Cheadle, United Kingdom

Paul Cookson

USPTO Granted Patents = 2 

Average Co-Inventor Count = 6.9

ph-index = 1


Company Filing History:


Years Active: 2025

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2 patents (USPTO):Explore Patents

Title: The Innovative Contributions of Paul Cookson

Introduction

Paul Cookson is a notable inventor based in Cheadle, GB. He has made significant contributions to the field of materials science, particularly in the development of advanced films and machine learning methods for predicting chemical formulation properties. With a total of two patents to his name, Cookson's work exemplifies the intersection of innovation and technology.

Latest Patents

Cookson's latest patents include "Zeolite containing polyolefins films," which describes a method for producing zeolite-embedded polyolefin films for packaging polyurethane foams also embedded with zeolite. This innovation aims to enhance the properties of packaging materials, making them more effective and sustainable. His second patent, "Hybrid machine learning methods of training and using models to predict formulation properties," outlines methods for training machine learning modules to predict target product properties for prospective chemical formulations. This patent includes a comprehensive approach to constructing training datasets, performing feature selection, and validating machine learning models, ultimately optimizing the prediction of formulation properties.

Career Highlights

Cookson is currently employed at Dow Global Technologies LLC, where he applies his expertise in materials science and machine learning. His work at Dow has allowed him to contribute to cutting-edge research and development projects that push the boundaries of technology in the industry.

Collaborations

Some of Cookson's notable coworkers include Fabio Aguirre Vargas and Sukrit Mukhopadhyay. Their collaborative efforts in research and development have further advanced the innovative projects at Dow Global Technologies LLC.

Conclusion

Paul Cookson's contributions to the field of materials science and machine learning demonstrate his commitment to innovation. His patents reflect a deep understanding of both practical applications and theoretical advancements, making him a valuable asset in his field.

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