Artificial intelligence is changing everything—from how we work to how we invent. But the law hasn’t fully caught up. One of the biggest questions right now is this: can you patent an AI model?
It sounds straightforward. You build something smart. You want to protect it. But when you dig into the rules, the answers get complicated fast.
This article explores the tricky ground around patenting AI. We’ll explain what you can protect, what you can’t, and why courts and regulators are still figuring it all out. Whether you’re an AI developer, startup founder, or IP attorney, you’ll walk away knowing where the lines are today—and how to stay on the right side of them.
What Makes AI So Hard to Patent?
AI Isn’t a Single Thing
Before we talk about patents, it’s important to understand what we mean by “AI model.”
When someone refers to an AI model, they’re often talking about a trained algorithm—a system that’s been fed huge amounts of data and adjusted itself to make predictions or generate content.
But that’s just one layer.
There’s also the architecture (how the model is built), the training data (what it learned from), and the code that runs everything. These pieces are deeply connected, but from a legal perspective, they may be treated very differently.
Patents like to work with things that are clearly defined. AI models are layered, adaptable, and often difficult to pin down in technical terms. This complexity is one reason why patenting them is so challenging.
Is the Model an Invention or a Method?
One of the core issues in patent law is defining what counts as an “invention.”
Traditional inventions are mechanical. You can describe how they work, what they’re made of, and how they’re used. Patent examiners can review your diagrams, read your claims, and decide if your creation is new and useful.
AI models don’t always fit that mold.
They may not operate the same way twice. They often depend on data sets that change over time. And many of them “learn” through training, meaning they’re not completely programmed by humans.
This makes it harder to describe them in a way that meets patent standards. You can’t just say, “It thinks like a human.” You need to show how it works, step by step. That’s not easy with systems designed to be adaptive, flexible, and—sometimes—impossible to fully explain.
What the Law Currently Allows
You Can Patent Certain Parts
Despite the gray areas, some parts of AI can be patented under today’s laws.
For example, if you’ve developed a new architecture or a novel way to train a model, that can sometimes be protected. The same goes for systems that combine AI with hardware—like a medical device that uses machine learning to analyze blood samples.
In these cases, the patent usually focuses on the process, not the result. You’re not protecting the “smartness” of the AI. You’re protecting how the system is built and used.
But even here, it gets tricky. You have to describe everything in a way that meets the legal tests of novelty, non-obviousness, and utility. That means your invention must be new, not something any expert could have guessed, and useful in a clear way.
Software Patents Set the Tone
To understand how AI patents are treated, it helps to look at how software patents are handled. In many countries—including the U.S.—pure software is hard to patent.
Courts often rule that algorithms or code are abstract ideas. And abstract ideas, by themselves, aren’t patentable. To get a patent, your invention needs to be tied to something practical—something more than just “a better way to do math.”
This is one reason why many AI patents don’t get approved. If the application just says “this model predicts sales better,” without explaining the technical process behind it, it’s likely to get rejected.
You need to ground the invention in specific, technical steps. The more your patent application sounds like an engineering solution, not just a business idea, the better your chances.
Where Patent Offices Draw the Line
The U.S. Patent and Trademark Office (USPTO)
In the United States, the rules are evolving. The USPTO has granted patents for AI-related inventions, but they’re very selective.
Generally, the office requires detailed technical disclosures. That means you have to explain exactly how the AI model is trained, how it operates, and how it solves a specific technical problem. Vague claims don’t work.
The USPTO also wants to see human involvement. If an AI tool was trained entirely by other AI, the office may argue that the invention wasn’t truly made by a person—and patents, by law, are granted to human inventors.
This creates tension for companies that rely heavily on automated processes. If your AI writes its own code, is that code patentable? If the machine does the inventing, who owns the invention?
There are no clear answers yet. But the USPTO leans toward requiring human control and technical clarity.
The European Patent Office (EPO)
Europe has a similar approach. The EPO also looks closely at whether an invention has a technical character.
That means you can’t just say “this AI improves hiring decisions.” You need to show a technical effect—something that changes the way a system runs, reduces computation time, or solves a problem in a new way.
The EPO is also stricter about what counts as an “invention.” They often reject applications that sound too much like business methods or social tools, even if those tools use advanced AI.
But one area where Europe may be more open is in recognizing machine-generated outputs—provided the original invention still reflects human intent and control.
Human Inventorship vs Machine Contribution
Can a Machine Be an Inventor?
One of the thorniest legal questions in the AI patent debate is whether a machine can be listed as an inventor.
