MBHB (Chicago, IL)
Michael Borella received his Ph.D. from the University of California, Davis in Computer Science. His dissertation focused on data-link layer protocols for optical local area networks. Since then he has held a number of academic and industry positions, including teaching graduate level courses at DePaul University and Northwestern University, as well as engineering, management, and executive roles at high-tech companies. In particular, Dr. Borella was director of product development for 3Com’s wireless access gateway division, and led a team of over 75 engineers develop core 2G and 3G wireless technologies that were deployed worldwide. He also served as vice president of engineering at Fastmobile, a Chicago-area software startup, that developed and sold pre-iPhone email and messaging solutions in the U.S., Europe, and Asia.
Dr. Borella graduated summa cum laude from the Chicago-Kent School of Law, and joined MBHB in 2007. Since then, he has systematically built his practice around developing and executing patent strategies for clients from numerous industries. Dr. Borella has drafted, prosecuted, and overseen the drafting and prosecution of dozens of machine learning patent applications in diverse areas including image processing, speech recognition, facial recognition, supply chain management, IT service management, and bioinformatics. Thus, he is well versed in both the theory and practice of neural networks, decision trees, support vector machines, and various types of classifiers and clustering algorithms.
Dr. Borella is the author of over 70 peer-reviewed scientific articles and several AI-related legal publications and blog posts, including:
- The Subject Matter Eligibility of Machine Learning: An Early Take, Patent Docs (September 23, 2018)
- How to Draft Patent Claims for Machine Learning Inventions, Patent Docs (November 25, 2018)
- On the Patent-Eligibility of Machine Learning, Managing IP (December 17, 2018)
Dr. Borella also presented a webinar in February 2019 on subject matter eligibility issues. In that presentation, he provided detailed examples of claim drafting strategies for avoiding and overcoming eligibility rejections for machine learning inventions.