San Diego, CA, United States of America

John Scott Skellenger

USPTO Granted Patents = 3 

Average Co-Inventor Count = 3.8

ph-index = 1

Forward Citations = 2(Granted Patents)


Company Filing History:


Years Active: 2020-2025

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3 patents (USPTO):Explore Patents

Title: John Scott Skellenger: Innovator in Physiological Response Prediction Systems

Introduction

John Scott Skellenger is a notable inventor based in San Diego, California. He has made significant contributions to the field of physiological response prediction systems, holding a total of three patents. His work focuses on modeling the evolution of systems over time, utilizing advanced mathematical processes.

Latest Patents

One of Skellenger's latest patents is a physiological response prediction system. This innovative system models the evolution of a system over time using an advection-based process. It continuously evolves a probability density function (PDF) for a characteristic of the state of the system and its time-varying parameters. The PDF is evolved based on advection by solving an advection partial differential equation that is based on a system model. The system model incorporates time-varying parameters to effectively model the characteristic of the state of the system. Ultimately, the system uses the continuously evolving PDF to make predictions about the characteristic of the state of the system.

Career Highlights

Throughout his career, John Scott Skellenger has worked with several prominent companies, including Intermountain Intellectual Asset Management, LLC and Human Longevity, Inc. His experience in these organizations has contributed to his expertise in the field of physiological modeling and prediction systems.

Collaborations

Skellenger has collaborated with notable professionals in his field, including David John Balaban and Mark Joseph Durst. These collaborations have further enriched his work and expanded the impact of his innovations.

Conclusion

John Scott Skellenger is a distinguished inventor whose work in physiological response prediction systems has advanced the understanding of system evolution over time. His patents reflect a commitment to innovation and a deep understanding of complex mathematical processes.

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