Electronic Arts Inc.

Nitish Victor

Navid Aghdaie

Kazi Atif-Uz Zaman

Harold Henry Chaput

Sundeep Narravula

Mohsen Sardari

John Kolen

Igor Borovikov

Tushar Agarwal

Preethi Ganeshan

Yu Jin

Chinmay Mukund Sumant

A system and method for providing live gameplay updates receives a modification for a videogame, the modification affecting a gameplay aspect of the videogame during execution of the videogame. The system and method determine a target group for deploying the modification. The target group includes first one or more live instances of the videogame. The system and method deploy the modification to the target group and receive gameplay data associated with the gameplay aspect from the target group. The system and method deploy the modification to second one or more live instances of the videogame based at least in part on an analysis of the received gameplay data.

Live gameplay updates

A video reenactment system and method analyze a video clip that a video game player wishes to reenact and maps objects and actions within the video clip to virtual objects and virtual actions within the video game. A reenactment script indicating a sequence of virtual objects and virtual actions as mapped to objects and actions in the video clip is generated using a video translation model and stored for use in reenacting the video clip. The reenactment script can be used within the video game to reenact the objects and actions of the video clip. The reenactment of the video clip may be interactive, where a player may assume control within the reenactment and when the player relinquishes control, the reenactment will continue at an appropriate part of the sequence of actions by skipping actions corresponding to the ones played by the player.

Interactive reenactment within a video game

Systems presented herein may automatically and dynamically modify a video game being played by a user based at least in part on a determined or predicted emotional state of a user. Using one or more machine learning algorithms, a parameter function can be generated that uses sensory and/or biometric data obtained by monitoring a user playing a video game. Based on the sensory and/or biometric data, an emotional state of the user can be predicted. For example, it can be determined whether a user is likely to be bored, happy, or frightened while playing the video game. Based at least in part on the determination of the user's emotional state, the video game can be modified to improve positive feelings and reduce negative feelings occurring in response to the video game.

Sensory-based dynamic game-state configuration

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Rochester, NY, United States of America

Nitish Victor