Patent systems around the world generally require that an inventor must be a natural person. That means a human being—someone with legal standing who can apply for and own a patent.
But what happens when an AI model does something truly creative? What if it generates a new material or solves a complex problem that no one directly programmed it to solve?
In 2021, courts in the U.S., UK, and Australia were asked to decide whether a machine named “DABUS,” which had allegedly invented a food container and a light beacon, could be listed as the inventor on a patent. In most of these cases, the answer was no. The reasoning was simple: machines can’t hold legal rights or responsibilities.
Still, the discussion isn’t over. As AI becomes more autonomous, the legal world may need to revisit the definition of inventorship. But for now, human inventors must be the ones listed on patent applications.
Why Inventorship Matters
Inventorship isn’t just a box to check. It has legal consequences.
If the listed inventor isn’t the real inventor, the patent can be invalidated. That makes it critical for AI developers and companies to decide who really contributed to an invention.
Did a human choose the architecture? Did they select the data set? Did they tweak the model to improve its performance?
If yes, that person can likely be listed as the inventor.
But if the model did everything on its own, without specific guidance, then the waters get murky. For now, legal systems assume that someone must be held responsible—and that someone needs to be human.
Trade Secrets vs Patents: A Strategic Choice
The Case for Trade Secrets
Given all these hurdles, many companies choose not to patent their AI models at all. Instead, they keep them as trade secrets.
A trade secret is any valuable information that isn’t publicly known and is kept confidential. That could include the model’s architecture, training methods, or the data sets used to improve it.
The main benefit of a trade secret is that it lasts forever—so long as you keep it secret. There are no filing costs, no public disclosures, and no expiration dates. If a competitor can’t figure out how your model works, your edge remains intact.
For AI models, this is often appealing. Many models are hard to reverse-engineer, especially if they’re run behind closed systems or embedded within other platforms.
The Risks of Trade Secrets
But trade secrets come with risk. If someone independently creates a similar model or reverse engineers yours, there’s no legal protection.
Also, once a trade secret becomes public—through a leak, breach, or accidental disclosure—you lose all rights to it.
That’s why companies must have strong internal controls, such as secure servers, limited access, and non-disclosure agreements. Without those, even the best-kept secret can slip out.
For high-value AI systems, the choice between patenting and keeping things secret isn’t simple. It depends on how easily the model can be copied, how long you expect it to remain useful, and whether you’re ready to disclose its workings to the world.
How to Maximize Protection in a Shifting Landscape
Drafting Stronger Patent Applications
If you decide to pursue a patent, the way you draft your application makes a huge difference.
First, focus on technical details. You need to describe how the model was trained, what specific problems it solves, and how it does so in a novel way. Vague language won’t survive examination.
Second, include as much experimental data as possible. Show performance benchmarks. Include diagrams. The more concrete your claims, the more likely they are to be seen as inventive and non-obvious.
Third, avoid relying on generic AI phrases like “the model learns.” That’s not enough. You need to walk the examiner through the structure and function—clearly and in technical language.
Filing Internationally
Patent law varies from country to country, which creates an opportunity—and a challenge—for AI innovators.
Some jurisdictions are more open to AI patents than others. For example, Japan has begun issuing clearer guidelines on how AI-related inventions should be examined. Meanwhile, the European Union and the United States are still developing case law that defines the limits.
If your invention has global potential, you may need to file in multiple countries. That means managing cost, translation, and legal complexity. But it also offers broader protection if your model gains traction worldwide.
Ownership of AI Outputs
Who Owns What an AI Creates?
This question might sound simple at first, but it quickly becomes complicated. If an AI model writes a piece of code, generates product designs, or invents a new algorithm, who actually owns that output?
The general rule today is that the person or company using the AI tool is considered the owner of its output. But this hinges on how the tool is used, how much control the human has, and whether the tool is considered more like a “helper” or an “inventor.”
If a human sets the objective, configures the model, chooses the training data, and then uses the model’s results as part of their broader process, courts will typically credit that human with the invention. But if the tool acts with a high degree of independence, and the human simply observes the outcome, the legal waters become much less clear.
What matters most is intent and direction. Courts and patent offices want to know that a person—not a machine—was ultimately steering the innovation.
AI-as-a-Service and Licensing Complications
Things get even trickier when AI tools are offered as services. For example, if you use a cloud-based AI tool to generate a design or optimize a system, and that model is owned by someone else, do you really own what it creates?
