers 260 for other input or control devices. One or more input controllers 260 receive / send electrical signals from other input control devices 216. Other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slide switches, joysticks, click wheels, etc. In some alternative embodiments, the input controller 260 is optionally coupled to any of the following (or none of the following): a keyboard, an infrared port, a USB port, and a pointing device such as a mouse. One or more buttons (e.g., Figure 3 308 in ) optionally includes an up / down button for volume control of the speaker 211 and / or the microphone 213. The one or more buttons optionally include a push button (e.g., Figure 3 306 in ).

[0055] A quick press of the push button disengages the lock of the touch screen 212 or begins the process of using gestures on the touch screen to unlock the device, as described in U.S. Patent Application 11 / 322,549, entitled "Unlocking a Device by Performing Gestures on an Unlock Image," filed on December 23, 2005, U.S. Patent 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) turns the device 200 on or off. The user can customize the function of one or more buttons. The touch screen 212 is used to implement virtual buttons or soft buttons and one or more soft keyboards.

[0056] The touch screen 212 provides an input interface and an output interface between the device and the user. The display controller 256 receives electrical signals from the touch screen 212 and / or sends electrical signals to the touch screen 212. The touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, videos, and any combination thereof (collectively referred to as "graphics"). In some embodiments, some or all of the visual output corresponds to user interface objects.

[0057] The touch screen 212 has a touch-sensitive surface, sensor, or sensor group that accepts input from a user based on tactile and / or haptic contact. The touch screen 212 and display controller 256 (together with any associated modules and / or instruction sets in memory 202) detect contact (and any movement or interruption of the contact) on the touch screen 212 and convert the detected contact into interaction with a user interface object (e.g., one or more soft keys, icons, web pages, or images) displayed on the touch screen 212. In an exemplary embodiment, the point of contact between the touch screen 212 and the user corresponds to the user's finger.

[0058] The touch screen 212 uses LCD (liquid crystal display) technology, LPD (light emitting polymer display) technology, or LED (light emitting diode) technology, although other display technologies may be used in other embodiments. The touch screen 212 and display controller 256 use any of a variety of touch sensing technologies now known or later developed, as well as other proximity sensor arrays or other elements for determining one or more points of contact with the touch screen 212 to detect contact and any movement or interruption thereof, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as in the Apple ® from Apple Inc. (Cupertino, California). and iPod The technology used in.

[0059] In some embodiments, the touch-sensitive display of the touch screen 212 is similar to the multi-touch-sensitive trackpads described in U.S. Patents 6,323,846 (Westerman et al.), 6,570,557 (Westerman et al.), and / or 6,677,932 (Westerman), and / or U.S. Patent Publication 2002 / 0015024A1, all of which are hereby incorporated by reference in their entirety. However, the touch screen 212 displays visual output from the device 200, whereas the touch-sensitive trackpad does not provide visual output.

[0060] In some embodiments, the touch-sensitive display of the touch screen 212 is as described in the following applications: (1) U.S. patent application 11 / 381,313, entitled “Multipoint Touch Surface Controller,” filed on May 2, 2006; (2) U.S. patent application No. 10 / 840,862, entitled “Multipoint Touchscreen,” filed on May 6, 2004; (3) U.S. patent application No. 10 / 903,964, entitled “Gestures For Touch Sensitive Input Devices,” filed on July 30, 2004; (4) U.S. patent application No. 11 / 048,264, entitled “Gestures For Touch Sensitive Input Devices,” filed on January 31, 2005; (5) U.S. patent application No. 11 / 086,864, entitled “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed on January 18, 2005 and (9) U.S. patent application No. 11 / 367,749, filed on March 3, 2006, entitled “Multi-Functional Hand-Held Device.” All of these applications are hereby incorporated by reference in their entirety.

[0061] The touch screen 212 has, for example, a video resolution of more than 100 dpi. In some embodiments, the touch screen has a video resolution of about 160 dpi. The user contacts the touch screen 212 using any suitable object or appendage, such as a stylus, a finger, etc. In some embodiments, the user interface is designed to work primarily through finger-based contacts and gestures, which may not be as precise as stylus-based input due to the larger contact area of ​​a finger on the touch screen. In some embodiments, the device converts rough finger-based input into precise pointer / cursor positions or commands for performing the actions desired by the user.

[0062] In some embodiments, in addition to the touch screen, the device 200 also includes a touchpad (not shown) for activating or deactivating specific functions. In some embodiments, the touchpad is a touch-sensitive area of ​​the device that, unlike the touch screen, does not display visual output. The touchpad is a touch-sensitive surface that is separate from the touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.

[0063] Device 200 also includes a power system 262 for powering the various components. Power system 262 includes a power management system, one or more power sources (e.g., batteries, alternating current (AC)), a recharging system, power fault detection circuits, power converters or inverters, power status indicators (e.g., light emitting diodes (LEDs)), and any other components associated with the generation, management, and distribution of power in a portable device.

[0064] The device 200 also includes one or more optical sensors 264 . Figure 2A An optical sensor coupled to an optical sensor controller 258 in the I / O subsystem 206 is shown. The optical sensor 264 includes a charge coupled device (CCD) or a complementary metal oxide semiconductor (CMOS) phototransistor. The optical sensor 264 receives light projected through one or more lenses from the environment and converts the light into data representing an image. In conjunction with the imaging module 243 (also called a camera module), the optical sensor 264 captures a static image or video. In some embodiments, the optical sensor is located at the rear of the device 200, opposite to the touch screen 212 at the front of the device, so that the touch screen display is used as a viewfinder for static image and / or video image acquisition. In some embodiments, the optical sensor is located at the front of the device so that the image of the user is obtained for video conferencing while the user is viewing other video conference participants on the touch screen display. In some embodiments, the position of the optical sensor 264 can be changed by the user (e.g., by rotating the lens and sensor in the device housing) so that a single optical sensor 264 is used with the touch screen display for both video conferencing and static image and / or video image acquisition.

[0065] Device 200 optionally also includes one or more contact intensity sensors 265 . Figure 2A A contact force sensor is shown coupled to a force sensor controller 259 in the I / O subsystem 206. The contact force sensor 265 optionally includes one or more piezoresistive strain gauges, capacitive force sensors, electrical force sensors, piezoelectric force sensors, optical force sensors, capacitive touch-sensitive surfaces, or other force sensors (e.g., sensors for measuring the force (or pressure) of contact on a touch-sensitive surface). The contact force sensor 265 receives contact force information (e.g., pressure information or a surrogate for pressure information) from the environment. In some embodiments, at least one contact force sensor is juxtaposed or adjacent to a touch-sensitive surface (e.g., touch-sensitive display system 212). In some embodiments, at least one contact force sensor is located on the back of the device 200, opposite to the touch screen 212 located on the front of the device 200.

[0066] Device 200 also includes one or more proximity sensors 266 . Figure 2A A proximity sensor 266 is shown coupled to the peripherals interface 218. Alternatively, the proximity sensor 266 is coupled to the input controller 260 in the I / O subsystem 206. The proximity sensor 266 is implemented as described in the following U.S. patent applications: 11 / 241,839, entitled "Proximity Detector In Handheld Device"; No. 11 / 240,788, entitled "Proximity Detector In Handheld Device"; No. 11 / 620,702, entitled "Using Ambient Light Sensor To Augment Proximity Sensor Output"; No. 11 / 586,862, entitled "Automated Response To And Sensing Of User Activity In Portable Devices"; and No. 11 / 638,251, entitled "Methods And Systems For Automatic Configuration Of Peripherals", which are hereby incorporated by reference in their entirety. In some embodiments, when the multifunction device is placed near the user's ear (e.g., when the user is making a phone call), the proximity sensor turns off and the touch screen 212 is disabled.

[0067] Device 200 optionally also includes one or more tactile output generators 267 . Figure 2AA tactile output generator coupled to a tactile feedback controller 261 in the I / O subsystem 206 is shown. The tactile output generator 267 optionally includes one or more electroacoustic devices such as a speaker or other audio component; and / or an electromechanical device for converting energy into linear motion such as a motor, solenoid, electroactive polymer, piezoelectric actuator, electrostatic actuator, or other tactile output generating component (e.g., a component for converting an electrical signal into a tactile output on the device). The contact force sensor 265 receives tactile feedback generation instructions from the tactile feedback module 233 and generates a tactile output on the device 200 that can be felt by a user of the device 200. In some embodiments, at least one tactile output generator is arranged in juxtaposition or proximity to a touch-sensitive surface (e.g., a touch-sensitive display system 212) and optionally generates tactile output by moving the touch-sensitive surface vertically (e.g., inward / outward of the surface of the device 200) or laterally (e.g., backward and forward in the same plane as the surface of the device 200). In some embodiments, at least one tactile output generator sensor is located on the back of the device 200, opposite the touch screen 212 located on the front of the device 200.

[0068] Device 200 also includes one or more accelerometers 268 . Figure 2A An accelerometer 268 coupled to the peripheral device interface 218 is shown. Alternatively, the accelerometer 268 is coupled to the input controller 260 in the I / O subsystem 206. The accelerometer 268 is implemented as described in the following U.S. patent publications: U.S. Patent Publication 20050190059, "Acceleration-basedTheft Detection System for Portable Electronic Devices" and U.S. Patent Publication 20060017692, "Methods And Apparatuses For Operating A Portable Device Based OnAn Accelerometer", both of which are incorporated herein by reference in their entirety. In some embodiments, information is displayed in a portrait view or a landscape view on the touch screen display based on analysis of data received from one or more accelerometers. The device 200 optionally includes a magnetometer (not shown) and a GPS (or GLONASS or other global navigation system) receiver (not shown) in addition to one or more accelerometers 268 for obtaining information about the position and orientation (e.g., portrait or landscape) of the device 200.

[0069] In some embodiments, the software components stored in memory 202 include an operating system 226, a communication module (or instruction set) 228, a contact / motion module (or instruction set) 230, a graphics module (or instruction set) 232, a text input module (or instruction set) 234, a global positioning system (GPS) module (or instruction set) 235, a digital assistant client module 229, and an application (or instruction set) 236. In addition, memory 202 stores data and models, such as user data and models 231. In addition, in some embodiments, memory 202 ( Figure 2A ) or 470( Figure 4 ) storage device / global internal state 257, such as Figure 2A and Figure 4 . The device / global internal state 257 includes one or more of the following: an active application state, which indicates which applications (if any) are currently active; a display state, which indicates what applications, views, or other information occupy various areas of the touch screen display system 212; a sensor state, including information obtained from the device's various sensors and input control devices 216; and position information about the device's position and / or posture.

[0070] The operating system 226 (e.g., Darwin, RTXC, LINUX, UNIX, OS X, iOS, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.), and facilitating communication between various hardware components and software components.

[0071] The communication module 228 facilitates communication with other devices through one or more external ports 224, and also includes various software components for processing data received by the RF circuit 208 and / or the external port 224. The external port 224 (e.g., Universal Serial Bus (USB), FireWire, etc.) is suitable for coupling directly to other devices, or indirectly through a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is connected to (trademark of Apple Inc.) devices.

[0072] The contact / motion module 230 optionally detects contact with the touch screen 212 (in conjunction with the display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). The contact / motion module 230 includes various software components for performing various operations related to contact detection, such as determining whether contact has occurred (e.g., detecting a finger press event), determining the contact strength (e.g., the force or pressure of the contact, or a substitute for the force or pressure of the contact), determining whether there is movement of the contact and tracking the movement on the touch-sensitive surface (e.g., detecting one or more finger drag events), and determining whether the contact has stopped (e.g., detecting a finger lift event or contact disconnection). The contact / motion module 230 receives contact data from the touch-sensitive surface. Determining the movement of the contact point optionally includes determining the rate (magnitude), velocity (magnitude and direction), and / or acceleration (change in magnitude and / or direction) of the contact point, the movement of the contact point being represented by a series of contact data. These operations are optionally applied to a single point of contact (e.g., a single finger contact) or multiple points of simultaneous contact (e.g., "multi-touch" / multiple finger contacts). In some embodiments, the contact / motion module 230 and display controller 256 detect contact on the touch pad.

[0073] In some embodiments, the contact / motion module 230 uses a set of one or more intensity thresholds to determine whether an operation has been performed by a user (e.g., to determine whether a user has "clicked" an icon). In some embodiments, at least a subset of the intensity thresholds are determined based on software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of specific physical actuators and can be adjusted without changing the physical hardware of the device 200). For example, without changing the touchpad or touchscreen display hardware, the mouse "click" threshold of a touchpad or touchscreen can be set to any one of a large range of predefined thresholds. In addition, in some specific implementations, a software setting is provided to the user of the device for adjusting one or more intensity thresholds in a set of intensity thresholds (e.g., by adjusting individual intensity thresholds and / or by utilizing a system-level click on an "intensity" parameter to adjust multiple intensity thresholds at once).

[0074] The contact / motion module 230 optionally detects gesture input from a user. Different gestures on a touch-sensitive surface have different contact patterns (e.g., different motions, timings, and / or intensities of the detected contacts). 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 (lift-off) event at the same location (or substantially the same location) as the finger press event (e.g., at the location of an icon). As another example, detecting a finger swipe gesture on a touch-sensitive surface includes detecting a finger press event, then detecting one or more finger drag events, and then detecting a finger lift (lift-off) event.

[0075] The graphics module 232 includes various known software components for rendering and displaying graphics on the touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual characteristics) of the displayed graphics. As used herein, the term "graphics" includes any object that can be displayed to a user, including, without limitation, text, web pages, icons (such as user interface objects including soft keys), digital images, videos, animations, etc.

[0076] In some embodiments, the graphics module 232 stores data representing graphics to be used. Each graphic is optionally assigned a corresponding code. The graphics module 232 receives one or more codes specifying the graphics to be displayed from an application program, etc., along with coordinate data and other graphic attribute data if necessary, and then generates screen image data to be output to the display controller 256.

[0077] The tactile feedback module 233 includes various software components for generating instructions used by the one or more tactile output generators 267 to produce tactile output at one or more locations on the device 200 in response to user interaction with the device 200 .

[0078] The text input module 234, which in some examples 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 requiring text input).

[0079] The GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to the phone 238 for use in location-based dialing; to the camera 243 as picture / video metadata; and to applications that provide location-based services, such as the Weather widget, the Local Pages widget, and the Maps / Navigation widget).

[0080] The digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functions of the digital assistant. For example, the digital assistant client module 229 can accept sound 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 output in the form of audio (e.g., voice output), output in the form of visual output and / or output in the form of tactile output through various output interfaces of the portable multifunction device 200 (e.g., speaker 211, touch-sensitive display system 212, one or more tactile output generators 267, etc.). For example, the output is provided as voice, sound, alarm, text message, menu, graphic, video, animation, vibration and / or a combination of two or more of the above. During operation, the digital assistant client module 229 uses the RF circuit 208 to communicate with the DA server 106.

[0081] User data and models 231 include various data associated with the user (e.g., user-specific vocabulary data, user preference data, user-specified name pronunciations, data from the user's electronic address book, to-do items, shopping lists, etc.) to provide client-side functionality of the digital assistant. In addition, user data and models 231 include various models for processing user input and determining user intent (e.g., speech recognition models, statistical language models, natural language processing models, knowledge ontologies, task flow models, service models, etc.).

[0082] In some examples, the digital assistant client module 229 utilizes the various sensors, subsystems, and peripherals of the portable multifunction device 200 to collect additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with the user, the current user interaction, and / or the current user input. In some examples, the digital assistant client module 229 provides the context information, or a subset thereof, to the DA server 106 along with the user input to help infer the user's intent. In some examples, the digital assistant also uses the context information to determine how to prepare and transmit output to the user. The context information is referred to as context data.

[0083] In some examples, contextual information accompanying the user input includes sensor information, such as lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information may also include the physical state of the device, such as device orientation, device location, device temperature, power level, speed, acceleration, motion pattern, cellular signal strength, etc. In some examples, information related to the software state of the DA server 106, such as the running process of the portable multifunction device 200, installed programs, past and current network activities, background services, error logs, resource usage, etc., is provided to the DA server 106 as contextual information associated with the user input.

[0084] In some examples, the digital assistant client module 229 selectively provides information stored on the portable multifunction device 200 (e.g., user data 231) in response to a request from the DA server 106. In some examples, the digital assistant client module 229 also elicits additional input from the user via a natural language dialog or other user interface upon request by the DA server 106. The digital assistant client module 229 communicates the additional input to the DA server 106 to assist the DA server 106 in inferring intent and / or implementing the user intent expressed in the user request.

[0085] Reference below 7A to 7C A more detailed description of the digital assistant is provided. It should be appreciated that the digital assistant client module 229 may include any number of sub-modules of the digital assistant module 726 described below.

[0086] The application 236 includes the following modules (or instruction sets) or a subset or superset thereof:

[0087] Contacts module 237 (sometimes referred to as address book or contact list);

[0088] Telephone module 238;

[0089] Video conferencing module 239;

[0090] Email client module 240;

[0091] Instant messaging (IM) module 241;

[0092] Fitness support module 242;

[0093] A camera module 243 for still images and / or video images;

[0094] Image management module 244;

[0095] Video player module;

[0096] Music player module;

[0097] Browser module 247;

[0098] Calendar module 248;

[0099] A widget module 249, which in some examples includes one or more of the following: a weather widget 249-1, a stock widget 249-2, a calculator widget 249-3, an alarm clock widget 249-4, a dictionary widget 249-5, and other widgets acquired by the user and a widget 249-6 created by the user;

[0100] A widget creator module 250 for forming a user-created widget 249-6;

[0101] Search module 251;

[0102] Video and music player module 252, which merges the video player module and the music player module;

[0103] Notepad module 253;

[0104] Map module 254; and / or

[0105] Online video module 255.

[0106] Examples of other applications 236 stored in memory 202 include other word processing applications, other image editing applications, drawing applications, rendering applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice reproduction.

[0107] In combination with the touch screen 212, display controller 256, touch / motion module 230, graphics module 232, and text input module 234, the contact module 237 is used to manage an address book or contact list (for example, stored in the application internal state 292 of the contact module 237 in memory 202 or memory 470), including: adding one or more names to the address book; deleting names from the address book; associating telephone numbers, email addresses, physical addresses, or other information with names; associating images with names; categorizing and classifying names; providing telephone numbers or email addresses to initiate and / or facilitate communications via telephone 238, video conferencing module 239, email 240, or IM 241; and the like.

[0108] In conjunction with RF circuit 208, audio circuit 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, phone module 238 is used to enter a character sequence corresponding to a phone number, access one or more phone numbers in contact module 237, modify an already entered phone number, dial a corresponding phone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As described above, wireless communications use any of a variety of communication standards, protocols, and technologies.

