Yahoo Assets LLC

Verizon Patent and Licensing Inc.

Yahoo Ad Tech LLC

Donald J Mcqueen

Lachlan Ahren Maxwell

William C Wakefield, Iii

Paul S Rock

Systems and methods are disclosed for identifying human users on a network. One method includes receiving network data comprising data transmitted over a network over predetermined time period, the network data comprising a plurality of usernames and a plurality of events, wherein each of the plurality of events is associated with at least one of the plurality of usernames; determining a plurality of pairs, each pair of the plurality of pairs comprising a username of the plurality of usernames and an associated event of the plurality of events; determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more predetermined event frequency thresholds; determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more predetermined event frequency thresholds; generating at least one distribution associated with the qualifying pairs and non-qualifying pairs; and based on the at least one distribution, determining if at least one username of the plurality of usernames is associated with a human user or a non-human user.

Systems and methods for analyzing network data to identify human and non-human users in network communications

Systems and methods are disclosed for determining if an account identifier is computer-generated. One method includes receiving the account identifier, dividing the account identifier into a plurality of fragments, and determining one or more features of at least one of the fragments. The method further includes determining the commonness of at least one of the fragments, and determining if the account identifier is computer-generated based on the features of at least one of the fragments, and the commonness of at least one of the fragments.

Machine learning and validation of account names, addresses, and/or identifiers

Systems, methods, and computer-readable media are provided for pairing users for electronic communications over a network. In accordance with one implementation, a method is provided for pairing users that request a chat encounter or other form of electronic communication. The operations of the method include receiving a request from a first user and calculating a plurality of pairing scores, each pairing score based on a collaborative filtering score. The method further includes selecting a second user based on the plurality of pairing scores and pairing the first user with the second user to enable electronic communications.

Pairing systems and methods for electronic communications

Systems and methods are disclosed for identifying malicious traffic associated with a website. One method includes receiving website traffic metadata comprising a plurality of variables, the website traffic metadata being associated with a plurality of website visitors to the website; determining a total number of occurrences associated with at least two of the plurality of variables of the website traffic metadata; generating a plurality of pairs comprising combinations of the plurality of variables of the website traffic metadata; determining a total number of occurrences associated with each pair of the plurality of pairs of combinations of the plurality of variables of the website traffic metadata; determining a plurality of visitor actions associated with the plurality of variables of the website traffic metadata; clustering each of the plurality of pairs and the plurality of visitor actions associated with the plurality of variables of the website traffic metadata into groups; and determining, based on the clustering of the plurality of pairs and the plurality of visitor actions, whether each of the plurality of website visitors are malicious visitors.

Systems and methods for network traffic analysis

Systems and methods are disclosed for utilizing sender-recipient pair data to establish sender-level trust in future communication. One method comprises receiving raw communication data over a network and testing the received raw communication data against trained machine learning data to predict whether the raw communication data is associated with expected communication data. The raw communication data is sorted for expected communication data, which is further analyzed for sender-recipient pair data and assigned an expected communication pair data score. Senders associated with an expected communication pair data score that meets or exceeds a threshold are labeled and stored in a database as trusted. As a result of the sender-recipient pair analysis, recipients at-risk for being scammed can be identified, senders misidentified as spammers can be properly classified, and machine learning techniques utilized for analyzing raw communication data can be fine-tuned.

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Leesburg, VA, United States of America

Donald J Mcqueen