Draper, UT, United States of America

Matthew Bennett

USPTO Granted Patents = 4 

Average Co-Inventor Count = 1.5

ph-index = 1

Forward Citations = 1(Granted Patents)


Location History:

  • Draper, UT (US) (2023)
  • San Mateo, CA (US) (2023)

Company Filing History:


Years Active: 2023-2025

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4 patents (USPTO):Explore Patents

Title: Innovations of Matthew Bennett

Introduction

Matthew Bennett is an accomplished inventor based in Draper, Utah. He has made significant contributions to the field of technology, particularly in machine learning and gaming. With a total of four patents to his name, Bennett continues to push the boundaries of innovation.

Latest Patents

Bennett's latest patents include "Encoding input for machine learning," which describes a method and system for providing synchronized input feedback. This invention involves receiving an input event, encoding it in an output stream, and reproducing the output stream through an output device, ensuring that the encoded input event remains imperceptible to the user. Another notable patent is "Adaptive time dilation based on player performance." This invention allows for the adjustment of game latency based on a player's performance, providing a tailored gaming experience that can slow down gameplay during high activity periods.

Career Highlights

Matthew Bennett is currently employed at Sony Interactive Entertainment Inc., where he applies his expertise in developing innovative solutions for gaming and machine learning. His work has garnered attention for its practical applications and potential to enhance user experiences.

Collaborations

Bennett collaborates with talented individuals such as Joshua M Eads and Brendan Rehon, contributing to a dynamic work environment that fosters creativity and innovation.

Conclusion

Matthew Bennett's contributions to technology through his patents and work at Sony Interactive Entertainment Inc. highlight his role as a leading inventor in the field. His innovative approaches continue to shape the future of gaming and machine learning.

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