Patent Description:
As mentioned above, many automated assistants are configured to be interacted with via spoken utterances, such as an invocation indication followed by a spoken query. To preserve user privacy and/or to conserve resources, a user must often explicitly invoke an automated assistant before the automated assistant will fully process a spoken utterance. The explicit invocation of an automated assistant typically occurs in response to certain user interface input being received at a client device. The client device includes an assistant interface that provides, to a user of the client device, an interface for interfacing with the automated assistant (e.g., receives spoken and/or typed input from the user, and provides audible and/or graphical responses), and that interfaces with one or more additional components that implement the automated assistant (e.g., remote server device(s) that process user inputs and generate appropriate responses).

Some user interface inputs that can invoke an automated assistant via a client device include a hardware and/or virtual button at the client device for invoking the automated assistant (e.g., a tap of a hardware button, a selection of a graphical interface element displayed by the client device). Many automated assistants can additionally or alternatively be invoked in response to one or more spoken invocation phrases, which are also known as "hot words/phrases" or "trigger words/phrases". For example, a spoken invocation phrase such as "Hey Assistant," "OK Assistant", and/or "Assistant" can be spoken to invoke an automated assistant. Also, for example, an assistant may be invoked based on one or more gestures of the user, such as pressing a button on a device and/or motioning in a particular manner such that the motion can be captured by a camera of a device.

Often, a client device that includes an assistant interface includes one or more locally stored models that the client device utilizes to monitor for an occurrence of a spoken invocation phrase. Such a client device can locally process received audio data utilizing the locally stored model, and discards any audio data that does not include the spoken invocation phrase. However, when local processing of received audio data indicates an occurrence of a spoken invocation phrase, the client device will then cause that audio data and/or following audio data to be further processed by the automated assistant. For instance, if a spoken invocation phrase is "Hey, Assistant", and a user speaks "Hey, Assistant, what time is it", audio data corresponding to "what time is it" can be processed by an automated assistant based on detection of "Hey, Assistant", and utilized to provide an automated assistant response of the current time. If, on the other hand, the user simply speaks "what time is it" (without first speaking an invocation phrase or providing alternate invocation input), no response from the automated assistant will be provided as a result of "what time is it" not being preceded by an invocation phrase (or other invocation input). Example of prior art techniques for selectively invoking automated assistants are known from the patent applications <CIT>, <CIT>, <CIT>, <CIT>, <CIT> and <CIT>.

The invention provides a method according to claim <NUM>, a system according to claim <NUM> and a computer-readable storage medium according to claim <NUM>. Further preferable aspects are set forth in the dependent claims. Techniques are described herein for selecting, from multiple candidate automated assistants, a particular automated assistant to invoke for processing a request when an invocation is received that is capable of selectively invoking any of the multiple candidate automated assistants. Put another way, according to techniques disclosed herein in some situations the invocation, when received, can result in invocation of the particular automated assistant exclusively, while in other situations the invocation, when received, can result in the invocation of an alternate automated assistant, of the multiple candidate automated assistants, exclusively. For example, various techniques are directed to utilizing a general automated assistant, which can receive an invocation from a user, to determine which of a plurality of secondary automated assistants to invoke based on features of the invocation and/or based on features of audio data that is provided with the invocation. For instance, the user can utter a general invocation phrase, such as "OK Assistant", that is capable of invoking both a first automated assistant and a second automated assistant. The general automated assistant can determine, based on one or more prosodic features of the invocation, features of a query provided by the user, to determine whether to invoke and subsequently provide the query to the first automated assistant in lieu of invoking and providing the query to the second automated assistant.

As an example, the user may have a device that has multiple automated assistants installed on the client device (e.g., at least client applications for the multiple automated assistants). Both a first automated assistant and a second automated assistant executing on the device are capable of being invoked with the invocation phrase "OK Assistant. " The invocation input of "OK Assistant" can be processed to determine, based on one or more features of the invocation input (including at least prosodic features, optionally further including usage of vocabulary) whether to invoke the first automated assistant or the second automated assistant. Also, for example, one or more other features, other than speech recognition features (e.g., other applications executing by the client device, location of the client device, a classification of the location where the device is located, presence of others in proximity to the client device), can be processed to determine whether to invoke the first automated assistant or the second automated assistant.

As another example, the user may have a client device that has a first instantiation of an automated assistant installed on the device and a second instantiation of the same automated assistant installed on the same device. For example, the user may have a "home" automated assistant that the user utilizes when in a home setting and a "work" automated assistant instantiation that the user utilizes when in a work setting. The invocation input of "OK Assistant" may be capable of invoking both the "home" assistant and the "work" assistant, and the invocation input can be processed to determine, based further on one or more features as described herein, whether to invoke the "home" instantiation or the "work" instantiation. A location can be a geographical location and/or can be a classification of the location of the client device, such as "public" versus "private" location.

