Beijing, China

De Bo Xiong

This inventor holds 1 USPTO granted patent. Top assignee: International Business Machines Corporation. Active years: 2024.


% Patents Active = 100.0

Average Co-Inventor Count = 5.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):Explore Patents

Title: Innovations of De Bo Xiong in Flaw Detection Technology

Introduction

De Bo Xiong is an accomplished inventor based in Beijing, China. He has made significant contributions to the field of flaw detection technology, particularly in the area of printed matter inspection. His innovative approach utilizes machine learning to enhance the accuracy and efficiency of defect detection.

Latest Patents

De Bo Xiong holds a patent for a technology that automatically generates defect data of printed matter for flaw detection. This invention employs machine logic informed by machine learning to inspect and detect defects in printed materials. Some key features of his patent include the generation of defect datasets, the creation of defect libraries, and the application of these libraries for deep learning training. Additionally, his technology uses machine learning to detect defects by analyzing computer code corresponding to images of printed matter, rather than relying solely on visual images.

Career Highlights

De Bo Xiong is currently associated with International Business Machines Corporation, commonly known as IBM. His work at IBM has allowed him to collaborate with other talented professionals in the field, further advancing the development of innovative technologies.

Collaborations

Some of his notable coworkers include Zhuo Jp Cai and Chao Xin. Their collaborative efforts contribute to the ongoing research and development in the realm of machine learning and defect detection technologies.

Conclusion

De Bo Xiong's contributions to flaw detection technology exemplify the intersection of innovation and practical application in the field of printed matter inspection. His work continues to influence advancements in machine learning and defect detection methodologies.

Profile summary based on public USPTO records.
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