Google Inc.

Wideorbit Inc.

Matt Chalawsky

Alexandr Y Smolyanov

Chad Steelberg

Ryan Steelberg

Bin Tian

Bradley David Townsend

Neil Campbell Rhodes

William Irvin

Matthew S Marzilli

Eugen Cristian Nistor

Xiaoli Chen

Dipankar Gupta

Jeffrey W Houston

Bradley D Townsend

Ryan S Steelberg

Chad E Steelberg

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, are described for frequency optimization of advertisement streams. The methods and systems described in this specification may enable determination of an optimal presentation frequency of an ad stream, or a number of times the ad stream is to be broadcast and/or rebroadcast, prior to the audience becoming interested in the ad, or acting on the ad to generate a conversion event.

Frequency optimization of advertisement insertion in media streams

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for characterizing effectiveness of online advertisements inserted into media streams based at least in part on monitoring events indicative of an audience skipping ad streams inserted into the media streams. The methods and systems described in this specification enable tracking the number of impressions prior to detecting events indicative of interest or disinterest for ad streams inserted into a media stream.

Ad skip feature for characterizing advertisement effectiveness

External-signal influence on content item performance is determined. Content item performance data is received that reflects historic performance of a content item for multiple presentations of the content item. Signal data is received that corresponds to at least one signal that is temporally correlated with the content item performance data and that is external to each user, publisher and content provider involved in any of the presentations. Using the content item performance data and the signal data, an influence value for the signal with regard to the content item is determined. A content item prediction model is modified based on the influence value.

External-signal influence on content item performance

Sharing media content among families of broadcast stations includes receiving information relating to media content from families of broadcast stations, storing the information such that the information is accessible to at least two of the families of broadcast stations via a computer network, receiving a query for specific media content from a broadcast station, searching the stored information in accordance with the query to identify the specific media content; and enabling the broadcast station to access to the specific media content if the specific media content is identified among the stored information.

Sharing media content among families of broadcast stations

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Los Angeles, CA, United States of America

Matt Chalawsky