Electronic Arts Inc.

Fernando De Mesentier Silva

Navid Aghdaie

Sundeep Narravula

Laura Greige

Kazi Atif-Uz Zaman

Reza Pourabolghasem

Meredith Trotter

Alexander Sulimanov

A collusion detection system may detect collusion between entities participating in online gaming. The collusion detection system may identify a plurality of entities associated with and opponents within an instance of an online game, determine social data associated with the plurality of entities, determine in-game behavior data associated with the plurality of entities, and determine, for one or more pairings of the plurality of entities, respective pairwise feature sets based at least in part on the social data and the in-game behavior data. The collusion detection system may then perform anomaly detection on the respective pairwise feature sets and, in response to the anomaly detection detecting one or more anomalous pairwise feature sets, output one or more suspect pairings of the plurality of entities corresponding to the one or more anomalous pairwise feature sets as suspected colluding pairings.

Detecting collusion in online games

A persona system determines a player persona for a player of a gaming system based on gameplay information for the user and, for example, performs dynamic content generation or additional product recommendations based on the player persona. The persona system may receive a request for content based on a persona of a player and receive gameplay data associated with gameplay of the player in a plurality of games. The persona system may then generate a player persona of the player based on the gameplay data associated with the gameplay of the player in the plurality of games, determine persona based content based at least in part on a portion of the player persona, and output the persona based content in response to the request.

Persona driven dynamic content framework

A high-skilled-low-level detection system may detect high-skilled entities in low-level matches of an online gaming. The system may identify a plurality of entities that are within a first category of entities eligible to be matched by a matchmaking algorithm. The system may then determine respective feature sets based at least in part on gameplay data associated with the plurality of entities and perform anomaly detection on the respective feature sets. The system may then determine, based on the anomaly detection, an anomalous entity of the plurality of entities and cause the matchmaking algorithm to match the anomalous entity with other entities that are in a second category of entities.

Detecting high-skilled entities in low-level matches in online games

Growing community of inventors

San Francisco, CA, United States of America

Fernando De Mesentier Silva