Kirk Sigmon authored a LexisNexis practice note on patent protection for artificial intelligence and machine learning.
Kirk Sigmon authored this LexisNexis practice note on patent protection for artificial intelligence and machine learning, which is available for free on the LexisNexis website here.
This practice note discusses patenting artificial intelligence (AI), machine learning (ML), and related inventions. The note provides a high-level overview of AI and ML, provides tips for drafting a patent application directed to inventions relating to AI and ML, and discusses trends and strategies for handling prosecution of such inventions.
Summary
The practice note has a variety of useful sections for practitioners interested in the AI/ML space – a brief survey:
- AI / ML Basics: Kirk’s guidance introduces the foundational concepts behind AI, machine learning, and deep learning, clarifying terminology so legal and technical audiences can align on what is actually being patented.
- Types of AI Inventions: The guidance explains the distinction between core AI innovations and AI-enabled applications, and why that distinction drives different patenting strategies.
- Patent Eligibility (§101): Kirk’s guidance surveys the legal challenges facing AI patents—especially abstract idea rejections—and outlines how to frame inventions to survive eligibility scrutiny.
- Drafting Strategies: The guidance walks through how to write stronger AI patent applications by emphasizing technical detail, real-world implementation, and claim diversity.
- Disclosure Requirements: Kirk’s guidance highlights what must be disclosed to satisfy enablement and written description, with a focus on avoiding overly “black box” AI disclosures.
- Prosecution Strategies: The guidance then covers how AI applications are examined in practice and how to respond effectively to common rejections during USPTO prosecution.
- Industry Trends: Kirk’s guidance provides context on the rapid growth of AI patent filings and the increasing complexity and competitiveness of the space.
- Key Takeaways: The guidance then synthesizes best practices for securing meaningful protection in AI, emphasizing technical specificity, strategic drafting, and awareness of evolving legal standards.