Ann Arbor, MI, United States of America

Hiroyuki Funaya

USPTO Granted Patents = 3 

Average Co-Inventor Count = 1.8

ph-index = 1

Forward Citations = 8(Granted Patents)


Company Filing History:


Years Active: 2021-2023

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3 patents (USPTO):Explore Patents

Title: Hiroyuki Funaya: Innovator in Sensor Data Simulation

Introduction

Hiroyuki Funaya is a prominent inventor based in Ann Arbor, MI, known for his contributions to the field of sensor data simulation. With a total of 3 patents, he has made significant strides in developing systems and methods that enhance the understanding and generation of simulated sensor data.

Latest Patents

Hiroyuki's latest patents include innovative systems and methods for inferring simulated data. One of his notable inventions focuses on simulating sensor data by acquiring real sensor data and using machine learning models to generate simulated information that includes attributes absent from the original data. This method allows for a more comprehensive understanding of the perceived environment. Another patent involves simulating sensor data using a generative model, where computer-generated simulation data is processed through a generative neural network to produce simulated sensor data that reflects the scene accurately.

Career Highlights

Hiroyuki Funaya is currently employed at the Toyota Research Institute, Inc., where he applies his expertise in sensor data simulation to advance automotive technologies. His work is pivotal in enhancing the capabilities of autonomous systems and improving the interaction between vehicles and their environments.

Collaborations

Hiroyuki collaborates with esteemed colleagues such as Randall J St Romain and Michael James Delp, contributing to a dynamic research environment that fosters innovation and development in sensor technologies.

Conclusion

Hiroyuki Funaya's work in simulating sensor data represents a significant advancement in the field, showcasing his innovative spirit and dedication to improving technology. His contributions are essential for the future of autonomous systems and sensor applications.

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