[0109] In combination with the RF circuit 208, the audio circuit 210, the speaker 211, the microphone 213, the touch screen 212, the display controller 256, the optical sensor 264, the optical sensor controller 258, the contact / motion module 230, the graphics module 232, the text input module 234, the contact module 237 and the telephone module 238, the video conferencing module 239 includes executable instructions for initiating, conducting and terminating a video conference between a user and one or more other participants in accordance with user instructions.

[0110] In conjunction with RF circuit 208, touch screen 212, display controller 256, contact / motion module 230, graphics module 232, and text input module 234, email client module 240 includes executable instructions for creating, sending, receiving, and managing emails in response to user instructions. In conjunction with image management module 244, email client module 240 makes it very easy to create and send emails with still images or video images captured by camera module 243.

[0111] In conjunction with the RF circuit 208, the touch screen 212, the display controller 256, the contact / motion module 230, the graphics module 232, and the text input module 234, the instant messaging module 241 includes executable instructions for the following operations: inputting a character sequence corresponding to an instant message, modifying previously input characters, transmitting a corresponding instant message (e.g., using a short message service (SMS) or multimedia message service (MMS) protocol for telephone-based instant messaging or using XMPP, SIMPLE, or IMPS for Internet-based instant messaging), receiving instant messages, and viewing received instant messages. In some embodiments, the transmitted and / or received instant messages include graphics, photos, audio files, video files, and / or other attachments such as those supported in MMS and / or enhanced messaging services (EMS). As used herein, "instant messaging" refers to both telephone-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).

[0112] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the contact / motion module 230, the graphics module 232, the text input module 234, the GPS module 235, the map module 254, and the music player module, the fitness support module 242 includes executable instructions for: creating a workout (e.g., with time, distance, and / or calorie burn goals); communicating with fitness sensors (sports equipment); receiving fitness sensor data; calibrating sensors for monitoring fitness; selecting and playing music for fitness; and displaying, storing, and transmitting fitness data.

[0113] In combination with the touch screen 212, the display controller 256, one or more optical sensors 264, the optical sensor controller 258, the touch / motion module 230, the graphics module 232, and the image management module 244, the camera module 243 includes executable instructions for the following operations: capturing still images or videos (including video streams) and storing them in the memory 202, modifying the characteristics of the still images or videos, or deleting the still images or videos from the memory 202.

[0114] In conjunction with the touch screen 212, display controller 256, touch / motion module 230, graphics module 232, text input module 234, and camera module 243, the image management module 244 includes executable instructions for arranging, modifying (e.g., editing), or otherwise manipulating, labeling, deleting, presenting (e.g., in a digital slide show or album), and storing still images and / or video images.

[0115] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232 and the text input module 234, the browser module 247 includes executable instructions for browsing the Internet in accordance with user instructions, including searching, linking to, receiving and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.

[0116] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234, the email client module 240 and the browser module 247, the calendar module 248 includes executable instructions for creating, displaying, modifying and storing calendars and data associated with the calendar (e.g., calendar entries, to-do items, etc.) in accordance with user instructions.

[0117] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the contact / motion module 230, the graphics module 232, the text input module 234, and the browser module 247, the desktop widget module 249 is a mini-application that can be downloaded and used by the user (e.g., the weather desktop widget 249-1, the stock desktop widget 249-2, the calculator desktop widget 249-3, the alarm desktop widget 249-4, and the dictionary desktop widget 249-5) or a mini-application created by the user (e.g., the user-created desktop widget 249-6). In some embodiments, the desktop widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, the desktop widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., the Yahoo! desktop widget).

[0118] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234 and the browser module 247, the desktop widget creator module 250 is used by users to create desktop widgets (for example, to turn a user-specified portion of a web page into a desktop widget).

[0119] In combination with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232 and text input module 234, the search module 251 includes executable instructions for searching the memory 202 for text, music, sound, images, videos and / or other files that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.

[0120] In conjunction with touch screen 212, display controller 256, contact / motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, and browser module 247, video and music player module 252 includes executable instructions that allow a user to download and play back recorded music and other sound files stored in one or more file formats (such as MP3 or AAC files), as well as executable instructions for displaying, presenting, or otherwise playing back video (e.g., on touch screen 212 or on an external display connected via external port 224). In some embodiments, device 200 optionally includes the functionality of an MP3 player such as an iPod (trademark of Apple Inc.).

[0121] In conjunction with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232 and text input module 234, the notepad module 253 includes executable instructions for creating and managing notes, to-do lists, etc. according to user instructions.

[0122] In combination with the RF circuit 208, the touch screen 212, the display controller 256, the touch / motion module 230, the graphics module 232, the text input module 234, the GPS module 235 and the browser module 247, the map module 254 is used to receive, display, modify and store maps and data associated with the maps (e.g., driving directions, data related to stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.

[0123] In conjunction with the touch screen 212, display controller 256, contact / motion module 230, graphics module 232, audio circuit 210, speaker 211, RF circuit 208, text input module 234, email client module 240, and browser module 247, the online video module 255 includes instructions that allow a user to access, browse, receive (e.g., by streaming and / or downloading), play back (e.g., on the touch screen or on a connected external display via external port 224), send an email with a link to a specific online video, and otherwise manage online videos in one or more file formats (such as H.264). In some embodiments, the instant messaging module 241 is used instead of the email client module 240 to send a link to a specific online video. Additional descriptions of online video applications can be found in U.S. Provisional Patent Application No. 60 / 936,562, filed on June 20, 2007, entitled “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” and U.S. Patent Application No. 11 / 968,067, filed on December 31, 2007, entitled “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” the contents of which are hereby incorporated by reference in their entirety.

[0124] Each of the modules and applications described above corresponds to an executable instruction set for performing one or more of the functions described above and the methods described in this patent application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., instruction sets) do not have to be implemented as separate software programs, processes, or modules, and therefore various subsets of these modules may be combined or otherwise rearranged in various embodiments. For example, a video player module may be combined with a music player module into a single module (e.g., Figure 2AIn some embodiments, the memory 202 stores a subset of the above modules and data structures. In addition, the memory 202 stores additional modules and data structures not described above.

[0125] In some embodiments, the device 200 is a device on which the operation of a predefined set of functions is performed exclusively through a touch screen and / or a touch pad. By using a touch screen and / or a touch pad as the primary input control device for the operation of the device 200, the number of physical input control devices (such as push buttons, dials, etc.) on the device 200 is reduced.

[0126] A predefined set of functions that are performed exclusively through a touch screen and / or a touchpad optionally includes navigation between user interfaces. In some embodiments, the touchpad, when touched by a user, navigates the device 200 to a main menu, a home menu, or a root menu from any user interface displayed on the device 200. In such embodiments, a touchpad is used to implement a "menu button." In some other embodiments, the menu button is a physical push button or other physical input control device, rather than a touchpad.

[0127] Figure 2B is a block diagram illustrating exemplary components for event processing according to some embodiments. In some embodiments, memory 202 ( Figure 2A ) or memory 470( Figure 4 ) includes an event classifier 270 (e.g., in the operating system 226) and a corresponding application 236-1 (e.g., any one of the aforementioned applications 237 to 251, 255, 480 to 490).

[0128] The event classifier 270 receives event information and determines the application 236-1 and the application view 291 of the application 236-1 to which the event information is to be delivered. The event classifier 270 includes an event monitor 271 and an event distributor module 274. In some embodiments, the application 236-1 includes an application internal state 292 that indicates one or more current application views displayed on the touch-sensitive display system 212 when the application is active or executing. In some embodiments, the device / global internal state 257 is used by the event classifier 270 to determine which application(s) is currently active, and the application internal state 292 is used by the event classifier 270 to determine the application view 291 to which the event information is to be delivered.

[0129] In some embodiments, the application internal state 292 includes additional information, such as one or more of the following: resumption information to be used when the application 236-1 resumes execution, user interface state information indicating that information is being displayed or is ready to be displayed by the application 236-1, a state queue for enabling a user to return to a previous state or view of the application 236-1, and a repeat / undo queue of previous actions taken by the user.

[0130] Event monitor 271 receives event information from peripherals interface 218. Event information includes information about sub-events (e.g., a user touch on touch-sensitive display system 212 as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I / O subsystem 206 or sensors such as proximity sensor 266, one or more accelerometers 268, and / or microphone 213 (through audio circuit 210). The information that peripherals interface 218 receives from I / O subsystem 206 includes information from touch-sensitive display system 212 or a touch-sensitive surface.

[0131] In some embodiments, event monitor 271 sends requests to peripheral device interface 218 at predetermined intervals. In response, peripheral device interface 218 transmits event information. In other embodiments, peripheral device interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and / or receiving an input for more than a predetermined duration).

[0132] In some embodiments, the event classifier 270 also includes a hit view determination module 272 and / or an active event identifier determination module 273.

[0133] When the touch-sensitive display system 212 displays more than one view, the hit view determination module 272 provides software procedures for determining where within one or more views a sub-event has occurred. A view consists of controls and other elements that a user can see on the display.

[0134] Another aspect of the user interface associated with an application is a set of views, sometimes referred to herein as application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of the respective application) in which a touch is detected correspond to a programmatic level within a programmatic hierarchy of applications or a view hierarchy. For example, the lowest level view in which a touch is detected is called a hit view, and the set of events that are considered to be correct input is determined at least in part based on the hit view of the initial touch that started the touch-based gesture.

[0135] The hit view determination module 272 receives information related to sub-events of touch-based gestures. When an application has multiple views organized in a hierarchy, the hit view determination module 272 identifies the hit view as the lowest view in the hierarchy where 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 that form an event or potential event) occurs. Once a hit view is identified by the hit view determination module 272, the hit view typically receives all sub-events related to the same touch or input source for which it was identified as the hit view.

[0136] Active event recognizer determination module 273 determines which view or views within the view hierarchy should receive a particular sequence of sub-events. In some embodiments, active event recognizer determination module 273 determines that only the hit view should receive a particular sequence of sub-events. In other embodiments, active event recognizer determination module 273 determines that all views that include the physical location of the sub-event are actively participating views, and therefore determines that all actively participating views should receive a particular sequence of sub-events. In other embodiments, even if a touch sub-event is completely confined to an area associated with one particular view, higher views in the hierarchy will still remain as actively participating views.

[0137] Event distributor module 274 distributes event information to event identifiers (e.g., event identifier 280). In embodiments including active event identifier determination module 273, event distributor module 274 delivers the event information to the event identifier determined by active event identifier determination module 273. In some embodiments, event distributor module 274 stores the event information in an event queue, which is retrieved by corresponding event receiver 282.

[0138] In some embodiments, operating system 226 includes event classifier 270. Alternatively, application 236-1 includes event classifier 270. In yet another embodiment, event classifier 270 is a standalone module or is part of another module stored in memory 202, such as contact / motion module 230.

[0139] In some embodiments, the application 236-1 includes multiple event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events occurring in a corresponding view of the user interface of the application. Each application view 291 of the application 236-1 includes one or more event identifiers 280. Typically, the corresponding application view 291 includes multiple event identifiers 280. In other embodiments, one or more event identifiers in the event identifier 280 are part of an independent module, which is a higher-level object such as a user interface toolkit (not shown) or the application 236-1 from which methods and other properties are inherited. In some embodiments, the corresponding event handler 290 includes one or more of the following: data updater 276, object updater 277, GUI updater 278, and / or event data 279 received from the event classifier 270. The event handler 290 utilizes or calls the data updater 276, object updater 277, or GUI updater 278 to update the application internal state 292. Alternatively, one or more of the application views in the application views 291 include one or more corresponding event handlers 290. In addition, in some embodiments, one or more of the data updater 276, the object updater 277, and the GUI updater 278 are included in the corresponding application view 291.

[0140] 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 metadata 283 and at least a subset of event delivery instructions 288 (which includes sub-event delivery instructions).

[0141] The event receiver 282 receives event information from the event classifier 270. The event information includes information about sub-events such as touches or touch movements. Depending on the sub-event, the event information also includes additional information, such as the location of the sub-event. When the sub-event involves the movement of a touch, the event information also includes the rate and direction of the sub-event. In some embodiments, the event includes the device rotating from one orientation to another orientation (e.g., from a longitudinal orientation to a transverse orientation, or vice versa), and the event information includes corresponding information about the current orientation of the device (also referred to as the device posture).

[0142] Event comparator 284 compares event information with predefined event or sub-event definitions, and based on the comparison, determines an event or sub-event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definition 286. Event definition 286 includes the definition of an event (e.g., a predefined sequence 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, the definition of event 1 (287-1) is a double-click on a displayed object. For example, a double-click includes a first touch (touch start) of a predetermined duration on a displayed object, a first lift-off (touch end) of a predetermined duration, a second touch (touch start) of a predetermined duration on a displayed object, and a second lift-off (touch end) of a predetermined duration. In another example, the definition of event 2 (287-2) is a drag on a displayed object. For example, dragging includes a touch (or contact) of a predetermined duration on a displayed object, movement of the touch on the touch-sensitive display system 212, and lifting of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.

[0143] In some embodiments, event definition 287 includes a definition of an event for a corresponding user interface object. In some embodiments, event comparator 284 performs a hit test to determine which user interface object is associated with a sub-event. For example, in an application view displaying three user interface objects on touch-sensitive display system 212, when a touch is detected on touch-sensitive display system 212, event comparator 284 performs a hit test to determine which of the three user interface objects is associated with the touch (sub-event). If each displayed object is associated with a corresponding event handler 290, the event comparator uses the result of the hit test to determine which event handler 290 should be activated. For example, event comparator 284 selects an event handler associated with a sub-event and the object that triggered the hit test.

[0144] In some embodiments, the definition of the corresponding event (287) also includes a delay action that delays the delivery of the event information until it has been determined that the sub-event sequence does or does not correspond to the event type of the event identifier.

[0145] When a corresponding event recognizer 280 determines that a sequence of sub-events does not match any event in event definition 286, the corresponding event recognizer 280 enters an event impossible, event failed, or event ended state, after which subsequent sub-events of the touch-based gesture are ignored. In this case, other event recognizers (if any) that remain active for the hit view continue to track and process sub-events of the ongoing touch-based gesture.

[0146] In some embodiments, the corresponding event identifier 280 includes metadata 283 with configurable properties, flags, and / or lists that indicate how the event delivery system should perform sub-event delivery to actively participating event identifiers. In some embodiments, metadata 283 includes configurable properties, flags, and / or lists that indicate how event identifiers interact or can interact with each other. In some embodiments, metadata 283 includes configurable properties, flags, and / or lists that indicate whether sub-events are delivered to different levels in a view or programmatic hierarchy.

[0147] In some embodiments, when one or more specific sub-events of an event are identified, the corresponding event identifier 280 activates the event handler 290 associated with the event. In some embodiments, the corresponding event identifier 280 delivers the event information associated with the event to the event handler 290. Activating the event handler 290 is different from sending (and deferred sending) the sub-events to the corresponding hit view. In some embodiments, the event identifier 280 throws a tag associated with the identified event, and the event handler 290 associated with the tag obtains the tag and performs a predefined process.

[0148] In some embodiments, event delivery instructions 288 include sub-event delivery instructions that deliver event information about a sub-event without activating an event handler. Instead, the sub-event delivery instruction delivers the event information to an event handler associated with a sub-event sequence or to an actively participating view. An event handler associated with a sub-event sequence or with an actively participating view receives the event information and executes a predetermined process.

[0149] In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates phone numbers used in contact module 237 or stores video files used in video player module. In some embodiments, object updater 277 creates and updates objects used in application 236-1. For example, object updater 277 creates new user interface objects or updates the position of user interface objects. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends the display information to graphics module 232 for display on a touch-sensitive display.

[0150] In some embodiments, event handler 290 includes or has access to data updater 276, object updater 277, and GUI updater 278. In some embodiments, data updater 276, object updater 277, and GUI updater 278 are included in a single module of the corresponding application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.

[0151] It should be understood that the above discussion of event processing of user touches on a touch-sensitive display also applies to other forms of user input that utilize input devices to operate the multifunction device 200, and not all user input is initiated on the touch screen. For example, mouse movement and mouse button presses, optionally in conjunction with single or multiple keyboard presses or hold; contact movement on a touch pad, such as tapping, dragging, scrolling, etc.; stylus input; movement of the device; verbal commands; detected eye movement; biometric input; and / or any combination thereof are optionally used as input corresponding to sub-events defining the event to be recognized.

[0152] Figure 3A portable multifunction device 200 with a touch screen 212 according to some embodiments is shown. The touch screen optionally displays one or more graphics within a user interface (UI) 300. In this embodiment and other embodiments described below, a user can select one or more of these graphics by, for example, making gestures on the graphics using one or more fingers 302 (not drawn to scale in the figure) or one or more styluses 303 (not drawn to scale in the figure). In some embodiments, when the user interrupts contact with one or more graphics, selection of one or more graphics will occur. In some embodiments, gestures optionally include one or more taps, one or more swipes (from left to right, from right to left, up and / or down) and / or rolling of fingers that have been in contact with the device 200 (from right to left, from left to right, up and / or down). In some specific implementations or in some cases, inadvertent contact with a graphic will not select the graphic. For example, when the gesture corresponding to the selection is a tap, a swipe gesture that sweeps over an application icon optionally does not select the corresponding application.

[0153] The device 200 also includes one or more physical buttons, such as a "home" or menu button 304. As previously described, the menu button 304 is used to navigate to any application 236 in a set of applications executing on the device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on the touch screen 212.

[0154] In some embodiments, the device 200 includes a touch screen 212, a menu button 304, a push button 306 for powering the device on / off and for locking the device, one or more volume adjustment buttons 308, a user identity module (SIM) card slot 310, an earphone jack 312, and a docking / charging external port 224. The push button 306 is optionally used to turn the device on / off by pressing the button and keeping the button in a pressed state for a predefined time interval; lock the device by pressing the button and releasing the button before the predefined time interval passes; and / or unlock the device or initiate an unlocking process. In an alternative embodiment, the device 200 also accepts speech input for activating or deactivating certain functions through a microphone 213. The device 200 also optionally includes one or more contact strength sensors 265 for detecting the strength of contact on the touch screen 212, and / or one or more tactile output generators 267 for generating tactile output for a user of the device 200.

