Drafting Machine Learning Patents Applications

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

• •What exactly is machine learning? What is a good working definition of the term? What is the distinction between machine learning and supervised learning, unsupervised learning, and reinforcement learning?
• •What can patent drafters do to make machine learning claims more likely to overcome examiner rejections based on eligibility issues?
• •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 Core Wireless Licensing S.A.R.L. v. LG Electronics impact the risk of machine learning claims being deemed abstract?
• •Which relevant art units are most likely to reject machine learning patent applications, and how can applications be steered away from such art units?
• •How might the Berkheimer v. HP memorandum affect eligibility of machine learning patent applications?
• •What should in-house counsel do when approving invention disclosures to overcome abstract-idea rejections?
• •How important are rigorous prior art searches before filing machine learning patent applications?
• •How should responses to office actions be drafted?

Course Leader

Gregory Rabin, Senior Attorney, Schwegman, Lundberg & Woessner

Greg is a senior patent attorney with over a decade of experience drafting and prosecuting patent applications in the United States and abroad.

He has worked with European, Chinese, Japanese, Korean, Taiwanese, Indian, Canadian, and Australian counsel and frequently conducts patent mining sessions with clients.

Greg has spoken on artificial intelligence and machine learning patenting before AIPLA, the U.S. Patent & Trademark Office, Strafford Publications, and industry audiences.

Course Length

Approx. 1 hour

Pricing

$175.00 per user