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For the Inventor, By the Inventor

Seattle, WA, United States of America

Junyuan Lin

USPTO Granted Patents = 17 

Average Co-Inventor Count = 3.8

ph-index = 4

Forward Citations = 59(Granted Patents)


Location History:

  • Seattle, WA (US) (2016 - 2022)
  • Seattle, CA (US) (2021 - 2023)
  • Bellevue, WA (US) (2023)

Company Filing History:

goldMedal17 out of 5,475 
 
Vmware, Inc.
 patents

Years Active: 2016-2025

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17 patents (USPTO):Explore Patents
1. 12348581 - Streaming anomaly detection (2025-07-01),
2. 11748230 - Exponential decay real-time capacity planning (2023-09-05),
3. 11640465 - Methods and systems for troubleshooting applications using streaming anomaly detection (2023-05-02),
4. 11316727 - Method and system for clustering event messages and manage event-message clusters (2022-04-26),
5. 11048608 - Probability-distribution-based log-file analysis (2021-06-29),
6. 11016870 - Exponential decay real-time capacity planning (2021-05-25),
7. 10810103 - Method and system for identifying event-message transactions (2020-10-20),
8. 10810052 - Methods and systems to proactively manage usage of computational resources of a distributed computing system (2020-10-20),
9. 10776439 - Efficient log-file-based query processing (2020-09-15),
10. 10776166 - Methods and systems to proactively manage usage of computational resources of a distributed computing system (2020-09-15),
11. 10740211 - Methods and systems to tag tokens in log messages (2020-08-11),
12. 10616038 - Method and system for clustering event messages and managing event-message clusters (2020-04-07),
13. 10205627 - Method and system for clustering event messages (2019-02-12),
14. 10120928 - Method and system for clustering event messages and managing event-message clusters (2018-11-06),
15. 10116675 - Methods and systems to detect anomalies in computer system behavior based on log-file sampling (2018-10-30),

Title: Innovations of Junyuan Lin

Introduction

Junyuan Lin is a prominent inventor based in Seattle, WA (US). He has made significant contributions to the field of computing, holding a total of 17 patents. His work focuses on enhancing the efficiency and reliability of distributed computing systems.

Latest Patents

One of his latest patents is related to streaming anomaly detection. This patent describes computational methods and systems designed to detect anomalous behavior in resources and objects within a distributed computing system. The system sends multiple streams of metric data representing the usage of various resources to a management system. This management system updates a performance model based on the newly received metric values, which is then used to identify changes in the streams of metric data. Such changes may indicate anomalous behavior, and an anomaly listener is notified when such behavior is detected.

Another notable patent is for exponential decay real-time capacity planning. This innovation involves transitioning usage forecasting in a computing environment. It allows for the forecasting of computing resource usage using a first forecasting data model and actual usage measurements. The transition to a second forecasting data model occurs without any downtime, ensuring continuous operation. The second model exponentially decays usage measurements based on the time period in which they were obtained.

Career Highlights

Junyuan Lin is currently employed at VMware, Inc., where he continues to develop innovative solutions in the computing sector. His work has significantly impacted how organizations manage and forecast resource usage in distributed systems.

Collaborations

He has collaborated with notable coworkers, including Darren Brown and Nicholas Kushmerick, contributing to various projects that enhance the capabilities of their computing solutions.

Conclusion

Junyuan Lin's contributions to the field of computing through his patents and innovations demonstrate his expertise and commitment to advancing technology. His work continues to influence the way distributed computing systems operate and manage resources effectively.

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