If you’re building software, AI tools, or data-driven products in 2024, you’re probably working with algorithms at the core. But can you patent them?
The answer isn’t simple. Patent law doesn’t always treat code, logic, or formulas like inventions. And yet, some startups get algorithm-based patents granted every year.
So where’s the line?
This article will walk you through exactly how patent offices view algorithms today, what makes one patentable, and how startups can approach protection the right way. No legal jargon. No vague theory. Just clear guidance, so you don’t waste time or risk losing your competitive edge.
Why Algorithms Raise Patent Questions
What Is an Algorithm?
At its core, an algorithm is a set of steps to solve a problem or complete a task.
It’s the logic behind a recommendation engine, a fraud detection system, or a real-time traffic router.
But while algorithms drive innovation, they’re often seen by the law as abstract ideas.
This is where startups hit a wall.
Why Patent Law Treats Algorithms Differently
Patent law doesn’t allow the protection of ideas in their raw form.
That means if you just describe what your algorithm does—without showing how it ties to a real-world use—your patent won’t pass.
Courts and patent offices look for a technical solution to a technical problem.
In other words, it’s not enough that your algorithm is clever. It has to be part of a larger invention that makes something work better in the real world.
This idea is known as the “abstract idea” doctrine—and it’s the reason many algorithm-based patents get rejected.
The Legal Landscape in 2024
In the U.S., the key case is still Alice Corp. v. CLS Bank.
That Supreme Court decision set the current standard: you can’t patent abstract ideas unless you add something “significantly more.”
In 2024, that standard still applies—but the interpretation has become more refined.
Patent examiners are now trained to look at specific elements. They want to see how your algorithm improves computer performance, transforms data in a meaningful way, or creates a new kind of system or device.
So, startups must be very clear not just about what the algorithm does, but how it fits into something useful.
What Makes an Algorithm Patentable?
Tie It to a Practical Application
One of the biggest mistakes startups make is focusing only on the logic.
Patent law needs more.
Let’s say you’ve created a way to sort data faster. That sounds useful—but unless you show how it works within a computer system or device, it will look like just an abstract idea.
Now, if your sorting algorithm helps compress files for streaming video, or speeds up machine learning training in a novel way, that’s different.
That’s a technical result with a clear use case. And that’s where patent eligibility begins.
Be Specific About the Technical Contribution
General descriptions like “this improves efficiency” or “this is faster” don’t carry much weight.
You need to go deeper.
How exactly does your algorithm reduce processing load? What part of the system is changed? Is there less memory usage? Does it allow the device to do something it couldn’t before?
If you can answer those questions in your patent application, you have a stronger shot at approval.
Show Implementation, Not Just Theory
Another critical point—your patent has to explain how the algorithm is carried out.
You can’t just say, “we use machine learning to improve results.”
You must describe the method. What’s the input? What are the steps? How is the result generated?
Even if you don’t reveal source code, you need to walk through the technical architecture so that someone skilled in the field could recreate the process.
That level of detail shows the algorithm isn’t just a theory—it’s a tool with real, working parts.
Common Scenarios Startups Face
You’ve Built a Recommendation Engine
Let’s say your startup has built a new way to recommend products to users.
That’s algorithmic—but can it be patented?
It depends.
If you simply describe the logic behind the recommendations, it won’t qualify. But if your method improves the speed of recommendations by rethinking how the data is queried or clustered, now you’re talking about performance.
And if that improved performance reduces server costs or enables real-time feedback—those are the kinds of results patent examiners value.
You’re Training a Unique Model
Maybe you’ve developed a special method for training AI models with less data.
That could be very patentable—if it’s more than just a math trick.
Does your method solve a known bottleneck? Does it integrate with hardware in a specific way? Does it reshape how training happens under constrained computing?
Those angles take you out of abstract territory and into invention.
You’ve Created a Security Protocol
Security algorithms are some of the strongest candidates for patent protection.
That’s because they often tie directly to devices, systems, or real-world outcomes.
