The Evolving Landscape of Software and AI Patents

Few areas of patent law have shifted as dramatically in recent years as software and artificial intelligence. As machine learning, generative AI, and autonomous systems reshape entire industries, inventors and companies face unique challenges in securing and enforcing patent protection for their innovations.

Can Software Be Patented?

Yes — but with important limitations. In the United States, pure software (as in a list of instructions) is not patentable. However, software that produces a concrete technical result or improves a computer's functionality in a specific, practical way often can be patented.

The landmark 2014 Supreme Court decision in Alice Corp. v. CLS Bank International significantly tightened software patent eligibility. The Court ruled that simply implementing an abstract idea on a computer is not patentable. As a result, many software patent applications are rejected under 35 U.S.C. § 101 as directed to abstract ideas.

The Alice Two-Step Test

Patent examiners use a two-part framework (derived from Alice) to evaluate software claims:

  1. Step 1: Is the claim directed to a patent-ineligible concept — an abstract idea, law of nature, or natural phenomenon?
  2. Step 2: If yes, does the claim include additional elements that amount to "significantly more" than the abstract idea itself? Does it transform the abstract idea into a patent-eligible application?

The key to surviving this test is framing your claims around specific technical improvements — not broad abstract concepts like "organizing data" or "performing calculations."

What About AI Inventions?

AI introduces new layers of complexity. The USPTO has issued guidance clarifying that AI-generated inventions require a human inventor — an AI system alone cannot be listed as an inventor. This has significant implications for companies that heavily use AI in their R&D processes.

Patentable AI-related innovations may include:

  • A novel neural network architecture that solves a specific technical problem.
  • A new training methodology that produces measurably better results.
  • An AI-powered system applied to a specific, concrete use case (e.g., medical diagnosis, fraud detection).
  • Hardware improvements designed specifically for AI acceleration.

Abstract claims like "using AI to improve predictions" without technical specificity are unlikely to survive examination.

Drafting Strategies for Software and AI Patents

To maximize patentability in this space, consider these drafting approaches:

  • Focus on technical outcomes: Emphasize how the invention improves computer performance, reduces processing time, conserves memory, or solves a specific technical problem.
  • Avoid purely result-oriented claims: Claiming "a method for better recommendations" is abstract. Claiming "a method for generating ranked recommendations using a sparse attention transformer trained on event-sequence data to reduce inference latency by X%" is far more concrete.
  • Include system and method claims: Draft claims across multiple formats (method, system, computer-readable medium) to broaden protection.
  • Detailed technical disclosure: A robust specification with architectural diagrams, training procedures, and performance benchmarks supports stronger claims and aids in overcoming examiner rejections.

Alternative Protections for Software and AI

Given the challenges of software patentability, a layered IP strategy is often the wisest approach:

  • Copyright: Automatically protects your source code as a literary work.
  • Trade secrets: Highly effective for protecting proprietary training datasets, model weights, and algorithms you don't want to disclose. Major AI companies rely heavily on trade secret protection.
  • Patents: Best suited for novel technical architectures and deployment systems where disclosure creates a competitive moat.

The Global Picture

Software patent eligibility varies internationally. The European Patent Office (EPO) applies a "technical character" requirement — software must solve a technical problem in a technical way. China has been expanding software patent protection and has become an increasingly active AI patent jurisdiction. Understanding jurisdictional differences is critical for global tech companies crafting IP strategy.

Looking Ahead

As AI continues to evolve, patent law is struggling to keep pace. Questions about AI inventorship, ownership of AI-generated IP, and the patentability of emergent AI behaviors remain open. Inventors and companies operating in this space should work closely with patent counsel experienced in both technology and IP law to stay ahead of these developments.