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Patenting Machine Learning and AI Innovations: Strengthen Your Claims to Avoid Rejection

Format: On-Demand Video, DVD, or PDF Transcript
Originally presented: October 16, 2018

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Machine learning and artificial intelligence innovations are quickly becoming commonplace in the life sciences, informatics, automotive, security, and medical device fields just to name a few. Many universities are dedicating research centers and funding dollars to support these inventions, but risks abound in this emerging market and legal landscape — and ensuring valid and enforceable patents is a must.

Already this fast-paced innovation sector has become a hotbed of patent protection controversy. USPTO is providing some guidance, but as is often the case there is room for interpretation – and confusion for patent applicants. As we see with many software claims, the key appears to be in the description.

Patenting Machine Learning and AI Innovations: Strengthen Your Claims to Avoid Rejection

To help TTOs best protect their growing portfolios of machine learning and AI technologies, Technology Transfer Tactics has tapped Gregory Rabin, Senior Attorney with law firm Schwegman, Lundberg Woessner, to present this critical webinar that will help you effectively protect and prepare to enforce machine learning and AI inventions. Here’s a quick look at the agenda:

  • Definition of machine learning and types of machine learning
  • Drafting claims for machine learning technology (simple non-real-life example)
  • Real-life example that overcame a patentable subject matter (35 USC 101) rejection
  • Patent Office Art Units for machine learning inventions
  • Berkheimer v. HP and its departure from Alice
  • Patent Office Berkheimer Memorandum – defining “well-understood, routine, conventional activity”
  • Suggestions for improving invention disclosures and claims

Meet Your Presenter:

Gregory RabinGregory Rabin
Senior Patent Attorney
Schwegman, Lundberg & Woessner 

Mr. Rabin has been practicing patent law for a decade and has drafted and prosecuted multiple patent applications in the United States and abroad. He has spoken about patenting inventions in artificial intelligence and machine learning before the American Intellectual Property Law Association and the United States Patent & Trademark Office, among others. In addition to machine learning and artificial intelligence, Mr. Rabin has also drafted and prosecuted patents related to mobile and WiFi networks, operating systems, cryptography, security systems, control systems, and robotics. Prior to joining Schwegman, he practiced at McDermott, Will & Emery in Boston, MA.  Mr. Rabin holds a J.D. from the University of Michigan Law School, dual Bachelor’s Degrees in Computer Science and Mathematics from MIT, and a Master’s Degree in Computer Science from MIT. 

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