Google Inc.

Ioan Marius Pungaru

Anurag Gupta

James R Giles

Michael David McNally

Vinay Rao

Jiayao Yu

Fei Xiao

Christopher Dinn

Tammy Wu

Bill Davis

Systems and methods for distributing network traffic during streaming are hereby provided. A server can provide a first content pod to a client device for insertion in a content break separating a first portion and a second portion of the streaming content. The server can receive, from the client device during playback of the first content pod, a request for content to insert into the content break for playback subsequent to playback of the first content pod. The request for content can be generated at a first elapsed time from initiation of playback of the first content pod via a request generation policy. The server can select a second content pod. The server can provide the second content pod to the client device during the playback of the first content pod for playback subsequent to completion of the playback of the first content pod.

Distribution of network traffic for streaming content

This specification describes technologies relating to detecting anomalous user accounts. A computer implemented method is disclosed which evaluates an unknown status user account. The method described compares features associated with a plurality of known anomalous user accounts stored in a database to features present in the unknown account. A correlation value corresponding to the probability of a specific feature occurring in a particular anomalous user account is calculated and a dependence value corresponding to the degree of dependence between the given feature and at least one other feature is also calculated. A subset of features in the unknown account is generated comprising those features that possess a correlation value less than a threshold value and a dependence value below a maximum correlation value. A risk score for the unknown account is calculated by selecting those features from the subset that maximizes the correlation value. The unknown account is then reviewed by an account reviewer if the risk score exceeds a threshold value.

Detection of anomalous instances through dynamic feature selection analysis

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Brooklyn, NY, United States of America

Ioan Marius Pungaru