Staufen im Breisgau, Germany

Jona Boeddinghaus

This inventor holds 1 USPTO granted patent and 2 published patent applications and 1 EPO patent. Top assignee: Daiki Gmbh. Active years: 2024.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Jona Boeddinghaus. Retrieved from https://idiyas.com/inventor/jona-boeddinghaus

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

USPTO Granted Patents = 1 

% Patents Active = 100.0

 

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Jona Boeddinghaus: Innovator in Machine Learning Provenance

Introduction

Jona Boeddinghaus is a notable inventor based in Staufen im Breisgau, Germany. He has made significant contributions to the field of machine learning, particularly in verifying the provenance of machine learning systems. His innovative approach has garnered attention in the tech community.

Latest Patents

Jona holds a patent titled "Verifying the provenance of a machine learning system." This patent involves receiving a header, metadata, and content associated with a machine learning system. A digital envelope is generated based on these components, which includes a first value corresponding to the header and a combined value for the metadata and content. This invention aims to enhance the reliability and traceability of machine learning systems.

Career Highlights

Jona is currently employed at Daiki GmbH, where he applies his expertise in machine learning and innovation. His work at the company reflects his commitment to advancing technology and improving system integrity.

Collaborations

Jona collaborates with talented individuals such as Mauritz Kop and Paolo Campigotto. Their combined efforts contribute to the development of cutting-edge solutions in the field of machine learning.

Conclusion

Jona Boeddinghaus is a pioneering inventor whose work in machine learning provenance is shaping the future of technology. His contributions are vital for ensuring the integrity of machine learning systems.

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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