Kolkata, India

Kaustav Banerjee

This inventor holds 2 USPTO granted patents. Top assignee: Other. Active years: 2020-2023.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Kaustav Banerjee. Retrieved from https://idiyas.com/inventor/kaustav-banerjee

Data source: USPTO (United States Patent and Trademark Office) public patent records. Weekly synchronization. How IDiyas builds this profile


% Patents Active = 100.0

Average Co-Inventor Count = 3.0

ph-index = 1


Company Filing History:

goldMedal2 out of 834,513 
Other
 patents

Years Active: 2020-2023

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2 patents (USPTO):Explore Patents

Title: Innovations of Kaustav Banerjee

Introduction

Kaustav Banerjee is an accomplished inventor based in Kolkata, India. He has made significant contributions to the field of technology, particularly in the areas of pattern recognition and emotion detection through innovative methods.

Latest Patents

Kaustav holds 2 patents that showcase his expertise. His first patent is titled "Pattern recognition by convolutional neural networks." This method involves training a convolutional neural network (CNN) by sliding multiple windows on training images to create a target matrix that helps the CNN recognize features of interest. The second patent, "Method and system for detecting emotions based on typing behaviour," focuses on analyzing an individual's typing data to detect emotional states based on hand speed and key-press duration.

Career Highlights

Throughout his career, Kaustav has demonstrated a strong commitment to advancing technology. His innovative approaches have garnered attention in the tech community, and his patents reflect his dedication to solving complex problems.

Collaborations

Kaustav has worked alongside talented individuals such as Abhishek Biswas and Samudraneel Saha. Their collaborative efforts have contributed to the development of groundbreaking technologies.

Conclusion

Kaustav Banerjee's work exemplifies the spirit of innovation and creativity in technology. His contributions through patents not only enhance our understanding of machine learning and human behavior but also pave the way for future advancements in these fields.

Profile summary based on public USPTO records.
Data Sources: USPTO Patent Grant XML, Patent Center, EPO & CIPO • Normalized by IDiyas Innovation Graph. Methodology & provenance architecturePlease report any incorrect information to [email protected]
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