Patent Grant
10733375
This relates generally to intelligent automated assistants and, more specifically, to using knowledge bases to improve the natural language understanding capabilities of intelligent automated assistants.
Intelligent automated assistants (or digital assistants) can provide a beneficial interface between human users and electronic devices. Such assistants can allow users to interact with devices or systems using natural language in spoken and/or text forms. For example, a user can provide natural language input representing a user request to a digital assistant operating on an electronic device. The digital assistant can interpret the user's intent from the input and operationalize the user's intent into tasks. The tasks can then be performed by executing one or more services of the electronic device, and a relevant output responsive to the user request can be returned to the user.
Digital assistants can interpret natural language input by parsing the natural language input to obtain one or more natural language parsing results. The one or more parsing results can identify relevant semantic information in the natural language input and can be used to generate one or more tasks corresponding to the natural language input. In some instances, the one or more generated tasks, when performed by the digital assistant, may fail to accomplish the user request represented by the natural language input. This may be because the one or more parsing results contain insufficient and/or erroneous semantic information about the natural language input. Accordingly, improved natural language processing techniques are desirable.
Example methods are disclosed herein. An example method includes, at an electronic device having one or more processors and memory, receiving natural language input and determining, from a plurality of domains, a domain corresponding to the natural language input. The method further includes, in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result. The method further includes determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base, and in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result. The method further includes, based on the ranking, generating a task flow using the second parsing result and executing the task flow to provide an output based on the data item.
An example method includes at an electronic device with one or more processors and memory, obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain. The method further includes determining, from the metadata, whether the output satisfies a predetermined criterion and, in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain. The method further includes providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain.
Exemplary non-transitory computer-readable media are disclosed herein. An example non-transitory computer-readable storage medium stores one or more programs. The one or more programs comprise instructions which, when executed by one or more processors of an electronic device, cause the electronic device to receive natural language input; determine, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determine a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determine whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, rank the second parsing result higher than the first parsing result; based on the ranking, generate a task flow using the second parsing result; and execute the task flow to provide an output based on the data item.
An example non-transitory computer readable storage medium stores one or more programs. The one or more programs comprise instructions which, when executed by one or more processors of an electronic device, cause the electronic device to obtain a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determine from the metadata whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, compare the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and provide the natural language input and the determined parsing result to generate a natural language parsing model of the domain.
Exemplary electronic devices are disclosed herein. An example electronic device comprises one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for receiving natural language input; determining, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result based on the ranking, generating a task flow using the second parsing result; and executing the task flow to provide an output based on the data item.
An example electronic device comprises one or more processors; a memory; and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determining, from the metadata, whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain.
An example electronic device comprises means for receiving natural language input; determining, from a plurality of domains, a domain corresponding to the natural language input; in accordance with determining that the domain corresponds to a predetermined type of domain, determining a first and a second parsing result for the natural language input, wherein each of the first and the second parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the natural language input, and wherein a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result; determining whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base; in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, ranking the second parsing result higher than the first parsing result based on the ranking, generating a task flow using the second parsing result; and executing the task flow to provide an output based on the data item.
An example electronic device comprises means for obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, wherein the natural language input corresponds to a domain of a predetermined type, the output includes a data item, and the metadata includes a set of attributes corresponding to the data item, the set of attributes defining a set of values for a respective set of properties of the domain; determining, from the metadata, whether the output satisfies a predetermined criterion; in accordance with a determination that the output satisfies the predetermined criterion, comparing the set of values to the natural language input to determine a parsing result, the parsing result mapping one or more words of the natural language input to one or more properties of the respective set of properties of the domain; and providing the natural language input and the determined parsing result to generate a natural language parsing model of the domain.
Determining one or more parsing results for natural language input and determining whether the one or more parsing results for the natural language input correspond to respective data items of a plurality of data items in a knowledge base allow an electronic device to validate and/or rank parsing results using data items contained in the knowledge base. Accordingly, parsing results determined to correspond to data items in a knowledge base accessible to the electronic device may be used to execute tasks. This reduces the chance that the electronic device incorrectly, or fails to, provide data items responsive to natural language inputs requesting data items and also increases the accuracy of the response provided to the natural language inputs. Improving the accuracy of the response in this manner enhances the operability of the electronic device and makes the user-device interface more efficient (e.g., by improving interpretation of user requests, by reducing mistakes in interpreting natural language input, and by reducing repetitive user inputs) which, additionally, reduces power usage and improves battery life of the device by enabling the user to user the device more quickly and efficiently.
Obtaining a natural language input and metadata corresponding to an output provided responsive to the natural language input, determining whether the output satisfies a predetermined criterion, and determining a parsing result for the natural language input using the metadata allows an electronic device to generate accurate parsing results that can be used to generate and/or train natural language parsing models. In particular, the above techniques allow the electronic device to use information contained in the metadata to infer whether the output provided responsive to the natural language input was correct. If the output is inferred to be correct, the metadata corresponding to the output can be used to determine an accurate parsing result for the natural language input. Determining accurate parsing results in this manner allows the electronic device to collect large amounts of accurate training data (e.g., the accurate parsing results) and to train natural language parsing models using the training data. Collecting accurate training data and training natural language parsing models in this manner improves the accuracy of parsing results, and therefore the accuracy of the response provided to natural language inputs. Improving the accuracy of response provided to natural language inputs enhances the operability of the electronic device and makes the user-device interface more efficient (e.g., by improving interpretation of user requests, by reducing mistakes in interpreting natural language input, and by reducing repetitive user inputs) which, additionally, reduces power usage and improves battery life of the device by enabling the user to user the device more quickly and efficiently.
In the following description of examples, reference is made to the accompanying drawings in which are shown by way of illustration specific examples that can be practiced. It is to be understood that other examples can be used and structural changes can be made without departing from the scope of the various examples.
Digital assistants can determine one or more parsing results for user natural language inputs to generate task flows to accomplish tasks requested by the user. The parsing results depend on a domain corresponding to the natural language input and map one or more properties of the domain to one or more words of the natural language input. For example, the natural language input “play Hello by Adele” can be determined to correspond to a media domain. In this example, a first parsing result can map “Hello by Adele” to the “musicTitle” property of the media domain and a second parsing result can map “Hello” to the “musicTitle” property of the media domain.
In the present example, a task flow generated using the first parsing result mapping the “musicTitle” domain property to “Hello by Adele” can lead to an error message (e.g., “Sorry I couldn't find the song “Hello by Adele”) when executed because there is no song titled “Hello by Adele.” However, a task flow generated using the second parsing result can correctly cause playback of the song titled “Hello” by the artist “Adele” when executed because there is a song titled “Hello” by the artist “Adele.” Accordingly, before generating task flows using one or more parsing results for natural language input, it may be desirable to validate whether the one or more parsing results correspond to a data item (e.g., a song) available to the digital assistant.
Thus, the present disclosure contemplates using knowledge bases (e.g., information databases comprising data items, such as the iTunes® database by Apple Inc.) to rank and/or validate parsing results before generating and executing task flows based on the parsing results. For example, it can be determined whether the first and the second parsing results for the natural language input “play Hello by Adele” respectively correspond to a data item in a knowledge base. Here, the first parsing result mapping the domain property “musicTitle” to “Hello by Adele” can be determined to not correspond to any data items in the knowledge base, while the second parsing result mapping the domain property “musicTitle” to “Hello” can be determined to correspond to a data item in the knowledge base. Accordingly, the second parsing result can be ranked higher than the first parsing result, and the second parsing result can be used to generate a task flow. The task flow generated using the second parsing result can cause correct playback of the song “Hello” by the artist “Adele” when executed.
The present disclosure further contemplates that parsing results correctly determined for natural language inputs can be used to generate and/or train natural language parsing models that generate parsing results. For example, the correct parsing result mapping “musicTitle” to “Hello” and the natural language input “play Hello by Adele” can be provided to train a natural language parsing model so that subsequent parsing results for the natural language input “play Hello by Adele” correctly map “musicTitle” to “Hello,” instead of incorrectly mapping “musicTitle” to “Hello by Adele.”
To determine correct parsing results for a natural language input, the present disclosure contemplates using “user engagement data” corresponding to outputs provided responsive to the natural language input. User engagement data corresponding to outputs can indicate that the output was played for greater than a predetermined duration (e.g., 30 seconds). For example, user engagement data corresponding to the song “Rolling in the Deep,” correctly provided responsive to the natural language input “play Rolling in the Deep,” can indicate that the song was played for greater than 30 seconds. In some examples, an output provided for greater than the predetermined duration is interpreted as an indication that the output was correct (e.g., because otherwise the user would cancel the output within the predetermined duration). Accordingly, information associated with a data item (e.g., a song) included in the output can be used to determine a correct parsing result for the natural language input. For example, information associated with the song “Rolling in the Deep” can specify that “Rolling in the Deep” is mapped to the “musicTitle” property of the media domain. This information can be compared to the natural language input “play Rolling in the Deep” to determine the correct parsing result mapping “Rolling in the Deep” to the “musicTitle” property of the domain. The correct parsing result for the natural language input and the natural language input can then be provided to train and/or generate a natural language parsing model.
Although the following description uses terms “first,” “second,” etc. to describe various elements, these elements should not be limited by the terms. These terms are only used to distinguish one element from another. For example, a first input could be termed a second input, and, similarly, a second input could be termed a first input, without departing from the scope of the various described examples. The first input and the second input are both inputs and, in some cases, are separate and different inputs.
The terminology used in the description of the various described examples herein is for the purpose of describing particular examples only and is not intended to be limiting. As used in the description of the various described examples and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that the term “and/or” as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms “includes,” “including,” “comprises,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term “if” may be construed to mean “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [a stated condition or event] is detected” may be construed to mean “upon determining” or “in response to determining” or “upon detecting [the stated condition or event]” or “in response to detecting [the stated condition or event],” depending on the context.
1. System and Environment
Specifically, a digital assistant is capable of accepting a user request at least partially in the form of a natural language command, request, statement, narrative, and/or inquiry. Typically, the user request seeks either an informational answer or performance of a task by the digital assistant. A satisfactory response to the user request includes a provision of the requested informational answer, a performance of the requested task, or a combination of the two. For example, a user asks the digital assistant a question, such as “Where am I right now?” Based on the user's current location, the digital assistant answers, “You are in Central Park near the west gate.” The user also requests the performance of a task, for example, “Please invite my friends to my girlfriend's birthday party next week.” In response, the digital assistant can acknowledge the request by saying “Yes, right away,” and then send a suitable calendar invite on behalf of the user to each of the user's friends listed in the user's electronic address book. During performance of a requested task, the digital assistant sometimes interacts with the user in a continuous dialogue involving multiple exchanges of information over an extended period of time. There are numerous other ways of interacting with a digital assistant to request information or performance of various tasks. In addition to providing verbal responses and taking programmed actions, the digital assistant also provides responses in other visual or audio forms, e.g., as text, alerts, music, videos, animations, etc.
As shown in
In some examples, DA server 106 includes client-facing I/O interface 112, one or more processing modules 114, data and models 116, and I/O interface to external services 118. The client-facing I/O interface 112 facilitates the client-facing input and output processing for DA server 106. One or more processing modules 114 utilize data and models 116 to process speech input and determine the user's intent based on natural language input. Further, one or more processing modules 114 perform task execution based on inferred user intent. In some examples, DA server 106 communicates with external services 120 through network(s) 110 for task completion or information acquisition. I/O interface to external services 118 facilitates such communications.
User device 104 can be any suitable electronic device. In some examples, user device 104 is a portable multifunctional device (e.g., device 200, described below with reference to
Examples of communication network(s) 110 include local area networks (LAN) and wide area networks (WAN), e.g., the Internet. Communication network(s) 110 is implemented using any known network protocol, including various wired or wireless protocols, such as, for example, Ethernet, Universal Serial Bus (USB), FIREWIRE, Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wi-Fi, voice over Internet Protocol (VoIP), Wi-MAX, or any other suitable communication protocol.
Server system 108 is implemented on one or more standalone data processing apparatus or a distributed network of computers. In some examples, server system 108 also employs various virtual devices and/or services of third-party service providers (e.g., third-party cloud service providers) to provide the underlying computing resources and/or infrastructure resources of server system 108.
In some examples, user device 104 communicates with DA server 106 via second user device 122. Second user device 122 is similar or identical to user device 104. For example, second user device 122 is similar to devices 200, 400, or 600 described below with reference to
In some examples, user device 104 is configured to communicate abbreviated requests for data to second user device 122 to reduce the amount of information transmitted from user device 104. Second user device 122 is configured to determine supplemental information to add to the abbreviated request to generate a complete request to transmit to DA server 106. This system architecture can advantageously allow user device 104 having limited communication capabilities and/or limited battery power (e.g., a watch or a similar compact electronic device) to access services provided by DA server 106 by using second user device 122, having greater communication capabilities and/or battery power (e.g., a mobile phone, laptop computer, tablet computer, or the like), as a proxy to DA server 106. While only two user devices 104 and 122 are shown in
Although the digital assistant shown in
2. Electronic Devices
Attention is now directed toward embodiments of electronic devices for implementing the client-side portion of a digital assistant.
As used in the 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 substitute (proxy) for 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 distinct values and more typically includes hundreds of distinct values (e.g., at least 256). Intensity of a contact is, optionally, determined (or measured) using various approaches and various sensors or combinations of sensors. For example, one or more force sensors underneath or adjacent to the touch-sensitive surface are, optionally, used to measure force at various points on the touch-sensitive surface. In some implementations, force measurements from multiple force sensors are combined (e.g., a weighted average) to determine an estimated force of a contact. Similarly, a pressure-sensitive tip of a stylus is, optionally, used to determine a pressure of the stylus on the touch-sensitive surface. Alternatively, the size of the contact area detected on the touch-sensitive surface and/or changes thereto, the capacitance of the touch-sensitive surface proximate to the contact and/or changes thereto, and/or the resistance of the touch-sensitive surface proximate to the contact and/or changes thereto are, optionally, used as a substitute for the force or pressure of the contact on the touch-sensitive surface. In some implementations, the substitute measurements for contact force or pressure are used directly to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is described in units corresponding to the substitute measurements). In some implementations, the substitute measurements for contact force or pressure are converted to an estimated force or pressure, and the estimated force or pressure is used to determine whether an intensity threshold has been exceeded (e.g., the intensity threshold is a pressure threshold measured in units of pressure). Using the intensity of a contact as an attribute of a user input allows for user access to additional device functionality that may otherwise not be accessible by the user on a reduced-size device with limited real estate for displaying affordances (e.g., on a touch-sensitive display) and/or receiving user input (e.g., via a touch-sensitive display, a touch-sensitive surface, or a physical/mechanical control such as a knob or a button).
As used in the specification and claims, the term “tactile output” refers to physical displacement of a device relative to a previous position of the device, physical displacement of a component (e.g., a touch-sensitive surface) of a device relative to another component (e.g., housing) of the device, or displacement of the component relative to a center of mass of the device that will be detected by a user with the user's sense of touch. For example, in situations where the device or the component of the device is in contact with a surface of a user that is sensitive to touch (e.g., a finger, palm, or other part of a 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 physical characteristics of the device or the 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 “down click” or “up click” of a physical actuator button. In some cases, a user will feel a tactile sensation such as an “down click” or “up click” even when there is no movement of a physical actuator button associated with the touch-sensitive surface that is physically pressed (e.g., displaced) by the user's movements. As another example, movement of the touch-sensitive surface is, optionally, interpreted or sensed by the user as “roughness” of the touch-sensitive surface, even when there is no change in smoothness of the touch-sensitive surface. While such interpretations of touch by a user will be subject to the individualized sensory perceptions of the user, there are many sensory perceptions of touch that are common to a large majority of users. Thus, when a tactile output is described as corresponding to a particular sensory perception of a user (e.g., an “up click,” a “down click,” “roughness”), unless otherwise stated, the generated tactile output corresponds to physical displacement of the device or a component thereof that will generate the described sensory perception for a typical (or average) user.
It should be appreciated 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. The various components shown in
Memory 202 includes one or more computer-readable storage mediums. The computer-readable storage mediums are, for example, tangible and non-transitory. Memory 202 includes high-speed random access memory and also includes non-volatile memory, such as one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices. Memory controller 222 controls access to memory 202 by other components of device 200.
In some examples, a non-transitory computer-readable storage medium of memory 202 is used to store instructions (e.g., for performing aspects of processes described below) for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. In other examples, the instructions (e.g., for performing aspects of the processes described below) are stored on a non-transitory computer-readable storage medium (not shown) of the 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.
