AI Patent Strategy for Startups: Can You Patent Your AI?

Why AI Patent Strategy Matters Now

If you're building an AI startup, your model architecture, training pipeline, and inference workflow may be the most valuable assets you create. But can you actually patent them? The answer is more nuanced than a simple yes or no—and getting it wrong can mean spending $20,000+ on patent applications that get rejected under 35 U.S.C. § 101, or worse, disclosing your secret sauce to competitors for nothing in return.

The landscape shifted significantly in 2024. The USPTO issued two major guidance updates—one on AI-assisted inventorship in February and another on AI subject matter eligibility in July. The Federal Circuit has continued to invalidate AI-related patents that claim nothing more than applying generic machine learning to known business problems. And the window for AI startups to lock in IP protection before competitors replicate is closing fast.

We've seen founders make the same avoidable mistakes: filing too broadly, claiming "applying AI to X" without describing how the AI actually works, or defaulting to trade secret protection without understanding when a patent would have been strategically superior. This guide walks through the legal framework, the 2024 guidance changes, recent case law, and the practical decisions you need to make about when to file, what to claim, and whether to patent at all.

Can You Patent AI? The § 101 Framework After Alice

Section 101 of the Patent Act says that whoever "invents or discovers any new and useful process, machine, manufacture, or composition of matter" may obtain a patent. That sounds broad—but the Supreme Court has long recognized exceptions. Laws of nature, natural phenomena, and abstract ideas are not patent-eligible.

In Alice Corp. v. CLS Bank International (2014), the Supreme Court established the two-step test—now known as the Alice/Mayo framework—that examiners and courts use to evaluate whether claims are directed to patent-ineligible subject matter:

Step 1 (Alice Step One): Is the claim directed to a patent-ineligible concept?

The examiner first determines whether the claim is "directed to" an abstract idea, law of nature, or natural phenomenon. For AI inventions, the relevant category is almost always "abstract ideas"—particularly mathematical concepts, mental processes, and certain methods of organizing human activity.

Step 2 (Alice Step Two): Does the claim contain an "inventive concept"?

If the claim is directed to an abstract idea, the examiner then asks whether the claim contains additional elements that amount to "significantly more" than the abstract idea itself. Generic computer implementation—"do it on a computer"—is not enough. But a specific, non-generic technological improvement can be.

The key takeaway for founders: your AI patent claims must describe how your AI works in technical terms, not just what it accomplishes. Claiming "using machine learning to optimize scheduling" without describing the specific model architecture, training method, or technical improvement will almost certainly fail Step One.

The 2024 USPTO AI Guidance: What Changed

On July 17, 2024, the USPTO issued its 2024 Guidance Update on Patent Subject Matter Eligibility, specifically addressing AI inventions. The guidance doesn't create new law—it clarifies how examiners should apply the existing Alice/Mayo framework to AI-related claims.

According to detailed analysis of the 2024 AI Guidance, the update includes three new hypothetical claim examples (Examples 47-49) that illustrate how the USPTO will analyze AI patent claims. The examples suggest that:

• Claims must describe how AI features operate, not just recite an "AI model" with functional language about how to "apply it."
• Specifications should explain how AI improves computer functioning or another technology—vague references to "machine learning" are insufficient.
• Applied AI claims tied to specific technical fields (like medical treatment) have a better chance of surviving than claims that simply apply generic ML to business problems.

The guidance also organizes Federal Circuit case examples around the three categories of abstract ideas that examiners most commonly cite: mathematical concepts, certain methods of organizing human activity, and mental processes. None of the cited cases are AI-specific—because AI-specific § 101 decisions have been rare—but the principles translate directly.

AI Inventorship: Who Can Be an Inventor?

In a separate February 2024 guidance, the USPTO addressed a question that sounds academic but has real consequences for AI startups: can an AI system be listed as an inventor?

The short answer is no. The USPTO's guidance makes clear that only natural persons can be inventors. But—and this is the part that matters for startups—AI-assisted inventions are not categorically unpatentable. A patent can still be obtained if at least one natural person made a "significant contribution" to each claim.

The guidance focuses on the human contribution. If your team used AI tools (like Copilot, GPT, or a custom model) during the invention process, you need to document which human team members contributed to the conception of each claimed invention. Simply prompting an AI to "design a better recommendation algorithm" and accepting its output likely doesn't qualify the human as an inventor. But using AI as a tool within a broader inventive process—where a human identifies the problem, designs the approach, and evaluates the results—can still result in patentable inventions with human inventors.

This matters because your invention assignment agreements need to account for AI-assisted invention scenarios. If you haven't already, review your founder IP assignment agreements to ensure they cover AI-assisted invention workflows.

Recentive v. Fox: The Federal Circuit's Warning to AI Patentees

In April 2025, the Federal Circuit issued a precedential decision in Recentive Analytics, Inc. v. Fox Corp. that sent a clear signal to AI startups. The court affirmed the dismissal of Recentive's patent claims at the pleading stage under § 101.

According to analysis of the decision, Recentive's patents claimed using machine learning to schedule live events and assign television programming. The patents expressly disclosed that any standard machine learning technique—regression, neural networks, decision trees—could be used. Recentive acknowledged that the patents did not claim any new machine learning techniques; they claimed applying known ML to specific business scenarios.

The Federal Circuit held that applying generic machine learning to known tasks, without disclosing any technological advance to the ML techniques themselves, fails § 101. The claims were directed to the abstract idea of scheduling and programming, and the use of generic ML was the equivalent of "do it with a computer"—not an inventive concept.

