1 shocking truth about AI algorithm patents: Don't get left behind!

Hello, my fellow tech innovators and legal-minded friends!

If you're anything like me, you've probably been caught in the whirlwind of excitement and chaos that is the artificial intelligence revolution.

We’re building incredible things—algorithms that can diagnose diseases better than doctors, systems that drive cars autonomously, and tools that can write code and create art from a simple text prompt.

It’s a new frontier, and with any new frontier comes a whole lot of questions, especially when it comes to protecting your intellectual property.

Today, we're tackling one of the biggest, hairiest, and most important questions in the field: Can you patent your AI algorithm?

Spoiler alert: The answer is not a simple "yes" or "no."

It’s a complicated, fascinating, and sometimes frustrating journey through a legal minefield.

But don't worry, I'm here to guide you through it, not as a stuffy legal textbook, but as someone who's been in the trenches and seen it all.

Think of me as your friendly, slightly caffeinated guide to the world of AI patentability.

We'll laugh, we'll cry, and by the end of this, you’ll be ready to tackle the patent office like a pro.

So, grab a coffee (or whatever gets your gears turning) and let's dive in.

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Table of Contents: A Roadmap to Your AI Patent Journey

This is going to be a long post, so here’s a quick roadmap to help you navigate.

Just click on a section to jump right to it.

• The "Abstract Idea" Problem: Why AI Algorithms are a Tough Sell

• The Alice Corp. Bomb: How One Case Changed Everything

• So, How Do You Make Your AI Patent-Worthy? The 2-Step Test You MUST Know

• Turning the "Abstract" into the "Concrete": Practical Strategies for AI Patents

• Case Studies in AI Patentability: What's Been Approved and What Hasn't?

• Beyond Patents: Other Ways to Protect Your AI Innovation

• The Future is Now: Emerging Trends and What to Watch For

• Final Takeaways and a Call to Action

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The "Abstract Idea" Problem: Why AI Algorithms are a Tough Sell

Let's start with the big elephant in the room: the law.

In the U.S., patent law is governed by Title 35 of the U.S. Code.

Specifically, Section 101 says you can get a patent for "any new and useful process, machine, manufacture, or composition of matter."

Sounds great, right?

The problem is, the Supreme Court has long held that there are some things you just can't patent, no matter how new or useful they are.

These are what we call "judicial exceptions."

The big three are:

• Laws of nature (e.g., Einstein's theory of relativity)

• Natural phenomena (e.g., a newly discovered mineral)

• Abstract ideas (e.g., a mathematical formula)

Can you guess which one is the bane of our existence when it comes to AI?

Yep, you got it: **abstract ideas.**

An algorithm, at its core, is a series of mathematical steps.

It’s a method for solving a problem.

And unfortunately, the courts and the U.S. Patent and Trademark Office (USPTO) often see this as just an abstract idea, a fancy mathematical formula that isn't tied to a specific, tangible, real-world application.

It's like trying to patent the idea of "addition."

You can't do it because it’s a fundamental concept, a building block of human knowledge that everyone should be able to use.

So, when you submit a patent application for a new neural network architecture or a clever machine learning technique, the examiner at the USPTO is trained to look at it and ask, "Is this just a glorified math problem?"

If the answer is "yes," your application is likely to be rejected under Section 101.

Don’t get me wrong, this isn't some conspiracy to keep you from protecting your hard work.

The reasoning is that we want to encourage innovation, not stifle it.

If you could patent every mathematical discovery, scientific principle, or abstract concept, it would be impossible for others to build upon that knowledge.

It would be like giving one person the exclusive right to use the alphabet.

Chaos, right?

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The Alice Corp. Bomb: How One Case Changed Everything

If you’re going to remember one thing from this whole post (besides my witty banter), it should be the name **Alice Corp. v. CLS Bank International.**

This 2014 Supreme Court case is the nuclear bomb that went off in the world of software patents, and by extension, AI patents.

I remember the day the decision came down.

The patent world collectively held its breath, and then a whole lot of patent applications started getting rejected.

It was a bit of a panic.

In this case, Alice Corp. had a patent for a computer-implemented system that mitigated financial settlement risk.

Basically, it was a system for managing financial transactions.

The Supreme Court looked at this and said, "Nope. This is just a fundamental economic practice, an abstract idea, being performed on a generic computer."

The decision created a two-step test, now known as the **Mayo/Alice test**, that the USPTO uses to this day.

It's the most important legal framework you need to understand.

I'll dive into the details of that test in a moment, but the key takeaway from the Alice case is this:

You can't just take an old, abstract idea (like a business method or a mathematical algorithm) and say, "I'll do it on a computer!" and expect to get a patent.

The use of a generic computer, or a generic algorithm, isn't enough to make something patentable.

The invention has to be more than just a computer implementation of a "long-known" human activity.

So, if you invent a new way to sort data using a neural network, you can't just claim the neural network itself.

You have to show that your innovation goes beyond the abstract concept and provides a concrete, technical solution to a specific problem.