Thus, utilization of techniques described herein mitigates occurrences of users inadvertently initially invoking incorrect automated assistant(s) in attempting to invoke an intended automated assistant. By mitigating instances where an unintended automated assistant is invoked inadvertently by the user, utilization of computational and/or network resources is mitigated as the computational and/or network resource(s), that would otherwise be utilized if the unintended automated assistant was invoked, are not utilized. Further, by accommodating a single invocation that can be utilized by multiple automated assistants, the quantity of inputs and/or duration of input(s) from a user to invoke a particular automated assistant can be reduced, thereby reducing processing time and memory resources. Thus, a user can utilize a single invocation to selectively invoke one of a plurality of automated assistants without requiring additional input from the user specifying which of the plurality of automated assistants the user is intending to invoke. Yet further, by accommodating a single invocation that can be utilized by multiple automated assistants, the need to load and/or utilize multiple invocation models, each being used in monitoring for invocation(s) for a respective automated assistant, can be reduced or eliminated. For example, having a first hot word detection model for a first automated assistant and a second hotword detection model for a second automated assistant continuously loaded and continuously being utilized can consume more processor, memory resources, and power than having a general hot word detection model loaded and utilized. Also, for example, having multiple hotword detection models that monitor for multiple phrases can result in higher incidences of false positive detection of hotwords, thus unnecessarily consuming resources. Moreover, digital signal processors (DSPs) of a client device are often utilized to process audio data utilizing hot word detection model(s). DSPs can have constrained memory and/or processor resources, preventing multiple hot word detection models from being loaded and/or executed in parallel (or at least preventing that without also having to forego not executing other process(es) on the DSP).

In some implementations, a first assistant and a second assistant can both be configured to be invoked by the same invocation. In some implementations, the invocation can be a spoken utterance from the user that indicates which automated assistant, of a plurality of automated assistants, the user has interest in providing a spoken query. For example, both the first and second automated assistant can be invoked by the invocation phrase "OK Assistant. " In some implementations, the invocation can be an action from the user. For example, both the first and second automated assistant can be invoked by the user performing a particular gesture, pressing a button on the client device, looking at the client device, and/or one or more other actions that can be captured by one or more cameras of the client device.

In some implementations, the invocation is received and/or detected by a general automated assistant that is not configured to generate responses to a query. For example, the general automated assistant can be a "meta assistant" that is configured to receive invocation input, process the invocation input to determine which of a plurality of secondary automated assistants to invoke based on one or more features, and invoke the intended secondary automated assistant. Thus, the invocation input, and/or additional audio input that precedes and/or follows the invocation input, can be processed before providing the invocation input and/or additional audio data to a secondary automated assistant for further processing.

In some implementations, one or more invocation features of the invocation input can be processed to determine whether to invoke a first automated assistant or a second automated assistant. The invocation features comprise at least one or more prosodic features of spoken invocation input, determined from the audio data detected by one or more microphones of the client device, and are utilized to determine whether to invoke the first automated assistant or the second automated assistant. By processing invocation input to determine prosodic features of the audio input (e.g., tone, speech rate), a first automated assistant can be invoked in lieu of invoking a second automated assistant based on determining that the prosodic features that are detected in the audio data are more likely to be present in invocations of the first automated assistant than in invocations of the second automated assistant.

In some implementations, one or more additional features detected by the client device can be further processed in addition to the invocation input features, to determine whether to invoke the first automated assistant or the second automated assistant. Additional features that are processed to determine whether to invoke the first automated assistant or the second automated assistant can be in addition to any automatic speech recognition features of the invocation input. For example, additional features can include one or more terms that are utilized by the user in a query that precedes or follows the invocation input that are more likely to be utilized when invoking (and/or providing a query) to a "home" automated assistant over invoking (and/or providing a query) to a "work" automated assistant.

In some implementations, the one or more additional features can include one or more applications that are executing (or that have recently been accessed and/or executed) by the client device when the invocation input is detected. For example, a client device may be executing a calendar application that includes calendar information for the "work" profile of a user. When invocation input is detected that can invoke both a "work" automated assistant and a "home" automated assistant, the "work" automated assistant may be invoked based on a likelihood that the user is currently engaged in work activities. Also, for example, a client device may be executing a game, a web browser, and/or other application that would more likely to accessed by the user in a non-work setting, and a non-work profile of an automated assistant may be invoked in lieu of invoking a "work" profile of the automated assistant.

In some implementations, the one or more additional features can include location information related to the location of the client device when the invocation input is detected. In some implementations, a location can be a physical location of the client device, such as the geographic location of the device. In some implementations, the location can be a classification of a location, such as a "public" location versus a "private" location. In some implementations, then location can be a specific area within a geographic location, such as "living room" and "home office. " Location classifications can be set as user preferences by the user and/or can be determined based on past interactions of the user with the client device (e.g., locations where the user commonly utilizes a "work" calendar can be classified as "work" or "private" locations).