[0155] Figure 44 is a block diagram of an exemplary multi-function device with a display and a touch-sensitive surface according to some embodiments. Device 400 does not have to be portable. In some embodiments, device 400 is a laptop computer, a desktop computer, a tablet computer, a multimedia player device, a navigation device, an educational device (such as a children's learning toy), a game system, or a control device (e.g., a home controller or an industrial controller). Device 400 typically includes one or more processing units (CPUs) 410, one or more network or other communication interfaces 460, a memory 470, and one or more communication buses 420 for interconnecting these components. Communication bus 420 optionally includes circuits (sometimes referred to as chipsets) that interconnect system components and control communications between system components. Device 400 includes an input / output (I / O) interface 430 with a display 440, which is typically a touch screen display. I / O interface 430 also optionally includes a keyboard and / or mouse (or other pointing device) 450 and a touchpad 455, a tactile output generator 457 for generating tactile output on device 400 (e.g., similar to the above reference Figure 2A The one or more tactile output generators 267 described above), sensors 459 (e.g., optical sensors, acceleration sensors, proximity sensors, touch sensors, and / or contact intensity sensors (similar to those described above with reference to Figure 2A The memory 470 may include one or more storage devices located away from the CPU 410. In some embodiments, the memory 470 stores information related to the portable multifunction device 200 ( Figure 2A ) or a subset thereof. In addition, memory 470 optionally stores additional programs, modules, and data structures not present in memory 202 of portable multifunction device 200. For example, memory 470 of device 400 optionally stores drawing module 480, presentation module 482, word processing module 484, website creation module 486, disk editing module 488, and / or spreadsheet module 490, while portable multifunction device 200( Figure 2A )'s memory 202 optionally does not store these modules.

[0156] Figure 4Each of the above-mentioned elements in some examples is stored in one or more previously mentioned memory devices. Each module in the above-mentioned modules corresponds to an instruction set for performing the above-mentioned functions. The above-mentioned modules or programs (e.g., instruction sets) do not have to be implemented as independent software programs, processes or modules, so the various subsets of these modules are combined or otherwise rearranged in various embodiments. In some embodiments, memory 470 stores a subset of the above-mentioned modules and data structures. In addition, memory 470 stores additional modules and data structures not described above.

[0157] Attention is now turned to embodiments of a user interface that may be implemented on, for example, portable multifunction device 200.

[0158] Figure 5A An exemplary user interface of an application menu on portable multifunction device 200 is shown according to some embodiments. A similar user interface is implemented on device 400. In some embodiments, user interface 500 includes the following elements, or a subset or superset thereof:

[0159] one or more signal strength indicators 502 of one or more wireless communications such as cellular signals and Wi-Fi signals;

[0160] Time 504;

[0161] Bluetooth indicator 505;

[0162] Battery status indicator 506;

[0163] A tray 508 with icons for commonly used applications, such as:

[0164] o An icon 516 labeled "Phone" of the phone module 238, which optionally includes an indicator 514 of the number of missed calls or voice messages;

[0165] o an icon 518 labeled “Mail” of the email client module 240, which optionally includes an indicator 510 of the number of unread emails;

[0166] o An icon 520 labeled "Browser" for the browser module 247; and

[0167] ○ Video and music player module 252 (also known as iPod (trademark of Apple Inc.)

[0168] module 252) and an icon 522 labeled "iPod"; and

[0169] Icons for other apps, such as:

[0170] o Icon 524 labeled "Message" of IM module 241;

[0171] o An icon 526 labeled “Calendar” of the calendar module 248;

[0172] o Icon 528 labeled "Photos" of the image management module 244;

[0173] o An icon 530 labeled “Camera” of the camera module 243;

[0174] o An icon 532 labeled "Online Video" of the online video module 255;

[0175] ○ Stock Market ○ Icon 534 labeled “Stock Market” of applet 249-2;

[0176] o An icon 536 labeled “Map” of the map module 254;

[0177] o Icon 538 labeled “Weather” of the Weather widget 249-1;

[0178] ○ Icon 540 labeled as “Clock” of the alarm clock widget 249-4;

[0179] o An icon 542 labeled “Fitness Support” of the fitness support module 242;

[0180] o An icon 544 labeled "Notepad" of the Notepad module 253; and

[0181] ○ An icon 546 labeled “Settings” for a settings application or module that provides access to settings for the device 200 and its various applications 236 .

[0182] It should be noted that Figure 5A The icon labels shown in are exemplary only. For example, icon 522 of video and music player module 252 is optionally labeled "Music" or "Music Player". Other labels are optionally used for various application icons. In some embodiments, the label of the corresponding application icon includes the name of the application corresponding to the corresponding application icon. In some embodiments, the label of a particular application icon is different from the name of the application corresponding to the particular application icon.

[0183] Figure 5B A touch-sensitive surface 551 (eg, touch screen 212) is shown having a touch-sensitive surface 551 (eg, Figure 4 tablet or touch pad 455) of the device (e.g., Figure 4Device 400 also optionally includes one or more contact intensity sensors (e.g., one or more of sensors 457) for detecting intensity of contacts on touch-sensitive surface 551 and / or one or more tactile output generators 459 for generating tactile output for a user of device 400.

[0184] Although some of the examples that follow will be given with reference to input on touch screen 212 (where a touch-sensitive surface and a display are combined), in some embodiments, the device detects input on a touch-sensitive surface that is separate from the display, such as Figure 5B In some embodiments, the touch-sensitive surface (e.g., Figure 5B 551) has a main axis (e.g., Figure 5B 553) corresponding to the principal axis (for example, Figure 5B According to these embodiments, the device detects a position corresponding to a corresponding position on the display (e.g., Figure 5B , 560 corresponds to 568 and 562 corresponds to 570) at a contact with touch-sensitive surface 551 (eg, Figure 5B Thus, on a touch-sensitive surface (e.g., Figure 5B 551) and a display of a multi-function device (e.g., Figure 5B When the user input detected by the device on the touch-sensitive surface (e.g., contacts 560 and 562 and their movement) is separated, the device is used to manipulate the user interface on the display. It should be understood that similar methods are optionally used for other user interfaces described herein.

[0185] In addition, although the following examples are primarily given with reference to finger inputs (e.g., finger contacts, single-finger tap gestures, finger swipe gestures), it should be understood that in some embodiments, one or more of these finger inputs are replaced by input from another input device (e.g., mouse-based input or stylus input). For example, a swipe gesture is optionally replaced by a mouse click (e.g., instead of contact), followed by movement of the cursor along the path of the swipe (e.g., instead of movement of the contact). As another example, a tap gesture is optionally replaced by a mouse click when the cursor is over the location of the tap gesture (e.g., instead of detecting contact, followed by ceasing to detect contact). Similarly, when multiple user inputs are detected simultaneously, it should be understood that multiple computer mice are optionally used simultaneously, or a mouse and finger contact are optionally used simultaneously.

[0186] Fig. 6AAn exemplary personal electronic device 600 is shown. Device 600 includes a body 602. In some embodiments, device 600 includes a body 602 relative to devices 200 and 400 (e.g., Figure 2A-Figure 4 ) in some or all of the features described in the preceding claims. In some embodiments, device 600 has a touch-sensitive display screen, hereinafter referred to as touch screen 604. As an alternative or in addition to touch screen 604, device 600 has a display and a touch-sensitive surface. As with devices 200 and 400, in some embodiments, touch screen 604 (or touch-sensitive surface) has one or more intensity sensors for detecting the intensity of contact (e.g., touch) being applied. One or more intensity sensors of touch screen 604 (or touch-sensitive surface) provide output data representing the intensity of the touch. The user interface of device 600 responds to touch based on the intensity of the touch, which means that touches of different intensities can invoke different user interface operations on device 600.

[0187] Technology for detecting and processing touch intensity may be found, for example, in related applications: International Patent Application PCT / US2013 / 040061, filed on May 8, 2013, entitled “Device, Method, and Graphical User Interface for Displaying UserInterface Objects Corresponding to an Application,” and International Patent Application PCT / US2013 / 069483, filed on November 11, 2013, entitled “Device, Method, and Graphical UserInterface for Transitioning Between Touch Input to Display OutputRelationships,” each of which is hereby incorporated by reference in its entirety.

[0188] In some embodiments, the device 600 has one or more input mechanisms 606 and 608. The input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, the device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, may allow the device 600 to be attached to, for example, hats, glasses, earrings, necklaces, shirts, jackets, bracelets, watchbands, bracelets, pants, belts, shoes, wallets, backpacks, etc. These attachment mechanisms allow the user to wear the device 600.

[0189] Figure 6BAn exemplary personal electronic device 600 is shown. In some embodiments, the device 600 includes a Figure 2A , Figure 2B and Figure 4 Some or all of the components described. Device 600 has a bus 612 that operatively couples an I / O portion 614 to one or more computer processors 616 and a memory 618. I / O portion 614 is connected to a display 604, which may have a touch-sensitive component 622 and, optionally, also a touch-intensity sensitive component 624. In addition, I / O portion 614 is connected to a communication unit 630 for receiving application and operating system data using Wi-Fi, Bluetooth, near field communication (NFC), cellular and / or other wireless communication technologies. Device 600 includes input mechanisms 606 and / or 608. For example, input mechanism 606 is a rotatable input device or a depressible input device and a rotatable input device. In some examples, input mechanism 608 is a button.

[0190] In some examples, input mechanism 608 is a microphone. Personal electronic device 600 includes, for example, various sensors such as GPS sensor 632, accelerometer 634, orientation sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and / or combinations thereof, all of which are operably connected to I / O portion 614.

[0191] The memory 618 of the personal electronic device 600 is a non-transitory computer-readable storage medium for storing computer-executable instructions that, when executed by one or more computer processors 616, for example, cause the computer processors to perform the above-described techniques and processes. The computer-executable instructions are also stored and / or transmitted, for example, in any non-transitory computer-readable storage medium for use by or in conjunction with an instruction execution system, device, or apparatus, such as a computer-based system, a system containing a processor, or other system that can obtain instructions from an instruction execution system, device, or apparatus and execute the instructions. The personal electronic device 600 is not limited to Figure 6B components and configurations, but may include other components or additional components in a variety of configurations.

[0192] As used herein, the term “indicative representation” refers to an indication on device 200, device 400, device 600, and / or device 900 ( Figure 2A , Figure 4 , FIG. 6A to FIG. 6B ,as well as 9A to 9C ) is a user-interactive graphical user interface object displayed on a display screen of a computer. For example, an image (e.g., an icon), a button, and text (e.g., a hyperlink) each constitute an affordance.

[0193] 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 implementations that include a cursor or other position marker, the cursor acts as a "focus selector" such that when the cursor is over a particular user interface element (e.g., a button, window, slider, or other user interface element), a focus selector is displayed on a touch-sensitive surface (e.g., Figure 4 Touchpad 455 or Figure 5B In the event that an input (e.g., a press input) is detected on the touch-sensitive surface 551 in FIG. 5 , the particular user interface element is adjusted according to the detected input. In the case that a touch screen display (e.g., Figure 2A A touch sensitive display system 212 or Figure 5A In some implementations of the touch screen 212 in FIG. 1 , a contact detected on the touch screen acts as a “focus selector” such that when an input (e.g., a press input by the contact) is detected at the location of a particular user interface element (e.g., a button, window, slider, or other user interface element) on the touch screen display, the particular user interface element is adjusted in accordance with the detected input. In some implementations, the focus moves from one area of ​​the user interface to another area of ​​the user interface without corresponding movement of a cursor or movement of a contact on the touch screen display (e.g., by using a tab key or arrow keys to move the focus from one button to another); in these implementations, the focus selector moves in accordance with the movement of the focus between different areas of the user interface. Regardless of the specific form the focus selector takes, the focus selector is typically a user interface element (or contact on the touch screen display) that is controlled by the user to deliver the user's intended interaction with the user interface (e.g., by indicating to the device the element of the user interface with which the user desires to interact). For example, when a press input is detected on a touch-sensitive surface (e.g., a trackpad or touch screen), the position of a focus selector (e.g., a cursor, contact, or selection box) over a corresponding button will indicate that the user intends to activate the corresponding button (rather than other user interface elements shown on the device display).

[0194] As used in the specification and claims, the term "characteristic intensity" of a contact refers to a characteristic of a contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is optionally based on a predefined number of intensity samples or a set of intensity samples collected during a predetermined time period (e.g., 0.05 seconds, 0.1 seconds, 0.2 seconds, 0.5 seconds, 1 second, 2 seconds, 5 seconds, 10 seconds) relative to a predefined event (e.g., after contact is detected, before contact is detected to be lifted off, before or after contact starts to move, before contact ends, before or after contact intensity is detected to increase, and / or before or after contact intensity is detected to decrease). The characteristic intensity of a contact is optionally based on one or more of the following: the maximum value of the contact intensity, the mean value of the contact intensity, the average value of the contact intensity, the value at the top 10% of the contact intensity, the half-maximum value of the contact intensity, the 90% maximum value of the contact intensity, etc. In some embodiments, the duration of the contact is used when determining the characteristic intensity (e.g., when the characteristic intensity is the average value of the intensity of the contact over time). In some embodiments, the feature strength is compared to a set of one or more strength thresholds to determine whether the user has performed an operation. For example, the set of one or more strength thresholds includes a first strength threshold and a second strength threshold. In this example, a contact whose feature strength does not exceed the first threshold results in a first operation, a contact whose feature strength exceeds the first strength threshold but does not exceed the second strength threshold results in a second operation, and a contact whose feature strength exceeds the second threshold results in a third operation. In some embodiments, a comparison between the feature strength and one or more thresholds is used to determine whether to perform one or more operations (e.g., whether to perform the corresponding operation or give up performing the corresponding operation), rather than to determine whether to perform the first operation or the second operation.

[0195] In some embodiments, a portion of a gesture is identified for determining characteristic strength. For example, a touch-sensitive surface receives a continuous swipe contact that transitions from a starting position and reaches an end position, where the strength of the contact increases. In this example, the characteristic strength of the contact at the end position is based only on a portion of the continuous swipe contact, rather than the entire swipe contact (e.g., the swipe contact is only located at the portion at the end position). In some embodiments, a smoothing algorithm is applied to the strength of the swipe contact before determining the characteristic strength of the contact. For example, a smoothing algorithm optionally includes one or more of the following: an unweighted sliding average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and / or an exponential smoothing algorithm. In some cases, these smoothing algorithms eliminate narrow peaks or depressions in the strength of the swipe contact to achieve the purpose of determining the characteristic strength.

[0196] The intensity of a contact on a touch-sensitive surface is characterized relative to one or more intensity thresholds such as a contact detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and / or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform an operation typically associated with clicking a button of a physical mouse or touchpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform an operation different from the operation typically associated with clicking a button of a physical mouse or touchpad. In some embodiments, when a contact having a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact detection intensity threshold, contacts lower than the nominal contact detection intensity threshold are no longer detected) is detected, the device will move the focus selector according to the movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally speaking, unless otherwise stated, these intensity thresholds are consistent between different groups of user interface illustrations.

[0197] An increase in contact feature intensity from an intensity below a light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a "light press" input. An increase in contact feature intensity from an intensity below a deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a "deep press" input. An increase in contact feature intensity from an intensity below a contact detection intensity threshold to an intensity between the contact detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting contact on the touch surface. A decrease in contact feature intensity from an intensity above the contact detection intensity threshold to an intensity below the contact detection intensity threshold is sometimes referred to as detecting contact lifted from the touch surface. In some embodiments, the contact detection intensity threshold is zero. In some embodiments, the contact detection intensity threshold is greater than zero.

[0198] In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a corresponding press input or in response to detecting a corresponding press input performed using a corresponding contact (or multiple contacts), wherein the corresponding press input is detected at least in part based on detecting that the strength of the contact (or multiple contacts) increases to above a press input strength threshold. In some embodiments, the corresponding operation is performed in response to detecting that the strength of the corresponding contact increases to above the press input strength threshold (e.g., a "down stroke" of the corresponding press input). In some embodiments, the press input includes an increase in the strength of the corresponding contact to above the press input strength threshold and the strength of the contact subsequently decreases to below the press input strength threshold, and the corresponding operation is performed in response to detecting that the strength of the corresponding contact subsequently decreases to below the press input threshold (e.g., an "up stroke" of the corresponding press input).

[0199] In some embodiments, the device employs intensity hysteresis to avoid unexpected inputs sometimes referred to as "jitter," where the device defines or selects a hysteresis intensity threshold that has a predefined relationship to a press input intensity threshold (e.g., the hysteresis intensity threshold is X intensity units lower than the press input intensity threshold, or the hysteresis intensity threshold is 75%, 90%, or some reasonable proportion of the press input intensity threshold). Thus, in some embodiments, a press input includes an increase in the intensity of the corresponding contact to above the press input intensity threshold and a subsequent decrease in the intensity of the contact to below the hysteresis intensity threshold corresponding to the press input intensity threshold, and a corresponding operation is performed in response to detecting that the intensity of the corresponding contact subsequently decreases to below the hysteresis intensity threshold (e.g., an "upward stroke" of the corresponding press input). Similarly, in some embodiments, a press input is detected only when the device detects that the contact intensity increases from an intensity equal to or below the hysteresis intensity threshold to an intensity equal to or above the press input intensity threshold and, optionally, that the contact intensity subsequently decreases to an intensity equal to or below the hysteresis intensity, and a corresponding operation is performed in response to detecting the press input (e.g., an increase in contact intensity or a decrease in contact intensity, depending on the circumstances).

[0200] For ease of explanation, optionally, a description of an operation performed in response to a press input associated with a press input intensity threshold or in response to a gesture including a press input is triggered in response to detecting any of the following: contact intensity increasing to above the press input intensity threshold, contact intensity increasing from an intensity below a hysteresis intensity threshold to an intensity above the press input intensity threshold, contact intensity decreasing to below the press input intensity threshold, and / or contact intensity decreasing to below a hysteresis intensity threshold corresponding to the press input intensity threshold. Additionally, in examples where an operation is described as being performed in response to detecting that the intensity of the contact decreases below the press input intensity threshold, the operation is optionally performed in response to detecting that the intensity of the contact decreases below a hysteresis intensity threshold that corresponds to and is less than the press input intensity threshold.

[0201] Figure 6C Detection of multiple contacts 652A through 652E on a touch-sensitive display screen using multiple intensity sensors 624A through 624D is shown. Figure 6C Also included is an intensity graph that shows the current intensity measurements of intensity sensors 624A to 624D relative to intensity units. In this example, the intensity measurements of intensity sensors 624A and 624D are each 9 intensity units, and the intensity measurements of intensity sensors 624B and 624C are each 7 intensity units. In some implementations, the cumulative intensity is the sum of the intensity measurements of multiple intensity sensors 624A to 624D, which is 32 intensity units in this example. In some embodiments, each contact is assigned a corresponding intensity, which is a portion of the cumulative intensity. Fig.6DThe cumulative strengths assigned to contacts 652A to 652E based on their distances from the center of force 654 are shown. In this example, each of contacts 652A, 652B, and 652E is assigned a strength of the contact of 8 strength units of the cumulative strength, and each of contacts 652C and 652D is assigned a strength of the contact of 4 strength units of the cumulative strength. More generally, in some embodiments, each contact j is assigned a corresponding strength Ij, which is a portion of the cumulative strength A, according to a predefined mathematical function Ij=A·(Dj / ΣDi), where Dj is the distance of the corresponding contact j from the center of force, and ΣDi is the sum of the distances of all corresponding contacts (e.g., i=1 to the last) from the center of force. The reference may be performed using an electronic device similar to or equivalent to device 104, 200, 400, or 600. FIG. 6C to FIG. 6D In some embodiments, the characteristic intensity of the contact is based on one or more intensities of the contact. In some embodiments, the intensity sensor is used to determine a single characteristic intensity (e.g., a single characteristic intensity of a single contact). It should be noted that the intensity map is not part of the displayed user interface, but is included in the FIG. 6C to FIG. 6D To help readers.

