Artificial intelligence is reshaping nearly every industry, but the legal question remains surprisingly unsettled for many companies: can you patent AI? The better framing, in most cases, is this: can you patent an AI-enabled invention, and if so, how do you draft, document, and prosecute it in a way that survives scrutiny?
That distinction matters. United States patent law does not reward buzzwords. It rewards inventions. Businesses that treat “AI” as a label rather than a technical and legal problem often end up with applications that are too abstract, too broad, or too detached from a concrete technological implementation. The result is predictable: weak claims, subject-matter eligibility problems, and intellectual property assets that do not deliver the strategic value leadership expected.
For companies building machine-learning platforms, generative AI tools, decision-support systems, computer vision pipelines, or workflow automation products, the right question is not whether AI is patentable in the abstract. The question is whether the invention solves a specific technical problem in a specific way, and whether that solution is claimed with enough technical precision to meet U.S. patent standards. The USPTO’s current subject-matter eligibility guidance remains rooted in traditional Section 101 analysis, and the agency’s 2025 revised inventorship guidance makes clear that AI-assisted inventions are still evaluated under the same human inventorship standard that applies generally. There is no separate, easier patent track simply because AI was involved.
Why AI Patents Are Under So Much Pressure
AI-related patent applications often face difficulty because many of them are drafted at too high a level of generality. Phrases like “using machine learning to optimize,” “applying artificial intelligence to identify,” or “employing a model to predict” can describe important business goals, but those phrases do not necessarily identify a patent-eligible invention.
Federal Circuit decisions over the last several years have continued to show skepticism toward claims that merely apply known computational techniques to data without explaining a concrete technological improvement. In 2025, for example, the Federal Circuit in Recentive Analytics, Inc. v. Fox Corp. addressed machine-learning-related claims and framed the issue through the familiar patent-eligibility lens rather than creating a special doctrinal path for AI. The court’s treatment reinforces the practical reality that AI inventions are being judged under existing software and abstract-idea jurisprudence, not under a new AI-specific regime.
That does not mean AI patents are unavailable. It means they must be developed carefully. Strong AI patent strategies usually focus on one or more of the following:
- a technical improvement to model training, inference, memory use, latency, or system performance
- a specific architecture tied to a concrete machine or data-processing environment
- a practical implementation that improves another technical system
- a novel interaction between software and hardware
- a workflow that is more than mere automation of human judgment
This is one reason businesses developing AI tools should evaluate patent strategy alongside BLTG’s Software Patentspractice. BLTG already positions that service around “software plus” technologies and around the overlap among patents, copyrights, and trade secrets—exactly where many AI products live in practice.
What Can Actually Be Patented in the AI Space?
The most patentable AI inventions are usually not “AI” in the abstract. They are inventions such as:
1. AI-Enabled Technical Systems
For example, an AI tool that improves network traffic allocation, anomaly detection in industrial equipment, robotics control, chip design, or medical imaging workflows may present stronger patent opportunities than a generic “AI assistant” concept.
2. Model Training or Deployment Improvements
If a company has developed a novel training pipeline, a better inferencing structure, reduced compute overhead, improved compression, or a system that materially changes how models are deployed at the edge, that may support a stronger patent position.
3. AI Integrated with Physical Processes
Where AI is tied to sensors, manufacturing systems, energy management, logistics hardware, or specialized devices, patent eligibility arguments often become stronger because the invention is easier to characterize as a technological solution rather than a purely abstract idea.
4. AI-Specific Workflow Architecture
Novel orchestration layers, multi-model coordination, agent systems with technical control logic, and secure retrieval or compliance architectures may all present meaningful filing opportunities if documented with sufficient specificity.
The global patent landscape also shows just how active this field has become. WIPO’s generative AI patent landscape report highlights rapid filing activity across generative AI technologies and application areas, reinforcing the strategic importance of filing early and filing thoughtfully when a company believes it has a defensible technical edge.
The Inventorship Problem in AI
Another issue many founders underestimate is inventorship. A company may use generative AI, coding copilots, modeling tools, or automated design systems throughout product development. But under current U.S. law, patents still require human inventors. The USPTO’s revised inventorship guidance issued in November 2025 emphasizes that the same legal test for inventorship applies to AI-assisted inventions as to all other inventions. In other words, using AI does not disqualify an invention, but the patent must still be tied to qualifying human contribution.