In some implementations, one or more additional features can include visual input features that are captured by one or more cameras of the client device when the invocation input is detected. For example, visual input that is received during the invocation input can indicate the presence of others in proximity to the client device. In response, a "public" automated assistant can be invoked in lieu of invoking a "private" automated assistant. Also, for example, visual input may indicate that the user is in a home setting and invoke a "home" automated assistant in lieu of invoking a "work" automated assistant.

In some implementations, the user may be provided with an indication of the automated assistant that was invoked by the invocation input. For example, the user may be provided, via an interface of the client device, with an indication that a "work" automated assistant has been invoked. Also, for example, the user may be provided with an audio indication that a first automated assistant has been invoked in lieu of invoking a second automated assistant. For example, the user may be provided with an audio indication of "Invoking your work assistant" when a "work" automated assistant has been invoked in lieu of invoking a "home" automated assistant. Also, for example, one or more audio prompts and/or responses from an invoked automated assistant can be provided using a different voice profile such that the user can differentiate between multiple automated assistants and/or profiles (e.g., a synthesized male voice for a "home" automated assistant and a synthesized female voice for a "work" automated assistant).

In some implementations, one or more machine learning models can be utilized to process features of the invocation input and/or one or more additional features. Invocation input, audio data received with the invocation input, and/or visual data that is received with the invocation input can be utilized to generate one or more vectors in an embedding space that indicate the location information, prosodic features of the input, and/or other features described herein. The vector can be processed utilizing a machine learning model that can generate, as output, probabilities (and/or other indications) of whether to invoke a first automated assistant in lieu of invoking a second automated assistant.

In some implementations, the user may provide feedback (e.g., additional audio feedback and/or visual feedback) that indicates whether the invoked automated assistant was the intended automated assistant to be invoked. In response to negative feedback, the non-invoked automated assistant can be invoked and one or more of the features that were processed to invoke the first automated assistant can be provided to the machine learning model as training data for further training based on the negative feedback. For example, a user may utter an invocation phrase of "OK Assistant," which may invoke both a first and a second automated assistant. Based on the invocation features and/or additional features, the first automated assistant can be invoked. The user may have instead intended to invoke the second automated assistant, and respond to the invoked automated assistant with "Use the second automated assistant instead. " In response, the second automated assistant can be invoked, and the originally provided invocation input can be utilized to further train the associated machine learning model so that future invocations with the same features are less likely to result in the first automated assistant being invoked.

In some implementations, the invocation input can be processed and any additional audio data that is captured immediately preceding or following the invocation input can be provided to the invoked automated assistant without further processing. For example, the client device may detect audio data that includes an invocation phrase and, based at least on the invocation features, and possibly on additional features, as described herein, invoke the first automated assistant. To minimize latency in processing additional input (e.g., a query that precedes and/or follows the invocation), the audio data that precedes and/or follows the invocation input can be directly provided to the invoked automated assistant. The invoked automated assistant can then process the audio data, such as performing natural language processing, automatic speech recognition, STT, and/or other processing to generate a response and/or perform an action based on the provided input.

In some implementations, the invocation input can be processed and any additional audio data that is captured immediately preceding or following the invocation input can be further processed to determine one or more features before the audio data is provided to the invoked automated assistant. For example, a general automated assistant can receive the invocation input and/or additional audio input that precedes and/or follows the invocation input. Based at least on the invocation features and possibly on additional features, a first automated assistant can be invoked. The general automated assistant can perform, for example, automatic speech recognition, natural language processing, and/or other processing of the invocation input and/or additional input on the input from the user, and detected features and/or processed input can be provided with the audio data (or in lieu of the audio data) to the invoked automated assistant.

Referring to <FIG>, an example environment is provided which includes multiple automated assistants that may be invoked by a user <NUM>. The environment includes a first standalone interactive speaker <NUM> with a microphone (not depicted) and a camera (also not depicted), and a second standalone interactive speaker <NUM> with a microphone (not depicted) and a camera (also not depicted). The first speaker may be executing, at least in part, a first automated assistant that may be invoked with an invocation phrase. The second speaker <NUM> may be executing a second automated assistant that may be invoked with an invocation phrase, either the same invocation phrase as the first automated assistant or a different phrase to allow the user, based on the phrase uttered, to select which automated assistant to invoke. In the example environment, the user <NUM> is speaking a spoken utterance <NUM> of "OK Assistant, What's on my calendar" in proximity to the first speaker <NUM> and the second speaker <NUM>. If one of the first and/or second automated assistants is configured to be invoked by the phrase "OK Assistant," the invoked assistant may process the query that follows the invocation phrase (i.e., "What's on my calendar"). In some implementations, one or both of the automated assistants <NUM> and <NUM> can be capable to be invoked by the user performing one or more actions that can be captured by the cameras of the automated assistants. For example, automated assistant <NUM> can be invoked by the user looking in the direction of automated assistant <NUM>, making a waving motion in the direction of automated assistant <NUM>, and/or one or more other actions that can be captured by the camera of automated assistant <NUM>.

