IP Advice for AI Startups: Patent, Profit, Progress
2025
5 mins
AI is taking over the technology industry and IP is an AI inventor’s best friend. Learn how to secure your creation and stand out in the crowded marketplace.
Written by: William Wathey
As AI adoption intensifies, it is more important than ever that genuine AI innovators distinguish themselves from the noise and maintain a competitive advantage. Having a solid understanding of your intellectual property (IP) and a strategy for protecting it are vital for establishing your position in the market and attracting investment.
Can I patent my AI?
There is a common misconception that AI, and software inventions more generally, cannot be patented. However, many AI and AI-adjacent inventions have been patented. Indeed, AI is now one of the fastest growing technology areas in terms of patent filings.
The legal landscape around the patentability of AI is still evolving and can vary between jurisdictions. However, when considering if patent protection is right for your AI solution, the key questions to ask yourself are:
❓ Are you using machine learning in an unusual way?
❓ Does your AI solution address a technical problem and provide measurable technical benefits?
❓ At each stage of your data processing pipeline (training, pre-processing to inference and post- processing), are there any bespoke adaptations you have made for the intended use case or data types?
If the answer to any of these is yes, your solution may be eligible for patent protection. For example, an AI solution may be patentable if it:
💡 Combines known algorithms in a new way to address a specific real-world problem. Examples of this are image classification or load balancing of renewable energy sources.
💡 Uses a new model architecture or training technique which improves the fundamental operation of the hardware it is run on.
In contrast, if your AI product simply applies off-the-shelf models to address a commercial problem (e.g. to identify new customers or to improve project management), patent protection may be less feasible.
If you would like to discuss the patentability of your AI solution with a patent attorney in our dedicated AI team, please get in touch via our enquiry form.
The wider IP picture
Regardless of whether your AI product is patentable, it is also important to consider other types IP it might include. These can range from training data and model architectures to model weights, hyperparameter values and underlying code.
Even if patent protection is not feasible, investors often want to see that you have thought about how you will protect all types of IP. For example, using trade secrets and other guards against information leakage can be effective ways to protect non-patentable IP.
Given the rate at which AI is evolving, it is also worth remembering that, like physical property, IP rights such as patents can be used to generate revenue though sales or licensing. Therefore, even if you are likely to move on to other models well before the end of the patent’s 20-year lifetime, securing and maintaining patents for your AI innovations can be extremely valuable to your business.
Third-party IP and regulation
Even if you have robust protection to stop others from using your inventions, this provides no guarantee that you are free to commercialise your AI product. Instead, whether you can use and sell your product will depend on IP rights held by third parties and regulatory requirements.
Almost all AI solutions will involve a mixture of proprietary and third-party IP. Therefore, understanding early on whether you are infringing any third-party IP rights allows you to reduce risk, and sight potential obstacles and costs for your business.
A full “freedom-to-operate” analysis can be relatively complex. However, for AI products, it is at least worth having an ongoing process to check whether licences are required for any third-party AI models or training data that you use as part of your product.
As we start to see the first binding regulations on AI coming into effect, such as the EU AI Act, it is also crucial to ensure compliance with AI regulation. For example, the EU AI Act places stricter requirements on providers and deployers of general-purpose AI (GPAI) models. On the other hand, it does include several SME-friendly provisions, such as:
✔ Regulatory sandboxes for testing AI products outside normal regulatory structures,
✔ Reduced compliance costs and fees,
✔ Simplified SME-specific documentation and training,
✔ Dedicated support channels for SMEs.