Abstract: Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

Abstract: Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

Abstract: Implementations relate to an automated assistant that is capable of interacting with non-assistant applications that do not have functionality explicitly provided for interfacing with certain automated assistants. Application data, such as annotation data and/or GUI data, associated with a non-assistant application, can be processed to map such data into an embedding space. An assistant input command can then be processed and mapped to the same embedding space, and a distance from the assistant input command embedding and the non-assistant application data embedding can be determined. When the distance between the assistant input command embedding and the non-assistant application data embedding satisfies threshold(s), the automated assistant can generate instruction(s), for the non-assistant application, that correspond to the non-assistant application data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for announcing and detecting automated conversation are disclosed. One of the methods includes initiating, over a natural language communication channel, a conversation with a communication participant using a natural language communication method that includes a dialogue of natural language communications. The communication participant is determined to be automated using a pre-defined adaptive interactive protocol that specifies natural language linguistic transformations defined in a sequence. The conversation can be transitioned to a communication method that is different form the natural language communication method in response to determining that the communication participant is automated.

Abstract: A navigation service determines that a first user intends to navigate to a shared destination from a first location, at a first time, and that a second user intends to navigate to the shared destination from a second location, at a second time within a threshold interval of the first time. The navigation service notifies the first user using an electronic notification that the second user intends to navigate to the shared destination, receives from the first user an electronic request to coordinate navigation to the shared destination with the second user, and in response to receiving the electronic request, provides navigation directions to the shared destination to a device associated with the first user in view of a progress of the second user toward the shared destination.

Abstract: This specification is generally directed to techniques for automatically transitioning applications—especially those that enable exchange of messages between users—into and/or out of a private state based on a variety of signals associated with the messages and/or the participants themselves. In various implementations, an ongoing message exchange thread between two or more participants operating two or more respective message exchange clients may be examined. Based at least in part on the examining, a likelihood may be determined that message(s) directed by one of the participants to another of the participants as part of the ongoing message exchange thread would be deemed private by at least a given participant of the two or more participants. A determination may be made of whether the determined likelihood satisfies one or more thresholds, and in response, one or more of the message exchange clients may be transitioned into a private state.

Abstract: Implementations relate to an automated assistant that is capable of interacting with non-assistant applications that do not have functionality explicitly provided for interfacing with certain automated assistants. Application data, such as annotation data and/or GUI data, associated with a non-assistant application, can be processed to map such data into an embedding space. An assistant input command can then be processed and mapped to the same embedding space, and a distance from the assistant input command embedding and the non-assistant application data embedding can be determined. When the distance between the assistant input command embedding and the non-assistant application data embedding satisfies threshold(s), the automated assistant can generate instruction(s), for the non-assistant application, that correspond to the non-assistant application data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for extending application access across devices. In some implementations, an electronic device receives a request to provide access to the electronic device to a particular user that is not registered as a user of the electronic device. The electronic device receives authentication credentials for the particular user. The electronic device provides the authentication credentials to a server system and receives data from the server system that (i) indicates that the providing access to the electronic device in a guest mode is authorized, and (ii) indicates a state of an instance of an application installed on a second device. The electronic device provides access to the electronic device in the guest mode that provides an interface that at least partially recreates the state of the instance of the application installed on the second device.

Abstract: The present disclosure provides an on-device machine learning platform that enables sharing of machine-learned models between applications on a computing device. For example, a first application which has a machine-learned model for a specific task can expose the model to other applications through a system level application programming interface (API) for the other applications to use. Communications using the API can be handled by the on-device machine learning platform. In some implementations, some exchange of resources (e.g., computing resources) can be provided so that the first application is compensated for sharing the machine-learned model (e.g., on a per model invocation basis).

Abstract: A navigation service determines that a first user intends to navigate to a shared destination from a first location, at a first time, and that a second user intends to navigate to the shared destination from a second location, at a second time within a threshold interval of the first time. The navigation service notifies the first user using an electronic notification that the second user intends to navigate to the shared destination, receives from the first user an electronic request to coordinate navigation to the shared destination with the second user, and in response to receiving the electronic request, provides navigation directions to the shared destination to a device associated with the first user in view of a progress of the second user toward the shared destination.

Abstract: This document describes techniques and systems that enable privacy controls for sharing embeddings for searching and indexing media content. A set of images of a user's face are obtained and a machine-learned model is applied to the set of images to generate a user-specific dataset of face embeddings for the user. Media content stored in a media storage is indexed by applying the machine-learned model to the media content to provide indexed media information identifying one or more faces shown in the media content. Access to the indexed media information by another user querying the media content for images or videos depicting the user is controlled based on a digital key shared by the user with the other user, where the digital key is associated with the user-specific dataset and the user-specific dataset is usable to identify the images or videos depicting the user.

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: Techniques are described herein for assembling/evaluating automated assistant responses for privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.

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: 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: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for collaboration between multiple voice controlled devices are disclosed. In one aspect, a method includes the actions of identifying, by a first computing device, a second computing device that is configured to respond to a particular, predefined hotword; receiving audio data that corresponds to an utterance; receiving a transcription of additional audio data outputted by the second computing device in response to the utterance; based on the transcription of the additional audio data and based on the utterance, generating a transcription that corresponds to a response to the additional audio data; and providing, for output, the transcription that corresponds to the response.

Abstract: Implementations set forth herein relate to generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

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 generating a pre-call analysis for one or more users that are receiving and/or initializing a call with one or more other users, and/or prioritizing pre-call content according to whether security-related value was gleaned from provisioning certain pre-call content. One or more machine learning models can be employed for determining the pre-call content to be cached and/or presented prior to a user accepting a call from another user. Feedback provided before, during, and/or after the call can be used as a basis from which to prioritize certain content and/or sources of content when generating pre-call content for a subsequent call. Other information, such as contextual data (e.g., calendar entries, available peripheral devices, location, etc.) corresponding to the previous call and/or the subsequent call, can also be used as a basis from which to provide a pre-call analysis.

Abstract: Automated assistant responses may be assembled and/or evaluated to address privacy concerns. In various implementations, a free-form natural language input may be received from a first user and may include a request for information pertaining to a second user. Multiple data sources may be identified that are accessible by an automated assistant to retrieve data associated with the second user. The multiple data sources may collectively include sufficient data to formulate a natural language response to the request. Respective privacy scores associated with the multiple data sources may be used to determine an aggregate privacy score associated with responding to the request. The natural language response may then be output at a client device operated by the first user in response to a determination that the aggregate privacy score associated with the natural language response satisfies a privacy criterion established for the second user with respect to the first user.