Carlsbad, CA, United States of America

Chuck Stroyeck

USPTO Granted Patents = 1 

Average Co-Inventor Count = 4.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2022

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1 patent (USPTO):Explore Patents

Title: Chuck Stroyeck: Innovator in Diabetes Prediction Technology

Introduction

Chuck Stroyeck is a notable inventor based in Carlsbad, California. He has made significant contributions to the field of diabetes prediction through innovative technology. His work focuses on utilizing machine learning to enhance the accuracy of diabetes classification.

Latest Patents

Stroyeck holds a patent for "Diabetes prediction using glucose measurements and machine learning." This patent describes a sophisticated observation analysis platform that incorporates a machine learning model. The model is trained using historical glucose measurements and outcome data from a user population to predict diabetes classifications for individual users. The glucose measurements are collected from wearable monitoring devices, while the outcome data includes diagnostic measurements from independent sources. Once trained, the model can predict a user's diabetes classification based on their glucose data collected over several days. The results can be communicated through notifications or user interfaces.

Career Highlights

Chuck Stroyeck is currently employed at Dexcom, Inc., a company renowned for its advancements in diabetes management technology. His work at Dexcom has allowed him to contribute to groundbreaking innovations that improve the lives of individuals with diabetes.

Collaborations

Stroyeck collaborates with talented professionals in his field, including Spencer Frank and David Price. Their combined expertise fosters an environment of innovation and progress in diabetes technology.

Conclusion

Chuck Stroyeck's contributions to diabetes prediction technology exemplify the impact of innovative thinking in healthcare. His patent and work at Dexcom, Inc. highlight the importance of machine learning in improving diabetes management.

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