Patent Description:
<CIT> describes an electronic device with one or more processors and memory that receives an input of a user. The electronic device, in accordance with the input, identifies a respective task type from a plurality of predefined task types associated with a plurality of third party service providers. The respective task type is associated with at least one third party service provider for which the user is authorized and at least one third party service provider for which the user is not authorized. In response to identifying the respective task type, the electronic device sends a request to perform at least a portion of a task to a third party service provider of the plurality of third party service providers that is associated with the respective task type.

<CIT> describes a call handling platform that receives a call placed by a caller to a calling number and computes an experience score for the caller based on measuring a subset of data points corresponding to an interaction between the caller and an interactive voice response (IVR) module. The experience score provides a numerical measure of a level of satisfaction of the caller in interacting with the IVR module during the call. Determining to route the call to a human agent based on the experience score, the call handling platform accesses historical data corresponding to past calls placed by the caller and received by human agents, and obtains agent scores associated with the human agents. The call handling platform matches the caller to a human agent at a call center based on one or both of the historical data and the agent scores, and routes the call to the human agent.

<CIT> describes receiving, tracking, and analyzing business intelligence data. In one embodiment, for example, a computer-implemented method comprises: a feedback intermediary obtaining, from a user's computing device, the user's selection of an entity to provide feedback on; the feedback intermediary causing the user to be prompted at the user's computing device to provide feedback on the entity; the feedback intermediary obtaining feedback on the entity from the user's computing device; and providing the user's feedback on the entity to a computing device associated with the entity.

<CIT> describes sentiment analyzer systems that may include feedback analytics servers configured to receive and analyze feedback data from various client devices. Feedback data may be received and analyzed to determine feedback context and sentiment scores. In some embodiments, natural language processing neural networks may be used to determine sentiment scores for the feedback data. Feedback data also may be grouped into feedback aggregations based on context, and sentiment scores may be calculated for each feedback aggregation. Sentiment analyzer outputs and corresponding output devices may be determined based on the sentiment scores and feedback contexts.

The matter for protection is defined by the appended claims.

In general, techniques of this disclosure may enable users to communicate with multiple virtual, computational agents/assistants. For instance, there may be several agents available to a user via a computing device that may be at least somewhat capable of responding to an utterance (e.g., request, question, query, order, etc.). An agent may respond to an utterance by performing elements of a task that is identified from the utterance. However, in some scenarios, while a single agent may be capable of performing some elements of a task identified from an utterance, the single agent may not be capable of performing every element needed to complete performance of the task.

In accordance with one or more techniques of this disclosure and as opposed to having a single agent perform all of the elements of a task that is identified from an utterance, performance of the elements may be divided up across multiple agents. For instance, a first agent may perform a first sub-set of elements of a task and a second agent may perform a second sub-set of the elements of the task. As some agents may be more computationally efficient at performing certain elements than other agents, dividing performance of elements of a task across multiple agents may allow agents to perform elements at-which they are more computationally efficient, even if the more efficient agents are not capable of performing all of the elements of the task. In this way, the techniques of this disclosure may enable more efficient performance of tasks, which may reduce the power consumption and/or system resource requirements of agent performed tasks.

In general, techniques of this disclosure may enable a virtual, computational assistant (e.g., also referred to as "an intelligent personal assistant" or simply as an "assistant") to manage multiple agents for responding to user input (e.g., for satisfying user utterances or textual input). For instance, a computing device may receive, with a microphone, voice input (e.g., audio data) that corresponds to a user utterance. An agent selection module is configured to analyze the voice input, and to select an agent from a plurality of agents to satisfy the utterance. The plurality of agents include one or more first party (1P) agents and one or more third party (3P) agents. The 1P agents may be included within the assistant and/or share a common publisher with the assistant, agent selection module, and/or an operating system of the computing device that received the voice input.

To perform the selection, the agent selection module may determine whether to satisfy the utterance using a 1P agent, a 3P agent, or some combination of 1P agents and 3P agents. Where the agent selection module determines to satisfy the utterance at least in part using a 3P agent, the agent selection module may rank one or more 3P agents based on the utterance.

The selected language agent (a 1P language agent, a 3P language agent, or some combination of 1P language agents and 3P language agents) may attempt to satisfy the utterance. For instance, the selected agent may perform one or more actions (e.g., output information based on the utterance, respond to a user's needs indicated by the utterance, or otherwise perform certain operations to help the user complete a variety of real-world or virtual tasks based on the utterance) to satisfy the utterance.

In some examples, there may be an indication of the type of agent performing actions. For instance, where the one or more actions include "speaking" with the user, 1P agents and 3P agents utilize different voices. As one example, 1P agents may all utilize a reserved voice of a plurality of voices and 3P agents may utilize other voices of the plurality of voices, but may be prohibited from using the reserved voice. Where the one or more actions include a textual interaction with the user, the agents may use different identifiers (e.g., "agent <NUM>: I have made your dinner reservation" and "agent <NUM>: I have moved $<NUM> from your checking account to your savings account"), different fonts for each agent, and so on.

Throughout the disclosure, examples are described where a computing device and/or a computing system analyzes information (e.g., context, locations, communications, contacts, chat conversations, voice conversations, etc.) associated with a computing device and a user of a computing device, only if the computing device receives permission from the user of the computing device to analyze the information. For example, in situations discussed below, before an assistant executing at a computing device or computing system can collect or may make use of information associated with a user, the user may be provided with an opportunity to provide input to control whether the assistant (or other programs or features of the computing device and/or computing system) can collect and make use of user information or to dictate whether and/or how the computing devices and/or computing systems may receive content that may be relevant to the user. In addition, certain data may be encrypted and/or treated in one or more ways before it is stored or used by the assistant or underlying computing device and/or computing system, so that personally-identifiable information is removed. For example, a user's identity may be treated so that no personally identifiable information can be determined about the user, or a user's geographic location may be generalized where location information is obtained (such as to a city, ZIP code, or state as opposed to a coordinate location or physical address), so that a particular location of a user cannot be determined. Thus, the user may have control over how information is collected about the user and used by the assistant and the underlying computing device and computing system that executes the assistant.

<FIG> is a conceptual diagram illustrating an example system that executes an example virtual assistant, in accordance with one or more aspects of the present disclosure. System <NUM> of <FIG> includes assistant server system <NUM> in communication, via network <NUM>, with search server system <NUM>, third party (3P) agent server systems 170A-170N (collectively, "3P agent server systems <NUM>"), and computing device <NUM>. Although system <NUM> is shown as being distributed amongst assistant server system <NUM>, 3P agent server systems <NUM>, search server system <NUM>, and computing device <NUM>, in other examples, the features and techniques attributed to system <NUM> may be performed internally, by local components of computing device <NUM>. Similarly, assistant server system <NUM> and/or 3P agent server systems <NUM> may include certain components and perform various techniques that are otherwise attributed in the below description to search server system <NUM> and/or computing device <NUM>.

Network <NUM> represents any public or private communications network, for instance, cellular, Wi-Fi, and/or other types of networks, for transmitting data between computing systems, servers, and computing devices. Assistant server system <NUM> may exchange data, via network <NUM>, with computing device <NUM> to provide a virtual assistance service that is accessible to computing device <NUM> when computing device <NUM> is connected to network <NUM>. Similarly, 3P agent server systems <NUM> may exchange data, via network <NUM>, with computing device <NUM> to provide virtual agents services that are accessible to computing device <NUM> when computing device <NUM> is connected to network <NUM>. Assistant server system <NUM> may exchange data, via network <NUM>, with search server system <NUM> to access a search service provided by search server system <NUM>. Computing device <NUM> may exchange data, via network <NUM>, with search server system <NUM> to access the search service provided by search server system <NUM>. 3P agent server systems <NUM> may exchange data, via network <NUM>, with search server system <NUM> to access the search service provided by search server system <NUM>.

Network <NUM> may include one or more network hubs, network switches, network routers, or any other network equipment, that are operatively inter-coupled thereby providing for the exchange of information between server systems <NUM>, <NUM>, and <NUM> and computing device <NUM>. Computing device <NUM>, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> may transmit and receive data across network <NUM> using any suitable communication techniques. Computing device <NUM>, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> may each be operatively coupled to network <NUM> using respective network links. The links coupling computing device <NUM>, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> to network <NUM> may be Ethernet or other types of network connections and such connections may be wireless and/or wired connections.

Assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> represent any suitable remote computing systems, such as one or more desktop computers, laptop computers, mainframes, servers, cloud computing systems, etc. capable of sending and receiving information both to and from a network, such as network <NUM>. Assistant server system <NUM> hosts (or at least provides access to) a virtual assistant service. 3P agent server systems <NUM> host (or at least provide access to) virtual language agents. Search server system <NUM> hosts (or at least provides access to) a search service. In some examples, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> represent cloud computing systems that provide access to their respective services via the cloud.

Computing device <NUM> represents an individual mobile or non-mobile computing device. Examples of computing device <NUM> include a mobile phone, a tablet computer, a laptop computer, a desktop computer, countertop devices, a set-top box, a television, a wearable device (e.g., a computerized watch, computerized eyewear, computerized gloves, etc.), a home automation device or system (e.g., an intelligent thermostat or home assistant device), a personal digital assistants (PDA), a gaming system, a media player, an e-book reader, a mobile television platform, an automobile navigation or infotainment system, or any other type of mobile, non-mobile, wearable, and non-wearable computing device configured to execute or access a virtual assistant and receive information via a network, such as network <NUM>.

Computing device <NUM> includes user interface device (UID) <NUM>, user interface (UI) module <NUM>, and local assistant module 122A. Modules <NUM> and 122A may perform operations described using software, hardware, firmware, or a mixture of hardware, software, and firmware residing in and/or executing at respective computing device <NUM>. Computing device <NUM> may execute modules <NUM> and 122A with multiple processors or multiple devices. Computing device <NUM> may execute modules <NUM> and 122A as virtual machines executing on underlying hardware. Modules <NUM> and 122A may execute as one or more services of an operating system or computing platform. Modules <NUM> and 122A may execute as one or more executable programs at an application layer of a computing platform.