[0202] In some embodiments, a portion of a gesture is identified for determining characteristic strength. For example, a touch-sensitive surface receives a continuous swipe contact that transitions from a starting position and reaches an end position, where the strength of the contact increases. In this example, the characteristic strength of the contact at the end position is based only on a portion of the continuous swipe contact, rather than the entire swipe contact (e.g., the swipe contact is only located at the portion at the end position). In some embodiments, a smoothing algorithm is applied to the strength of the swipe contact before determining the characteristic strength of the contact. For example, a smoothing algorithm optionally includes one or more of the following: an unweighted sliding average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and / or an exponential smoothing algorithm. In some cases, these smoothing algorithms eliminate narrow peaks or depressions in the strength of the swipe contact to achieve the purpose of determining the characteristic strength.

[0203] The intensity of contact on the touch-sensitive surface is optionally characterized relative to one or more intensity thresholds such as a contact detection intensity threshold, a light press intensity threshold, a deep press intensity threshold, and / or one or more other intensity thresholds. In some embodiments, the light press intensity threshold corresponds to an intensity at which the device will perform an operation typically associated with clicking a button of a physical mouse or touchpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform an operation different from the operation typically associated with clicking a button of a physical mouse or touchpad. In some embodiments, when a contact having a characteristic intensity lower than the light press intensity threshold (e.g., and higher than a nominal contact detection intensity threshold, contacts lower than the nominal contact detection intensity threshold are no longer detected) is detected, the device will move the focus selector according to the movement of the contact on the touch-sensitive surface without performing an operation associated with the light press intensity threshold or the deep press intensity threshold. Generally speaking, unless otherwise stated, these intensity thresholds are consistent between different groups of user interface illustrations.

[0204] An increase in contact feature intensity from an intensity below a light press intensity threshold to an intensity between the light press intensity threshold and the deep press intensity threshold is sometimes referred to as a "light press" input. An increase in contact feature intensity from an intensity below a deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a "deep press" input. An increase in contact feature intensity from an intensity below a contact detection intensity threshold to an intensity between the contact detection intensity threshold and the light press intensity threshold is sometimes referred to as detecting contact on the touch surface. A decrease in contact feature intensity from an intensity above the contact detection intensity threshold to an intensity below the contact detection intensity threshold is sometimes referred to as detecting contact lifted from the touch surface. In some embodiments, the contact detection intensity threshold is zero. In some embodiments, the contact detection intensity threshold is greater than zero.

[0205] In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a corresponding press input or in response to detecting a corresponding press input performed using a corresponding contact (or multiple contacts), wherein the corresponding press input is detected at least in part based on detecting that the strength of the contact (or multiple contacts) increases to above a press input strength threshold. In some embodiments, the corresponding operation is performed in response to detecting that the strength of the corresponding contact increases to above the press input strength threshold (e.g., a "down stroke" of the corresponding press input). In some embodiments, the press input includes an increase in the strength of the corresponding contact to above the press input strength threshold and the strength of the contact subsequently decreases to below the press input strength threshold, and the corresponding operation is performed in response to detecting that the strength of the corresponding contact subsequently decreases to below the press input threshold (e.g., an "up stroke" of the corresponding press input).

[0206] FIG. 6E to FIG. 6HDetection of a gesture is shown, the gesture comprising contact 662 with an intensity from below Fig. 6E The light press intensity threshold in L ”) increases to a strength higher than Figure 6H The deep compression intensity threshold in D ”) corresponding to a press input with an intensity of a deep press intensity threshold (e.g., “IT 2”). On the displayed user interface 670 including application icons 672A to 672D displayed in a predefined area 674, a cursor 676 is displayed above application icon 672B corresponding to application 2, and a gesture performed using contact 662 is detected on the touch-sensitive surface 660. In some embodiments, a gesture is detected on the touch-sensitive display 604. The intensity sensor detects the intensity of the contact on the touch-sensitive surface 660. The device determines whether the intensity of contact 662 is within a deep press intensity threshold (e.g., “IT 2”). D ”) reaches a peak above. Contact 662 is maintained on touch-sensitive surface 660. In response to detecting the gesture, and according to the intensity increasing to a deep press intensity threshold (e.g., “IT D ”) above contact 662, displays reduced-scale representations 678A to 678C (e.g., thumbnails) of documents recently opened for application 2, such as FIG. 6F to FIG. 6H In some embodiments, the intensity is a characteristic intensity of the contact compared to one or more intensity thresholds. It should be noted that the intensity map for contact 662 is not part of the displayed user interface, but is included in the FIG. 6E to FIG. 6H To help readers.

[0207] In some embodiments, the display of representations 678A to 678C includes animation. For example, representation 678A is initially displayed near application icon 672B, such as Fig. 6F As the animation progresses, representation 678A moves upward and representation 678B is displayed near application icon 672B, as shown in FIG. Figure 6G Then, representation 678A moves upward, 678B moves upward toward representation 678A, and representation 678C is displayed near application icon 672B, as shown in FIG. Figure 6H 678A to 678C form an array above icon 672B. In some embodiments, the animation progresses according to the intensity of contact 662, such as FIG. 6F to FIG. 6G , where representations 678A to 678C appear and increase as the strength of contact 662 approaches a deep press strength threshold (eg, “IT D ”) increases and moves upward. In some embodiments, the intensity according to which the animation progresses is the characteristic intensity of the contact. The reference may be performed using an electronic device similar to or equivalent to device 104, 200, 400, 600, or 800. FIG. 6E to FIG. 6H The operation described.

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

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

[0210] As used herein, an "installed application" refers to a software application that has been downloaded to an electronic device (e.g., device 100, 200, 400, 600, and / or 800) and is ready to be launched (e.g., become open) on the device. In some embodiments, a downloaded application becomes an installed application using an installer that extracts program portions from a downloaded software package and integrates the extracted portions with the operating system of a computer system.

[0211] As used herein, the term "open application" or "executing application" refers to a software application that has retained state information (e.g., as part of device / global internal state 157 and / or application internal state 192). An open or executing application is optionally any of the following types of applications:

[0212] The active application currently displayed on the display of the device on which the application is being used;

[0213] Background applications (or background processes), which are not currently displayed but one or more processes of the application are being processed by one or more processors; and

[0214] - A suspended or dormant application that is not running but has state information stored in memory (volatile and non-volatile, respectively) and can be used to resume execution of the application.

[0215] As used herein, the term "closed application" refers to a software application that does not have retained state information (e.g., the state information of a closed application is not stored in the memory of the device). Therefore, closing an application includes stopping and / or removing the application process of the application and removing the state information of the application from the memory of the device. Generally speaking, when in a first application, opening a second application does not close the first application. When the second application is displayed and the first application stops being displayed, the first application becomes a background application.

[0216] 3. Digital Assistant System

[0217] Fig. 7A A block diagram of a digital assistant system 700 according to various examples is shown. In some examples, the digital assistant system 700 is implemented on a stand-alone computer system. In some examples, the digital assistant system 700 is distributed across multiple computers. In some examples, some of the modules and functions of the digital assistant are divided into a server portion and a client portion, where the client portion is located on one or more user devices (e.g., device 104, device 122, device 200, device 400, device 600, or device 900) and communicates with the server portion (e.g., server system 108) via one or more networks, such as Figure 1 In some examples, the digital assistant system 700 is Figure 1 Specific implementation of the server system 108 (and / or DA server 106) shown in . It should be noted that the digital assistant system 700 is only one example of a digital assistant system, and the digital assistant system 700 has more or fewer components than shown, combines two or more components, or may have a different configuration or layout of components. Fig. 7AThe various components shown in the drawings are implemented in hardware, software instructions for execution by one or more processors, firmware (including one or more signal processing integrated circuits and / or application specific integrated circuits), or a combination thereof.

[0218] The digital assistant system 700 includes a memory 702, an input / output (I / O) interface 706, a network communication interface 708, and one or more processors 704. These components can communicate with each other via one or more communication buses or signal lines 710.

[0219] 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).

[0220] In some examples, the I / O interface 706 couples the input / output devices 716 of the digital assistant system 700, such as a display, keyboard, touch screen, and microphone, to the user interface module 722. The I / O interface 706 and the user interface module 722 receive user input (e.g., voice input, keyboard input, touch input, etc.) and process the input accordingly. In some examples, for example, when the digital assistant is implemented on a stand-alone user device, the digital assistant system 700 includes a plurality of components related to the user interface module 722. Figure 2A , Figure 4 , FIG. 6A to FIG. 6B and 9A to 9C In some examples, digital assistant system 700 represents a server portion of a specific implementation of a digital assistant, and can interact with a user through a client-side portion located on a user device (e.g., device 104, device 200, device 400, device 600, or device 900).

[0221] In some examples, the network communication interface 708 includes one or more wired communication ports 712 and / or wireless transmission and reception circuits 714. One or more wired communication ports receive and send communication signals via one or more wired interfaces such as Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuit 714 receives RF signals and / or optical signals from a communication network and other communication devices and sends RF signals and / or optical signals to a communication network and other communication devices. Wireless communication uses any of a variety of communication standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. The network communication interface 708 enables the digital assistant system 700 to communicate with other devices through a network, such as the Internet, an intranet and / or a wireless network such as a cellular telephone network, a wireless local area network (LAN) and / or a metropolitan area network (MAN).

[0222] In some examples, the memory 702 or the computer-readable storage medium of the memory 702 stores programs, modules, instructions, and data structures, including all or a subset of the following: operating system 718, communication module 720, user interface module 722, one or more application programs 724, and digital assistant module 726. Specifically, the memory 702 or the computer-readable storage medium of the memory 702 stores instructions for performing the above processes. One or more processors 704 execute these programs, modules, and instructions, and read data from or write data to data structures.

[0223] The operating system 718 (e.g., Darwin, RTXC, LINUX, UNIX, iOS, OS X, WINDOWS, or an embedded operating system such as VxWorks) includes various software components and / or drivers for controlling and managing general system tasks (e.g., memory management, storage device control, power management, etc.), and facilitates communication between various hardware, firmware, and software components.

[0224] The communication module 720 facilitates communication between the digital assistant system 700 and other devices through the network communication interface 708. For example, the communication module 720 and the electronic devices (such as Figure 2A , Figure 4 , FIG. 6A to FIG. 6B and 9A to 9C The communication module 720 also includes various components for processing data received by the wireless circuit 714 and / or the wired communication port 712.

[0225] The user interface module 722 receives commands and / or input from the user (e.g., from a keyboard, touch screen, pointing device, controller, and / or microphone) via the I / O interface 706 and generates user interface objects on the display. The user interface module 722 also prepares output (e.g., speech, sound, animation, text, icon, vibration, tactile feedback, lighting, etc.) and transmits it to the user via the I / O interface 706 (e.g., through a display, audio channel, speaker, touchpad, etc.).

[0226] Applications 724 include programs and / or modules configured to be executed by the one or more processors 704. For example, if the digital assistant system is implemented on a stand-alone user device, applications 724 include user applications such as games, calendar applications, navigation applications, or mail applications. If the digital assistant system 700 is implemented on a server, applications 724 include, for example, resource management applications, diagnostic applications, or scheduling applications.

[0227] The memory 702 also stores a digital assistant module 726 (or the server portion of the digital assistant). In some examples, the digital assistant module 726 includes the following submodules or subsets or supersets thereof: an input / output processing module 728, a speech-to-text (STT) processing module 730, a natural language processing module 732, a dialog flow processing module 734, a task flow processing module 736, a service processing module 738, and a speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726 or a subset or superset thereof: a knowledge ontology 760, a vocabulary index 744, user data 748, a task flow model 754, a service model 756, and an ASR system 758.

[0228] In some examples, using the processing modules, data, and models implemented in the digital assistant module 726, the digital assistant may perform at least some of the following: converting voice input into text; recognizing user intent expressed in natural language input received from a user; proactively eliciting and obtaining information needed to fully infer user intent (e.g., by disambiguating words, games, intents, etc.); determining a task flow for satisfying the inferred intent; and executing the task flow to satisfy the inferred intent.

[0229] In some examples, such as Figure 7B As shown in FIG. , the I / O processing module 728 can be Fig. 7A The I / O device 716 interacts with the user or through Fig. 7AThe network communication interface 708 in interacts with a user device (e.g., device 104, device 200, device 400, or device 600) to obtain 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 obtains context information associated with the user input from the user device along with or shortly after receiving the user input. The context information includes user-specific data, vocabulary, and / or preferences related to the user input. In some examples, the context information also includes the software state and hardware state of the user device when the user request is received, and / or information related to the user's surrounding environment when the user request is received. In some examples, the I / O processing module 728 also sends follow-up questions related to the user request to the user and receives answers from the user. When the user request is received by the I / O processing module 728 and the user request includes voice input, the I / O processing module 728 forwards the voice input to the STT processing module 730 (or speech recognizer) for speech-to-text conversion.

[0230] The STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input received by the I / O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech preprocessor. The front-end speech preprocessor extracts representative features from the speech input. For example, the front-end speech preprocessor performs Fourier transform on the speech input to extract the spectral features that characterize the speech input as a sequence of representative multidimensional vectors. In addition, each ASR system 758 includes one or more speech recognition models (e.g., acoustic models and / or language models) and implements one or more speech recognition engines. Examples of speech recognition models include hidden Markov models, Gaussian mixture models, deep neural network models, n-gram language models, and other statistical models. Examples of speech recognition engines include engines based on dynamic time warping and engines based on weighted finite state transformers (WFST). One or more speech recognition models and one or more speech recognition engines are used to process the extracted representative features of the front-end speech preprocessor to produce intermediate recognition results (e.g., phonemes, phoneme strings and sub-words), and finally produce text recognition results (e.g., words, word strings, or symbol sequences). In some examples, the voice input is at least partially processed by a third-party service or processed on the user's device (e.g., device 104, device 200, device 400 or device 600) to produce recognition results. Once the STT processing module 730 generates a recognition result containing a text string (e.g., a word, or a sequence of words, or a sequence of symbols), the recognition result is transmitted to the natural language processing module 732 for intention inference. In some examples, the STT processing module 730 generates multiple candidate text representations of the voice input. Each candidate text representation is a sequence of words or symbols corresponding to the voice input. In some examples, each candidate text representation is associated with a voice recognition confidence score. Based on the speech recognition confidence score, the STT processing module 730 ranks the candidate text representations and provides the n best (e.g., the n highest ranked) candidate text representations to the natural language processing module 732 for intent inference, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is delivered to the natural language processing module 732 for intent inference. For another example, the 5 highest ranked (n=5) candidate text representations are delivered to the natural language processing module 732 for intent inference.

[0231] Further details regarding speech-to-text processing are described in U.S. Utility Patent Application Serial No. 13 / 236,942, filed on September 20, 2011, entitled “Consolidating Speech Recognition Results,” the entire disclosure of which is incorporated herein by reference.

[0232] In some examples, the STT processing module 730 includes a vocabulary of recognizable words and / or accesses the vocabulary via the speech-to-alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in the speech recognition phonetic alphabet. Specifically, the vocabulary of recognizable words includes words associated with multiple candidate pronunciations. For example, the vocabulary includes words associated with and In some examples, the candidate pronunciations for the words are associated with candidate pronunciations for the word "tomato". Additionally, vocabulary words are associated with custom candidate pronunciations based on previous speech input from the user. Such custom candidate pronunciations are stored in the STT processing module 730 and associated with a particular user via a user profile on the device. In some examples, the candidate pronunciations for the words are determined based on the spelling of the words and one or more linguistic and / or phonetic rules. In some examples, the candidate pronunciations are manually generated, for example, based on a known standard pronunciation.

[0233] In some examples, candidate pronunciations are ranked based on their prevalence. Ranked higher than Because the former is a more common pronunciation (e.g., among all users, for users in a particular geographic region, or for any other suitable subset of users). In some examples, the candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, a custom candidate pronunciation is ranked higher than a standard candidate pronunciation. This can be used to identify proper nouns with unique pronunciations that deviate from the standard pronunciation. In some examples, the candidate pronunciation is associated with one or more speech features such as geographic origin, country, or ethnicity. For example, the candidate pronunciation associated with the United States, while the candidate pronunciation associated with the United Kingdom. In addition, the ranking of the candidate pronunciations is based on one or more characteristics of the user (e.g., geographic origin, country, ethnicity, etc.) in a user profile stored on the device. For example, it may be determined from the user profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciations are ranked (associated with the United States) Comparable candidate pronunciations (associated with the United Kingdom) is ranked higher. In some examples, one of the ranked candidate pronunciations may be selected as the predicted pronunciation (eg, the most likely pronunciation).

[0234] When speech input is received, the STT processing module 730 is used to determine (e.g., using an acoustic model) a phoneme corresponding to the speech input and then attempts to determine (e.g., using a language model) a word that matches the phoneme. For example, if the STT processing module 730 first identifies a phoneme sequence corresponding to a portion of the speech input It can then determine based on the lexical index 744 that the sequence corresponds to the word "tomato".

[0235] In some examples, the STT processing module 730 uses fuzzy matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines the phoneme sequence corresponds to the word "tomato", even though this particular phoneme sequence is not a candidate phoneme sequence for that word.

[0236] The digital assistant's natural language processing module 732 ("natural language processor") obtains the n best candidate text representations ("word sequences" or "symbol sequences") generated by the STT processing module 730 and attempts to associate each candidate text representation with one or more "executable intents" recognized by the digital assistant. An "executable intent" (or "user intent") represents a task that can be executed by the digital assistant and can have an associated task flow implemented in the task flow model 754. The associated task flow is a series of programmed actions and steps taken by the digital assistant to perform the task. The scope of the digital assistant's capabilities depends on the number and variety of task flows that have been implemented and stored in the task flow model 754, or in other words, on the number and variety of "executable intents" recognized by the digital assistant. However, the effectiveness of the digital assistant also depends on the assistant's ability to infer the correct "one or more executable intents" from user requests expressed in natural language.

