Abstract: Implementations are directed to receiving a search query from a user, obtaining environmental signal(s) associated with an environment in which the user is located when the search query is received, processing the environmental signal(s) to generate a privacy measure associated with submission of the search query, obtaining additional environmental signal(s) associated with the environment in which the user is located when user input directed to a search interface is received, processing the additional environmental signal(s) to generate an additional privacy measure associated with the user input, selecting, from a superset of historical search queries of the user, a subset of the historical search queries based on at least the privacy measure and the additional privacy measure, and causing the subset of the historical search queries to be presented to the user in response to receiving the user input directed to the search interface.

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: 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 set forth herein relate to an automated assistant that can bias speech processing towards certain requests according to whether those requests are relevant to content that is being rendered, or is expected to be rendered, at a computing device. In this way, speech processing can be dynamically biased according to features of content that may be rendered by a particular application and/or a particular device. Biasing can be performed during rendering of a portion of content determined to be relevant to a particular request by adjusting a score threshold that is used for determining whether a particular request was received. When the portion of content is no longer being rendered, the threshold can return to a particular value, or be adjusted again according to a subsequent portion of the content.

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: Techniques are described herein for identifying a failed hotword attempt. A method includes: receiving first audio data; processing the first audio data to generate a first predicted output; determining that the first predicted output satisfies a secondary threshold but does not satisfy a primary threshold; receiving second audio data; processing the second audio data to generate a second predicted output; determining that the second predicted output satisfies the secondary threshold but does not satisfy the primary threshold; in response to the first predicted output and the second predicted output satisfying the secondary threshold but not satisfying the primary threshold, and in response to the first spoken utterance and the second spoken utterance satisfying one or more temporal criteria relative to one another, identifying a failed hotword attempt; and in response to identifying the failed hotword attempt, providing a hint that is responsive to the failed hotword attempt.

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: Implementations relate to an automated assistant that can respond to communications received via a third party application and/or other third party communication modality. The automated assistant can determine that the user is participating in multiple different conversations via multiple different third party communication services. In some implementations, conversations can be processed to identify particular features of the conversations. When the automated assistant is invoked to provide input to a conversation, the automated assistant can compare the input to the identified conversation features in order to select the particular conversation that is most relevant to the input. In this way, the automated assistant can assist with any of multiple disparate conversations that are each occurring via a different third party application.

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: Implementations can receive audio data corresponding to a spoken utterance of a user, process the audio data to generate a plurality of speech hypotheses, determine an action to be performed by an automated assistant based on the speech hypotheses, and cause the computing device to render an indication of the action. In response to the computing device rendering the indication, implementations can receive additional audio data corresponding to an additional spoken utterance of the user, process the additional audio data to determine that a portion of the spoken utterance is similar to an additional portion of the additional spoken utterance, supplant the action with an alternate action, and cause the automated assistant to initiate performance of the alternate action. Some implementations can determine whether to render the indication of the action based on a confidence level associated with the action.

Abstract: To identify driving event sounds during navigation, a client device in a vehicle provides a set of navigation directions for traversing from a starting location to a destination location along a route. During navigation to the destination location, the client device identifies audio that includes a driving event sound from within the vehicle or an area surrounding the vehicle. In response to determining that the audio includes the driving event sound, the client device determines whether the driving event sound is artificial. In response to determining that the driving event sound is artificial, the client device presents a notification to the driver indicating that the driving event sound is artificial or masks the driving event sound to prevent the driver from hearing the driving event sound.

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: A method of training a speech model includes receiving, at a voice-enabled device, a fixed set of training utterances where each training utterance in the fixed set of training utterances includes a transcription paired with a speech representation of the corresponding training utterance. The method also includes sampling noisy audio data from an environment of the voice-enabled device. For each training utterance in the fixed set of training utterances, the method further includes augmenting, using the noisy audio data sampled from the environment of the voice-enabled device, the speech representation of the corresponding training utterance to generate noisy audio samples and pairing each of the noisy audio samples with the corresponding transcription of the corresponding training utterance. The method additionally includes training a speech model on the noisy audio samples generated for each speech representation in the fixed set of training utterances.

Abstract: Techniques are described herein for cross-device data synchronization based on simultaneous hotword triggers.

Abstract: While an assistant-enabled device is playing back media content, a method includes receiving a contextual signal from an environment of the assistant-enabled device and executing an event recognition routine to determine whether the received contextual signal is indicative of an event that conflicts with the playback of the media content from the assistant-enabled device. When the event recognition routine determines that the received contextual signal is indicative of the event that conflicts with the playback of the media content, the method also includes adjusting content playback settings of the assistant-enabled device.

Abstract: Techniques are described herein for determining an information gain score for one or more documents of interest to the user and present information from the documents based on the information gain score. An information gain score for a given document is indicative of additional information that is included in the document beyond information contained in documents that were previously viewed by the user. In some implementations, the information gain score may be determined for one or more documents by applying data from the documents across a machine learning model to generate an information gain score. Based on the information gain scores of a set of documents, the documents can be provided to the user in a manner that reflects the likely information gain that can be attained by the user if the user were to view the documents.

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: 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: Implementations are directed to receiving a search query from a user, obtaining environmental signal(s) associated with an environment in which the user is located when the search query is received, processing the environmental signal(s) to generate a privacy measure associated with submission of the search query, obtaining additional environmental signal(s) associated with the environment in which the user is located when user input directed to a search interface is received, processing the additional environmental signal(s) to generate an additional privacy measure associated with the user input, selecting, from a superset of historical search queries of the user, a subset of the historical search queries based on at least the privacy measure and the additional privacy measure, and causing the subset of the historical search queries to be presented to the user in response to receiving the user input directed to the search interface.

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.