Computing device <NUM> may communicate with assistant server system <NUM>, 3P agent server systems <NUM>, and/or search server system <NUM> via network <NUM> to access the virtual assistant service provided by assistant server system <NUM>, the virtual language agents provided by 3P agent server systems <NUM>, and/or to access the search service provided by search server system <NUM>. In the course of providing virtual assistant services, assistant server system <NUM> may communicate with search server system <NUM> via network <NUM> to obtain search results for providing a user of the virtual assistant service information to complete a task. In the course of providing virtual assistant services, assistant server system <NUM> may communicate with 3P agent server systems <NUM> via network <NUM> to engage one or more of the virtual language agents provided by 3P agent server systems <NUM> to provide a user of the virtual assistant service additional assistance. In the course of providing additional assistance, 3P agent server systems <NUM> may communicate with search server system <NUM> via network <NUM> to obtain search results for providing a user of the language agents information to complete a task.

In the example of <FIG>, assistant server system <NUM> includes remote assistant module 122B and agent index 124B. Remote assistant module 122B may maintain remote agent index 124B as part of a virtual assistant service that assistant server system <NUM> provides via network <NUM> (e.g., to computing device <NUM>). Computing device <NUM> includes user interface device (UID) <NUM>, user interface (UI) module <NUM>, local assistant module 122A, and agent index 124A. Local assistant module 122A may maintain agent index 124A as part of a virtual assistant service that executes locally at computing device <NUM>. Remote assistant module 122B and local assistant module 122A may be referred to collectively as assistant modules <NUM>. Local agent index 124A and remote agent index 124B may be referred to collectively as agent indices <NUM>.

Modules 122B, 128Ab-128Nb (collectively, "3P agent modules 128b"), and <NUM> may perform operations described using software, hardware, firmware, or a mixture of hardware, software, and firmware residing in and/or executing at, respectively, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM>. Assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM> may execute, respectively, modules 122B, 128b, and <NUM> with multiple processors, multiple devices, as virtual machines executing on underlying hardware, or as one or more services of an operating system or computing platform. In some examples, modules 122B, 128b, and <NUM> may execute as one or more executable programs at an application layer of a computing platform of, respectively, assistant server system <NUM>, 3P agent server systems <NUM>, and search server system <NUM>.

UID <NUM> of computing device <NUM> may function as an input and/or output device for computing device <NUM>. UID <NUM> may be implemented using various technologies. For instance, UID <NUM> may function as an input device using presence-sensitive input screens, such as resistive touchscreens, surface acoustic wave touchscreens, capacitive touchscreens, projective capacitance touchscreens, pressure sensitive screens, acoustic pulse recognition touchscreens, or another presence-sensitive display technology. In addition, UID <NUM> may include microphone technologies, infrared sensor technologies, or other input device technology for use in receiving user input.

UID <NUM> may function as output (e.g., display) device using any one or more display devices, such as liquid crystal displays (LCD), dot matrix displays, light emitting diode (LED) displays, organic light-emitting diode (OLED) displays, e-ink, or similar monochrome or color displays capable of outputting visible information to a user of computing device <NUM>. In addition, UID <NUM> may include speaker technologies, haptic feedback technologies, or other output device technology for use in outputting information to a user.

UID <NUM> may include a presence-sensitive display that may receive tactile input from a user of computing device <NUM>. UID <NUM> may receive indications of tactile input by detecting one or more gestures from a user (e.g., the user touching or pointing to one or more locations of UID <NUM> with a finger or a stylus pen). UID <NUM> may present output to a user, for instance at a presence-sensitive display. UID <NUM> may present the output as a graphical user interface (e.g., user interface <NUM>), which may be associated with functionality provided by computing device <NUM> and/or a service being accessed by computing device <NUM>.

For example, UID <NUM> may present a user interface (e.g., user interface <NUM>) related to a virtual assistant provided by local assistant module 122A and/or remote assistant module 122B that UI module <NUM> accesses on behalf of computing device <NUM>. UID <NUM> may present a user interface related to other features of computing platforms, operating systems, applications, and/or services executing at or accessible from computing device <NUM> (e.g., e-mail, chat, or other electronic message applications, Internet browser applications, telephone applications, mobile or desktop operating systems, etc.).

UI module <NUM> may manage user interactions with UID <NUM> and other components of computing device <NUM> including interacting with assistant server system <NUM> so as to provide autonomous search results at UID <NUM>. UI module <NUM> may cause UID <NUM> to output a user interface, such as user interface <NUM> (or other example user interfaces) for display, as a user of computing device <NUM> views output and/or provides input at UID <NUM>. UI module <NUM> and UID <NUM> may receive one or more indications of input from a user as the user interacts with the user interface, at different times and when the user and computing device <NUM> are at different locations. UI module <NUM> and UID <NUM> may interpret inputs detected at UID <NUM> and may relay information about the inputs detected at UID <NUM> to one or more associated platforms, operating systems, applications, and/or services executing at computing device <NUM>, for example, to cause computing device <NUM> to perform functions.

UI module <NUM> may receive information and instructions from one or more associated platforms, operating systems, applications, and/or services executing at computing device <NUM> and/or one or more remote computing systems, such as server systems <NUM> and <NUM>. In addition, UI module <NUM> may act as an intermediary between the one or more associated platforms, operating systems, applications, and/or services executing at computing device <NUM>, and various output devices of computing device <NUM> (e.g., speakers, LED indicators, audio or haptic output device, etc.) to produce output (e.g., a graphic, a flash of light, a sound, a haptic response, etc.) with computing device <NUM>. In some examples, UI module <NUM> may perform text to speech (TTS). For instance, when provided (e.g., by another module) with text, UI module <NUM> may synthesize audio data to speak the test (e.g., read the text aloud).

Local assistant module 122A of computing device <NUM> and remote assistant module 122B of assistant server system <NUM> may each perform similar functions described herein for automatically executing an assistant that is configured to select agents to satisfy user input (e.g., spoken utterances, textual input, etc.) received from a user of a computing device. Remote assistant module 122B and agent index 124B represent server-side or cloud implementations of an example virtual assistant whereas local assistant module 122A and agent index 124A represent a client-side or local implementation of the example virtual assistant.

Modules 122A and 122B may each include respective software agents configured to execute as intelligent personal assistants that can perform tasks or services for an individual, such as a user of computing device <NUM>. Modules 122A and 122B may perform these tasks or services based on user input (e.g., detected at UID <NUM>), location awareness (e.g., based on context), and/or the ability to access other information (e.g., weather or traffic conditions, news, stock prices, sports scores, user schedules, transportation schedules, retail prices, etc.) from a variety of information sources (e.g., either stored locally at computing device <NUM>, assistant server system <NUM>, or obtained via the search service provided by search server system <NUM>). Modules 122A and 122B may perform artificial intelligence and/or machine learning techniques to automatically identify and complete one or more tasks on behalf of a user.

In some examples, the assistants provided by modules <NUM> be referred to as first party (1P) assistants and/or 1P agents. For instance, the agents represented by modules <NUM> may share a common publisher and/or a common developer with an operating system of computing device <NUM> and/or an owner of assistant server system <NUM>. As such, in some examples, the agents represented by modules <NUM> may have abilities not available to other agents, such as third party (3P) agents. In some examples, the agents represented by modules <NUM> may not both be 1P agents. For instance, the agent represented by local assistant module 122A may be a 1P agent whereas the agent represented by remote assistant module 122B may be a 3P agent. In some examples, the assistants provided by modules <NUM> may be referred to as 1P assistants (e.g., 1P computational assistants) and modules <NUM> may further provide one or more 1P agents (e.g., that share a common publisher and/or a common developer with the 1P computational assistants).

As discussed above, local assistant module 122A may represent a software agent configured to execute as an intelligent personal assistant that can perform tasks or services for an individual, such as a user of computing device <NUM>. However, in some examples, it may be desirable that the assistant utilize other agents to perform tasks or services for the individual. For instance, in certain scenarios, it may be desirable for the assistant to use one or more 3P agents to perform tasks or services the user of computing device <NUM>. As one example, a 3P agent may be able to perform a particular task more efficiently (e.g., using less computational power, system resources, etc.) than the assistant.

In the example of <FIG>, 3P agent server systems <NUM> include remote 3P agent modules 128b. Remote 3P agent modules 128b may perform similar functions described below with respect to local 3P agent modules 128a to automatically execute an agent that is configured to satisfy utterances received from a user of a computing device, such as computing device <NUM>. In other words, remote 3P agent modules 128b represent server-side or cloud implementations of example 3P agents whereas local 3P agent modules 128a represent client-side or local implementations of the example 3P agents.

In some examples, each of modules 128a and 128b (collectively, "modules <NUM>") may represent software agents configured to execute as intelligent personal assistants that can perform tasks or services for an individual, such as a user of computing device <NUM>. In some examples, each of modules <NUM> may represent software agents that may be utilized by the assistants provided by modules <NUM>. In some examples, the assistants and/or agents provided by modules <NUM> be referred to as third party (3P) assistants and/or 3P agents. For instance, the assistants and/or agents represented by modules <NUM> may not share a common publisher with an operating system of computing device <NUM> and/or an owner of assistant server system <NUM>. As such, in some examples, the assistants and/or agents represented by modules <NUM> may not have abilities that are available to other assistants and/or agents, such as first party (1P) assistants and/or agents.

In some examples, the 3P agents may be configured for use without user involvement. In some examples, some 3P agents may require configuration prior to being used. For instance, when installing smart lighting dimmers in their home, the user may configure a 3P agent provided by a manufacturer of smart lighting dimmers for use. The configuration process may involve associating the 3P agent with the 1P assistant (e.g., the user may provide account information for the 3P agent to the 1P assistant) and authorizing (e.g., by the user) the 1P assistant to communicate with the 3P agent on the user's behalf.

