Dearborn, MI, United States of America

Harsh Bhupendra Bhate

USPTO Granted Patents = 1 

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Harsh Bhupendra Bhate: Innovator in Vehicle Machine Learning

Introduction

Harsh Bhupendra Bhate is an accomplished inventor based in Dearborn, MI (US). He has made significant contributions to the field of vehicle technology, particularly in the area of machine learning. His innovative approach has led to the development of a patent that enhances the validation process of machine learning models in vehicles.

Latest Patents

Harsh holds a patent for "Automatic onboard validation of a newly trained vehicle machine learning model." This invention involves a vehicle system that receives an indication of a newly trained machine learning model designated for validation. The system loads a copy of the model into shadow execution hardware, which is capable of background execution. It subscribes to data topics where input data for the model is published, allowing the system to execute the model in the background as the vehicle travels. The performance of the new model is benchmarked against a prior version to determine its suitability for deployment.

Career Highlights

Harsh is currently employed at Ford Global Technologies, LLC, where he applies his expertise in machine learning to develop innovative solutions for vehicle systems. His work focuses on improving the efficiency and reliability of machine learning applications in automotive technology.

Collaborations

Throughout his career, Harsh has collaborated with talented individuals such as Sahib Singh and Zaydoun Rawashdeh. These collaborations have fostered a creative environment that encourages innovation and the sharing of ideas.

Conclusion

Harsh Bhupendra Bhate is a notable inventor whose work in vehicle machine learning is paving the way for advancements in automotive technology. His patent demonstrates a commitment to enhancing the validation processes of machine learning models, contributing to safer and more efficient vehicles.

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