人工智能和机器学习专利具有一般软件专利的共性，但也有其特殊性。对于人工智能和/或机器学习专利审查的规则还在发展中。现将欧专局的相关规定（在欧专局专利审查指南Part G – Chapter II- 3.3.1节，网络链接https://www.epo.org/law-practice/legal-texts/html/guidelines/e/g_ii_3_3_1.htm）附列如下，同时提供参考译文。

Artificial intelligence and machine learning

人工智能和机器学习

Artificial intelligence and machine learning are based on computational models and algorithms for classification, clustering, regression and dimensionality reduction, such as 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" based on training data. Hence, the guidance provided in G‑II, 3.3 generally applies also to such computational models and algorithms.

人工智能和机器学习基于计算模型和算法用于分类、聚类、回归和降维，诸如神经网络、遗传算法、支持向量机、k-means、内核回归和判别分析。此类计算模型和算法本身是抽象数学性质的本身，而不管它们是否可以基于训练数据被"训练"。因此，G‑II, 3.3中提供的指导通常也适用于这样的计算模型和算法。

Terms such as "support vector machine", "reasoning engine" or "neural network" may, depending on the context, merely refer to abstract models or algorithms and thus do not, on their own, necessarily imply the use of a technical means. This has to be taken into account when examining whether the claimed subject-matter has a technical character as a whole (Art. 52(1), (2) and (3)).

根据上下文，诸如"支持向量机"，"推理引擎"或"神经网络"之类的术语可以仅指抽象模型或算法，并且因此不一定暗示使用技术手段。当检查所要求保护的主题是否具有作为整体的技术特征时，必须考虑这一点(Art. 52(1), (2) and (3))。

Artificial intelligence and machine learning find applications in various fields of technology. For example, the use of a neural network in a heart monitoring apparatus for the purpose of identifying irregular heartbeats makes a technical contribution. The classification of digital images, videos, audio or speech signals based on low-level features (e.g. edges or pixel attributes for images) are further typical technical applications of classification algorithms. Further examples of technical purposes for which artificial intelligence and machine learning could be used may be found in the list under G‑II, 3.3.

人工智能和机器学习在技术的各个领域中找到应用。例如，出于识别不规则心跳的目的，在心脏监测装置中使用神经网络做出技术贡献。基于低级特征（例如，图像的边缘或像素属性）的数字图像，视频，音频或语音信号的分类是分类算法的进一步典型的技术应用。可以使用人工智能和机器学习的技术目的的其他示例可以在G‑II, 3.3下的列表中找到。

Classifying text documents solely in respect of their textual content is however not regarded to be per se a technical purpose but a linguistic one (T 1358/09). Classifying abstract data records or even "telecommunication network data records" without any indication of a technical use being made of the resulting classification is also not per se a technical purpose, even if the classification algorithm may be considered to have valuable mathematical properties such as robustness (T 1784/06).

然而，仅对文本内容进行分类的文本文档不被认为本身是技术目的，而是语言的目的（t1358/09）。即使分类算法可以被认为具有诸如鲁棒性（T 1784/06）之类的有价值的数学属性，则对抽象数据记录或甚至"电信网络数据记录"进行分类，而没有对所得到的分类做出的技术使用的任何指示，也不是技术目的本身。

Where a classification method serves a technical purpose, the steps of generating the training set and training the classifier may also contribute to the technical character of the invention if they support achieving that technical purpose.

在分类方法用于技术目的的情况下，如果支持实现该技术目的，则生成训练集和训练分类器的步骤也可以有助于本发明的技术特性。