New York, NY, United States of America

Kevin Most

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Clarifai, Inc.. Active years: 2020.

USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2020

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1 patent (USPTO):Explore Patents

Title: Kevin Most: Innovator in Cooperative Machine Learning

Introduction

Kevin Most is a prominent inventor based in New York, NY (US). He has made significant contributions to the field of machine learning, particularly in the area of cooperative systems that enhance deep neural network operations. His innovative approach has paved the way for advancements in client computing platforms.

Latest Patents

Kevin Most holds a patent for "Systems and methods for cooperative machine learning across multiple client computing platforms and the cloud enabling off-line deep neural network operations on client computing platforms." This patent focuses on enabling off-line deep neural network operations through cooperative machine learning across various platforms. The technology allows client applications to locally access machine learning models to perform operations on structured data, while also facilitating the sharing of model states between cloud and client-side models.

Career Highlights

Kevin Most is currently employed at Clarifai, Inc., a company known for its cutting-edge work in artificial intelligence and machine learning. His role at Clarifai has allowed him to further develop his expertise and contribute to innovative projects that leverage machine learning technologies.

Collaborations

Some of Kevin's notable coworkers include Matthew Donald Zeiler and John Rogers. Their collaborative efforts have contributed to the advancement of machine learning applications and technologies.

Conclusion

Kevin Most's work in cooperative machine learning exemplifies the innovative spirit of modern technology. His contributions are shaping the future of machine learning and its applications across various platforms.

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