Electronic Arts Inc.

Electronics Arts, Inc.

John Kolen

Navid Aghdaie

Kazi Atif-Uz Zaman

Mohsen Sardari

Kenneth Alan Moss

Mohamed Marwan Mattar

Su Xue

Harold Henry Chaput

Meng Wu

Igor Borovikov

Jesse Hans Stokes Harder

Thomas Murray Perlinski

Graham Francis Scott

Zhengxing Chen

Daniel Lee Kading

John Christopher Epler

Sundeep Narravula

Reza Pourabolghasem

Caedmon Somers

Jason Rupert

Dhaval Shah

Jervis Pinto

Nitish Victor

Tushar Agarwal

Yunqi Zhao

Yu Jin

Ahmad Beirami

Polina Igorevna Gouskova

Farah Mariam Ali

Preethi Ganeshan

Chetan Nagaraja Rao

Mohamed Marwan Abdel Magid Mattar

Bhargav Rajendra

Chinmay Mukund Sumant

A video game includes a single player mode where completion of storyline objectives advances the single player storyline. The video game also includes a multiplayer mode where a plurality of players can play on an instance of a multiplayer map. Storyline objectives from the single player mode are selected and made available for completion to players in the multiplayer mode, and the single player storylines can be advanced by players completing respective storyline objectives while playing in the multiplayer mode. Combinations of storyline objectives are selected from pending storyline objectives for players connecting to a multiplayer game for compatibility with multiplayer maps. Constraints can be used to determine compatibility.

Dynamic modifications of single player and multiplayer mode in a video game

Some embodiments herein disclose intelligent priority evaluators configured to perform a method that prioritizes tasks submitted by various users, even if the tasks are similarly classified. The scheduling system can collect, calculate, and use various criteria to determine a reward score in order to prioritize one task over another, such as for dynamic scheduling purposes. This can be performed in addition to or as a replacement for receiving user designations of priority.

Intelligent system for automated allocation of computing resources

Embodiments of an automated fraud detection system are disclosed that can detect user accounts that are engaging in unauthorized activities within a game application. The fraud detection system can provide an automated system that identifies parasitic accounts. The fraud detection system may identify patterns using machine learning based on characteristics, such as gameplay and transaction characteristics, associated with the parasitic user accounts. The fraud detection system may generate a model that can be applied to existing accounts within the game in order to automatically identify users that are engaging in unauthorized activities. The fraud detection system may automatically identify these parasitic accounts and implement appropriate actions to prevent the accounts from impacting legitimate users within the game application.

Fraud detection system

Embodiments of the systems and methods described herein provide a three dimensional reconstruction system that can receive an image from a camera, and then utilize machine learning algorithms to identify objects in the image. The three dimensional reconstruction system can identify a geolocation of a user, identify features of the surrounding area, such as structures or geographic features, and reconstruct the scene including the identified features. The three dimensional reconstruction system can generate three dimensional object data for the features and/or objects, modify the three dimensional objects, arrange the objects in a scene, and render a two dimensional view of the scene.

Three dimensional reconstruction of objects based on geolocation and image data

Systems described herein may automatically and dynamically adjust the amount and type of computing resources usable to execute, process, or perform various tasks associated with a video game. Using one or more machine learning algorithms, a prediction model can be generated that uses the historical and/or current user interaction data obtained by monitoring the users playing the video game. Based on the historical and/or current user interaction data, future user interactions likely to be performed in the future can be predicted. Using the predictions of the users' future interactions, the amount and type of computing resources maintained in the systems can be adjusted such that a proper balance between reducing the consumption of computing resources and reducing the latency experienced by the users of the video game is achieved and maintained.

Predictive execution of distributed game engines

Embodiments of the systems and methods described herein can automatically measure the difficulty metrics associated with various aspects of a video game using an artificial intelligence system. The artificial intelligence system may include multiple game agents. Telemetry data associated with the gameplay of each game agent may be recorded while the game application is automatically executed by the game agents. The telemetry data may be communicated to a data analysis system which can calculate game difficulty metrics for various aspects of the game. The data analysis system can determine game difficulty associated with the various aspects based on the game difficulty metrics. The results from the data analysis system may be visualized and communicated to a game developer for updating the operations of the video game.

