Google Inc.

Nisarg Dilipkumar Kothari

Nicholas Delmonico Rizzolo

Ullas Gargi

Sanketh Shetty

Sudheendra Vijayanarasimhan

Balakrishnan Varadarajan

Kevin Snow McCurley

Charles DuHadway

Tomás Izo

Techniques identify time-sensitive content and present the time-sensitive content to communication devices of users interested or potentially interested in the time-sensitive content. A content management component analyzes video or audio content, and extracts information from the content and determines whether the content is time-sensitive content, such as recent news-related content, based on analysis of the content and extracted information. The content management component evaluates user-related information and the extracted information, and determines whether a user(s) is likely to be interested in the time-sensitive content based on the evaluation results. The content management component sends a notification to the communication device(s) of the user(s) in response to determining the user(s) is likely to be interested in the time-sensitive content.

Discovery of news-related content

This technology may generate recommendations of relevant content, based on determining intersections among one or more user interest profiles of users, who interact either synchronously or asynchronously. This technology may retrieve interest profiles for particular users, determine intersections among all user interest profiles (or among individual content recommendations), and create a group interest profile, update the particular users' interest profiles based on the group interest profile created, and generate recommendations of content that is determined to be relevant based on the group interest profile. A user may select items from these recommendations of content, add the user-selected content recommendations to a common group pool, generate a group interest profile based on the common group pool, and generate recommendations of content based on the group interest profile. Scores for the recommendations of content may be calculated and the top scoring ones may be displayed to the users in the group.

Recommending content based on intersecting user interest profiles

A method, computer program product, and system is described. A group including a plurality of individuals is defined based upon, at least in part, participation of the plurality of individuals in one or more consumption sessions. A group profile is designated for the group. Consumption of a first item of content by a portion of the group during a consumption session participated in by at least the portion of the group is identified. An indicator associated with the first item of content consumed by the portion of the group during the consumption session is associated with the group profile. A recommendation of a second item of content is provided to one or more members of the group, wherein the recommendation is based upon, at least in part, associating with the group profile the indicator associated with the first item of content.

Group pseudo-profiles for online sessions

A method for assessing sharing of items within a social network is provided. The method includes identifying a first sharing of a social item by a first user of a social network, determining one or more second sharings of the social item by one or more second users, the one or more second sharings being based on the first sharing. The method also includes determining a sharing score associated with the first user based on a number of the one or more second sharings, and updating a data structure based on the determined sharing score associated with the first user. The data structure stores respective sharing scores associated with the plurality of users of the social network. Systems and machine-readable media are also provided.

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Sunnyvale, CA, United States of America

Nisarg Dilipkumar Kothari