Google Inc.

Murali Krishna Viswanathan

Pravir Kumar Gupta

Ganesh Ramanarayanan

Lev Finkelstein

Tal Cohen

Jun Gong

Adi Mano

Daphne Dembo

Evan B Roseman

Brett Rolston Lider

Gheorghe Postelnicu

Sean Liu

Doug Sherrets

Varun Kacholia

Philo Juang

Palash Nandy

Nicola Muscettola

Richard Tucker

Tim Dasilva

Stanley Charles Ross Wolf

A system and method for externally verifying content popularity served via a content sharing service, including an access analyzer to create a list based on a number of accesses associated with content items served via the content sharing service, an external source interface module to monitor external sources for content items being served via the external sources, a list verifier to determine if the content items from the list match any of the content items from the external sources, and a list generator to create an externally verified list based on the determination from the list verifier.

External verification of content popularity

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for triggering music answer boxes. In one aspect, a method includes receiving a query, obtaining a plurality of search results responsive to the query, the search results being results from a search of web resources on the Internet, and determining from the plurality of search results that the query is a music query. Music data for a song responsive to the query is obtained, where the music data comprises a Uniform Resource Locator (URL) of song content of the song on the Internet. A music answer box is generated for the query, where the music answer box comprises the music data and a link to the URL of the song content, and the music answer box is provided in addition to search results.

Triggering music answer boxes relevant to user search queries

Systems and methods are provided herein relating to real-time duplicate detection of video content. Fingerprints can be generated for an uploaded video. The fingerprints can be used to match the uploaded video to a set of matching videos. The set of matching videos can be filtered based on the type of match, and the quality of the match. A unique cluster-id can be generated for the uploaded video containing an upload time, and that unique cluster-id can then be modified to associate the uploaded video with a cluster-id of potential duplicates. Cluster-ids can then be used in the context of a search to filter results that have identical cluster-ids. The benefits in using real-time duplicate detection can better maximize user experiences in a video sharing service that contains potential duplicates of the same content.

Real-time duplicate detection for uploaded videos

This disclosure relates to video segment identification and organization based on dynamic characterizations. A characteristics component analyzes a video, and determines a set of video characteristics based at least in part on the analysis. The video characteristics include but are not limited to a category, a type, an identity of a person, and/or an identity of an object. A segmenting component segments the video based in part on the set of video characteristics, and an indicator component associates indicators corresponding to respective video characteristics with the corresponding video segments.

Video segment identification and organization based on dynamic characterizations

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Mountain View, CA, United States of America

Murali Krishna Viswanathan