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

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 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 invention's novelty relates to each of these components and then draft claims that have a style and framing that optimally captures the invention based on its position in the workflow.

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

Counsel may also face challenges when seeking to comply with the disclosure requirements such as those outlined 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 that 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.