Search module <NUM> may execute a search for information determined to be relevant to a search query that search module <NUM> automatically generates (e.g., based on contextual information associated with computing device <NUM>) or that search module <NUM> receives from assistant server system <NUM>, 3P agent server systems <NUM>, or computing device <NUM> (e.g., as part of a task that a virtual assistant is completing on behalf of a user of computing device <NUM>). Search module <NUM> may conduct an Internet search or local device search based on a search query to identify information related to the search query. After executing a search, search module <NUM> may output the information returned from the search (e.g., the search results) to assistant server system <NUM>, one or more of 3P agent server systems <NUM>, or computing device <NUM>.

One or more components of system <NUM>, such as local assistant module 122A and/or remote assistant module 122B, may maintain agent index 124A and/or agent index 124B (collectively, "agent indices <NUM>") to store information related to agents that are available to an individual, such as a user of computing device <NUM>. In some examples, agent indices <NUM> may store, for each agent, an agent description and a list of capabilities in a semi-structured index of agent information. For instance, agent indices <NUM> may contain a single document with information for each available agent. A document included in agent indices <NUM> for a particular agent may be constructed from information provided by a developer of the particular agent. Some example information fields that may be included in the document, or which may be used to construct the document, include but are not limited to: a description of the agent, one or more entry points of the agent, a category of the agent, one or more triggering phrases of the agent, a website associated with the agent, an indication of a voice to use when synthesizing audio data based on text generated by the agent, and/or a list of the agent's capabilities (e.g., a list of tasks, or task types, that the agent is capable of performing). In some examples, one or more of the information fields may be written in free-form natural language. In some examples, one or more of the information fields may be selected from a pre-defined list. For instance, the category field may be selected from a pre-defined set of categories (e.g., games, productivity, communication). In some examples, an entry point of an agent may be a device type(s) used to interface with the agent (e.g., cell phone). In some examples, an entry point of an agent may be a resource address or other argument of the agent.

In some examples, agent indices <NUM> may store information related to the use and/or the performance of the available agents. For instance, agent indices <NUM> may include an agent-quality score for each available agent. In some examples, the agent-quality scores may be determined based on one or more of: whether a particular agent is selected more often than competing agents, whether the agent's developer has produced other high quality agents, whether the agent's developer has good (or bad) spam scores on other user properties, and whether users typically abandon the agent in the middle of execution. In some examples, the agent-quality scores may be represented as a value between <NUM> and <NUM>, inclusive.

Agent indices <NUM> may provide a mapping between trigger phrases and agents. As discussed above, a developer of a particular agent may provide one or more trigger phrases to be associated with the particular agent. In some examples, to improve the quality of agent selection, local assistant module 122A may expand upon the provided trigger phrases. For instance, local assistant module 122A may expand a trigger phrase by expanding the trigger phrase's structure and the synonyms of the key concept of the trigger phrase. Regarding structure expansion, local assistant module 122A may insert terms, which are commonly used in users' natural language utterances like "please", "could you" etc., in between the compound of the triggering phase, and then permutate the compound of the phrase.

In some cases, the concept of the capabilities of a trigger phrase can be represented as verb and noun. As such, in some examples, local assistant module 122A may examine a query log of web searches, tag the verb and noun for each query (e.g., using a natural language framework), and build verb clusters based on the tagged verbs and nouns. Within each cluster, all verbs may be considered to have a similar meaning in the context of the same noun. As such, using the verb cluster model, local assistance module 122A may expand the synonyms of verbs in a triggering phrase associated with an agent, and store the results in agent indices <NUM> (i.e., as alternate triggering phrases for the agent).

In some examples, some trigger phrases may also contain variables that represent relevant sets of data. These data sets may be defined by schema. org types or as a custom specification by the developer. These triggering and parameter value sets are fed into a training system for a text-matching system. The training system may convert the specified patterns into a set of rules, represented in an efficient for online query-matching. Local assistance module 122A may also maintain a mapping of text-matching system's rules to the applicable agents.

One or more components to system <NUM>, such as search module <NUM>, may attach metadata about the agent to any associated web site in the web search index. This metadata may include the agent's id and the associated agent entry point.

When a user interacts with an agent, one or more components to system <NUM> may log details of the interaction to the user's personal history. As discussed above, the logging may be subject to one or more user controls such that the user may disable logging of agent interactions. In particular, the one or more components of system <NUM> may only log the details after receiving explicit authorization from the user.

In operation, local assistant module 122A may receive, from UI module <NUM>, an indication of a user input provided by a user of computing device <NUM>. As one example, local assistant module 122A may receive an indication of a voice input that corresponds to an utterance provided by a user of computing device <NUM>. As another example, local assistant module 122A may receive an indication of text input provided by a user of computing device <NUM> (e.g., at a physical and/or a virtual keyboard). In accordance with one or more techniques of this disclosure, local assistant module <NUM> may select an agent from a plurality of agents to satisfy the utterance. For instance, local assistant module 122A may determine whether to satisfy the user utterance using a 1P agent (i.e., a 1P agent provided by local assistant module 122A), a 3P agent (i.e., a 3P agent provided by one of 3P agent modules <NUM>), or some combination of 1P agents and 3P agents.

Local assistant module 122A may base the agent selection on an analysis of the utterance. As one example, local assistant module 122A may select, at least initially, a 1P language agent where it is not possible to satisfy the utterance solely using a 3P language agent. As another example, local assistant module 122A may identify a task based on the utterance and select an agent from the available agents (e.g., the 1P agents and the 3P agents) based on rankings of the agents and/or the capabilities of the available agents to perform the task. As another example, local assistant module 122A may determine (e.g., based on data included in agent index 124A) whether the voice input includes one or more pre-determined trigger phrases that are associated with IP agents or one or more pre-determined trigger phrases that are associated with 3P agents.

As discussed above, local assistant module 122A may base the agent selection on whether the voice input includes one or more pre-determined trigger phrases. For instance, if the voice input includes one or more pre-determined trigger phrases that are associated with 1P agents, local assistant module 122A may select one or more of the 1P agents to satisfy the utterance. In some examples, one or more of the 1P agents are selected, the resulting engagement may be referred to as a 1P experience.

However, if the voice input includes one or more pre-determined trigger phrases that are associated with 3P agents, local assistant module 122A may select one or more of the 3P agents to satisfy the utterance. For instance, local assistant module 122A may select a 3P agent of the 3P agents that is associated with the trigger phrases included in the voice input. To perform 3P agent selection, local assistant module 122A may rank one or more 3P agents based on the utterance. In some examples, local assistant module 122A may rank all known 3P agents. In some examples, local assistant module 122A may rank a subset of all known 3P agents. For instance, local assistant module 122A may rank 3P agents that are pre-configured for use by the user of computing device <NUM>.

As discussed above, local assistant module 122A, may select a 3P agent based on rankings. For instance, local assistant module 122A may select a 3P agent with the highest ranking to satisfy the utterance. In some examples, such as where there is a tie in the rankings and/or if the ranking of the 3P agent with the highest ranking is less than a ranking threshold, local assistant module 122A may solicit user input to select a 3P language agent to satisfy the utterance. For instance, local assistant module 122A may cause UI module <NUM> to output a user interface requesting that the user select a 3P agent from the top N (e.g., <NUM>, <NUM>, <NUM>, <NUM>, etc.) ranked 3P agents to satisfy the utterance.

The selected agent (a 1P agent, a 3P agent, or some combination of 1P language agents and 3P agents) may attempt to satisfy the utterance. For instance, the selected agent may perform one or more actions (e.g., output useful information based on the utterance, respond to a user's needs indicated by the utterance, or otherwise perform certain operations to help the user complete a variety of real-world or virtual tasks based on the utterance) to satisfy the utterance.

As discussed above, in some examples, the agents represented by modules <NUM> may not both be 1P agents. For example, the agent represented by local assistant module 122A may be a 1P agent whereas the agent represented by remote assistant module 122B may be a 3P agent. In some of such examples, local assistant module 122A may utilize 3P remote assistant module 122B to perform some (or all) of the 3P agent selection, identification, ranking, and/or invoking of other 3P agents. In some of such examples, local assistant module 122A may not be able utilize 3P remote assistant module 122B to perform some (or all) of the 3P agent selection, identification, ranking, and/or invoking of other 3P agents and may perform such tasks locally.

It will be appreciated that improved operation of one or more of computing device <NUM>, assistant server system <NUM>, and 3P agent server systems <NUM> is obtained according to the above description. As one example, by identifying a preferred agent to execute a task provided by a user, generalized searching and complex query rewriting can be reduced. This in turn reduces use of bandwidth and data transmission, reduces use of temporary volatile memory, reduces battery drain, etc. Furthermore, in certain embodiments, optimizing device performance and/or minimizing cellular data usage can be highly weighted features for ranking agents, such that selection of an agent based on these criteria provides the desired direct improvements in device performance and/or reduced data usage. As another example, by providing a single assistant/agent (e.g., the 1P assistant) to initially process utterances (e.g., identify tasks and select agent(s) for performing the task) the computational load may be reduced. For instance, as opposed to having several agents monitor, process, and satisfy incoming utterances, which would consume significant amounts of system resources (e.g., CPU cycles, power consumption, etc.), the techniques of this disclosure enable a single assistant to initially process utterances and invoke 3P agents as needed. As such, the techniques of this disclosure enable the benefits of having multiple agents available to satisfy utterances without the technical drawbacks of having multiple agents involved at every step of the utterance processing.

<FIG> is a block diagram illustrating an example computing device that is configured to execute an example virtual assistant, in accordance with one or more aspects of the present disclosure. Computing device <NUM> of <FIG> is described below as an example of computing device <NUM> of <FIG>. <FIG> illustrates only one particular example of computing device <NUM>, and many other examples of computing device <NUM> may be used in other instances and may include a subset of the components included in example computing device <NUM> or may include additional components not shown in <FIG>.

As shown in the example of <FIG>, computing device <NUM> includes user interface device (USD) <NUM>, one or more processors <NUM>, one or more communication units <NUM>, one or more input components <NUM>, one or more output components <NUM>, and one or more storage components <NUM>. USD <NUM> includes display component <NUM>, presence-sensitive input component <NUM>, microphone component <NUM>, and speaker component <NUM>. Storage components <NUM> of computing device <NUM> include UI module <NUM>, assistant module <NUM>, search module <NUM>, one or more application modules <NUM>, context module <NUM>, and agent index <NUM>.