Peripherals interface 218 is used to couple input and output peripherals of the device to CPU 220 and memory 202. The one or more processors 220 run or execute various software programs and/or sets of instructions stored in memory 202 to perform various functions for device 200 and to process data. In some embodiments, peripherals interface 218, CPU 220, and memory controller 222 are implemented on a single chip, such as chip 204. In some other embodiments, they are implemented on separate chips.
RF (radio frequency) circuitry 208 receives and sends RF signals, also called electromagnetic signals. RF circuitry 208 converts electrical signals to/from electromagnetic signals and communicates with communications networks and other communications devices via the electromagnetic signals. RF circuitry 208 optionally includes well-known circuitry for performing these functions, including but not limited to an antenna system, an RF transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a CODEC chipset, a subscriber identity module (SIM) card, memory, and so forth. RF circuitry 208 optionally communicates with networks, such as the Internet, also referred to as the World Wide Web (WWW), an intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN), and other devices by wireless communication. The RF circuitry 208 optionally includes well-known circuitry for detecting near field communication (NFC) fields, such as by a short-range communication radio. The wireless communication optionally uses any of a plurality of communications standards, protocols, and technologies, including but not limited to Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), high-speed downlink packet access (HSDPA), high-speed uplink packet access (HSUPA), Evolution, Data-Only (EV-DO), HSPA, HSPA+, Dual-Cell HSPA (DC-HSPDA), long term evolution (LTE), near field communication (NFC), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Bluetooth Low Energy (BTLE), Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, and/or IEEE 802.11ac), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for e mail (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 Leveraging Extensions (SIMPLE), Instant Messaging and Presence Service (IMPS)), and/or Short Message Service (SMS), or any other suitable communication protocol, including communication protocols not yet developed as of the filing date of this document.
Audio circuitry 210, speaker 211, and microphone 213 provide an audio interface between a user and device 200. Audio circuitry 210 receives audio data from peripherals interface 218, converts the audio data to an electrical signal, and transmits the electrical signal to speaker 211. Speaker 211 converts the electrical signal to human-audible sound waves. Audio circuitry 210 also receives electrical signals converted by microphone 213 from sound waves. Audio circuitry 210 converts the electrical signal to audio data and transmits the audio data to peripherals interface 218 for processing. Audio data are retrieved from and/or transmitted to memory 202 and/or RF circuitry 208 by peripherals interface 218. In some embodiments, audio circuitry 210 also includes a headset jack (e.g., 312,
I/O subsystem 206 couples input/output peripherals on device 200, such as touch screen 212 and other input control devices 216, to peripherals interface 218. I/O subsystem 206 optionally includes display controller 256, optical sensor controller 258, intensity sensor controller 259, haptic feedback controller 261, and one or more input controllers 260 for other input or control devices. The one or more input controllers 260 receive/send electrical signals from/to other input control devices 216. The other input control devices 216 optionally include physical buttons (e.g., push buttons, rocker buttons, etc.), dials, slider switches, joysticks, click wheels, and so forth. In some alternate embodiments, input controller(s) 260 are, optionally, coupled to any (or none) of the following: a keyboard, an infrared port, a USB port, and a pointer device such as a mouse. The one or more buttons (e.g., 308,
A quick press of the push button disengages a lock of touch screen 212 or begin a process that uses gestures on the touch screen to unlock the device, as described in U.S. patent application Ser. No. 11/322,549, “Unlocking a Device by Performing Gestures on an Unlock Image,” filed Dec. 23, 2005, U.S. Pat. No. 7,657,849, which is hereby incorporated by reference in its entirety. A longer press of the push button (e.g., 306) turns power to device 200 on or off. The user is able to customize a functionality of one or more of the buttons. Touch screen 212 is used to implement virtual or soft buttons and one or more soft keyboards.
Touch-sensitive display 212 provides an input interface and an output interface between the device and a user. Display controller 256 receives and/or sends electrical signals from/to touch screen 212. Touch screen 212 displays visual output to the user. The visual output includes graphics, text, icons, video, and any combination thereof (collectively termed “graphics”). In some embodiments, some or all of the visual output correspond to user-interface objects.
Touch screen 212 has a touch-sensitive surface, sensor, or set of sensors that accepts input from the user based on haptic and/or tactile contact. Touch screen 212 and display controller 256 (along with any associated modules and/or sets of instructions in memory 202) detect contact (and any movement or breaking of the contact) on touch screen 212 and convert the detected contact into interaction with user-interface objects (e.g., one or more soft keys, icons, web pages, or images) that are displayed on touch screen 212. In an exemplary embodiment, a point of contact between touch screen 212 and the user corresponds to a finger of the user.
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. Touch screen 212 and display controller 256 detect contact and any movement or breaking thereof using any of a plurality of touch sensing technologies now known or later developed, including but not limited to capacitive, resistive, infrared, and surface acoustic wave technologies, as well as other proximity sensor arrays or other elements for determining one or more points of contact with touch screen 212. In an exemplary embodiment, projected mutual capacitance sensing technology is used, such as that found in the iPhone® and iPod Touch® from Apple Inc. of Cupertino, Calif.
A touch-sensitive display in some embodiments of touch screen 212 is analogous to the multi-touch sensitive touchpads described in the following U.S. Pat. No. 6,323,846 (Westerman et al.), U.S. Pat. No. 6,570,557 (Westerman et al.), and/or U.S. Pat. No. 6,677,932 (Westerman), and/or U.S. Patent Publication 2002/0015024A1, each of which is hereby incorporated by reference in its entirety. However, touch screen 212 displays visual output from device 200, whereas touch-sensitive touchpads do not provide visual output.
A touch-sensitive display in some embodiments of touch screen 212 is as described in the following applications: (1) U.S. patent application Ser. No. 11/381,313, “Multipoint Touch Surface Controller,” filed May 2, 2006; (2) U.S. patent application Ser. No. 10/840,862, “Multipoint Touchscreen,” filed May 6, 2004; (3) U.S. patent application Ser. No. 10/903,964, “Gestures For Touch Sensitive Input Devices,” filed Jul. 30, 2004; (4) U.S. patent application Ser. No. 11/048,264, “Gestures For Touch Sensitive Input Devices,” filed Jan. 31, 2005; (5) U.S. patent application Ser. No. 11/038,590, “Mode-Based Graphical User Interfaces For Touch Sensitive Input Devices,” filed Jan. 18, 2005; (6) U.S. patent application Ser. No. 11/228,758, “Virtual Input Device Placement On A Touch Screen User Interface,” filed Sep. 16, 2005; (7) U.S. patent application Ser. No. 11/228,700, “Operation Of A Computer With A Touch Screen Interface,” filed Sep. 16, 2005; (8) U.S. patent application Ser. No. 11/228,737, “Activating Virtual Keys Of A Touch-Screen Virtual Keyboard,” filed Sep. 16, 2005; and (9) U.S. patent application Ser. No. 11/367,749, “Multi-Functional Hand-Held Device,” filed Mar. 3, 2006. All of these applications are incorporated by reference herein in their entirety.
Touch screen 212 has, for example, a video resolution in excess of 100 dpi. In some embodiments, the touch screen has a video resolution of approximately 160 dpi. The user makes contact with touch screen 212 using any suitable object or appendage, such as a stylus, a finger, and so forth. In some embodiments, the user interface is designed to work primarily with finger-based contacts and gestures, which can be less precise than stylus-based input due to the larger area of contact of a finger on the touch screen. In some embodiments, the device translates the rough finger-based input into a precise pointer/cursor position or command for performing the actions desired by the user.
In some embodiments, in addition to the touch screen, device 200 includes a touchpad (not shown) for activating or deactivating particular 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 touch screen 212 or an extension of the touch-sensitive surface formed by the touch screen.
Device 200 also includes power system 262 for powering the various components. Power system 262 includes a power management system, one or more power sources (e.g., battery, alternating current (AC)), a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator (e.g., a light-emitting diode (LED)) and any other components associated with the generation, management and distribution of power in portable devices.
Device 200 also includes one or more optical sensors 264.
Device 200 optionally also includes one or more contact intensity sensors 265.
Device 200 also includes one or more proximity sensors 266.
Device 200 optionally also includes one or more tactile output generators 267.
Device 200 also includes one or more accelerometers 268.
In some embodiments, the software components stored in memory 202 include operating system 226, communication module (or set of instructions) 228, contact/motion module (or set of instructions) 230, graphics module (or set of instructions) 232, text input module (or set of instructions) 234, Global Positioning System (GPS) module (or set of instructions) 235, Digital Assistant Client Module 229, and applications (or sets of instructions) 236. Further, memory 202 stores data and models, such as user data and models 231. Furthermore, in some embodiments, memory 202 (
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 facilitates communication between various hardware and software components.
Communication module 228 facilitates communication with other devices over one or more external ports 224 and also includes various software components for handling data received by RF circuitry 208 and/or external port 224. External port 224 (e.g., Universal Serial Bus (USB), FIREWIRE, etc.) is adapted for coupling directly to other devices or indirectly over a network (e.g., the Internet, wireless LAN, etc.). In some embodiments, the external port is a multi-pin (e.g., 30-pin) connector that is the same as, or similar to and/or compatible with, the 30-pin connector used on iPod® (trademark of Apple Inc.) devices.
Contact/motion module 230 optionally detects contact with touch screen 212 (in conjunction with display controller 256) and other touch-sensitive devices (e.g., a touchpad or physical click wheel). Contact/motion module 230 includes various software components for performing various operations related to detection of contact, such as determining if contact has occurred (e.g., detecting a finger-down event), determining an intensity of the contact (e.g., the force or pressure of the contact or a substitute for the force or pressure of the contact), determining if there is movement of the contact and tracking the movement across the touch-sensitive surface (e.g., detecting one or more finger-dragging events), and determining if the contact has ceased (e.g., detecting a finger-up event or a break in contact). Contact/motion module 230 receives contact data from the touch-sensitive surface. Determining movement of the point of contact, which is represented by a series of contact data, optionally includes determining speed (magnitude), velocity (magnitude and direction), and/or an acceleration (a change in magnitude and/or direction) of the point of contact. These operations are, optionally, applied to single contacts (e.g., one finger contacts) or to multiple simultaneous contacts (e.g., “multitouch”/multiple finger contacts). In some embodiments, contact/motion module 230 and display controller 256 detect contact on a touchpad.
In some embodiments, 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” on an icon). In some embodiments, at least a subset of the intensity thresholds are determined in accordance with software parameters (e.g., the intensity thresholds are not determined by the activation thresholds of particular physical actuators and can be adjusted without changing the physical hardware of device 200). For example, a mouse “click” threshold of a trackpad or touch screen display can be set to any of a large range of predefined threshold values without changing the trackpad or touch screen display hardware. Additionally, in some implementations, a user of the device is provided with software settings for adjusting one or more of the set of intensity thresholds (e.g., by adjusting individual intensity thresholds and/or by adjusting a plurality of intensity thresholds at once with a system-level click “intensity” parameter).
Contact/motion module 230 optionally detects a gesture input by a user. Different gestures on the touch-sensitive surface have different contact patterns (e.g., different motions, timings, and/or intensities of detected contacts). Thus, a gesture is, optionally, detected by detecting a particular contact pattern. For example, detecting a finger tap gesture includes detecting a finger-down event followed by detecting a finger-up (liftoff) event at the same position (or substantially the same position) as the finger-down event (e.g., at the position of an icon). As another example, detecting a finger swipe gesture on the touch-sensitive surface includes detecting a finger-down event followed by detecting one or more finger-dragging events, and subsequently followed by detecting a finger-up (liftoff) event.
Graphics module 232 includes various known software components for rendering and displaying graphics on touch screen 212 or other display, including components for changing the visual impact (e.g., brightness, transparency, saturation, contrast, or other visual property) of graphics that are displayed. 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, and the like.
In some embodiments, graphics module 232 stores data representing graphics to be used. Each graphic is, optionally, assigned a corresponding code. Graphics module 232 receives, from applications etc., one or more codes specifying graphics to be displayed along with, if necessary, coordinate data and other graphic property data, and then generates screen image data to output to display controller 256.
Haptic feedback module 233 includes various software components for generating instructions used by tactile output generator(s) 267 to produce tactile outputs at one or more locations on device 200 in response to user interactions with device 200.
Text input module 234, which is, in some examples, a component of graphics module 232, provides soft keyboards for entering text in various applications (e.g., contacts 237, email 240, IM 241, browser 247, and any other application that needs text input).
GPS module 235 determines the location of the device and provides this information for use in various applications (e.g., to telephone 238 for use in location-based dialing; to camera 243 as picture/video metadata; and to applications that provide location-based services such as weather widgets, local yellow page widgets, and map/navigation widgets).
Digital assistant client module 229 includes various client-side digital assistant instructions to provide the client-side functionalities of the digital assistant. For example, digital assistant client module 229 is capable of accepting voice input (e.g., speech input), text input, touch input, and/or gestural input through various user interfaces (e.g., microphone 213, accelerometer(s) 268, touch-sensitive display system 212, optical sensor(s) 229, other input control devices 216, etc.) of portable multifunction device 200. Digital assistant client module 229 is also capable of providing output in audio (e.g., speech output), visual, and/or tactile forms through various output interfaces (e.g., speaker 211, touch-sensitive display system 212, tactile output generator(s) 267, etc.) of portable multifunction device 200. For example, output is provided as voice, sound, alerts, text messages, menus, graphics, videos, animations, vibrations, and/or combinations of two or more of the above. During operation, digital assistant client module 229 communicates with DA server 106 using RF circuitry 208.
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 lists, shopping lists, etc.) to provide the client-side functionalities of the digital assistant. Further, user data and models 231 include various models (e.g., speech recognition models, statistical language models, natural language processing models, ontology, task flow models, service models, etc.) for processing user input and determining user intent.
In some examples, digital assistant client module 229 utilizes the various sensors, subsystems, and peripheral devices of portable multifunction device 200 to gather additional information from the surrounding environment of the portable multifunction device 200 to establish a context associated with a user, the current user interaction, and/or the current user input. In some examples, digital assistant client module 229 provides the contextual information or a subset thereof with the user input to DA server 106 to help infer the user's intent. In some examples, the digital assistant also uses the contextual information to determine how to prepare and deliver outputs to the user. Contextual information is referred to as context data.
In some examples, the contextual information that accompanies the user input includes sensor information, e.g., lighting, ambient noise, ambient temperature, images or videos of the surrounding environment, etc. In some examples, the contextual information can also include the physical state of the device, e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion patterns, cellular signals strength, etc. In some examples, information related to the software state of DA server 106, e.g., running processes, installed programs, past and present network activities, background services, error logs, resources usage, etc., and of portable multifunction device 200 is provided to DA server 106 as contextual information associated with a user input.
In some examples, the digital assistant client module 229 selectively provides information (e.g., user data 231) stored on the portable multifunction device 200 in response to requests from DA server 106. In some examples, digital assistant client module 229 also elicits additional input from the user via a natural language dialogue or other user interfaces upon request by DA server 106. Digital assistant client module 229 passes the additional input to DA server 106 to help DA server 106 in intent deduction and/or fulfillment of the user's intent expressed in the user request.
A more detailed description of a digital assistant is described below with reference to
Applications 236 include the following modules (or sets of instructions), or a subset or superset thereof:
Examples of other applications 236 that are stored in memory 202 include other word processing applications, other image editing applications, drawing applications, presentation applications, JAVA-enabled applications, encryption, digital rights management, voice recognition, and voice replication.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, contacts module 237 are used to manage an address book or contact list (e.g., stored in application internal state 292 of contacts module 237 in memory 202 or memory 470), including: adding name(s) to the address book; deleting name(s) from the address book; associating telephone number(s), e-mail address(es), physical address(es) or other information with a name; associating an image with a name; categorizing and sorting names; providing telephone numbers or e-mail addresses to initiate and/or facilitate communications by telephone 238, video conference module 239, e-mail 240, or IM 241; and so forth.
In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, telephone module 238 are used to enter a sequence of characters corresponding to a telephone number, access one or more telephone numbers in contacts module 237, modify a telephone number that has been entered, dial a respective telephone number, conduct a conversation, and disconnect or hang up when the conversation is completed. As noted above, the wireless communication uses any of a plurality of communications standards, protocols, and technologies.