In some implementations, a device, such as first speaker <NUM>, may be executing multiple automated assistants. Referring to <FIG>, an example environment is illustrated that includes multiple client devices executing multiple automated assistants. The system includes a first client device <NUM> that is executing a first automated assistant <NUM> and a second automated assistant <NUM>. Each of the first and second automated assistants may be invoked by uttering an invocation phrase (unique to each assistant or the same phrase to invoke both assistants) proximate to the client device <NUM> such that the audio may be captured by a microphone <NUM> of client device <NUM> and/or performing an action that may be captured by camera <NUM> of client device <NUM>. For example, user <NUM> may invoke the first automated assistant <NUM> by uttering "OK Assistant <NUM>" in proximity to the client device <NUM>, and further invoke the second automated assistant <NUM> by uttering the phrase "OK Assistant <NUM>" in proximity to client device <NUM>. Further, user <NUM> may invoke the first automated assistant <NUM> by performing a first action and invoke the second automated assistant <NUM> by performing a second action. Based on which invocation phrase is uttered and/or which action is performed, the user can indicate which of the multiple assistants that are executing on the client device <NUM> that the user has interest in processing a spoken query. The example environment further includes a second client device <NUM> that is executing a third automated assistant <NUM>. The third automated assistant may be configured to be invoked using a third invocation phrase, such as "OK Assistant <NUM>" such that it may be captured by microphone <NUM>. Further, the third automated assistant <NUM> can be configured to be invoked using a third gesture and/or action that may be captured by camera <NUM> In some implementations, one or more of the automated assistants of <FIG> may be absent. Further, the example environment may include additional automated assistants that are not present in <FIG>. For example, the system may include a third device executing additional automated assistants and/or client device <NUM> and/or client device <NUM> may be executing additional automated assistants and/or fewer automated assistants than illustrated.

In some implementations, one or more automated assistants can be capable of being invoked based on constraints of the devices that are executing the automated assistants. For example, first client device <NUM> may include a camera to capture gestures of the user, whereas second client device <NUM> may include a microphone (and not a camera), thus being capable of only identifying audio invocations. In instances wherein a user performs a gesture, the gesture may be identified by first client device <NUM> and can invoke at least one of the first automated assistant <NUM> and/or second automated assistant <NUM>. In instances wherein a user utters an invocation phrase, only automated assistants on client devices that include a microphone may be invoked. Thus, in instances where both first automated assistant <NUM> and third automated assistant <NUM> are capable of being invoked with the same invocation input, the user can indicate a preference for one of the invocable automated assistants over the other based on the type of invocation input that is detected by one or more of the client devices <NUM> and <NUM>.

Each of the automated assistants <NUM>, <NUM>, and <NUM> can include one or more components of the automated assistants described herein. For example, automated assistant <NUM> may include its own speech capture component to process incoming queries, visual capture component to process incoming visual data, hotword detection engine, and/or other components. In some implementations, automated assistants that are executing on the same device, such as automated assistants <NUM> and <NUM>, can share one or more components that may be utilized by both of the automated assistants. For example, automated assistant <NUM> and automated assistant <NUM> may share an on-device speech recognizer, on-device NLU engine, and/or one or more of the other components.

In some implementations, two or more of the automated assistants may be invoked by the same invocation phrase, such as "OK Assistant," that is not unique to a single automated assistant. When the user utters an invocation phrase and/or provides other invocation input (e.g., a gesture that can invoke two or more of the automated assistants), one or more of the automated assistants may function as a general automated assistant and determine which, of the automated assistants that may be invoked, to invoke based on the invocation input. Referring to <FIG>, a general automated assistant <NUM> is illustrated along with two additional automated assistants <NUM> and <NUM>. The general automated assistant <NUM> may be configured to process invocation input, such as an utterance that includes the phrase "OK Assistant" or other invocation input, which may indicate that the user has interest in providing a query to one of multiple automated assistants that can be invoked by the invocation input. As described herein, the general automated assistant <NUM> may not include all of the functionality of an automated assistant. For example, the general automated assistant <NUM> may not include a query processing engine and/or functionality to perform actions other than processing invocation input to determine which of multiple automated assistants to invoke. In some implementations, the general automated assistant <NUM> may include the functionality of other automated assistants and may determine, for invocation input, whether to invoke itself or invoke a different automated assistant that is configured to be invoked by the same invocation input. For example, both general automated assistant <NUM> and first automated assistant <NUM> may be configured to be invoked in response to detecting a spoken invocation phrase such as "Hey Assistant," "OK Assistant", and/or "Assistant". General automated assistant <NUM> can continuously process (e.g., if not in an "inactive" mode) a stream of audio data frames that are based on output from one or more microphones <NUM> of the client device <NUM>, to monitor for an occurrence of a spoken invocation phrase. While monitoring for the occurrence of the spoken invocation phrase, the general automated assistant <NUM> discards (e.g., after temporary storage in a buffer) any audio data frames that do not include the spoken invocation phrase. However, when the general automated assistant <NUM> detects an occurrence of a spoken invocation phrase in processed audio data frames, the general automated assistant <NUM> can determine whether the invocation input is directed to the general automated assistant <NUM> or directed to one or more other automated assistants <NUM> and <NUM> that can be invoked with the same invocation input.

