Danville, CA, United States of America

Eric Christopher Hagen

USPTO Granted Patents = 1 

Average Co-Inventor Count = 2.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: The Innovations of Eric Christopher Hagen

Introduction

Eric Christopher Hagen is an accomplished inventor based in Danville, California. He has made significant contributions to the field of technology, particularly in the area of resource allocation using machine learning. His innovative approach has led to the development of a unique patent that addresses the challenges of assigning hardware and software resources efficiently.

Latest Patents

Hagen holds a patent for an invention titled "Automatic assignment of hardware/software resources to different entities using machine learning based on determined scores for assignment solutions." This patent outlines a method where tasks and resources are randomly assigned multiple times, and a score for each solution is calculated. By utilizing machine learning and artificial intelligence, a subset of the solutions is selected, allowing for dynamic changes in task/resource assignments. The process continues until specific conditions are met, ensuring optimal resource allocation.

Career Highlights

Eric Christopher Hagen is currently employed at Callidus Software, Inc., where he applies his expertise in machine learning to enhance software solutions. His work focuses on improving the efficiency of resource management within the company. Hagen's innovative mindset and technical skills have positioned him as a valuable asset in the tech industry.

Collaborations

Hagen collaborates with his coworker, Nick Pendar, to further develop and refine their projects. Their teamwork exemplifies the importance of collaboration in driving innovation and achieving successful outcomes.

Conclusion

Eric Christopher Hagen's contributions to the field of technology through his patent and work at Callidus Software, Inc. highlight his role as a forward-thinking inventor. His innovative methods for resource allocation using machine learning demonstrate the potential for advancements in efficiency and effectiveness in various applications.

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