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For the Inventor, By the Inventor

Tokyo, Japan

Eiichi Matsumoto

This inventor holds 15 USPTO granted patents and 7 published patent applications, primarily in Machine Learning Robotics. Top assignees: Preferred Networks, Inc., Fanuc Corporation. Active years: 2019-2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Eiichi Matsumoto. Retrieved from https://idiyas.com/inventor/eiichi-matsumoto-40s9nwf7

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

Calculating rank
USPTO Granted Patents = 15 

% Patents Active = 93.3

Average Co-Inventor Count = 3.5

ph-index = 4

Forward Citations = 47(Granted Patents)


Location History:

  • Tokyo-to, JP (2021 - 2023)
  • Tokyo, JP (2019 - 2024)

Company Filing History:

goldMedal15 out of 85 
 
Preferred Networks, Inc.
 patents
silverMedal8 out of 6,292 
 
Fanuc Corporation
 patents
where one patent can have more than one assignee

Years Active: 2019-2026

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Areas of Expertise:
Machine Learning
Robotics
Fault Prediction
3D Modeling
Object Picking
Action Pattern
Grasping System
Training Data
Detection Device
Robot Controller
Analysis Device
Learning Model
15 patents (USPTO):Explore Patents
1. 12682645 - Analysis device, analysis system, analysis method, and computer-readable medium (2026-07-14),
2. 12066797 - Fault prediction method and fault prediction system for predecting a fault of a machine (2024-08-20),
3. 12020376 - Method of generating three-dimensional model, training data, machine learning model, and system (2024-06-25),
4. 11904469 - Machine learning device, robot controller, robot system, and machine learning method for learning action pattern of human (2024-02-20),
5. 11845194 - Robot system and workpiece picking method (2023-12-19),
6. 11780095 - Machine learning device, robot system, and machine learning method for learning object picking operation (2023-10-10),
7. 11565407 - Learning device, learning method, learning model, detection device and grasping system (2023-01-31),
8. 11551419 - Method of generating three-dimensional model, training data, machine learning model, and system (2023-01-10),
9. 11475289 - Learning device unit (2022-10-18),
10. 11386587 - Automatic coloring of line drawing (2022-07-12),
11. 11275345 - Machine learning Method and machine learning device for learning fault conditions, and fault prediction device and fault prediction system including the machine learning device (2022-03-15),
12. 11034018 - Learning device, learning method, learning model, detection device and grasping system (2021-06-15),
13. 10807235 - Machine learning device, robot controller, robot system, and machine learning method for learning action pattern of human (2020-10-20),
14. 10717196 - Machine learning device, robot system, and machine learning method for learning workpiece picking operation (2020-07-21),
15. 10317853 - Machine learning method and machine learning device for learning fault conditions, and fault prediction device and fault prediction system including the machine learning device (2019-06-11),

Title: Innovations by Eiichi Matsumoto: Pioneering Fault Prediction Systems and 3D Modeling

Introduction

Eiichi Matsumoto, a notable inventor residing in Tokyo, Japan, has made significant contributions to the field of technology through his inventive work. With a remarkable portfolio of 14 patents, Matsumoto is recognized for his innovative solutions that enhance machine learning and fault prediction systems.

Latest Patents

Matsumoto's latest patents focus on cutting-edge technologies that show immense potential in various industries. One of his notable inventions is a fault prediction method and system aimed at predicting machine faults. This anomaly prediction system incorporates several components, including one or more memories and processors configured to assess state variables. These state variables may consist of output data from sensors detecting machine status or environmental conditions, internal control software data, or computational data derived from the aforementioned outputs. By utilizing a machine learning model, the system can generate a degree of anomaly, thus notifying users about potential issues within a machine's operation.

Another innovative patent revolves around a method of generating three-dimensional models from two-dimensional images. This system integrates multiple imaging devices designed to capture a real object for which a three-dimensional representation is to be developed. A marker is included to calculate the pose of these imaging devices, which is crucial for creating an accurate 3D representation. By capturing images from below the real object and marker, Matsumoto’s system efficiently translates two-dimensional data into three-dimensional models.

Career Highlights

Throughout his career, Matsumoto has been associated with leading technology companies, contributing to significant advancements within these organizations. He has worked with Preferred Networks, Inc., and Fanuc Corporation, where he honed his expertise in machine learning and robotics.

Collaborations

Eiichi Matsumoto has collaborated with notable professionals in the field, including Daisuke Okanohara and Ryosuke Okuta. These partnerships have allowed him to exchange ideas and further enhance his innovative capabilities in developing new technologies.

Conclusion

Eiichi Matsumoto remains a prominent figure in the realm of technology innovation with his substantial contributions through advanced patents. His work in fault prediction systems and 3D modeling showcases the importance of machine learning in today's technological landscape, displaying a unique blend of creativity and engineering expertise that paves the way for future advancements.

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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