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.

Abstract: Implementations utilize a hybrid use of a smaller LLM and a larger LLM to generate and refine content responsive to a user query/request for content generation. In various implementations, the smaller LLM is utilized to process the user query for content generation, to generate initial content responsive to the user query for content generation. The user query for content generation and the initial content can be utilized to generate a text prompt, where the text prompt can be configured to further include a request for focused edit(s). Such a text prompt can be processed using the larger LLM, to generate focused edit(s) to the initial content that refine the initiated content, so that revised content (with improved accuracy) responsive to the user query for content generation is acquired.

Abstract: A method includes, while a user is wearing stereo headphones in an environment, obtaining, from a target digital assistant, a response to a query issued by the user, and obtaining spatial audio preferences of the user. Based on the spatial audio preferences of the user, the method also includes determining a spatially disposed location within a playback sound-field for the user to perceive as a sound-source of the response to the query. The method further includes rendering output audio signals characterizing the response to the query through the stereo headphones to produce the playback sound-field. Here, the user perceives the response to the query as emanating from the sound-source at the spatially disposed location within the playback sound-field.

Abstract: A method includes detecting multiple users, receiving a first query issued by a first user, the first query including a command for a digital assistant to perform a first action, and enabling a round robin mode to control performance of actions commanded by queries. The method also includes, while performing the first action, receiving audio data corresponding to a second query including a command to perform a second action, performing speaker identification on the audio data, determining that the second query was spoken by the first user, preventing performing the second action, and prompting at least another user to issue a query. The method further includes receiving a third query issued by a second user, the third query including a command for the digital assistant to perform a third action, and when the digital assistant completes performing the first action, executing performance of the third 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: A method using media arbitration includes, while a first assistant-enabled device is performing a first long-standing operation, determining the first assistant-enabled device satisfies a co-presence condition with a second assistant-enabled device, and determining that the second assistant-enabled device is performing a second long-standing operation that conflicts with the first long-standing operation performed by the first assistant-enabled device. Based on determining that the first long-standing operation and the second long-standing operation conflict, the method also includes executing an operation arbitration routine to identify one or more compromise operations for at least one of the first assistant-enabled device or the second assistant-enabled device to perform, and instructing the first assistant-enabled device or the second assistant-enabled device to perform a selected compromise operation among the identified compromise operations.

Abstract: To provide dynamic generation and suggestion of map tiles, a server device receives from a user device a request for map data for a particular geographic region. The server device obtains a set of user contextual data and a set of candidate map tiles associated with the particular geographic region. The server device then selects one or more of the set of candidate map tiles based on the set of user contextual data, and transmits the one or more selected map tile to the user device for display.

Abstract: Implementations are directed to dynamically adapting which assistant on-device model(s) are locally stored at assistant devices of an assistant device group and/or dynamically adapting the assistant processing role(s) of the assistant device(s) of the assistant device group. In some of those implementations, the corresponding on-device model(s) and/or corresponding processing role(s), for each of the assistant devices of the group, is determined based on collectively considering individual processing capabilities of the assistant devices of the group. Implementations are additionally or alternatively directed to cooperatively utilizing assistant devices of a group, and their associated post-adaptation on-device model(s) and/or post-adaptation processing role(s), in cooperatively processing assistant requests that are directed to any one of the assistant devices of the group.

Abstract: Techniques include using a generative model to make changes to content such that the mechanisms used to guide the user into a decision become plain to the user and/or minimizes the perceived urgency. Implementations can operate as part of the browser or as an extension to the browser. Implementations may identify a targeted UI element in browser content (a web page) and use the generative model to modify the targeted UI element before presenting the browser content to the user. In some implementations, the identification of the targeted UI element may be performed by the generative model.

Abstract: Systems and methods for providing dialog data, from an initially invoked automated assistant to a subsequently invoked automated assistant. A first automated assistant may be invoked by a user utterance, followed by a dialog with the user that is processed by the first automated assistant. During the dialog, a request to transfer dialog data to a second automated assistant is received. The request may originate with the user, by the first automated assistant, and/or by the second automated assistant. Once authorized, the first automated assistant provides the previous dialog data to the second automated assistant. The second automated assistant performs one or more actions based on the dialog data.

Abstract: Disclosed implementations relate to structures that support an on-demand navigational corpus. An example method involves receiving a navigation request from a client device pertaining to an intent, determining seed content associated with the navigation request, utilizing a large foundational model to create a web page incorporating the seed content, based on a navigation model, and the intent, and delivering the generated web page for presentation on the client device. The method enables efficient and personalized web page generation based on user intent, enhancing user experience and facilitating dynamic navigation using raw seed content.

