Donaueschingen, Germany

Dominic Thomae


 

Average Co-Inventor Count = 4.0

ph-index = 1


Company Filing History:


Years Active: 2023

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1 patent (USPTO):Explore Patents

Title: The Innovations of Dominic Thomae

Introduction

Dominic Thomae is a notable inventor based in Donaueschingen, Germany. He has made significant contributions to the field of kinematic measurement through his innovative work. His expertise and creativity have led to the development of a unique encoder device that enhances the accuracy of movement detection.

Latest Patents

Dominic Thomae holds a patent for an encoder device and method of determining a kinematic value. This device is designed to determine the kinematic value of the movement of a first object relative to a second object. The encoder device includes a standard associated with the first object and at least one scanning unit linked to the second object. This scanning unit produces scanning signals by detecting the standard. Additionally, a control and evaluation unit is incorporated to determine the kinematic value from the scanning signal. Notably, this unit utilizes machine learning methods to evaluate the scanning signals, which are trained with a variety of scanning signals and their corresponding kinematic values. He has 1 patent to his name.

Career Highlights

Dominic Thomae is currently employed at Sick AG, a company renowned for its advancements in sensor technology and automation solutions. His role at Sick AG allows him to apply his innovative ideas in a practical setting, contributing to the company's reputation for excellence in the industry.

Collaborations

Dominic has collaborated with talented coworkers, including Simon Brugger and Christian Sellmer. Their combined expertise fosters a creative environment that encourages innovation and the development of cutting-edge technologies.

Conclusion

Dominic Thomae's contributions to the field of kinematic measurement through his encoder device exemplify the spirit of innovation. His work not only enhances the accuracy of movement detection but also showcases the potential of machine learning in engineering applications.

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