In 2019, Dr. Stephen Thaler filed two patent applications that would shake the foundation of intellectual property law. His inventor? An AI system called DABUS. The USPTO rejected them. Thaler sued. In 2022, the Federal Circuit delivered a unanimous verdict: only humans can be inventors. Yet paradoxically, AI patent filings have exploded: annual AI patent applications in the United States more than doubled from roughly 30,000 in 2002 to over 60,000 by 2018, and growth has continued to accelerate into the 2020s. AI has already made a positive impact across healthcare, finance, retail, and other industries, thanks to its advanced capabilities and tailored solutions that are transforming business operations and customer experiences. Top players in AI innovation—OpenAI, Google, and NVIDIA—are driving these advancements. The United States Patent and Trademark Office (USPTO) has granted hundreds of thousands of patents on artificial intelligence technology.
This tension captures the AI patent landscape perfectly: revolutionary technology governed by traditional legal frameworks, where knowing the rules determines whether you protect your innovation or lose it entirely. In this first-to-file environment, where one in five companies now patent AI innovations, hesitation means watching competitors secure protection for ideas similar to yours. Strong AI patent protections are increasingly viewed as a national security imperative to maintain competitiveness. San Francisco stands out as a hub for leading AI innovators and companies, further shaping the competitive landscape.
Artificial Intelligence Patents: Are AI Inventions Patentable in the United States?
Yes, and they have been for six decades. Frank Rosenblatt filed a patent application in 1960 for a pattern-recognition perceptron—granted in 1966 as U.S. Patent No. 3,287,649—establishing that neural networks qualify for patent protection. This was one of the earliest artificial intelligence inventions, utilizing an artificial neural network as its core technology. Modern machine learning systems, training pipelines, and AI-enabled applications continue this tradition.
Here’s what determines whether your AI innovation qualifies:
The exact requirements are the same as any patent. Artificial intelligence inventions must satisfy subject matter eligibility under 35 U.S.C. § 101, novelty (§ 102), non-obviousness (§ 103), and utility. The patent statute does not describe the issue of patent eligibility for AI inventions, making interpretation difficult. The USPTO explicitly stated it does not intend to exclude AI technologies from patent protection; they are treated like any other computer-implemented invention in eligibility analyses. If your AI-based invention is new, non-obvious, useful, and falls into a statutory category, it can be patented. An artificial intelligence system can be the subject of a patentable invention if it meets these legal criteria. Still, current law requires a natural person as the inventor, even when an artificial intelligence system is involved in the creation process.
However, not all AI patents are created equal. Weak patents don’t just fail to protect your innovation—they create roadmaps for competitors to design around your technology or beat you to market with improvements. The difference between patents that deter competitors and patents that help them often comes down to experienced patent prosecution from day one.
Modern AI patents cover diverse technologies. A recent USPTO report noted that AI-related patent applications increased by 33% since 2018 and accounted for 60% of all technology subclasses by 2023. From core algorithms to applied solutions, the scope is vast: Natural Language Processing (NLP), computer vision, speech recognition, recommender systems, and beyond. This broad range of industries and applications highlights AI’s versatility and wide-reaching benefits. Foundational architectures, autonomous systems, and specialized hardware dominate the patent landscape in 2026.
Figure 1: Explosive Figure 1: Growth in AI Patent Filings by USPTO Technology Center: AI-related patent application filings have risen sharply since the mid-2010s, driven primarily by Technology Center 2100 (Software) and Technology Center 2600 (Communications). The apparent dip after 2020 reflects publication delay rather than reduced filing activity—source: Juristat data via PatentNext (Ryan Phelan), 2022.
The numbers tell a dramatic story. From 2002 to 2018, AI’s share of all U.S. patent applications grew from about 9% to nearly 16%. The competitive landscape is fierce: WIPO found that Chinese inventors filed six times as many generative AI patent families from 2014 to 2023 as U.S. inventors, though U.S. patents are cited far more often. This explosive growth means your competitors are likely filing patents on innovations similar to yours right now.
AI has diffused into virtually every field. The percentage of U.S. inventors and companies that patent rose from under 5% in 1980 to over 20% by 2018. One in five patenting organizations now works on AI innovations, a remarkable broadening that demonstrates how mainstream AI research has become.
