Google Inc.

Vladimir Vuskovic

Gleb Skobeltsyn

Behshad Behzadi

Marcin M Nowak-Przygodzki

Joseph Lange

Yariv Adan

Martin Baeuml

Alexandru Ovidiu Dovlecel

Melvin Jose Johnson Premkumar

Mihaly Kozsevnyikov

Zineb Ait Bahajji

Stephan Wenger

James Kuczmarski

Zaheed Sabur

Ibrahim Badr

Lucas Mirelmann

Amarnag Subramanya

Adrian Zumbrunnen

Mugurel Ionut Andreica

Nimesh Ranjan

Luna Dai

Nihal Sandeep Balani

Jinna Lei

Mengmeng Niu

Vibhor Jain

Yossi Matias

David Kogan

Dhruv Bakshi

Michael Fink

Richard D Borovoy

Idan Szpektor

Eran Ofek

Asaf Revach

Deborah Cohen

Sharon Stovezky

Hongjie Chai

Amaury Forgeot d'Arc

Christoph Poropatits

Shimon Or Salant

Andrew Callahan

Andrew Richardson

Bradley Nelson

Daisuke Ikeda

Wangqing Yuan

Abraham Lee

Olga Kapralova

Yufei Zhao

Konstantin Shagin

Jonathan Berant

Gökhan Hasan Bakir

Jaroslaw Jerzy Wilkiewicz

Shirley Connie Gaw

Henry Scott Dlhopolsky

Alessio Macrí

Eric P Lundberg

Jeffrey Alan Posnick

Raul Marian Fumica

Alessio Macrì

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.

Subquery generation from a query

A method for dynamically generating training data for a model includes receiving a transcript corresponding to a conversation between a customer and an agent, the transcript comprising a customer input and an agent input. The method includes receiving a logic model including a plurality of responses, each response of the plurality of responses representing a potential reply to the customer input. The method further includes selecting, based on the agent input, a response from the plurality of responses of the logic model. The method includes determining that a similarity score between the selected response and the agent input satisfies a similarity threshold, and, based on determining that the similarity score between the selected response and the agent input satisfies the similarity threshold, training a machine learning model using the customer input and the selected response.

Building bots from raw logs and computing coverage of business logic graph

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. In various implementations, the incorporating may be performed in response to the determining that the automated assistant has responded to all natural language input received from the user during the human-to-computer dialog session.

Proactive incorporation of unsolicited content into human-to-computer dialogs

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.

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.

Adaptive interface in a voice-activated network

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.

Delayed responses by computational assistant

Generating expanded responses that guide continuance of a human-to computer dialog that is facilitated by a client device and that is between at least one user and an automated assistant. The expanded responses are generated by the automated assistant in response to user interface input provided by the user via the client device, and are caused to be rendered to the user via the client device, as a response, by the automated assistant, to the user interface input of the user. An expanded response is generated based on at least one entity of interest determined based on the user interface input, and is generated to incorporate content related to one or more additional entities that are related to the entity of interest, but that are not explicitly referenced by the user interface input.

Generating a composite response to natural language input using a trained generative model and based on responsive content from disparate content agents

Implementations are set forth herein for creating an order of execution for actions that were requested by a user, via a spoken utterance to an automated assistant. The order of execution for the requested actions can be based on how each requested action can, or is predicted to, affect other requested actions. In some implementations, an order of execution for a series of actions can be determined based on an output of a machine learning model, such as a model that has been trained according to supervised learning. A particular order of execution can be selected to mitigate waste of processing, memory, and network resources—at least relative to other possible orders of execution. Using interaction data that characterizes past performances of automated assistants, certain orders of execution can be adapted over time, thereby allowing the automated assistant to learn from past interactions with one or more users.

Orchestrating execution of a series of actions requested to be performed via an automated assistant

Implementations described herein relate to providing suggestions, via a display modality, for completing a spoken utterance for an automated assistant, in order to reduce a frequency and/or a length of time that the user will participate in a current and/or subsequent dialog session with the automated assistant. A user request can be compiled from content of an ongoing spoken utterance and content of any selected suggestion elements. When a currently compiled portion of the user request (from content of a selected suggestion(s) and an incomplete spoken utterance) is capable of being performed via the automated assistant, any actions corresponding to the currently compiled portion of the user request can be performed via the automated assistant. Furthermore, any further content resulting from performance of the actions, along with any discernible context, can be used for providing further suggestions.

Supplementing voice inputs to an automated assistant according to selected suggestions

Techniques described herein relate to facilitating end-to-end multilingual communications with automated assistants. In various implementations, speech recognition output may be generated based on voice input in a first language. A first language intent may be identified based on the speech recognition output and fulfilled in order to generate a first natural language output candidate in the first language. At least part of the speech recognition output may be translated to a second language to generate an at least partial translation, which may then be used to identify a second language intent that is fulfilled to generate a second natural language output candidate in the second language. Scores may be determined for the first and second natural language output candidates, and based on the scores, a natural language output may be selected for presentation.

Facilitating communications with automated assistants in multiple languages

Systems and methods for optimizing videos are disclosed. A method of the present disclosure includes analyzing a video using a plurality of rules to determine one or more optimizations for the video, the one or more optimizations reflecting a subset of properties to improve viewership statistics of the video by automatically modifying characteristics of the video based on the subset of properties that is indicative of improved viewership statistics pertaining to a plurality of users of a media hosting service, wherein the characteristics comprise at least one characteristic, which when automatically modified, results in a change to the video that correlates to improved viewership statistics pertaining to the plurality of users. The method further includes causing the characteristics of the video to be modified to implement the one or more optimizations, and causing the video with the modified characteristics to be presented to at least a subset of the plurality of users.

System and method for optimizing videos

Facilitating end-to-end communications with automated assistants in multiple languages

Growing community of inventors

Zollikerberg, Switzerland

Vladimir Vuskovic