In conjunction with RF circuitry 208, audio circuitry 210, speaker 211, microphone 213, touch screen 212, display controller 256, optical sensor 264, optical sensor controller 258, contact/motion module 230, graphics module 232, text input module 234, contacts module 237, and telephone module 238, video conference module 239 includes executable instructions to initiate, conduct, and terminate a video conference between a user and one or more other participants in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, e-mail client module 240 includes executable instructions to create, send, receive, and manage e-mail in response to user instructions. In conjunction with image management module 244, e-mail client module 240 makes it very easy to create and send e-mails with still or video images taken with camera module 243.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, the instant messaging module 241 includes executable instructions to enter a sequence of characters corresponding to an instant message, to modify previously entered characters, to transmit a respective instant message (for example, using a Short Message Service (SMS) or Multimedia Message Service (MMS) protocol for telephony-based instant messages or using XMPP, SIMPLE, or IMPS for Internet-based instant messages), to receive instant messages, and to view received instant messages. In some embodiments, transmitted and/or received instant messages include graphics, photos, audio files, video files and/or other attachments as are supported in an MMS and/or an Enhanced Messaging Service (EMS). As used herein, “instant messaging” refers to both telephony-based messages (e.g., messages sent using SMS or MMS) and Internet-based messages (e.g., messages sent using XMPP, SIMPLE, or IMPS).
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, map module 254, and music player module, workout support module 242 includes executable instructions to create workouts (e.g., with time, distance, and/or calorie burning goals); communicate with workout sensors (sports devices); receive workout sensor data; calibrate sensors used to monitor a workout; select and play music for a workout; and display, store, and transmit workout data.
In conjunction with touch screen 212, display controller 256, optical sensor(s) 264, optical sensor controller 258, contact/motion module 230, graphics module 232, and image management module 244, camera module 243 includes executable instructions to capture still images or video (including a video stream) and store them into memory 202, modify characteristics of a still image or video, or delete a still image or video from memory 202.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and camera module 243, image management module 244 includes executable instructions to arrange, modify (e.g., edit), or otherwise manipulate, label, delete, present (e.g., in a digital slide show or album), and store still and/or video images.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, browser module 247 includes executable instructions to browse the Internet in accordance with user instructions, including searching, linking to, receiving, and displaying web pages or portions thereof, as well as attachments and other files linked to web pages.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, e-mail client module 240, and browser module 247, calendar module 248 includes executable instructions to create, display, modify, and store calendars and data associated with calendars (e.g., calendar entries, to-do lists, etc.) in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, widget modules 249 are mini-applications that can be downloaded and used by a user (e.g., weather widget 249-1, stocks widget 249-2, calculator widget 249-3, alarm clock widget 249-4, and dictionary widget 249-5) or created by the user (e.g., user-created widget 249-6). In some embodiments, a widget includes an HTML (Hypertext Markup Language) file, a CSS (Cascading Style Sheets) file, and a JavaScript file. In some embodiments, a widget includes an XML (Extensible Markup Language) file and a JavaScript file (e.g., Yahoo! Widgets).
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, and browser module 247, the widget creator module 250 are used by a user to create widgets (e.g., turning a user-specified portion of a web page into a widget).
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, search module 251 includes executable instructions to search for text, music, sound, image, video, and/or other files in memory 202 that match one or more search criteria (e.g., one or more user-specified search terms) in accordance with user instructions.
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 the user to download and play back recorded music and other sound files stored in one or more file formats, such as MP3 or AAC files, and executable instructions to display, present, or otherwise play back videos (e.g., on touch screen 212 or on an external, connected display 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.).
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, and text input module 234, notes module 253 includes executable instructions to create and manage notes, to-do lists, and the like in accordance with user instructions.
In conjunction with RF circuitry 208, touch screen 212, display controller 256, contact/motion module 230, graphics module 232, text input module 234, GPS module 235, and browser module 247, map module 254 are used to receive, display, modify, and store maps and data associated with maps (e.g., driving directions, data on stores and other points of interest at or near a particular location, and other location-based data) in accordance with user instructions.
In conjunction with touch screen 212, display controller 256, contact/motion module 230, graphics module 232, audio circuitry 210, speaker 211, RF circuitry 208, text input module 234, e-mail client module 240, and browser module 247, online video module 255 includes instructions that allow the user to access, browse, receive (e.g., by streaming and/or download), play back (e.g., on the touch screen or on an external, connected display via external port 224), send an e-mail with a link to a particular online video, and otherwise manage online videos in one or more file formats, such as H.264. In some embodiments, instant messaging module 241, rather than e-mail client module 240, is used to send a link to a particular online video. Additional description of the online video application can be found in U.S. Provisional Patent Application No. 60/936,562, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Jun. 20, 2007, and U.S. patent application Ser. No. 11/968,067, “Portable Multifunction Device, Method, and Graphical User Interface for Playing Online Videos,” filed Dec. 31, 2007, the contents of which are hereby incorporated by reference in their entirety.
Each of the above-identified modules and applications corresponds to a set of executable instructions for performing one or more functions described above and the methods described in this application (e.g., the computer-implemented methods and other information processing methods described herein). These modules (e.g., sets of instructions) need not be implemented as separate software programs, procedures, or modules, and thus various subsets of these modules can be combined or otherwise rearranged in various embodiments. For example, video player module can be combined with music player module into a single module (e.g., video and music player module 252,
In some embodiments, device 200 is a device where operation of a predefined set of functions on the device is performed exclusively through a touch screen and/or a touchpad. By using a touch screen and/or a touchpad as the primary input control device for operation of device 200, the number of physical input control devices (such as push buttons, dials, and the like) on device 200 is reduced.
The predefined set of functions that are performed exclusively through a touch screen and/or a touchpad optionally include navigation between user interfaces. In some embodiments, the touchpad, when touched by the user, navigates device 200 to a main, home, or root menu from any user interface that is displayed on device 200. In such embodiments, a “menu button” is implemented using a touchpad. In some other embodiments, the menu button is a physical push button or other physical input control device instead of a touchpad.
Event sorter 270 receives event information and determines the application 236-1 and application view 291 of application 236-1 to which to deliver the event information. Event sorter 270 includes event monitor 271 and event dispatcher module 274. In some embodiments, application 236-1 includes application internal state 292, which indicates the current application view(s) displayed on touch-sensitive display 212 when the application is active or executing. In some embodiments, device/global internal state 257 is used by event sorter 270 to determine which application(s) is (are) currently active, and application internal state 292 is used by event sorter 270 to determine application views 291 to which to deliver event information.
In some embodiments, application internal state 292 includes additional information, such as one or more of: resume information to be used when application 236-1 resumes execution, user interface state information that indicates information being displayed or that is ready for display by application 236-1, a state queue for enabling the user to go back to a prior state or view of application 236-1, and a redo/undo queue of previous actions taken by the user.
Event monitor 271 receives event information from peripherals interface 218. Event information includes information about a sub-event (e.g., a user touch on touch-sensitive display 212, as part of a multi-touch gesture). Peripherals interface 218 transmits information it receives from I/O subsystem 206 or a sensor, such as proximity sensor 266, accelerometer(s) 268, and/or microphone 213 (through audio circuitry 210). Information that peripherals interface 218 receives from I/O subsystem 206 includes information from touch-sensitive display 212 or a touch-sensitive surface.
In some embodiments, event monitor 271 sends requests to the peripherals interface 218 at predetermined intervals. In response, peripherals interface 218 transmits event information. In other embodiments, peripherals interface 218 transmits event information only when there is a significant event (e.g., receiving an input above a predetermined noise threshold and/or for more than a predetermined duration).
In some embodiments, event sorter 270 also includes a hit view determination module 272 and/or an active event recognizer determination module 273.
Hit view determination module 272 provides software procedures for determining where a sub-event has taken place within one or more views when touch-sensitive display 212 displays more than one view. Views are made up of controls and other elements that a user can see on the display.
Another aspect of the user interface associated with an application is a set of views, sometimes herein called application views or user interface windows, in which information is displayed and touch-based gestures occur. The application views (of a respective application) in which a touch is detected correspond to programmatic levels within a programmatic or view hierarchy of the application. For example, the lowest level view in which a touch is detected is called the hit view, and the set of events that are recognized as proper inputs is determined based, at least in part, on the hit view of the initial touch that begins a touch-based gesture.
Hit view determination module 272 receives information related to sub events of a touch-based gesture. When an application has multiple views organized in a hierarchy, hit view determination module 272 identifies a hit view as the lowest view in the hierarchy which should handle the sub-event. In most circumstances, the hit view is the lowest level view in which an initiating sub-event occurs (e.g., the first sub-event in the sequence of sub-events that form an event or potential event). Once the hit view is identified by the hit view determination 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.
Active event recognizer determination module 273 determines which view or views within a 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 a sub-event are actively involved views, and therefore determines that all actively involved views should receive a particular sequence of sub-events. In other embodiments, even if touch sub-events were entirely confined to the area associated with one particular view, views higher in the hierarchy would still remain as actively involved views.
Event dispatcher module 274 dispatches the event information to an event recognizer (e.g., event recognizer 280). In embodiments including active event recognizer determination module 273, event dispatcher module 274 delivers the event information to an event recognizer determined by active event recognizer determination module 273. In some embodiments, event dispatcher module 274 stores in an event queue the event information, which is retrieved by a respective event receiver 282.
In some embodiments, operating system 226 includes event sorter 270. Alternatively, application 236-1 includes event sorter 270. In yet other embodiments, event sorter 270 is a stand-alone module, or a part of another module stored in memory 202, such as contact/motion module 230.
In some embodiments, application 236-1 includes a plurality of event handlers 290 and one or more application views 291, each of which includes instructions for handling touch events that occur within a respective view of the application's user interface. Each application view 291 of the application 236-1 includes one or more event recognizers 280. Typically, a respective application view 291 includes a plurality of event recognizers 280. In other embodiments, one or more of event recognizers 280 are part of a separate module, such as a user interface kit (not shown) or a higher level object from which application 236-1 inherits methods and other properties. In some embodiments, a respective event handler 290 includes one or more of: data updater 276, object updater 277, GUI updater 278, and/or event data 279 received from event sorter 270. Event handler 290 utilizes or calls 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 291 include one or more respective event handlers 290. Also, in some embodiments, one or more of data updater 276, object updater 277, and GUI updater 278 are included in a respective application view 291.
A respective event recognizer 280 receives event information (e.g., event data 279) from event sorter 270 and identifies an event from the event information. Event recognizer 280 includes event receiver 282 and event comparator 284. In some embodiments, event recognizer 280 also includes at least a subset of: metadata 283, and event delivery instructions 288 (which include sub-event delivery instructions).
Event receiver 282 receives event information from event sorter 270. The event information includes information about a sub-event, for example, a touch or a touch movement. Depending on the sub-event, the event information also includes additional information, such as location of the sub-event. When the sub-event concerns motion of a touch, the event information also includes speed and direction of the sub-event. In some embodiments, events include rotation of the device from one orientation to another (e.g., from a portrait orientation to a landscape orientation, or vice versa), and the event information includes corresponding information about the current orientation (also called device attitude) of the device.
Event comparator 284 compares the event information to predefined event or sub-event definitions and, based on the comparison, determines an event or sub event, or determines or updates the state of an event or sub-event. In some embodiments, event comparator 284 includes event definitions 286. Event definitions 286 contain definitions of events (e.g., predefined sequences of sub-events), for example, event 1 (287-1), event 2 (287-2), and others. In some embodiments, sub-events in an event (287) include, for example, touch begin, touch end, touch movement, touch cancellation, and multiple touching. In one example, the definition for event 1 (287-1) is a double tap on a displayed object. The double tap, for example, comprises a first touch (touch begin) on the displayed object for a predetermined phase, a first liftoff (touch end) for a predetermined phase, a second touch (touch begin) on the displayed object for a predetermined phase, and a second liftoff (touch end) for a predetermined phase. In another example, the definition for event 2 (287-2) is a dragging on a displayed object. The dragging, for example, comprises a touch (or contact) on the displayed object for a predetermined phase, a movement of the touch across touch-sensitive display 212, and liftoff of the touch (touch end). In some embodiments, the event also includes information for one or more associated event handlers 290.
In some embodiments, event definition 287 includes a definition of an event for a respective 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 in which three user-interface objects are displayed on touch-sensitive display 212, when a touch is detected on touch-sensitive display 212, event comparator 284 performs a hit test to determine which of the three user-interface objects is associated with the touch (sub-event). If each displayed object is associated with a respective 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 the sub-event and the object triggering the hit test.
In some embodiments, the definition for a respective event (287) also includes delayed actions that delay delivery of the event information until after it has been determined whether the sequence of sub-events does or does not correspond to the event recognizer's event type.
When a respective event recognizer 280 determines that the series of sub-events do not match any of the events in event definitions 286, the respective event recognizer 280 enters an event impossible, event failed, or event ended state, after which it disregards subsequent sub-events of the touch-based gesture. In this situation, other event recognizers, if any, that remain active for the hit view continue to track and process sub-events of an ongoing touch-based gesture.
In some embodiments, a respective event recognizer 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 involved event recognizers. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate how event recognizers interact, or are enabled to interact, with one another. In some embodiments, metadata 283 includes configurable properties, flags, and/or lists that indicate whether sub-events are delivered to varying levels in the view or programmatic hierarchy.
In some embodiments, a respective event recognizer 280 activates event handler 290 associated with an event when one or more particular sub-events of an event are recognized. In some embodiments, a respective event recognizer 280 delivers event information associated with the event to event handler 290. Activating an event handler 290 is distinct from sending (and deferred sending) sub-events to a respective hit view. In some embodiments, event recognizer 280 throws a flag associated with the recognized event, and event handler 290 associated with the flag catches the flag and performs a predefined process.
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 instructions deliver event information to event handlers associated with the series of sub-events or to actively involved views. Event handlers associated with the series of sub-events or with actively involved views receive the event information and perform a predetermined process.
In some embodiments, data updater 276 creates and updates data used in application 236-1. For example, data updater 276 updates the telephone number used in contacts module 237, or stores a video file 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 a new user-interface object or updates the position of a user-interface object. GUI updater 278 updates the GUI. For example, GUI updater 278 prepares display information and sends it to graphics module 232 for display on a touch-sensitive display.
In some embodiments, event handler(s) 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 a respective application 236-1 or application view 291. In other embodiments, they are included in two or more software modules.
It shall be understood that the foregoing discussion regarding event handling of user touches on touch-sensitive displays also applies to other forms of user inputs to operate multifunction devices 200 with input devices, not all of which are initiated on touch screens. For example, mouse movement and mouse button presses, optionally coordinated with single or multiple keyboard presses or holds; contact movements such as taps, drags, scrolls, etc. on touchpads; pen stylus inputs; movement of the device; oral instructions; detected eye movements; biometric inputs; and/or any combination thereof are optionally utilized as inputs corresponding to sub-events which define an event to be recognized.
Device 200 also includes one or more physical buttons, such as “home” or menu button 304. As described previously, menu button 304 is used to navigate to any application 236 in a set of applications that is executed on device 200. Alternatively, in some embodiments, the menu button is implemented as a soft key in a GUI displayed on touch screen 212.
In one embodiment, device 200 includes touch screen 212, menu button 304, push button 306 for powering the device on/off and locking the device, volume adjustment button(s) 308, subscriber identity module (SIM) card slot 310, headset jack 312, and docking/charging external port 224. Push button 306 is, optionally, used to turn the power on/off on the device by depressing the button and holding the button in the depressed state for a predefined time interval; to lock the device by depressing the button and releasing the button before the predefined time interval has elapsed; and/or to unlock the device or initiate an unlock process. In an alternative embodiment, device 200 also accepts verbal input for activation or deactivation of some functions through microphone 213. Device 200 also, optionally, includes one or more contact intensity sensors 265 for detecting intensity of contacts on touch screen 212 and/or one or more tactile output generators 267 for generating tactile outputs for a user of device 200.
Each of the above-identified elements in
Attention is now directed towards embodiments of user interfaces that can be implemented on, for example, portable multifunction device 200.
It should be noted that the icon labels illustrated in
Although some of the examples which follow will be given with reference to inputs on touch screen display 212 (where the touch-sensitive surface and the display are combined), in some embodiments, the device detects inputs on a touch-sensitive surface that is separate from the display, as shown in
Additionally, while the following examples are given primarily with reference to finger inputs (e.g., finger contacts, finger tap gestures, finger swipe gestures), it should be understood that, in some embodiments, one or more of the finger inputs are replaced with input from another input device (e.g., a mouse-based input or stylus input). For example, a swipe gesture is, optionally, replaced with a mouse click (e.g., instead of a 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 with a mouse click while the cursor is located over the location of the tap gesture (e.g., instead of detection of the contact followed by ceasing to detect the contact). Similarly, when multiple user inputs are simultaneously detected, it should be understood that multiple computer mice are, optionally, used simultaneously, or a mouse and finger contacts are, optionally, used simultaneously.
