Tokyo, Japan

Takaomi Kanda

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Hitachi-Kokusai Electric Inc.. Active years: 2024.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Takaomi Kanda. Retrieved from https://idiyas.com/inventor/takaomi-kanda

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

Calculating rank
USPTO Granted Patents = 1 

% Patents Active = 100.0

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Takaomi Kanda - Innovator in Video Recognition Technology

Introduction

Takaomi Kanda is a notable inventor based in Tokyo, Japan. He has made significant contributions to the field of video recognition technology. His innovative approach combines machine learning with advanced imaging systems.

Latest Patents

Kanda holds a patent for a video system that enhances video recognition through machine learning. This system configuration includes an imaging device and a video processing device. The imaging device generates video data and incorporates a neural network unit designed for recognition processing. The video processing device further processes the intermediate feature quantities obtained from the imaging device.

Career Highlights

Kanda is associated with Hitachi Kokusai Electric Inc., where he applies his expertise in developing cutting-edge video systems. His work focuses on integrating machine learning techniques to improve video recognition capabilities.

Collaborations

Due to space limitations, the collaborations section will be omitted.

Conclusion

Takaomi Kanda's contributions to video recognition technology exemplify the innovative spirit of modern inventors. His work continues to influence advancements in the field.

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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