Abstract: Systems and methods for generating a domain-specific conversational automated assistant. In some examples, a conversational language model is used to generate a target answer and a target action recommendation in response to each of a set of in-domain training questions. In some examples, the conversational language model is further used to generate follow-up questions to one or more of its generated target answers, and to then generate a target answer and target action recommendation to each generated follow-up question. In some examples, the processing system also generates a set of out-of-domain training examples including an out-of-domain question, a predetermined target answer, and a predetermined target action recommendation. The automated assistant may then be trained to predict the generated target answers and target action recommendations based on the associated training question or generated follow-up question, as well as any prior questions and answers in the conversation.

Abstract: A computing system receives requests from client devices to process voice queries that have been detected in local environments of the client devices. The system identifies that a value that is based on a number of requests to process voice queries received by the system during a specified time interval satisfies one or more criteria. In response, the system triggers analysis of at least some of the requests received during the specified time interval to trigger analysis of at least some received requests to determine a set of requests that each identify a common voice query. The system can generate an electronic fingerprint that indicates a distinctive model of the common voice query. The fingerprint can then be used to detect an illegitimate voice query identified in a request from a client device at a later time.

Abstract: Implementations set forth herein relate to a robotic computing device that can perform certain operations, such as communicating between users in a common space, according to certain preferences of the users. When interacting with a particular user, the robotic computing device can perform an operation at a preferred location relative to the particular user based on an express or implied preference of that particular user. For instance, certain types of operations can be performed at a first location within a room, and other types of operations can be performed at a second location within the room. When an operation involves following or guiding a user, parameters for driving the robotic computing device can be selected based on preferences of the user and/or a context in which the robotic computing device is interacting with the user (e.g., whether or not the context indicates some amount of urgency).

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: Techniques are described herein for warm word arbitration between automated assistant devices. A method includes: determining that warm word arbitration is to be initiated between a first assistant device and one or more additional assistant devices, including a second assistant device; broadcasting, by the first assistant device, to the one or more additional assistant devices, an active set of warm words for the first assistant device; for each of the one or more additional assistant devices, receiving, from the additional assistant device, an active set of warm words for the additional assistant device; identifying a matching warm word included in the active set of warm words for the first assistant device and included in the active set of warm words for the second assistant device; and enabling or disabling detection of the matching warm word by the first assistant device, in response to identifying the matching warm word.

Abstract: Systems and methods are provided for a personalized entity repository. For example, a computing device comprises a personalized entity repository having fixed sets of entities from an entity repository stored at a server, a processor, and memory storing instructions that cause the computing device to identify fixed sets of entities that are relevant to a user based on context associated with the computing device, rank the fixed sets by relevancy, and update the personalized entity repository using selected sets determined based on the rank and on set usage parameters applicable to the user. In another example, a method includes generating fixed sets of entities from an entity repository, including location-based sets and topic-based sets, and providing a subset of the fixed sets to a client, the client requesting the subset based on the client's location and on items identified in content generated for display on the client.

Abstract: In some implementations, a language proficiency of a user of a client device is determined by one or more computers. The one or more computers then determines a text segment for output by a text-to-speech module based on the determined language proficiency of the user. After determining the text segment for output, the one or more computers generates audio data including a synthesized utterance of the text segment. The audio data including the synthesized utterance of the text segment is then provided to the client device for output.

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: To present a navigation directions preview, a server device receives a request for navigation directions from a starting location to a destination location and generates a set of navigation directions in response to the request. The set of navigation directions includes a set of route segments for traversing from the starting location to the destination location. The server device selects a subset of the route segments based on characteristics of each route segment in the set of route segments. For each selected route segment, the server device provides a preview of the route segment to be displayed on a client device. The preview of the route segment includes panoramic street level imagery depicting the route segment.

Abstract: A computing device may implement a method for providing route information regarding a completed or ongoing trip by a user without the user having previously initiated a navigation session. The method may include receiving a query regarding a previous or ongoing trip by a user prior to the user initiating a navigation session; determining an origin for the previous or ongoing trip; obtaining route information for the previous or ongoing trip; generating one or more route attributes associated with the query based at least on the origin for the previous or ongoing trip and the route information for the previous or ongoing trip; and providing a response to the query based at least on the one or more route attributes.