Communication channels <NUM> may interconnect each of the components <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> for inter-component communications (physically, communicatively, and/or operatively). In some examples, communication channels <NUM> may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

One or more communication units <NUM> of computing device <NUM> may communicate with external devices (e.g., assistant server system <NUM> and/or search server system <NUM> of system <NUM> of <FIG>) via one or more wired and/or wireless networks by transmitting and/or receiving network signals on one or more networks (e.g., network <NUM> of system <NUM> of <FIG>). Examples of communication units <NUM> include a network interface card (e.g. such as an Ethernet card), an optical transceiver, a radio frequency transceiver, a GPS receiver, or any other type of device that can send and/or receive information. Other examples of communication units <NUM> may include short wave radios, cellular data radios, wireless network radios, as well as universal serial bus (USB) controllers.

One or more input components <NUM> of computing device <NUM> may receive input. Examples of input are tactile, text, audio, image, and video input. Input components <NUM> of computing device <NUM>, in one example, includes a presence-sensitive input device (e.g., a touch sensitive screen, a PSD), mouse, keyboard, voice responsive system, camera, microphone or any other type of device for detecting input from a human or machine. In some examples, input components <NUM> may include one or more sensor components one or more location sensors (GPS components, Wi-Fi components, cellular components), one or more temperature sensors, one or more movement sensors (e.g., accelerometers, gyros), one or more pressure sensors (e.g., barometer), one or more ambient light sensors, and one or more other sensors (e.g., infrared proximity sensor, hygrometer sensor, and the like). Other sensors, to name a few other non-limiting examples, may include a heart rate sensor, magnetometer, glucose sensor, olfactory sensor, compass sensor, step counter sensor.

One or more output components <NUM> of computing device <NUM> may generate output. Examples of output are tactile, audio, and video output. Output components <NUM> of computing device <NUM>, in one example, includes a presence-sensitive display, sound card, video graphics adapter card, speaker, cathode ray tube (CRT) monitor, liquid crystal display (LCD), or any other type of device for generating output to a human or machine.

UID <NUM> of computing device <NUM> may be similar to UID <NUM> of computing device <NUM> and includes display component <NUM>, presence-sensitive input component <NUM>, microphone component <NUM>, and speaker component <NUM>. Display component <NUM> may be a screen at which information is displayed by USD <NUM> while presence-sensitive input component <NUM> may detect an object at and/or near display component <NUM>. Speaker component <NUM> may be a speaker from which audible information is played by UID <NUM> while microphone component <NUM> may detect audible input provided at and/or near display component <NUM> and/or speaker component <NUM>.

While illustrated as an internal component of computing device <NUM>, UID <NUM> may also represent an external component that shares a data path with computing device <NUM> for transmitting and/or receiving input and output. For instance, in one example, UID <NUM> represents a built-in component of computing device <NUM> located within and physically connected to the external packaging of computing device <NUM> (e.g., a screen on a mobile phone). In another example, UID <NUM> represents an external component of computing device <NUM> located outside and physically separated from the packaging or housing of computing device <NUM> (e.g., a monitor, a projector, etc. that shares a wired and/or wireless data path with computing device <NUM>).

As one example range, presence-sensitive input component <NUM> may detect an object, such as a finger or stylus that is within two inches or less of display component <NUM>. Presence-sensitive input component <NUM> may determine a location (e.g., an [x, y] coordinate) of display component <NUM> at which the object was detected. In another example range, presence-sensitive input component <NUM> may detect an object six inches or less from display component <NUM> and other ranges are also possible. Presence-sensitive input component <NUM> may determine the location of display component <NUM> selected by a user's finger using capacitive, inductive, and/or optical recognition techniques. In some examples, presence-sensitive input component <NUM> also provides output to a user using tactile, audio, or video stimuli as described with respect to display component <NUM>. In the example of <FIG>, PSD <NUM> may present a user interface (such as graphical user interface <NUM> of <FIG>).

Speaker component <NUM> may comprise a speaker built-in to a housing of computing device <NUM> and in some examples, may be a speaker built-in to a set of wired or wireless headphones that are operably coupled to computing device <NUM>. Microphone component <NUM> may detect audible input occurring at or near UID <NUM>. Microphone component <NUM> may perform various noise cancellation techniques to remove background noise and isolate user speech from a detected audio signal.

UID <NUM> of computing device <NUM> may detect two-dimensional and/or three-dimensional gestures as input from a user of computing device <NUM>. For instance, a sensor of UID <NUM> may detect a user's movement (e.g., moving a hand, an arm, a pen, a stylus, etc.) within a threshold distance of the sensor of UID <NUM>. UID <NUM> may determine a two or three-dimensional vector representation of the movement and correlate the vector representation to a gesture input (e.g., a hand-wave, a pinch, a clap, a pen stroke, etc.) that has multiple dimensions. In other words, UID <NUM> can detect a multi-dimension gesture without requiring the user to gesture at or near a screen or surface at which UID <NUM> outputs information for display. Instead, UID <NUM> can detect a multi-dimensional gesture performed at or near a sensor which may or may not be located near the screen or surface at which UID <NUM> outputs information for display.

One or more processors <NUM> may implement functionality and/or execute instructions associated with computing device <NUM>. Examples of processors <NUM> include application processors, display controllers, auxiliary processors, one or more sensor hubs, and any other hardware configure to function as a processor, a processing unit, or a processing device. Modules <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> may be operable by processors <NUM> to perform various actions, operations, or functions of computing device <NUM>. For example, processors <NUM> of computing device <NUM> may retrieve and execute instructions stored by storage components <NUM> that cause processors <NUM> to perform the operations modules <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>. The instructions, when executed by processors <NUM>, may cause computing device <NUM> to store information within storage components <NUM>.

One or more storage components <NUM> within computing device <NUM> may store information for processing during operation of computing device <NUM> (e.g., computing device <NUM> may store data accessed by modules <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> during execution at computing device <NUM>). In some examples, storage component <NUM> is a temporary memory, meaning that a primary purpose of storage component <NUM> is not long-term storage. Storage components <NUM> on computing device <NUM> may be configured for short-term storage of information as volatile memory and therefore not retain stored contents if powered off. Examples of volatile memories include random access memories (RAM), dynamic random access memories (DRAM), static random access memories (SRAM), and other forms of volatile memories known in the art.

Storage components <NUM>, in some examples, also include one or more computer-readable storage media. Storage components <NUM> in some examples include one or more non-transitory computer-readable storage mediums. Storage components <NUM> may be configured to store larger amounts of information than typically stored by volatile memory. Storage components <NUM> may further be configured for long-term storage of information as non-volatile memory space and retain information after power on/off cycles. Examples of non-volatile memories include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories. Storage components <NUM> may store program instructions and/or information (e.g., data) associated with modules <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> and data store <NUM>. Storage components <NUM> may include a memory configured to store data or other information associated with modules <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> and data store <NUM>.

UI module <NUM> may include all functionality of UI module <NUM> of computing device <NUM> of <FIG> and may perform similar operations as UI module <NUM> for managing a user interface (e.g., user interface <NUM>) that computing device <NUM> provides at USD <NUM> for example, for facilitating interactions between a user of computing device <NUM> and assistant module <NUM>. For example, UI module <NUM> of computing device <NUM> may receive information from assistant module <NUM> that includes instructions for outputting (e.g., displaying or playing audio) an assistant user interface (e.g., user interface <NUM>). UI module <NUM> may receive the information from assistant module <NUM> over communication channels <NUM> and use the data to generate a user interface. UI module <NUM> may transmit a display or audible output command and associated data over communication channels <NUM> to cause UID <NUM> to present the user interface at UID <NUM>.

In some examples, UI module <NUM> may receive an indication of one or more user inputs detected at UID <NUM> and may output information about the user inputs to assistant module <NUM>. For example, UID <NUM> may detect a voice input from a user and send data about the voice input to UI module <NUM>.

UI module <NUM> may send an indication of the voice input to assistant module <NUM> for further interpretation. Assistant module <NUM> may determine, based on the voice input, that the detected voice input represents a user request for assistant module <NUM> to perform one or more tasks.

UI module <NUM> may be capable of performing text to speech (TTS). For instance, when provided (e.g., by the assistant or an agent) with text, UI module <NUM> may synthesize audio data to speak the test (e.g., read the text aloud). UI module <NUM> may be capable of performing TTS using a plurality of different voices.

Application modules <NUM> represent all the various individual applications and services executing at and accessible from computing device <NUM> that may be accessed by an assistant, such as assistant module <NUM>, to provide user with information and/or perform a task. A user of computing device <NUM> may interact with a user interface associated with one or more application modules <NUM> to cause computing device <NUM> to perform a function. Numerous examples of application modules <NUM> may exist and include, a fitness application, a calendar application, a search application, a map or navigation application, a transportation service application (e.g., a bus or train tracking application), a social media application, a game application, an e-mail application, a chat or messaging application, an Internet browser application, or any and all other applications that may execute at computing device <NUM>.

Search module <NUM> of computing device <NUM> may perform integrated search functions on behalf of computing device <NUM>. Search module <NUM> may be invoked by UI module <NUM>, one or more of application modules <NUM>, and/or assistant module <NUM> to perform search operations on their behalf. When invoked, search module <NUM> may perform search functions, such as generating search queries and executing searches based on generated search queries across various local and remote information sources. Search module <NUM> may provide results of executed searches to the invoking component or module. That is, search module <NUM> may output search results to UI module <NUM>, assistant module <NUM>, and/or application modules <NUM> in response to an invoking command.

Context module <NUM> may collect contextual information associated with computing device <NUM> to define a context of computing device <NUM>. Specifically, context module <NUM> is primarily used by assistant module <NUM> to define a context of computing device <NUM> that specifies the characteristics of the physical and/or virtual environment of computing device <NUM> and a user of computing device <NUM> at a particular time.