That depends on the license agreement. Some tools say that any output is yours to keep and use however you want. Others claim partial or full rights to that output.
This means IP professionals and companies need to read service agreements carefully. You might assume you own a new AI-created feature, but if the fine print says otherwise, the service provider may have a legal claim to it.
To avoid surprises, define the boundaries in advance. If you’re relying heavily on an outside AI platform, negotiate terms that clarify who owns what, especially if the output is valuable or could be patented later.
The Role of Data in AI Patents
Data as the New Secret Sauce
Behind every great AI model is a great dataset. And while the model might get the spotlight, it’s often the data that gives it power.
In many cases, the uniqueness of a model isn’t the code—it’s the data used to train it. If you’ve gathered proprietary data, curated it carefully, and cleaned it for training, that data has real value. But it’s often invisible in a patent application.
Why? Because patent offices typically focus on the model itself—its structure, performance, and use cases—not the specific data behind it.
This raises a strategic question: should you include your data in the application or keep it secret?
Protecting Training Data
Disclosing your training data in a patent could make it harder for others to copy your system. It also helps prove how your model works and why it’s different from others.
But once published, your data is out there forever. That’s why many companies choose to describe their data broadly but not in detail. They’ll say things like “a proprietary dataset of clinical notes” or “customer behavior logs from two million transactions,” without actually disclosing the raw data.
You can also split your protection strategy: patent the model, but treat the data as a trade secret. This gives you coverage on both fronts—public rights for the structure and confidential rights for the fuel.
The key is to make sure the data can’t be easily reverse-engineered. If others can recreate your dataset from public sources, then its value as a trade secret drops. But if your data is rare, expensive to gather, or deeply proprietary, keeping it confidential may give you an edge that no patent can.
Algorithm vs Application: What’s Patentable?
The Abstract Idea Problem
One of the biggest legal challenges in AI patenting is the “abstract idea” hurdle. Patent law doesn’t allow you to protect pure ideas, algorithms, or mathematical formulas unless they’re tied to something useful in the real world.
This has led to many AI patents being rejected for being “too abstract.” You can’t patent a general method for predicting customer churn or optimizing traffic flows unless you show how it’s implemented in a specific, useful way.
To succeed, your application must describe how the AI model is used in practice—how it connects to sensors, devices, workflows, or physical outcomes. The more concrete and applied the invention is, the more likely it is to survive legal scrutiny.
Showing Technical Effect
Patent offices often look for a “technical effect”—something that improves the function of a computer, system, or machine.
For example, if your AI model makes a medical device safer or speeds up signal processing in a smartphone, that technical gain can support patentability.
But if your model just gives better predictions without changing how the device or system works, it may still be seen as abstract.
That’s why patent claims must be carefully crafted. Focus on how the AI integrates with a broader process. Explain the step-by-step interaction between the model and the system it improves. The more grounded your explanation is, the more persuasive your patent will be.
Collaborative Inventions and Joint Ownership
Who Gets Credit in a Team Setting?
AI development is often collaborative. You might have data scientists, software engineers, product leads, and even outside consultants contributing to an invention. When it comes time to file a patent, who gets listed?
This is where things get sensitive. Only those who contributed to the inventive concept should be named as inventors. People who carried out instructions or helped build the system, but didn’t have a hand in the key ideas, usually don’t qualify.
Getting this wrong can cost you. Incorrect inventorship is one of the top reasons patents get challenged. That’s why it’s important to track contributions as you go—and document who suggested what, when, and how.
A good practice is to have invention disclosure meetings as new features are developed. This creates a clear record and helps avoid disputes later on.
Navigating Joint Ownership
In some projects, especially in academia or corporate partnerships, two or more entities may jointly own the rights to an AI invention.
Joint ownership sounds fair, but it can get messy. Unless there’s a written agreement, each party can use the patent freely—but they can also license it to others without permission or sharing revenue.
To avoid this, joint owners should set clear rules. Who can license the patent? How will revenue be split? Can one party sell their share?
These questions should be answered early—ideally before filing. That way, both sides know their rights and responsibilities from the start.
Global Differences in AI Patent Policy
The United States vs. Europe
The United States Patent and Trademark Office (USPTO) and the European Patent Office (EPO) take different views on AI patents. This matters when companies are looking to protect their models in multiple regions.
In the U.S., the key challenge is the “abstract idea” bar. If your invention sounds too much like a basic concept or mathematical process, it risks being rejected. To pass, you need to show how your AI is applied to a real-world task in a new and useful way.
