Allen, TX, United States of America

Fred Crable


Average Co-Inventor Count = 8.0

ph-index = 1

Forward Citations = 6(Granted Patents)


Company Filing History:


Years Active: 2018

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1 patent (USPTO):Explore Patents

Title: Innovations by Fred Crable in Computational Systems

Introduction

Fred Crable is an accomplished inventor based in Allen, TX (US). He has made significant contributions to the field of computational systems, particularly in the area of analytical modeling. His innovative work focuses on enhancing decision-making processes through advanced technological solutions.

Latest Patents

Fred Crable holds a patent for "Automated systems for reducing computational loads in the mass execution of analytical models using scale-out computing." This patent discloses systems and techniques that facilitate the mass execution of analytical models across various dimensions, including client, collateral, deal structure, and third-party data. The disclosed embodiments aim to predict optimal decisions in real-time. By reducing computational loads on decisioning systems, these innovations increase decisioning speed. Furthermore, the systems may scale-out analytical modeling computations through advanced execution environments that are asynchronous and non-blocking, allowing for parallel execution of multiple analytical models and optimizing results.

Career Highlights

Fred Crable is currently employed at Capital One Services, LLC, where he applies his expertise in computational systems. His work has been instrumental in developing solutions that enhance operational efficiency and decision-making capabilities within the organization.

Collaborations

Fred has collaborated with notable colleagues, including Chandra Dhandapani and Raman Bajaj, who share a commitment to innovation and excellence in their respective fields.

Conclusion

Fred Crable's contributions to the field of computational systems exemplify the impact of innovative thinking on decision-making processes. His patent and work at Capital One Services, LLC highlight the importance of reducing computational loads to enhance analytical modeling.

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