Seoul, South Korea

Sneha Kanchan

This inventor holds 1 USPTO granted patent. Top assignee: Soongsil University. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Sneha Kanchan. Retrieved from https://idiyas.com/inventor/sneha-kanchan

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 = 2.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Sneha Kanchan: Innovator in Federated Learning

Introduction

Sneha Kanchan is a prominent inventor based in Seoul, South Korea. She has made significant contributions to the field of federated learning, particularly through her innovative patent. Her work focuses on enhancing privacy and efficiency in machine learning processes.

Latest Patents

Sneha Kanchan holds a patent titled "Group signature-based federated learning method and system, and recording medium for performing same." This invention outlines a method that utilizes a group signature-based federated learning system, which includes at least one client terminal and an administrator terminal. The process involves requesting a group signature from the administrator terminal, generating update information of signed federated learning, and ensuring the integrity of the federated learning model through signature verification.

Career Highlights

Sneha Kanchan is affiliated with Soongsil University, where she continues to advance her research in federated learning. Her academic background and innovative mindset have positioned her as a key figure in her field.

Collaborations

Sneha collaborates with Bongjun Choi, who is also involved in research related to federated learning. Their partnership enhances the development and application of innovative solutions in this area.

Conclusion

Sneha Kanchan's contributions to federated learning exemplify her commitment to innovation and research. Her patent reflects a significant advancement in the field, showcasing her expertise and dedication to improving machine learning methodologies.

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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