Patent Drafting for Machine Learning: Claim Strategy, Patent Eligibility, and Disclosure Requirements for the U.S. and Europe

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


Conducted on Tuesday, March 2, 2021

Recorded event now available

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Course Materials

This CLE course will assist patent practitioners in drafting high quality patent applications for machine learning inventions. The panel will discuss different claim styles and framings which may be appropriate for different machine learning inventions, how to anticipate and minimize the risks of Sect. 101 or 112 rejections, and best practices for success before the European Patent Office (EPO).

Description

Machine learning is a cutting-edge field of technology that is being increasingly applied in many different contexts, from pure information technologies to practical applications such as autonomous robotics. However, inventions in the field of machine learning pose challenges for patent practitioners drafting patent applications.

As one example, current machine learning workflows include multiple components such as data acquisition, data preparation, model selection or design, model learning or training, and deployment of the trained model within a larger computing context or application. Counsel should examine the invention to understand how the novelty of the invention relates to each of these components and then draft claims which have a style and framing which optimally captures the invention based on its position in the workflow.

Further, the scope of machine learning technology that is patent eligible is an evolving area of law in the United States and Europe. Understanding the contours of subject matter eligibility in the United States and Europe enables improved filing decisions and drafting with eligibility requirements in mind that can lead to a greater likelihood of success.

Counsel may also face challenges when seeking to comply with the disclosure requirements such as those set forth in Section 112. Considering and addressing these challenges when drafting patent applications for machine learning inventions can benefit applicants by producing valid and enforceable patents.

This webinar will assist counsel in understanding both the USPTO’s and the EPO's approach to examining machine learning inventions. Counsel that is well versed in both the U.S. and EU approaches can successfully navigate challenges in obtaining patent protection in multiple jurisdictions.

Listen as our authoritative panel of patent attorneys examines different claim styles and framings which may be appropriate for different machine learning inventions, how to anticipate and minimize the risks of Sect. 101 or 112 rejections, and best practices for success before the EPO.

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Outline

  1. Understanding the components of a machine learning invention
  2. Claim strategies for machine learning inventions, style, and framing
  3. Current state of subject matter eligibility for machine learning in the U.S. and Europe
  4. Disclosure requirements in the U.S. and Europe
  5. Drafting techniques for satisfying patent eligibility and disclosure requirements

Benefits

The panel will review these and other relevant issues:

  • What is the appropriate way to claim machine learning inventions?
  • How can patent counsel meet the requirements under Sect. 101 and 112 in machine learning patent applications?
  • How does the EPO treat patent applications for machine learning technologies differently than the USPTO?
  • What steps should patent counsel take to satisfy patent eligibility and disclosure requirements?

Faculty

Kennedy, Richard
Richard Kennedy

Partner
Venner Shipley

Mr. Kennedy is a UK and European patent attorney in the firm’s Electronics and Engineering Department. He deals...  |  Read More

Probst, Joseph
Joseph J. Probst

Principal
Dority & Manning

Mr. Probst advises clients regarding intellectual property matters, with a particular emphasis on assisting clients in...  |  Read More

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