Google Inc.

James A Kunz

Yew Jin Lim

Joseph Garrett Linn

Navneet Panda

Wei Lwun Lu

Dong Hyun Kim

Min Yang

Charles Jordan Gilliland

David Faden

Lauren Koepnick

Carsten Steinebach

Sanjit Jhala

Yuling Liang

Randolph Gregory Brown

David Muquing Hou

Tao Feng

Kyle Zaragoza

Yi Li

Nikita Kirnosov

Beidou Wang

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

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 determining duration performance of websites. In one aspect, a method includes, for each website of a plurality of websites: obtaining duration measurements of user device visits to resources of the website for a plurality of different users, obtaining data describing a plurality of categories to which the website belongs; determining, for each category of the plurality of categories to which the website belongs, a category duration score for the category based on the website duration score for the website; and determining, from one or more of the category duration scores, a duration performance score for the website.

Website duration performance based on category durations

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining duration performance of websites. In one aspect, a method includes, for each of a plurality of websites, obtaining duration measurements of user device session visits to resources of the website, obtaining data describing a plurality of categories to which the website belongs; determining for each of the plurality of categories to which the website belongs, a category duration score based on the duration measurements, each category durations score being proportional to durations of time from the duration measurements; and determining from one or more of the category duration scores, a duration performance score for the website, the duration performance score proportional to the one or more category duration scores from which the duration performance score is determined.

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San Jose, CA, United States of America

James A Kunz