Google Inc.

Conviva Inc.

Xi Xiong

Radu Jurca

Chiu Wah Kelvin So

Yan Li

Mahesh Keralapura Manjunatha

Shen Wang

Yao Liu

Jakub Ocwieja

Andrei Dragus

Vinay Vyas Vemuri

Md Mahbubul Hasan

Muruo Liu

Daniel Svonava

David Fan

Yasar Arafath Rafi Ahmed

A set of Internet Protocol (IP) addresses is received wherein each IP address is associated with a corresponding set of features. For an IP address in the set, the IP address is evaluated based at least in part on a set of inclusion criteria. For the IP address in the set, a likelihood that the IP address is residential or non-residential is generated based at least in part on the corresponding set of features and the evaluation of the IP address based at least in part on the set of inclusion criteria. For the IP address in the set, a training sample is generated that includes the IP address, at least some of the corresponding set of features, and a label. A labeled training data set is output that includes the training sample, where an IP address classifier is trained using the labeled training data set.

IP classification

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for a content platform that receives a request to provide a digital component. The request includes information about users to which the digital component is directed. Futurized queries are obtained from serving logs of a serving system that is configured to execute an existing digital component using serving code that directs digital content to the set of users. The futurized queries are loaded as data structures in memory of a forecasting system. The system uses an instruction set derived from the serving code to determine that similarity between the particular futurized query and the request exceeds a threshold similarity. The system then generates a forecast output as a response to the request based on futurized queries that exceed the threshold similarity. The forecast output includes data describing future provision of the digital component.

Modeling distribution of digital components over a network

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San Jose, CA, United States of America

Xi Xiong