In Europe, the test is more focused on whether the invention has a “technical character.” If the AI contributes to the technical functioning of a machine or system, it’s more likely to be accepted. But like the U.S., the EPO does not allow protection of pure algorithms or data models in isolation.
These differences can lead to mixed results. A model that’s patentable in the U.S. might be denied in Europe—or vice versa. That’s why drafting strategies need to be tailored for each region.
For example, in Europe, it helps to emphasize how the AI solves a technical problem or works more efficiently than previous systems. In the U.S., it’s better to stress the practical application of the model in industry, commerce, or technology.
Asia’s Growing Influence
Countries like China and South Korea are becoming important players in AI patenting. China, in particular, is granting a large number of AI-related patents, and its examiners have developed detailed guidance for handling applications in this area.
Chinese patent law tends to be more favorable to applicants who clearly define the technical improvement their AI model brings. If the system controls a robot, improves hardware function, or enhances real-time communication, it stands a better chance of success.
South Korea also favors AI applications with industrial uses. Their patent office is modernizing quickly and keeping pace with global trends in AI development.
For global protection, it’s wise to understand how these regions treat AI. A one-size-fits-all approach no longer works. Successful companies often file tailored applications in different jurisdictions, highlighting the features that matter most to each region’s examiners.
Defensive Publishing and Trade Secrets
When Not to Patent
There are times when filing a patent isn’t the best move. If your AI model evolves quickly or relies on secret data that would be hard to replicate, keeping it confidential may be a better strategy.
Trade secrets can protect algorithms, training data, optimization methods, and even performance metrics. The key is to maintain strong internal controls—limit access, mark documents as confidential, and use clear contracts with anyone involved.
Another tactic is defensive publishing. By publishing your method publicly (in a blog, white paper, or journal), you prevent others from patenting the same idea. This can be useful in fast-moving fields where the goal is to preserve freedom to operate, not exclusive rights.
Of course, once something is published, it’s no longer protectable as a trade secret. So this decision must be made carefully, based on the company’s goals and the technology’s shelf life.
Balancing Patent and Trade Secret Strategy
Many businesses use a hybrid model. They patent core systems—like model architectures or integrated solutions—and keep complementary elements as trade secrets.
This mix gives you legal protection while still preserving competitive advantages. It also makes enforcement easier. If a competitor copies your patented process, you can take legal action. If they try to reverse-engineer your secret sauce, your confidentiality program helps support a trade secret claim.
In short, patents give you public rights, but secrets give you silence. The right mix depends on how your technology is used, how fast it changes, and how easy it would be for others to copy.
Looking Ahead: Preparing for an Evolving Legal Landscape
The Unfinished Story of AI and Patents
The conversation around patenting AI is far from over. Courts, patent offices, and lawmakers are still catching up. Every few months, a new ruling or policy shift changes how we understand what’s patentable.
This uncertainty can be frustrating. But it also creates opportunity. Those who move early, document clearly, and understand how to frame their inventions have a better chance of success.
The future will likely bring clearer rules, especially around ownership, inventorship, and the role of algorithms. But until then, the best approach is to stay flexible, work closely with experienced counsel, and never treat AI like a standard invention.
Action Steps for Innovators
If you’re building or using AI models and wondering how to protect them, here are a few concrete actions to take:
First, start with documentation. Keep a clean paper trail of what the model does, how it works, who contributed, and how it connects to real-world use. This helps whether you’re filing for a patent, proving inventorship, or defending trade secrets.
Second, talk to a patent lawyer early. The sooner you define what’s novel and useful, the easier it is to shape a protection strategy. Filing too late—or too broadly—can waste time and money.
Third, think globally. If your market spans countries, your IP strategy should too. One region’s rejection doesn’t mean your model lacks value—it might just need a different presentation elsewhere.
Lastly, be realistic. Not every piece of code needs a patent. Sometimes, the real edge is in how fast you innovate, not how long you block competitors.
Where Innovation and Law Must Meet
Artificial intelligence will only keep growing in power, influence, and value. Whether it’s generating new products, diagnosing disease, or writing the next hit song, AI models are reshaping what it means to invent.
The law is being challenged like never before—but that’s nothing new. Every big shift in technology has forced the rules to evolve.
As creators, developers, and rights holders, we don’t need perfect clarity to move forward. We just need good tools, smart strategies, and an understanding of what’s possible today.
The key is to treat patent protection not as a checkbox, but as a living process—one that adapts to both innovation and regulation.
Stay informed. Stay flexible. And keep building.