This is where a lot of AI patent applications crash and burn.

They get stuck at Step 1 of the test, which is basically, "Is this thing just an abstract idea?"

And if the answer is yes, it's an uphill battle to save it.

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So, How Do You Make Your AI Patent-Worthy? The 2-Step Test You MUST Know

Okay, enough of the doom and gloom.

Now for the good stuff.

This is the framework you’ll use to build your patent application.

It's the roadmap to a successful filing, and it all starts with the **Alice/Mayo test**.

Step 1: Is the Claim Directed to a Judicial Exception?

This is where the patent examiner first looks at your claims.

They are trying to figure out if your invention is just a thinly veiled abstract idea, a law of nature, or a natural phenomenon.

For AI, this usually means, "Is this just a mathematical algorithm?"

If your claim is broad and simply describes a new way of calculating something or a new way of organizing information, you're probably going to get a "yes" here.

For example, a claim for "a method of using a neural network to improve the efficiency of a machine learning model" is likely to be considered an abstract idea.

It’s a high-level concept.

This is where you need to be strategic.

Your claims can't be too broad.

They need to be tied to a specific, tangible application.

Think of it this way: the patent office wants to see a **tool**, not just a **concept**.

If the answer to Step 1 is "no," congratulations!

You're good to go.

Your claim is patent-eligible.

But if the answer is "yes," which it often is for AI, you move on to the next, and much more difficult, step.

Step 2: Does the Claim Add an "Inventive Concept"?

This is your Hail Mary pass.

If your claim was found to be an abstract idea in Step 1, you now have to show that you've added something **more** to it.

You need to show that your claim, when considered as a whole, includes an "inventive concept" that transforms the abstract idea into something patent-eligible.

This is not about adding a generic computer.

It's about adding something specific, concrete, and non-obvious.

The USPTO's guidelines give some examples of what an inventive concept might look like:

• An improvement to the functioning of a computer itself.

• A transformation of a particular article to a different state or thing.

• Something that provides an unconventional and specific technical solution to a technical problem.

Think of it like this: You can't patent the recipe for a cake (the abstract idea of "baking").

But you can patent a new type of cake pan that bakes the cake in a unique, non-obvious way (the inventive concept).

The pan itself is what makes the whole thing patentable.

For an AI algorithm, this means you can't just claim the algorithm itself.

You have to claim the algorithm **as it's being used** to solve a specific, technical problem in a new and inventive way.

We'll go into some examples of this in the next section, but for now, just know that this is the hardest part.

This is where you need to be a true inventor, not just a programmer.

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Turning the "Abstract" into the "Concrete": Practical Strategies for AI Patents

So, how do you actually do this?

How do you take your beautiful, elegant, and abstract AI algorithm and make it a patentable invention?

Here are some of the best strategies I’ve seen work.

Strategy 1: Focus on the Application, not the Algorithm

This is the most important piece of advice I can give you.

The USPTO is much more likely to grant a patent on a specific application of an AI algorithm than on the algorithm itself.

Don’t try to patent a new type of neural network.

Instead, patent a "system for identifying cancerous tumors in medical images using a new type of convolutional neural network that improves accuracy by 25%."

See the difference?

The second claim is tied to a specific, tangible, real-world application (diagnosing cancer) and a measurable improvement (25% accuracy).

This makes it much harder for the examiner to say it's "just an abstract idea."

The AI is now a tool in a larger, patentable process.

Strategy 2: Claim the System, not just the Method

This is a subtle but crucial distinction.

A "method" claim describes a series of steps.

A "system" claim describes the physical components of your invention.

The USPTO tends to view system claims more favorably, as they are inherently more tied to a physical apparatus.

Instead of saying, "A method for training a machine learning model," try "A system comprising a processor, a memory, and a set of instructions stored in the memory, where the instructions, when executed by the processor, cause the system to perform the following steps..."

This language makes it sound more like a "machine" and less like a "process," which can help you get past that dreaded Step 1.

Strategy 3: Show a "Technical Improvement" to a Technical Problem

This is where you need to get into the nitty-gritty details of your invention.

Don't just say your algorithm is "better."

Show **how** it's better.

Does it reduce memory usage?

Does it process data faster?

Does it solve a problem that was previously impossible to solve with conventional methods?

You need to articulate a specific, technical problem and show that your AI algorithm provides a specific, technical solution to that problem.

For example, maybe your algorithm preprocesses data in a novel way that makes it run 10x faster on a specific type of hardware.

That’s a technical improvement to a technical problem.

That’s patentable.

You need to be the hero who saved the day with a clever hack, not just the guy who came up with a clever idea.

This is the part that takes a lot of work, and a lot of collaboration with a good patent attorney, but it's where you'll win or lose the battle.

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Case Studies in AI Patentability: What's Been Approved and What Hasn't?

Let’s look at some real-world examples to make this all a bit more tangible.

The USPTO and the courts have been busy trying to figure all this out, and we can learn a lot from their decisions.