[0237] In some examples, in addition to the sequence of words or symbols obtained from the STT processing module 730, the natural language processing module 732 also receives context information associated with the user request, for example, from the I / O processing module 728. The natural language processing module 732 optionally uses the context information to clarify, supplement and / or further qualify the information contained in the candidate text representation received from the STT processing module 730. The context information includes, for example, user preferences, hardware and / or software state of the user's device, sensor information collected before, during, or shortly after the user's request, previous interactions (e.g., conversations) between the digital assistant and the user, and the like. As described herein, in some examples, the context information is dynamic and changes with the time, location, content, and other factors of the conversation.

[0238] In some examples, natural language processing is based on, for example, knowledge ontology 760. Knowledge ontology 760 is a hierarchical structure containing many nodes, each node representing an "executable intent" or an "attribute" related to one or more of the "executable intent" or other "attributes". As described above, an "executable intent" represents a task that the digital assistant can perform, that is, the task is "executable" or can be performed. "Attributes" represent parameters associated with sub-aspects of an executable intent or another attribute. The connection between executable intent nodes and attribute nodes in knowledge ontology 760 defines how the parameters represented by the attribute nodes are subordinate to the tasks represented by the executable intent nodes.

[0239] In some examples, the knowledge ontology 760 is composed of executable intent nodes and attribute nodes. In the knowledge ontology 760, each executable intent node is directly connected to or connected to one or more attribute nodes through one or more intermediate attribute nodes. Similarly, each attribute node is directly connected to or connected to one or more executable intent nodes through one or more intermediate attribute nodes. For example, Figure 7C As shown, ontology 760 includes a "restaurant reservation" node (i.e., an executable intent node). The attribute nodes "restaurant", "date / time" (for reservations), and "party size" are all directly connected to the executable intent node (i.e., the "restaurant reservation" node).

[0240] In addition, the attribute nodes "cuisine", "price range", "telephone number", and "location" are child nodes of the attribute node "restaurant", and are all connected to the "restaurant reservation" node (i.e., executable intent node) through the intermediate attribute node "restaurant". Figure 7C As shown, ontology 760 also includes a "set reminder" node (i.e., another executable intent node). The attribute nodes "date / time" (for setting reminders) and "subject" (for reminders) are both connected to the "set reminder" node. Since the attribute "date / time" is related to both the task of making a restaurant reservation and the task of setting a reminder, the attribute node "date / time" is connected to both the "restaurant reservation" node and the "set reminder" node in ontology 760.

[0241] Executable intent nodes, together with their linked attribute nodes, are described as "domains". In this discussion, each domain is associated with a corresponding executable intent and refers to a set of nodes (and the relationships between these nodes) associated with a particular executable intent. For example, Figure 7CThe ontology 760 shown in includes an example of a restaurant reservation domain 762 and an example of a reminder domain 764 within the ontology 760. The restaurant reservation domain includes an executable intent node "restaurant reservation", attribute nodes "restaurant", "date / time" and "party size", and sub-attribute nodes "cuisine", "price range", "phone number" and "location". The reminder domain 764 includes an executable intent node "set reminder" and attribute nodes "subject" and "date / time". In some examples, the ontology 760 consists of multiple domains. Each domain shares one or more attribute nodes with one or more other domains. For example, in addition to the restaurant reservation domain 762 and the reminder domain 764, the "date / time" attribute node is also associated with many different domains (e.g., itinerary scheduling domain, travel booking domain, movie ticket domain, etc.).

[0242] although Figure 7C Two exemplary domains within ontology 760 are shown, but other domains include, for example, "find a movie," "make a phone call," "find directions," "schedule a meeting," "send a message," and "provide answers to questions," "read a list," "provide navigation instructions," "provide instructions for a task," etc. The "send message" domain is associated with the "send message" executable intent node, and further includes attribute nodes such as "one or more recipients," "message type," and "message body." The attribute node "recipients" is further defined, for example, by child attribute nodes such as "recipient name" and "message address."

[0243] In some examples, ontology 760 includes all domains (and thus executable intents) that a digital assistant can understand and act upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying the relationships between nodes within ontology 760.

[0244] In some examples, nodes associated with multiple related executable intents are clustered under a "superdomain" in the knowledge ontology 760. For example, the "travel" superdomain includes a cluster of attribute nodes and executable intent nodes related to travel. Executable intent nodes related to travel include "ticket booking", "hotel booking", "car rental", "get directions", "find 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, the executable intent nodes for "ticket booking", "hotel booking", "car rental", "get directions", and "find points of interest" share one or more of the attribute nodes "starting location", "destination", "departure date / time", "arrival date / time", and "party size".

[0245] In some examples, each node in the knowledge ontology 760 is associated with a set of words and / or phrases related to the attribute or executable intent represented by the node. The corresponding set of words and / or phrases associated with each node is a so-called "vocabulary" associated with the node. The corresponding set of words and / or phrases associated with each node is stored in the vocabulary index 744 associated with the attribute or executable intent represented by the node. For example, return Figure 7B , the vocabulary associated with the node of the "restaurant" attribute includes words such as "food", "drinks", "cuisine", "hunger", "eat", "pizza", "fast food", "meals", etc. For another example, the vocabulary associated with the node of the "make phone call" executable intent includes words and phrases such as "call", "make a phone call", "dial", "talk to...", "call the number", "call", etc. The vocabulary index 744 optionally includes words and phrases in different languages.

[0246] The natural language processing module 732 receives candidate text representations (e.g., one or more text strings or one or more symbol sequences) from the STT processing module 730, and for each candidate representation, determines which nodes the words in the candidate text representation involve. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in the knowledge ontology 760 (via the vocabulary index 744), the word or phrase "triggers" or "activates" those nodes. Based on the number and / or relative importance of the activated nodes, the natural language processing module 732 selects one of the executable intents as the task that the user intends the digital assistant to perform. In some examples, the domain with the most "triggered" nodes is selected. In some examples, the domain with the highest confidence (e.g., based on the relative importance of its various triggered nodes) is selected. In some examples, the domain is selected based on a combination of the number and importance of the triggered nodes. In some examples, additional factors are also considered in the process of selecting nodes, such as whether the digital assistant has previously correctly interpreted similar requests from the user.

[0247] User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default second language, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses user-specific information to supplement the information contained in the user input to further qualify the user's intent. For example, for a user request to "invite my friends to my birthday party," natural language processing module 732 can access user data 748 to determine who "friends" are and when and where the "birthday party" will be held, without requiring the user to explicitly provide such information in their request.

[0248] It should be recognized that in some examples, the natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). Specifically, the one or more machine learning mechanisms are configured to receive candidate text representations and context information associated with the candidate text representations. Based on the candidate text representations and the associated context information, the one or more machine learning mechanisms are configured to determine an intent confidence score based on a set of candidate executable intents. The natural language processing module 732 may select one or more candidate executable intents from a set of candidate executable intents based on the determined intent confidence scores. In some examples, a knowledge ontology (e.g., knowledge ontology 760) is also utilized to select one or more candidate executable intents from a set of candidate executable intents.

[0249] Additional details of searching knowledge ontologies based on symbolic strings are described in U.S. Utility Patent Application Serial No. 12 / 341,743, filed on December 22, 2008, entitled “Method and Apparatus for Searching Using An Active Ontology,” the entire disclosure of which is incorporated herein by reference.

[0250] In some examples, once the natural language processing module 732 identifies an executable intent (or domain) based on a user request, the natural language processing module 732 generates a structured query to represent the identified executable intent. In some examples, the structured query includes parameters for one or more nodes within the domain of the executable intent, and at least some of the parameters are filled with specific information and requirements specified in the user request. For example, the user says "help me reserve a seat at 7pm at the sushi restaurant." In this case, the natural language processing module 732 is able to correctly identify the executable intent as "restaurant reservation" based on the user input. According to the knowledge ontology, the structured query of the "restaurant reservation" domain includes parameters such as {cuisine}, {time}, {date}, {party number}, etc. In some examples, based on voice input and text derived from the voice input using the STT processing module 730, the natural language processing module 732 generates a partial structured query for the restaurant reservation domain, wherein the partial structured query includes parameters {cuisine = "sushi type"} and {time = "7pm"}. However, in this example, the user utterance contains insufficient information to complete the structured query associated with the domain. Therefore, based on the currently available information, other necessary parameters such as {number of party members} and {date} are not specified in the structured query. In some examples, the natural language processing module 732 populates some parameters of the structured query with the received contextual information. For example, in some examples, if the user requests a sushi restaurant "nearby", the natural language processing module 732 populates the {location} parameter in the structured query with the GPS coordinates from the user's device.

[0251] In some examples, the natural language processing module 732 identifies multiple candidate executable intentions for each candidate text representation received from the STT processing module 730. In addition, in some examples, a corresponding structured query is generated (partially or completely) for each identified candidate executable intention. The natural language processing module 732 determines the intent confidence score for each candidate executable intention and ranks the candidate executable intention based on the intent confidence score. In some examples, the natural language processing module 732 transmits the generated one or more structured queries (including any completed parameters) to the task flow processing module 736 ("task flow processor"). In some examples, one or more structured queries for the m best (e.g., the m highest ranked) candidate executable intentions are provided to the task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, one or more structured queries for the m best candidate executable intentions are provided to the task flow processing module 736 together with the corresponding one or more candidate text representations.

[0252] Additional details for inferring user intent based on multiple candidate executable intents determined based on multiple candidate textual representations of speech input are described in U.S. utility model patent application 14 / 298,725, entitled “System and Method for Inferring User Intent From Speech Inputs,” filed on June 6, 2014, the entire disclosure of which is incorporated herein by reference.

[0253] The task flow processing module 736 is configured to receive one or more structured queries from the natural language processing module 732, complete the structured queries (if necessary), and perform the actions required to "complete" the user's final request. In some examples, the various processes necessary to complete these tasks are provided in the task flow model 754. In some examples, the task flow model 754 includes a process for obtaining additional information from the user, and a task flow for performing actions associated with an executable intent.

[0254] As described above, in order to complete the structured query, the task flow processing module 736 needs to initiate an additional dialogue with the user in order to obtain additional information and / or clarify the utterances that may be ambiguous. When it is necessary to conduct such an interaction, the task flow processing module 736 calls the dialogue flow processing module 734 to participate in the dialogue with the user. In some examples, the dialogue flow processor module 734 determines how (and / or when) to request additional information from the user, and receives and processes the user response. The question is provided to the user and the answer is received from the user through the I / O processing module 728. In some examples, the dialogue flow processing module 734 presents the dialogue output to the user via an audible output and / or a visual output, and receives input from the user via a verbal or physical (e.g., click) response. Continuing the above example, when the task flow processing module 736 calls the dialogue flow processing module 734 to determine the "party number" and "date" information for the structured query associated with the domain "restaurant reservation", the dialogue flow processing module 734 generates questions such as "how many people in a row?" and "which day to book?" and passes them to the user. Once the answer is received from the user, the dialog flow processing module 734 fills in the structured query with the missing information, or passes the information to the task flow processing module 736 to complete the missing information based on the structured query.

[0255] Once the task flow processing module 736 has completed the structured query for the executable intent, the task flow processing module 736 begins to execute the final task associated with the executable intent. Therefore, the task flow processing module 736 executes the steps and instructions in the task flow model according to the specific parameters contained in the structured query. For example, the task flow model for the executable intent "restaurant reservation" includes steps and instructions for contacting a restaurant and actually requesting a reservation for a specific party size at a specific time. For example, using a structured query such as: {restaurant reservation, restaurant = ABC Cafe, date = 3 / 12 / 2012, time = 7pm, party size = 5}, the task flow processing module 736 can perform the following steps: (1) Log in to the server of ABC Cafe or such A restaurant reservation system that (2) enters date, time, and party size information into a form on the website, (3) submits the form, and (4) creates a calendar entry for the reservation in the user's calendar.

[0256] In some examples, the task flow processing module 736 completes the task requested in the user input or provides the informational answer requested in the user input with the assistance of the service processing module 738 ("service processing module"). For example, the service processing module 738 initiates a phone call, sets a calendar entry, invokes a map search, invokes or interacts with other user applications installed on the user device, and invokes or interacts with third-party services (e.g., restaurant reservation portals, social networking sites, bank portals, etc.) on behalf of the task flow processing module 736. In some examples, the protocols and application programming interfaces (APIs) required for each service are specified by the corresponding service models in the service model 756. The service processing module 738 accesses the appropriate service model for the service and generates a request for the service according to the protocol and API required by the service based on the service model.

[0257] For example, if the restaurant has enabled an online reservation service, the restaurant submits a service model that specifies the necessary parameters for making a reservation and transmits the values ​​of the necessary parameters to the API of the online reservation service. When requested by the task flow processing module 736, the service processing module 738 can use the web address stored in the service model to establish a network connection with the online reservation service and send the necessary parameters for the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.

[0258] In some examples, the natural language processing module 732, the dialog flow processing module 734, and the task flow processing module 736 are used together and repeatedly to infer and define the user's intention, obtain information to further clarify and refine the user's intention, and finally generate a response (i.e., output to the user, or complete the task) to meet the user's intention. The generated response is a dialogue response to the voice input that at least partially meets the user's intention. In addition, in some examples, the generated response is output as a voice output. In these examples, the generated response is sent to a speech synthesis processing module 740 (e.g., a speech synthesizer), where the generated response can be processed to synthesize the dialogue response in the form of speech. In other examples, the generated response is data content related to satisfying the user request in the voice input.

[0259] In an example where the task flow processing module 736 receives multiple structured queries from the natural language processing module 732, the task flow processing module 736 first processes the first structured query of the received structured query in an attempt to complete the first structured query and / or perform one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest-ranked executable intent. In other examples, the first structured query is selected from the structured query received based on the combination of the corresponding speech recognition confidence score and the corresponding intent confidence score. In some examples, if the task flow processing module 736 encounters an error during the processing of the first structured query (e.g., due to the inability to determine the necessary parameters), the task flow processing module 736 may continue to select and process the second structured query corresponding to the executable intent with a lower ranking in the received structured query. For example, the second structured query is selected based on the speech recognition confidence score represented by the corresponding candidate text, the intent confidence score of the corresponding candidate executable intent, the missing necessary parameters in the first structured query, or any combination thereof.

[0260] The speech synthesis processing module 740 is configured to synthesize speech output for presentation to the user. The speech synthesis processing module 740 synthesizes speech output based on the text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. The speech synthesis processing module 740 converts the text string into an audible speech output. The speech synthesis processing module 740 uses any appropriate speech synthesis technology to generate speech output from the text, including but not limited to: concatenation synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, pronunciation synthesis, synthesis based on hidden Markov model (HMM) and sine wave synthesis. In some examples, the speech synthesis processing module 740 is configured to synthesize individual words based on phoneme strings corresponding to these words. For example, the phoneme string is associated with the words in the generated dialogue response. The phoneme string is stored in the metadata associated with the word. The speech synthesis processing module 740 is configured to directly process the phoneme string in the metadata to synthesize the words in speech form.

[0261] In some examples, instead of (or in addition to) using the speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this may occur in some implementations where the output of the digital assistant is generated at the server system. And because the server system typically has more processing power or more resources than the user device, it is possible to obtain higher quality speech output than client-side synthesis would achieve.

[0262] Additional details about digital assistants can be found in U.S. utility patent application 12 / 987,982, entitled “Intelligent Automated Assistant,” filed on January 10, 2011, and U.S. utility patent application 13 / 251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed on September 30, 2011, the entire disclosures of which are incorporated herein by reference.

[0263] 4. System for generating avatars

[0264] Figure 8 A system 800 for generating an avatar and displaying the avatar in an animated manner according to some examples is shown. In some examples, the system 800 is implemented on one or more electronic devices (e.g., device 104, device 122, device 200, device 400, device 600, or device 900), and the modules and functions of the system 800 can be distributed between the devices in any manner. In some examples, some of the modules and functions of the system 800 are divided into a server portion and a client portion, where the client portion is located on one or more user devices (e.g., device 104, device 122, device 200, device 400, device 600, and device 900) and communicates with the server portion (e.g., server system 108) via one or more networks, such as Figure 1 The system 800 is implemented using hardware, software, or a combination of hardware and software to perform the principles discussed herein. In some examples, the modules and functions of the system 800 are implemented within a digital assistant module, as described above with respect to 7A to 7C described.

[0265] It should be noted that system 800 is exemplary, and thus system 800 may have more or fewer components than those shown, may combine two or more components, or may have a different configuration or arrangement of components. In addition, although the following discussion describes functions performed at a single component of system 800, it should be understood that these functions may be performed at other components of system 800, and these functions may be performed at more than one component of system 800.

[0266] like Figure 8 As shown, system 800 includes a neural network 810, an avatar animator 820, and an output processor 830. As described in more detail below, system 800 receives text 802 and determines an emotional state.

[0267] In some examples, as discussed further below, the system 800 determines the emotional state based on the received text 802, and thus does not need to provide any further indication of the emotional state. In other examples, the system 800 may receive an indication of the emotional state 804, such as by prompting the user or from explicit user input. In other examples, the system 800 may optionally be provided with an indication of the emotional state 804 and the text 802, and determine the emotional state based on the provided indication of the emotional state 804 and the text 802.

[0268] The system 800 then generates a speech output (e.g., speech output 832) of the text 802 and a set of parameters (e.g., animation parameters 814) representing one or more actions of an avatar (e.g., avatars 906, 914, 920) based on the text 802 and the determined emotional state. In this way, a more realistic avatar can be produced with greater flexibility. Specifically, different actions of the avatar can be informed by the determined emotional state to make the avatar look more vivid and realistic. In some examples, the determined emotional state can affect the posture of the avatar. For example, when the determined emotional state is "happy", the avatar can be animated to tilt its head upward in a posture indicating the emotional state. Similarly, in some examples, the determined emotional state can affect the actions of various features of the face, including the actions of the mouth, nose, ears, etc. For example, when the determined emotional state is "happy", the avatar can be animated to make the mouth move like a smile, the eyes open wider and toward the observer, and so on. It should be understood that this is just one example, and that the avatar may be affected by many different emotional states, including happy, sad, angry, neutral, etc. Several different examples are provided further below.

[0269] In some examples, text 802 is received from an input / output component (e.g., I / O subsystem 206, I / O interface 430, and I / O processing module 728) of a user device, a server, or a combination of the two. In some examples, text 802 is received from a user. For example, a user may provide text 802 through a user interface of an application (e.g., a messaging application such as a text messaging application, an email application, etc.). In some examples, text 802 is received from another electronic device. For example, system 800 may be located on a server, and text 802 may be received from a user device located away from the server. In some examples, text 802 is received from another application located on a user device. For example, a user may provide text 802 in a messaging application and then indicate that they want to generate an avatar based on the text 802 provided to the messaging application.

