Electronic Arts Inc.

Tushar Agarwal

Navid Aghdaie

Mohsen Sardari

Kazi Atif-Uz Zaman

Harold Henry Chaput

Sundeep Narravula

Kilol Gupta

Zahra Shakeri

Preethi Ganeshan

Yu Jin

John Kolen

Nitish Victor

Chinmay Mukund Sumant

Cong Feng

Drew John Zagieboylo

An example method of automated selection of audio asset synthesizing pipelines includes: receiving an audio stream comprising human speech; determining one or more features of the audio stream; selecting, based on the one or more features of the audio stream, an audio asset synthesizing pipeline; training, using the audio stream, one or more audio asset synthesizing models implementing respective stages of the selected audio asset synthesizing pipeline; and responsive to determining that a quality metric of the audio asset synthesizing pipeline satisfies a predetermined quality condition, synthesizing one or more audio assets by the selected audio asset synthesizing pipeline.

Automated pipeline selection for synthesis of audio assets

A system can manage distribution of computing jobs among a plurality of third-party network or cloud computing providers to maximize utilization of available computing resources purchased or otherwise obtained by an entity. The system can determine a dependency relationship between jobs and distribute the jobs among the network computing providers based at least in part on the dependency relationship between the jobs. Moreover, the system can use machine learning algorithms to generate one or more prediction algorithms to predict future computing resource usage demands for performing a set of scheduled and unscheduled jobs. Based at least in part on the resource prediction, the dependency relationship between jobs, service level agreements with network computing service providers, and job resource requirements, the system can determine an improved or optimal distribution of jobs among the network computing service providers that satisfies or best satisfies one or more objective functions to maximize resource utilization.

Distributed computer task management of interrelated network computing tasks

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

Growing community of inventors

San Carlos, CA, United States of America

Tushar Agarwal