New York, NY, United States of America

Zahra Ebrahimzadeh

This inventor holds 1 USPTO granted patent and 1 published patent application. Top assignee: Paige.ai, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Zahra Ebrahimzadeh. Retrieved from https://idiyas.com/inventor/zahra-ebrahimzadeh

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 

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Zahra Ebrahimzadeh: Inventor in Medical Image Processing.

Introduction

Zahra Ebrahimzadeh is an inventor associated with New York, NY, and Paige.ai, Inc. Her patent work focuses on processing medical images and connecting image data with descriptive information.

Latest Patents

Ebrahimzadeh is associated with 1 patent, titled “Systems and Methods for Processing Electronic Images with Metadata Integration.” The invention describes a computer-implemented method for receiving medical images of a pathology specimen and a gross description containing information about those images. It extracts data from the description, uses machine learning to identify corresponding locations in the images, and displays visual indications of the description data in relation to the images.

Career Highlights

Her listed patent reflects work at the intersection of medical imaging, metadata integration, and machine learning. The approach is designed to help associate descriptive information with relevant locations in pathology images.

Collaborations

Ebrahimzadeh’s listed coworkers include Patricia Raciti and Jeremy Daniel Kunz. Her company affiliation is Paige.ai, Inc.

Conclusion

Zahra Ebrahimzadeh’s patent centers on using machine learning to connect pathology-image locations with information from gross descriptions, supporting more integrated medical image processing.

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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