Artificial intelligence is moving faster than patent law. Protecting an AI invention requires navigating eligibility challenges that most industries never face, inventorship rules that the courts have only recently clarified, and a competitive patent landscape that can shift significantly in months. It also requires counsel that genuinely understands the technology, not just the legal framework surrounding it.

At Gearhart Law, our AI patent practice is led by attorneys who have built AI and AR/VR portfolios through university technology transfer, stayed current on every development in USPTO examination practice for AI inventions, and worked directly with companies commercializing AI-enabled products. Our attorneys work with AI startups, technology companies, life sciences organizations deploying AI in diagnostics and drug discovery, and enterprises building proprietary machine learning systems.

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What Is an AI Patent?

An AI patent is a patent granted for an invention that involves artificial intelligence, machine learning, neural networks, or related computational technologies. Like all patents, it must meet the standard requirements of novelty, non-obviousness, and utility. But AI patents face a challenge that most other technology patents do not: subject matter eligibility under 35 U.S.C. § 101.

The Supreme Court’s Alice Corp. v. CLS Bank International decision established that abstract ideas — including mathematical concepts and software-implemented methods — cannot be patented without something more. For AI inventions, that something more is typically a demonstrable technical improvement: the system doesn’t just perform a calculation differently; it enables a computer or device to operate more efficiently, produces a new technical capability, or solves a specific problem in a defined technical domain.

This distinction is not academic. It determines whether your patent gets granted, whether it survives an IPR challenge, and whether it holds up when a competitor or a VC’s due diligence team runs your claims through their own analysis. Getting the framing right in the application, before you file, is the single most consequential decision in AI patent prosecution.

Beyond § 101, AI patents present practical challenges around the following:

  • Inventorship (who is the inventor when AI tools contribute substantially to the innovation?)
  • Prior art (AI research moves fast and the landscape is crowded)
  • Claim scope (how broadly can you claim an architecture or training method before the claims become abstract?)

These are not generic patent questions; they require attorneys who think about AI specifically.

The AI Patent Framework

AI patent law is governed by a combination of statute, Supreme Court precedent, Federal Circuit case law, and evolving USPTO examination practice. Here is what every company building an AI IP strategy needs to understand.

  • 35 U.S.C. § 101 – Subject Matter Eligibility: The Alice/Mayo two-step framework requires that AI patent claims either integrate into a practical application or include an additional element reflecting an inventive concept beyond the abstract idea. The USPTO has issued AI-specific examination guidance, most recently in 2024, updating the examples it considers eligible. The Federal Circuit continues to decide § 101 cases involving AI and software, and outcomes at this stage are highly fact-specific. Our attorneys monitor these developments as part of active practice, not periodic research.
  • 35 U.S.C. § 102 / § 103 – Novelty and Non-Obviousness: In fast-moving AI fields, the prior art landscape can shift within months. A thorough prior art search before filing is not a box-checking exercise — it is the foundation of a defensible claim set.
  • AI Inventorship – Post-Thaler: In Thaler v. Vidal, the Federal Circuit confirmed that inventors under U.S. patent law must be natural persons. AI systems cannot be listed as inventors. In practice, this requires companies using AI in R&D to carefully identify the human inventors whose intellectual contributions shaped each claimed invention, a more complex analysis than it may initially appear in AI-assisted development workflows.
  • The USPTO Is Using AI in Examination: Patent examiners are now conducting prior art searches with AI-enhanced tools. This raises the standard of rigor needed in application drafting and office action responses. An application written to withstand a traditional manual search may face more comprehensive prior art in examination than its authors anticipated. Our approach to drafting accounts for this shift.
  • The District of New Jersey (D.N.J.): AI patent infringement actions can be brought in any federal district court with proper jurisdiction. D.N.J. is an active venue for IP matters, and New Jersey’s growing AI deployment in pharmaceutical, financial, and enterprise technology sectors makes it an increasingly relevant one. The Federal Circuit hears all patent appeals.
  • Statute of Limitations: Patent infringement claims must be brought within six years of the alleged infringement under 35 U.S.C. § 286.

How an AI Patent Engagement Works

Here is what working with Gearhart Law on an AI patent matter typically looks like, from first conversation to granted patent and beyond.

  1. Technical Disclosure & Patentability Assessment – We start with the technology itself: the architecture, the training methodology, the specific problem it solves, and the result it produces. We evaluate § 101 eligibility upfront, identify the claim structures most likely to survive examination, and flag any publication or on-sale bar risks before you present, publish, or launch.
  2. Prior Art Search – AI patent landscapes evolve rapidly. We conduct a targeted search across patent databases and technical literature, identifying both what has been filed and what an examiner is likely to cite. In AI, the difference between a crowded prior art landscape and a defensible one can be a matter of how the innovation is framed.
  3. Claims Strategy – For AI patents, this is the critical step. We determine what type of claim (system, method, computer-readable medium) best covers your invention, how to articulate the technical improvement so that § 101 challenges are anticipated rather than responded to, and how to build in fallback positions that preserve scope if claims are narrowed during prosecution.
  4. Application Drafting – We write the specification to support the broadest defensible claims. For AI inventions, the specification must describe the system with enough technical specificity to enable a skilled practitioner to implement it — which is a more demanding standard in rapidly evolving fields than in established technology areas.
  5. USPTO Filing & Prosecution – We file and manage examinations. § 101 rejections in AI cases typically require substantive legal and technical argumentation; we respond with both and request examiner interviews where productive.
  6. Office Action Responses – We respond to rejections with amended claims, arguments under the Alice framework, and supporting technical analysis. Many AI patent rejections that initially appear fatal can be overcome with the right response strategy.
  7. Post-Grant Portfolio Strategy – A single AI patent rarely provides adequate protection. We advise on continuation applications, continuation-in-part filings as the technology evolves, trade secret strategy for what cannot or should not be patented, and building a portfolio that reflects the full scope of the innovation.