The practical lesson: if your patent claims merely recite "using machine learning" or "applying a neural network" to solve a business problem, without describing a specific technical improvement to how the model works, your patent will likely be invalidated. This is true even if your product is commercially successful.

Patent vs. Trade Secret: The Strategic Calculus

Not every AI innovation should be patented. In fact, for many AI startups, trade secret protection is strategically superior. Here's how to think about the decision:

When to Pursue a Patent

• Your innovation is in the model architecture or training method—not just the application. If you've invented a new attention mechanism, a novel training pipeline, or a hardware-software co-design that improves inference speed, those are more likely to survive § 101.
• You need to deter competitors or attract investors. Patents are visible assets on your cap table. VCs and acquirers often count patents during diligence, and a strong patent portfolio can justify a higher valuation.
• Your innovation could be reverse-engineered. If competitors can figure out your approach from your product's behavior, trade secret protection won't help. A patent gives you exclusive rights regardless.
• You're in a hardware-adjacent space. AI hardware innovations (chips, accelerators, sensor fusion) tend to face fewer § 101 problems than pure software claims. Our guide to hardware patent strategy for early-stage founders covers this in more depth.

When to Hold as a Trade Secret

• Your innovation is your training data or model weights. These are almost never patentable in themselves and are best protected as trade secrets with appropriate technical and legal safeguards.
• Your innovation is a workflow or business process that's not externally visible. If competitors can't see how you produce your output, trade secret protection lasts indefinitely—no disclosure, no expiration.
• You can't afford to disclose your method. A patent application requires you to describe your invention in enough detail that someone skilled in the art can reproduce it. If that disclosure would give competitors too much, trade secret is the better path.
• Your innovation is incremental and ongoing. ML models evolve constantly. A patent filed today may be obsolete by the time it issues in 2-4 years. Trade secrets can protect each iteration without filing costs.

The trade-off is fundamental: patents require disclosure in exchange for a time-limited monopoly (20 years from filing). Trade secrets require secrecy in exchange for potentially perpetual protection—but only against misappropriation, not independent discovery.

Claim Drafting Strategies to Survive § 101 Rejections

If you decide to file, how you draft your claims determines whether your patent survives. Based on the 2024 USPTO guidance and recent Federal Circuit decisions, here are practical claim drafting strategies:

1. Describe the technical improvement, not the business result. Instead of claiming "a method for optimizing content recommendations using machine learning," claim the specific architectural change that makes your recommendation engine faster, more accurate, or more efficient than prior approaches.
2. Avoid reciting mathematical formulas in the claims. The 2024 guidance notes that claims which merely recite mathematical equations are directed to abstract ideas. Describe the formula in the specification, but claim the system or method that uses it in a specific technical context.
3. Tie claims to a specific technological field. The USPTO's Example 49 (a medical treatment claim) suggests that claims tied to a specific technical domain—where the AI improves the functioning of that domain—are more likely to survive. Generic "apply ML to any field" claims are at high risk.
4. Claim the machine, not just the method. Apparatus claims that describe specific hardware configurations, data flows, or system architectures often fare better than pure method claims that read like algorithms.
5. Include specific, non-generic implementation details. The Federal Circuit in Recentive emphasized that the patents disclosed "any standard machine learning technique" could be used. Don't make that mistake. Specify your model type, training approach, and technical parameters.

When to File: Timing Decisions for AI Startups

Timing matters in patent strategy, especially for AI startups operating in a fast-moving competitive landscape:

• File early, but not too early. You need enough technical detail to describe your invention with specificity—but if you wait until your product is fully built, a competitor may have already filed or published. Many startups file provisional applications early (which preserve your priority date for 12 months) and then file the non-provisional once the architecture is more refined.
• File before public disclosure. U.S. patent law gives you a one-year grace period, but most foreign jurisdictions require absolute novelty. If you plan to seek international protection, file before any public demo, publication, or product launch.
• Consider the competitive landscape. If competitors are filing in your space, the first-to-file system rewards speed. Monitor competitor patent filings and consider filing defensively before a competitor's patent can be used to block you.
• Align filing with fundraising. Investors increasingly ask about IP strategy during diligence. Having at least one provisional application filed before a raise signals that you're protecting your technology—and can support a higher valuation.

Actionable Next Steps

1. Audit your AI IP. List every innovation in your model architecture, training pipeline, inference workflow, and data processing. For each, decide: patent candidate or trade secret? Document the rationale.
2. Review your invention assignment agreements. Ensure they cover AI-assisted invention scenarios and clearly identify human inventors for each patentable invention. This is critical for both patent validity and cap table cleanliness.
3. File a provisional if you have a patentable innovation. A provisional application costs significantly less than a non-provisional and preserves your priority date for 12 months. Use that time to refine your claims and decide whether to proceed.
4. Implement trade secret protections. For innovations you're holding as trade secrets, put technical controls (access restrictions, encryption, logging) and legal controls (NDAs, employee agreements, vendor contracts) in place. Without reasonable measures, you don't have a trade secret—you have a disclosure.
5. Talk to a patent attorney before your next fundraise. Your IP strategy should be aligned with your business strategy. A patent attorney who understands AI technology can help you identify what's worth patenting, draft claims that survive § 101, and build a portfolio that strengthens your position with investors and acquirers.

AI patent strategy is not a one-time decision—it's an ongoing process that should evolve with your product, your competitive landscape, and the law. The 2024 USPTO guidance and the Federal Circuit's recent decisions have made the rules clearer, but they've also raised the bar. The founders who win will be the ones who understand the framework, draft claims that describe real technical improvements, and know when to patent and when to keep their innovations secret.