Techniques for detecting and processing touch intensity are found, for example, in related applications: International Patent Application Serial No. PCT/US2013/040061, titled “Device, Method, and Graphical User Interface for Displaying User Interface Objects Corresponding to an Application,” filed May 8, 2013, and International Patent Application Serial No. PCT/US2013/069483, titled “Device, Method, and Graphical User Interface for Transitioning Between Touch Input to Display Output Relationships,” filed Nov. 11, 2013, each of which is hereby incorporated by reference in their entirety.
In some embodiments, device 600 has one or more input mechanisms 606 and 608. Input mechanisms 606 and 608, if included, are physical. Examples of physical input mechanisms include push buttons and rotatable mechanisms. In some embodiments, device 600 has one or more attachment mechanisms. Such attachment mechanisms, if included, can permit attachment of device 600 with, for example, hats, eyewear, earrings, necklaces, shirts, jackets, bracelets, watch straps, chains, trousers, belts, shoes, purses, backpacks, and so forth. These attachment mechanisms permit device 600 to be worn by a user.
Input mechanism 608 is a microphone, in some examples. Personal electronic device 600 includes, for example, various sensors, such as GPS sensor 632, accelerometer 634, directional sensor 640 (e.g., compass), gyroscope 636, motion sensor 638, and/or a combination thereof, all of which are operatively connected to I/O section 614.
Memory 618 of personal electronic device 600 is a non-transitory computer-readable storage medium, for storing computer-executable instructions, which, when executed by one or more computer processors 616, for example, cause the computer processors to perform the techniques and processes described below. The computer-executable instructions, for example, are also stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. Personal electronic device 600 is not limited to the components and configuration of
As used here, the term “affordance” refers to a user-interactive graphical user interface object that is, for example, displayed on the display screen of devices 200, 400, and/or 600 (
As used herein, the term “focus selector” refers to an input element that indicates a current part of a user interface with which a user is interacting. In some implementations that include a cursor or other location marker, the cursor acts as a “focus selector” so that when an input (e.g., a press input) is detected on a touch-sensitive surface (e.g., touchpad 455 in
As used in the specification and claims, the term “characteristic intensity” of a contact refers to a characteristic of the contact based on one or more intensities of the contact. In some embodiments, the characteristic intensity is based on multiple intensity samples. The characteristic intensity is, optionally, based on a predefined number of intensity samples, or a set of intensity samples collected during a predetermined time period (e.g., 0.05, 0.1, 0.2, 0.5, 1, 2, 5, 10 seconds) relative to a predefined event (e.g., after detecting the contact, prior to detecting liftoff of the contact, before or after detecting a start of movement of the contact, prior to detecting an end of the contact, before or after detecting an increase in intensity of the contact, and/or before or after detecting a decrease in intensity of the contact). A characteristic intensity of a contact is, optionally based on one or more of: a maximum value of the intensities of the contact, a mean value of the intensities of the contact, an average value of the intensities of the contact, a top 10 percentile value of the intensities of the contact, a value at the half maximum of the intensities of the contact, a value at the 90 percent maximum of the intensities of the contact, or the like. In some embodiments, the duration of the contact is used in determining the characteristic intensity (e.g., when the characteristic intensity is an average of the intensity of the contact over time). In some embodiments, the characteristic intensity is compared to a set of one or more intensity thresholds to determine whether an operation has been performed by a user. For example, the set of one or more intensity thresholds includes a first intensity threshold and a second intensity threshold. In this example, a contact with a characteristic intensity that does not exceed the first threshold results in a first operation, a contact with a characteristic intensity that exceeds the first intensity threshold and does not exceed the second intensity threshold results in a second operation, and a contact with a characteristic intensity that exceeds the second threshold results in a third operation. In some embodiments, a comparison between the characteristic intensity and one or more thresholds is used to determine whether or not to perform one or more operations (e.g., whether to perform a respective operation or forgo performing the respective operation) rather than being used to determine whether to perform a first operation or a second operation.
In some embodiments, a portion of a gesture is identified for purposes of determining a characteristic intensity. For example, a touch-sensitive surface receives a continuous swipe contact transitioning from a start location and reaching an end location, at which point the intensity of the contact increases. In this example, the characteristic intensity of the contact at the end location is based on only a portion of the continuous swipe contact, and not the entire swipe contact (e.g., only the portion of the swipe contact at the end location). In some embodiments, a smoothing algorithm is applied to the intensities of the swipe contact prior to determining the characteristic intensity of the contact. For example, the smoothing algorithm optionally includes one or more of: an unweighted sliding-average smoothing algorithm, a triangular smoothing algorithm, a median filter smoothing algorithm, and/or an exponential smoothing algorithm. In some circumstances, these smoothing algorithms eliminate narrow spikes or dips in the intensities of the swipe contact for purposes of determining a characteristic intensity.
The intensity of a contact on the 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 operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, the deep press intensity threshold corresponds to an intensity at which the device will perform operations that are different from operations typically associated with clicking a button of a physical mouse or a trackpad. In some embodiments, when a contact is detected with a characteristic intensity below the light press intensity threshold (e.g., and above a nominal contact-detection intensity threshold below which the contact is no longer detected), the device will move a focus selector in accordance with 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, unless otherwise stated, these intensity thresholds are consistent between different sets of user interface figures.
An increase of characteristic intensity of the contact from an intensity below the 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 of characteristic intensity of the contact from an intensity below the deep press intensity threshold to an intensity above the deep press intensity threshold is sometimes referred to as a “deep press” input. An increase of characteristic intensity of the contact from an intensity below the 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 the contact on the touch-surface. A decrease of characteristic intensity of the contact from an intensity above the contact-detection intensity threshold to an intensity below the contact-detection intensity threshold is sometimes referred to as detecting liftoff of the contact 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.
In some embodiments described herein, one or more operations are performed in response to detecting a gesture that includes a respective press input or in response to detecting the respective press input performed with a respective contact (or a plurality of contacts), where the respective press input is detected based at least in part on detecting an increase in intensity of the contact (or plurality of contacts) above a press-input intensity threshold. In some embodiments, the respective operation is performed in response to detecting the increase in intensity of the respective contact above the press-input intensity threshold (e.g., a “down stroke” of the respective press input). In some embodiments, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the press-input threshold (e.g., an “up stroke” of the respective press input).
In some embodiments, the device employs intensity hysteresis to avoid accidental inputs sometimes termed “jitter,” where the device defines or selects a hysteresis intensity threshold with a predefined relationship to the 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, the press input includes an increase in intensity of the respective contact above the press-input intensity threshold and a subsequent decrease in intensity of the contact below the hysteresis intensity threshold that corresponds to the press-input intensity threshold, and the respective operation is performed in response to detecting the subsequent decrease in intensity of the respective contact below the hysteresis intensity threshold (e.g., an “up stroke” of the respective press input). Similarly, in some embodiments, the press input is detected only when the device detects an increase in intensity of the contact from an intensity at or below the hysteresis intensity threshold to an intensity at or above the press-input intensity threshold and, optionally, a subsequent decrease in intensity of the contact to an intensity at or below the hysteresis intensity, and the respective operation is performed in response to detecting the press input (e.g., the increase in intensity of the contact or the decrease in intensity of the contact, depending on the circumstances).
For ease of explanation, the descriptions of operations performed in response to a press input associated with a press-input intensity threshold or in response to a gesture including the press input are, optionally, triggered in response to detecting either: an increase in intensity of a contact above the press-input intensity threshold, an increase in intensity of a contact from an intensity below the hysteresis intensity threshold to an intensity above the press-input intensity threshold, a decrease in intensity of the contact below the press-input intensity threshold, and/or a decrease in intensity of the contact below the 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 a decrease in intensity of a contact below the press-input intensity threshold, the operation is, optionally, performed in response to detecting a decrease in intensity of the contact below a hysteresis intensity threshold corresponding to, and lower than, the press-input intensity threshold.
3. Digital Assistant System
Digital assistant system 700 includes memory 702, one or more processors 704, input/output (I/O) interface 706, and network communications interface 708. These components can communicate with one another over one or more communication buses or signal lines 710.
In some examples, memory 702 includes a non-transitory computer-readable medium, such as high-speed random access memory and/or a non-volatile computer-readable storage medium (e.g., one or more magnetic disk storage devices, flash memory devices, or other non-volatile solid-state memory devices).
In some examples, I/O interface 706 couples input/output devices 716 of digital assistant system 700, such as displays, keyboards, touch screens, and microphones, to user interface module 722. I/O interface 706, in conjunction with user interface module 722, receives user inputs (e.g., voice input, keyboard inputs, touch inputs, etc.) and processes them accordingly. In some examples, e.g., when the digital assistant is implemented on a standalone user device, digital assistant system 700 includes any of the components and I/O communication interfaces described with respect to devices 200, 400, or 600 in
In some examples, the network communications interface 708 includes wired communication port(s) 712 and/or wireless transmission and reception circuitry 714. The wired communication port(s) receives and send communication signals via one or more wired interfaces, e.g., Ethernet, Universal Serial Bus (USB), FIREWIRE, etc. The wireless circuitry 714 receives and sends RF signals and/or optical signals from/to communications networks and other communications devices. The wireless communications use any of a plurality of communications standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol. Network communications interface 708 enables communication between digital assistant system 700 with networks, such as the Internet, an intranet, and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN), and/or a metropolitan area network (MAN), and other devices.
In some examples, memory 702, or the computer-readable storage media of memory 702, stores programs, modules, instructions, and data structures including all or a subset of: operating system 718, communications module 720, user interface module 722, one or more applications 724, and digital assistant module 726. In particular, memory 702, or the computer-readable storage media of memory 702, stores instructions for performing the processes described below. One or more processors 704 execute these programs, modules, and instructions, and reads/writes from/to the data structures.
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 communications between various hardware, firmware, and software components.
Communications module 720 facilitates communications between digital assistant system 700 with other devices over network communications interface 708. For example, communications module 720 communicates with RF circuitry 208 of electronic devices such as devices 200, 400, and 600 shown in
User interface module 722 receives commands and/or inputs from a user via I/O interface 706 (e.g., from a keyboard, touch screen, pointing device, controller, and/or microphone), and generate user interface objects on a display. User interface module 722 also prepares and delivers outputs (e.g., speech, sound, animation, text, icons, vibrations, haptic feedback, light, etc.) to the user via the I/O interface 706 (e.g., through displays, audio channels, speakers, touch-pads, etc.).
Applications 724 include programs and/or modules that are configured to be executed by one or more processors 704. For example, if the digital assistant system is implemented on a standalone user device, applications 724 include user applications, such as games, a calendar application, a navigation application, or an email application. If digital assistant system 700 is implemented on a server, applications 724 include resource management applications, diagnostic applications, or scheduling applications, for example.
Memory 702 also stores digital assistant module 726 (or the server portion of a digital assistant). In some examples, digital assistant module 726 includes the following sub-modules, or a subset or superset thereof: input/output processing module 728, speech-to-text (STT) processing module 730, natural language processing module 732, dialogue flow processing module 734, task flow processing module 736, service processing module 738, and speech synthesis processing module 740. Each of these modules has access to one or more of the following systems or data and models of the digital assistant module 726, or a subset or superset thereof: ontology 760, vocabulary index 744, user data 748, task flow models 754, service models 756, and ASR systems 758.
In some examples, using the processing modules, data, and models implemented in digital assistant module 726, the digital assistant can perform at least some of the following: converting speech input into text; identifying a user's intent expressed in a natural language input received from the user; actively eliciting and obtaining information needed to fully infer the user's intent (e.g., by disambiguating words, games, intentions, etc.); determining the task flow for fulfilling the inferred intent; and executing the task flow to fulfill the inferred intent.
In some examples, as shown in
STT processing module 730 includes one or more ASR systems 758. The one or more ASR systems 758 can process the speech input that is received through I/O processing module 728 to produce a recognition result. Each ASR system 758 includes a front-end speech pre-processor. The front-end speech pre-processor extracts representative features from the speech input. For example, the front-end speech pre-processor performs a Fourier transform on the speech input to extract spectral features that characterize the speech input as a sequence of representative multi-dimensional vectors. Further, each ASR system 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 the dynamic time warping based engines and weighted finite-state transducers (WFST) based engines. The one or more speech recognition models and the one or more speech recognition engines are used to process the extracted representative features of the front-end speech pre-processor to produce intermediate recognitions results (e.g., phonemes, phonemic strings, and sub-words), and ultimately, text recognition results (e.g., words, word strings, or sequence of tokens). In some examples, the speech input is processed at least partially by a third-party service or on the user's device (e.g., device 104, 200, 400, or 600) to produce the recognition result. Once STT processing module 730 produces recognition results containing a text string (e.g., words, or sequence of words, or sequence of tokens), the recognition result is passed to natural language processing module 732 for intent deduction. In some examples, STT processing module 730 produces multiple candidate text representations of the speech input. Each candidate text representation is a sequence of words or tokens corresponding to the speech input. In some examples, each candidate text representation is associated with a speech recognition confidence score. Based on the speech recognition confidence scores, STT processing module 730 ranks the candidate text representations and provides the n-best (e.g., n highest ranked) candidate text representation(s) to natural language processing module 732 for intent deduction, where n is a predetermined integer greater than zero. For example, in one example, only the highest ranked (n=1) candidate text representation is passed to natural language processing module 732 for intent deduction. In another example, the five highest ranked (n=5) candidate text representations are passed to natural language processing module 732 for intent deduction.
More details on the speech-to-text processing are described in U.S. Utility application Ser. No. 13/236,942 for “Consolidating Speech Recognition Results,” filed on Sep. 20, 2011, the entire disclosure of which is incorporated herein by reference.
In some examples, STT processing module 730 includes and/or accesses a vocabulary of recognizable words via phonetic alphabet conversion module 731. Each vocabulary word is associated with one or more candidate pronunciations of the word represented in a speech recognition phonetic alphabet. In particular, the vocabulary of recognizable words includes a word that is associated with a plurality of candidate pronunciations. For example, the vocabulary includes the word “tomato” that is associated with the candidate pronunciations of // and /
/. Further, vocabulary words are associated with custom candidate pronunciations that are based on previous speech inputs from the user. Such custom candidate pronunciations are stored in STT processing module 730 and are associated with a particular user via the user's profile on the device. In some examples, the candidate pronunciations for words are determined based on the spelling of the word and one or more linguistic and/or phonetic rules. In some examples, the candidate pronunciations are manually generated, e.g., based on known canonical pronunciations.
In some examples, the candidate pronunciations are ranked based on the commonness of the candidate pronunciation. For example, the candidate pronunciation // is ranked higher than /
/, because the former is a more commonly used pronunciation (e.g., among all users, for users in a particular geographical region, or for any other appropriate subset of users). In some examples, candidate pronunciations are ranked based on whether the candidate pronunciation is a custom candidate pronunciation associated with the user. For example, custom candidate pronunciations are ranked higher than canonical candidate pronunciations. This can be useful for recognizing proper nouns having a unique pronunciation that deviates from canonical pronunciation. In some examples, candidate pronunciations are associated with one or more speech characteristics, such as geographic origin, nationality, or ethnicity. For example, the candidate pronunciation /
/ is associated with the United States, whereas the candidate pronunciation /
/ is associated with Great Britain. Further, the rank of the candidate pronunciation is based on one or more characteristics (e.g., geographic origin, nationality, ethnicity, etc.) of the user stored in the user's profile on the device. For example, it can be determined from the user's profile that the user is associated with the United States. Based on the user being associated with the United States, the candidate pronunciation /
/ (associated with the United States) is ranked higher than the candidate pronunciation /
/ (associated with Great Britain). In some examples, one of the ranked candidate pronunciations is selected as a predicted pronunciation (e.g., the most likely pronunciation).
When a speech input is received, STT processing module 730 is used to determine the phonemes corresponding to the speech input (e.g., using an acoustic model), and then attempt to determine words that match the phonemes (e.g., using a language model). For example, if STT processing module 730 first identifies the sequence of phonemes // corresponding to a portion of the speech input, it can then determine, based on vocabulary index 744, that this sequence corresponds to the word “tomato.”