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AI Technologies and Patent Matters We Handle in New Jersey

AI encompasses a wide range of technologies, each presenting distinct patentability questions. Gearhart Law has experience across the AI innovation landscape, including:

  • Machine Learning & Deep Learning – Novel neural network architectures, training methodologies, optimization algorithms, convolutional and recurrent neural networks, transformer architectures
  • Natural Language Processing (NLP) – Text classification systems, language model innovations, conversational AI, speech recognition methods, document analysis
  • Computer Vision – Image recognition systems, object detection architectures, medical imaging AI, visual inspection and quality control systems
  • Generative AI – Generative adversarial network (GAN) innovations, diffusion model architectures, content generation methods with specific technical applications
  • AI in Life Sciences & Healthcare – AI-enabled diagnostic tools, drug discovery ML methods, clinical decision support systems, AI-integrated medical devices and Software as a Medical Device (SaMD)
  • Reinforcement Learning – Novel RL algorithms, reward function innovations, autonomous system decision-making, robotics AI
  • Explainable AI (XAI) – Methods for making AI outputs interpretable, model transparency techniques, AI audit tools
  • AI Hardware / Software Integration – AI accelerator architectures, AI-specific chip designs, edge AI systems, hardware-software co-optimization methods
  • AR/VR and Spatial Computing – AI-driven spatial computing systems, mixed reality AI, gesture and gaze recognition, environment-aware AI

Our AI Patent Services

Beyond patent prosecution, our AI IP services may include:

  • Patentability analysis and prior art landscape assessments before filing
  • Freedom-to-operate analysis before AI product launch or significant development investment
  • Intellectual property strategy development, including patent vs. trade secret analysis for training data, model weights, and proprietary pipelines
  • AI inventorship analysis and documentation in AI-assisted R&D environments
  • IP due diligence for AI companies raising capital, licensing technology, or preparing for acquisition
  • AI patent licensing and technology transfer, structuring agreements, advising on royalty strategy
  • IP litigation support including § 101 appeal strategy, IPR/PGR proceedings, infringement opinions, claim construction
  • Trademark protection for AI product names, platforms, and brands

What a Sound AI IP Strategy Delivers

AI companies that invest in intellectual property strategy early—before product launch, before the funding round, before a competitor files—may be able to:

  • Establish patent positions that create barriers to fast-followers and deter copycat development
  • Build a portfolio that supports licensing conversations with larger technology or industry partners
  • Reduce the risk that a competitor’s existing patent blocks your product or forces a costly design-around
  • Demonstrate IP depth to investors and acquirers, portfolios that hold up under independent due diligence scrutiny
  • Protect the first-to-file advantage before publishing research, presenting at conferences, or launching publicly
  • Develop a trade secret program that protects training data, model weights, and proprietary pipelines that should not be disclosed in patent filings

AI patents are not a guarantee of business success, and not every AI innovation is patentable in its current form. What a well-structured IP strategy does is reduce risk exposure, create optionality, and build the kind of IP foundation that scales with the company, through funding rounds, product launches, and eventual liquidity events.

We do not guarantee specific outcomes. Patent allowance, enforceability, and litigation results depend on the specific facts, prior art landscape, and legal arguments in each matter.

Why Technology Companies Choose Gearhart Law for AI Patents

AI innovations move fast, and protecting them requires intellectual property counsel that understands both the technology and the evolving legal landscape surrounding it, from § 101 eligibility to inventorship questions that courts have only recently resolved.

  • Industry-Focused Perspective: We work with AI startups, enterprise technology companies, life sciences organizations deploying AI in diagnostics and drug discovery, university spinouts commercializing AI research, and investors conducting IP due diligence on AI companies. This range of experience provides practical insight into the IP challenges that arise at every stage, from pre-seed patent filing through acquisition-stage portfolio review.
  • Strategic Patent Portfolio Development: A single AI patent rarely provides adequate protection. The technology evolves, the product changes, and competitors file aggressively. We help clients build portfolios that keep pace — through continuation strategy, trade secret programs for what should not be disclosed, and licensing structures that monetize the IP as the business grows.
  • Personalized Client Service: As a boutique IP firm, we emphasize direct attorney involvement at every stage. The attorney you consult with is the attorney who drafts your claims. In AI patent prosecution, where the framing of a technical improvement in the original application often determines the entire downstream value of the patent, that direct involvement is not a courtesy; it is a material advantage.

