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 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: 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: Methods, apparatus, and computer readable media related to receiving textual input of a user during a dialog between the user and an automated assistant (and optionally one or more additional users), and generating responsive reply content based on the textual input and based on user state information. The reply content is provided for inclusion in the dialog. In some implementations, the reply content is provided as a reply, by the automated assistant, to the user's textual input and may optionally be automatically incorporated in the dialog between the user and the automated assistant. In some implementations, the reply content is suggested by the automated assistant for inclusion in the dialog and is only included in the dialog in response to further user interface input.

Abstract: Techniques and a framework are described herein for constructing and/or updating, e.g., on top of a general-purpose knowledge graph, an “event-specific provisional knowledge graph.” In various implementations, live data stream(s) may be analyzed to identify entity(s) associated with a developing event. The entity(s) may form part of a general-purpose knowledge graph that includes entity nodes and edges between the entity nodes. Based on the identified one or more entities, an event-specific provisional knowledge graph may be constructed or updated in association with the developing event. In some implementations, the event-specific provisional knowledge graph may be queried for new information about the developing event. Computing devices may be caused to render, as output, the new information.

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: The present disclosure is directed to a new framework the enables the combination of symbolic programming with machine learning, where the programmer maintains control of the overall architecture of the functional mapping and the ability to inject domain knowledge while allowing their program to evolve by learning from examples. In some instances, the framework provided herein can be referred to as “predictive programming.

Abstract: Implementations set forth herein relate to intervening notifications provided by an application for mitigating computationally wasteful application launching behavior that is exhibited by some users. A state of a module of a target application can be identified by emulating user inputs previously provided by the user to the target application. In this way, the state of the module can be determined without visibly launching the target application. When the state of the module is determined to satisfy criteria for providing a notification to the user, the application can render a notification for the user. The application can provide intervening notifications for a variety of different target applications in order to reduce a frequency at which the user launches and closes applications to check for variations in target application content.

Abstract: Systems and methods enable a computing system to recognize a sequence of repeated actions and offer to automatically repeat any such recognized actions. An example method includes determining a current sequence of user actions is similar to a previous sequence of user actions, determining whether the previous sequence is reproducible and, when reproducible, initiating display of a prompt that requests approval for completing the current sequence based on the previous sequence and, responsive to receiving an indication of approval, completing the previous sequence. Another example method includes determining that a first current sequence of user interactions is complete and is not similar to any saved sequence of user interactions, saving the first current sequence as a previous sequence, identifying a second current sequence as satisfying a similarity threshold with the previous sequence, and initiating display of a prompt that requests approval for saving the previous sequence as a shortcut.

Abstract: Implementations set forth herein relate to an automated assistant that provides a response for certain user queries based on a level of interaction of the user with respect to the automated assistant. Interaction can be characterized by sensor data, which can be processed using one or more trained machine learning models in order to identify parameters for generating a response. In this way, the response can be limited to preserve computational resources and/or ensure that the response is more readily understood given the amount of interaction exhibited by the user. In some instances, a response that embodies information that is supplemental, to an otherwise suitable response, can be provided when a user is exhibiting a particular level of interaction. In other instances, such supplemental information can be withheld when the user is not exhibiting that particular level of interaction, at least in order to preserve computational resources.

Abstract: User interaction may be supported with an audio presentation by an automated assistant, and in particular with the spoken content of such an audio presentation that is presented at particular points within the audio presentation. Analysis of an audio presentation may be performed to identify one or more entities addressed by, mentioned by, or otherwise associated with the audio presentation, and utterance classification may be performed to determine whether an utterance received during playback of the audio presentation is directed to the audio presentation, and in some instances, to a particular entity and/or point of playback in the audio presentation, thereby enabling a suitable response to be generated to the utterance.

Abstract: Implementations set forth herein relate to an automated assistant that can be customized by a user to provide custom assistant responses to certain assistant queries, which may originate from other users. The user can establish certain custom assistant responses by providing an assistant response request to the automated assistant and/or responding to a request from the automated assistant to establish a particular custom assistant response. In some instances, a user can elect to establish a custom assistant response when the user determines or acknowledges that certain common queries are being submitted to the automated assistant—but the automated assistant is unable to resolve the common query. Establishing such custom assistant responses can therefore condense interactions between other users and the automated assistant.

Abstract: The present disclosure provides systems and methods for on-device machine learning. In particular, the present disclosure is directed to an on-device machine learning platform and associated techniques that enable on-device prediction, training, example collection, and/or other machine learning tasks or functionality. The on-device machine learning platform can include a mode controller that allows a user to provide data input indicating whether to operate one or more applications on the device in a first collection mode (e.g., permission mode) for storing training examples or a second collection mode for (e.g., incognito mode) for not storing training examples. The training examples can be generated based on user interaction with the one or more applications and used to personalize one or more machine-learned models used by the application(s) by retraining the models using the user-specific training examples.

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 set forth herein relate to intervening notifications provided by an application for mitigating computationally wasteful application launching behavior that is exhibited by some users. A state of a module of a target application can be identified by emulating user inputs previously provided by the user to the target application. In this way, the state of the module can be determined without visibly launching the target application. When the state of the module is determined to satisfy criteria for providing a notification to the user, the application can render a notification for the user. The application can provide intervening notifications for a variety of different target applications in order to reduce a frequency at which the user launches and closes applications to check for variations in target application content.

Abstract: A dynamic platform in a map application allows users to discover various types of goods at a selected geographic region. A method for utilizing the dynamic platform includes providing, by one or more processors, a map for display on a computing device. The method includes receiving, by the one or more processors, a request for displaying a business content in the map, wherein the business content includes information in relation to physical stores. The method includes displaying, by the one or more processors, the business content at a selected geographic location. The method includes collecting, by the one or more processors, computational data of local market information related to the selected geographic location. The method includes presenting, by the one or more processors, the local market information in the map. The method includes providing a recommendation whether to update the business content based on the local market information.

Abstract: Aspects of the subject technology relate to dynamically adjusting a UI based on the current modality. Layout features of a UI may be determined based on an input modality of a computing device. The arrangement of the UI elements may be determined based on the layout features and respective importance scores of the UI elements. The UI elements arranged based on the arrangement may be provided for display of the computing device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, identify and classify the various video pathways in an interactive video based on the content of these video pathways. A video comprising multiple video segments is obtained from a video library. Each video segment is directly linked to at least one other video segment and the multiple video segments comprise a beginning segment, intermediate segments (including interactive segments), and final segments. Multiple video pathways in the video are identified. For each identified video pathway, classification data is generated and each such video pathway is then stored in the video library. When the video is selected from a particular category of the video library, the video segments of a video pathway that has a classification which is the same as the classification associated with the particular category, is then displayed.

Abstract: An example method includes establishing a single-user login session associated with a first user-account such that the single-user login session has read and/or write access to first user data associated with the first user-account. The method further includes accepting, within the single-user login session, a further login associated with a second user-account to convert the single-user login session to a multi-user login session having read and/or write access to second user data associated with the second user-account in addition to having read and/or write access to the first user data. Computer readable media and computing devices related to the example method are disclosed herein as well.

Abstract: The disclosure provides technology for adaptively creating and adjusting reminder notifications for a first user based on activity of a second user. An example method includes identifying an electronic message transmitted between a first user device and a second user device; accessing data associated with a user of the second device, wherein the data comprises activity state data for the user; determining a notification time for the first device based on the data associated with the user of the second device; and updating a reminder related to the electronic message based on the notification time.