Munich, Germany

Robert Seidl

USPTO Granted Patents = 2 

Average Co-Inventor Count = 6.5

ph-index = 1


Company Filing History:


Years Active: 2025

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2 patents (USPTO):Explore Patents

Title: The Innovative Mind of Robert Seidl

Introduction

Robert Seidl is a prominent inventor based in Munich, Germany. He has made significant contributions to the field of sports predictions through his innovative computing systems. With a total of 2 patents, Seidl is recognized for his unique approaches to analyzing player performances in tennis.

Latest Patents

Seidl's latest patents include groundbreaking technologies that enhance the accuracy of sports predictions. One of his notable inventions is a system for live prediction of player performances in tennis. This computing system receives pre-match data for an upcoming match and generates predictions using advanced models. Additionally, it processes in-match data to provide live match predictions, allowing for real-time analysis.

Another significant patent is focused on implementing a feature set of high-dimensional spatial data in sports predictions. This method generates probabilities for specific actions during sporting events by utilizing machine learning models. It involves obtaining initial data related to player positions and target areas, which is then used to modify projected scoring probabilities.

Career Highlights

Robert Seidl works at Stats LLC, a company known for its expertise in sports analytics. His work has positioned him as a key figure in the intersection of technology and sports, where data-driven insights are becoming increasingly vital.

Collaborations

Seidl collaborates with notable professionals in the field, including Patrick Joseph Lucey and Joe Dominic Gallagher. Their combined expertise contributes to the development of innovative solutions in sports analytics.

Conclusion

Robert Seidl's contributions to sports prediction technology exemplify the power of innovation in enhancing athletic performance analysis. His patents reflect a commitment to advancing the field through data-driven methodologies.

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