Buchanan, NY, United States of America

Srinivas V R Gutta


Average Co-Inventor Count = 3.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2011

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1 patent (USPTO):Explore Patents

Title: Innovations of Srinivas V R Gutta

Introduction

Srinivas V R Gutta is an accomplished inventor based in Buchanan, NY (US). He has made significant contributions to the field of program recommendation systems. His innovative approach has led to the development of a unique patent that enhances user experience in program recommendations.

Latest Patents

Srinivas holds a patent for a "Nearest neighbor recommendation method and system." This invention involves a program recommendation system that utilizes a program record module and various nearest neighbor modules. Upon receiving a program record, the program record module converts each key field into a feature value. The system includes a single neighbor module that generates recommendations based on a stored program record qualifying as a nearest neighbor. Additionally, a multiple neighbor module generates recommendations based on N number of stored program records qualifying as N nearest neighbors. Furthermore, a neighbor cluster module generates recommendations based on a cluster of stored program records qualifying as the nearest neighbor.

Career Highlights

Srinivas is currently employed at Koninklijke Philips Corporation N.V., where he continues to innovate and contribute to advancements in technology. His work focuses on improving recommendation systems, which are essential in various applications today.

Collaborations

Srinivas has collaborated with notable colleagues, including J David Schaffer and Kaushal Kurapati. These collaborations have fostered a creative environment that encourages the development of cutting-edge technologies.

Conclusion

Srinivas V R Gutta's contributions to the field of program recommendation systems exemplify the impact of innovative thinking in technology. His patent reflects a significant advancement in how programs are recommended to users, enhancing their overall experience.

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