Atlanta, GA, United States of America

Conor Perreault

This inventor holds 1 USPTO granted patent and 5 published patent applications. Top assignee: Intuitive Surgical Operations, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Conor Perreault. Retrieved from https://idiyas.com/inventor/conor-perreault

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 = 2.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Conor Perreault: Inventor in Medical Procedure Analysis

Introduction

Conor Perreault is an inventor based in Atlanta, Georgia. His patent work concerns machine learning tools for analyzing medical procedures performed with robotic medical systems.

Latest Patents

Perreault is associated with 1 patent: “Machine Learning Based Medical Procedure Analysis with Interpretable Model Confidence Rankings.” The disclosed system uses a first machine-learning model to make a prediction from a video stream of a robotic medical procedure. A second model estimates confidence in that prediction and attributes the confidence level to at least two input features. The system can then display those attributions as an overlay on the video, helping users interpret the model’s confidence.

Career Highlights

Perreault is associated with Intuitive Surgical Operations, Inc. The patent describes an approach to making machine-learning predictions in medical procedure analysis more interpretable by showing which input features contribute to confidence.

Collaborations

Aneeq Zia is identified as Perreault’s coworker. The available information does not specify their roles or describe a particular joint project.

Conclusion

Conor Perreault’s patent focuses on interpretable confidence rankings for machine-learning analysis of robotic medical procedure videos, connecting predictive models with visual explanations of their confidence.

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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