Automated assistants <NUM> and <NUM> can include multiple components for processing a query, once invoked, for example, a local speech-to-text ("STT") engine (that converts captured audio to text), a local text-to-speech ("TTS") engine (that converts text to speech), a local natural language processor (that determines semantic meaning of audio and/or text converted from audio), and/or other local components. Because the client devices executing automated assistants may be relatively constrained in terms of computing resources (e.g., processor cycles, memory, battery, etc.), the local components may have limited functionality relative to any counterparts that are included in any cloud-based automated assistant components that are executing remotely in conjunction with the automated assistant(s).

In some implementations, one or more of the automated assistants may be invoked by one or more gestures that indicate that the user has interest in interacting with the primary automated assistant. For example, a user may demonstrate intention to invoke an automated assistant by interacting with a device, such as pressing a button or a touchscreen, perform a movement that is visible and may be captured by an image capture device, such as camera, and/or may look at a device such that the image capture device can recognize the user movement and/or positioning. When a user performs a gesture or action, the automated assistant may be invoked and begin capturing audio data that follows the gesture or action, as described above. Further, as described above, multiple automated assistants may be invoked by the same invocation input such that a particular gesture may be a common invocation to more than one automated assistant.

In some implementations, one or more automated assistants <NUM> and <NUM> may share one or more modules, such as a natural language processor and/or the results of a natural language, TTS, and/or STT processor. For example, referring again to <FIG>, both first automated assistant <NUM> and second automated assistant <NUM> may share natural language processing so that, when client device <NUM> receives audio data, the audio data is processed once into text that may then be provided to both automated assistants <NUM> and <NUM>. Also, for example, one or more components of client device <NUM> may process audio data into text and provide the textual representation of the audio data to third automated assistant <NUM>, as further described herein. In some implementations, the audio data may not be processed into text and may instead be provided to one or more of the automated assistants as raw audio data.

In some implementations, a user may utter a query after uttering an invocation phrase, indicating that the user has interest in receiving a response to the query from a primary automated assistant. In some implementations, the user may utter a query before or in the middle of an invocation phrase, such as "What is the weather, Assistant" and/or "What is the weather today, Assistant, and what is the weather tomorrow. " The general automated assistant <NUM> can process the invocation input (e.g., "Assistant") and other captured audio data (e.g., "What is the weather") to determine which automated assistant to invoke based on features further described herein.

Referring to <FIG>, two instantiations of an automated assistant <NUM> is illustrated, each with a different profile for the same user. The user may configure the two instantiations of the automated assistant <NUM> such that both are responsive to the same user voice and are both capable of being invoked with the same invocation phrase. However, depending on which instantiation of the automated assistant <NUM> performs an action, different results may be provided to the user. For example, the user may have a work calendar and a home calendar, each of which operates independently and handles appointments and/or other calendar functionality for particular purposes. When the user has interest in being provided with information from a "work" automated assistant instantiation <NUM>, the user can be provided with information from the "work" profile <NUM> of the user. Similarly, when the user is interacting with the "home" instantiation of the automated assistant <NUM>, the user can be provided with information from the "home" profile <NUM> of the user. In some implementations, both automated assistants <NUM> have the same general invocation input that is capable of generally invoking the automated assistant <NUM> but does not specify between the instantiations. For example, one or both automated assistants <NUM> may be invoked with the invocation input "OK Assistant" without specifying whether the invocation is intended for the instantiation with the home profile <NUM> or work profile <NUM>. Thus, one or both instantiations of the automated assistant <NUM> can be configured to determine which profile to utilize upon detecting a general invocation phrase, in a manner similar to the general automated assistant <NUM> of <FIG>.