If your encryption method prevents a type of attack or adds a unique authentication step, you’ve got a strong technical story.
Again, the key is not just saying “we secure data.” The key is showing how.
How to Draft Algorithm-Based Patent Applications
Speak the Language of Systems, Not Just Logic
One major tactic when writing patents for algorithms is to describe them in terms of systems.
For example, don’t say: “the algorithm calculates X from Y.”
Instead, say: “a processor receives input Y, performs operation X using memory module Z, and generates output for display or storage.”
Why?
Because patent examiners want to see how your algorithm fits into real hardware, software, or platforms. If you frame it this way from the start, you avoid a common pitfall—being dismissed as too abstract.
Anticipate Pushback
Even well-written algorithm patents often face initial rejection.
That’s normal.
The U.S. Patent and Trademark Office uses a two-part test to screen software inventions. The first part asks whether the claim is directed to an abstract idea. The second part asks whether it includes “significantly more.”
Your job, when writing the application, is to address both.
Explain clearly how your method solves a technical problem. Show how it integrates into a device or improves performance. And be ready to defend those points if the examiner challenges them.
File Before You Launch
Startups often delay filing because they think the algorithm isn’t fully finished.
But that can be risky.
If you start showing your invention publicly—or even just demo it at a pitch event—you might lose patent rights in some countries.
In the U.S., you have a one-year grace period. But internationally, many jurisdictions expect you to file first and disclose later.
So, if your algorithm is working well enough to describe in technical detail, it may be time to file—even if you’re still refining the edge cases.
Jurisdiction Matters: Different Countries, Different Standards
Why One Patent Strategy Doesn’t Fit All
You may assume that if your algorithm gets a U.S. patent, it’ll automatically work worldwide.
That’s not the case.
Different countries have different standards for what counts as patentable subject matter—especially when it comes to software and algorithms.
While the U.S. uses a “technical solution” test after Alice, Europe relies heavily on the “technical effect” rule, and places like China and Japan have their own distinct frameworks.
That means what passes in one country might be rejected in another unless the application is carefully tailored.
Europe: A Focus on Technical Effect
In the European Patent Office (EPO), algorithms fall under “computer-implemented inventions.”
To be accepted, your algorithm must produce a technical effect that goes beyond just running on a computer.
Examples include controlling a machine, improving network speed, or enhancing the inner workings of a device.
It’s not enough to automate a business process. The improvement must be tied to the technical workings of a system.
So, if you’re filing in Europe, your claims should reflect that logic. The emphasis is less on abstract logic and more on real-world function.
China and Japan: Rapid Growth, Tight Standards
Both China and Japan are becoming more important in the world of AI and algorithms.
And both have strict standards for software patents.
China requires that the claimed algorithm be part of a technical solution to a technical problem, and they’re especially skeptical of financial or marketing algorithms.
Japan looks for practical application and avoids claims that are just mathematical formulas in disguise.
The good news is that both countries are more open to algorithm patents than they were a decade ago.
But again, your strategy must be jurisdiction-specific.
Business Strategy Behind Algorithm Patents
Protecting the Core vs. the Ecosystem
When thinking about patent strategy, you don’t always need to protect the entire algorithm.
Sometimes, it’s smarter to protect how it’s used.
You might have a core algorithm that sorts data. But what’s valuable is how it plugs into your user interface, mobile app, or cloud backend.
So you file claims that cover those components.
This way, even if someone tweaks the logic slightly, they can’t copy your product without running into infringement issues.
Blocking Competitors Without Overreaching
Another tactic is filing narrow patents that target key features your competitors will likely need.
For example, if your algorithm enables real-time personalization and cuts latency by 30%, you can focus your claims on that specific performance edge.
This blocks rivals from using the same advantage, even if they code it differently.
The idea isn’t to patent everything under the sun—it’s to fence off your unique strengths.
IP as a Signal for Investors and Partners
A strong patent portfolio can also be a business signal.
If you’re in the AI or SaaS space, investors often want to see more than just traction—they want defensibility.
