San Francisco, CA, United States of America

Brad Vancho

This inventor holds 2 USPTO granted patents and 1 published patent application. Top assignee: Fair Isaac Corporation. Active years: 2023-2025.

USPTO Granted Patents = 2 

% Patents Active = 100.0

Average Co-Inventor Count = 2.0

ph-index = 1

Forward Citations = 4(Granted Patents)


Company Filing History:


Years Active: 2023-2025

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2 patents (USPTO):Explore Patents

Title: Innovations by Brad Vancho

Introduction

Brad Vancho is an accomplished inventor based in San Francisco, CA. He has made significant contributions to the field of machine learning and predictive modeling. With a total of 2 patents, his work focuses on developing advanced systems for understanding sensitivities to potential disruptions.

Latest Patents

Brad's latest patents include innovative approaches to supervised machine learning-based modeling. The first patent describes a computer-implemented system, method, and product for modeling sensitivities to potential disruptions. This involves observing performances of entities in two sub-populations using a machine learning model that predicts expected performance based on known attributes and binary indicators. The second patent outlines a sensitivity index model that predicts how sensitive an entity is to future disruptions. This model is trained by dividing a population into matched pairs and using supervised learning techniques to quantify differences in expected performances.

Career Highlights

Brad Vancho is currently employed at Fair Isaac Corporation, a leader in analytics and decision management technology. His work at the company has allowed him to apply his expertise in machine learning to real-world challenges.

Collaborations

Brad collaborates with Gerald Fahner, leveraging their combined knowledge to enhance their projects and drive innovation within their field.

Conclusion

Brad Vancho's contributions to machine learning and predictive modeling demonstrate his commitment to advancing technology. His innovative patents reflect a deep understanding of complex systems and their applications in predicting disruptions.

Profile summary based on public USPTO records.
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