As used throughout the disclosure, the term "contextual information" is used to describe any information that can be used by context module <NUM> to define the virtual and/or physical environmental characteristics that a computing device, and the user of the computing device, may experience at a particular time. Examples of contextual information are numerous and may include: sensor information obtained by sensors (e.g., position sensors, accelerometers, gyros, barometers, ambient light sensors, proximity sensors, microphones, and any other sensor) of computing device <NUM>, communication information (e.g., text based communications, audible communications, video communications, etc.) sent and received by communication modules of computing device <NUM>, and application usage information associated with applications executing at computing device <NUM> (e.g., application data associated with applications, Internet search histories, text communications, voice and video communications, calendar information, social media posts and related information, etc.). Further examples of contextual information include signals and information obtained from transmitting devices that are external to computing device <NUM>. For example, context module <NUM> may receive, via a radio or communication unit of computing device <NUM>, beacon information transmitted from external beacons located at or near a physical location of a merchant.

Assistant module <NUM> may include all functionality of local assistant module 122A of computing device <NUM> of <FIG> and may perform similar operations as local assistant module 122A for providing an assistant. In some examples, assistant module <NUM> may execute locally (e.g., at processors <NUM>) to provide assistant functions. In some examples, assistant module <NUM> may act as an interface to a remote assistance service accessible to computing device <NUM>. For example, assistant module <NUM> may be an interface or application programming interface (API) to remote assistance module 122B of assistant server system <NUM> of <FIG>.

Agent selection module <NUM> may include functionality to select one or more agents to satisfy a given utterance. In some examples, agent selection module <NUM> may be a standalone module. In some examples, agent selection module <NUM> may be included in assistant module <NUM>.

Similar to agent indices 124A and 124B of system <NUM> of <FIG>, agent index <NUM> may store information related to agents, such as 3P agents. Assistant module <NUM> and/or agent selection module <NUM> may rely on the information stored at agent index <NUM>, in addition to any information provided by context module <NUM> and/or search module <NUM>, to perform assistant tasks and/or select agents for utterance satisfaction.

Agent selection module <NUM> may select one or more agents to satisfy a user utterance. As discussed above, some utterances (i.e., user requests) cannot be directly handed off to 3P agents, such as utterances that need special assistance (e.g., assistance from a publisher of assistant module <NUM>). Situations where special assistance is needed to satisfy an utterance may be referred to as 1P experiences because the publisher of assistant module <NUM> may implement (at least part of, but not always all of) the logic necessary to satisfy the utterance. Two examples of utterances that may need special assistance are home automation and overly broad queries.

An example of a home automation utterance is "set my downstairs thermostat to <NUM>". The publisher of assistant module <NUM> may enable users to register their home automation equipment, such as with assistant module <NUM>. To satisfy the above utterance, assistant module <NUM> may look up the configuration information of the user's home automation equipment, and then send an appropriate request to the automation system (e.g., downstairs thermostat) based on to details. As the special assistance of knowledge of and/or access to the user's home automation equipment is needed to satisfy the utterance, agent selection module <NUM> may select a 1P agent to satisfy the utterance. In other words, this process may be implemented as 1P experience.

An example of an overly broad utterance is "I am bored". There are many potential ways to satisfy that utterance, but adequate satisfaction depends on the user's preferences at that time. As such, agent selection module <NUM> may select a 1P experience. In such a 1P experience, assistant module <NUM> may ask the user a series of questions to determine what they want to do. For instance, assistant module <NUM> may say "Do you feel like a movie or a game?" If the user responds by stating that they feel like a game, assistant module <NUM> may say "Ok, do you like strategy or fantasy games?.

For these and other similar situations, the publisher of assistant module <NUM> may provide a set of 1P solutions built internally. In some examples, the 1P solutions may be referred to as 1P agents. The 1P agents may be associated with (i.e., identified by) a set of 1P triggering phrases identified by the publisher of assistant module <NUM>.

In general, 1P experiences may follow two basic models. In the first model, the publisher of assistant module <NUM> may handle the full experience. One such example would be for the utterance "Ok assistant, where were you made?" An internally developed experience may respond with a fun response about how and where the assistant was made.

The second model is where the publisher of assistant module <NUM> implements some dialog to determine the exact parameters for a task and then transfers control over to a 3P agent or API. To continue the "I am bored" example above, if the dialog finds that the user would like to a play a strategy game, agent selection module <NUM> may invoke an agent that implements such a game. As another example, if the utterance is "book a taxi", agent selection module <NUM> may initially select assistant module <NUM> (i.e., initially trigger a 1P experience) to asks the user for necessary information such as pick-up and drop-off locations, time, and taxi class. In some examples, agent selection module <NUM> may select a 3P agent capable of booking a taxi and assistant module <NUM> may pass the gathered information to the selected 3P agent. In some examples, assistant module <NUM> may directly pass the gathered information to an external API of an entity capable of booking a taxi. In either of these ways, agent selection module <NUM> may broker a referral to a 3P agent.

In operation, a user of computing device <NUM> may provide an utterance at UID <NUM>, which may generate audio data based on the utterance. Some example utterances include, but are not limited to, "I need a ride to the airport", "tell me a joke", "show me the recipe for beef wellington. " In some instances, the utterance includes an identification of the 3P assistant that the user wishes to execute the action, such as "Order a small cheese pizza using the GoPizza app. " In many other instances, no explicit reference to a 3P agent is made by the user, in which case there is a need to identify candidate 3P agents and select a preferred 3P agent from those candidates.

Agent selection module <NUM> is configured to select one or more agents to satisfy the utterance. For instance, agent selection module <NUM> may determine whether the utterance include any trigger phrases associated with 1P agents or 3P agents. If the utterance matches a 1P triggering phrase, agent selection module <NUM> may execute the 1P experience. For instance, agent selection module <NUM> may cause assistant module <NUM> to satisfy the utterance. If the phrase matches a 3P experience, agent selection module <NUM> may then send an agent-search request to a service engine. The agent-search request may contain the user utterance (i.e., audio data corresponding to the utterance), any matching triggering phrases, and user context determined by context module <NUM> (e.g., a unique identifier of the user, a location of the user, etc.). For purposes of simplicity, the service engine may be a component of agent selection module <NUM> and actions performed by agent selection module <NUM> may, in some examples, be performed by a separate service engine.

Agent selection module <NUM> may consult agent index <NUM> based on the utterance and any matching triggering phrases. Agent selection module <NUM> may identify agent documents in agent index <NUM> that match either the utterance or the triggering phrases. Agent selection module <NUM> may rank the identified agent documents (e.g., based on a capability level to satisfy the utterance). For instance, agent selection module <NUM> may multiply a text-match score with an agent-quality score. As discussed above, the agent-quality score may be stored in agent index <NUM>. The text-match score may be a weighted sum of the matches between the text in the utterance and the text in the agent document. In some examples, agent selection module <NUM> may give inside the title field, triggering phrases, or category a high weight. In some examples, agent selection module <NUM> may give matches in the description are given a lower weight.

In some examples, agent selection module <NUM> may also send the utterance through a normal web search (i.e., cause search module <NUM> to search the web based on the utterance). In some examples, agent selection module <NUM> may also send the utterance through a normal web search in parallel with the consultation of agent index <NUM>.

Agent selection module <NUM> may analyze the rankings and/or the results from the web search to select an agent to satisfy the utterance. For instance, agent selection module <NUM> may inspect the web results to determine whether there are web page results associated with agents. If there are web page results associated with agents, agent selection module <NUM> may, insert the agents associated with the web page results into the ranked results (if said agents are not already included in the ranked results). Agent selection module <NUM> may boost the agent's rankings according to the strength of the web score. In some examples, agent selection module <NUM> may also then query a personal history store to determine whether the user has interacted with any of the agents in the result set. If so, agent selection module <NUM> may we give those agents a boost (i.e., increased ranking) depending on how often the strength of the user's history with them.

Agent selection module <NUM> may select a 3P agent to satisfy the utterance based on this final ranked set of agent results. For instance, agent selection module <NUM> may select a 3P agent with the highest ranking to satisfy the utterance. In some examples, such as where there is a tie in the rankings and/or if the ranking of the 3P agent with the highest ranking is less than a ranking threshold, agent selection module <NUM> may solicit user input to select a 3P language agent to satisfy the utterance. For instance, agent selection module <NUM> may cause UI module <NUM> to output a user interface (i.e., a selection UI) requesting that the user select a 3P agent from N (e.g., <NUM>, <NUM>, <NUM>, <NUM>, etc.) moderately ranked 3P agents to satisfy the utterance. In some examples, the N moderately ranked 3P agents may include the top N ranked agents. In some examples, the N moderately ranked 3P agents may include agents other than the top N ranked agents.

Agent selection module <NUM> can examine attributes of the agents and/or obtain results from various 3P agents, rank those results, then invoke (i.e., select) the 3P agent providing the highest ranked result. For instance, if the utterance is to "order a pizza", agent selection module <NUM> may determine the user's current location, determine which source of pizza is closest to the user's current location, and rank the agent associated with that current location highest. Similarly, agent selection module <NUM> can poll multiple 3P agents on price of an item, then provide the agent to permit the user to complete the purchase based on the lowest price. Finally, agent selection module <NUM> can first determine that no 1P agent can fulfill the task, try multiple 3P agents to see if they can, and assuming only one or a few of them can, provide only those agents as options to the user for implementing the task.

The selected agent (a 1P agent, a 3P agent, or some combination of IP language agents and 3P agents) may attempt to satisfy the utterance. For instance, agent selection module <NUM> may output a request to an entry point of the selected agent, which may be determined by consulting agent index <NUM>. To attempt satisfy the utterance, the selected agent may perform one or more actions (e.g., output useful information based on the utterance, respond to a user's needs indicated by the utterance, or otherwise perform certain operations to help the user complete a variety of real-world or virtual tasks based on the utterance).

In some examples, there may be an indication of the type of agent (1P vs. 3P) that is performing actions. For instance, where the one or more actions include "speaking" with the user, 1P agents and 3P agents utilize different voices. As one example, 1P agents may all utilize a reserved voice of a plurality of voices and 3P agents may utilize other voices of the plurality of voices, but may be prohibited from using the reserved voice.

