San Jose, CA, United States of America

Mingran Li

This inventor holds 2 USPTO granted patents. Top assignee: Cohesity, Inc.. Active years: 2024-2026.


% Patents Active = 50.0

Average Co-Inventor Count = 2.0

ph-index = 1


Company Filing History:


Years Active: 2024-2026

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2 patents (USPTO):Explore Patents

Title: Innovations by Mingran Li

Introduction

Mingran Li is an accomplished inventor based in San Jose, CA. He has made significant contributions to the field of technology, particularly in the area of anomaly detection. His innovative work has led to the development of a patent that enhances the understanding of anomalous events through graphical representation.

Latest Patents

Mingran Li holds a patent titled "Providing a graphical representation of anomalous events." This patent involves the analysis of one or more event logs using various models to detect anomalous events. The invention provides a graphical representation of risk entities associated with the detected anomalous events. Additionally, it offers a visual representation of automatically detected relationships between these risk entities. The patent also includes indications of measures of anomaly associated with the detected events for the relevant risk entities. He has 1 patent to his name.

Career Highlights

Mingran Li is currently employed at Cohesity, Inc., where he applies his expertise in technology and innovation. His work at Cohesity focuses on enhancing data management and security solutions. His contributions have been instrumental in advancing the company's mission to simplify data protection and management.

Collaborations

Mingran collaborates with various professionals in his field, including his coworker Colin Scott Johnson. Their teamwork fosters an environment of innovation and creativity, leading to the development of cutting-edge solutions.

Conclusion

Mingran Li's innovative work in anomaly detection and his contributions to Cohesity, Inc. highlight his role as a significant inventor in the technology sector. His patent demonstrates the importance of graphical representation in understanding complex data, paving the way for future advancements in the field.

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