Abstract: Implementations set forth herein relate to an automated assistant that can jointly process spoken utterances that can include at least one corrective utterance and one utterance that may have been misinterpreted by the automated assistant. In this way, the automated assistant does not need to exclusively rely on the corrective utterance to determine a request of a user but, rather, can utilize data capturing any prior spoken utterance to more accurately determine the request. The automated assistant can process utterances jointly after determining that they are acoustically similar and/or received within a threshold duration of time. The joint processing can involve generating a single input, or multiple inputs, to characterize the series of utterances and processing the single input, or multiple inputs, with one or more neural network models and/or machine learning models. The automated assistant can then proactively correct any previous responses to misinterpreted utterances.

Abstract: Some implementations relate to performing speech biasing, NLU biasing, and/or other biasing based on historical assistant interaction(s). It can be determined, for one or more given historical interactions of a given user, whether to affect future biasing for (1) the given user account, (2) additional user account(s), and/or (3) the shared assistant device as a whole. Some implementations disclosed herein additionally and/or alternatively relate to: determining, based on utterance(s) of a given user to a shared assistant device, an association of first data and second data; storing the association as accessible to a given user account of the given user; and determining whether to store the association as also accessible by additional user account(s) and/or the shared assistant device.

Abstract: Implementations described herein include detecting a stream of audio data that captures a spoken utterance of the user and that captures ambient noise occurring within a threshold time period of the spoken utterance being spoken by the user. Implementations further include processing a portion of the audio data that includes the ambient noise to determine ambient noise classification(s), processing a portion of the audio data that includes the spoken utterance to generate a transcription, processing both the transcription and the ambient noise classification(s) with a machine learning model to generate a user intent and parameter(s) for the user intent, and performing one or more automated assistant actions based on the user intent and using the parameter(s).

Abstract: Systems and methods for steganographic embedding of metadata in media, and improved generation of synthetic media files. In some examples, a steganography encoder may be trained to encode a media file with data such that it will be more likely to be accurately decoded, and/or less likely to be perceptible to a user or other applications. In some examples, the media file may be a synthetically generated media file, and the data may be some or all of the data used to generate the synthetically generated media file. In some examples, a generative model may be trained to create synthetically generated media files that are more likely to be accurately interpreted by an interpretive model. In some examples, data encoded into a synthetically generated media file may be used to output an indication that the file was synthetically generated.

Abstract: Implementations are directed to biasing speaker authentication on a per-user basis and on a device-by-device basis and/or contextual feature(s) basis. In some of those implementations, in performing speaker authentication based on a spoken utterance, different biasing parameters are determined for each of multiple different registered users of an assistant device at which the spoken utterance was detected. In those implementations, each of the biasing parameters can be used to make it more likely or less likely (in dependence of the biasing parameter) that a corresponding registered user will be verified using the speaker authentication. Through utilization of biasing parameter(s) in performing speaker authentication, accuracy and/or robustness of speaker authentication can be increased.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing tasks. One of the methods includes obtaining a sequence of input tokens, where each token is selected from a vocabulary of tokens that includes text tokens and audio tokens, and wherein the sequence of input tokens includes tokens that describe a task to be performed and data for performing the task; generating a sequence of embeddings by embedding each token in the sequence of input tokens in an embedding space; and processing the sequence of embeddings using a language model neural network to generate a sequence of output tokens for the task, where each token is selected from the vocabulary.

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: Systems and methods for determining whether to combine responses from multiple automated assistants. An automated assistant may be invoked by a user utterance, followed by a query, which is provided to a plurality of automated assistants. A first response is received from a first automated assistant and a second response is received from a second automated assistant. Based on similarity between the responses, a primary automated assistant determines whether to combine the responses into a combined response. Once the combined response has been generated, one or more actions are performed in response to the combined response.

Abstract: A computer-implemented technique can include detecting, by a first computing device, a set of user communications at least one of transmitted to and received by from a second computing device via a first communication mode, identifying a second communication mode that is available for communication between the first and second computing devices, and obtaining an appropriateness score for the first and second communication modes based on a contextual feature of the set of user communications, wherein the contextual feature relates an appropriateness of a particular communication mode for the set of user communications, and wherein each appropriateness score is indicative of a level of the appropriateness of a particular communication mode for the set of user communications. The technique can also include selectively outputting a suggestion to switch from the first communication mode to the second communication mode.

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.