Having patent protection around your algorithms shows that you’ve built something others can’t easily copy.
And when it comes to M&A or licensing deals, those patents become powerful assets. They’re not just paperwork—they’re leverage.
What to Avoid When Patenting Algorithms
Don’t Be Vague
Many startups draft patents that are too general.
They describe the benefits but not the mechanics. They talk about what the algorithm achieves, but not how.
That’s a fast way to get rejected.
Patent examiners want detail. They want diagrams, steps, and explanations. They want to see how your algorithm actually works in a system.
So avoid high-level buzzwords. Dive into the specifics.
Don’t Wait Too Long
Filing late is another common pitfall.
If your algorithm is published, shared, or demoed before filing, that can count as prior art—and in some countries, that means you’re out of luck.
Even if your product isn’t fully launched, you can file a provisional patent. It locks in your filing date and gives you a year to refine your full application.
The key is to get ahead of public disclosure.
Don’t Skip Legal Help
It might be tempting to DIY your patent application, especially in the early stages.
But writing a strong algorithm patent isn’t like writing a product spec.
You need to frame the invention in the right legal language, anticipate examiner objections, and structure claims to withstand challenges.
A good patent attorney will help you do all that while keeping costs in check.
Trying to save money upfront can cost much more if your patent gets rejected or ends up too narrow to be useful.
Real-World Examples and What They Teach
Google’s PageRank Patent
One of the most famous algorithm patents is Google’s original PageRank.
This algorithm was granted a patent not because it ranked pages—but because of how it calculated value based on link structure in a network.
The technical innovation was in the way it measured importance mathematically and applied that to search.
This shows that even foundational logic can be patentable—if tied to a specific technical improvement.
Amazon’s Personalization Engine
Amazon has secured many patents for how it recommends products.
They don’t just describe “recommending things based on user data.”
Instead, they patent methods for clustering users, tracking behavior in unique ways, and optimizing the engine’s speed and accuracy.
These patents protect specific systems and flows—not vague concepts.
That’s a lesson for any startup working with user-driven algorithms.
A Startup Example: HealthTech AI
A small healthtech company created an algorithm that could detect heart rhythm issues from smartwatch data.
Rather than patenting “a method for detecting heart problems,” they focused on the unique way their algorithm processed sensor noise, filtered data, and triggered alerts.
Their claims were deeply tied to the device architecture and the signal pipeline.
That gave them strong IP—even against larger players—because the improvement was concrete and clear.
The Role of Training Data in Patent Eligibility
Data Isn’t Always Neutral
A surprising but important point in algorithm patenting is the role of training data.
Most machine learning models rely on data sets to become accurate.
But the way you curate, process, or structure that data can sometimes become part of the invention itself.
If your training method is unique, or if your data pipeline produces better results than others, that process can add to your patent claim strength.
Examiners often care not just about the model, but how it gets built.
If the data helps create better outcomes in a technical way—like fewer errors, faster results, or reduced memory usage—that can tip your algorithm into the “patentable” category.
Preprocessing Techniques Can Be Protectable
Let’s say you’ve invented a way to remove bias from your data before training your algorithm.
That preprocessing step might seem like a background task—but if it has technical impact, you may be able to claim it.
Many granted patents today cover not just the model output, but the steps taken to get there.
So don’t treat data prep as an afterthought in your application.
Describe it clearly. Show how it connects to better system behavior.
That could make the difference between a weak and a strong patent.
Data Alone Can’t Be Patented, But How You Use It Can
Here’s the catch—raw data isn’t patentable on its own.
You can’t claim ownership of user clicks, emails, or heartbeat records.
But the methods you use to structure or transform that data for training purposes can be.
If your system uses a unique embedding, compression, or classification process, that’s where IP protection becomes possible.
The takeaway is simple: in AI and algorithm patents, your data story matters.
Don’t gloss over it.
Algorithms and Trade Secrets: Choosing Between IP Tools
When a Patent Isn’t the Best Fit
Patents are public. Once granted, the core of your invention becomes visible to the world.
