Google Inc.

Jindong Chen

Anna Lynn Patterson

Blaise Hilary Aguera-Arcas

Advay Mengle

Abhinav Kumar Rastogi

Zecheng He

Xiaoxue Zang

Srinivas Kumar Sunkara

Charmaine Cynthia Rose D'Silva

Gabriel Overholt Schubiner

Nevan Holt Wichers

Lijuan Liu

Ying Xu

Marcin M Nowak-Przygodzki

Gabriel Taubman

Ilya Firman

Ankit K Jain

Adam Coimbra

Joseph Lange

James Stout

Wei Chai

Thomas Weedon Hume

Abhanshu Sharma

Gökhan Bakir

Jacek Szmigiel

Kyle Pedersen

Reed Enger

Vishwath Mohan

Yunfan Jin

Gilles Baechler

Generally, the present disclosure is directed to user interface understanding. More particularly, the present disclosure relates to training and utilization of machine-learned models for user interface prediction and/or generation. A machine-learned interface prediction model can be pre-trained using a variety of pre-training tasks for eventual downstream task training and utilization (e.g., interface prediction, interface generation, etc.).

Machine-learned models for user interface prediction, generation, and interaction understanding

Implementations set forth herein relate to an automated assistant that can interact with applications that may not have been pre-configured for interfacing with the automated assistant. The automated assistant can identify content of an application interface of the application to determine synonymous terms that a user may speak when commanding the automated assistant to perform certain tasks. Speech processing operations employed by the automated assistant can be biased towards these synonymous terms when the user is accessing an application interface of the application. In some implementations, the synonymous terms can be identified in a responsive language of the automated assistant when the content of the application interface is being rendered in a different language. This can allow the automated assistant to operate as an interface between the user and certain applications that may not be rendering content in a native language of the user.

Automated assistant control of non-assistant applications via identification of synonymous term and/or speech processing biasing

Methods and apparatus are described herein for classifying user reviews or portions thereof as being related to various entities, and for associating extracted descriptive segments of text contained in those user reviews or portions thereof with entities based on the classifications. In various implementations, one or more categories of observed user interest may be identified based on a corpus of user queries. One or more segments of text related to the one or more categories of observed user interest may be detected in one or more user reviews associated with a product. Based on the detecting, the product may be indexed on the one or more categories of observed user interest in a searchable database. In some implementations, the searchable database may be accessible to one or more remote client devices, and may be searchable by the one or more categories of observed user interest to provide search results to be rendered by the one or more remote client devices.

Analyzing user reviews to determine entity attributes

Methods and apparatus are described herein for classifying user reviews or portions thereof as being related to various entities, and for associating extracted descriptive segments of text contained in those user reviews or portions thereof with entities based on the classifications. In various implementations, one or more portions of a corpus of user reviews may be classified as being related to a first entity or a second entity. One or more descriptive segments of text may be extracted from the one or more classified portions. The one or more extracted descriptive segments of text may be associated with the first or second entity based on classifications of the one or more classified portions.

Systems and methods are disclosed for determining media consumption preferences. A method may include accessing media consumption history associated with a user. The media consumption history may include at least one of media purchase history of the user, media viewing history of the user, and media listening history of the user. A media category preference of the user may be determined, based on the media consumption history. The media category preference may include a popularity indication for each of a plurality of media categories of media items in the media consumption history. Search results provided in response to a search query by the user and/or media recommendations prepared for the user may be scored based on the media category preference. The media may include a video, a movie, a TV show, a book, an audio recording, a music album and/or another type of digital media.

Determining media consumption preferences

Growing community of inventors

Hillsborough, CA, United States of America

Jindong Chen