Figure 2: Diffusion of AI Patenting Among U.S. Inventors and Companies: This chart shows how artificial intelligence patenting has spread from a niche activity to a mainstream practice over four decades. The solid green line shows the percentage of U.S. organizations that hold at least one AI patent, while the dashed blue line shows the percentage of U.S. inventor-patentees involved in AI inventions. Both measures rose from under 5% in the early 1980s to roughly 20–25% by 2018, illustrating how AI has become a core component of inventive activity across industries—source: USPTO Office of the Chief Economist, Inventing AI report (Figure 4), 2020.
Foreign patent offices recognize AI inventions. The European Patent Office, Japan Patent Office, and China’s CNIPA all grant patents on AI-based inventions. However, each applies its own standards for what constitutes a patent-eligible technical contribution. The EPO requires a “technical effect”: an AI invention must solve a technical problem or have a concrete technical application to be patentable. A pure algorithm isn’t patentable in Europe, but AI applied within a device or industrial process can be.
The USPTO actively encourages AI patents. In January 2025, as part of the USPTO’s comprehensive AI Strategy, Director John A. Squires emphasized that Section 101 should not be misused “as a blunt instrument to exclude entire technological fields” like artificial intelligence. Director Squires told inventors that “the doors to America’s Innovation Agency are wide open” for AI and emerging technologies.
Patents provide market exclusivity, encouraging investment in high-risk, high-reward AI R&D.
Key Technology Areas Where AI Patents Are Granted
AI patents span from fundamental architectures to industry-specific applications. The range reflects how deeply machine learning has penetrated modern commerce. Understanding which areas are actively patented helps you identify where your innovations fit and what protection strategies competitors are deploying. Established fields like Knowledge Processing, Speech Recognition, and AI Hardware are also significant areas for AI patents.
Generative AI and Large Language Models. According to WIPO’s July 2024 Patent Landscape Report, over 25% of all generative AI patent families filed between 2014 and 2023 were published in 2023 alone, indicating explosive recent growth.
Companies are patenting transformer neural network architectures, training techniques for large language models, prompt engineering methods, retrieval-augmented generation systems, and safety mechanisms for AI-generated content. Generative Adversarial Networks (GANs) are another central area of patent activity, covering innovations in architectures and training techniques.
Computer Vision Systems. Patents protect convolutional neural networks, vision transformers, and image processing pipelines used in medical imaging diagnostics, autonomous vehicle perception, and industrial quality control. Autonomous vehicles, such as those developed by Waymo and Tesla, rely on AI-powered sensors and algorithms to interpret data and navigate roads safely. Google holds patents for core NLP technologies, including BERT and Gemini, as well as for applications in autonomous vehicles and healthcare diagnostics. Waymo holds over 1,898 granted patents globally on self-driving technology, including AI vision and control systems.
Reinforcement Learning and Decision-Making. Patent protection extends to Reinforcement Learning (RL) algorithms used in robotics control, game AI, autonomous navigation, and resource allocation. These systems learn optimal actions through trial and error: patents have been granted on AI for traffic signal optimization, algorithmic trading strategies, and recommendation engines that adjust content based on user behavior.
Multimodal and Hybrid AI Systems. Some patents cover AI models that combine text, audio, images, and sensor inputs, or hybrid approaches that integrate neural networks with symbolic logic or knowledge graphs. These cross-cutting inventions propose specific ways to fuse modalities or switch between AI and symbolic reasoning to yield better results.
Natural Language Processing Applications. A considerable number of patents fall under NLP: systems for understanding and generating human language. This includes chatbots, virtual assistants, call center AI, machine translation systems, and speech recognition algorithms—companies frequently patent improvements in language model training, semantic understanding, dialogue management, and speech-to-text accuracy.
AI Training Infrastructure and Hardware. Beyond algorithms, patents cover the infrastructure that enables AI training: methods for distributed training across GPUs/TPUs, model compression techniques such as quantization and pruning, and specialized AI hardware, including neural network accelerators. The USPTO has granted patents on everything from efficient backpropagation schemes to new chip architectures tailored for deep learning. NVIDIA holds an extensive patent portfolio covering GPU architectures, AI acceleration hardware, and deep learning optimization technologies that enable the training of large-scale AI models.
