Powhatan, VA, United States of America

Carlos Iga Garza

This inventor holds 1 USPTO granted patent. Top assignee: Landis+gyr Technology, Inc.. Active years: 2025.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Carlos Iga Garza. Retrieved from https://idiyas.com/inventor/carlos-iga-garza

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


% Patents Active = 100.0

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2025

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1 patent (USPTO):Explore Patents

Title: Innovations by Carlos Iga Garza

Introduction

Carlos Iga Garza is an accomplished inventor based in Powhatan, Virginia. He has made significant contributions to the field of electrical engineering, particularly in the area of power distribution systems. His innovative approach utilizes machine learning techniques to enhance the detection of electrical anomalies.

Latest Patents

Carlos holds a patent for a method titled "Service Location Anomalies." This patent discloses techniques that involve using machine learning to detect electrical anomalies in power distribution systems. The method includes accessing voltage measurements from an electric metering device over a specified time period. It calculates average and minimum voltages for each time window and applies a machine learning model to identify predetermined electrical anomalies. The model is trained to classify these anomalies and send alerts to utility operators based on its findings.

Career Highlights

Carlos Iga Garza is currently employed at Landis+Gyr Technology, Inc., where he continues to develop innovative solutions in the energy sector. His work focuses on improving the reliability and efficiency of power distribution systems through advanced technology.

Conclusion

Carlos Iga Garza exemplifies the spirit of innovation in electrical engineering. His contributions through patents and his role at Landis+Gyr Technology, Inc. highlight the importance of integrating machine learning into traditional power systems. His work not only enhances operational efficiency but also contributes to the safety and reliability of electrical services.

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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