Artificial intelligence (AI) is no longer confined to research labs and academic conferences. In just over a decade, it has evolved from a niche discipline into a primary driver of global innovation—and the patent system has taken notice. According to recent estimates, there are now more than 340,000 AI patent families worldwide, with over 200,000 granted since 2013. AI-related patents have overtaken every other technology category to become the fastest-growing area in intellectual property filings.
This surge is more than a statistical curiosity. It signals a measurable shift from theory to commercial application, as companies across industries move to secure legal protections for the algorithms, systems, and processes that underpin their competitive advantage. But the path to obtaining an AI patent is complex, especially in jurisdictions where rules differ on what constitutes an “invention” in the context of software and algorithms.
For technology businesses—from two-person startups to multinational corporations—understanding how to navigate these complexities can be the difference between safeguarding a breakthrough and watching it slip into the public domain.
Why Patent AI Innovations?
Patents are more than just legal shields; they are strategic business assets. A granted patent gives its owner the right to exclude others from making, using, or selling the claimed invention. This exclusivity can translate into several tangible benefits:
- Increased company valuation – Patents enhance the perceived value of a company, particularly when attracting investors. For startups, they can represent a significant portion of overall asset value.
- Competitive protection – A patent can deter competitors from entering a specific technological space, making it harder for them to replicate core features.
- Negotiation leverage – Patents can be licensed, sold, or used as bargaining chips in cross-licensing deals.
- Investor confidence – A robust patent portfolio signals technological sophistication and foresight, reassuring venture capital firms and strategic partners.
- Founders’ equity retention – Patents can help founders maintain a larger share of ownership by demonstrating asset value during funding negotiations.
Patent Valuation: How Much Is It Worth?
Determining the value of a patent is part science, part art. Companies often assess patents using one or more approaches:
- Replacement value – Estimating how much time and money a competitor would need to develop a workaround.
- Capitalized royalty method – Projecting licensing fees over time and converting them into a present-day value.
The actual worth depends on the uniqueness of the invention, the market potential, and the enforceability of the claims. Importantly, a patent that seems minor to its inventors may still have substantial value in blocking competitors or enabling strategic licensing deals.
How Many Patents Should a Company File?
The answer varies with company size, resources, and market ambitions:
- Startups with deep tech roots often file 5–10 patents to cover the most crucial aspects of their technology.
- Growing companies expand their portfolio steadily, filing additional patents as new features and improvements are developed.
- Large enterprises tend to patent aggressively, covering as many aspects as possible to build strong defensive walls around their intellectual property.
The guiding principle is to secure patent protection for innovations that provide a meaningful edge—whether in performance, efficiency, usability, or integration.
Understanding What a Patent Is—and Isn’t
Before drafting an application, it’s essential to distinguish patents from other intellectual property (IP) rights:
- Trademarks protect brand identity, such as product names, logos, and slogans.
- Copyrights safeguard original works like source code or documentation.
- Design rights protect the visual appearance of products, including graphical user interfaces. (In the U.S., this may be called a “design patent.”)
A utility patent—the most relevant type for AI—protects the functional aspects of an invention. In the United States, eligibility is broad, provided the invention is more than a law of nature, natural phenomenon, or abstract idea. In Europe, the definition is narrower: the invention must contribute a technical effect toward a technical purpose.
US vs. EU: Key Differences in Patent Standards
Although both the United States Patent and Trademark Office (USPTO) and the European Patent Office (EPO) ultimately aim to protect innovative technology, they differ in terminology, procedures, and thresholds for approval:
- Technical contribution – The EPO requires a clear technical effect, whereas the USPTO focuses on avoiding claims that fall under “abstract ideas.”
- Amendments – The USPTO allows more flexibility to amend claims after filing. The EPO is stricter, making it critical to get the application right from the start.
- Granting trends – The EPO generally applies higher standards in examination, potentially making it harder to obtain patents without thorough preparation.
AI Patenting Pitfalls and How to Avoid Them
AI inventions often face rejections for the same reasons as other computer-implemented technologies. Common pitfalls include:
- Abstract ideas – Claiming only a mathematical model without tying it to a specific technical implementation.
- Non-technical subject matter – Under EPO rules, pure algorithms and computational models are excluded unless they produce a measurable technical effect.
- Overly broad claims – Attempting to protect an entire field of AI use rather than a specific, non-obvious solution.
Statistics underscore the challenge: while roughly one-third of general tech patent applications succeed, the success rate for AI-specific patents over the last five years has been closer to 10%.
What Can You Patent in an AI Pipeline?
AI systems are often built from existing frameworks, libraries, and pre-trained models. Even so, there are numerous patentable elements—provided they meet eligibility requirements:
- Speech technologies – Natural Language Understanding (NLU), Natural Language Generation (NLG), Automatic Speech Recognition (ASR), and Text-to-Speech (TTS) implementations can be patentable if they involve non-obvious technical solutions.
- Computer vision – Optical recognition of low-level features, or classification of images, videos, or audio signals using inventive techniques, may qualify.
- Pipeline optimizations – Innovations outside the core algorithm—such as improved data preprocessing or hardware-specific performance gains—can form the basis for a patent.
The common denominator is technical contribution: the improvement must go beyond standard use of known AI toolkits and solve a defined technical problem in a novel way.
Incremental Innovation: The “Small Steps” Philosophy
Contrary to the popular image of patents as protecting giant leaps forward, most patents cover incremental improvements. This could mean:
- Adapting an existing model to a specific hardware environment.
- Reducing computational requirements through a new memory-management strategy.
- Combining known techniques in a way that solves a long-standing technical bottleneck.
The EPO applies a problem-solution approach to assess whether an improvement is obvious. The USPTO uses different tests but ultimately seeks the same determination: is the improvement something a skilled professional would not naturally think to do?
Do’s and Don’ts for Patenting AI
Do:
- Focus on a specific technical implementation, not just the concept.
- Consult engineers to identify real-world technical hurdles your solution overcomes.
- Think small—many small patents can collectively create a large protective wall.
Don’t:
- File patents that are just a wish list of system requirements.
- Assume improvements are too minor to patent.
- Overlook the importance of jurisdiction-specific drafting.
Case Examples: What Flies and What Fails
- Patentable – A computer vision system that improves low-level feature extraction under poor lighting conditions, enabling higher accuracy in real-time recognition.
- Not patentable (EPO) – A text classification algorithm that assigns documents to categories based solely on content without additional technical contribution.
- Potentially patentable – A document classification system that overcomes hardware limitations, reduces latency, or improves integration into existing infrastructures.
Final Takeaways for AI Innovators
For AI companies, patenting is not just an optional layer of legal protection—it is a strategic necessity in an increasingly competitive and fast-moving market. The most effective patents focus on how a technical problem is solved, not merely what the problem is.
Understanding jurisdiction-specific nuances, avoiding common drafting mistakes, and recognizing that incremental improvements can be valuable are critical steps toward building a meaningful portfolio.
When approached thoughtfully, patents can transform AI inventions from fleeting technical milestones into long-term competitive advantages—ensuring that the small steps taken today pave the way for the giant leaps of tomorrow.