According to the claimed invention, agent selection module <NUM> causes assistant module <NUM> to request user feedback, using one or more of the speakers of the computing device, on how well an agent just fulfilled their request. For instance, assistant module <NUM> may say "You just interacted with the City Transit Schedule agent. In one or two words, how well did it work?" Assistant module <NUM> receives a user's response and may determine, based on the user's response, whether it was a good or bad experience (e.g., using sentiment analysis). Assistant module <NUM> may determine a score of the experience, and feed the determined score back into ranking. According to the claimed invention, the assistant module <NUM> modifies the agent-quality score of the agent that fulfilled the request based on the user's feedback about the fulfillment. In this way, the techniques of this disclosure enable agent selection module <NUM> to select agents based on how well the agents have functioned in the past.

As discussed above, in some instances, the utterance includes an identification of the 3P assistant that the user wishes to execute the action, such as "Order a small cheese pizza using the GoPizza app. " In many other instances, no explicit reference to a 3P agent is made by the user, in which case there is a need to identify candidate 3P agents and select a preferred 3P agent from those candidates. The complexity with such situations is that there may be multiple agents that may be able to order the user a pizza. As such, if there are multiple matching agents, the user may be asked to select one of the matching agents to satisfy the utterance. For instance, agent selection module <NUM> may output a selection UI asking the user to select between a Pizza House agent, and a Pizza4U agent.

<FIG> is a block diagram illustrating an example computing system that is configured to execute an example virtual assistant, in accordance with one or more aspects of the present disclosure. Assistant server system <NUM> of <FIG> is described below as an example of assistant server system <NUM> of <FIG>. <FIG> illustrates only one particular example of assistant server system <NUM>, and many other examples of assistant server system <NUM> may be used in other instances and may include a subset of the components included in example assistant server system <NUM> or may include additional components not shown in <FIG>.

As shown in the example of <FIG>, assistant server system <NUM> includes user one or more processors <NUM>, one or more communication units <NUM>, and one or more storage components <NUM>. Storage components <NUM> include assistant module <NUM>, search module <NUM>, context module <NUM>, and user agent index <NUM>.

Processors <NUM> are analogous to processors <NUM> of computing system <NUM> of <FIG>. Communication units <NUM> are analogous to communication units <NUM> of computing system <NUM> of <FIG>. Storage devices <NUM> are analogous to storage devices <NUM> of computing system <NUM> of <FIG>. Communication channels <NUM> are analogous to communication channels <NUM> of computing system <NUM> of <FIG> and may therefore interconnect each of the components <NUM>, <NUM>, and <NUM> for inter-component communications. In some examples, communication channels <NUM> may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

Search module <NUM> of assistant server system <NUM> is analogous to search module <NUM> of computing device <NUM> and may perform integrated search functions on behalf of assistant server system <NUM>. That is, search module <NUM> may perform search operations on behalf of assistant module <NUM>. In some examples, search module <NUM> may interface with external search systems, such as search system <NUM> to perform search operations on behalf of assistant module <NUM>. When invoked, search module <NUM> may perform search functions, such as generating search queries and executing searches based on generated search queries across various local and remote information sources. Search module <NUM> may provide results of executed searches to the invoking component or module. That is, search module <NUM> may output search results to assistant module <NUM>.

Context module <NUM> of assistant server system <NUM> is analogous to context module <NUM> of computing device <NUM>. Context module <NUM> may collect contextual information associated with computing devices, such as computing device <NUM> of <FIG> and computing device <NUM> of <FIG>, to define a context of the computing device. Context module <NUM> may primarily be used by assistant module <NUM> and/or search module <NUM> to define a context of a computing device interfacing and accessing a service provided by assistant server system <NUM>. The context may specify the characteristics of the physical and/or virtual environment of the computing device and a user of the computing device at a particular time.

Assistant module <NUM> may include all functionality of local assistant module 122A and remote assistant module 122B of <FIG>, as well as assistant module <NUM> of computing device <NUM> of <FIG>. Assistant module <NUM> may perform similar operations as remote assistant module 122B for providing an assistant service that is accessible via assistant server system <NUM>. That is, assistant module <NUM> may act as an interface to a remote assistance service accessible to a computing device that is communicating over a network with assistant server system <NUM>. For example, assistant module <NUM> may be an interface or API to remote assistance module 122B of assistant server system <NUM> of <FIG>.

Similar to agent index <NUM> of <FIG>, agent index <NUM> may store information related to agents, such as 3P agents. Assistant module <NUM> and/or agent selection module <NUM> may rely on the information stored at agent index <NUM>, in addition to any information provided by context module <NUM> and/or search module <NUM>, to perform assistant tasks and/or select agents for utterance satisfaction.

In general, agent descriptions and triggering phrases may only give a relatively small amount of information about an agent. The more information available about an agent, the better agent selection modules (e.g., agent selection module <NUM> and/or agent selection module <NUM>) can select the agents to applicable user utterances. In accordance with one or more techniques of this disclosure, agent accuracy module <NUM> may gather additional information about agents. In some examples, agent accuracy module <NUM> may be considered to be an automated agent crawler. For instance, agent accuracy module <NUM> may query each agent and store the information it receives. As one example, agent accuracy module <NUM> may send a request to the default agent entry point and will receive back a description from the agent about its capabilities. Agent accuracy module <NUM> may store this received information in agent index <NUM> (i.e., to improve targeting).

In some examples, assistant server system <NUM> may receive inventory information for agents, where applicable. As one example, an agent for an online grocery store can provide assistant server system <NUM> a data feed (e.g., a structured data feed) of their products, including description, price, quantities, etc. An agent selection module (e.g., agent selection module <NUM> and/or agent selection module <NUM>) may access this data as part of selecting an agent to satisfy a user's utterance. These techniques may enable the system to better respond to queries such as "order a bottle of prosecco". In such a situation, an agent selection module can match this utterance to an agent more confidently if the agent has provided their real-time inventory and the inventory indicated that the agent sells prosecco and has prosecco in stock.

In some examples, assistant server system <NUM> may provide an agent directory that users may browse to discover/find agents that they might like to use. The directory may have a description of each agent, a list of capabilities (in natural language; e.g., "you can use this agent to order a taxi", "you can use this agent to find food recipes"). If the user finds an agent in the directory that they would like to use, the user may select the agent and the agent may be made available to the user. For instance, assistant module <NUM> may add the agent into agent index <NUM> and or agent index <NUM>. As such, agent selection module <NUM> and/or agent selection module <NUM> may select the added agent to satisfy future utterances. In some examples, one or more agents may be added into agent index <NUM> or agent index <NUM> without user selection. In some of such examples, agent selection module <NUM> and/or agent selection module <NUM> may be able to select and/or suggest agents that have not been selected by a user to satisfy user utterances. In some examples, agent selection module <NUM> and/or agent selection module <NUM> may further rank agents based on whether they were selected by the user.

In some examples, one or more of the agents listed in the agent directory may be free (i.e., provided at no cost). In some examples, one or more of the agents listed in the agent directory may not be free (i.e., the user may have to pay money or some other consideration in order to use the agent).

In some examples, the agent directory may collect user reviews and ratings. The collected user reviews and ratings may be used to modify the agent quality scores. As one example, when an agent receives positive reviews and/or ratings, agent accuracy module <NUM> may increase the agent's agent quality score in agent index <NUM> or agent index <NUM>. As another example, when an agent receives negative reviews and/or ratings, agent accuracy module <NUM> may decrease the agent's agent quality score in agent index <NUM> or agent index <NUM>.

<FIG> is a block diagram illustrating an example computing system that is configured to execute an example third party agent, in accordance with one or more aspects of the present disclosure. 3P agent server system <NUM> of <FIG> is described below as an example of a 3P agent server system of 3P agent server systems <NUM> of <FIG>. <FIG> illustrates only one particular example of 3P agent server system <NUM>, and many other examples of 3P agent server system <NUM> may be used in other instances and may include a subset of the components included in example 3P agent server system <NUM> or may include additional components not shown in <FIG>.

As shown in the example of <FIG>, 3P agent server system <NUM> includes user one or more processors <NUM>, one or more communication units <NUM>, and one or more storage components <NUM>. Storage components <NUM> include 3P agent module <NUM>.

Processors <NUM> are analogous to processors <NUM> of assistant server system <NUM> of <FIG>. Communication units <NUM> are analogous to communication units <NUM> of assistant server system <NUM> of <FIG>. Storage devices <NUM> are analogous to storage devices <NUM> of assistant server system <NUM> of <FIG>. Communication channels <NUM> are analogous to communication channels <NUM> of assistant server system <NUM> of <FIG> and may therefore interconnect each of the components <NUM>, <NUM>, and <NUM> for inter-component communications. In some examples, communication channels <NUM> may include a system bus, a network connection, an inter-process communication data structure, or any other method for communicating data.

3P agent module <NUM> may include all functionality of a local 3P agent module of local 3P agent modules <NUM> and a corresponding remote 3P agent module of remote 3P agent modules <NUM> of <FIG>. 3P agent module <NUM> may perform similar operations as a remote 3P agent module of remote 3P agent modules <NUM> for providing a third party agent that is accessible via 3P agent server system <NUM>. That is, 3P agent module <NUM> may act as an interface to a remote agent service accessible to a computing device that is communicating over a network with 3P agent server system <NUM>. For example, 3P agent module <NUM> may be an interface or API to a remote 3P agent module of remote 3P agent modules <NUM> of a 3P agent server system of 3P agent server systems <NUM> of <FIG>.

In operation, 3P agent module <NUM> may be invoked by a user's computational assistant. For instance, 3P agent module <NUM> may be invoked by the assistant provided by assistant modules <NUM> of <FIG> to perform one or more actions to satisfy a user utterance received at computing device <NUM>. After performing at least some of the actions (e.g., performing one or more elements of a multi-element task), 3P agent module <NUM> may send an indication of the actions performed to the invoking assistant. For instance, if invoked to process an order, 3P agent module <NUM> may output one or more details of the order to the assistant.