Referring to <FIG>, components of a general automated assistant <NUM> are illustrated in which implementations described herein can be implemented. Although described herein for an environment whereby a general automated assistant processes invocation input and determines which automated assistant to invoke, components described with respect to general automated assistant <NUM> may be present in instantiations of automated assistant <NUM> and be utilized to determine whether to selectively invoke automated assistant <NUM> utilizing home profile <NUM> over utilizing automated assistant <NUM> with work profile <NUM>.

Invocation input analysis engine <NUM> is configured to process invocation input to determine one or more invocation features that are utilized to determine which automated assistant to invoke. In some implementations, invocation features can be determined based on general invocation input that is capable of invoking multiple automated assistants. For example, referring to <FIG>, general automated assistant <NUM> can process invocation input of the user uttering an invocation phrase of "OK Assistant" that is capable of invoking both first automated assistant <NUM> and second automated assistant <NUM>. Also, for example, general automated assistant <NUM> can process invocation input of the performing a gesture that is captured by one or more cameras and is capable of invoking both first automated assistant <NUM> and second automated assistant <NUM>.

According to the claimed invention, the one or more invocation features include one or more prosodic features of audio input that includes the invocation input. Prosodic features can include, for example, a tone of the speaker, speech rate, inflection, volume, and/or other features of human speech that can be indications of whether the user intends to invoke one automated assistant in lieu of invoking a second automated assistant. As an example, a user may utilize first automated assistant <NUM> for non-work purposes, and may, when speaking a general invocation phrase, speak in a more relaxed manner (e.g., slower, friendly, louder). Conversely, a user may utilize second automated assistant <NUM> for work purposes, and may, when speaking a general invocation phrase, speak in a more formal manner (e.g., quieter, less inflection, more rapidly). Thus, based on processing the user speaking the invocation phrase, invocation features can be determined that may be utilized by invocation determination engine <NUM> to determine which automated assistant to invoke.

Additional input analysis engine <NUM> can further determine one or more additional features that can be additionally utilized to determine which automated assistant to invoke. In some implementations, additional features can be based on a location that is associated with the client device that is executing the general automated assistant <NUM>. For example, a user may have interest in utilizing a particular automated assistant when at work and a different automated assistant when at home. In instances where both automated assistants are invocable utilizing the same invocation input, the location of the user can be an indication of whether to invoke a first automated assistant (e.g., a work automated assistant) in lieu of invoking a second automated assistant (e.g., a home automated assistant).

In some implementations, a location can be based on a geographic location of the client device that is executing the general automated assistant <NUM>. For example, additional input analysis engine <NUM> can identify a current location of the client device that is executing the automated assistant based on GPS and determine whether the user has previously indicated that the location is a particular classification of location. Also, for example, additional input analysis engine <NUM> can identify a current location of the client device that is executing the automated assistant based on WiFi, signal strength of a wireless communication signal, and/or other indication of a location of the device. In some implementations, one or more locations can be associated with a location type, such as "airport" and/or "restaurant. " In some implementations, one or more locations can be associated with an area within an identified geographic location, such as a room of a house and/or a particular office of an office building.

In some implementations, a location can be based on a classification of the location where the client device that is executing the general automated assistant <NUM> is located. For example, a user may be located in a location that has been tagged as an "airport" location and additional input analysis engine <NUM> can determine that the location is a "public" location based on the type of location. Also, for example, additional input analysis engine <NUM> can determine that the user is at a location that the user has previously indicated is a "home" location, and additional input analysis engine <NUM> can determine that the location is classified as a "private" location.

In some implementations, additional features can be determined based on additional audio data that precedes and/or follows the invocation input. For example, additional features can include prosodic features of the user speaking a query that precedes and/or follows the invocation input. Also, for example, additional input analysis engine <NUM> can determine that, based on word usage, vocabulary selections, and/or other terms that are included in audio data whether the spoken utterance of the user is more closely associated with an intent of the user to invoke a first automated assistant in lieu of invoking a second automated assistant. For example, the user may utilize a more formal vocabulary when uttering a query when intending to utilize a "private" automated assistant and additional input analysis engine <NUM> can process audio input from the user to determine whether the user's vocabulary selection is more "formal" or more "casual.

In some implementations, additional features can be determined based on background and/or other audio data other than the query and/or invocation that was uttered by the user. For example, if audio data that precedes and/or follows the invocation input includes background noise (e.g., other speakers), an additional feature can be determined that indicates that the user is likely in a public location. Also, for example, is audio data that precedes and/or follows the invocation input includes noise from a television and/or radio, an additional feature can be determined that indicates that the user is more likely in a private setting.

In some implementations, additional features can include features that are determined based on visual input that is received proximate to detecting the invocation input. For example, the client device that is executing general automated assistant <NUM> can include a camera that can capture visual input while (or proximate to) the user providing invocation input. Additional input analysis engine <NUM> can determine, based on the visual input, one or more visual input features that can indicate whether the user has interest in accessing one of the invocable automated assistants over another automated assistant.