Systems and methods for automatically measuring a video game difficulty

Embodiments of the systems and methods disclosed herein provide a request distribution system in which a request for resources may be executed by a plurality of workers. Upon receiving a request for resources from a user computing system, the request distribution system may select a subset of workers from the plurality of workers to execute the request within a time limit. Once the workers generate a plurality of outputs, each output associated with a quality level, the request distribution system may transmit the output associated with the highest quality level to the user computing system.

Request distribution system

Embodiments presented herein include systems and methods for performing dynamic difficulty adjustment. Further, embodiments disclosed herein perform dynamic difficulty adjustment using processes that may not be detectable or are more difficult to detect by users compared to static and/or existing difficulty adjustment processes. In some embodiments, historical user information utilized by a machine learning system to generate a prediction model that predicts an expected duration of game play, such as for example, an expected churn rate, a retention rate, the length of time a user is expected to play the game, or an indication of the user's expected game play time relative to a historical set of users who have previously played the game. Before or during game play, the prediction model can be applied to information about the user to predict the user's expected duration of game play. Based on the expected duration, in some embodiments, the system may then utilize a mapping data repository to determine how to dynamically adjust the difficulty of the game, such as, for example, changing the values of one or more gameplay parameters to make portions of the game less difficult.

Realtime dynamic modification and optimization of gameplay parameters within a video game application

Systems and methods for generating a customized virtual character are disclosed. A system may obtain video data or other media depicting a real person, and then may provide the obtained media to one or more machine learning models configured to learn visual appearance and behavior information regarding the particular person depicted in the video or other media. The system may then generate a custom visual appearance model and a custom behavior model corresponding to the real person, which may subsequently be used to render, within a virtual environment of a video game, a virtual character that resembles the real person in appearance and in-game behavior.

Virtual character generation from image or video data

Embodiments of systems presented herein may perform automatic granular difficulty adjustment. In some embodiments, the difficulty adjustment is undetectable by a user. Further, embodiments of systems disclosed herein can review historical user activity data with respect to one or more video games to generate a game retention prediction model that predicts an indication of an expected duration of game play. The game retention prediction model may be applied to a user's activity data to determine an indication of the user's expected duration of game play. Based on the determined expected duration of game play, the difficulty level of the video game may be automatically adjusted.

Dynamic difficulty adjustment

Embodiments of systems presented herein may identify users to play a multiplayer video game together using a mapping system and machine learning algorithms to create sets of matchmaking plans for the multiplayer video game that increases player or user retention. Embodiments of systems presented herein can determine the predicted churn rate, or conversely retention rate, of a user waiting to play a video game if the user is matched with one or more additional users in a multiplayer instance of the video game.

Multiplayer video game matchmaking system and methods

The present disclosure provides embodiments of a virtual mapping system for using real world geographical locations to generate virtual environments within game applications. The virtual mapping system allows a game application to identify geographical information associated with real world locations. The geographical information can be used by the game application to recreate the selected geographical location using virtual assets from the game application.

Virtual environment mapping system

Methods for providing multi-objective experiments with dynamic group assignment are provided. In one aspect, a method includes receiving, from a configuration component of an experiment management system, a plurality of objectives and at least one constraint for an experiment. The method also includes assigning a test population of the experiment into a plurality of groups. The method also includes determining a progress summary that predicts completion rates for the plurality of objectives according to the at least one constraint. The method also includes modifying a prioritization of the plurality of objectives to optimize the predicted completion rates. The method also includes receiving a request to add additional users into the test population. The method also includes distributing the additional users into the plurality of groups according to the modified prioritization. Systems and machine-readable media are also provided.

Growing community of inventors

Half Moon Bay, CA, United States of America

John Kolen