[0270] In some examples, system 800 processes text 802 before providing text 802 to neural network 810. In some examples, system 800 processes text 802 to determine a representation of text 802. In some examples, the representation of text 802 is a sequence of phonemes corresponding to text 802. In some examples, text 802 is determined based on a larger amount of text input before being provided to neural network 810. For example, Fig.9A As shown and as discussed further below, a user provides text input 904 of "Send 'Happy Birthday!' to my mom" to a digital assistant of a user device 900. The digital assistant can then process the entire text input 904 to determine that the text 802 to be provided to the neural network 810 should include "Happy Birthday!" and not "Send to my mom." Thus, after the text input 904 is pre-processed by the digital assistant, the neural network 810 receives the text 802 to be included in the speech output 832.

[0271] In some examples, text 802 is a text representation of the output of a digital assistant. Fig. 9C As shown and described further below, a user may provide an input request 916 to a digital assistant of device 900, "What is the temperature in Palo Alto?" The digital assistant may then use the above context relative to FIG. 7A to FIG. 7C The user request may be processed by one or more modules discussed to determine an appropriate response, including but not limited to an input / output processing module 728 on the user device or on another electronic device (such as a server), a speech-to-text (STT) processing module 730, a natural language processing module 732, a knowledge ontology 760, a vocabulary index 744, user data 748, a task flow model 754, a service model 756, and an ASR system 758. The digital assistant may then provide a response including "It's 48 degrees Fahrenheit outside" as text 802 to the system 800 to determine a speech output 832 and avatar data 822 for display as an avatar 920. The text 802 may then be provided as output 918 along with an avatar 920 representing the digital assistant on the display 902 of the user device 900.

[0272] In some examples, the digital assistant processes the digital assistant output to determine a portion of the output to be provided as text 802. For example, a user may provide an input request to the digital assistant at a user device, such as "What restaurants are near me?" In response to the user request "What restaurants are near me?", the digital assistant may determine that an appropriate response is "I found these restaurants near your current location," followed by a list of nearby restaurants. The digital assistant may determine that "I found these restaurants near your current location" should be provided to the system 800 as text 802 because this is a portion of the output that is expected to be provided as voice output, and the list of restaurants should be displayed on the screen of the user device. Therefore, the system 800 will only use the portion of this output provided as text 802 to generate voice output 832 and avatar data 822, and will not use the list of restaurants.

[0273] In some examples, the system 800 optionally receives an indication of an emotional state 804. In some examples, the indication of the emotional state 804 is determined based on user input received simultaneously with the user input providing the text 802. For example, a user may provide a text input of "Send 'Happy Birthday!' to my mom with a happy avatar" (e.g., through a user interface of a user device). Based on the input provided by the user, the system 800 receives the text 802 "Happy Birthday!" and an indication of the emotional state 804 "Happy" based on the text input provided by the user.

[0274] In some examples, the indication of emotional state 804 is determined by another neural network independent of neural network 810 before being provided to neural network 810. In some examples, the indication of emotional state 804 is determined by another neural network independent of neural network 810 based on text 802. For example, text 802 can be provided to a neural network that is trained to determine the indication of emotional state 804, such as a classifier discussed below with respect to training neural network 810. The neural network can then evaluate text 802 to determine the indication of emotional state 804 using techniques similar to those discussed below with respect to evaluating text 802 by neural network 810 to determine emotional state. Thus, the independent neural network (e.g., the classifier) ​​can evaluate individual words, punctuation marks, or phrases of text 802 to determine whether they provide any indication of a particular emotional state (e.g., "happy"). The independent neural network can then provide the determined emotional state as the indication of emotional state 804 to neural network 810.

[0275] In some examples, the indication of the emotional state 804 is received separately from the text 802. For example, Fig. 9BAs shown and discussed further below, a user may provide a text input 908 of "send 'I'm not sure I can do this' to my mom with an avatar" and then separately indicate in another input (e.g., input 912) provided to the user device 900 that the avatar should have an emotional state of "sad". In some examples, the indication 804 of the emotional state is based on a selection of possible emotional states provided to the user. For example, a user may provide a text input of "send 'Happy Birthday!' to my mom with an avatar", and the user device may provide a selection menu of possible emotional states, including happy, angry, sad, etc. The user may then select "happy" as the desired emotional state, which is provided to the system 800 as the indication 804 of the emotional state.

[0276] In some examples, the indication 804 of the emotional state is based on contextual data of the user or user device. As described above, the contextual data includes user-specific data, vocabulary, and / or preferences related to the user input. In some examples, the contextual data also includes the software state and hardware state of the user device when the text input is received, and / or information related to the user's surrounding environment when the text input is received. In some examples, the contextual data includes sensor information collected before, during, or shortly after receiving the text input, previous interactions (e.g., conversations) between the digital assistant of the user device and the user, etc. In some examples, the contextual data includes one or more default settings of the user or user device.

[0277] In some examples, the system 800 provides text 802 to the neural network 810. In some examples, the neural network 810 includes a neural network trained to determine a voice data set 812 and animation parameters 814 based on the text 802. The neural network 810 may include any type of neural network capable of receiving text and determining a voice data set and avatar animation parameters. For example, the neural network 810 may include a tacotron network, a modified tacotron network, a long short-term memory (LSTM) network, a recurrent neural network (RNN), any other suitable network, or any combination of these or other suitable networks. In some examples, the neural network 810 includes a machine learning model trained to determine a voice data set 812 and animation parameters 814 based on the text 802. In some examples, the neural network 810 includes one or more of an encoder, a decoder, or any other suitable module, which can be implemented with a tactron network, a modified tactron network, a LSTM network, a RNN network, any other suitable network, or any combination of these or other suitable networks.

[0278] In some examples, neural network 810 determines an emotional state. In some examples, neural network 810 determines an emotional state based on received text 802. In some examples, neural network 810 determines whether received text 802 or a representation of received text 802 includes one or more words that indicate a particular emotional state. For example, when text 802 includes "Happy Birthday!", neural network 810 may determine that text 802 includes the word "happy", and therefore text 802 indicates a happy emotional state. For another example, when text 802 includes "I fell and hurt myself", neural network 810 determines that text 802 includes the word "hurt", and therefore indicates an emotional state of sadness or worry.

[0279] In some examples, neural network 810 determines whether text 802 or a representation of received text 802 includes punctuation that indicates a particular emotional state. For example, when text 802 includes "Happy Birthday!" as previously described, neural network 810 determines that text 802 indicates a happy emotional state based on the use of an exclamation point. In some examples, neural network 810 determines an emotional state based on the words and punctuation included in text 802. For example, neural network 810 may determine that the presence of an exclamation point in "Happy Birthday!" may indicate an emotional state of happiness or anger. Therefore, by determining that text 802 also includes the word "happy," neural network 810 may determine that text 802 indicates an emotional state of happiness rather than anger.

[0280] In some examples, when system 800 receives indication of emotional state 804, system 800 provides indication of emotional state 804 to neural network 810. In some examples, neural network 810 determines the emotional state based on indication of emotional state 804, regardless of text 802. For example, when indication of emotional state 804 is "happy", neural network 810 determines the emotional state to be "happy" regardless of text 802. In some examples, neural network 810 determines the emotional state based on text 802 and indication of emotional state 804. For example, neural network 810 may determine that the presence of an exclamation mark in text 802 of "Happy Birthday!" may indicate an emotional state of happiness or anger, and thus may refer to indication of emotional state 804 of "happy". Therefore, neural network 810 may determine that the emotional state is "happy".

[0281] In some examples, neural network 810 determines a vector representation of text 802 and a vector representation of the determined emotional state. In some examples, the vector representation of text 802 and the vector representation of the determined emotional state are determined by an encoder of neural network 810. In some examples, neural network 810 determines a vector representation of both text 802 and the determined emotional state. In some examples, neural network 810 determines a vector representation of both text 802 and the determined emotional state by linking the vector representation of text 802 and the vector representation of the determined emotional state together. In some examples, the vector representation of both text 802 and the determined emotional state is a first intermediate vector representing text 802 and the determined emotional state. In some examples, this first intermediate vector representing text 802 and the determined emotional state is determined by an encoder of neural network 810.

[0282] In some examples, neural network 810 determines speech dataset 812 and animation parameters 814 based on the first intermediate vector representing text 802 and the determined emotional state. Thus, the output of neural network 810 (including speech dataset 812 and animation parameters 814) is based on both text 802 and the determined emotional state, and thus based on both text 802 and the optional indication of emotional state 804. In some examples, speech dataset 812 and animation parameters 814 are determined using a decoder of neural network 810.

[0283] In some examples, the neural network 810 determines a second intermediate vector based on the first intermediate vector representing the text 802 and the determined emotional state (e.g., using a decoder). For example, the neural network 810 may determine a second intermediate vector corresponding to the speech data set 812 and the animation parameters 814 based on the first intermediate vector representing the text 802 and the determined emotional state. In some examples, the neural network 810 applies a linear projection to the second intermediate vector to determine the speech data set 812 and the animation parameters 814. In some examples, the neural network 810 applies a different linear projection to the second intermediate vector to determine each corresponding output. For example, the neural network 810 may apply a first linear projection to the second intermediate vector to determine the speech data set 812, and apply a second linear projection to the second intermediate vector to determine the animation parameters 814.

[0284] In some examples, the neural network 810 determines the speech data set 812 based on the vector representation of the text 802 or the vector representation of the determined emotional state, without considering the other vector representation. For example, the speech data set 812 can be generated similarly regardless of the determined emotional state, and therefore can be determined based on the vector representation of the text 802 without considering the determined emotional state. Similarly, in some examples, the neural network 810 determines the animation parameters 814 or a portion of the animation parameters 814 based on the vector representation of the determined emotional state or the vector representation of the text 802, without considering the other vector representation. Therefore, the neural network 810 can determine some outputs or portions of outputs (e.g., the speech data set 812 and the animation parameters 814) based on the text 802 or the indication 804 of the emotional state without any other input.

[0285] In some examples, the animation parameters 814 include one or more sets of parameters. For example, the animation parameters 814 may include a first set of parameters for animating the action of the avatar and a second set of parameters for animating the posture of the avatar. Therefore, the neural network 810 may determine each set of parameters (e.g., the first set of parameters and the second set of parameters) included in the animation parameters 814 based on the first intermediate vector representing the text 802 and the determined emotional state. For example, as described above, the neural network 810 may determine the second intermediate vector and then apply the corresponding linear projection to the second intermediate vector to determine each corresponding output, including the speech data set 812, the first set of parameters for animating the action of the avatar, and the second set of parameters for animating the posture of the avatar.

[0286] In some examples, each set of parameters (e.g., a first set of parameters and a second set of parameters) included in the animation parameters 814 is based on a vector representation of the text 802 or a vector representation of the determined emotional state, without regard to the other vector representation. For example, when the determined emotional state changes, the first set of parameters for the action of animating the avatar may not change. Therefore, the first set of parameters for the action of animating the avatar may be determined based only on the vector representation of the text 802, and the determined emotional state may not be considered. Similarly, when the text 802 changes, the second set of parameters for the gesture of the avatar may not change. Therefore, the second set of parameters for the gesture of the avatar may be determined based only on the vector representation of the determined emotional state, and the text 802 may not be considered.

[0287] In some examples, prior to receiving text 802 and determining speech data set 812 and animation parameters 814, neural network 810 is trained to determine a speech data set and a set of parameters representing one or more actions of the avatar. To train neural network 810, a set of training text data is provided to neural network 810 along with a reference speech data set and a set of parameters representing a reference of one or more actions of the avatar. Neural network 810 then determines a training speech data set and a set of training parameters representing the actions of the avatar based on the training text data. The training speech data set and the set of training parameters representing the actions of the avatar are then evaluated to determine whether neural network 810 has been successfully trained. Specifically, the training speech data set and the set of training parameters representing the actions of the avatar are compared with the reference speech data set and a set of parameters representing the reference of one or more actions of the avatar, respectively. Typically, the training of the neural network 810 is accomplished in an iterative manner by repeatedly providing training text data to the neural network 810 so that the neural network 810 learns to synthesize a training speech dataset and a set of training parameters representing the actions of the avatar, wherein the training speech dataset and the set of training parameters representing the actions of the avatar are respectively approximate to a reference speech dataset and a set of parameters representing a reference of one or more actions of the avatar.

[0288] In some examples, the same training text data may be provided to the neural network 810 in an iterative manner. When the neural network 810 is trained in this manner, the training speech data set and training parameters determined by the neural network 810 will gradually become more similar to the reference speech data set and a reference set of parameters representing one or more actions of the avatar, respectively. For example, the training text data provided to the neural network 810 may be "Happy Birthday". When the training text is first provided to the neural network 810, the determined training speech data set and training parameters may be very different from the reference speech data set representing "Happy Birthday" and the reference parameters for displaying the avatar in an animated manner to make an action as if saying "Happy Birthday". When the training text is provided to the neural network 810 multiple times during the process of training the neural network 810, the training speech data set and training parameters will change until the training speech data set approximates the reference speech data set representing "Happy Birthday" and the training parameters approximates the reference parameters for displaying the avatar in an animated manner to make an action as if saying "Happy Birthday".

[0289] Similarly, in some examples, training text data including different words or phrases may be provided to neural network 810 during the process of training neural network 810. Training text data including different words or phrases may also be iteratively provided to neural network 810 to effectively train neural network 810, thereby generating many speech data sets and parameters that are different from speech data set 812 and animation parameters 814, respectively.

[0290] As described above, after neural network 810 determines each training speech data set and each set of training parameters, they are evaluated to determine whether neural network 810 has been successfully trained. In some examples, when the training speech data set and the set of training parameters sufficiently match the reference speech data set and the set of parameters, it is determined that neural network 810 has been successfully trained. In some examples, when multiple training speech data sets and multiple sets of training parameters are correctly determined based on multiple sets of training text data, it is determined that neural network 810 has been successfully trained. Once it is determined that neural network 810 has been successfully trained, the system can stop providing training text data to neural network 810.

[0291] In some examples, after training of neural network 810 is completed, neural network 810 is adjusted to recognize and determine an emotional state. In some examples, adjusting neural network 810 to recognize and determine an emotional state includes adjusting neural network 810 to determine speech data set 812 and animation parameters 814 based on the emotional state. To adjust neural network 810 to recognize and determine an emotional state, a predetermined emotional state is provided to neural network 810. In some examples, the predetermined emotional state is provided to neural network 810 as a vector representation of the predetermined emotional state.

[0292] In some examples, the predetermined emotional state is generated by a classifier that is independently trained to determine the emotional state. The classifier may be provided with each set of training text data and the corresponding emotional state for each set of training text data. For example, the classifier may be provided with the training text data "Happy Birthday!" and the corresponding emotional state "Happy". The training text data and the corresponding emotional state may be provided to the classifier in a supervised iterative manner until the classifier can correctly identify one of several different emotional states (e.g., happy, sad, angry, neutral, etc.) from the text. Once the classifier is trained, an intermediate representation of the predetermined emotional state is extracted and then provided to the neural network 810. In some examples, an intermediate representation of each different predetermined emotional state (e.g., happy, sad, angry, neutral, etc.) is extracted and provided to the neural network 810.

[0293] In some examples, the predetermined emotional state is generated by a classifier that is independently trained to determine the emotional state based on the speech data. The classifier may be provided with each set of training speech data and the corresponding emotional state for each set of training speech data. For example, the classifier may be provided with the training speech data "Happy Birthday!" and the corresponding emotional state "Happy". Similarly, the classifier may be provided with the training speech data "I'm sorry" and the corresponding emotional state "Sad". The training speech data and the corresponding emotional state may be provided to the classifier in a supervised iterative manner until the classifier can correctly identify one of several different emotional states (e.g., happy, sad, angry, neutral, etc.) from the speech. Once the classifier is trained, an intermediate representation of the predetermined emotional state is extracted and then provided to the neural network 810. In some examples, an intermediate representation of each different predetermined emotional state (e.g., happy, sad, angry, neutral, etc.) is extracted and provided to the neural network 810.

[0294] In some examples, the neural network 810 is provided with a predetermined emotional state and a set of training text data representing the predetermined emotional state. For example, when an intermediate representation of the emotional state "happy" is provided to the neural network 810, the training text data "Happy Birthday!" is also provided to teach the neural network 810 the correlation between similar text data and the emotional state "happy". The neural network 810 can then generate a training speech data set and a set of training parameters representing the predetermined emotional state (e.g., happy). As described above with respect to training the neural network 810, this process of providing the predetermined emotional state and the training text data can be iteratively repeated until the training speech data set and the set of training parameters are sufficiently matched with a set of reference training speech data sets and a set of reference parameters, respectively. In addition, the process can be iteratively repeated using multiple sets of training text data and multiple predetermined emotional states until the neural network 810 is adjusted to recognize many different emotional states and generate speech data sets and animation parameter sets based on those different emotional states.

[0295] In some examples, a set of training parameters representing a predetermined emotional state is generated in a manner similar to animation parameters 814. Thus, the set of training parameters is generated by determining a vector representation of the training text data and a vector representation of the predetermined emotional state. In some examples, the vector representation of the predetermined emotional state is an intermediate representation extracted from the trained classifier described above. In other examples, the vector representation of the predetermined emotional state is determined by the neural network 810 based on another indication of the predetermined emotional state. Once the neural network 810 obtains or generates a vector representation of the training text data and a vector representation of the predetermined emotional state, the vector representations may be linked together to create an intermediate vector representing both the training text data and the predetermined emotional state. The neural network 810 may then apply appropriate linear projections to the intermediate vectors representing both the training text data and the predetermined emotional state to determine the training speech data and / or one or more sets of training parameters.

[0296] In some examples, neural network 810 is trained to generate speech output and is adjusted to recognize emotional states in separate sequential steps as described above. In some examples, neural network 810 is trained to generate speech output and is adjusted to recognize emotional states simultaneously (e.g., simultaneously). Thus, the iterative process of providing training text data and predetermined emotional states to generate training speech data sets and multiple sets of training parameters can overlap and occur simultaneously, such that neural network 810 is gradually trained to generate speech data sets and animation parameters based on text data and determined emotional states.

[0297] In some examples, neural network 810 optionally includes multiple neural networks. In some examples, the functions of the multiple neural networks are similar to the description of neural network 810 above. In some examples, the multiple neural networks include a first neural network trained to generate a speech data set representing text 802 based on text 802 and the determined emotional state, and a second neural network trained to generate a set of parameters for animating an avatar based on the generated speech data set. In some examples, the multiple neural networks also include a neural network trained to determine an emotional state based on text 802 or an indication 804 of an emotional state that is optionally received. In some examples, the first neural network trained to generate the speech data set is trained to determine the emotional state.

