Oakland, CA, United States of America

Patrick Ryan Driscoll

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2024.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Patrick Ryan Driscoll. Retrieved from https://idiyas.com/inventor/patrick-ryan-driscoll

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 = 10.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: The Innovations of Patrick Ryan Driscoll

Introduction

Patrick Ryan Driscoll is an accomplished inventor based in Oakland, CA. He has made significant contributions to the field of data analysis through his innovative patent. His work focuses on identifying and ranking reversal points in data, which can be crucial for various applications.

Latest Patents

Driscoll holds a patent for a method titled "Reversal-point-based detection and ranking." This invention involves training a model to identify reversal points in data, defined as points where a first order derivative crosses from positive to negative or vice versa. The model evaluates these points based on their abnormality and significance, catering to users' interests in significant changes in data.

Career Highlights

Patrick Ryan Driscoll is currently associated with Microsoft Technology Licensing, LLC. His role at the company allows him to leverage his expertise in data analysis and machine learning to develop innovative solutions.

Collaborations

Driscoll has collaborated with notable colleagues, including Chaofan Huang and Kristina Caroline Ryan. Their combined efforts contribute to the advancement of technology and innovation within their field.

Conclusion

Patrick Ryan Driscoll's work exemplifies the intersection of technology and data analysis. His innovative patent demonstrates the potential for machine learning to enhance our understanding of data dynamics.

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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