Beijing, China

Xiu Fang Zhu

USPTO Granted Patents = 3 

Average Co-Inventor Count = 8.0

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2017-2021

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3 patents (USPTO):

Title: Innovations of Xiu Fang Zhu

Introduction

Xiu Fang Zhu is a notable inventor based in Beijing, China. He has made significant contributions to the field of transaction data analysis, holding a total of 3 patents. His work focuses on predicting transactions and identifying promotions for entities, showcasing his expertise in data analysis and pattern recognition.

Latest Patents

Xiu Fang Zhu's latest patents revolve around transaction data analysis. These embodiments include methods for predicting transactions by an entity and identifying suitable promotions to offer. The process involves parsing multiple event records corresponding to various entities. Additionally, it includes identifying a sequence of events for the entity and discretizing time durations and event values into discrete symbolic values. Furthermore, the patents detail generating a temporal pattern of events, which includes a sequence of transaction-symbols that represent the time duration and event value of the events. This innovative approach allows for predicting the next transaction based on the established temporal pattern.

Career Highlights

Xiu Fang Zhu is currently employed at International Business Machines Corporation, commonly known as IBM. His role at IBM allows him to apply his innovative ideas in a corporate environment, contributing to advancements in technology and data analysis.

Collaborations

Xiu Fang Zhu has collaborated with several talented individuals, including Chao Zhang and Yea-Jane Chu. Their teamwork has likely fostered a creative environment that enhances the development of innovative solutions in their field.

Conclusion

Xiu Fang Zhu's contributions to transaction data analysis and his innovative patents highlight his role as a significant inventor in the technology sector. His work continues to influence the way transactions are predicted and analyzed, showcasing the importance of innovation in today's data-driven world.

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