That’s why some startups choose to protect their algorithms as trade secrets instead.
If your algorithm is hard to reverse-engineer, and doesn’t need to be disclosed to users, keeping it confidential might make more sense.
Think of secret ranking formulas, fraud detection logic, or optimization techniques that don’t leave fingerprints.
In such cases, filing a patent might just hand the keys to your competitors.
So you must choose carefully.
Pros and Cons of Trade Secrets vs. Patents
Patents offer clear legal protection—but they expire, and they require disclosure.
Trade secrets don’t expire—as long as you keep them secret—but they’re harder to enforce.
If someone independently discovers your method, you can’t stop them unless there’s proof they took it from you.
So in fast-moving tech, some companies use both strategies.
They patent the core logic, and keep surrounding tricks and enhancements as secrets.
It’s not always either/or.
The right mix depends on your product, your market, and your risk tolerance.
What’s Most Valuable to Your Business?
When deciding what to patent, ask yourself:
What would hurt my business most if a competitor copied it?
That’s the piece to protect.
Whether that’s your main algorithm, your scaling technique, or your user profiling model, the answer isn’t always obvious.
Sometimes the real value isn’t in the model, but in how you integrate it into a larger system.
Patent strategy is about picking your battles. Not everything needs a fence around it.
But the parts that do should be locked down early and smartly.
Litigation, Licensing, and Defensive Filing
Preparing for Infringement Before It Happens
Let’s be honest—most startups don’t want to go to court.
But patent litigation happens. And if your core algorithm is copied by a competitor, you need a defense plan.
That starts with strong claims that cover likely variations.
You don’t want a patent that only protects one version of your model.
You want protection that follows the idea as it evolves across platforms and languages.
That’s why claim scope matters.
And it’s why you should build a portfolio, not just one patent.
If you ever need to enforce, you’ll be glad you did.
Algorithm Licensing as a Revenue Stream
Some companies patent their algorithms not to block others—but to license them.
That works best when your model is modular.
If others can plug it into their system without needing your full product, licensing can bring in revenue.
But you can’t license what you haven’t protected.
You need clearly defined rights. You need claims that explain what they can use and what they can’t.
And you need contracts that match your patent language.
Licensing isn’t passive. It’s a business model—and patents are the asset that makes it work.
Defensive Patents Still Have Strategic Value
Even if you never plan to sue anyone, having patents can protect you.
If a bigger company claims you’re infringing their algorithm, your patent portfolio gives you leverage.
You can cross-license. You can negotiate from strength.
Without patents, you’re exposed.
That’s why many startups file defensively—to avoid being boxed out by players with deeper pockets.
Think of your algorithm patents as armor. You may never need to swing, but you want them in place before the fight begins.
Global Patent Differences: Why Geography Matters
Not All Countries Treat Algorithms the Same
One of the biggest traps startups fall into is assuming that if an algorithm is patentable in one country, it’s patentable everywhere. That’s not true.
Patent laws vary by jurisdiction.
In the United States, the standard revolves around whether the invention adds a concrete, practical improvement to a technical process.
In Europe, the focus is on whether the algorithm solves a “technical problem” with a “technical solution.”
Even small changes in phrasing or claim focus can make the difference between a granted and rejected patent across borders.
That’s why international filings need tailored strategies—not copy-paste jobs.
Filing First in the U.S.? That’s Often Smart
The U.S. Patent and Trademark Office (USPTO) is often the first stop for AI startups.
That’s partly due to speed, but also because the U.S. system allows for relatively broad claims when structured well.
It also allows provisional patent applications, which give you a 12-month head start to refine your strategy.
That said, filing in the U.S. doesn’t automatically protect you in Europe, Asia, or elsewhere.
You’ll need to file separate applications through regional systems like the EPO (European Patent Office) or WIPO (World Intellectual Property Organization) if you want global rights.
And you’ll need to adapt your claims to local rules.
International patent protection takes planning—but it’s worth it if your market is global.
