Shandong, China

Faling Rui

This inventor holds 1 USPTO granted patent. Top assignee: Inspur Suzhou Intelligent Technology Co., Ltd. Active years: 2026.

IDiyas Innovation Intelligence. (2026). Inventor Profile: Faling Rui. Retrieved from https://idiyas.com/inventor/faling-rui

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

Calculating rank

% Patents Active = 100.0

Average Co-Inventor Count = 1.0

ph-index = 1


Company Filing History:


Years Active: 2026

Loading Chart...
1 patent (USPTO):Explore Patents

Title: Faling Rui - Innovator in Deep Learning Systems

Introduction

Faling Rui is a prominent inventor based in Shandong, China. He has made significant contributions to the field of artificial intelligence, particularly in the deployment of deep learning systems. His innovative approach has garnered attention in the tech community.

Latest Patents

Faling Rui holds a patent for a "Method and apparatus for deploying deep learning system." This patent outlines a comprehensive method for deploying a deep learning system, which includes defining a node group template for different node groups, validating cluster templates, and configuring virtual machines with artificial intelligence frameworks. His work emphasizes the importance of rational configuration and efficient communication benchmarks in deep learning applications.

Career Highlights

Faling Rui is currently associated with Inspur Suzhou Intelligent Technology Co., Ltd. His role at the company allows him to leverage his expertise in artificial intelligence and contribute to cutting-edge technological advancements. His patent reflects his commitment to enhancing the capabilities of deep learning systems.

Collaborations

Due to space constraints, the collaborations section will be omitted.

Conclusion

Faling Rui's innovative work in deep learning systems showcases his dedication to advancing technology in artificial intelligence. His contributions are paving the way for more efficient and effective deployment of deep learning applications.

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]
Loading…