Redondo Beach, CA, United States of America

Daniel Micah Pipe-Mazo

This inventor holds 1 USPTO granted patent and 2 published patent applications. Top assignee: Lumafield, Inc.. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Daniel Micah Pipe-Mazo. Retrieved from https://idiyas.com/inventor/daniel-micah-pipe-mazo

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 = 3.0

ph-index = 1


Company Filing History:


Years Active: 2026

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1 patent (USPTO):Explore Patents

Title: Daniel Micah Pipe-Mazo: Inventor in AI-Assisted X-Ray Inspection.

Introduction

Daniel Micah Pipe-Mazo is an inventor based in Redondo Beach, California. His patent work concerns applying artificial intelligence to X-ray computed tomography (CT) data to detect anomalies in scanned objects.

Latest Patents

Pipe-Mazo is associated with 1 patent: “Artificial Intelligence Anomaly Detection Using X-Ray Computed Tomography Scan Data.” The invention describes methods, systems, apparatuses, and computer program products that use machine-learning models to identify anomalies. The process derives data from an X-ray CT scan to reveal interior structures, analyzes that data with a model trained on scans of objects of the same type, and provides an output to a physical device when an anomaly is detected.

Career Highlights

Pipe-Mazo works at Lumafield, Inc., a company whose work includes industrial X-ray CT technology. His patent reflects an application of machine learning to scan analysis, with the aim of helping identify irregularities in an object’s internal structure.

Collaborations

His listed coworkers include Andreas Linas Bastian and Anne Winnefeld Sandman. The available information identifies them as coworkers but does not specify their roles in the patent.

Conclusion

Daniel Micah Pipe-Mazo’s patent connects X-ray CT imaging with machine-learning-based anomaly detection, highlighting his contribution to AI-assisted inspection technology.

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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