Domain-Specific Applications. AI innovation within specific industries represents fast-growing patent activity:
Medical Devices and Healthcare: Companies are patenting AI systems for medical image analysis, diagnostic algorithms that process patient data, and machine learning models embedded in medical devices. For instance, AI that analyzes X-rays or MRI scans to detect anomalies, or systems that predict patient outcomes based on electronic health records. AI is also being used to improve processes, reduce unnecessary hospital visits, and assist in drug development. Roche alone filed 72 AI-related patents in Q1 2024 [Note: Client should verify this figure if accuracy is critical], many in digital pathology and protein design.
Automotive Systems: The competition in self-driving tech has led to aggressive patenting by Waymo, GM Cruise, Tesla, and Uber, which famously litigated with Waymo over LiDAR patents. Patents cover sensor fusion algorithms, decision-making systems for autonomous navigation, and safety protocols.
Financial Services: Financial services companies patent AI models for fraud detection, credit scoring, algorithmic trading, and risk management. Capital One and JPMorgan have patented machine-learning techniques for detecting credit card fraud in real time. Intelligent machines and AI solutions are used to automate complex tasks, improve efficiency, and enhance customer experience across the financial sector.
Manufacturing and IoT: AI for predictive maintenance, quality control via computer vision on production lines, and supply chain optimization are hot areas. These systems use sensors and machine learning to analyze data and detect equipment failures before they occur or identify defective products on assembly lines.
E-commerce and Retail: Recommendation engines used by Amazon, Netflix, and others are often patented. These AI systems drive user personalization, increase sales, and represent competitive advantages worth protecting.
Section 101 Patent Eligibility for AI Inventions
AI inventions must be patent-eligible subject matter under 35 U.S.C. § 101, meaning they can’t fall within excluded categories such as abstract ideas or mathematical formulas. Understanding the evolving eligibility requirements is essential, and successfully navigating these challenges requires the calibration that comes only from years of sophisticated prosecution experience with complex AI inventions.
The “Abstract Idea” Challenge. Many AI-related patent claims face scrutiny as potential “abstract ideas”: mathematical optimization or mental processes that humans could theoretically perform. The Supreme Court’s Alice Corp. v. CLS Bank (2014) decision established the test for such situations. Determining whether your AI invention crosses the line from an abstract idea to a concrete technical improvement requires experienced prosecution counsel.
The Alice Two-Step Test. Step 1 asks: Is the claim directed to a judicial exception like an abstract idea? If yes, Step 2 asks whether the claim includes “significantly more” than the abstract idea: a specific practical application that transforms it into a patent-eligible invention. Successful AI patents usually claim a specific technical solution (e.g., improved accuracy, faster processing, or a novel network architecture that reduces memory usage) rather than merely the idea of using AI to achieve a result.
USPTO Guidance for Examiners. In August 2025, the USPTO’s Deputy Commissioner issued a memo (the “Kim Memo”) reminding examiners how to evaluate AI-related claims under Alice. This guidance reinforced existing policy rather than introducing new standards. Key points:
- Examiners were cautioned not to over-generalize AI claims; merely because a claim involves math doesn’t automatically make it a “mental process.”
- A claim reciting “training a neural network in a first stage using a training set” does not recite a judicial exception, whereas a claim explicitly reciting a formula might.
- A claim is eligible if it covers a particular solution to a problem or a specific way to achieve a desired outcome; neither the claim nor the specification is required to explicitly spell out the improvement, as long as it would be apparent to a skilled person.
- A Section 101 rejection should issue only if there is >50% probability the claim is ineligible; mere uncertainty is not enough to reject.
When drafting claims for AI-assisted inventions, it is essential to consider how claim limitations are evaluated, especially in light of recent USPTO guidance. The eligibility of such claims often hinges on whether a human inventor made a significant contribution to the conception of each claim limitation.
USPTO Inventorship Guidance and Evolving Standards. In November 2025, the USPTO revised its inventorship guidance for AI-assisted inventions, withdrawing the framework that had required a “significant contribution” analysis specific to AI. The revised guidance clarifies that inventorship must be determined solely under traditional conception principles established in case law, without any AI-specific heightened standard. This shift introduces additional uncertainty in how inventorship is determined for AI-assisted inventions, particularly when evaluating the human inventor’s role in claim limitations.
Successful Claims Emphasize Technical Improvements. Both USPTO policy and court decisions indicate that AI patent claims survive eligibility challenges when they clearly articulate a technical improvement. If an AI invention reduces database query latency, improves image recognition https://vepga.org/contract-materials/ accuracy using less computing power, or enables new network functionality, these concrete benefits confer eligibility. Experienced patent attorneys know how to frame AI innovations to emphasize these technical contributions rather than abstract computational concepts, engineering Litigation Quality Patents® that withstand both USPTO examination and potential court challenges.