Watch Out for China and Emerging Jurisdictions
China is becoming a major player in patent filings, including AI-related inventions.
Their system has unique requirements, and the examiners are getting stricter, especially around abstract algorithms.
Still, they are willing to grant patents when the model is tied to real-world applications—like factory automation, smart cities, or logistics.
If your product touches those areas, don’t ignore Chinese protection.
Also, keep an eye on emerging jurisdictions like India, Brazil, and Southeast Asia.
They’re still catching up, but if your user base or production chain operates there, local filings might be smart.
How Examiners Read Algorithm Patents
Focus on the “Problem” First
Examiners don’t start by reading your code.
They start by reading your problem statement.
They ask: what issue is this solving, and is it a technical one?
That’s why your patent should begin with a clear, well-structured description of the problem you’re fixing—and how current methods fail.
Then show how your approach works better.
This story-first approach matters because it frames your invention as necessary, not just clever.
Make it easy for the examiner to say: “Yes, I see why this matters.”
Avoid Overclaiming or Underspecifying
One common mistake is writing claims that are too broad or too vague.
Saying “an improved machine learning model” doesn’t help.
What makes it improved? What changes, step by step?
On the other hand, if your claims are too narrow—such as only applying to a very specific use case—you risk making your patent easy to design around.
Strike a balance.
Be specific enough to show how your algorithm works, but broad enough to apply to multiple use cases.
That’s the sweet spot where patents become valuable assets.
Give the Examiner What They Need
Examiners are not software engineers. They are legal analysts trained to look for certain clues.
If your filing includes diagrams, workflows, training processes, and comparisons to existing models, you’re doing it right.
Explain what each part of your algorithm does—and why.
Even better, show metrics: speed, accuracy, latency, power usage.
These concrete numbers can help prove technical value, which is key to passing the eligibility bar.
Think of your patent application like a pitch deck to someone who has to check boxes—not fall in love with your brand.
Future-Proofing Your Algorithm Patents
Plan for Evolutions, Not Just Snapshots
Tech doesn’t sit still.
If your algorithm is evolving—adding features, new data types, or optimizations—your patent strategy should evolve too.
That’s why you might want to file multiple related patents, often called a family.
One covers the core. Others cover updates, improvements, or new domains.
This lets you build layers of protection that stay relevant as your product grows.
It also lets you react fast when competitors try to work around your claims.
Don’t think of patents as one-and-done. Think of them as living documents that follow your business growth.
Watch the Headlines, Watch the Courts
The laws around algorithm patenting are changing.
New court rulings, changes in examiner guidelines, and even public pressure around AI bias can shift what gets approved.
Stay updated on major cases, especially in the U.S. and Europe.
If a new ruling tightens the rules, you’ll want to know before your next filing.
Also, trends like explainable AI, energy-efficient models, and federated learning are becoming hot topics.
If your work touches these, it may have stronger chances at getting protection.
Use legal trends as a compass to guide your patent efforts—not just technical trends.
Invest in the Right Help Early
Many founders wait too long to bring in IP professionals.
That delay can cost you—either in missed deadlines or weak filings.
A good patent attorney (especially one who understands AI) can help you frame your algorithm in a way that speaks both legal and technical language.
They can also help build your filing calendar, prepare you for international routes, and steer clear of common traps.
Yes, it’s an investment. But when your algorithm is your moat, it’s one worth making early.
Closing Thoughts: Your Algorithm Is Worth Protecting
Algorithms aren’t just math—they’re the engines behind products, platforms, and profits.
But they live in a tricky legal space.
You can’t patent abstract logic. You can’t own a math formula.
But if your model improves a technical process, solves a real problem, or delivers performance no one else can match—you may have something protectable.
And in 2024, those protections matter more than ever.
From investor confidence to market differentiation, strong IP around algorithms can drive real value.
So don’t guess.
Work with experts. File early. Evolve your strategy.
And always ask: if someone copied this, how would it hurt us?
That’s your patent priority, right there.