In some examples, STT processing module 730 uses approximate matching techniques to determine words in an utterance. Thus, for example, the STT processing module 730 determines that the sequence of phonemes // corresponds to the word “tomato,” even if that particular sequence of phonemes is not one of the candidate sequence of phonemes for that word.
Natural language processing module 732 (“natural language processor”) of the digital assistant takes the n-best candidate text representation(s) (“word sequence(s)” or “token sequence(s)”) generated by STT processing module 730, and attempts to associate each of the candidate text representations with one or more “actionable intents” recognized by the digital assistant. An “actionable intent” (or “user intent”) represents a task that can be performed by the digital assistant, and can have an associated task flow implemented in task flow models 754. The associated task flow is a series of programmed actions and steps that the digital assistant takes in order to perform the task. The scope of a digital assistant's capabilities is dependent on the number and variety of task flows that have been implemented and stored in task flow models 754, or in other words, on the number and variety of “actionable intents” that the digital assistant recognizes. The effectiveness of the digital assistant, however, also dependents on the assistant's ability to infer the correct “actionable intent(s)” from the user request expressed in natural language.
In some examples, in addition to the sequence of words or tokens obtained from STT processing module 730, natural language processing module 732 also receives contextual information associated with the user request, e.g., from I/O processing module 728. The natural language processing module 732 optionally uses the contextual information to clarify, supplement, and/or further define the information contained in the candidate text representations received from STT processing module 730. The contextual information includes, for example, user preferences, hardware, and/or software states of the user device, sensor information collected before, during, or shortly after the user request, prior interactions (e.g., dialogue) between the digital assistant and the user, and the like. As described herein, contextual information is, in some examples, dynamic, and changes with time, location, content of the dialogue, and other factors.
In some examples, the natural language processing is based on, e.g., ontology 760. Ontology 760 is a hierarchical structure containing many nodes, each node representing either an “actionable intent” or a “property” relevant to one or more of the “actionable intents” or other “properties.” As noted above, an “actionable intent” represents a task that the digital assistant is capable of performing, i.e., it is “actionable” or can be acted on. A “property” represents a parameter associated with an actionable intent or a sub-aspect of another property. A linkage between an actionable intent node and a property node in ontology 760 defines how a parameter represented by the property node pertains to the task represented by the actionable intent node.
In some examples, ontology 760 is made up of actionable intent nodes and property nodes. Within ontology 760, each actionable intent node is linked to one or more property nodes either directly or through one or more intermediate property nodes. Similarly, each property node is linked to one or more actionable intent nodes either directly or through one or more intermediate property nodes. For example, as shown in
In addition, property nodes “cuisine,” “price range,” “phone number,” and “location” are sub-nodes of the property node “restaurant,” and are each linked to the “restaurant reservation” node (i.e., the actionable intent node) through the intermediate property node “restaurant.” For another example, as shown in
An actionable intent node, along with its linked property nodes, is described as a “domain.” In the present discussion, each domain is associated with a respective actionable intent, and refers to the group of nodes (and the relationships there between) associated with the particular actionable intent. For example, ontology 760 shown in
While
In some examples, ontology 760 includes all the domains (and hence actionable intents) that the digital assistant is capable of understanding and acting upon. In some examples, ontology 760 is modified, such as by adding or removing entire domains or nodes, or by modifying relationships between the nodes within the ontology 760.
In some examples, nodes associated with multiple related actionable intents are clustered under a “super domain” in ontology 760. For example, a “travel” super-domain includes a cluster of property nodes and actionable intent nodes related to travel. The actionable intent nodes related to travel includes “airline reservation,” “hotel reservation,” “car rental,” “get directions,” “find points of interest,” and so on. The actionable intent nodes under the same super domain (e.g., the “travel” super domain) have many property nodes in common. For example, the actionable intent nodes for “airline reservation,” “hotel reservation,” “car rental,” “get directions,” and “find points of interest” share one or more of the property nodes “start location,” “destination,” “departure date/time,” “arrival date/time,” and “party size.” In another example, a “media” super-domain includes a cluster of property nodes and actionable intent nodes related to media. The actionable intent nodes related to media include, for example, “search media,” “play media item,” “buy media item,” or the like.
In some examples, each node in ontology 760 is associated with a set of words and/or phrases that are relevant to the property or actionable intent represented by the node. The respective set of words and/or phrases associated with each node are the so-called “vocabulary” associated with the node. The respective set of words and/or phrases associated with each node are stored in vocabulary index 744 in association with the property or actionable intent represented by the node. For example, returning to
Natural language processing module 732 receives the candidate text representations (e.g., text string(s) or token sequence(s)) from STT processing module 730, and for each candidate representation, determines what nodes are implicated by the words in the candidate text representation. In some examples, if a word or phrase in the candidate text representation is found to be associated with one or more nodes in ontology 760 (via vocabulary index 744), the word or phrase “triggers” or “activates” those nodes. Based on the quantity and/or relative importance of the activated nodes, natural language processing module 732 selects one of the actionable intents as the task that the user intended the digital assistant to perform. In some examples, the domain that has the most “triggered” nodes is selected. In some examples, the domain having the highest confidence value (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 the importance of the triggered nodes. In some examples, additional factors are considered in selecting the node as well, such as whether the digital assistant has previously correctly interpreted a similar request from a user.
User data 748 includes user-specific information, such as user-specific vocabulary, user preferences, user address, user's default and secondary languages, user's contact list, and other short-term or long-term information for each user. In some examples, natural language processing module 732 uses the user-specific information to supplement the information contained in the user input to further define the user intent. For example, for a user request “invite my friends to my birthday party,” natural language processing module 732 is able to access user data 748 to determine who the “friends” are and when and where the “birthday party” would be held, rather than requiring the user to provide such information explicitly in his/her request.
It should be recognized that in some examples, natural language processing module 732 is implemented using one or more machine learning mechanisms (e.g., neural networks). In particular, the one or more machine learning mechanisms are configured to receive a candidate text representation and contextual information associated with the candidate text representation. Based on the candidate text representation and the associated contextual information, the one or more machine learning mechanisms are configured to determine intent confidence scores over a set of candidate actionable intents. Natural language processing module 732 can select one or more candidate actionable intents from the set of candidate actionable intents based on the determined intent confidence scores. In some examples, an ontology (e.g., ontology 760) is also used to select the one or more candidate actionable intents from the set of candidate actionable intents.
Other details of searching an ontology based on a token string are described in U.S. Utility application Ser. No. 12/341,743 for “Method and Apparatus for Searching Using An Active Ontology,” filed Dec. 22, 2008, the entire disclosure of which is incorporated herein by reference.
In some examples, once natural language processing module 732 identifies an actionable intent (or domain) based on the user request, natural language processing module 732 generates a structured query to represent the identified actionable intent. In some examples, the structured query includes parameters for one or more nodes within the domain for the actionable intent, and at least some of the parameters are populated with the specific information and requirements specified in the user request. For example, the user says “Make me a dinner reservation at a sushi place at 7.” In this case, natural language processing module 732 is able to correctly identify the actionable intent to be “restaurant reservation” based on the user input. According to the ontology, a structured query for a “restaurant reservation” domain includes parameters such as {Cuisine}, {Time}, {Date}, {Party Size}, and the like. In some examples, based on the speech input and the text derived from the speech input using STT processing module 730, natural language processing module 732 generates a partial structured query for the restaurant reservation domain, where the partial structured query includes the parameters {Cuisine=“Sushi”} and {Time=“7 pm”}. However, in this example, the user's utterance contains insufficient information to complete the structured query associated with the domain. Therefore, other necessary parameters such as {Party Size} and {Date} are not specified in the structured query based on the information currently available. In some examples, natural language processing module 732 populates some parameters of the structured query with received contextual information. For example, in some examples, if the user requested a sushi restaurant “near me,” natural language processing module 732 populates a {location} parameter in the structured query with GPS coordinates from the user device.
In some examples, natural language processing module 732 identifies multiple candidate actionable intents for each candidate text representation received from STT processing module 730. Further, in some examples, a respective structured query (partial or complete) is generated for each identified candidate actionable intent. Natural language processing module 732 determines an intent confidence score for each candidate actionable intent and ranks the candidate actionable intents based on the intent confidence scores. In some examples, natural language processing module 732 passes the generated structured query (or queries), including any completed parameters, to task flow processing module 736 (“task flow processor”). In some examples, the structured query (or queries) for the m-best (e.g., m highest ranked) candidate actionable intents are provided to task flow processing module 736, where m is a predetermined integer greater than zero. In some examples, the structured query (or queries) for the m-best candidate actionable intents are provided to task flow processing module 736 with the corresponding candidate text representation(s).
Other details of inferring a user intent based on multiple candidate actionable intents determined from multiple candidate text representations of a speech input are described in U.S. Utility application Ser. No. 14/298,725 for “System and Method for Inferring User Intent From Speech Inputs,” filed Jun. 6, 2014, the entire disclosure of which is incorporated herein by reference.
Task flow processing module 736 is configured to receive the structured query (or queries) from natural language processing module 732, complete the structured query, if necessary, and perform the actions required to “complete” the user's ultimate request. In some examples, the various procedures necessary to complete these tasks are provided in task flow models 754. In some examples, task flow models 754 include procedures for obtaining additional information from the user and task flows for performing actions associated with the actionable intent.
As described above, in order to complete a structured query, task flow processing module 736 needs to initiate additional dialogue with the user in order to obtain additional information, and/or disambiguate potentially ambiguous utterances. When such interactions are necessary, task flow processing module 736 invokes dialogue flow processing module 734 to engage in a dialogue with the user. In some examples, dialogue flow processing module 734 determines how (and/or when) to ask the user for the additional information and receives and processes the user responses. The questions are provided to and answers are received from the users through I/O processing module 728. In some examples, dialogue flow processing module 734 presents dialogue output to the user via audio and/or visual output, and receives input from the user via spoken or physical (e.g., clicking) responses. Continuing with the example above, when task flow processing module 736 invokes dialogue flow processing module 734 to determine the “party size” and “date” information for the structured query associated with the domain “restaurant reservation,” dialogue flow processing module 734 generates questions such as “For how many people?” and “On which day?” to pass to the user. Once answers are received from the user, dialogue flow processing module 734 then populates the structured query with the missing information, or pass the information to task flow processing module 736 to complete the missing information from the structured query.
Once task flow processing module 736 has completed the structured query for an actionable intent, task flow processing module 736 proceeds to perform the ultimate task associated with the actionable intent. Accordingly, 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 actionable intent of “restaurant reservation” includes steps and instructions for contacting a restaurant and actually requesting a reservation for a particular party size at a particular time. For example, using a structured query such as: {restaurant reservation, restaurant=ABC Café, date=3/12/2012, time=7 pm, party size=5}, task flow processing module 736 performs the steps of: (1) logging onto a server of the ABC Café or a restaurant reservation system such as OPENTABLE®, (2) entering the date, time, and party size information in a form on the website, (3) submitting the form, and (4) making a calendar entry for the reservation in the user's calendar.
In some examples, task flow processing module 736 employs the assistance of service processing module 738 (“service processing module”) to complete a task requested in the user input or to provide an informational answer requested in the user input. For example, service processing module 738 acts on behalf of task flow processing module 736 to make a phone call, set a calendar entry, invoke a map search, invoke or interact with other user applications installed on the user device, and invoke or interact with third-party services (e.g., a restaurant reservation portal, a social networking website, a banking portal, etc.). In some examples, the protocols and application programming interfaces (API) required by each service are specified by a respective service model among service models 756. Service processing module 738 accesses the appropriate service model for a service and generates requests for the service in accordance with the protocols and APIs required by the service according to the service model.
For example, if a restaurant has enabled an online reservation service, the restaurant submits a service model specifying the necessary parameters for making a reservation and the APIs for communicating the values of the necessary parameter to the online reservation service. When requested by task flow processing module 736, service processing module 738 establishes a network connection with the online reservation service using the web address stored in the service model, and sends the necessary parameters of the reservation (e.g., time, date, party size) to the online reservation interface in a format according to the API of the online reservation service.
In some examples, natural language processing module 732, dialogue flow processing module 734, and task flow processing module 736 are used collectively and iteratively to infer and define the user's intent, obtain information to further clarify and refine the user intent, and finally generate a response (i.e., an output to the user, or the completion of a task) to fulfill the user's intent. The generated response is a dialogue response to the speech input that at least partially fulfills the user's intent. Further, in some examples, the generated response is output as a speech output. In these examples, the generated response is sent to speech synthesis processing module 740 (e.g., speech synthesizer) where it can be processed to synthesize the dialogue response in speech form. In yet other examples, the generated response is data content relevant to satisfying a user request in the speech input.
In examples where task flow processing module 736 receives multiple structured queries from natural language processing module 732, task flow processing module 736 initially processes the first structured query of the received structured queries to attempt to complete the first structured query and/or execute one or more tasks or actions represented by the first structured query. In some examples, the first structured query corresponds to the highest ranked actionable intent. In other examples, the first structured query is selected from the received structured queries based on a combination of the corresponding speech recognition confidence scores and the corresponding intent confidence scores. In some examples, if task flow processing module 736 encounters an error during processing of the first structured query (e.g., due to an inability to determine a necessary parameter), the task flow processing module 736 can proceed to select and process a second structured query of the received structured queries that corresponds to a lower ranked actionable intent. The second structured query is selected, for example, based on the speech recognition confidence score of the corresponding candidate text representation, the intent confidence score of the corresponding candidate actionable intent, a missing necessary parameter in the first structured query, or any combination thereof.
Speech synthesis processing module 740 is configured to synthesize speech outputs for presentation to the user. Speech synthesis processing module 740 synthesizes speech outputs based on text provided by the digital assistant. For example, the generated dialogue response is in the form of a text string. Speech synthesis processing module 740 converts the text string to an audible speech output. Speech synthesis processing module 740 uses any appropriate speech synthesis technique in order to generate speech outputs from text, including, but not limited, to concatenative synthesis, unit selection synthesis, diphone synthesis, domain-specific synthesis, formant synthesis, articulatory synthesis, hidden Markov model (HMM) based synthesis, and sinewave synthesis. In some examples, speech synthesis processing module 740 is configured to synthesize individual words based on phonemic strings corresponding to the words. For example, a phonemic string is associated with a word in the generated dialogue response. The phonemic string is stored in metadata associated with the word. Speech synthesis processing module 740 is configured to directly process the phonemic string in the metadata to synthesize the word in speech form.
In some examples, instead of (or in addition to) using speech synthesis processing module 740, speech synthesis is performed on a remote device (e.g., the server system 108), and the synthesized speech is sent to the user device for output to the user. For example, this can occur in some implementations where outputs for a digital assistant are generated at a server system. And because server systems generally have more processing power or resources than a user device, it is possible to obtain higher quality speech outputs than would be practical with client-side synthesis.
Additional details on digital assistants can be found in the U.S. Utility application Ser. No. 12/987,982, entitled “Intelligent Automated Assistant,” filed Jan. 10, 2011, and U.S. Utility application Ser. No. 13/251,088, entitled “Generating and Processing Task Items That Represent Tasks to Perform,” filed Sep. 30, 2011, the entire disclosures of which are incorporated herein by reference.
Natural language parsing system 800 includes natural language parsing module 802, knowledge base 804, post-processing module 806, and natural language parsing model generating module 808. In some examples, natural language parsing system 800 is at least partially implemented within natural language processing module 732 discussed above and the output of natural language parsing system 800 (e.g., the output of post-processing module 806) is provided as a structured query to task flow processing module 736.
As discussed in greater detail below, in some examples, natural language parsing module 802 uses information contained in knowledge base 804 and a natural language parsing model to generate parsing results for natural language input. In some examples, the parsing results have confidence scores and are ranked based on their respective confidence scores. In some examples, post-processing module 806 re-ranks parsing results generated by natural language parsing module 802 (e.g., by adjusting the respective confidence scores of the parsing results) and generates new parsing results using the parsing results generated by natural language parsing module 802. In some examples, natural language parsing model generating module 808 generates and/or trains natural language parsing models. For example, generating module 808 generates any natural language parsing model that is implemented within natural language parsing module 802.