Abstract: Training data is obtained. The training data includes (a) route information indicative of a route from a starting location to a destination location, wherein the route comprises a plurality of route segments comprising a first subset of route segments and a second subset of route segments, and (b) route characteristic information descriptive of one or more route characteristics. At least the first subset of route segments and a portion of the route characteristic information associated with the first subset of route segments is processed with a machine-learned semantic routing model to obtain one or more predicted route segments for the second subset of route segments. One or more parameters of the machine-learned semantic routing model are adjusted based on an optimization function that evaluates a difference between the one or more predicted route segments and the second subset of route segments.

Abstract: Implementations set forth herein relate to an automated assistant that can solicit other devices for data that can assist with user authentication. User authentication can be streamlined for certain requests by removing a requirement that all authentication be performed at a single device and/or by a single application. For instance, the automated assistant can rely on data from other devices, which can indicate a degree to which a user is predicted to be present at a location of an assistant-enabled device. The automated assistant can process this data to make a determination regarding whether the user should be authenticated in response to an assistant input and/or pre-emptively before the user provides an assistant input. In some implementations, the automated assistant can perform one or more factors of authentication and utilize the data to verify the user in lieu of performing one or more other factors of authentication.

Abstract: Techniques are described herein for detecting and handling failures in other automated assistants. A method includes: executing a first automated assistant in an inactive state at least in part on a computing device operated by a user; while in the inactive state, determining, by the first automated assistant, that a second automated assistant failed to fulfill a request of the user; in response to determining that the second automated assistant failed to fulfill the request of the user, the first automated assistant processing cached audio data that captures a spoken utterance of the user comprising the request that the second automated assistant failed to fulfill, or features of the cached audio data, to determine a response that fulfills the request of the user; and providing, by the first automated assistant to the user, the response that fulfills the request of the user.

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: An overall endpointing measure can be generated based on an audio-based endpointing measure and (1) an accelerometer-based endpointing measure and/or (2) a gaze-based endpointing measure. The overall endpointing measure can be used in determining whether a candidate endpoint is an actual endpoint. Various implementations include generating the audio-based endpointing measure by processing an audio data stream, capturing a spoken utterance of a user, using an audio model. Various implementations additionally or alternatively include generating the accelerometer-based endpointing measure by processing a stream of accelerometer data using an accelerometer model. Various implementations additionally or alternatively include processing an image data stream using a gaze model to generate the gaze-based endpointing measure.

Abstract: The present disclosure provides a computing device and method for providing personal specific information based on semantic queries. The semantic queries may be input in a natural language form, and may include user specific context, such as by referring to prior or future events related to a place the user is searching for. With the user's authorization, data associated with prior or planned activities of the user may be accessed and information from the accessed data may be identified, wherein the information is correlated with the user specific context. One or more query results are determined based on the identified information and provided for output to the user.

Abstract: Methods, systems, and computer readable media related to generating a combined search query based on search parameters of a current search query of a user and search parameters of one or more previously submitted search quer(ies) of the user that are determined to be of the same line of inquiry as the current search query. Two or more search queries may be determined to share a line of inquiry when it is determined that they are within a threshold level of semantic similarity to one another. Once a shared line of inquiry has been identified and a combined search query generated, users may interact with the search parameters and/or the search results to update the search parameters of the combined search query.

Abstract: A method includes receiving a natural language query specifying an action for an assistant interface to perform and selecting one or more business large language models (LLMs) for the assistant interface to interact with to fulfill performance of the action. For each business LLM, method also includes accessing an adapter module to structure the natural language query into a respective prompt specifically formulated for the corresponding business LLM, issuing, for input to the corresponding business LLM, the respective prompt, and receiving corresponding response content from the corresponding business LLM that conveys details regarding performance of a corresponding portion of the action. The method also includes presenting, for output from the user device, presentation content based on the corresponding response content received from each corresponding business LLM.

Abstract: A computing system can include one or more machine-learned models configured to receive context data that describes one or more entities to be named. In response to receipt of the context data, the machine-learned model(s) can generate output data that describes one or more names for the entity or entities described by the context data. The computing system can be configured to perform operations including inputting the context data into the machine-learned model(s). The operations can include receiving, as an output of the machine-learned model(s), the output data that describes the name(s) for the entity or entities described by the context data. The operations can include storing at least one name described by the output data.