Nisshin, Japan

Masahiro Mohri

This inventor holds 1 USPTO granted patent. Top assignee: Toyota Jidosha Kabushiki Kaisha. Active years: 2022.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Masahiro Mohri. Retrieved from https://idiyas.com/inventor/masahiro-mohri-9bhuzmra

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

Calculating rank

% Patents Active = 100.0

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Masahiro Mohri: Inventor in Engine Knock Detection.

Introduction

Masahiro Mohri is an inventor associated with Nisshin, Japan, and Toyota Jidosha Kabushiki Kaisha. His patent work applies neural-network learning to detect engine knocking.

Latest Patents

The provided information lists one patent for Mohri: “Knocking Detection System and Knocking Detection Method of Internal Combustion Engine.” The invention uses a knocking sensor to detect engine-body vibration and a pressure sensor to measure combustion-chamber pressure. Pressure-sensor readings provide training data representing knocking strength, while vibration readings serve as neural-network inputs. Once trained, the network estimates knocking strength from the knocking sensor’s output.

Career Highlights

Mohri’s listed patent reflects work at the intersection of internal-combustion-engine sensing and neural-network-based estimation. His recorded patent portfolio includes 1 patent.

Collaborations

The provided information identifies Daisuke Kobayashi and Kenji Senda as Mohri’s coworkers at Toyota Jidosha Kabushiki Kaisha.

Conclusion

Masahiro Mohri’s listed invention combines engine sensors and a trained neural network to estimate knocking strength, contributing to technology for internal-combustion-engine monitoring.

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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