AI Is Advancing Globally
Artificial intelligence is advancing rapidly worldwide—but it is not equally patentable everywhere
In Europe, patent applications in the field of AI are significantly more restricted than in the United States or China. The reason lies in the legal framework: under the European Patent Convention (EPC), mathematical methods, schemes, rules, and methods for mental activities are excluded from patent protection—at least when claimed "as such.” The German Patent and Trade Mark Office (DPMA) follows the same principle: protection is only granted if there is a concrete technical contribution.
This disparity is also reflected in the numbers: over the past ten years, more than 38,000 patents in the field of generative AI have been filed in China, around 6,300 in the United States, and only 708 in Germany. Notably, more than a quarter of these filings were made in the past year alone.
The challenge becomes particularly evident in complex technical fields: with conventional tools that rely primarily on keywords, searches often return either an overwhelming number of results—or, more critically, almost nothing relevant.
This is not because relevant patents do not exist, but because they are described differently. Technical content is rarely expressed in a standardized way. Two documents may describe the same concept without sharing a single identical keyword.
In practice, this means that without modern approaches, a significant portion of relevant prior art remains invisible.
This is not a theoretical issue - it is a structural limitation of traditional search methods.
This is exactly where patentbutler.ai comes in: instead of relying solely on keywords, the technology analyzes the technical substance of an invention. Individual features are extracted and mapped against existing solutions—independent of how they are described linguistically.
As a result, relevant findings become visible that would be nearly impossible to identify using conventional methods.
The difference is clearly noticeable in day-to-day work: questions that previously led to limited and unreliable results after hours or even days now produce a well-founded and significantly more comprehensive set of findings. This not only improves efficiency, but more importantly, increases confidence in the assessment.
The key point is no longer to search faster—but to find what truly matters. And this is exactly where traditional search ends and modern, AI-driven analysis begins.
