其中「The EPO has responded to the emergence of AI by refining its approach to patentability of inventions. AI is considered a branch of computer science, and therefore, inventions involving AI are considered "computer-implemented inventions" (CII). Patents may be granted when AI leaves the abstract realm of mathematical algorithms and computational models and is applied to solve a technical problem in a field of technology.」
這裡提到兩個條件：當 AI發明 (1)超越數學演算法和計算模型的抽象概念時，並(2)應用於解決特定技術領域中的技術問題時，便可獲得專利授權。
審查指南Part G/Chapter II/3.3.1 Artificial intelligence and machine learning：（以下穿插中文摘要，可以得出EPO對於AI相關發明的看法）
Artificial intelligence and machine learning are based on computational models and algorithms such as artificial neural networks, genetic algorithms, support vector machines, k-means, kernel regression and discriminant analysis. Such computational models and algorithms are per se of an abstract mathematical nature, irrespective of whether they can be "trained" using training data. （計算模型與演算法本身是抽象數學本質，無關於其如何被訓練） However, their use does not by itself render inventions related to artificial intelligence or machine learning non-patentable（然而，他們關於AI應用／user並非不能專利）, and the guidance provided in G‑II, 3.3 generally applies. This means that, if a claim of an invention related to artificial intelligence or machine learning is directed either to a method involving the use of technical means (e.g. a computer) or to a device, its subject-matter has technical character as a whole and is thus not excluded from patentability under Art. 52(2) or (3)（整體上若有技術手段或是裝置等技術特徵，仍是可以專利的標的）. In such cases, the computational models and algorithms themselves contribute to the technical character of the invention if they contribute to a technical solution to a technical problem, for example by being applied in a field of technology and/or by being adapted to a specific technical implementation.（AI、演算法、電腦軟體等，若有技術特徵、解決技術問題，有技術貢獻(相對前案而言)，為可專利標的）
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以下段落都是反映上述的精神，而特別在最後一段，提到「機器學習演算法」實現的技術效果應為顯而易見的，並可通過解釋、數學證明、實驗數據等方式確立其技術效果，但沒有必要提出詳細的證明，但如果「技術效果」取決於所用「訓練資料集」的特定特徵，則必須公開重現所述技術效果所需的特徵。除非本領域技術人員可以知悉，通常並不需要公開所述訓練資料集本身。
＂The technical effect that a machine learning algorithm achieves may be readily apparent or established by explanations, mathematical proof, experimental data or the like. Mere assertions are not enough, but comprehensive proof is not required either. If the technical effect depends on particular characteristics of the training dataset used, the characteristics required to reproduce the technical effect must be disclosed unless the skilled person can determine them without undue burden using common general knowledge. However, in general, there is no need to disclose the specific training dataset itself (see also F‑III, 3 and G‑VII, 5.2).＂
Ron
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