Natural language parsing module 802 generates parsing results for natural language input based on a domain determined to correspond to the natural language input. In particular, natural language parsing module 802 generates one or more parsing results for natural language input using a determined domain corresponding to the natural language input (e.g., determined by natural language processing module 732) and a received text representation of the natural language input (e.g., received from STT processing module 730). Each parsing result comprises a mapping of one or more properties of the determined domain to one or more words of the natural language input. In some examples, the one or more parsing results are generated using a neural network (e.g., neural network 900 discussed with respect to
For example, the natural language input “play Don't Let Me down by the Chainsmokers” is determined by natural language processing module 732 to correspond to the media domain. The media domain is a domain (e.g., a domain of ontology 760) associated with the actionable intents of searching for and playing media items such as songs, movies, books, video games etc. The media domain includes properties such as “default,” “mediaPlayerItem,” “mediaPlayerMood,” “musicArtist,” “musicAlbum,” “musicTitle,” and “musicGenre.” When these properties are mapped to words of the natural language input, they respectively indicate one or more default words, one or more words associated with media items, one or more words associated with a music artist, one or more words associated with a music album, one or more words associated with a music title (e.g., a song title), and one or more words associated with a music genre.
In some examples, natural language parsing module 802 generates a first and a second parsing result for the natural language input. For example, a first parsing result comprises the mapping of “Don't Let Me Down by the Chainsmokers” to “musicArtist” and a second parsing result comprises the mappings of “Don't Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist.” In the present example, the first parsing result indicates that the words “Don't Let Me Down by the Chainsmokers” are interpreted as a music artist and the second parsing result indicates that the words “Don't Let Me Down” are interpreted as a music title (e.g., a song title) and that the word “Chainsmokers” is interpreted as a music artist.
In some examples, natural language parsing module 802 generates respective confidence scores and/or rankings for the parsing results it generates (e.g., using neural network 900). For example, a first confidence score of the first parsing result is higher than a second confidence score of the second parsing result and the first parsing result is thus initially ranked higher than the second parsing result.
In some examples, natural language parsing module 802 uses information contained in knowledge base 804 when generating and/or ranking parsing results. Knowledge base 804 includes a plurality of data items (e.g., songs, books, audiobooks, movies, video games, emails, websites, contacts, calendar events, etc.) and, in some examples, includes additional information related to the data items (e.g., the number of views, plays, and/or purchases of the data items, the ranking of the data items, the popularity of the data items, the length of the data items, the source of the data items, etc.). In one example, knowledge base 804 is a media database (e.g., iTunes® by Apple Inc.). In some examples, the data items comprising knowledge base 804 and the additional information about the data items in knowledge base 804 are updated in real-time (e.g., live updates, daily updates, etc.). For example, a media database such as iTunes® is updated live as new data items are added and information associated with the data items changes (e.g., a song gets more plays and/or purchases).
In some examples, knowledge base 804 is embedded in natural language parsing module 802. For example, knowledge base 804 is embedded into neural network 900 implemented within natural language parsing module 802. Exemplary techniques for embedding knowledge bases into neural networks are described in Bishan Yang's and Tom Mitchell's, “Leveraging Knowledge Bases in LSTMs for Improving Machine Reading,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017, pp. 1436-1446, the content of which is incorporated by reference herein in its entirety.
Embedding knowledge base 804 in natural language parsing module 802 enables natural language parsing module 802 to use information stored in knowledge base 804 when determining parsing results and their respective confidence scores. For example, because knowledge base 804 includes the song “Don't Let Me Down” by the artist “The Chainsmokers,” natural language parsing module 802 (with embedded knowledge base 804) determines a high confidence score for a parsing result indicating that the words “Don't Let Me Down” are interpreted as a music title and that the word “Chainsmokers” is interpreted as an artist. In contrast, natural language parsing module 802 determines a low confidence score for a parsing result indicating that “Don't Let Me Down by the Chainsmokers” is interpreted as a music artist because knowledge base 804 does not include a music artist titled “Don't Let Me Down by the Chainsmokers.”
In some examples, knowledge base 804 validates parsing results generated by natural language parsing module 802. In particular, natural language parsing module 802 determines whether generated parsing results correspond to any one of a plurality of data items included in knowledge base 804. For example, natural language parsing module 802 determines that the first parsing result comprising the mapping of “Don't Let Me Down by the Chainsmokers” to “musicArtist” does not correspond to any one of the plurality of data items in knowledge base 804 because there is no data item in knowledge base 804 by the music artist “Don't Let Me Down by the Chainsmokers.” However, natural language parsing module 802 determines that the second parsing result comprising the mappings of “Don't Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist corresponds to one of the plurality of data items in knowledge base 804 because knowledge base 804 includes the song “Don't′ Let Me Down” by the artist “The Chainsmokers.”
Post-processing module 806 re-ranks parsing results generated by natural language parsing module 802 using validation results determined from knowledge base 804. For example, because the first parsing result comprising the mapping of “Don't Let Me Down by the Chainsmokers” to “musicArtist” does not correspond to any one of the plurality of data items in knowledge base 804 and the second parsing result comprising the mappings of “Don't Let Me Down” to “musicTitle” and “Chainsmokers” to “musicArtist” is determined to correspond to a data item in knowledge base 804, post-processing module 806 ranks the second parsing result higher than the first parsing result. As discussed, the second parsing result is initially ranked lower than the first parsing result (e.g., because the first parsing result had a higher confidence value). Accordingly, in some examples, post-processing module 806 adjusts the respective confidence scores associated with parsing results generated by natural language parsing module 802 to re-rank the parsing results.
In some examples, post-processing module 806 generates a final parsing result using parsing results generated by natural language parsing module 802. In particular, in some examples, the final parsing result is generated using parsing results output from different output layers of a neural network (e.g., output layers 906 and 908 of neural network 900 within parsing module 802). For example, the second parsing result discussed above is generated by a first output layer of a neural network (e.g., output layer 906) and a third parsing result is generated by a second output layer of a neural network (e.g., output layer 908). Post-processing module 806 uses the first parsing result and the second parsing result to generate the final parsing result. In the present example, the third parsing result for the natural language input “play Don't Let Me Down by the Chainsmokers” comprises the mapping of the “default” property to “play” and the mappings of the “mediaPlayerItem” property to “Don't Let Me Down” and to “Chainsmokers.” To generate the final parsing result, post-processing module 806 replaces the mapping of the “mediaPlayerItem” property to “Don't Let Me Down” with the mapping of “musicTitle” to “Don't Let Me Down” and replaces the mapping of the “mediaPlayerItem” property to “Chainsmokers” with the mapping of “musicArtist” to “Chainsmokers.” Accordingly, the final parsing result comprises the mappings of “default” to “play,” “musicTitle” to “Don't Let Me Down,” and “musicArtist” to “Chainsmokers.”
In some examples, post-processing module 806 provides one or more parsing results (e.g., the first, second, third, and/or final parsing results) to task flow processing module 736. In some examples, natural language parsing module 802 provides one or more parsing results to task flow processing module 736 without having post-processing 806 module re-rank parsing results and/or generate a final parsing result.
Natural language parsing model generating module 808 is configured to generate and/or train natural language parsing models of domains (e.g., a natural language parsing model within natural language parsing module 802). In some examples, generating module 808 obtains a data item (e.g., a song) and a natural language input that caused the data item to be provided. Generating module 808 then trains a natural language parsing model using the data item and the natural language input. For example, generating module 808 uses the data item and the natural language input to train a neural network (e.g., neural network 900 discussed below).
In some examples, natural language parsing model generating module 808 uses natural language input and metadata corresponding to an output provided responsive to the natural language input (e.g., from I/O processing module 728 and/or from STT processing module 730) to generate training data for natural language parsing models and to train natural language parsing models. For example, natural language parsing model generating module 808 uses information included in the metadata to determine parsing results for the natural language input as training data. Generating module 808 then uses the natural language input and the determined parsing result to train a natural language parsing model. In some examples, before determining the parsing result using the metadata, generating module 808 determines, using the metadata, whether the output satisfies a predetermined criterion, such as a predetermined duration of the output (e.g., 30 seconds).
For example, generating module 808 obtains the natural language input “Play Hello by Adele” and metadata corresponding to the song “Hello” by “Adele” provided responsive to the natural language input. The metadata specifies the song “Hello” by “Adele” is played for over 30 seconds and that “Hello” is mapped to “musicTitle” and “Adele” is mapped to “musicArtist.” Because the output of the song “Hello” is played for over 30 seconds, the output satisfies the predetermined criterion, and thus generating module 808 determines a parsing result for “Play Hello by Adele” by comparing the mappings of “Hello” to “musicTitle” and “Adele” to “musicArtist” to “Play Hello by Adele.” In the present example, the parsing result is determined to include the mappings of “Hello” to “musicTitle” and “Adele” to “musicArtist.” In some examples, generating module 808 compares the mappings specified in the metadata to the natural language input to determine the parsing result using fuzzy matching techniques. Generating module 808 then uses the natural language input “Play Hello by Adele” and the determined parsing result to generate and/or train a natural language parsing model.
Neural network 900 includes multiple layers. In particular, neural network 900 includes input layer 902, one or more hidden layers 904, first output layer 906, and second output layer 908. As shown in
Each layer of neural network 900 includes multiple units. The units are the basic computational elements of neural network 900 and are referred to as dimensions, neurons, or nodes. As shown in
Input layer 902 is configured to receive a textual representation of natural language input and the output of input layer 902 is provided to one or more hidden layers 904. The output of one or more hidden layers 904 is provided to first output layer 906 and to second output layer 908. In particular, as shown in
In some examples, first output layer 906 and second output layer 908 each generate one or more parsing results for the natural language input. In some examples, first output layer 906 is a named entity recognition (NER) output layer configured to map properties of the domain corresponding to named entities (e.g., domain properties such as “musicTitle,” “musicArtist,” and “musicAlbum”) to one or more words of the natural language input. In some examples, second output layer 908 is a shallow parsing output layer configured to map properties of the domain to the natural language input.
In some examples, first output layer 906 and second output layer 908 map different properties of the domain to one or more words of the natural language input. For example, second output layer 908 maps the media domain property “mediaPlayerMood” to one or more words of the natural language input, while first output layer 906 is not configured to map the “mediaPlayerMood” property to one or more words of natural language input. Similarly, in some examples, first output layer 906 maps the media domain property “musicArtist” to one or more words of the natural language input, while second output layer 908 is not configured to map the “musicArtist” property to one or more words of natural language input.
Because in some examples first output layer 906 and second output layer 908 respectively map different properties of a domain to one or more words of the natural language input in some examples, using both first output layer 906 and second output layer 908 to generate parsing results enables use of many different domain properties when generating parsing results for natural language input. This can improve the accuracy of the parsing results and thus the accuracy of the task flows generated using the parsing results (e.g., generated by task flow processing module 754) by providing more detailed information about the natural language input (e.g., which words correspond to song titles, which words correspond to moods, etc.).
4. Process for Using a Knowledge Base to Perform Natural Language Processing
At block 1002, the electronic device (e.g., device 200, 400, 600 and/or server system 108) receives natural language input. In some examples, the natural language input comprises a request for a media item (e.g., “play Hello by Adele”). In some examples, the natural language input is received from a user of the electronic device, or from a second electronic device separate from the electronic device. Exemplary natural language inputs include speech, text, or a combination of speech and text.
At block 1004, the electronic device determines, from a plurality of domains, a domain corresponding to the natural language input. For example, the electronic device determines that the natural language input “play Hello by Adele” corresponds to the media domain. In some examples, the plurality of domains are domains of an ontology (e.g., ontology 760). The domain corresponding to the natural language input is determined, for example, using any of the techniques described with respect to
At block 1006, the electronic device determines whether the domain corresponds to a predetermined type of domain. The predetermined type of domain corresponds to a specific domain of the ontology. For example, the predetermined type of domain is the media domain. As discussed, in some examples, the media domain is associated with the actionable intents of searching for and playing media items such as music, videos, books, video games, etc. The determined domain is associated with one or more properties. In some examples, the one or more properties each correspond to a named entity (e.g., any object that can be given a proper name). For instance, in examples where the determined domain corresponds to a media domain, the one or more properties each correspond to media-related named entities, such as artists, song titles, albums, authors and the like.
In some examples, in accordance with determining that the domain does not correspond to a predetermined type of domain, the device forgoes performing the operations described with respect to blocks 1008-1016 below. Rather, for example, the electronic device processes the natural language input to determine parsing results using a domain that is not of the predetermined type (e.g., domains other than the media domain). In some examples, parsing results determined using domains not of the predetermined type do not include mappings of domain properties corresponding to named entities to one or more words of the natural language input. In some examples, parsing results determined using domains not of the predetermined type are not validated and/or re-ranked using a knowledge base. Processing the natural language input with respect to domains not of the predetermined type to determine parsing results to execute one or more tasks is described with respect to
At block 1008, in accordance with determining that the domain corresponds to a predetermined type of domain, the device determines parsing results for the natural language input (e.g., using neural network 900 and/or using natural language parsing system 800). As discussed, each parsing result represents a semantic interpretation of the natural language input and each parsing result comprises respective mappings of one or more domain properties to one or more words of the natural language input. In determining the parsing results, one or more words of the natural language input are mapped to one or more properties of the domain to obtain the parsing results. The parsing results represent, for example, which words in the natural language input correspond to song titles, music albums, music artists, etc.
For example, at block 1008, in accordance with determining that the domain corresponds to a predetermined type of domain, the device determines a first and a second parsing result for the natural language input. The first and the second parsing results each comprise a respective mapping of one or more properties of the domain to one or more words of the natural language input. A first confidence score of the first parsing result is higher than a second confidence score of the second parsing result.
For example, as shown in
As shown in
In some examples, the first parsing result and the second parsing result respectively comprise one or more mappings of the same property of the domain to the same one or more words in the natural language input. For example, for the natural language input “play Frozen Let It Go by Idina Menzel” (determined to correspond to the media domain) a first parsing result maps the media domain property “musicArtist” to “Idina Menzel” while a second parsing result also maps the media domain property “musicArtist” to “Idina Menzel.”
Although block 1008 is described above with respect to a first and a second parsing result for the natural language input, it is to be understood that, in some examples, more than two parsing results are determined for the natural language input. In particular, in some examples, a plurality of parsing results are determined for the natural language input, where each parsing result of the plurality of parsing results respectively includes a mapping of one or more properties of the domain to one or more words of the natural language input. In some examples, each parsing result of the plurality of parsing results is generated using a neural network (e.g., neural network 900). For example, the first parsing result and the second parsing result are each generated by neural network 900. In some examples, the first and the second parsing result for the natural language input are each generated by a first output layer of a neural network (e.g., output layer 906 of neural network 900), while a third parsing result for the natural language input is generated by a second output layer of a neural network (e.g., output layer 908 of neural network 900) different from the first output layer. The third parsing result comprises a mapping of a set of one or more properties of the domain (e.g., the domain determined at step 1004) to the one or more words of the natural language input.
As discussed, in some examples, the first output layer and the second output layer are configured to map different properties of the domain to one or more words of the natural language input. For example, the first output layer is a named entity recognition (NER) output layer configured to map properties of the domain corresponding to named entities (e.g., domain properties such as “musicTitle,” “musicArtist,” and “musicAlbum,”) to one or more words of the natural language input and the second output layer is a shallow parsing output layer configured to coarsely map properties of the domain to one or more words of the natural language input. For the exemplary natural language input “Play Hello by Adele,” the first output layer generates the second parsing result mapping “Hello” to “musicTitle” and “Adele” to “musicArtist shown in
In some examples, the parsing results generated by the second output layer (e.g., the third parsing result) provide coarser information about the natural language input than the parsing results generated using the first output layer. For example, the third parsing result mapping the domain property “mediaPlayerItem” to “Hello by Adele” generated by the second output layer does not provide as detailed information about the natural language input as the parsing result mapping the domain properties “musicTitle” and “musicArtist” respectively to “Hello” and “Adele” generated by the first output layer. However, in some examples, the parsing results generated by the second output layer provide semantic interpretations of the natural language input that the parsing results generated by the first output layer are unable to provide. In particular, in some examples, the parsing results generated using the second output layer map a set of properties of the domain to the one or more words of the natural language input, where the first output layer is not configured to map the set of properties of the domain to the one or more words.
For example, for the natural language input “play me a sad song by Adele,” (determined to correspond to the media domain) the second output layer generates a parsing result mapping the “mediaPlayerMood” property of the media domain to “sad song.” The “mediaPlayerMood” property of the domain indicates a mood (e.g., sad, happy, angry) of a media item specified in the natural language input. In the present example, the first output layer is not configured to map the “mediaPlayerMood” property of the domain to the one or more words of the natural language input. Accordingly, using both the first output layer and the second output layer to generate parsing results for natural language input can allow for a greater variety of domain properties to be mapped to one or more words of the natural language input. This in turn can allow for more accurate processing and generation of task flows from natural language input.