That has immediate operational consequences. Companies should document:
- who framed the problem
- who selected the inputs and technical constraints
- who evaluated outputs and made inventive decisions
- who transformed AI-generated material into a concrete implementation
- who contributed to the conception of the claimed subject matter
Without that record, inventorship disputes can become a serious weakness later during diligence, licensing, financing, or litigation.
Patent Protection Is Only One Part of AI IP Strategy
An AI company that relies only on patents may leave major assets unprotected. In many cases, the most valuable parts of the business are not fully disclosed in the patent at all. Training data curation, system prompts, fine-tuning methods, model evaluation processes, deployment architecture, customer-specific tuning, and internal guardrails may be better protected as trade secrets.
That is why many AI businesses need a dual-track strategy: patent what should be disclosed and monopolized, while preserving secret know-how that should never appear in a public filing. BLTG’s Trade Secret Protection and Intellectual Property Agreements pages are especially relevant here because trade secret rights are only as strong as the confidentiality measures and contractual controls behind them.
In practice, companies protecting AI should think in layers:
- patents for protectable technical inventions
- trade secrets for confidential know-how, pipelines, prompts, and processes
- copyrights for code, documentation, and certain expressive material
- contracts for ownership, confidentiality, and downstream restrictions
Practical Steps Before Filing an AI Patent
Before a company rushes to file, it should pressure-test the invention. That usually means asking:
- What is the technical problem?
- What is the technical solution?
- Is the improvement measurable?
- What is novel about the architecture, workflow, or implementation?
- Which parts should remain confidential instead of being disclosed publicly?
The answers should be written down before the application is drafted. A patent filing built on vague marketing language is rarely repaired by better prosecution alone.
It is also important to think internationally. If the product has commercial relevance outside the United States, filing strategy may need to account for foreign patent timing, publication risk, and local treatment of software-related inventions. WIPO data and international filing patterns show that AI is not just a domestic race; it is a global one.
Common AI Patent Mistakes
Businesses repeatedly make the same errors in this area:
Treating a Business Idea Like a Patentable Invention
A market concept is not the same thing as a patent claim.
Overusing “AI” and Under-Describing the Technology
The more generic the language, the easier it is for an examiner or court to characterize the claim as abstract.
Filing Before Ownership and Inventorship Are Clean
If contractors, employees, or founders are not properly assigned and documented, the asset may be impaired.
Disclosing Too Much Instead of Preserving Secret Know-How
Some competitive advantages should be protected through controlled secrecy, not public patent disclosure.
Ignoring Contracts
Founders often focus on patents while overlooking the confidentiality, assignment, and licensing agreements that determine who actually owns the IP.
FAQs About AI Patents
Can AI itself be listed as an inventor on a U.S. patent?
No. Under current U.S. law, inventorship remains a human-centered requirement, and the USPTO’s revised guidance continues to apply the traditional inventorship standard to AI-assisted inventions.
Can software using machine learning be patented?
Sometimes, yes. But it usually depends on whether the claimed invention is framed as a concrete technical solution rather than an abstract result. The current law remains demanding in this area.
Should an AI company rely on patents alone?
Usually not. Many AI businesses need a portfolio approach that combines patents, trade secrets, copyright, and contract strategy.
Is generative AI code or output automatically protected the same way as human-created work?
Not necessarily. The U.S. Copyright Office’s 2025 AI copyrightability report reaffirmed that copyright protection depends on human authorship principles and does not automatically attach to purely machine-generated output in the same way it does to traditional human-authored works.
Final Thought
Companies working in AI should not ask whether AI is “patentable” in the abstract and stop there. That question is too blunt to be useful. The more strategic inquiry is whether the business has developed a specific technical invention, whether the inventorship story is clean, whether the patent can be drafted around concrete implementation details, and whether the broader IP portfolio is structured to preserve what should remain confidential.
That is the difference between filing for optics and building an asset that can withstand diligence, support valuation, and create leverage in a competitive market. For businesses evaluating next steps, BLTG’s Software Patents and Contact page are natural internal next steps if the goal is to assess patentability, inventorship, and the right mix of patent and trade secret protection.
