Foster City, CA, United States of America

Song Ge

This inventor holds 1 USPTO granted patent. Top assignee: Amazon Technologies, Inc.. Active years: 2023.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Song Ge. Retrieved from https://idiyas.com/inventor/song-ge-tbgl0ewg

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 = 12.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: The Innovative Contributions of Song Ge

Introduction

Song Ge is a notable inventor based in Foster City, California. He has made significant contributions to the field of machine learning through his innovative patent. His work focuses on enhancing the efficiency of executing machine learning models across different devices.

Latest Patents

Song Ge holds a patent for "Automated distribution of models for execution on a non-edge device and an edge device." This patent describes techniques for generating and executing an execution plan for a machine learning (ML) model. The execution plan is generated based on comparative execution information and network latency information, which optimizes the performance of ML models.

Career Highlights

Song Ge is currently employed at Amazon Technologies, Inc., where he continues to develop cutting-edge technologies. His expertise in machine learning and model execution has positioned him as a valuable asset in the tech industry.

Collaborations

He has collaborated with talented coworkers, including Nagajyothi Nookula and Poorna Chand Srinivas Perumalla. Their combined efforts contribute to the advancement of innovative solutions in their field.

Conclusion

Song Ge's contributions to machine learning and his innovative patent demonstrate his commitment to advancing technology. His work at Amazon Technologies, Inc. continues to influence the development of efficient execution strategies for machine learning models.

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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