As discussed, in some examples, each of the parsing results of the plurality of the parsing results has a respective confidence score. The respective confidence score indicates a likelihood or probability (e.g., determined by the device) that the respective parsing result is correct for the natural language input. For example, the first parsing result mapping “Hello by Adele” to the “musicArtist” property of the domain has a first confidence score and the second parsing result mapping “Hello” to the “musicTitle” property of the domain and mapping “Adele” to the “musicArtist” property of the domain has a second confidence score. In some examples, the confidence score of the first parsing result is higher than the confidence score of the second parsing result when the first and the second parsing result are initially generated.
In some examples, each of the parsing results of the plurality of parsing results is initially ranked based on their respective confidence scores. For example, because the confidence score of the first parsing result is higher than the confidence score of the second parsing result, the first parsing result is ranked higher than the second parsing result. In some examples including more than two parsing results, the respective confidence score of each parsing result determines the respective ranking of the respective parsing result. In some examples, the ranking of the plurality of parsing results is independent of the respective confidence scores of the plurality of parsing results or the respective confidence scores of the plurality of parsing results are only one factor considered in ranking the plurality of parsing results.
At block 1010, in accordance with determining that the domain corresponds to a predetermined type of domain, the electronic device determines whether the parsing results determined for the natural language input correspond to respective data items of a plurality of data items in a knowledge base. For example, the electronic device determines whether the second parsing result corresponds to a data item of a plurality of data items in a knowledge base (e.g., knowledge base 804). In some examples, in addition to determining whether the second parsing result corresponds to a data item of the plurality of data items in the knowledge base, the electronic device determines whether the first parsing result corresponds to any of the plurality of data items in the knowledge base. In some examples, the data item is a media item (e.g., a song, book, movie, video game, etc.) and the knowledge base is a database of media items (e.g., the iTunes® database by Apple Inc.). As will be discussed in greater detail below, in some examples, the parsing results are re-ranked based on whether they are determined to correspond to respective data items in the knowledge base. In this way, parsing results determined to correspond to respective data items have their rankings increased, which allows for execution of task flows using knowledge base validated parsing results.
In the example shown in
As discussed, determining whether a parsing result corresponds to any of a plurality of data items in a knowledge base comprises determining whether a data item exists in the knowledge base that is associated with a mapping that matches one or more mappings comprising the parsing result. In particular, in some examples, the second parsing result comprises a mapping of a fourth property of the domain to a first set of one or more words of the natural language input (e.g., the property “musicTitle” to the first set of one or more words “Hello”) and the data item is associated with a mapping of a fifth property of the domain to one or more words (e.g., the property “musicTitle” to the one or more words “Hello”). The device determines that the mapping of the fourth property of the domain to the first set of one or more words matches the mapping of the fifth property of the domain to the one or more words. In the present example, the second parsing result is determined to correspond to the data item because there is a data item (e.g., the song “Hello”) associated with the mapping of the fifth property of the domain to the one or more words that matches the mapping of the fourth property of the domain to the first set of one or more words. In other words, because the second parsing result indicates a song titled “Hello,” and there is a song titled “Hello” in the knowledge base, the second parsing result is determined to correspond to a data item of the plurality of data items in the knowledge base.
At block 1012, in accordance with determining that the domain corresponds to a predetermined type of domain, the electronic device re-ranks parsing results in accordance with determining whether the parsing results respectively correspond to a data item in the knowledge base. In some examples, any parsing result determined to correspond to a data item in the knowledge base has its ranking increased relative to any parsing result determined not to correspond to a data item in the knowledge base. For example, the confidence scores of the parsing results determined to correspond to respective data items in the knowledge base are increased relative to the confidence scores of the parsing results determined not to correspond to respective data items in the knowledge base. In some examples, this is accomplished by decreasing the respective confidence scores of the parsing results determined to not correspond to respective data items and/or by increasing the respective confidence scores of the parsing results determined to correspond to data items. In the event that none of the parsing results are determined to correspond to a data item in the knowledge base, the respective ranking of each of the parsing results is unchanged, and the respective confidence score of each of the parsing results is also unchanged.
As an example of the process implemented at block 1012, in accordance with a determination that the second parsing result corresponds to the data item of the plurality of data items in the knowledge base, and in accordance with a determination that the first parsing result does not correspond to any of the plurality of data items in the knowledge base, the electronic device ranks the second parsing result higher than the first parsing result (e.g., using post-processing module 806). As discussed, the first parsing result (mapping “Hello by Adele” to the “musicArtist” property) is initially ranked higher than the second parsing result (mapping “Hello” to the “musicTitle” property and “Adele” to the “musicArtist” property) because the first parsing result initially has a confidence score higher than the second parsing result. However, because the first parsing result is determined not to correspond to any of the plurality of items in the knowledge base and because the second parsing result is determined to correspond to the data item of the plurality of data items in the knowledge base, the second parsing result is ranked higher than the first parsing result. For example, the second confidence score of the second parsing result is adjusted to be greater than the first confidence score of the first parsing result. Thus, in the present example, the second parsing result is the highest ranked parsing result of a plurality of parsing results determined for the natural language input.
For some natural language inputs, more than one of the determined parsing results is determined to correspond to a respective data item in the knowledge base. This occurs, for example, when one or more words of the natural language input correspond to both a song name and a music artist name. For example, for the natural language input “play Death Cab for Cutie” a first parsing result comprises a mapping of “musicTitle” to “Death Cab for Cutie” and a second parsing result comprises a mapping of “musicArtist” to “Death Cab for Cutie.” Because “Death Cab for Cutie” refers to both a song name and an artist name, both the first parsing result and the second parsing result are determined to correspond to a respective data item of the plurality of data items of the knowledge base.
In examples where more than one parsing result corresponds to a respective data item in the knowledge base, the electronic device uses the multiple techniques discussed below, alone or in combination, to determine the respective rankings (and/or the respective confidence scores) of the parsing results.
In some examples, the electronic device uses the number of words of the natural language input mapped to a same property to determine the respective rankings of the parsing results. For example, for the natural language input “play Death Cab for Cutie,” a first parsing result maps “musicTitle” to “Death Cab” and a second parsing result maps “musicTitle” to “Death Cab for Cutie.” In this example, because the number of words the second parsing result maps to “musicTitle” (e.g., four words) is greater than the number of words the first parsing result maps to “musicTitle” (e.g., two words), the second parsing result is ranked higher than the first parsing result. In other examples, the parsing result that maps the lesser number of words to the same property is ranked higher.
In some examples, the electronic device uses the number of mapped domain properties of the natural language input to determine the respective rankings of the parsing results. For example, for the natural language input “play Thriller by Michael Jackson,” the first parsing result includes mapping of “Thriller by Michael Jackson” to “musicTitle” and the second parsing result includes the mappings of “Thriller” to “musicTitle” and “Michael Jackson” to “musicArtist.” Because the number of domain properties mapped to the natural language input in the second parsing result (e.g., two) is greater than the number of domain properties mapped to the natural language input in the first parsing result (e.g., one), the second parsing result is ranked higher than the first parsing result. In other examples, the parsing result mapping the lesser number of domain properties to the natural language input is ranked higher.
In some examples, the electronic device uses the popularity of the respective data items in the knowledge base to determine the respective rankings of the parsing results. Accordingly, in some examples, ranking the second parsing result higher than the first parsing result is performed in accordance with the electronic device determining that a popularity of a second data item corresponding to the second parsing result is greater than a popularity of a first data item corresponding to the first parsing result. In some examples, the popularity of a data item is determined using information in the knowledge base containing the data item. For example, the popularity of a data item is based on the number of views (and/or listens) of the data item, the number of purchases of the data item, the position of the data item on a chart, and the like.
Accordingly, in the example where the first parsing result comprises a mapping of “musicTitle” to “Death Cab for Cutie” and the second parsing result comprises a mapping of “musicArtist” to “Death Cab for Cutie,” the second parsing result is ranked higher than the first parsing result because the popularity of the music artist “Death Cab for Cutie” is greater than the popularity of the song “Death Cab for Cutie.” For example, there are more plays of songs by the artist “Death Cab for Cutie” than there are plays of the song “Death Cab for Cutie.”
As discussed, using knowledge base (e.g., knowledge base 804) information to rank parsing results for natural language input both validates parsing results (e.g., by ranking parsing results determined not to correspond to data items lower) and provides techniques for selecting between multiple parsing results each corresponding to a respective data item (e.g., by determining which parsing result corresponds to a data item with the greatest popularity). In addition, because exemplary knowledge bases contain real-time information about the popularity of data items, using a knowledge base to rank parsing results provides the benefit of selecting parsing results corresponding to relevant (e.g., currently popular) data items to execute task flows. This can increase the accuracy with which digital assistants process natural language inputs to execute user requests, thus improving the efficiency of the man-machine interface, and conserving battery power on electronic devices.
In some examples, instead of, or in addition to, using the knowledge base to re-rank and/or validate parsing results (e.g., block 1012), the knowledge base (e.g., knowledge base 804) is directly incorporated into the natural language processing module 732 (e.g., directly incorporated into neural network 900 and/or natural language parsing module 802). By directly incorporating knowledge base 804 into neural network 900 and/or natural language parsing module 802, parsing results (and their respective confidence scores) generated by neural network 900 and/or natural language parsing module 802 for natural language inputs account for information about the data items in knowledge base 804. For example, because the knowledge base indicates that “Death Cab for Cutie” is more likely to be a music artist than to be a song title (e.g., based on popularity), parsing results mapping “Death Cab for Cutie” to “musicArtist” have a greater confidence score than parsing results mapping “Death Cab for Cutie” to “musicTitle.” Exemplary techniques for incorporating knowledge bases into neural networks are described in Bishan Yang's and Tom Mitchell's, “Leveraging Knowledge Bases in LSTMs for Improving Machine Reading,” Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2017, pp. 1436-1446, the content of which is incorporated by reference herein in its entirety.
Additionally, in some examples, parsing results generated for natural language input (e.g., generated using parsing module 802) implicitly account for the popularity of data items respectively corresponding to the parsing results. In particular, as discussed below with respect to
For example, for the natural language input “play Death Cab for Cutie,” the first parsing result maps “musicTitle” to “Death Cab for Cutie,” and the second parsing result maps “musicArtist” to “Death Cab for Cutie.” The second parsing result is ranked higher than the first parsing result when the parsing results are initially generated because more people intend to play a song by the artist “Death Cab for Cutie” than the song “Death Cab for Cutie.” As discussed below, the parsing model generating the parsing results accounts for this popularity information because it is trained using user-engagement data. For example, user engagement data indicates that most users are dissatisfied with the output of the song “Death Cab for Cutie” responsive to “play Death Cab for Cutie” and instead intend to play a song by the artist “Death Cab for Cutie.” Accordingly, a parsing model is trained using this user engagement data to prefer (e.g., rank higher) parsing results mapping “Death Cab for Cutie” to “musicArtist.”
In some examples, in accordance with ranking the second parsing result higher than the first parsing result, the electronic device (e.g., post-processing module 806) generates a final parsing result for the natural language input based on the second parsing result and a third parsing result In some examples, the second parsing result is generated by a first output layer of a neural network (e.g., output layer 906) and the third parsing result is generated by a second output layer of a neural network (e.g., output layer 908). The third parsing result comprises a mapping of a third property of the domain to a second word of the natural language input and the second parsing result comprises a mapping of a sixth property of the domain to the second word. As discussed below, in some examples, the final parsing result combines mappings from a plurality of parsing results to provide the parsing result with the greatest specificity for the natural language input (e.g., when mapped to one or more words, the property “musicArtist” provides greater specificity than the property “default”). The parsing result with the greatest specificity may provide the most detailed information about the natural language input, which allows for accurate task flows to be generated responsive to the natural language input.
In some examples, to generate the final parsing result, the device determines whether the second confidence score of the second parsing result exceeds a confidence threshold. In accordance with a determination that the second confidence score of the second parsing result exceeds the confidence threshold, the device uses the mapping of the sixth property of the domain to the second word to generate the final parsing result. In some examples, the mapping of the third property of the domain to the second word is not used to generate the final parsing result.
In some examples, the device uses the mapping of the third property of the domain to the second word to generate the final parsing result. For example, as shown in
In the above example, the mapping of “mediaPlayerItem” property to “Hello” is replaced by the mapping of the “musicTitle” property to “Hello” because the “mediaPlayerItem” property does not provide as specific information about the word “Hello” as the “musicTitle” property does. Accordingly, in some examples, only properties that do not provide specific named-entity information about the natural language input (e.g., the “mediaPlayerItem” property and the “default” property) are replaced. As discussed, this may provide the advantage of determining a final parsing result for the natural language input that provides the greatest specificity for the natural language input.
Accordingly, in some examples, using the mapping (comprising the second parsing result) of the sixth property of the domain to the second word to generate the final parsing result is performed in accordance with a determination that the third property of the domain (comprising the third parsing result) is of a predetermined type. In some examples, the predetermined type of property is a property of the domain that does not provide named entity information about the natural language input (e.g., “default” or “mediaPlayerItem”). In some examples, the predetermined type of property is any property of a domain included in ontology 760 discussed above. For example, with respect to the media domain, the predetermined type of properties are selected from the “mediaPlayerItem,” “musicArtist,” “musicTitle,” “musicAlbum,” and “default” properties.
As discussed, in some examples, parsing results generated by the second output layer (e.g., the third parsing result) provide information that parsing results generated by the first output layer (e.g., the second parsing result) are unable to provide. Thus, in some examples, parsing results generated by the second output layer provide more specific information about the natural language input than parsing results generated by the first output layer. To prevent this specific information included in the parsing results generated by the second output layer from being excluded in the final parsing result, in some examples, replacing the mapping (comprising the third parsing result) of the third property of the domain to the second word with the mapping (comprising the second parsing result) of the sixth property of the domain to the second word is not performed if the third property of the domain is of a predetermined type. Exemplary predetermined types of properties of the domain are properties that only the second output layer is configured to map to the one or more words of the natural language input. For example, the predetermined type of property is the “mediaPlayerMood” property. Accordingly, the above technique preserves properties of the domain that the first output layer is not configured to map to the one or more words of the natural language input in the final parsing result.
For example, for the natural language input “play me a sad song by Adele,” the second parsing result (e.g., determined using the first output layer) maps the media domain properties “default” and “musicArtist” respectively to the words “sad” and “Adele.” The third parsing result (e.g., determined using the second output layer) maps the media domain properties “mediaPlayerMood” and “mediaPlayerItem” respectively to the words “sad” and “Adele.” Because the “mediaPlayerMood” property is a predetermined type of property, the mapping of “mediaPlayerMood” to “sad” is not replaced by the mapping of “default” to “sad” when the final parsing result is generated using the second parsing result and the third parsing result. Accordingly, in the present example, the final parsing result comprises the mappings of “sad” to “mediaPlayerMood” and “Adele” to “musicArtist.”
At block 1014, in accordance with determining that the domain corresponds to a predetermined type of domain, based on the ranking (e.g., the ranking of the second parsing result higher than the first parsing result), the device generates a task flow using the second parsing result. For example, the second parsing result for the natural language input “play Hello by Adele” is used to generate the task flow of searching for and playing the song “Hello” by the artist “Adele.” In some examples, the device uses the final parsing result when generating the task flow at block 1014.
At block 1016, in accordance with determining that the domain corresponds to a predetermined type of domain, the device executes the task flow (e.g., using task flow processing module 736) to provide an output based on the data item. For example, the generated task flow is executed to provide the output of (e.g., play and/or display) the data item of the song “Hello” by the artist “Adele.” In some examples, the generated task flow is executed to provide output to an external electronic device, the output comprising instructions for the external electronic device (e.g., any of devices 200, 400, and/or 600) to provide the data item.
In some examples, the device receives an updated natural language parsing model of the domain (e.g., an updated version of a natural language parsing model within natural language parsing module 802). In some examples, the first and the second parsing results have been determined using an initial natural language parsing model of the domain. The updated natural language parsing model is trained using the natural language input and the data item.
For example, the device provides the natural language input and the data item to natural language parsing model generating module 808, and generating module 808 uses the data item and the natural language input to train a natural language parsing model within system 800 to obtain the updated natural language parsing model. For example, the natural language input and the data item are provided to train neural network 900. Generating module 808 trains neural network 900 using the data item and the natural language input to update one or more weighting values associated with connections 918. Updated neural network 900 is then provided to the device.