In some implementations, visual input features can include identifying whether additional users are in proximity of the user when the user provided the invocation input. For example, when the user provides the invocation input, additional input analysis engine <NUM> can determine, based on captured video, whether the user is alone or whether there are additional people in the vicinity of the user. In some implementations, the presence of others may be an indication that the user intends to access a "public" automated assistant in lieu of accessing a "private" automated assistant.

In some implementations, the user may be provided with an indication of the automated assistant that was invoked when the invocation input was received. In some implementations, the indication can be a visual indication, such as an icon and/or message that is displayed on an interface of a client device of the user. In some implementations, the indication can be audible, such as a synthesized voice indicating the name of the invoked automated assistant and/or a sound (e.g., a beep of a particular frequency) that indicates one automated assistant has been invoked in lieu of invoking another automated assistant. In some implementations, the indication can be a variation in a synthesized speech that is provided to the user by the automated assistant. For example, a first automated assistant may have a synthesized male voice when invoked and a second automated assistant may have a synthesized female voice when invoked such that the user can determine which automated assistant was invoked when multiple automated assistants are capable of being invoked.

Invocation determination engine <NUM> can determine, based on at least the processed invocation input and possibly on additional input features, whether to invoke a first automated assistant in lieu of invoking a second automated assistant. Invocation determination engine <NUM> can receive the invocation features and/or the additional input features from the invocation input analysis engine <NUM> and the additional input analysis engine <NUM>, and determine, based on the features, whether to invoke a first automated assistant over invoking a second automated assistant. In some implementations, invocation determination engine <NUM> can utilize one or more machine learning models to determine which automated assistant to invoke. For example, invocation determination engine <NUM> can provide a machine learning model with one or more vectors representing invocation and additional features in an embedding space. The machine learning model can provide, as output, probabilities that a first automated assistant is to be invoked and that a second automated assistant is to be invoked.

In some implementations, once an automated assistant has been invoked, additional audio data and/or other data can be provided to the invoked automated assistant. For example, once invoked, general automated assistant <NUM> can provide a spoken utterance of the user that precedes and/or follows the invocation input. In some implementations, the general automated assistant <NUM> can communicate with the invoked automated assistant via one or more communication protocols, such as API <NUM>. Also, for example, general automated assistant <NUM> can communicate via a speaker that is received by the invoked automated assistant at a microphone (e.g., an ultrasonic signal that includes audio data).

In some implementations, general automated assistant <NUM> can provide audio data that includes the user speaking an utterance. For example, once general automated assistant <NUM> has determined that a first automated assistant is to be invoked in lieu of invoking a second automated assistant, audio data of the user uttering a query can be directly provided to the invoked automated assistant. In some implementations, general automated assistant <NUM> can process audio data that includes a spoken utterance of the user prior to providing the audio data and/or additional data to the invoked automated assistant. For example, general automated assistant <NUM> can process at least a portion of the audio data utilizing STT, natural language processing, and/or automatic speech recognition. The general automated assistant <NUM> can provide, in addition to or in lieu of the audio data, the processed information to further reduce latency in the invoked automated assistant generating a response for the user.

In some implementations, the user can provide feedback once an automated assistant has been invoked. For example, based on features described herein, general automated assistant <NUM> may determine that a first automated assistant is to be invoked in lieu of invoking a second automated assistant. The first automated assistant can then be invoked and provided with a spoken query of the user. Further, the user may be provided with an indication that the first automated assistant was invoked. In response, the user may provide a spoken utterance of "No, I was talking to Assistant <NUM>," "I was speaking to the other Assistant," and/or other negative feedback indicating that the incorrect automated assistant was invoked. In response, general automated assistant <NUM> can invoke the intended automated assistant (and/or the next most likely automated assistant to invoke, in instances wherein the user does not specify the intended automated assistant), and provide the intended automated assistant with the spoken query of the user. Further, one or more of the invocation and possibly additional features that were utilized to initially determine to invoke the first automated assistant can be provided, along with a supervised output generated based on the negative feedback, as training data for training a machine learning model that was utilized by invocation determination engine <NUM>. For example, a training example can be generated that includes the feature(s) as input and that includes, as a supervised output, an indication that Assistant <NUM> should be invoked based on those feature(s). The training example can be used in training the machine learning model. In some implementations, positive feedback from the user can additionally or alternatively be utilized to generate training data for training the machine learning model. For example, if Assistant <NUM> is invoked based on processing of feature(s) using the machine learning model, and the user continues to interact with Assistant <NUM> (implicit positive feedback) and/or has explicit positive feedback regarding invoking of Assistant <NUM>, then a training example can be generated that includes the feature(s) and, as supervised output, an indication that Assistant <NUM> should be invoked.

