Did you know that since 2013, patent applications related to artificial intelligence have exploded? I saw a 2019 report from the World Intellectual Property Organization confirming this surge. Protecting machine learning (ML) inventions is a must. I am often asked: is it possible to patent machine learning models? The short answer is yes, although the path may be complex. If you want to file a patent in India, the success of your machine learning model depends heavily on how it is presented and the specifics of what you are claiming. Patent examiners will be all over these models.
Let us consider what makes something eligible for a patent.
Before we get into machine learning, keep in mind that patentability revolves around some basic ideas. Generally, an invention has to be:
- Novel: Completely new. Never seen or described before.
- Non obvious: Not just a minor change or combination of existing technology that someone in the field could easily come up with.
- Useful: It has to have a real purpose.
These principles also apply to machine learning inventions, of course. Showing that a machine learning creation meets these requirements, particularly that it is not obvious, can be challenging.
So, what are patent examiners really looking for?
When examiners review a patent application for a machine learning creation, there are several things they focus on:
The Real Innovation
Examiners look for the true innovation in the machine learning arrangement. This is the special, non obvious part. It could be a brand new algorithm, a unique structure or a specific way of training the system.
Example: Think about a new neural network structure that significantly improves image recognition. The real innovation here could be how the layers are arranged and connected.
Technical Nature and Actual Effect
A machine learning arrangement must have a technical nature and achieve a technical result. It needs to address a technical problem using technical methods, leading to a noticeable improvement. Abstract math with no practical application? Probably not patentable.
Example: Imagine a machine learning creation that improves delivery truck routes, reducing fuel use. That is a clear technical effect. It uses algorithms to tackle a routing problem, which saves fuel.
Full Explanation
You must explain the machine learning creation in enough detail so that someone skilled in the field could recreate it. Include specifics on the design, training data, methods and settings. If you are vague or do not provide enough data, your application will be rejected.
Example: Specify the type of neural network (convolutional, recurrent or something else), how many layers there are, what activation functions are used, the optimization algorithm and details about the training dataset.
Practical Use Is Key
Examiners need to know that the machine learning arrangement is more than just theory. Applications must describe how it is used in the real world. This shows that the invention is useful.
Example: For a fraud detection arrangement, explain how it fits into a fraud detection system, where the data comes from and how it identifies fraudulent transactions.
Importance of Data and Training
The data that drives the machine learning arrangement affects whether it can be patented. Using unique or custom data makes the invention seem less obvious. New training methods can also be patented.
Example: A machine learning creation trained using a carefully chosen medical image dataset might seem more inventive than one that uses data anyone can get.
Handling Bias
Reducing algorithmic bias is increasingly important, even if it does not directly determine patent eligibility. Showing that you have worked to reduce bias strengthens your application and demonstrates responsible AI development. While it is not required, it shows that you are thinking ahead and considering ethical issues, which can be a plus.
Here is how you can increase your chances of getting a patent for a machine learning creation:
- Stress Technical Improvement: Clearly state the technical problem the arrangement solves and how it is better than existing solutions.
- Show Originality and Non Obviousness: Highlight unique aspects like the design, algorithm or training method. Explain why these would not be obvious to experts.
- Give Complete Details: Fully describe the design, training data, methods and how it is implemented.
- Show Practical Use: Describe real world applications and their benefits.
- Handle Algorithmic Bias: Detail the steps you have taken to minimize bias in the data or design.
If you want to file a patent in India for a machine learning creation, you need to understand India’s specific rules. The Indian Patent Office (IPO) typically follows similar rules as other patent offices, but there are some differences:
- Software Patents: India has specific rules for software related inventions. Section 3(k) of the Patents Act prevents patents for “a mathematical or business method or a computer program per se or algorithms.” That being said, a machine learning creation that has a “technical effect” beyond just being a computer program may be eligible.
- Focus on Technical Improvement: The IPO emphasizes that you must demonstrate technical improvements over existing technology. Applications need to highlight significant advancements.
- Clarity Matters: Patent application claims must be clear, precise and supported by the description. If your claims are vague or too broad, they will be rejected.
- Local Representation Needed: If you are a foreign applicant, you will need a registered Indian patent agent to represent you before the IPO.
Patentability rules for machine learning are always changing. As AI gets better, patent offices around the world struggle with how to assess these inventions. Staying up to date on changes to patent law and best practices is essential if you want to protect AI innovations.
If you want to patent machine learning creations, you need to show that they are new, not obvious and useful. Focus on technical improvements, provide detailed information and handle any potential biases to improve your chances, especially if you want to file a patent in India. The field is always changing, so protection methods will change as well. Expect that examination and legal interpretations will evolve. Stay informed and protect your machine learning intellectual property.