[0298] When neural network 810 includes multiple neural networks as described above, the multiple neural networks may be trained and adjusted sequentially or simultaneously as described above with respect to neural network 810. Thus, in some examples, multiple neural networks may be first trained to generate a speech data set and a set of parameters for animating an avatar, and then adjusted to recognize and generate outputs based on emotional states, and in some examples, multiple neural networks may be trained to generate outputs and adjusted to recognize emotional states simultaneously.

[0299] In some examples, an audio input may be provided to a plurality of neural networks. In some examples, the audio input is received from a user and includes a request to generate an avatar. In some examples, the audio input may be processed before being provided to the plurality of neural networks to determine the user's intent and to determine which portions of the audio input are to be provided to the plurality of networks. Additionally, the audio input may be processed to determine a representation of the audio input. In some examples, the representation of the audio input is a sequence of phonemes corresponding to the audio input.

[0300] In some examples, an audio input or a representation of the audio input is processed to determine whether to provide the audio input to one or more neural networks of a plurality of neural networks. In some examples, determining whether to provide the representation of the audio input to one or more neural networks of a plurality of neural networks includes determining whether to provide the representation of the audio input to a second neural network. In some examples, determining whether to provide the representation of the audio input to a second neural network includes determining whether the audio input will be provided with an avatar. In some examples, determining whether the audio input will be provided with an avatar includes determining that an audio output based on the text input will not be generated. In some examples, the representation of the audio input is provided to the neural network based on determining that the audio input will be provided with an avatar and / or determining that the representation of the audio input will be provided to a second neural network.

[0301] In some examples, the audio input or a representation of the audio input is also provided to another neural network to determine an emotional state associated with the audio input, such as a classifier trained to recognize emotional states based on the audio data described above. In some examples, the emotional state is determined based on another input provided by a user that indicates an emotional state. Once the emotional state is determined, the determined emotional state may also be provided to a second neural network.

[0302] Once the audio input or a representation of the audio input and the determined emotional state are provided to the second neural network, the second neural network determines a set of parameters for animating the avatar based on the representation of the audio input and the determined emotional state. In this way, an avatar can be generated that realistically moves as if providing the user-provided input and can convey the desired emotional state together with the provided audio input.

[0303] For example, a user may provide an audio input to the digital assistant "Send an avatar that says 'Happy Birthday!' to my mom." The digital assistant may then determine, based on the audio input, that the user wishes to have an avatar generated and sent to the contact "Mom" along with an audio recording of the user saying "Happy Birthday!" The digital assistant may also provide the portion of the audio input that will be sent with the avatar (e.g., "Happy Birthday!") to a neural network (e.g., a classifier) ​​to determine that the emotional state of the audio input is "happy." Alternatively, the user may provide an emotional state in the input by specifying that the emotional state should be "happy." A representation of the selected portion of the audio input along with the determined emotional state "happy" may then be provided to a second neural network, which generates a set of parameters for animating the avatar so that the avatar appears happy and says "Happy Birthday!" The parameters for animating the avatar and the audio recording of the user saying "Happy Birthday!" may later be processed (e.g., by output processor 830) to synchronize the avatar with the audio recording so that the avatar may be provided with the audio recording, as discussed further below.

[0304] It should be understood that this is one example of a multi-network implementation of neural network 810, and such a multi-network implementation may include more or fewer different neural networks as desired. Thus, in some examples, neural network 810 may include three, four, five, six, or more neural networks, all of which may be trained sequentially or simultaneously to produce a desired output.

[0305] like Figure 8As shown, the system 800 includes an avatar animator 820. In some examples, the avatar animator 820 receives animation parameters 814 from the neural network 810. Once the avatar animator 820 receives the animation parameters, the avatar animator 820 generates avatar data 822 for animating an avatar (e.g., avatars 906, 914, 920) using the animation parameters 814. In some examples, the avatar animator 820 uses different portions of the animation parameters 814 to animate different portions or actions of the avatar. In some examples, the avatar animator 820 uses a first set of parameters of the animation parameters 814 (e.g., pose animation parameters) to animate a first feature of the avatar (e.g., head pose), and uses a second set of parameters of the animation parameters 814 (e.g., action animation parameters) to animate a second feature of the avatar (e.g., lip motion). For example, when the neural network 810 determines that the emotional state is "happy", the animation parameters 814 may include a first set of parameters for a "happy" head pose, which the avatar animator 820 uses to generate avatar data 822 including a head pose that looks up. Similarly, the animation parameters 814 may also include a second set of parameters for a "happy" action, which the avatar animator 820 uses to generate avatar data 822 including lips that are upturned like a smile. In some examples, the animation parameters 814 may include multiple sets of parameters, where each set of parameters corresponds to a different feature of the avatar (e.g., pose, lips, nose, eyebrows, ears, etc.). In some examples, the avatar animator 820 uses each set of parameters in the multiple sets of parameters that corresponds to a different feature of the avatar to generate a portion of the avatar data 822.

[0306] In some examples, the avatar animator 820 uses the animation parameters 814 to generate avatar data 822 for animating features of an avatar's face (e.g., avatars 906, 914, 920) and / or states of features of the avatar's face. For example, the avatar animator 820 may generate avatar data 822 for animating the specific locations of many different points of the avatar's lips, how far the avatar's upper lip is from its nose, the shape of the avatar's lips, whether the avatar's cheeks are convex or concave, the distance between the avatar's eyebrows, the distance between the avatar's eyebrows and its eyes, whether the avatar's forehead or nose is wrinkled, the location of the avatar's ears, the direction of the avatar's eyes, and whether the avatar's eyes are closed.

[0307] In some examples, the avatar animator 820 determines the type of avatar to be used when generating the avatar data 822. In some examples, the types of avatars include different types of animals, human-like avatars, robots, aliens, anthropomorphic inanimate objects, or any other type of avatar. In some examples, the avatar animator 820 determines the type of avatar to be generated based on a user selection. For example, a user can select a desired type of avatar on a user interface of a user device or using voice input, which is then provided to the system 800 and to the avatar animator 820. In some examples, the avatar animator 820 determines the type of avatar based on default settings. In some examples, the avatar animator 820 determines the type of avatar based on user-specific default settings.

[0308] like Figure 8 As shown, the system 800 also includes an output processor 830. In some examples, the output processor 830 processes the avatar data 822 to generate an avatar for display (e.g., avatars 906, 914, 920). In some examples, the output processor 830 receives a speech data set 812 generated by the neural network 810 and avatar data 822 generated by the avatar animator 820. In some examples, the output processor 830 processes the speech data set 812 to generate speech output 832. In some examples, the output processor 830 includes a neural network (e.g., WaveRNN) trained to generate speech output 832 based on the speech data set 812.

[0309] In some examples, the output processor 830 synchronizes at least one movement of the avatar data 822 with the speech output 832. In some examples, the output processor 830 synchronizes the movement of at least one feature of the avatar data 822 with the speech output 832. For example, for the speech output "Happy Birthday!", the output processor 830 may synchronize the movement of the avatar's lips or at least a portion of the movement of the avatar's lips with the provided audio. In some examples, the output processor 830 does not synchronize the movement of at least one feature of the avatar with the speech output 832. For example, the output processor 830 may not synchronize the posture or posture change of the avatar with the speech output 832.

[0310] In some examples, after the avatar data 822 and the voice output 832 are processed by the output processor 830, the avatar (e.g., avatars 906, 914, 920) is displayed on a screen of an electronic device such as 9A to 9C902. In some examples, the avatar is displayed on screen 902 as avatar 906 to provide a preview of avatar 822 that can be edited, changed, or deleted. In some examples, the avatar can be provided to an application for use by the application. In some examples, the avatar can be included in a message provided to the user or another user. In some examples, the avatar can be displayed on screen 902 as avatar 920 to provide an interactive digital assistant to the user.

[0311] 9A to 9C Avatars generated and animated according to various examples are shown. Fig.9A An example is shown in which a user requests to send a message to the contact "Mom" and an avatar 906 is generated based on default settings. Fig.9A It is shown that during interaction with the digital assistant, the user provides input 904 to user device 900, "Text 'Happy Birthday!' to my mom with an avatar." In some examples, input 904 is provided as voice input. In some examples, input 904 is provided as text input entered using a user interface of user device 900 or the digital assistant, which may include, for example, a keyboard or other affordance for providing text.

[0312] As described above, input 904 is pre-processed to determine which portion of input 904 should be provided to system 800 as text 802. In this example, user device 900 or digital assistant may pre-process input 904 to determine that "Happy Birthday!" is to be provided to system 800 as text 802 to determine audio output and to animate the avatar. Specifically, user device 900 or digital assistant may perform natural language understanding as described above to determine that the user's intent is to send a message to the contact "Mom" in the phonebook application, so "Send my mom a message with an avatar..." is not part of the intended message.

[0313] After pre-processing the input, the text 802 is provided to the system 800 and thus to the neural network 810. The neural network 810 then determines that the emotional state is "happy" based on "Happy Birthday!", based on the use of the word "happy" in the text 802, and the use of an exclamation mark. The neural network 810 then determines a speech data set 812 representing "Happy Birthday!" and animation parameters 814 that are a set of parameters representing one or more actions of the avatar 906. Based on the determined "happy" emotional state, the animation parameters 814 may include parameters that cause one or more actions of the avatar 906 to indicate a "happy" emotional state. For example, the parameter may cause the head posture of the avatar 906 to look upward, which is generally associated with a person in a happy state, or the parameter may cause the lips of the avatar 906 to make a movement similar to a smile.

[0314] The animation parameters 814 are then received by the avatar animator 820, which generates avatar data 822 for animating the avatar 906. In this example, the avatar animator 820 determines which avatar to generate as the avatar 906 based on the default setting of "monkey". The avatar animator 820 then generates the avatar data 822 for animating the avatar 906 based on a set of parameters (e.g., the animation parameters 814) that represent one or more actions of the avatar 906. As described above, in this case, in addition to other actions representing the avatar 906 speaking the provided text, the avatar animator 820 also generates the avatar data 822 for animating the avatar 906 looking up (exposing the neck of the avatar 906) to indicate the emotional state of "happy".

[0315] The avatar data 822 is then provided to an output processor 830 along with the voice data set 812. The output processor 830 generates a voice output 832 based on the voice data set 812 and displays the avatar data 822 as an avatar 906 in an animated manner. The output processor 830 then synchronizes some of the movements of the avatar 906 (in this case, the movements of the mouth, ears, nose, etc.) with the voice output 832 so that when the voice output 832 is provided, the avatar 906 appears to be speaking the voice output 832. The output processor 830 then provides the avatar 906 with the voice output 832 on the display 902 of the user device 900 so that the user can view a preview of the avatar 906. The user can then choose to edit the avatar 906 or send 906 to "Mom" in the form of a message.

[0316] Fig. 9B An example is shown where a user requests to send a message to the contact "Mom" and requests further information to generate an avatar 914. Fig. 9B An input 908 of "sending 'I'm not sure I can do this' to my mom with an avatar" is shown provided to the device 900 and displayed on the screen 902. In this example, the input 908 is pre-processed to determine that the portion of the input 908 that will be provided to the system 800 as text 802 is "I'm not sure I can do this." During the pre-processing, the digital assistant may recognize that there is not enough information within the input 908 for the neural network 810 to determine the emotional state. Therefore, the digital assistant provides an output "OK, what emotion do you want to convey," and further asks the user "Which avatar do you want to generate?" as an output 910 on the screen 902. The user then provides an input 912 specifying the emotional state as "sad" and adding that the desired avatar is "robot avatar." In some examples, the text 802 may be provided to the neural network 810, and after the neural network 810 determines that the emotional state cannot be determined based on the text 802, the digital assistant provides an output 910 requesting more information from the user.

[0317] The user device 900 and / or the digital assistant may then process the input 912 to determine that the indication 804 of the emotional state to be provided to the neural network 810 is “sad”. The neural network 810 then receives the text 802 of “I’m not sure I can do this” and the indication 804 of the emotional state of “sad”. The neural network 810 then determines a speech data set 812 representing “I’m not sure I can do this” and animation parameters 814 as a set of parameters representing one or more actions of the avatar 914. Based on the determined “sad” emotional state, the animation parameters 814 may include parameters that cause one or more actions of the avatar 914 to indicate the “sad” emotional state. For example, the parameter may cause the head posture of the avatar 914 to look downward, which is typically associated with a person in a sad state, or the parameter may cause the lips of the avatar 914 to make a motion similar to a frown.

[0318] The animation parameters 814 are then received by the avatar animator 820, which generates avatar data 822 for animating the avatar 914. In this example, the avatar animator 820 determines which avatar to generate as the avatar 914 based on the input 912 specifying that the avatar should be a "robot." The avatar animator 820 then generates the avatar data 822 for animating the avatar 914 based on a set of parameters (e.g., the animation parameters 814) representing one or more actions of the avatar 914. As described above, in this case, the avatar animator 820 generates the avatar data 822 for animating the avatar 914 looking down (exposing the top and / or back of the avatar's 914 head) to indicate an emotional state of "sadness," in addition to other actions representing the avatar 914 speaking the provided text.

[0319] The avatar data 822 is then provided to an output processor 830 along with the voice data set 812. The output processor 830 generates a voice output 832 based on the voice data set 812 and displays the avatar data 822 as an avatar 914 in an animated manner. The output processor 830 then synchronizes some of the movements of the avatar 914 (in this case, the movements of the mouth, ears, nose, etc.) with the voice output 832 so that when the voice output 832 is provided, the avatar 914 appears to be speaking the voice output 832. The output processor 830 then provides the avatar 914 with the voice output 832 on the display 902 of the user device 900 so that the user can view a preview of the avatar 914. The user can then choose to edit the avatar 914 or send 914 to "Mom" in the form of a message.

[0320] Fig. 9C An example of a digital assistant providing a message to be provided to a user is shown along with an avatar 920 representing the digital assistant. Fig. 9CThe user is shown to have provided input 916 “What is the temperature in Palo Alto?” to the digital assistant of the user device 900. As described above, in some examples, this user input may be provided as voice input, and in some examples, the user input may be provided as text input via a user interface or affordance such as a keyboard.

[0321] After receiving input 916, the digital assistant of user device 900 can determine based on the natural language understanding techniques described above that the user's intent is to determine the current temperature in Palo Alto. The digital assistant then determines that the appropriate response to the user's question is "It's 48 degrees Fahrenheit outside." The digital assistant can further process the response to determine that it should be provided to neural network 810 as text 802.

[0322] Then, neural network 810 receives text 802 including "It's 48 degrees Fahrenheit outside." Based on text 802, neural network 810 determines that the emotional state of text 802 is "neutral." Neural network 810 determines the emotional state based on the lack of any words with strong emotional cues in the text and the lack of strong punctuation. Neural network 810 may also determine the emotional state because text 802 is a response to an informational question and therefore does not represent any particular emotion. Based on text 802 and the determined "neutral" emotional state, neural network 810 determines a speech data set 812 and animation parameters 814 as a set of parameters representing one or more actions of avatar 920. Based on the determined "neutral" emotional state, animation parameters 814 may include parameters that cause one or more actions of avatar 914 to indicate a "neutral" emotional state. For example, the parameters may cause the head posture of avatar 914 to look straight ahead, which is typically not associated with any particular emotion.

[0323] The animation parameters 814 are then received by the avatar animator 820, which generates avatar data 822 for animating the avatar 920. In this example, the avatar animator 820 determines which avatar to generate as the avatar 920 based on default settings for how the digital assistant should be represented as an avatar. The avatar animator 820 then generates the avatar data 822 for animating the avatar 920 based on a set of parameters (e.g., the animation parameters 814) representing one or more actions of the avatar 920. As described above, in this case, in addition to other actions representing the avatar 920 speaking the provided text, the avatar animator 820 also generates the avatar data 822 for animating the avatar 920 looking straight ahead to indicate a "neutral" emotional state.

[0324] The avatar data 822 is then provided to an output processor 830 along with the voice data set 812. The output processor 830 generates a voice output 832 based on the voice data set 812 and displays the avatar data 822 as an avatar 920 in an animated manner. The output processor 830 then synchronizes some of the movements of the avatar 920 (in this case, the movements of the mouth, ears, nose, etc.) with the voice output 832 so that when the voice output 832 is provided, the avatar 920 appears to be speaking the voice output 832. The output processor 830 then provides the avatar 920 with the voice output 832 on the display 902 of the user device 900 to convey the response to the user's question. Fig. 9C In the example shown, the digital assistant also provides text 802 as output 918 on the display 902 of the user device 900 to provide the user with a different way to view the digital assistant's responses to their questions.

[0325] FIG. 10A to FIG. 10C A process 1000 for generating an avatar and displaying the avatar in an animated manner according to various examples is shown. For example, process 1000 is performed using one or more electronic devices that implement system 800. In some examples, process 1000 is performed using a client-server system (e.g., system 100), and the boxes of process 1000 are divided in any manner between a server (e.g., DA server 106) and a client device. In other examples, the boxes of process 1000 are divided between a server and multiple client devices (e.g., a mobile phone and a smart watch). Therefore, although parts of process 1000 are described herein as being performed by a specific device of a client-server system, it should be understood that process 1000 is not limited thereto. In other examples, process 1000 is performed using only a client device (e.g., user device 104) or only multiple client devices. In process 1000, some boxes are optionally combined, the order of some boxes is optionally changed, and some boxes are optionally omitted. In some examples, additional steps may be performed in conjunction with process 1000.

[0326] At block 1002, text (e.g., text 802) is received. At block 1004, a representation of the received text is determined. At block 1006, an emotional state is determined. In some examples, the emotional state is determined based on user input (e.g., input 912) separate from the received text. In some examples, the user input separate from the received text specifies the emotional state. In some examples, the emotional state is determined based on the received text.

[0327] At box 1008, a neural network (e.g., neural network 810) is used to generate a speech dataset (e.g., speech dataset 812) representing the received text (e.g., text 802) and a set of parameters (e.g., animation parameters 814) representing one or more actions of an avatar (e.g., avatars 906, 914, 920) based on the received text and the determined emotional state.

[0328] At block 1010, a vector representation of the received text (e.g., text 802) is determined. At block 1012, a vector representation of the determined emotional state is determined. At block 1014, an intermediate vector based on the vector representation of the received text and the vector representation of the determined emotional state is determined. At block 1016, a speech dataset (e.g., speech dataset 812) representing the received text and a set of parameters (e.g., animation parameters 814) representing one or more actions of an avatar (e.g., avatars 906, 914, 920) are determined based on the intermediate vector.

[0329] In some examples, the neural network (e.g., neural network 810) is a first neural network and further includes a plurality of neural networks, the plurality of neural networks including a second neural network and a third neural network. At block 1018, a speech data set (e.g., speech data set 812) representing the received text (e.g., text 802) is generated using the second neural network based on the received text and the determined emotional state. At block 1020, a set of parameters (e.g., animation parameters 814) representing one or more actions of an avatar (e.g., avatars 906, 914, 920) is generated using the third neural network based on the speech data set representing the received text.