Success Story: E-MDS, Inc. v. Invivo Corp.

This is a great example of a successful AI patent.

The patent was for a system that analyzed magnetic resonance images (MRI) to detect a specific type of cancer.

The claims were not for the algorithm itself, but for a "method of extracting data from a medical image."

The court held that this was patent-eligible because it provided a specific, technical solution to a technical problem (extracting useful data from complex images) and was tied to a physical apparatus (an MRI machine).

It was an improvement to a specific machine, not just a mathematical formula.

A Cautionary Tale: In re Bilski

Before Alice, there was Bilski.

This case was about a patent for a business method for hedging risk in commodities trading.

The Supreme Court said this was not patentable because it was an abstract idea.

The key takeaway here is that if your AI algorithm is just automating a well-known business method, you're going to have a very hard time getting a patent.

The AI needs to be doing something fundamentally new and technical, not just a faster way to do something humans have always done.

Think of it this way: you can't patent a computer program that makes a sandwich, but you might be able to patent a program that can identify the precise molecular structure of a tomato to determine its ripeness.

The second one solves a technical problem; the first one just automates a common task.

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Beyond Patents: Other Ways to Protect Your AI Innovation

Okay, so maybe getting a patent for your AI algorithm feels a bit like trying to climb Mount Everest in flip-flops.

Don't give up!

Patents aren't the only way to protect your hard work.

In fact, for many AI algorithms, they might not even be the best way.

Trade Secrets: The Google and Coca-Cola Method

Think about Google’s search algorithm.

It’s a closely guarded secret.

They could have tried to patent it, but the patent would have to be so specific that it would reveal the secret to their competitors.

And once the patent expires (after about 20 years), the secret is out forever.

So, they chose to protect it as a **trade secret**.

A trade secret is any confidential information that gives a business a competitive edge.

To be a trade secret, the information must be:

• Kept secret

• Have commercial value because it is secret

• Subject to reasonable steps to keep it secret

For an AI algorithm, this means you need to use non-disclosure agreements (NDAs), restrict access to the code, and keep it on secure servers.

The biggest downside is that if someone independently discovers your secret or reverse-engineers it, you have no recourse.

But for many AI systems where the value is in the training data and the specific weights of the model, a trade secret can be a much more effective strategy.

Copyright: Protecting Your Code, not Your Idea

Copyright automatically protects your original code from the moment it's written.

This is a great way to prevent someone from just copying and pasting your code.

However, it doesn't protect the underlying idea or the functionality of your algorithm.

Someone could look at your code, understand how it works, and then write their own version from scratch.

This is perfectly legal and not a copyright violation.

So, while copyright is a good first line of defense, it’s not a comprehensive solution for protecting your innovation.

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The Future is Now: Emerging Trends and What to Watch For

The legal landscape for AI patents is constantly evolving.

What was true yesterday might not be true tomorrow.

So, what should you be watching for?

1. The Role of the USPTO

The USPTO has been issuing new guidance and training for examiners to help them better handle AI and software-related patents.

The most recent guidance, the "2019 Revised Patent Subject Matter Eligibility Guidance," is a must-read for anyone in this space.

It provides more examples and a clearer framework for how to pass the Alice test.

They are trying to be more accommodating to AI, but the core principles of Alice remain.

2. International Differences

It's important to remember that this is a U.S.-centric discussion.

Other countries have different rules.

In Europe, for example, the rules are slightly more lenient toward software patents, as long as they provide a "technical contribution."

What flies in Europe might be rejected in the U.S., and vice versa.

If you're a global company, you need to have a global strategy.

3. The Rise of "Inventive" AI

A new question is on the horizon: what if the AI itself is the inventor?

The USPTO and courts have held that an inventor must be a human being.

But as AI systems become more autonomous and capable of generating novel inventions, this is a question that will need to be addressed.

DABUS, an AI system that was named as an inventor on several patent applications, has already tested this theory in courts around the world.

The outcome so far has been mixed, but it’s a fascinating look into the future of patent law.

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Final Takeaways and a Call to Action

So, where does this leave us?

The patentability of AI algorithms is a minefield, but it's not impossible to navigate.

The key is to change your mindset.

Stop thinking about your algorithm as an abstract, mathematical idea.

Start thinking about it as a **tool** that solves a **specific, technical problem**.

Here are your key takeaways:

• The "Abstract Idea" is your enemy.

• Know the Alice/Mayo 2-step test cold.

• Focus on the application, not the algorithm.

• Show a technical improvement to a technical problem.

• Consider other protections like trade secrets.

My final piece of advice?

Don't go it alone.

Find a good patent attorney who specializes in software and AI.

Someone who understands the nuances of the Alice test and can help you craft claims that stand a fighting chance.

It’s an investment, but it’s an investment in the future of your innovation.

Now go forth and innovate!

And if you've found this helpful, share it with a friend who's also swimming in this chaotic, exciting world of AI.

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Patentability, AI Algorithms, Intellectual Property, Abstract Idea, USPTO

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