Recent Court Decisions Add Complexity. In Recentive Analytics, Inc. v. Fox Corp. (April 2025), the Federal Circuit issued what many consider the first precedential decision addressing machine learning patent eligibility. The court held that “claims that do no more than apply established methods of machine learning to a new data environment” are patent-ineligible absent a specific improvement to the ML technique. The court found the claims were just about collecting data, training a model, and outputting a result, without any innovative how in achieving better performance; the patent lacked details on how the model achieved the purported result.
This case demonstrates why technical depth matters: weak patents don’t just fail to protect; they can actively help competitors by revealing your approach without providing legal barriers.
USPTO Optimism vs. Court Skepticism. The USPTO (especially with Director Squires’ influence in 2025) is encouraging a broad view of AI eligibility, instructing examiners to be more hands-off with 101 rejections. Meanwhile, the Federal Circuit still applies Alice strictly. Congress has revisited the Patent Eligibility Restoration Act (PERA, S. 1546), which, as of early 2026, remains pending and could roll back some of Alice‘s impact. Retired Federal Circuit Judge O’Malley called it “absurd” that the Supreme Court won’t provide more guidance on 101.
Bottom line: AI inventions can be patented under 101, but you must draft claims strategically with counsel who understands both the technology and the evolving legal landscape. Focus on specific technical innovations and tangible applications. The more you highlight an improvement over existing technology (speed, accuracy, efficiency, capability), the better your chances.
AI Inventorship: Can an AI Be an Inventor?
The short answer under current U.S. law is no: only human beings can be named as inventors.
Thaler v. Vidal (Fed. Cir. 2022). Dr. Stephen Thaler filed patent applications listing his AI system “DABUS” as the sole inventor; the Federal Circuit unequivocally held that under the Patent Act, an inventor must be a natural person. The Supreme Court declined to hear Thaler’s appeal in 2023.
USPTO’s Stance and Guidance. Following the Thaler case, the USPTO issued Inventorship Guidance for AI-Assisted Inventions in 2023, making clear there is no heightened inventorship standard for inventions developed with AI assistance. The USPTO emphasizes that patents exist to reward human ingenuity, so the focus should be on what human inventors conceived. The determination of patentability in the context of AI-assisted inventions depends on human inventorship and whether a human made a significant contribution to the inventive process. Examiners generally won’t investigate inventorship unless there’s a clear issue. Applicants have no new duty to disclose AI involvement in the inventive process beyond existing disclosure obligations.
Human Contribution Must Be Documented. If you use AI systems in your R&D, you need to identify the human inventors who made the inventive decisions. This might be the person who defined the problem, selected and tuned the AI model’s parameters, or recognized the significance of the AI’s output and molded it into a real-world invention. Keep detailed records: lab notebooks, version control logs, and meeting notes that show how an invention was conceived and who contributed key ideas.
Global Consensus on Human-Only Inventors. The European Patent Office and the UK Intellectual Property Office both rejected applications that named DABUS as the inventor because an inventor must be a real person. The EPO’s Legal Board of Appeal confirmed in 2021 that only humans can be inventors under the European Patent Convention.
Practical Tip. If you use AI to assist in R&D, articulate how you used it and who drove the inventive concept. Your patent application might note that the inventor used an AI tool to accelerate experimentation, but ultimately, the inventor selected and implemented the solution. Make sure the patent application’s narrative reflects the human inventors’ contributions. The named inventors should https://eskaparate.co/contacto/ be those who conceived the invention’s core idea, even if an AI helped by crunching data or providing candidate designs. Documenting a significant contribution by a human inventor is essential for establishing inventorship in AI-assisted inventions.
Practical Strategies for Protecting AI Innovations
Building an IP strategy around AI requires balancing patents with other forms of protection, filing quickly before disclosures, and drafting applications that hold up under scrutiny. In the AI field, where patent applications increased 33% since 2018, speed and strategy matter more than ever.
Break down your AI stack and identify protectable elements separately. AI systems are multi-component. Consider filing separate patent applications for a novel core model, a unique training method, an innovative user interface, and specialized hardware. This modular approach ensures you cover key innovative pieces and can result in a stronger portfolio with claims of varying scope.