[0330] In some examples, prior to receiving the text, the neural network (e.g., neural network 810) is trained, as shown in block 1022. At block 1024, a set of training text data (e.g., text 802) is provided to the neural network. At block 1026, a first training speech data set (e.g., speech data set 812) and a first set of training parameters (e.g., animation parameters 814) representing the actions of an avatar (e.g., avatars 906, 914, 920) are determined based on the set of training text data. At block 1028, a determination is made whether the neural network has been trained based on the first training speech data set and the first set of training parameters representing the actions of the avatar. At block 1030, based on determining that the neural network has not been trained, a second training speech data set (e.g., speech data set 812) and a second set of training parameters (e.g., animation parameters 814) representing the actions of the avatar are determined based on the set of training text data.

[0331] In some examples, prior to receiving the text, a neural network (e.g., neural network 810) is conditioned using a plurality of predetermined emotional states, as shown in block 1032. At block 1034, a vector representation of the set of training text data (e.g., text 802) and a predetermined emotional state from the plurality of predetermined emotional states are determined. At block 1036, the vector representation of the set of training text data and the vector representation of the predetermined emotional state are linked together. In some examples, the vector representation of the predetermined emotional state is an intermediate vector extracted from a classifier that is trained in a supervised manner to recognize emotional states. At block 1038, a third set of training parameters (e.g., animation parameters 814) representing an action of an avatar (e.g., avatar 906, 914, 920) is determined based on the vector representing the set of training text data and the predetermined emotional state. At block 1040, a determination is made as to whether the neural network has been conditioned based on the third set of parameters representing the action of the avatar.

[0332] In some examples, the set of parameters (e.g., animation parameters 814) representing one or more actions of the avatar (e.g., avatars 906, 914, 920) also includes a first set of parameters for controlling the avatar's posture. In some examples, the first set of parameters for controlling the avatar's posture is determined based on the determined emotional state. In some examples, the set of parameters representing one or more actions of the avatar also includes a second set of parameters for controlling multiple features of the avatar.

[0333] In some examples, an avatar (e.g., avatars 906, 914, 920) is generated (e.g., using avatar animator 820), as shown in box 1042. In some examples, the avatar to be generated is determined based on a user selection (e.g., input 912). At box 1044, the avatar is animated using the set of parameters (e.g., animation parameters 814) representing one or more actions of the avatar (e.g., using avatar animator 820 or output processor 830). At box 1046, the animated avatar is displayed on a display (e.g., display 902) of an electronic device (e.g., user device 900). At box 1048, a speech output (e.g., speech output 832) is generated based on a speech data set (e.g., speech data set 812) representing the received text (e.g., text 802).

[0334] In some examples, at least a first one of the one or more actions of the avatar is synchronized with the audio output. In some examples, at least a second one of the one or more actions of the avatar is not synchronized with the audio output.

[0335] References FIG. 10A to FIG. 10C The described operations are optionally performed by Figures 1 to 4 , FIG. 6A to FIG. 6B , 7A to 7C , Figure 8 as well as 9A to 9C For example, the operations of process 1000 may be implemented by system 800. A person skilled in the art will clearly know how to implement the system 800 based on the system 800. Figures 1 to 4 , FIG. 6A to FIG. 6B , 7A to 7C , Figure 8 as well as 9A to 9C The components depicted in the figure are used to implement other processes.

[0336] According to some specific implementations, a computer-readable storage medium (e.g., a non-transitory computer-readable storage medium) is provided, which stores one or more programs for execution by one or more processors of an electronic device, the one or more programs including instructions for performing any of the methods or processes described herein.

[0337] According to some implementations, an electronic device (eg, a portable electronic device) is provided that includes means for performing any of the methods and processes described herein.

[0338] According to some implementations, an electronic device (eg, a portable electronic device) is provided that includes a processing unit configured to perform any of the methods and processes described herein.

[0339] According to some specific implementations, an electronic device (e.g., a portable electronic device) is provided, which includes one or more processors and a memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods and processes described herein.

[0340] For the purpose of explanation, the preceding description is described by reference to specific embodiments. However, the above exemplary discussion is not intended to be exhaustive or limit the invention to the disclosed precise form. According to the above teachings, many modifications and variations are possible. These embodiments are selected and described in order to best explain the principles of these technologies and their practical applications. Others skilled in the art can thus best utilize these technologies and various embodiments with various modifications suitable for the intended specific use.

[0341] Although the disclosure and examples have been fully described with reference to the accompanying drawings, it should be noted that various changes and modifications will become apparent to those skilled in the art. It should be understood that such changes and modifications are considered to be included within the scope of the disclosure and examples defined by the claims.

[0342] As described above, one aspect of the present technology is to collect and use data from various resources to improve the accuracy and authenticity of the generated avatars. The present disclosure contemplates that, in some instances, these collected data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data may include demographic data, location-based data, phone numbers, email addresses, Twitter IDs, home addresses, data or records related to the user's health or health level (e.g., vital sign measurements, medication information, exercise information), date of birth, or any other identifying or personal information.

[0343] The present disclosure recognizes that the use of such personal information data in the present technology can be used to benefit users. For example, personal information data can be used to generate more credible and realistic avatars. Therefore, the use of such personal information data enables users to have planned control over the content delivered. In addition, the present 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 the user's overall health status, or can be used as positive feedback for individuals who use technology to pursue health goals.

[0344] This disclosure envisions that entities responsible for collecting, analyzing, disclosing, transmitting, storing or otherwise using such personal information data will comply with established privacy policies and / or privacy practices. Specifically, such entities should implement and adhere to privacy policies and practices that are recognized as meeting or exceeding industry or government 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 purposes of the entity and not shared or sold outside of these legitimate uses. In addition, such collection / sharing should be performed after receiving informed consent from the user. In addition, such entities should consider taking any necessary steps to defend and secure access to such personal information data and ensure that others who have access to personal information data comply with their privacy policies and processes. In addition, such entities may subject themselves to third-party assessments to demonstrate their compliance with widely accepted privacy policies and practices. In addition, policies and practices should be adjusted to specific types of personal information data collected and / or accessed, and to applicable laws and standards including specific considerations of jurisdiction. For example, in the United States, the collection or access of certain health data may be governed by federal and / or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA), while health data in other countries may be subject to other regulations and policies and should be handled accordingly. Therefore, different privacy practices should be maintained in each country for different types of personal data.

[0345] Regardless of the foregoing, the present disclosure also contemplates implementation schemes in which users selectively block the use or access of personal information data. That is, the present disclosure contemplates that hardware elements and / or software elements may be provided to prevent or block access to such personal information data. For example, with respect to avatar generation services, the technology of the present invention may be configured to allow users to choose to "opt in" or "opt out" to participate in the collection of personal information data at any time during or after registration for the service. As another example, a user may choose to use a default, non-personalized avatar. As another example, a user may choose to limit the type of data used when generating an avatar to a specific type. In addition to providing "opt-in" and "opt-out" options, the present disclosure contemplates providing notifications related to access or use of personal information. For example, a user may be notified that their personal information data will be accessed when downloading an application, and then the user may be reminded again just before the personal information data is accessed by the application.

[0346] Furthermore, it is an object of the present disclosure that personal information data should be managed and processed to minimize the risk of unintentional or unauthorized access or use. Risks can be minimized by limiting data collection and deleting data once it is no longer needed. Additionally, and when applicable, including in certain health-related applications, data de-identification can be used to protect the privacy of users. Where appropriate, de-identification can be facilitated by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or characteristics of stored data (e.g., collecting location data at a city level rather than an address level), controlling how data is stored (e.g., aggregating data across users), and / or other methods.

[0347] Thus, while the present disclosure broadly covers the use of personal information data to implement one or more of the various disclosed embodiments, the present disclosure also contemplates that various embodiments may also be implemented without access to such personal information data. That is, various embodiments of the present technology will not fail to function properly due to the lack of all or a portion of such personal information data. For example, an avatar may be generated for a user by inferring preferences based on non-personal information data or an absolute minimum amount of personal information (such as an avatar requested by a device associated with the user, other non-personal information available, or publicly available information).

Claims

1. A method for avatar representation, comprising: At one or more electronic devices having a memory and one or more processors: Training the neural network, wherein training the neural network further comprises: providing a set of training text data to the neural network; Determine a first training speech data set and a first set of training parameters representing the action of the avatar based on the set of training text data; determining whether the neural network has been trained based on the first training speech data set and the first set of training parameters representing the action of the avatar; and Determining, based on the set of training text data, a second training speech data set and a second set of training parameters representing the action of the avatar according to determining that the neural network has not been trained; conditioning the neural network using a plurality of predetermined emotional states; After training and tuning the neural network: Receive text; determining an emotional state based on the received text or user input separate from the received text; and A speech data set representing the received text, a first set of parameters for controlling a pose of the avatar, and a second set of parameters for controlling a plurality of features of the avatar are generated using the neural network based on both the received text and the determined emotional state, respectively.

2. The method of claim 1, further comprising determining a representation of the received text.

3. The method of any one of claims 1 to 2, wherein the emotional state is determined based on the user input and the user input specifies the emotional state.

4. The method according to any one of claims 1 to 2, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the determined emotional state also includes: determining a vector representation of the received text; determining a vector representation of the emotional state; determining an intermediate vector based on the vector representation of the received text and the vector representation of the emotional state; and The speech data set representing the received text and the set of parameters representing one or more actions of the avatar are determined based on the intermediate vector.

5. The method of any one of claims 1 to 2, wherein the neural network is a first neural network and further comprises a plurality of neural networks, the plurality of neural networks comprising a second neural network and a third neural network.

6. The method according to claim 5, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the indication of the emotional state further comprises: generating, using the second neural network, the speech data set representing the received text based on the received text and the determined emotional state; and The set of parameters representing one or more actions of the avatar is generated based on the speech data set representing the received text using the third neural network.

7. The method of claim 1 , wherein conditioning the neural network using a plurality of predetermined emotional states further comprises: determining a vector, the vector representing the set of training text data and a predetermined emotional state from the plurality of predetermined emotional states; determining a third set of training parameters representing the action of the avatar based on the vector representing the set of training text data and the predetermined emotional state; as well as A determination is made as to whether the neural network has been adjusted based on the third set of training parameters representing the action of the avatar.

8. The method of claim 7, wherein determining the vector representing the set of training text data and the predetermined emotional state further comprises: The vector representation of the set of training text data is associated with the vector representation of the predetermined emotional state.

9. The method of claim 8, wherein the vector representation of the predetermined emotional state is an intermediate vector extracted from a classifier trained in a supervised manner to recognize emotional states.

10. The method of any one of claims 1 to 2, wherein the first set of parameters for controlling the pose of the avatar is determined based on the determined emotional state.

11. The method according to any one of claims 1 to 2, further comprising: generating the avatar; as well as The avatar is animated using the set of parameters representing one or more actions of the avatar.

12. The method according to claim 11, further comprising: The animated avatar is displayed on the display of the electronic device. The method of claim 11 , wherein the avatar to be generated is determined based on a user's selection.

14. The method according to any one of claims 1 to 2, further comprising: A speech output is generated based on the speech data set representing the received text.

15. The method of claim 14, wherein at least a first one of the one or more actions of the avatar is synchronized with the voice output.

16. The method of claim 15, wherein at least a second one of the one or more actions of the avatar is not synchronized with the voice output.

17. A computer-readable storage medium storing one or more programs, the one or more programs comprising instructions that, when executed by one or more processors of one or more electronic devices, cause the one or more electronic devices to: Training the neural network, wherein training the neural network further comprises: providing a set of training text data to the neural network; Determine a first training speech data set and a first set of training parameters representing the action of the avatar based on the set of training text data; determining whether the neural network has been trained based on the first training speech data set and the first set of training parameters representing the action of the avatar; as well as Determining, based on the set of training text data, a second training speech data set and a second set of training parameters representing the action of the avatar according to determining that the neural network has not been trained; conditioning the neural network using a plurality of predetermined emotional states; After training and tuning the neural network: Receive text; determining an emotional state based on the received text or user input separate from the received text; as well as A speech data set representing the received text, a first set of parameters for controlling a pose of an avatar, and a second set of parameters for controlling a plurality of features of the avatar are generated using the neural network based on both the received text and the determined emotional state, respectively.

18. The computer-readable storage medium of claim 17, the one or more programs further comprising instructions for determining a representation of the received text.

19. The computer-readable storage medium of any one of claims 17 to 18, wherein the emotional state is determined based on the user input and the user input specifies the emotional state.

20. The computer-readable storage medium according to any one of claims 17 to 18, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the determined emotional state also includes: determining a vector representation of the received text; determining a vector representation of the emotional state; determining an intermediate vector based on the vector representation of the received text and the vector representation of the emotional state; and The speech data set representing the received text and the set of parameters representing one or more actions of the avatar are determined based on the intermediate vector.

21. The computer-readable storage medium of any one of claims 17 to 18, wherein the neural network is a first neural network and further comprises a plurality of neural networks, the plurality of neural networks comprising a second neural network and a third neural network.

22. The computer-readable storage medium of claim 21, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the indication of the emotional state further comprises: generating, using the second neural network, the speech data set representing the received text based on the received text and the determined emotional state; and The set of parameters representing one or more actions of the avatar is generated based on the speech data set representing the received text using the third neural network.

23. The computer-readable storage medium of claim 17, wherein using the plurality of predetermined emotional states to modulate the neural network further comprises: determining a vector, the vector representing the set of training text data and a predetermined emotional state from the plurality of predetermined emotional states; determining a third set of training parameters representing the action of the avatar based on the vector representing the set of training text data and the predetermined emotional state; as well as A determination is made as to whether the neural network has been adjusted based on the third set of training parameters representing the action of the avatar.

24. The computer-readable storage medium of claim 23, wherein determining the vector representing the set of training text data and the predetermined emotional state further comprises: The vector representation of the set of training text data is associated with the vector representation of the predetermined emotional state.

25. The computer-readable storage medium of claim 24, wherein the vector representation of the predetermined emotional state is an intermediate vector extracted from a classifier trained in a supervised manner to recognize emotional states.

26. The computer-readable storage medium of any one of claims 17-18, wherein the first set of parameters for controlling the pose of the avatar is determined based on the determined emotional state.

27. The computer-readable storage medium of any one of claims 17 to 18, wherein the one or more programs further comprise instructions for: generating the avatar; and The avatar is animated using the set of parameters representing one or more actions of the avatar.

28. The computer-readable storage medium of claim 27, wherein the one or more programs further comprise instructions for: The animated avatar is displayed on the display of the electronic device.

29. The computer-readable storage medium of claim 27, wherein the avatar to be generated is determined based on a selection by a user.

30. The computer-readable storage medium of any one of claims 17 to 18, wherein the one or more programs further comprise instructions for: A speech output is generated based on the speech data set representing the received text.

31. The computer-readable storage medium of claim 30, wherein at least a first one of the one or more actions of the avatar is synchronized with the voice output.

32. The computer-readable storage medium of claim 31, wherein at least a second one of the one or more actions of the avatar is asynchronous with the speech output.

33. An electronic device comprising: one or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for: Training the neural network, wherein training the neural network further comprises: providing a set of training text data to the neural network; Determine a first training speech data set and a first set of training parameters representing the action of the avatar based on the set of training text data; determining whether the neural network has been trained based on the first training speech data set and the first set of training parameters representing the action of the avatar; and Determining, based on the set of training text data, a second training speech data set and a second set of training parameters representing the action of the avatar according to determining that the neural network has not been trained; conditioning the neural network using a plurality of predetermined emotional states; After training and tuning the neural network: Receive text; determining an emotional state based on the received text or user input separate from the received text; and A speech data set representing the received text, a first set of parameters for controlling a pose of the avatar, and a second set of parameters for controlling a plurality of features of the avatar are generated using the neural network based on both the received text and the determined emotional state, respectively.

34. The electronic device of claim 33, the one or more programs further comprising instructions for determining a representation of the received text.

35. An electronic device as claimed in any one of claims 33 to 34, wherein the emotional state is determined based on the user input and the user input specifies the emotional state.

36. The electronic device according to any one of claims 33 to 34, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the determined emotional state also includes: determining a vector representation of the received text; determining a vector representation of the emotional state; determining an intermediate vector based on the vector representation of the received text and the vector representation of the emotional state; and The speech data set representing the received text and the set of parameters representing one or more actions of the avatar are determined based on the intermediate vector.

37. The electronic device of any one of claims 33 to 34, wherein the neural network is a first neural network and further comprises a plurality of neural networks, the plurality of neural networks comprising a second neural network and a third neural network.

38. The electronic device according to claim 37, wherein: Generating a speech data set representing the received text and a set of parameters representing one or more actions of the avatar based on the received text and the indication of the emotional state further comprises: generating, using the second neural network, the speech data set representing the received text based on the received text and the determined emotional state; and The set of parameters representing one or more actions of the avatar is generated based on the speech data set representing the received text using the third neural network.

39. The electronic device of claim 33, wherein using the plurality of predetermined emotional states to adjust the neural network further comprises: determining a vector, the vector representing the set of training text data and a predetermined emotional state from the plurality of predetermined emotional states; determining a third set of training parameters representing the action of the avatar based on the vector representing the set of training text data and the predetermined emotional state; as well as A determination is made as to whether the neural network has been adjusted based on the third set of training parameters representing the action of the avatar.

40. The electronic device of claim 39, wherein determining the vector representing the set of training text data and the predetermined emotional state further comprises: The vector representation of the set of training text data is associated with the vector representation of the predetermined emotional state.

41. The electronic device of claim 40, wherein the vector representation of the predetermined emotional state is an intermediate vector extracted from a classifier trained in a supervised manner to recognize emotional states.

42. An electronic device according to any one of claims 33 to 34, wherein the first set of parameters for controlling the posture of the avatar is determined based on the determined emotional state.

43. The electronic device according to any one of claims 33 to 34, wherein the one or more programs further comprise instructions for: generating the avatar; and The avatar is animated using the set of parameters representing one or more actions of the avatar.

44. The electronic device of claim 43, wherein the one or more programs further comprise instructions for: The animated avatar is displayed on the display of the electronic device.

45. The electronic device of claim 43, wherein the avatar to be generated is determined based on a user's selection.

46. ​​The electronic device of any one of claims 33 to 34, wherein the one or more programs further comprise instructions for: A speech output is generated based on the speech data set representing the received text.

47. An electronic device according to claim 46, wherein at least a first action of the one or more actions of the avatar is synchronized with the voice output.

48. An electronic device according to claim 47, wherein at least a second one of the one or more actions of the avatar is not synchronized with the voice output.

49. An electronic device comprising: Device for carrying out the method according to any one of claims 1 to 2.

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