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Frequently Asked Questions About AI Patent

1. Can you actually get a patent on an AI invention, or does the abstract idea problem block it?

Yes, AI inventions can be patented, but they have to clear a hurdle that most other technologies do not face: subject matter eligibility under 35 U.S.C. § 101. The Supreme Court’s Alice Corp. v. CLS Bank decision established that abstract ideas, including mathematical concepts and software-implemented methods, are not patentable on their own. An AI patent application has to show something more than an abstract calculation. It needs to demonstrate a real technical improvement, such as a computer or device operating more efficiently, a new technical capability, or a specific solution to a defined technical problem.

This is not a minor drafting detail. How the invention is framed in the application determines whether the USPTO grants the patent, whether it survives a later IPR challenge, and whether it holds up when a competitor or an investor’s due diligence team scrutinizes the claims. Because § 101 outcomes are highly fact-specific and the case law from the Federal Circuit keeps developing, this framing decision has to be made carefully before filing, not fixed afterward.

2. Who counts as the inventor on a patent when AI tools were used to help develop the invention?

Under current law, only natural persons can be listed as inventors. In Thaler v. Vidal, the Federal Circuit confirmed that AI systems themselves cannot be named as inventors on a patent, no matter how much they contributed to generating or refining the invention.

That does not mean AI-assisted inventions cannot be patented. It means the company has to correctly identify which humans made the intellectual contributions that shaped each claimed invention. In modern AI-assisted R&D workflows, this analysis is often more complicated than it sounds, since the people who trained the model, designed the experiments, or interpreted the results may all have played different roles. Getting inventorship right matters because incorrect inventorship can create validity problems for the patent later, so it needs to be documented carefully as part of the application process rather than treated as an afterthought.

3. How is prior art different for AI patents compared to other technologies?

AI research moves fast, and the prior art landscape can shift meaningfully within just a few months. This creates two practical challenges. First, a prior art search done early in the process can become outdated by the time an application is ready to file, so timing and thoroughness both matter. Second, the USPTO itself is now using AI-enhanced tools during examination, which means patent examiners can surface more comprehensive prior art than an application drafted with only a traditional manual search in mind might anticipate.

Because of this, a prior art search for an AI invention is not a formality to check off before filing. It is the foundation the claims are built on. A thorough search identifies not just what has already been filed, but what an examiner is likely to find and cite, which shapes how the claims should be framed and how broad they can reasonably be.

4. How broadly can you claim an AI architecture or training method before the claims become too abstract to hold up?

There is no fixed formula for this, which is exactly why claim scope is one of the most consequential decisions in AI patent prosecution. Claim too narrowly and the patent is easy for competitors to design around. Claim too broadly and the claims risk being challenged as an abstract idea under § 101, particularly for architectures or training methods that could be described in purely mathematical or algorithmic terms.

The way around this tension is to anchor the claims in a specific, demonstrable technical improvement rather than the underlying concept alone. This usually means choosing the right claim type (system, method, or computer-readable medium), articulating exactly how the invention improves computer or device function, and building fallback claim positions so that if broader claims get narrowed during examination, the application still preserves meaningful protection. This strategy needs to be built into the application from the start, since it is far harder to fix after an examiner has already issued a rejection.

5. Does a single AI patent provide enough protection, or does AI IP strategy need to go further than that?

Generally, a single AI patent is not enough. AI technology evolves quickly, products change as the underlying models and methods improve, and competitors in crowded AI fields tend to file aggressively. A single patent covering one version of an architecture or method can become outdated or easy to design around as the technology moves forward.

A more durable approach combines several tools. Continuation applications and continuation-in-part filings let a portfolio keep pace as the technology evolves. Trade secret protection covers elements that should not be disclosed in a patent filing at all, such as specific training data, model weights, or proprietary pipelines. Freedom-to-operate analysis before a major launch or investment reduces the risk that a competitor’s existing patent blocks the product or forces a costly redesign. Together, these pieces build an IP position that scales with the company through funding rounds, product launches, and eventual acquisition or licensing, rather than one patent that ages out of relevance.

File before you present, publish, or launch. The U.S. is first-inventor-to-file, and public disclosure starts a one-year clock domestically. Most foreign jurisdictions allow no grace period at all. A provisional application establishes priority at lower cost.

6. Does Gearhart Law handle international AI patents?

Yes. We advise on international AI patent strategy and file through the PCT and in key jurisdictions including Europe, Japan, and China. Note: the EPO applies different eligibility rules for AI than the USPTO. We work with trusted foreign counsel in each jurisdiction.

Speak With an AI Patent Attorney at Gearhart Law

Artificial intelligence technologies continue to evolve across industries, creating new opportunities as well as unique intellectual property considerations. From software platforms and machine learning applications to AI-enabled products and research-driven innovations, protecting valuable technology often requires careful planning and a clear understanding of available IP options.

Gearhart Law works with startups, established companies, researchers, and innovators to help identify, secure, and manage intellectual property rights related to AI technologies. To learn more about your options, contact our team at 908.273.0700 to schedule a consultation.