Lahore, Pakistan

Aneeq Zia

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

IDiyas Innovation Intelligence. (2026). Inventor Profile: Aneeq Zia. Retrieved from https://idiyas.com/inventor/aneeq-zia-sboytdhc

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

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Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Aneeq Zia and Interpretable Medical Procedure Analysis

Introduction

Aneeq Zia is an inventor based in Lahore, Pakistan. His work includes a patent for applying machine learning to medical procedure analysis, with a focus on making model confidence more interpretable.

Latest Patents

Zia’s patent, “Machine Learning Based Medical Procedure Analysis with Interpretable Model Confidence Rankings,” describes a system that analyzes video of a procedure performed with a robotic medical system. A first machine-learning model makes a prediction from input features, while a second model estimates confidence in that prediction. The system then attributes confidence across at least two input features and displays that attribution as an overlay on the video stream. Zia is associated with 1 patent.

Career Highlights

Zia is associated with Intuitive Surgical Operations, Inc. The disclosed invention addresses how confidence in machine-generated predictions can be presented in the context of robotic medical procedures.

Collaborations

Zia’s listed coworker is Conor Perreault. The available information identifies their professional association but does not specify further details about their collaboration.

Conclusion

Aneeq Zia’s patent focuses on making machine-learning predictions in robotic medical procedures more transparent by showing how input features contribute to model 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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