Drafting Machine Learning Patents Applications




About the Course:

Don’t miss this unique opportunity to listen to a skilled patent attorney discuss numerous case studies for drafting machine learning patents. If you seek patent coverage of a machine learning-related invention, you really should listen to this webinar.

The following are among the issues discussed in this crucial webinar:

  • What exactly is machine learning? What is good working definition of the term? What is the distinction between machine learning and supervised learning? Between machine learning and unsupervised learning? Between machine learning and reinforcement learning?
  • What can patent drafters do to make machine learning claims more likely to overcome examiner rejections based on eligibility issues? Where in the claims should claims limitations be applied?
  • How can eligibility challenges during examination benefit patentees in later years?
  • What is the significance of the Enfish v. Microsoft case on machine learning claims?
  • How does the decision in the Core Wireless Licensing S.A.R.L. v. LG Electronics case impact the risk of machine learning claims being deemed “abstract”?
  • Which relevant art units are most likely to reject machine learning patent applications? What can be done to steer machine learning patent applications away from such art units?
  • How might the Berkheimer v. HP memorandum affect the eligibility of machine learning patent applications? What are the Berkheimer tests for eligibility? To what extent must examiners consider Berkheimer before rejecting patent applications based on eligibility?
  • What should in-house counsellors do with respect to approving invention disclosures to increase the odds of overcoming examiner rejections based on abstract concerns?
  • How important are rigorous prior art searches before filing machine learning patent applications?
  • How should responses to office actions be drafted?

Course LeaderGregory Rabin, Senior Attorney, Schwegman, Lundberg & Woessner

Greg is a senior patent attorney at Schwegman, Lundberg & Woessner. Greg has been practicing patent law for a decade and has drafted and prosecuted multiple patent applications to issue in the United States and abroad. Greg has worked with European, Chinese, Japanese, Korean, Taiwanese, Indian, Canadian, and Australian counsel to prepare and prosecute foreign patent applications for his clients. Greg frequently conducts “patent mining” sessions with clients, where he visits the client’s office, meets with inventors and in-house counsel, and identifies and selects inventions for patenting (or for coverage as a trade secret).

Greg has spoken about patenting inventions in artificial intelligence and machine learning before the American Intellectual Property Law Association (AIPLA), the United States Patent & Trademark Office, Strafford Publications, and internally at Schwegman and with clients. In addition to machine learning and artificial intelligence, Greg 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, Greg practiced at McDermott, Will & Emery in Boston, MA. Greg 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. Greg is a native speaker of both English and Russian.

Course Length: Approx. 1 hour


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