Jiaxing, China

Randolph Yao

This inventor holds 1 USPTO granted patent. Top assignee: Microsoft Technology Licensing, LLC. Active years: 2020.


% Patents Active = 100.0

Average Co-Inventor Count = 6.0

ph-index = 1

Forward Citations = 3(Granted Patents)


Company Filing History:


Years Active: 2020

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1 patent (USPTO):Explore Patents

Title: Innovations by Randolph Yao

Introduction

Randolph Yao is a notable inventor based in Jiaxing, China. He has made significant contributions to the field of cloud computing, particularly in the area of node failure prediction. His work is instrumental in enhancing the reliability and efficiency of cloud-based data centers.

Latest Patents

Randolph Yao holds a patent for a system designed to predict computing node failures and health in cloud-based data centers. The patent, titled "Computing node failure and health prediction for cloud-based data center," describes a system that includes a node historical state data store. This store contains historical node state data, which includes metrics representing the health status or attributes of a node prior to a failure. The system also features a node failure prediction algorithm creation platform that generates a machine learning trained algorithm. Additionally, an active node data store holds information about computing nodes, allowing for the calculation of node failure probability scores. This innovative approach enables the assignment of virtual machines to selected computing nodes based on their predicted health status.

Career Highlights

Randolph Yao is currently employed at Microsoft Technology Licensing, LLC, where he continues to develop cutting-edge technologies. His expertise in machine learning and cloud computing has positioned him as a valuable asset in the tech industry.

Collaborations

Randolph has collaborated with Murali Mohan Chintalapati, further enhancing the innovative projects at Microsoft Technology Licensing, LLC.

Conclusion

Randolph Yao's contributions to cloud computing through his patent on node failure prediction exemplify the importance of innovation in technology. His work not only improves the reliability of cloud services but also showcases the potential of machine learning in predictive analytics.

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