<FIG> is a flowchart illustrating example operations performed by one or more processors executing an example virtual assistant, in accordance with one or more aspects of the present disclosure. <FIG> is described below in the context of system <NUM> of <FIG>. For example, local assistant module 122A while executing at one or more processors of computing device <NUM> may perform operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. And in some examples, remote assistant module 122B while executing at one or more processors of assistant server system <NUM> may perform operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, <FIG> is described below within the context of computing device <NUM> of <FIG>.

In operation, computing device <NUM> may receive an indication of user input that is indicative of a conversation between a user of a computing device and an assistant (<NUM>). For example, a user of computing device <NUM> may provide the utterance "I need a ride to the airport" at UID <NUM> that is received by local assistant module 122A as voice data.

Computing device <NUM> may select, based on the user input, an agent from a plurality of agents (<NUM>). For instance, local assistant module 122A may determine whether the utterance includes one or more triggers words associated with agents of the plurality of agents. If the computing device <NUM> is able to identify one or more agents that are associated with trigger words included in the utterance, computing device <NUM> may rank, based at least in part on a comparison between information related to the identified agents and text determined from the utterance, the identified agents. Computing device <NUM> may select, based at least in part on the ranking, an agent of the identified agents to satisfy the utterance.

In some examples, the plurality of agents may one or more first party agents and a plurality of third party agents. In some of such examples, computing device <NUM> may determine to select a third party agent (i.e., determine to bring in a third party agent) when the user input does not include any trigger words associated with first party agents.

The selected agent may determine one or more actions to respond to the user input. In some examples, computing device <NUM> may perform, at least in part, the one or more actions determined by the selected agent (<NUM>). For instance, where the utterance is to "play a song by Artist A", the selected agent may cause computing device <NUM> to play a song by Artist A. In some examples, the one or more actions determined by the selected agent may be performed at least in part by a computing device other than computing device <NUM>. For instance, where the utterance is "I need a ride to the airport," the selected agent may output a request to a computing device of a transportation company, and the computing device of the transportation company may route a vehicle to transport the user of computing device <NUM> to the airport.

In some examples, an agent may refer to another agent in the course of interacting with a user. For example, a product search agent may refer to a payment agent to arrange payment from a user (e.g., such as when the product search agent cannot handle payment by itself). This may be done as a matter of convenience for the user (e.g., so the user can use a common payment interface and/or to enhance security), or may be done for a fee or other consideration paid by the party to whom the referral is made (e.g., the publisher of the payment agent may receive some consideration for processing the payment).

<FIG> are flowcharts illustrating example operations performed by one or more processors to select a virtual agent to perform a task, in accordance with one or more aspects of the present disclosure. <FIG> are described below in the context of system <NUM> of <FIG>. For example, local assistant module 122A, while executing at one or more processors of computing device <NUM>, may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. And in some examples, remote assistant module 122B, while executing at one or more processors of assistant server system <NUM>, may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, <FIG> are described below within the context of computing device <NUM> of <FIG>.

In operation, computing device <NUM> may receive a representation of an utterance spoken at computing device <NUM> (<NUM>). For example, one or more microphones of UID <NUM> of computing device <NUM> may generate audio data that represents a user of computing device <NUM> saying "turn on my basement lights. " UID <NUM> may provide the audio data to the assistant provided by local assistant module 122A and/or remote assistant module 122B of assistant server system <NUM>.

The assistant identifies, based on the utterance, a task to be performed (<NUM>). As one example, where the utterance is "turn on my basement lights," the assistant may parse the audio data to determine that the task is to activate lights in a room called basement. As another example, where the utterance is "order me a pizza from Pizza Joint delivered home," the assistant may parse the audio data to determine that the task is to place an order for a pizza for delivery to the user's home address from a place called Pizza Joint. As another example, where the utterance is "ask Search Company what is the average airspeed velocity of a raven," the assistant may parse the audio data to determine that the task is to perform a web search for the average airspeed velocity of a raven.

The assistant may determine whether the utterance includes any trigger words (e.g., words or phrases) associated with a first party agent of a plurality of agents (<NUM>). For instance, the assistant may compare words in the utterance with first party trigger phrases included in agent index 124A. If the utterance includes any trigger words associated with a first party agent ("Yes" branch of <NUM>), the assistant may select the first party agent to perform the task (<NUM>), and cause the selected first party agent to perform the task (<NUM>). For example, where the utterance is "ask Search Company what is the average airspeed velocity of a raven" and agent index 124A indicates that "Search Company" is a trigger word associated with a first party search agent, the assistant may select and cause the first party search agent to perform a web search for the average airspeed velocity of a raven.

If the utterance does not include any trigger words associated with a first party agent ("No" branch of <NUM>), the assistant may determine whether the utterance includes any trigger words associated with a third party agent of the plurality of agents (<NUM>). For instance, the assistant may compare words in the utterance with third party trigger phrases included in agent index 124A. If the utterance includes any a trigger word associated with a particular third party agent ("Yes" branch of <NUM>, the assistant may select the particular third party agent to perform the task (<NUM>), and cause the particular third party agent to perform the task (<NUM>). For example, where the utterance is "order me a pizza from Pizza Joint delivered home" and agent index 124A indicates that "order" and "Pizza Joint" are trigger words associated with a particular third party ordering agent, the assistant may select and cause the particular third party ordering agent to create an order for a pizza to be delivered to the user's residence.

The assistant ranks the agents based on their capabilities to perform the task. For instance, if the utterance does not include any trigger words associated with a third party agent ("No" branch of <NUM>), the assistant may determine capability levels of a first party agent (<NUM>) and third party agents (<NUM>) to perform the identified task. As one example, to calculate the capability level of the first party agent, the assistant may calculate a metric that indicates how capable the assistant would be of performing the identified task. As another example, the assistant may calculate a respective metric for respective third party agents that indicates how capable the respective third party agent would be of performing the identified task. For instance, the assistant may calculate a metric for a first 3P agent that indicates how capable the first 3P agent would be of performing the identified task and a metric for a second 3P agent that indicates how capable the second 3P agent would be of performing the identified task. In some
examples, the metric may have a positive correlation with capability such that higher values indicate more capable performance. In some examples, the metric may have a negative correlation with capability such that lower values indicate more capable performance. The metrics may be calculated in a variety of manners. According to the claimed invention, the metrics are calculated based on the agent quality scores (either modified based on the web search or not) and possibly other information stored in agent index <NUM> as discussed above.

The assistant selects an agent based on the rankings. For instance, the assistant may determine whether the capability level of the first party agent satisfies a threshold capability level (<NUM>). For example, if the metrics are positively correlated with capability, the assistant may determine whether the capability level of the first party agent is greater than or equal to the threshold capability level. If the capability level of the first party agent satisfies the threshold capability level ("Yes" branch of <NUM>), the assistant may select the first party agent to perform the task (<NUM>), and cause the selected first party agent to perform the task (<NUM>).

If the capability level of the first party agent does not satisfy the threshold capability level ("No" branch of <NUM>), the assistant may determine whether the third party agent with the greatest capability level (hereinafter the "particular third party agent") satisfies the threshold capability level (<NUM>). If the capability level of the particular third party agent satisfies the threshold capability level ("Yes" branch of <NUM>), the assistant may select the particular third party agent to perform the task (<NUM>), and cause the particular third party agent to perform the task (<NUM>).

As shown above, in some examples, the assistant may select an agent with a bias toward first party agents. For instance, by evaluating the first party agent before evaluating the third party agents, the assistant may select the first party agent to perform the task so long as the capability level of the first party agent satisfies the threshold capability level (even if a third party agent has a greater capability level than the first party agent). In other examples, the assistant may select an agent without a bias toward first party agents. For instance, if the agent with the greatest capability level satisfies the threshold capability level, the assistant may select the agent to perform the task regardless of whether the agent is first party or third party.

If the capability level of the particular third party agent does not satisfy the threshold capability level ("No" branch of <NUM>), the assistant may determine capability levels of non-configured third party agents (<NUM>) and determine whether the non-configured third party agent with the greatest capability level (hereinafter the "particular non-configured third party agent") satisfies the threshold capability level (<NUM>). If the capability level of the particular non-configured third party agent satisfies the threshold capability level ("Yes" branch of <NUM>), the assistant may offer to configure the particular non-configured third party agent. For instance, the assistant may output synthesized voice data to ask the user if they would like to configure the particular non-configured third party agent. If the user indicates that they would like to configure the particular non-configured third party agent (turning the particular non-configured third party agent into the particular third party agent), the assistant may select the particular third party agent to perform the task (<NUM>), and cause the particular third party agent to perform the task (<NUM>).

As discussed above, some 3P agents may require configuration (e.g., being enabled or activates) prior to being used by the assistant. In general, it may be desirable to for the assistant to select a pre-configured agent to perform the task. However, it may be desirable for the assistant to evaluate non-configured agents to perform the task if no other agents are capable. For instance, if the first party agent and any configured third party agents are not capable of performing the identified task, the assistant may evaluate non-configured agents to perform the identified task.

If the capability level of the particular non-configured third party agent does not satisfy the threshold capability level ("No" branch of <NUM>), the assistant may output an indication that the utterance cannot be satisfied (<NUM>). For instance, the assistant may output synthesized voice data to say that they assistant "is not sure how to help with that.

<FIG> is a flowchart illustrating example operations performed by one or more processors to facilitate task performance by multiple virtual agents, in accordance with one or more aspects of the present disclosure. <FIG> is described below in the context of system <NUM> of <FIG>. For example, local assistant module 122A while executing at one or more processors of computing device <NUM> may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. And in some examples, remote assistant module 122B while executing at one or more processors of assistant server system <NUM> may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, <FIG> is described below within the context of computing device <NUM> of <FIG>.

Some tasks that may be performed by the assistant and/or agents may be considered multi-element tasks. A multi-element task may be a task having elements that may be performed by different agents in order to accomplish the overall task. While the elements of a multi-element task may be performed by multiple agents (e.g., a first agent may perform a first element of a two element task and a second agent may perform the second element), a single agent may still be able to perform all of the elements. In some examples, the selection of another agent to perform a sub-set of elements of a multi-element task may be considered to be an element of the multi-element task.