<FIG> depicts a flowchart illustrating an example method <NUM> of selectively determining which automated assistant to invoke. For convenience, the operations of the method <NUM> are described with reference to a system that performs the operations. This system of method <NUM> includes one or more processors and/or other component(s) of a client device. Moreover, while operations of the method <NUM> are shown in a particular order, this is not meant to be limiting. One or more operations may be reordered, omitted, or added.

At step <NUM>, invocation input is detected. In some implementations, the invocation input is at least an audio input from the user. For example, the invocation input can be the user uttering a particular phrase that, when uttered, is capable of invoking both a first and a second automated assistant. In some implementations (not encompassed by the claims), the invocation input can be the user performing one or more actions that are captured by a camera of a device that is executing one or more of the automated assistants. For example, the user may wave in the direction of a client device that is executing both a first and a second instantiation of an automated assistant, both of which are invocable utilizing the same gesture.

At step <NUM>, invocation input is processed to determine one or more invocation input features that are utilized to determine whether to invoke a first automated assistant in lieu of invoking a second automated assistant. Invocation features include at least prosodic features of the user uttering an invocation phrase. For example, a user may speak with a particular tone, speed, and/or inflection when intending to invoke a first automated assistant and speak with a different tone, speed, and/or inflection when intending to invoke a second automated assistant. Also, for example, in instances (not encompassed by the claims) where the invocation input is a gesture that is visible via a camera of a client device that is executing one or more of the automated assistants, visual input features can be identified that can indicate a particular automated assistant that the user has interest in invoking (e.g., the presence of other users). In some implementations, invocation input features can be determined by a component that shares one or more characteristics with invocation input analysis engine <NUM>.

At step <NUM>, additional input can further be processed to determine additional features that can be indications of whether the user has interest in invoking a first automated assistant in lieu of invoking a second automated assistant. Additional features can be determined by a component that shares one or more characteristics with additional input analysis engine <NUM>. Additional features can include, for example, a location and/or classification of a location where the client device of the user is located, visual input indicating the presence of one or more other users when the invocation input was provided, vocabulary and/or terms utilized by the user when providing additional audio (e.g., a query) that precedes and/or follows the invocation input, and/or other features that can indicate an intent of the user to invoke a first automated assistant in lieu of invoking a second automated assistant that is capable of being invoked with the same general invocation input.

At step <NUM>, the output from step <NUM> and <NUM> is processed to determine whether to invoke the first automated assistant or the second automated based on the invocation and possibly additional features. In some implementations, the determination is performed by a component that shares one or more characteristics with invocation determination engine <NUM>. For example, invocation determination engine <NUM> can utilize one or more machine learning models that receive, as input, invocation and additional feature vectors, and provide, as output, probabilities of the user intending to invoke a first and second automated assistant. Based on the output, at step <NUM>, invocation determination engine <NUM> can invoke a first automated assistant or a second automated assistant, in lieu of invoking the other automated assistant. Once invoked, the invoked automated assistant can be provided with a spoken utterance of the user that precedes and/or follows the invocation input.

In some implementations, once the invoked automated assistant has been provided with a spoken query, the automated assistant can generate a response to the query. Based on user feedback, the second automated assistant can be invoked (e.g., in the case that the user indicates that the incorrect automated assistant was invoked). Feedback from the user can be utilized to further train a machine learning model that can be utilized to determine whether to invoke the first and/or second automated assistant.

Storage subsystem <NUM> stores programming and data constructs that provide the functionality of some or all of the modules described herein. For example, the storage subsystem <NUM> may include the logic to perform selected aspects of the method of <FIG>, and/or to implement various components depicted in <FIG>, <FIG>, and <FIG>.

For example, a user is provided with control over whether programs or features collect user information about that particular user or other users relevant to the program or feature. Each user for which personal information is to be collected is presented with one or more options to allow control over the information collection relevant to that user, to provide permission or authorization as to whether the information is collected and as to which portions of the information are to be collected. For example, users can be provided with one or more such control options over a communication network. In addition, certain data may be treated in one or more ways before it is stored or used so that personally identifiable information is removed. As one example, a user's identity may be treated so that no personally identifiable information can be determined. As another example, a user's geographic location may be generalized to a larger region so that the user's particular location cannot be determined.

Claim 1:
A method implemented by one or more processors, the method comprising:
detecting, at a client device, an invocation input that at least selectively invokes a first automated assistant and a second automated assistant;
determining whether the invocation input is directed to the first automated assistant or is directed to the second automated assistant, wherein the determining is based on processing:
one or more invocation features of the invocation input, wherein the one or more invocation features includes one or more prosodic features determined from audio data that includes the invocation input; and
in response to determining that the invocation input is directed to the first automated assistant:
invoking the first automated assistant in lieu of invoking the second automated assistant.