Patent Drafting for Machine Learning: Structural Claim Limitations, Avoiding §101 or §112 Rejections

Recording of a 90-minute CLE webinar with Q&A


Conducted on Tuesday, February 13, 2018

Recorded event now available

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Program Materials

This CLE webinar will provide guidance to patent practitioners on overcoming the challenges when seeking patent protection for machine learning inventions. The panel will also discuss what can be done to anticipate and minimize the risks of §101 or §112 rejections.

Description

Machine learning is increasingly important and is at work in many things used today, from Internet searches to navigating traffic. Inventions in the field of machine learning pose challenges for patent practitioners seeking patent protection. However, what is patent eligible in the context of software inventions is not always clear.

Machine learning inventions should be eligible for protection, but for patent-eligibility purposes such inventions must be described and claimed correctly and in view of the current state of the law. Another challenge for patent counsel is determining inventorship and who would hold the patent rights.

Counsel may also face challenges when seeking to comply with the enablement requirement under § 112. Structural claim limitations can be used to strengthen patents for machine learning inventions and will benefit applicants by producing valid and enforceable patents.

Listen as our authoritative panel of patent attorneys examines the challenges under §§ 101 and 112 and what patent counsel can do to overcome those challenges. The panel will also provide an overview of the technology involved with machine learning and the legal issues concerning inventorship.

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Outline

  1. Challenges in drafting patents for machine learning under §§ 101 and 112
  2. Anticipating and avoiding § 101 or § 112 rejections
  3. Addressing inventorship issues when patenting inventions directed to machine learning

Benefits

The panel will review these and other key issues:

  • What hurdles must patent counsel overcome to demonstrate inventorship?
  • How can patent counsel meet the requirements under § 101 and § 112 in machine learning patent applications?
  • What steps should patent counsel take to minimize the likelihood of § 101 or § 112 rejections?

Faculty

Rabin, Gregory
Gregory Rabin

Patent Attorney
Schwegman Lundberg & Woessner

Mr. Rabin is a registered patent attorney whose practice includes patent procurement in a wide range of computer...  |  Read More

Stein, Michael
Michael D. Stein

Partner
Baker & Hostetler

Mr. Stein has 30 years of experience working with technology companies and nearly 25 years of experience advising...  |  Read More

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