Boston, MA, United States of America

Michael Schmidt

USPTO Granted Patents = 5 

Average Co-Inventor Count = 1.0

ph-index = 4

Forward Citations = 41(Granted Patents)


Company Filing History:


Years Active: 2016-2022

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5 patents (USPTO):Explore Patents

Title: Michael Schmidt: Innovator in Machine Learning Technologies

Introduction

Michael Schmidt is a prominent inventor based in Boston, MA, known for his contributions to the field of machine learning. With a total of five patents to his name, Schmidt has developed innovative methods that enhance the automation of machine learning processes. His work focuses on creating intuitive user interfaces and methodologies that simplify the formulation of machine learning queries.

Latest Patents

Among his latest patents, Schmidt has developed methods for automating aspects of machine learning and related systems and apparatus. These innovations provide an intuitive user interface that enables automatic specification of queries and constraints for analysis by machine learning components. His methodologies facilitate the automatic generation of machine learning and optimization queries, utilizing examples to streamline the process. Another significant patent involves systems and methods for symbolic analysis, which allow for the searching of related data sets. This system can automatically reduce multivariate data sets to a set of best-fit data models, enhancing the analysis of data model similarities and quality.

Career Highlights

Throughout his career, Schmidt has made significant strides in the tech industry. He has worked with notable companies such as DataRobot, Inc. and Nutomian, Inc., where he has applied his expertise in machine learning to develop cutting-edge technologies. His contributions have not only advanced the field but have also provided practical solutions for various applications.

Collaborations

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Conclusion

Michael Schmidt's innovative work in machine learning continues to shape the future of technology. His patents reflect a deep understanding of the complexities involved in automating machine learning processes, making significant contributions to the field.

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