Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, based on content of an existing human-to-computer dialog session between a user and an automated assistant, an entity mentioned by the user or automated assistant may be identified. Fact(s)s related to the entity or to another entity that is related to the entity may be identified based on entity data contained in database(s). For each of the fact(s), a corresponding measure of potential interest to the user may be determined. Unsolicited natural language content may then be generated that includes one or more of the facts selected based on the corresponding measure(s) of potential interest. The automated assistant may then incorporate the unsolicited content into the existing human-to-computer dialog session or a subsequent human-to-computer dialog session.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, based on content of an existing human-to-computer dialog session between a user and an automated assistant, an entity mentioned by the user or automated assistant may be identified. Fact(s)s related to the entity or to another entity that is related to the entity may be identified based on entity data contained in database(s). For each of the fact(s), a corresponding measure of potential interest to the user may be determined. Unsolicited natural language content may then be generated that includes one or more of the facts selected based on the corresponding measure(s) of potential interest. The automated assistant may then incorporate the unsolicited content into the existing human-to-computer dialog session or a subsequent human-to-computer dialog session.

Abstract: The systems and methods of the present disclosure generally relate to a data processing system that can identify and surface alternative requests when presented with ambiguous, unclear, or other requests to which a data processing system may not be able to respond. The data processing system can improve the efficiency of network transmissions to reduce network bandwidth usage and processor utilization by selecting alternative requests that are responsive to the intent of the original request.

Abstract: Systems and methods for optimizing videos are disclosed. A method of the present disclosure includes providing a user interface (UI) that presents a plurality of metrics for analysis of a plurality of videos of an entity, and receiving, via the UI, a user selection of a subset of metrics of the plurality of metrics. The method also includes in response to the user selection of the subset of metrics, processing a video of the plurality of videos using a machine learning algorithm or a plurality of rules to determine one or more optimizations for the video, the one or more optimizations reflecting the subset of metrics to improve viewership statistics of the video. The method further includes modifying the video to produce a modified version of the video that implements the one or more optimizations, and causing the modified version of the video to be presented to viewers to demonstrate results of the one or more optimizations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating subqueries from a query. In one aspect, a method includes obtaining a query, generating a set of two subqueries from the query, where the set includes a first subquery and a second subquery, determining a quality score for the set of two subqueries, determining whether the quality score for the set of two subqueries satisfies a quality threshold, and in response to determining that the quality score for the set of two subqueries satisfies the quality threshold, providing a first response to the first subquery that is responsive to a first operation that receives the first subquery as input and providing a second response to the second subquery that is responsive to a second operation that receives the second subquery as input.

Abstract: A computing device executing an optimizer analyzes a video. The computing device identifies one or more optimizations for the video based on the analysis, the one or more optimizations pertaining to a modification of original contents of the video. The computing device implements the one or more optimizations for the video.

Abstract: Implementations disclose tagging a content item with a knowledge database. A method includes determining, using a processing device, a content item to be tagged, determining, using the processing device, an identification that is associated with the content item, the identification from a knowledge database comprising a collection of structured data corresponding to topics, tagging, using the processing device, the content item with the identification from the knowledge database, and storing, using the processing device, the content item and the identification corresponding to the tagged content item in a tag database.

Abstract: Techniques are described herein for automated assistants that search various alternative corpora for information. In various implementations, a method may include receiving, by an automated assistant via an input component of a first client device, a free form input, wherein the free form input includes a request for specific information; searching a general purpose corpus of online documents to obtain a first set of candidate response(s) to the request for specific information; searching a user-specific corpus of active document(s) to obtain a second set of candidate response(s) to the request for specific information; comparing the first and second sets of candidate responses; based on the comparing, selecting a given response to the request for specific information from the first or second set; and providing, by the automated assistant, output indicative of the given response.

Abstract: Techniques are described herein for automated assistants that proactively provide content to participant(s) of multi-participant message exchange threads (e.g., group chats, audio and/or video calls in which oral messages are transcribed for analysis, etc.) based on signals such as individual participant profiles associated with participant(s). In various implementations, automated assistant(s) that may not be explicitly invoked may analyze content of a message exchange thread involving multiple human participants and/or document(s) associated with the message exchange thread. Based on the analyzing, the automated assistant(s) may identify topic(s) pertinent to the message exchange thread. Based on individual participant profiles associated with the participants, the automated assistant(s) may identify shared interest(s) of the participants.

