Oakland, CA, United States of America

Geraldine Mitchley

This inventor holds 2 USPTO granted patents. Top assignee: Visa International Service Association. Active years: 2026.


% Patents Active = 100.0

Average Co-Inventor Count = 6.9

ph-index = 1


Company Filing History:


Years Active: 2026

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2 patents (USPTO):Explore Patents

Title: Geraldine Mitchley: Innovator in Multivariable Regression Processes

Introduction

Geraldine Mitchley is a notable inventor based in Oakland, CA. She has made significant contributions to the field of data analysis through her innovative patent. Her work focuses on enhancing interaction data processing using advanced statistical methods.

Latest Patents

Geraldine holds a patent for a method titled "Token interaction using multivariable regression process." This method involves receiving interaction data related to multiple interactions over a specified time period. It determines a multiple variable regression formula and calculates a coefficient of determination associated with this formula and the interaction data. The process includes assessing whether the coefficient meets a predetermined threshold or is maximized. If it does not, the slope coefficients are adjusted accordingly. Conversely, if the threshold is satisfied, the formula is utilized to evaluate risks associated with future interactions. Geraldine has 1 patent to her name.

Career Highlights

Geraldine is currently employed at Visa International Service Association, where she applies her expertise in data analysis and regression processes. Her innovative approach has the potential to significantly impact how companies assess and manage interaction data.

Collaborations

Geraldine collaborates with talented colleagues, including Yuexi Chen and Ratna Deepthi Jarugu. Their combined efforts contribute to the advancement of innovative solutions in their field.

Conclusion

Geraldine Mitchley is a pioneering inventor whose work in multivariable regression processes showcases her commitment to innovation. Her contributions are poised to influence the future of data interaction analysis significantly.

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