Seattle, WA, United States of America

Ming Chen

USPTO Granted Patents = 1 

Average Co-Inventor Count = 11.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2024

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1 patent (USPTO):

Title: The Innovations of Ming Chen

Introduction

Ming Chen is an accomplished inventor based in Seattle, WA. He is known for his contributions to the field of anomaly detection, particularly through his work at Amazon Technologies, Inc. His innovative approach has led to the development of techniques that enhance the accuracy and efficiency of detecting anomalies in data.

Latest Patents

Ming Chen holds a patent titled "Anomaly detection using feedback." This patent describes techniques for performing anomaly detection, which includes receiving a request to detect potential anomalies using an anomaly detection system. The exemplary method involves processing received data to score it and determine when it is potentially anomalous based on one or more thresholds. Additionally, the method includes requesting feedback on determined potential anomalies and adjusting thresholds used to identify anomalies without altering the anomaly scoring model. This patent showcases his expertise in leveraging feedback to improve anomaly detection systems. He has 1 patent to his name.

Career Highlights

Ming Chen has made significant strides in his career at Amazon Technologies, Inc. His work focuses on developing advanced technologies that improve data analysis and anomaly detection. His innovative contributions have positioned him as a key player in the tech industry, particularly in the realm of data science and machine learning.

Collaborations

Ming Chen has collaborated with notable colleagues such as Laurent Callot and Jasmeet Chhabra. These collaborations have fostered an environment of innovation and creativity, leading to the development of cutting-edge technologies in their field.

Conclusion

Ming Chen's work in anomaly detection exemplifies the impact of innovative thinking in technology. His contributions continue to shape the future of data analysis and enhance the capabilities of anomaly detection systems.

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