Google Inc.

Yew Jin Lim

Joseph Garrett Linn

James A Kunz

David Faden

Lauren Koepnick

Vikram Aggarwal

Adam Goldstein

Sung-jung Cho

Maureen Heymans

Jeffrey Chin

Hui Tan

Dong Hyun Kim

Wei Lwun Lu

Alexandre Kojoukhov

Min Yang

William Zhang

Mario Tanev

Charles Jordan Gilliland

Xingjian Zhang

Ruben Adamyan

Yuling Liang

Sagar Gandhi

Carsten Steinebach

Sanjit Jhala

Jon M Wiley

Jonathan James Effrat

Randolph Gregory Brown

Mark Andrew Paskin

David Muquing Hou

Tao Feng

Kelly Poon

Beidou Wang

Kyle Zaragoza

Yi Li

Nikita Kirnosov

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that identify and issue search queries expected to be issued in the future. A set of search queries that have been issued by multiple user devices can be obtained. For each query instance, contextual data can be obtained. A first query and its contextual data can be input to a model that outputs the query's likelihood of being issued in the future. The model can be trained using contextual data for training queries and a corresponding labels for the training queries. The learning model outputs the first query's likelihood of being issued in future, and this query is stored as a repeatable query if the likelihood satisfying a repeatability threshold. Subsequently, a stored repeatable query is issued upon a selection of a user selectable interface component and the search engine provides search results for the query.

Identification and issuance of repeatable queries

Methods, apparatus, and computer readable media related to soliciting feedback from a user regarding one or more content parameters of a suggestion or other content provided by the automated assistant. The user's feedback may be used to influence future suggestions and/or other content subsequently provided, by the automated assistant in future dialog sessions, to the user and/or to other users. In some implementations, content is provided to a user by an automated assistant in a dialog session between the user and the automated assistant—and the automated assistant provides a prompt that solicits user feedback related to the provided content in a future dialog session between the user and the automated assistant. In some of those implementations, the prompt is provided following input from the user and/or output from the automated assistant, in the future dialog session, that is unrelated to the content provided in the previous dialog session.

Providing prompt in an automated dialog session based on selected content of prior automated dialog session

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for receiving multiple resources, each resource including electronic media content. For each resource, a computing system identifies one or more entities that are associated with the resource. For at least one pair of identified entities, the system determines a quantity of resources in which each entity of the pair is associated. The system determines, based on the quantity of resources, an occurrence of an event that is associated with a particular pair of entities. The system generates a representation that corresponds to the event and based on the resources in which each entity of the pair of entities is associated. The system provides, for output to a user device, the representation that corresponds to the event.

Content selection and presentation of electronic content

Processor(s) of a client device can: analyze one or more features of an electronic resource that is under consideration for solicitation to a user; determine a notification likelihood that the user will access the electronic resource in response to an unsolicited notification of the electronic resource being output to the user; determine a baseline likelihood that the user will access the electronic resource without being solicited; compare the notification likelihood with the baseline likelihood; and cause, based on the comparing, the unsolicited notification to be output to the user. In some implementations, determining the notification likelihood and/or the baseline likelihood is based on applying data associated with the electronic resource as input across a machine learning model to generate output indicative of the notification likelihood and/or the baseline likelihood. In other implementations, determining the notification likelihood and/or the baseline likelihood is based on past behavior or preference(s) of the user.

Modeling lift of metrics for triggering push notifications

A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

Predicting intent of a search for a particular context

Methods, apparatus, and computer readable media related to soliciting feedback from a user regarding one or more content parameters of a suggestion or other content provided by the automated assistant. The user's feedback may be used to influence future suggestions and/or other content subsequently provided, by the automated assistant in future dialog sessions, to the user and/or to other users. In some implementations, content is provided to a user by an automated assistant in a dialog session between the user and the automated assistant&#x2014;and the automated assistant provides a prompt that solicits user feedback related to the provided content in a future dialog session between the user and the automated assistant. In some of those implementations, the prompt is provided following input from the user and/or output from the automated assistant, in the future dialog session, that is unrelated to the content provided in the previous dialog session.

A computing system is described that identifies, based on search histories associated with a group of computing devices for a particular context, a task performed by users of the group of computing devices for the particular context. The computing system determines a first degree of likelihood of the task being performed by the users of the group of computing devices for the particular context and determines a second degree of likelihood of the task being performed by the users of the group of computing devices for a broader context that includes the particular context and at least one other context. Responsive to determining that the first degree of likelihood exceeds the second degree of likelihood by a threshold amount, and that a current context of a particular computing device corresponds to the particular context, the computing system transmits, to the particular computing device, information for completing the task for the particular context.

Predicting user needs for a particular context

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross-language information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of receiving a query in a source language, the query including one or more query terms; automatically determining one or more target languages relevant to the query; translating the query into one or more translated queries in the respective one or more target languages; determining search results responsive to the respective one or more translated queries; and providing one or more of the search results.

Language selection for information retrieval

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for cross-language information retrieval. In general, one aspect of the subject matter described in this specification can be embodied in methods that include the actions of identifying a structured collection of documents, the collection of documents being organized according to a hierarchy of categories; extracting entities from structured collection of document; assigning language scores to each document in the collection of documents; assigning language scores to entities based on scores of associated documents of the collection of documents; and generating a mapping between entities and language scores.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for presenting spelling suggestions. In one aspect, a method includes receiving first search results for a first search query and a plurality of spelling suggestions for a first term in the first search query, wherein the first search results are provided by a search engine as a response to the first search query; displaying the plurality of spelling suggestions and the first search results in a user interface; receiving a user input selecting one of the plurality of spelling suggestions; transmitting data identifying the selected spelling suggestion to request second search results for a second search query comprising the selected spelling suggestion from the search engine; and displaying the second search results in place of the first search results in the user interface.

Displaying multiple spelling suggestions

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Saratoga, CA, United States of America

Yew Jin Lim