Fort Wayne, IN, United States of America

John G Watts

This inventor holds 1 USPTO granted patent. Top assignee: Raytheon Company. Active years: 2016.


% Patents Active = 100.0

Average Co-Inventor Count = 2.0

ph-index = 1

Forward Citations = 1(Granted Patents)


Company Filing History:


Years Active: 2016

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1 patent (USPTO):Explore Patents

Title: John G Watts - Innovator in Data Clustering Methods

Introduction

John G Watts is a notable inventor based in Fort Wayne, IN (US). He has made significant contributions to the field of data analysis through his innovative patent. His work focuses on methods for identifying clusters within collections of data entities, which is crucial for various applications in data science and analytics.

Latest Patents

John G Watts holds a patent for a "Method and system for identifying clusters within a collection of data entities." This patent describes embodiments of a method that includes defining a metric space over data entities. The distance function of the metric space satisfies the triangle inequality. The method determines a value for the number of clusters that minimizes the number of data bits used to define a model of the collection. This model describes the collection using a minimum description length (MDL). Additionally, the method assigns data entities to the clusters based on the determined value.

Career Highlights

John G Watts is associated with Raytheon Company, where he applies his expertise in data clustering. His innovative approach has contributed to advancements in data processing and analysis within the organization. His work is recognized for its potential to enhance the efficiency of data-driven decision-making processes.

Collaborations

John has collaborated with Richard J Kenefic, a fellow innovator, to further explore advancements in data clustering techniques. Their combined efforts have led to a deeper understanding of data entity relationships and clustering methodologies.

Conclusion

John G Watts is a distinguished inventor whose work in data clustering has paved the way for improved data analysis techniques. His contributions are vital in the evolving landscape of data science and analytics.

Profile summary based on public USPTO records.
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