Kirkland, WA, United States of America

Justin Hobart

USPTO Granted Patents = 1 

Average Co-Inventor Count = 5.0

ph-index = 1

Forward Citations = 20(Granted Patents)


Company Filing History:


Years Active: 2017

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1 patent (USPTO):Explore Patents

Title: The Innovative Mind of Justin Hobart

Introduction

Justin Hobart is an accomplished inventor based in Kirkland, WA (US). He has made significant contributions to the field of predictive analytics through his innovative patent. His work exemplifies the intersection of technology and data science, showcasing the potential of self-learning systems.

Latest Patents

Justin holds a patent for a "Self-learning adaptive modeling system." This invention employs self-learning and adaptive modeling techniques to enhance predictive analytics. The hierarchical model structure he developed comprises a set of predictive models that are automatically built from accumulated data and distributed across multiple levels. For any given input type, a set of candidate models can be identified across varying levels of granularity. The best model is selected based on a comparison of performance metrics, ensuring optimal predictions. As more data becomes available, the best model can change, allowing for continuous improvement in predictive accuracy.

Career Highlights

Justin is currently associated with Microsoft Technology Licensing, LLC, where he applies his expertise in predictive analytics. His innovative approach has positioned him as a valuable asset in the technology sector. With a focus on developing adaptive systems, he continues to push the boundaries of what is possible in data-driven decision-making.

Collaborations

Some of Justin's notable coworkers include Sandipan Ganguly and Lu Xia. Their collaborative efforts contribute to the advancement of technology and innovation within their organization.

Conclusion

Justin Hobart's contributions to predictive analytics through his self-learning adaptive modeling system highlight his innovative spirit and dedication to advancing technology. His work not only enhances predictive capabilities but also sets a foundation for future developments in the field.

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