Prioritize detailed technical disclosure in patent filings. The USPTO and courts have expressed concern about “black box” AI claims that are overly functional and fail to explain how the result is achieved. Include specifics: model architectures (layer types, sizes), training parameters, examples of training data, flowcharts of algorithm steps, and quantitative results demonstrating improvements. The August 2025 USPTO memo explicitly reinforced this: the spec should describe technological improvements and implementation details such that the benefit is apparent.
This level of technical detail requires experienced counsel who can translate your innovation into patent language that meets both USPTO requirements and potential litigation challenges. Applications that skimp on technical disclosure risk rejection and can reveal your approach to competitors without providing protection.
Decide what to patent vs. keep as trade secrets. Not every innovation is best protected by a patent. Patents eventually publish and reveal your invention. If you have proprietary datasets, secret feature engineering processes, or AI model tuning that’s hard to reverse-engineer, keep them as trade secrets. Many companies choose hybrid strategies: patent visible aspects (such as product functionality or model architecture) but keep internal details confidential (model weights and specific training data selection criteria).
Consider cross-border filing early and be aware of jurisdictional differences. AI is global. Plan foreign filings early: you typically have 12 months from your U.S. filing to file in other countries claiming priority. Each jurisdiction has its own stance. Europe requires technical application and has stringent rules against claiming algorithms per se. China is generally open to AI patents. Remember: the U.S. has a one-year grace period for filing after public disclosure, but most other countries do not. If you demo or publish your AI invention publicly, file before that disclosure, or you lose foreign rights.
Document human contributions for inventorship. Keep records of who did what during the AI development. Maintain version histories and meeting notes. Ensure employment contracts have clear IP assignment clauses so the company automatically obtains rights to any inventions created by employees.
File before public disclosure, especially for academic AI projects. AI research is highly open, with papers published at conferences such as NeurIPS and ICML. In the U.S., you get a 1-year grace period after publishing. But in Europe, China, and elsewhere, publishing even one day before filing can forever bar the patent. The safest practice: file a provisional patent application before any public disclosure (paper, poster, blog, open-source code release).
When filing provisional patent applications for AI inventions—and be clear, there is no such thing as a ‘provisional patent,’ only provisional patent applications—understand what you’re actually getting. A provisional patent application is like a stock option: it’s a limited right for a set time period that expires worthless unless you exercise it by filing a non-provisional application within 12 months. The value isn’t just defensive; provisional patent applications create monetizable property rights for licensing, collateral, and balance sheet assets that sophisticated investors and corporate partners expect to see. Most established companies require inventors to file provisional patent applications before discussions, protecting the company from idea-submission lawsuits and demonstrating serious IP development.
The Prototype Purchase Trap in AI Hardware Development. If your AI innovation involves custom hardware, specialized chips, or the need for manufacturing quotes for ML accelerators or embedded AI systems, be extremely careful. Requesting or receiving commercial quotes for prototype manufacturing can trigger the one-year “on sale” bar under 35 U.S.C. § 102(a)(1), potentially destroying your patent rights. This is particularly dangerous in AI hardware development, where companies often need foundry services, custom ASICs, or specialized circuit boards. The clock starts with the commercial offer or quote, not the actual purchase. File your provisional patent application before requesting any manufacturing quotes or prototypes from external vendors.
Coordinate Patent Strategy with R&D Tax Credits. Your AI development expenses qualify for enhanced R&D tax credits under current tax law. Strategic coordination of your patent filing timeline with R&D expenditure documentation can maximize both IP protection and tax benefits. Thompson Patent Law partners with Paychex to provide free R&D tax credit assessments, helping you offset the costs of strategic patent protection while ensuring your innovation investments deliver maximum value. This coordination is particularly valuable in AI development, where R&D costs are substantial and proper documentation can significantly reduce the effective cost of patent protection.
Engage specialized AI patent counsel early. The calibration required to navigate Section 101 eligibility challenges comes only from years of sophisticated prosecution experience on complex AI inventions. Generic patent attorneys who handle “a little bit of everything” lack the depth to engineer patents that survive Federal Circuit scrutiny. Thompson Patent Law’s team brings backgrounds serving Fortune 500 technology companies with specific expertise in AI, machine learning, and computer vision—registered patent attorneys with engineering degrees who understand your technology as profoundly as your development team.
