San Francisco, CA, United States of America

Todd Sawyer

USPTO Granted Patents = 2 

Average Co-Inventor Count = 6.0

ph-index = 1


Company Filing History:


Years Active: 2020-2024

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2 patents (USPTO):Explore Patents

Title: The Innovations of Todd Sawyer

Introduction

Todd Sawyer is an accomplished inventor based in San Francisco, California. He has made significant contributions to the field of machine learning and predictive modeling. With a total of two patents to his name, Sawyer's work focuses on enhancing the way interactions are predicted using advanced computational methods.

Latest Patents

One of Todd Sawyer's latest patents is titled "Machine learning and prediction using graph communities." This invention is directed towards methods and devices for predicting interactions. The embodiment of this invention involves receiving interaction data for a variety of known interactions between resource providers and users. It also includes creating a topological graph based on these interactions. The method further involves determining a plurality of communities to form a predictive model and receiving requests for predictions. By applying these requests to the predictive model, the system identifies a community corresponding to the request, determines a node within that community, and provides information regarding the node as the requested prediction.

Career Highlights

Todd Sawyer is currently employed at Visa International Service Association, where he continues to innovate and develop new technologies. His work at Visa allows him to apply his expertise in machine learning and predictive analytics to real-world applications.

Collaborations

Some of Todd Sawyer's notable coworkers include Theodore David Harris and Craig O'Connell. Their collaboration contributes to a dynamic work environment that fosters innovation and creativity.

Conclusion

Todd Sawyer's contributions to machine learning and predictive modeling demonstrate his commitment to advancing technology. His patents reflect a deep understanding of complex interactions and the potential for predictive analytics in various applications.

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