Abstract: An example method includes receiving, by a computational assistant executing at one or more processors, a representation of an utterance spoken at a computing device; identifying, based on the utterance, a task to be performed by the computational assistant; responsive to determining, by the computational assistant, that complete performance of the task will take more than a threshold amount of time, outputting, for playback by one or more speakers operably connected to the computing device, synthesized voice data that informs a user of the computing device that complete performance of the task will not be immediate; and performing, by the computational assistant, the task.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, based on content of an existing human-to-computer dialog session between a user and an automated assistant, an entity mentioned by the user or automated assistant may be identified. Fact(s)s related to the entity or to another entity that is related to the entity may be identified based on entity data contained in database(s). For each of the fact(s), a corresponding measure of potential interest to the user may be determined. Unsolicited natural language content may then be generated that includes one or more of the facts selected based on the corresponding measure(s) of potential interest. The automated assistant may then incorporate the unsolicited content into the existing human-to-computer dialog session or a subsequent human-to-computer dialog session.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, in an existing human-to-computer dialog session between a user and an automated assistant, it may be determined that the automated assistant has responded to all natural language input received from the user. Based on characteristic(s) of the user, information of potential interest to the user or action(s) of potential interest to the user may be identified. Unsolicited content indicative of the information of potential interest to the user or the action(s) may be generated and incorporated by the automated assistant into the existing human-to-computer dialog session.

Abstract: Methods, apparatus, and computer readable media are described related to automated assistants that proactively incorporate, into human-to-computer dialog sessions, unsolicited content of potential interest to a user. In various implementations, based on content of an existing human-to-computer dialog session between a user and an automated assistant, an entity mentioned by the user or automated assistant may be identified. Fact(s)s related to the entity or to another entity that is related to the entity may be identified based on entity data contained in database(s). For each of the fact(s), a corresponding measure of potential interest to the user may be determined. Unsolicited natural language content may then be generated that includes one or more of the facts selected based on the corresponding measure(s) of potential interest. The automated assistant may then incorporate the unsolicited content into the existing human-to-computer dialog session or a subsequent human-to-computer dialog session.

Abstract: A user device's browser receives a page from a publishing system and presents content of the page within a window. Instructions from a media hosting system cause a frame to be created within the window. The frame includes a media upload object from a media hosting system that has a different address domain that the publishing system. A communication channel is established between the window and the frame to allow messages to be exchanged. The media upload object allows a user to create a media item and upload it to the media hosting system. Throughout the process of creating and uploading the media item, the media upload object transmits one or more messages to the page via the communication channel. Based on the messages, the page responds to what is occurring within the frame.

Abstract: A computing device executing an optimizer analyzes a video. The computing device identifies one or more optimizations for the video based on the analysis, the one or more optimizations pertaining to a modification of original contents of the video. The computing device implements the one or more optimizations for the video.

Abstract: A system and method for searching media storage with a knowledge database is provided. The system includes a search retrieving unit to retrieve a search request, an association retrieval unit to retrieve an association between a content item from the media storage and an identification from the knowledge database based on the search request, and an output unit to output data generated by the association retrieval unit. A method tagging a content item with a knowledge database includes determining the content item to be tagged, tagging the content item with an identification from the knowledge database, and storing the content item and the tagged identification in a tag database.

Abstract: A computing device executing an optimizer analyzes a video. The computing device identifies one or more optimizations for the video based on the analysis. The computing device suggests the one or more optimizations to an entity associated with the video. In response to the entity accepting the one or more optimizations, the computing device implements the one or more optimizations for the video.

Abstract: A user device's browser receives a page from a publishing system and presents content of the page within a window. Instructions from a media hosting system cause a frame to be created within the window. The frame includes a media upload object from a media hosting system that has a different address domain that the publishing system. A communication channel is established between the window and the frame to allow messages to be exchanged. The media upload object allows a user to create a media item and upload it to the media hosting system. Throughout the process of creating and uploading the media item, the media upload object transmits one or more messages to the page via the communication channel. Based on the messages, the page responds to what is occurring within the frame.

Abstract: A computing device executing an optimizer analyzes a video. The computing device identifies one or more optimizations for the video based on the analysis. The computing device suggests the one or more optimizations to an entity associated with the video. In response to the entity accepting the one or more optimizations, the computing device implements the one or more optimizations for the video.