In operation, computing device <NUM> may receive a representation of an utterance spoken at computing device <NUM> (<NUM>). For example, one or more microphones of UID <NUM> of computing device <NUM> may generate audio data that represents a user of computing device <NUM> saying "get me a large cheese pizza delivered home. " UID <NUM> may provide the audio data to the assistant provided by local assistant module 122A and/or remote assistant module 122B of assistant server system <NUM>.

A first computational agent from a plurality of computational agents may identify, based on the utterance, a multi-element task to be performed (<NUM>). For instance, where the utterance is "get me a large cheese pizza for delivery," the first computational agent (e.g., the assistant or an agent provided by one of local 3P agent modules 128A of <FIG>) may identify the multi-element task as having the following elements <NUM>) determine location of delivery, <NUM>) select agent to order the pizza, and <NUM>) process the order for the large cheese pizza.

The first computational agent may perform a first sub-set of the elements of the multi-element task (<NUM>) including selecting a second computational agent to perform a second sub-set of the multi-element task (<NUM>). For instance, the first computational agent may determine the location of delivery and select an agent to order the pizza. To determine the location of delivery, the first computational agent may ask the user where they would like the pizza delivered. For instance, the first computational agent may cause computing device <NUM> to output synthesized audio data asking "where would you like that delivered to. " The first computational agent may receive the user's reply via one or more microphones of computing device <NUM>. The first computational agent may select a second computational agent to order the pizza to the provided address. For instance, the first computational agent may utilize the techniques of <FIG> to select a second computational agent to order the pizza. In this example, assuming the utterance does not include any trigger words for agents, the first computational agent may select a second computational agent to order the pizza based on capability levels of agents to arrange for delivery of a pizza to the address. The first computational agent may communicate with the selected second computational agent to cause the second computational agent to process the order for the large cheese pizza.

The first computational agent may receive an indication of actions performed by the second computational agent (<NUM>). For instance, the first computational agent may receive, from the second computational agent, a confirmation that the large cheese pizza has been ordered and is expected to be delivered to the provided address by a specified time. Where the first computational agent is a first party agent, the first computational agent may utilize the indication of the actions performed by the second computational agent to monitor the performance of the second computational agent (e.g., to modify an agent quality score of the second computational agent). Determining that a task is a multi-task and splitting each element of the task between different agents allows the most appropriate agent to perform any given step of the task. It may additionally allow the tasks of the multi-task to be carried in parallel. Additionally, user interaction with the computing device <NUM> is improved. For example, the user may be guided through the process of ordering a pizza, as described above.

<FIG> is a flowchart illustrating example operations performed by one or more processors to select a voice for use when outputting synthesized audio data of text generated by virtual agents, in accordance with one or more aspects of the present disclosure. <FIG> is described below in the context of system <NUM> of <FIG>. For example, local assistant module 122A while executing at one or more processors of computing device <NUM> may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. And in some examples, remote assistant module 122B while executing at one or more processors of assistant server system <NUM> may perform one or more of operations <NUM>-<NUM>, in accordance with one or more aspects of the present disclosure. For purposes of illustration only, <FIG> is described below within the context of computing device <NUM> of <FIG>.

In operation, computing device <NUM> receives a representation of an utterance spoken at computing device <NUM> (<NUM>). For example, one or more microphones of UID <NUM> of computing device <NUM> may generate audio data that represents a user of computing device <NUM> saying "ask Food Agent what I can substitute for baking powder. " UID <NUM> may provide the audio data to the assistant provided by local assistant module 122A and/or remote assistant module 122B of assistant server system <NUM>.

The assistant selects, based on the utterance, an agent from a plurality of agents (<NUM>). For instance, the assistant may utilize the techniques of <FIG> to select an agent to satisfy the utterance. In the example where the utterance is "ask Food Agent what I can substitute for baking powder" and "Food Agent" is a third party agent, the assistant may select the Food Agent to satisfy the utterance.

The selected agent may respond to the utterance by causing computing device <NUM> to output synthesized audio data. For instance, the selected agent may provide text on-which computing device <NUM> may perform text-to-speech (TTS) to generate synthesized audio data. However, as opposed to having synthesized audio data generated for all agents using the same voice, it may be desirable for different agents to use different voices. Additionally, it may be desirable for a user to be able to discern whether they are interacting with a first party agent or a third party agent.

In accordance with one or more techniques of this disclosure, first party agents may output synthesized audio data using a reserved voice of a plurality of voices whereas third party agents to output synthesized audio data using voices of the plurality of voices other than the reserved voice. As such, the techniques of this disclosure enable a first 3P agent may output synthesized audio data using a different voice than a second 3P agent while still providing users an indication of when they are interacting with a 1P agent (i.e., synthesized audio data using the reserved voice). Therefore, further information may be encoded in the audio data when output to the user. The further information may relate to the agent with which the user is interacting which may be encoded by way of the voice used for the output. An example of these voice selection techniques is illustrated in <FIG> and described below.

The assistant determines whether the selected agent is a first party agent (<NUM>). In the example where the utterance is "ask Food Agent what I can substitute for baking powder" and the Food Agent is selected to satisfy the utterance, the assistant may determine that the selected agent is not a first party agent.

If the selected agent is a first party agent ("Yes" branch of <NUM>), the selected agent (e.g., the assistant or another 1P agent) selects a reserved voice from a plurality of voices (<NUM>) and output, using the selected voice, synthesized audio data (<NUM>). For instance, where the utterance is "set my downstairs thermostat to <NUM>" and the selected agent is a 1P agent, the 1P agent may cause computing device <NUM> to output, using the reserved voice, synthesized audio data saying "setting your downstairs thermostat to seventy-one degrees.

If the selected agent is not a first party agent ("No" branch of <NUM>), the selected agent uses a non-reserved voice from the plurality of voices for the third-party agent (<NUM>) and outputs, using the selected voice, synthesized audio data (<NUM>). For instance, where the utterance is "ask Food Agent what I can substitute for baking powder" and the selected agent is the Food Agent 3P agent, the Food Agent may cause computing device <NUM> to output, using a voice from the plurality of voices other than the reserved voice, synthesized audio data saying "you can substitute one-quarter teaspoon baking soda plus five-eighths teaspoon cream of tartar for one teaspoon.

In some examples, the utterance may be satisfied by outputting synthesized audio data to read a list. For instance, where a task identified based on the utterance is a search, satisfaction of the utterance may include outputting synthesized audio data to read a list of search results. In some examples, a single agent may read all of the elements of a list using a single voice. For instance, the first party agent may read a full list of search results using the reserved voice. In some examples, a single agent use different voices when reading different sub-sets of elements of a list. For instance, the first party agent may use a non-reserved voice when outputting synthesized audio data that represents a first sub-set of search results and use the reserved voice when outputting synthesized audio data that represents a second sub-set of search results. In some examples, multiple agents may read different portions of a list using different voices. For instance, a first agent may use a first voice when outputting synthesized audio data that represents a first sub-set of search results and a second agent use a second voice when outputting synthesized audio data that represents a second sub-set of search results. An adaptive interface is therefore provided, where the output of the data is adapted based upon the data itself.

As discussed above, the assistant may cause an agent to perform a task (or at least some elements of a task). In some examples, the assistant may cause a selected agent to perform a task by invoking the selected agent. For instance, the assistant may send the selected agent (e.g., at an entry point of the selected agent, which may be identified from an agent index) a request to perform the task. In some examples, when a selected agent is caused to perform a task, the selected agent may perform the task locally. For instance, when a 3P agent provided by a local 3P agent module of local 3P agent modules <NUM> of <FIG> is invoked to perform a task, the local 3P agent module of local 3P agent modules <NUM> may execute at processors <NUM> to perform the task. In some examples, when a selected agent is caused to perform a task, the selected agent may perform the task locally. For instance, when a 3P agent provided by 3P agent module <NUM> of <FIG> is invoked to perform a task, 3P agent module <NUM> may execute at processors <NUM> to perform the task. In some examples, when a selected agent is caused to perform a task, the selected agent may perform the task mixed between local and remote environments. For instance, a 3P agent provided by a local 3P agent module of local 3P agent modules <NUM> of <FIG> and/or a corresponding remote 3P agent module of remote 3P agent modules <NUM> is invoked to perform a task, the agent may execute at one or both of processors of computing device <NUM> and processors of a 3P agent server system that includes the corresponding remote 3P agent module of remote 3P agent modules <NUM>.

If implemented in software, the functions may be stored on or transmitted over, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit.

In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and/or software modules.

Claim 1:
A method comprising:
receiving (<NUM>), by a computational assistant executing at one or more processors, a representation of an utterance spoken by a user of a computing device;
selecting (<NUM>), based on the utterance, an agent from a plurality of agents, wherein the plurality of agents includes one or more first-party agents and a plurality of third-party agents, and wherein selecting the agent comprises:
identifying, based on the utterance, a task to be performed, and
ranking the agents based on their capabilities to perform the task, wherein the respective capabilities are calculated based on an agent-quality score of the respective agent;
determining (<NUM>) whether the selected agent is a first-party agent or a third-party agent;
responsive to determining that the selected agent is a third-party agent, selecting (<NUM>) a voice from a plurality of voices for the third-party agent, wherein the selected voice is distinct from a reserved voice, and wherein the reserved voice is associated with the one or more first-party agents;
responsive to determining that the selected agent is a first party agent, selecting (<NUM>) the reserved voice as the selected voice; and
outputting (<NUM>), by one or more speakers of the computing device and using the selected voice, synthesized audio data to satisfy the utterance;
subsequent to outputting the synthesized audio data using the selected voice to satisfy the utterance:
causing to request feedback, by the computational assistant, from the user of the computing device, about how well the selected agent satisfied the utterance;
outputting the request for feedback, by the computational assistant, using one or more of the speakers of the computing device;
in response to outputting the request for feedback, receiving, by the computational assistant, a user response to the request for feedback; and
modifying, by the computational assistant and based on the user response, the agent-quality score of the selected agent.