Engaging experienced counsel early can help you determine how to structure your invention or what data to collect to strengthen your patent. A good AI patent attorney might suggest, “Include an example running on X dataset showing 30% speed improvement, because that will demonstrate a concrete benefit.” Experienced firms with backgrounds serving Fortune 500 technology https://medialabufrj.net/blog/ companies such as Apple, Google, Intel, and Microsoft bring the calibration needed to navigate the complexities of AI patent prosecution. With proprietary techniques and sophisticated prosecution strategies, proper counsel can spare you 1-2 years and five figures in prosecution costs while engineering stronger patents that actually deter competitors. [Note: If the “1,500 patents issued” and “94% allowance rate” figures are marketing claims, they should either be qualified as estimates/examples or supported by verifiable data.]
A robust AI patent portfolio is a primary indicator of a company’s future revenue potential and valuation. But in this first-to-file environment, every day you delay is a day your competitors gain ground. Strategic patent protection isn’t just defensive; it’s a competitive weapon that determines whether you profit from your innovation or watch competitors use your ideas against you.
Why Thompson Patent Law’s Approach to AI Patents Differs
The complexity of patent law multiplies with cutting-edge AI technology. Specialized AI patent counsel brings deep technical knowledge (understanding neural networks, machine learning concepts) and up-to-date legal expertise.
Technical fluency in AI. Your counsel should discuss architectures such as transformers, convolutional networks, and reinforcement learning strategies as if they were part of your dev team. This enables them to truly grasp the inventive step and draft a patent that accurately captures it. During patent drafting, they’ll flesh out details that a generalist might overlook. Look for a team of registered patent attorneys with engineering degrees in electrical, computer, and mechanical engineering who understand the actual technology, not just legal concepts.
Knowledge of evolving case law and USPTO policy. An AI-focused attorney follows the latest Federal Circuit decisions (Recentive, etc.) and USPTO guidance updates; they know that including specific technical details in claims and spec is crucial post-Recentive. If they’re aware that Director Squires designated a pro-AI decision as precedential in 2025, they might leverage that reasoning in arguing for your claim’s eligibility.
Comprehensive IP strategy services. AI patent attorneys go beyond writing applications. They help with prior art searches, portfolio strategy (deciding which inventions to patent vs. keep secret), and prosecution (responding to USPTO rejections). When interviewing counsel, ask whether they have experience with AI-related office actions; their approach to overcoming 101 rejections for ML algorithms is revealing.
Engage early for maximum benefit. Bringing in patent counsel early in the development cycle pays dividends. They can identify which parts of your R&D are most patentable and urge you to document certain aspects. Many AI companies hold regular invention disclosure meetings with their patent attorneys, where engineers present what they’re working on, and the attorney spots potential inventions.
Ongoing relationship and expertise retention. Build a long-term relationship with your AI patent counsel team. As your company grows from one patent to 50, having a team that’s been along for the ride means they deeply understand your technology stack and business objectives. They’ll ensure new patent filings fit the overall strategy, avoid overlap, and cover new ground, while monitoring competitors’ AI patents to alert you to trends or potential conflicts.
Key Takeaways
AI patents have been around since the 1960s and remain attainable, but not all AI patents provide equal protection. The USPTO continues to grant AI-related patents under its current AI-optimistic approach. Strategic, well-engineered Litigation Quality Patents® from experienced counsel make the difference between patents that deter competitors and patents that reveal your roadmap without protecting it.
AI inventions must meet standard patentability criteria. Many AI patents succeed when they present concrete technical improvements. Always frame claims around the specific technical contribution (faster model, improved accuracy, novel architecture).
Patent eligibility (Section 101) requires more than an abstract idea. Recent cases, such as Recentive v. Fox, highlight the need to disclose the “how” of the improvement. The USPTO is on the inventors’ side with recent guidance, but draft claims carefully to avoid pitfalls involving the “abstract idea” doctrine.
Only humans can be inventors. Under Thaler v. Vidal (Fed. Cir. 2022), U.S. patents cannot list AI systems as inventors; only natural persons qualify. This aligns with EPO, UK, and others. Document the role of human inventors when using AI in R&D.
Draft applications with details. Successful AI patents have thorough technical disclosures (architecture diagrams, data flows, experimental results) and well-thought-out claims that highlight technical improvements. Avoid purely functional claiming: add specifics about the model or steps.
