Abstract: A computer-implemented method for selectively applying audiovisual enhancement functions to an audiovisual communications stream includes transmitting, by a sender computing system, an audiovisual communication stream to a receiver computing system, obtaining, by the sender computing system, one or more receiver perception feedback signals associated with the audiovisual communication stream, the one or more receiver perception feedback signals obtained as output from one or more receiver perception feedback models at the receiver computing system and descriptive of perception of the audiovisual communication stream by a user operating the receiver computing system, and applying, by the sender computing system, one or more audiovisual enhancement functions to the audiovisual communication stream based at least in part on the one or more receiver perception feedback signals.

Abstract: Implementations can identify a given assistant device from among a plurality of assistant devices in an ecosystem, obtain device-specific signal(s) that are generated by the given assistant device, process the device-specific signal(s) to generate candidate semantic label(s) for the given assistant device, select a given semantic label for the given semantic device from among the candidate semantic label(s), and assigning, in a device topology representation of the ecosystem, the given semantic label to the given assistant device. Implementations can optionally receive a spoken utterance that includes a query or command at the assistant device(s), determine a semantic property of the query or command matches the given semantic label to the given assistant device, and cause the given assistant device to satisfy the query or command.

Abstract: Training and/or utilizing an interaction prediction model to generate a predicted interaction value that indicates a likelihood of interaction with a corresponding application on the basis of an electronic communication. The application can be in addition to any electronic communication application that is utilized in formulating the electronic communication and/or that is utilized in rendering the electronic communication. The predicted interaction value can be generated based on processing, utilizing the interaction prediction model, of features of the electronic communication and/or of other features. The predicted interaction value can be utilized to determine whether to perform further action(s) that interact with, and/or enable efficient interaction with, the application on the basis of the electronic communication.

Abstract: Implementations described herein relate to receiving user input directed to an automated assistant, processing the user input to determine whether data from a server and/or third-party application is needed to perform certain fulfillment of an assistant command included in the user input, and generating a prompt that requests a user consent to transmitting of a request to the server and/or the third-party application to obtain the data needed to perform the certain fulfillment. In implementations where the user consents, the data can be obtained and utilized to perform the certain fulfillment. In implementations where the user does not consent, client data can be generated locally at a client device and utilized to perform alternate fulfillment of the assistant command. In various implementations, the request transmitted to the server and/or third-party application can be modified based on ambient noise captured when the user input is received.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for limiting provision and display of redundant digital components on a client. Methods can include storing, by a client device, a list of digital components specifying digital components available for provision to the client device. For a first digital component received within a first application, the client device detects a set of signals specifying a first user interaction with the first digital component and a second user interaction with content provided in response to the first user interaction. Based on whether an affirmative user action was performed, the client device can modify the list of digital components. When a request to access a content page within a second application is received, the client device can receive the second digital component, which can be selected from among digital components included on the modified list of digital components.

Abstract: A method includes receiving an automated speech recognition (ASR) request from a user device that includes a speech input captured by the user device and content metadata associated with the speech input. The content metadata is generated by the user device. The method also includes determining a priority score for the ASR request based on the content metadata associated with the speech input and caching the ASR request in a pre-processing backlog of pending ASR requests each having a corresponding priority score. The pending ASR requests in the pre-processing backlog are ranked in order of the priority scores. The method also includes providing, from the pre-processing backlog, one or more of the pending ASR requests to a backend-side ASR module, wherein pending ASR requests associated with higher priority scores are processed before pending ASR requests associated with lower priority scores.

Abstract: A method includes obtaining proximity information for each of a plurality of assistant-enabled devices within an environment of a user device. Each assistant-enabled device is controllable by an assistant application to perform a respective set of available actions associated with the assistant-enabled device. For each assistant-enabled device, the method also includes determining a proximity score based on the proximity information indicating a proximity estimation of the corresponding assistant-enabled device relative to the user device. The method further includes generating, using the proximity scores determined for the assistant-enabled devices, a ranked list of candidate assistant-enabled devices, and for each corresponding assistant-enabled device in the ranked list, displaying, in a graphical user interface (GUI), a respective set of controls for performing the respective set of actions associated with the corresponding assistant-enabled device.

Abstract: Implementations set forth herein relate to an automated assistant that can control a camera according to one or more conditions specified by a user. A condition can be satisfied when, for example, the automated assistant detects a particular environment feature is apparent. In this way, the user can rely on the automated assistant to identify and capture certain moments without necessarily requiring the user to constantly monitor a viewing window of the camera. In some implementations, a condition for the automated assistant to capture media data can be based on application data and/or other contextual data that is associated with the automated assistant. For instance, a relationship between content in a camera viewing window and other content of an application interface can be a condition upon which the automated assistant captures certain media data using a camera.

Abstract: Techniques are described herein for multi-factor audio watermarking. A method includes: receiving audio data; processing the audio data to generate predicted output that indicates a probability of one or more hotwords being present in the audio data; determining that the predicted output satisfies a threshold that is indicative of the one or more hotwords being present in the audio data; in response to determining that the predicted output satisfies the threshold, processing the audio data using automatic speech recognition to generate a speech transcription feature; detecting a watermark that is embedded in the audio data; and in response to detecting the watermark: determining that the speech transcription feature corresponds to one of a plurality of stored speech transcription features; and in response to determining that the speech transcription feature corresponds to one of the plurality of stored speech transcription features, suppressing processing of a query included in the audio data.