In some examples, the device receives the updated natural parsing language model in accordance with determining that the output satisfies a predetermined criterion. For example, the predetermined criterion comprises a duration for which the output is provided.
Receiving an updated natural language model in accordance with a determination that the output satisfies a predetermined criterion (e.g., a predetermined duration) may allow the received updated natural language parsing model to be trained and/or updated using a data item that was correctly determined to correspond to the natural language input. For example, if the electronic device correctly provides the output of playing the song “Hello” by the artist “Adele,” the output is provided for greater than a predetermined duration (e.g., 30 seconds) because the user was satisfied with the output. In other words, provision of outputs for greater than the predetermined duration indicates user satisfaction with the provided output, which in turn indicates that the correct data item was determined to correspond to the natural language input. Accordingly, in some examples, natural language parsing system 800 is trained using the data item of the song “Hello” by the “Artist” Adele and the natural language input of “play Hello by Adele,” where the data item was correctly determined to correspond to the natural language input. This provides the benefit of using both natural language input and the correct data item determined to correspond to the natural language input to train natural language parsing models, resulting in more accurate natural language parsing results.
The operations described above with reference to
5. Process for Natural Language Parsing Model Generation Based on User Engagement Data
At block 1102, an electronic device obtains a natural language input and metadata corresponding to an output provided responsive to the natural language input. For example, generating module 808 obtains the natural language input and the metadata (e.g., from I/O processing module 728 and/or STT processing module 730.) The natural language input corresponds to a domain of a predetermined type (e.g., the media domain, or any other domain that can be within ontology 760.) The output provided responsive to the natural language input includes a data item (e.g., a media item such as a song, movie, book, and the like). The metadata includes a set of attributes corresponding to the data item, where the set of attributes defines a set of values for a respective set of properties of the domain. For example, the metadata includes attributes such as the name of the data item (e.g., a song title), the artist of the data item (e.g., a music artist), and the album of the data item (e.g., the album a song is from). These attributes define a set of values for the respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the media domain.
In some examples, the metadata includes additional information about the output provided responsive to the natural language input. For example, the metadata includes one or more of: a duration for which the output is provided, whether the user cancelled or interrupted the output within a predetermined duration, and a timestamp of when the output was provided. This additional information about the output included in the metadata is referred to as “user engagement data” (e.g., user generated data about the output). In some examples, the user engagement data is collected (e.g., collected by any one of devices 200, 400, and/or 600) for a predetermined duration after provision of the output. In some examples, the predetermined duration is 30 seconds, 1 minute, 2 minutes, 5 minutes, etc. As discussed below, user engagement data indicates a user's satisfaction with the output provided responsive to the natural language input. Accordingly, positive user engagement data can indicate that a correct output was provided responsive to the natural language input and thus information corresponding to the correct output and the natural language input is be provided as “user-validated” training data for a natural language parsing model of the domain.
As an example of the process described with respect to block 1102, the electronic device obtains the natural language input “play Rolling in the Deep” that corresponds to the media domain. The output provided responsive to the natural language input includes the data item of the song “Rolling in the Deep” by the music artist “Adele.” The electronic device receives metadata corresponding to the output including the song “Rolling in the Deep.” For example, the metadata corresponding to the output includes the attributes of the song name “Rolling in the Deep,” the music artist name “Adele,” and the music album name “21.” These attributes respectively define a set of values for the respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the media domain. The additional information included in the metadata specifies that the song “Rolling in the Deep” by “Adele” was played for 3 minutes and 48 seconds (the entire length of “Rolling in the Deep” by Adele) and that the user did not cancel playback of the song for 3 minutes and 48 seconds.
At block 1104, the electronic device determines, from the metadata, whether the output satisfies a predetermined criterion (e.g., using generating module 808). In some examples, the predetermined criterion is a duration for which the output is provided (e.g., 30 seconds). For example, the electronic device determines from the metadata specifying that the song “Rolling in the Deep” was played for 3 minutes and 48 seconds that the output including the song “Rolling in the Deep” satisfies the predetermined criterion. In some examples, the predetermined criterion is whether or not the user cancelled and/or interrupted the output within a predetermined duration (e.g., 30 seconds). For example, the electronic device determines from the metadata specifying that playback of the song “Rolling in the Deep” was not canceled by the user within 30 seconds that the output including the song “Rolling in the Deep” satisfies the predetermined criterion.
An output provided for more than a predetermined duration (e.g., 30 seconds) is interpreted by the device as a strong indication of user satisfaction with the output provided responsive to the natural language input. For example, if the output provided responsive to the natural language input “Play Rolling in the Deep” includes playback of the song “Rolling in the Deep” by “Adele” for more than 30 seconds, the user is likely satisfied with the output. In contrast, if the output provided responsive to “Play Rolling in the Deep” includes playback of the song “Hello” by “Adele,” the user quickly (e.g., within less than 30 seconds) cancels playback of the song because he or she is dissatisfied with the output. Accordingly, determining whether the output satisfies a predetermined criterion allows for determining whether a correct output was provided responsive to a natural language input based on user engagement data. As described below, in some examples, information corresponding to the correct output and the natural language input is thus provided as “user validated” training data for a natural language parsing model of the domain.
At block 1106, in accordance with a determination that the output satisfies the predetermined criterion, the device compares the set of values to the natural language input to determine a parsing result (e.g., using generating module 808). The parsing result maps one or more words of the natural language input to one or more properties of the respective set of properties of the domain. For example, for the natural language input “play Rolling in the Deep,” the value “Rolling in the Deep” (for the “musicTitle” property of the domain) is compared to the natural language input to determine the parsing result mapping the domain property “musicTitle” to “Rolling in the Deep.”
In some examples, comparing the set of values to the natural language input to determine the parsing result comprises comparing the set of values to the natural language input using fuzzy matching. Comparing the set of values to the natural language input using fuzzy matching can be desirable in situations where the natural language input does not contain an exact match to one or more values specified in the metadata (corresponding to output provided responsive to the natural language input). For example, for the natural language input “play Roll in the Deep by Adele” and the output provided responsive to the natural language input including the song of “Rolling in the Deep,” the metadata specifies the set of values “Rolling in the Deep,” “Adele,” and “21.” However, despite the value “Rolling in the Deep” not matching “Roll in the Deep” of the natural language input, fuzzy matching techniques still associate the value “Rolling in the Deep” with the words “Roll in the Deep.” Accordingly, the parsing result maps the words “Roll in the Deep” to the “musicTitle” property of the domain.
In some examples, the parsing result determined by comparing the set of values to the natural language input is associated with a confidence score determined using the fuzzy matching. For example, the parsing result mapping the “musicTitle” property of the domain to “Roll in the Deep” is associated with a relatively high confidence score because the fuzzy matching algorithm determines that the value “Rolling in the Deep” (for the “musicTitle” property) closely matches the words “Roll in the Deep” of the natural language input. In contrast, if the value does not closely match one or more words of the natural language input, the parsing result mapping one or more words of natural language input to a property of the domain is determined to have a low confidence score.
At block 1108, in accordance with a determination that the output satisfies the predetermined criterion, the electronic device provides the natural language input and the determined parsing result to generate and/or train a natural language parsing model of the domain. For example, the natural language input and the determined parsing result are provided to natural language parsing model generating module 808 and generating module 808 generates and/or trains a natural language model of the domain. For example, the electronic device provides the natural language input “play Rolling in the Deep” and the parsing result mapping “Rolling in the Deep” to “musicTitle” to generating module 808. Accordingly, the generated natural language parsing model of the domain is trained to generate parsing results mapping the words “Rolling in the Deep” to “musicTitle.”
In some examples, the device provides the natural language input and the determined parsing result in accordance with a determination that the confidence score associated with the parsing result exceeds a threshold. Providing the natural language input and the determined parsing result in accordance with determining that the confidence score exceeds a threshold may be desirable to allow parsing results with sufficient confidence scores to be provided to train and/or generate a natural language parsing model of the domain. For example, for the natural language input “Play Rolling in the Deep by Adele,” the confidence score associated with the parsing result mapping “Rolling in the Deep” to the domain property “musicTitle” and “Adele” to the domain property “musicArtist” exceeds the threshold. Accordingly, the natural language input and the parsing result are provided to generate a natural language parsing model of the domain.
In some examples, generating and/or training the natural language parsing model of the domain using the natural language input and the determined parsing result comprises providing the natural language input and the determined parsing result as training data for a neural network (e.g., neural network 900). For example, a set of optimal weighting values for the connections (e.g., connections 918) in a neural network are determined using the training data. The neural network is thus trained (e.g., trained by generating module 808) to generate parsing results mapping, for example, “Rolling in the Deep” to “musicTitle” when the natural language input including “Rolling in the Deep” is input into the neural network.
In some examples, the natural language parsing model of the domain is configured to receive a second natural language input and to generate a plurality of candidate parsing results. Each candidate parsing result of the plurality of candidate parsing results comprises a respective mapping of one or more properties of the domain to one or more words of the second natural language input. For example, the second natural language input is a subsequent natural language input of “Play Rolling in the Deep by Adele” and the generated plurality of candidate parsing results includes a first parsing result mapping “Rolling in the Deep” to “musicTitle” and a second parsing result mapping “Rolling in the Deep by Adele” to “musicTitle.” Because the natural language parsing model of the domain has been trained to prefer parsing results mapping “Rolling in the Deep” to “musicTitle,” the first parsing result is ranked higher than the second parsing result.
In some examples, the electronic device provides the natural language parsing model of the domain to a second electronic device, the second electronic device configured to perform natural language processing services. For example, the device that performs blocks 1102-1108 provides the natural language parsing model to one or more devices (e.g., devices 200, 400, or, 600).
In some examples, in accordance with a determination that the output does not satisfy the predetermined criterion, the electronic device forgoes performing the operations described with respect to blocks 1106 and 1108. In particular, the parsing result is not determined using the metadata corresponding to the output and thus the parsing result is not provided to generate a natural language parsing model of the domain. Rather, in some examples, in accordance with a determination that the output does not satisfy the predetermined criterion, the metadata corresponding to the output is discarded.
Forgoing the operations in block 1106 and 1108 when the output provided responsive to the natural language input does not satisfy the predetermined criterion can be desirable, as the electronic device does not waste processing power and/or battery resources determining a parsing result using metadata corresponding to an output the device interprets as an incorrect response to the natural language input. In this way, the generation of erroneous parsing results as training data for the natural language parsing model may be avoided.
At block 1110, in accordance with a determination that the output does not satisfy the predetermined criterion, the electronic device obtains a second set of metadata corresponding to a second output provided after the output responsive to the natural language input. The second output is output within a second predetermined duration of the first output ceasing to be provided. The second output includes a second data item and the second set of metadata includes a second set of attributes corresponding to the second data item. The second set of attributes defines a second set of values for a second respective set of properties of the domain.
For example, suppose the output provided responsive to the natural language input “Play Rolling in the Deep by Adele” erroneously includes playback of the data item “Hello” by “Adele.” In some examples, this is because the parsing result used to generate the task flow of searching for and playing a song erroneously maps “in the Deep” to “musicTitle” and “Adele” to “musicArtist.” Because “in the Deep” is not a song by the artist “Adele,” the electronic device causes playback of a random erroneous song by the artist “Adele” (e.g., the song “Hello”). A user cancels playback of “Hello” by “Adele” within a predetermined duration (e.g., within 30 seconds) because the user intended to play “Rolling in the Deep” by “Adele,” not “Hello” by “Adele.” Within the second predetermined duration (e.g., a short duration such as thirty seconds) of cancelling playback of “Hello” by “Adele,” the user manually searches for the song “Rolling in the Deep” by Adele and causes it to be played. Accordingly, in the present example, the electronic device determines from the metadata corresponding to the output including the song “Hello,” that the output does not satisfy the predetermined criterion (e.g., a duration of 30 seconds) and determines that the second output “Rolling in the Deep” is output within the second predetermined duration of canceling the output of the song “Hello.” Because the second output (e.g., “Rolling in the Deep”) is output within a short duration of the previous output ceasing to be provided, the second output is likely the correct output that should be provided responsive to the natural language input. Thus, in the present example, the electronic device obtains a second set of metadata corresponding to the second output including the song “Rolling in the Deep” by “Adele.” The second set of metadata includes the second set of attributes of “Rolling in the Deep,” “Adele,” and “21” corresponding to the song “Rolling in the Deep” by “Adele.” The second set of attributes defines a second set of values for the second set of respective “musicTitle,” “musicArtist,” and “musicAlbum” properties of the domain.
In some examples, after the electronic device obtains the second set of metadata corresponding to the second output provided responsive to the natural language input, process 1100 returns to block 1104, where the electronic device determines from the second set of metadata, whether the second output satisfies the predetermined criterion. For example, because the user intended to play the song “Rolling in the Deep” by “Adele,” the song “Rolling in the Deep” is played for a predetermined duration that satisfies the predetermined criterion. Accordingly, the electronic device determines that the second output including the song “Rolling in the Deep” satisfies the predetermined criterion. At block, 1106 in accordance with a determination that the second output satisfies the predetermined criterion, the electronic device compares the second set of values to the natural language input to determine a second parsing result. The second parsing result maps one or more words of the natural language input to one or more properties of the second respective set of properties of the domain. For example, the electronic device compares the values of “Rolling in the Deep,” “Adele,” and, “21” to the natural language input “Play Rolling in the Deep by Adele” to determine the second parsing result mapping “Rolling in the Deep” to “musicTitle” and “Adele” to “musicArtist.”
At block 1108, in accordance with a determination that the second output satisfies the predetermined criterion, the electronic device provides the natural language input and the determined second parsing result to generate a natural language parsing model of the domain.
As described, providing the natural language input and the determined second parsing result to generate a natural language parsing model of the domain can allow the device to generate correct training data even when the initial output provided responsive to the natural language input was incorrect (e.g., determined not to satisfy a predetermined criterion). In particular, as described, the device uses metadata corresponding to a correct output that should have been provided responsive to the natural language input to generate a second parsing result for the natural language input. The second parsing result for the natural language input and the natural language input can thus be provided as correct training data to generate a natural language parsing model of the domain.
The operations described above with reference to
In accordance with some implementations, a computer-readable storage medium (e.g., a non-transitory computer readable storage medium) is provided, the computer-readable storage medium storing 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.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises means for performing any of the methods or processes described herein.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises a processing unit configured to perform any of the methods or processes described herein.
In accordance with some implementations, an electronic device (e.g., a portable electronic device) is provided that comprises one or more processors and memory storing one or more programs for execution by the one or more processors, the one or more programs including instructions for performing any of the methods or processes described herein.
The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. The embodiments were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various embodiments with various modifications as are suited to the particular use contemplated.
Although the disclosure and examples have been fully described with reference to the accompanying drawings, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims.
As described above, one aspect of the present technology is the gathering and use of data available from various sources to improve natural language processing. The present disclosure contemplates that in some instances, this gathered data may include personal information data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include demographic data, location-based data, telephone numbers, email addresses, twitter IDs, home addresses, data or records relating to a user's health or level of fitness (e.g., vital signs measurements, medication information, exercise information), date of birth, or any other identifying or personal information.
The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to improve natural language processing and to more accurately provide media items to users. Further, other uses for personal information data that benefit the user are also contemplated by the present disclosure. For instance, health and fitness data may be used to provide insights into a user's general wellness, or may be used as positive feedback to individuals using technology to pursue wellness goals.
The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by 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 uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.
Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of collecting user engagement data, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In another example, users can select not to provide user engagement data to train natural language parsing models. In yet another example, users can select to limit the length of time user engagement data is maintained or entirely prohibit the collection of user engagement data. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.
Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data at a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.
Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data. For example, natural language parsing results and the presentation of media items can be based on non-personal information data or a bare minimum amount of personal information, such as the content being requested by the device associated with a user, other non-personal information available to natural language parsing models, or publicly available information.
This application claims priority to U.S. Application No. 62/624,464, entitled “KNOWLEDGE-BASED FRAMEWORK FOR IMPROVING NATURAL LANGUAGE UNDERSTANDING,” filed on Jan. 31, 2018, the content of which is hereby incorporated by reference herein in its entirety.
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| Number | Date | Country | |
|---|---|---|---|
| 20190236130 A1 | Aug 2019 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62624464 | Jan 2018 | US |