The legal landscape continues evolving. The USPTO’s Administrative Review Panel decisions in 2025 signaled a more permissive stance, while courts still sometimes strike down broad AI claims. Companies should stay updated through their counsel.
Use an IP strategy as a competitive advantage. Patents can secure market exclusivity for core AI features, attract investment, and provide leverage for cross-licensing. But also consider trade secrets for aspects best kept confidential. Perform freedom-to-operate checks, especially in crowded fields.
Engage professionals and act early. Working with an AI-savvy patent attorney team with backgrounds serving Fortune 500 companies is highly recommended. Experienced counsel can help navigate complex prosecution challenges and develop stronger patents. Start the patent process early, ideally well before any public release or publication. Missing the timing can permanently forfeit rights in this first-to-file system, where competitors are actively filing on similar innovations.
Your Next Steps to AI Patent Success
Whether you’re developing generative AI applications, computer vision systems, or machine learning infrastructure, patent protection remains a powerful tool for securing competitive advantage. But the statistics and case law make clear: patent quality determines outcomes.
The bottom line: In the AI field, where Section 101 eligibility challenges are common, and the Federal Circuit has invalidated patents for insufficient technical detail, you need strategic, well-engineered Litigation Quality Patents® that can withstand both USPTO examination and potential court challenges. Generic patent preparation risks creating applications that reveal your innovations to competitors without providing meaningful protection.
The urgency is real. In a field where AI patent applications increased 33% since 2018 and one in five companies now file AI patents, every day you delay is a day your competitors gain ground. Poor decisions about AI patent protection directly translate into lost revenue, market share, and control over how your innovations are monetized. In our first-to-file system, the second filer gets nothing—even if they independently developed the same invention. Your competitors are already working on ideas similar to yours.
Take action now:
- Schedule a Free Patent Needs Assessment to evaluate your AI invention’s patentability, assess Section 101 eligibility risks specific to your innovation, and develop a comprehensive protection strategy that addresses both USPTO prosecution and potential competitive challenges.
- Document your AI development process thoroughly, including contributions from human inventors, technical improvements over existing solutions, and quantitative results that demonstrate concrete benefits. This documentation is essential for both patent applications and inventorship determinations.
- File provisional patent applications before any public disclosure, especially if you’re planning conference presentations, academic publications, or product demonstrations. The 12-month provisional period gives you breathing room to refine your technology while establishing priority dates that protect against competitor filings.
- Coordinate your patent strategy with R&D tax credit planning to maximize both IP protection and tax benefits from your AI development expenses. This coordination can significantly offset the costs of strategic patent protection.
- Work with experienced patent attorneys with deep AI technical expertise and backgrounds serving Fortune 500 technology companies. The calibration required to navigate Section 101 eligibility challenges, overcome obviousness rejections on complex ML algorithms, and engineer claims that survive both USPTO examination and litigation comes only from years of specialized experience.
Looking ahead, the AI patent landscape will continue to evolve rapidly as the technology advances and legal precedents develop. Strategic patent protection isn’t a one-time decision; it’s an ongoing competitive advantage that requires sophisticated guidance tailored to the latest USPTO policies, Federal Circuit decisions, and your specific business objectives.
Remember: Your invention may be brilliant, but the quality of your patent protection determines whether you profit from that brilliance or watch competitors use your ideas against you. Experienced patent attorneys don’t just file paperwork—they engineer patents that withstand scrutiny and create genuine competitive barriers through strategic Litigation Quality Patent® services.
Don’t let competitors turn your AI innovations against you. In a field moving this fast, with competition this intense, you need strategic, well-engineered patent protection that delivers valuable patents efficiently. The difference between weak patents and strong patents often comes down to the quality of counsel guiding your strategy from day one—a team with the technical expertise, legal experience, and proven methodologies to compete with Fortune 500 patent portfolios.
About the Author
Craige Thompson is a registered patent attorney and founder of Thompson Patent Law, leading a team of registered patent attorneys with engineering degrees and extensive patent experience as lawyers. The Thompson Patent Law team brings backgrounds from industry and major law firms, with collective experience serving clients ranging from individual inventors to Fortune 500 companies, including Apple, Google, Intel, and Microsoft. The team provides Litigation Quality Patent® services across electrical engineering, software, mechanical systems, and medical device technologies.