Abstract: Automated content switching rules may be generated and/or utilized for automatically switching away from certain interactive content during presentation of that interactive content when one or more switch conditions are met. In some instances, automated content switching rules may define one or more non-temporal switch conditions, e.g., based upon reaching certain points or milestones in certain interactive content, that may be used to initiate actions that switch away from the interactive content. In addition, in some instances, automated content switching rules may be used to not only switch away from particular interactive content, but additionally switch to other interactive content, thereby enabling a user to effectively schedule a workflow across different interactive content, applications and/or other computer-related tasks.

Abstract: The technology relates to a navigation system that provides one or more routes from a starting location to a destination location, including a candidate parking location at or near the destination location. The starting location may be the current location of a user or the parking location from a previous navigation session. In some examples, the current location of the user may be the same location as the parking location from the previous navigation session. Each route suggestion may include one or more segments. For example, a route may include a first segment from the location of the user to the candidate parking spot and a second segment may be from the candidate parking location to the destination location. In some examples, there may be a third segment from the user's current location to the parking location from the previous navigation session.

Abstract: A first computing device may implement a method for sharing a navigation session to minimize driver distraction. The method includes requesting access to a shared navigation session synchronized with a navigation session at a second computing device indicative of a navigation route, and the navigation session includes route data. In response to receiving access to the shared navigation session, the first computing device may receive synchronized route data indicative of the navigation route at the second computing device. The method further comprises obtaining one or more first navigation instructions corresponding to the synchronized route data including at least one navigation instruction that augments or is output in a different manner than one or more second navigation instructions output at the second computing device, and outputting the one or more first navigation instructions.

Abstract: Implementations set forth herein relate to an automated assistant that can operate in a transient personalization mode, and/or assist a separate automated assistant with providing output according to a transient personalization mode. The transient personalization mode can allow a guest user of an assistant enabled-device to receive personalized responses from the assistant-enabled device—despite not being signed into the assistant-enabled device. A host automated assistant of the assistant-enabled device can securely communicate with a guest user's automated assistant through a backend process. In this way, input queries from the guest user to the host automated assistant can be personalized according to the guest automated assistant—without the guest user directly engaging with their own personal device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for assigning operations of a computational graph to a plurality of computing devices are disclosed. Data characterizing a computational graph is obtained. Context information for a computational environment in which to perform the operations of the computational graph is received. A model input is generated, which includes at least the context information and the data characterizing the computational graph. The model input is processed using the machine learning model to generate an output defining placement assignments of the operations of the computational graph to the plurality of computing devices. The operations of the computational graph are assigned to the plurality of computing device according to the defined placement assignments.

Abstract: Group actions may be performed on behalf of multiple users based in part on the suitability of the various user devices of the different users to perform such group actions. Different user devices may also be used to generate different query intent determinations for a query such that the query intent determination made by a particular user device may be used to fulfill the query.

Abstract: A method includes a first assistant-enabled device (AED) receiving an assignment instruction assigning a group hotword to a selected group of AEDs that includes the first AED and one or more other AEDs. Each AED is configured to wake-up from a low-power state when the group hotword is detected in streaming audio by at least one of the AEDs. The method also includes receiving audio data that corresponds to an utterance spoken by the user and includes a query that specifies an operation to perform. In response to detecting the group hotword in the audio data, the method also includes triggering the first AED to wake-up from the low-power state and executing a collaboration routine to cause the first AED and each other AED in the selected group of AEDs to collaborate with one another to fulfill performance of the operation specified by the query.

Abstract: Implementations described herein receive audio data that captures a spoken utterance, generate, based on processing the audio data, a recognition that corresponds to the spoken utterance, and determine, based on processing the recognition, that the spoken utterance is ambiguous (i.e., is interpretable as requesting performance of a first particular action exclusively and is also interpretable a second particular action exclusively). In response to determining that the spoken utterance is ambiguous, implementations determine to provide an enhanced clarification prompt that renders output that is in addition to natural language. The enhanced clarification prompt solicits further user interface input for disambiguating between the first particular action and the second particular action.

Abstract: Implementations can detect respective audio data that captures an acoustic event at multiple assistant devices in an ecosystem that includes a plurality of assistant devices, process the respective audio data locally at each of the multiple assistant devices to generate respective measures that are associated with the acoustic event using respective event detection models, process the respective measures to determine whether the detected acoustic event is an actual acoustic event, and cause an action associated with the actional acoustic event to be performed in response to determining that the detected acoustic event is the actual acoustic event. In some implementations, the multiple assistant devices that detected the respective audio data are anticipated to detect the respective audio data that captures the actual acoustic event based on a plurality of historical acoustic events being detected at each of the multiple assistant devices.

Abstract: Methods, systems, and apparatus for receiving, from a user, a request that includes an entity identifier associated with an entity that is referenced by one or more query terms of a search query, determining that the entity is identified in a media consumption database as a media item that has been indicated as consumed by the user or that the entity is associated with a media item that is identified in the media consumption database as a media item that has been indicated as consumed by the user, and based on the determination, providing a response to the request, the response including data indicating that the entity is a media item that has been indicated as consumed by the user or that the entity is associated with a media item that has been indicated as consumed by the user.

Abstract: Implementations relate to an automated assistant that can automate repeatedly performed procedures. The automation can involve communicating with different users, organizations, and/or other automated assistants. The automated assistant, with prior permission from respective user(s), can detect repeated performance of a particular series of manually initiated computational actions. Based on this determination, the automated assistant can determine automated assistant computational action(s) that can be performed by the automated assistant in order to reduce latency in performing a procedure, reduce quantity and/or size of transmissions in performing the procedure, and/or reduce an amount of client device resources required for performing the procedure. Such actions can include communicating with an additional automated assistant that may be associated with another user and/or organization.