Google Inc.

Randolph Gregory Brown

Sylvain Gelly

Oskar Sandberg

Olivier Jean Andre Bousquet

Amit Behal

Akash Nanavati

Bharat Kalyanpur

Andrew William Hogue

John R Provine

Vinicius J Fortuna

Benjamin J Mann

Yew Jin Lim

James A Kunz

David Muquing Hou

Tao Feng

Yi Li

Nikita Kirnosov

Beidou Wang

Kyle Zaragoza

Methods, systems, and apparatus for generating user interface elements for an entity in response to a search query. The method includes receiving a request that includes the entity and one or more context terms. In response to determining to provide the search results page including knowledge elements, the method identifies knowledge elements for the entity that include facts relating to the entity and identifies content in at least one knowledge element of the knowledge elements for the entity that relates to data identifying the entity and the one or more context terms. The method generates the user interface elements for at least some of the knowledge elements, including generating a user interface element for the knowledge element that includes a change of appearance of the content. The user interface elements are provided in the search results page in response to the request.

Contextualizing knowledge panels

Methods, systems, and apparatus for receiving a request that includes an entity identifier of an entity referenced by a search query and one or more context terms that are included in the search query; determining that the one or more context terms describe a relationship connecting the entity referenced by the search query with a plurality of other entities; and in response to determining that the one or more context terms describe the relationship, generating user interface elements that provide facts related to the entity referenced. Generating the user interface may include identifying relationship knowledge elements that include facts relating to at least some of the other entities connected to the entity by the relationship, and identifying one or more additional knowledge for the entity referenced by the search query. The ranking the relationship knowledge elements may be highest ranked knowledge elements in the user interface elements.

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

Methods and apparatus for associating an entity with at least one search query. Some implementations are directed to methods and apparatus for identifying multiple queries associated with an entity and identifying one or more of the queries as an entity search query that provides desired search results for the entity. Some implementations are directed to methods and apparatus for identifying a particular entity and, in response to identifying the particular entity, identifying an entity search query corresponding to the particular entity.

Associating an entity with a search query

Methods, systems, and apparatus for receiving a request that includes an entity identifier of an entity that is referenced by a search query submitted by a user and one or more context terms that are referenced by the search query; identifying a plurality of knowledge elements that are related to the entity; identifying one or more context terms that are associated with the entity that is referenced by the search query; assigning, by one or more computers, rank scores to the plurality of knowledge elements, based at least on identifying the one or more context terms that are associated with the entity that is referenced by the search query; selecting one or more of the knowledge elements from among the knowledge elements based at least on the rank scores assigned to the knowledge elements; and providing, in response to the request, information associated with the entity and the one or more selected knowledge elements.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for query analysis. Queries are identified in query data, and an entity-descriptive portion and a suffix are determined in each query. Query counts are determined for a number of times that the respective queries occur in the query data. Based on the query counts, an entity-level count is estimated, which represents a number of query submissions that include the particular suffix and are considered to refer to a first entity. The entity is determined to be a particular type of entity. A type-level count is determined, which represents a number of query submissions that include the first suffix and are estimated to refer to entities of the particular type of entity. A score is assigned to the particular suffix based on the entity-level count and the type-level count.

Identifying and ranking attributes of entities

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Sunnyside, NY, United States of America

Randolph Gregory Brown