向 EPO 申请计算机实施发明专利：第 二部分
Patenting Computer Implemented Inventions at the EPO: Part Two
我们在第二份资讯中讲述了如何对 AI 发 明适用充分性要求，包括可能需要纳入 申请的多种数据类型。
In this second of our newsletters we examine how the requirement of sufficiency is applied to AI inventions, including the various types of data that may need to be included in the application.
充分性 EPC 第 83 条规定“欧洲专利申请应当 对发明作出充分、清晰、完整的披露， 确保该领域技术人员能够实施。”因 此，EPO 审查员必须考虑专利申请是 否包含充分的信息，能够确保技术人员 复制出发明；而申请必须提供充分的信 息，确保技术人员不会承受不当负担， 并且不需要创造性技能就可以实施发 明。 针对 AI 发明，EPO 审查员可能会提出 以下问题： •
是否在整个权利要求保护范围内合理 实现了技术效果？
•
机器学习过程的输入和输出之间是否 存在明确的因果关系？
•
经过训练的模型能否进行可靠的预 测？
缺乏可复制性可能会引发充分性或创造 性异议。如果权利要求表达了预期的技 术效果，但申请缺乏可复制性，则可以 根据 EPC 第 83 条提出披露不充分的 异议。如果技术效果不具备可复制性， 并且未在权利要求中表达，而是纳入待 解决的客观技术问题部分，则可以根据 EPC 第 56 条提出缺乏创造性的异议。
1
Sufficiency Article 83 of the EPC requires that “[t]he European Patent Application shall disclose the invention in a manner sufficiently clear and complete for it to be carried out by a person skilled in the art”. EPO Examiners must therefore consider whether there is enough information in a patent application to allow the skilled person to reproduce the invention, and the application must provide sufficient information that the burden on the skilled person is not undue and that no inventive skill is required to work the invention. For AI inventions, the EPO Examiner may ask: •
Is the technical effect plausibly achieved over the whole of the claimed scope?
•
Is there a clear causal link between the input and output of a machine learning process?
•
Will the trained model produce reliable predictions?
A lack of reproducibility can lead to sufficiency or inventive step objections. If the desired technical effect is expressed in the claim but the application lacks reproducibility, an objection of lack of sufficient disclosure under Article 83 EPC may be raised. If the technical effect is not reproducible and is not expressed in the claim but is instead part of the objective technical problem to be solved, an objection of lack of inventive step under Article 56 EPC may be raised. www.hlk-ip.cn
化学领域的经验教训
Lessons From the Field of Chemistry
如果 AI 相关申请没有一目了然地说明 AI 发明的工作方式或原理，则可以根据 EPC 第 83 条提出异议。
Objections under Article 83 EPC may be raised against applications concerning AI because it is not always immediately clear how or why an AI invention works.
例如，一个经过精心设计的训练数据集 和奖励函数可以引导您使用一台非常实 用的机器，但发明人却从未真正地理解 这台机器背后的数字为什么能够成功实 现引导。 化学领域的代理律师长期以来都在处理 发明人不知道组合物 A 为什么比组合 物 B 性能更好，或者改变反应变量 Y 时特定反应产物 X 的产率为什么会增加 等问题，尽管有大量实验可以表明这类 发明有效。虽然对化学家来说制造新型 组合物本身会比较容易，但是要想制造 出能够根据预期实现新效果或改善以往 效果的新型组合物仍然十分困难。本质 上来讲，新化学反应的结果是无法预测 的。 从化学领域得到的这些经验与 AI 领域 具有相似之处。例如，考量一种图像分 类 AI 机器学习方法时，机器的重点是 找到正确的答案，而不是理解问题本 身。这就意味着，在神经网络的特定阶 段，与特定像素组合相关的权重为何如 此之大并不明确。但是测试机器并分别 检查对应的结果后，发明人可以确定他 们的 AI 机器的确是好机器，就像能够 提高生存率的药就是好药一样，即使没 人知道它实现疗效的确切机制，它仍然 是一种好药。 因此，现在我们建议在 AI 相关申请中 加入实验数据和比较测试，这种做法在 化学申请中很常见。根据 EPO 审查指 南和判例法，最好在专利申请本身中同 时加入实验数据和比较测试。仅在某些 情况下，即使原申请中未包含后期提交 的证据，审查员仍然会考虑这些证据。 但是确定申请是否解决它旨在解决的问 题时，不能只依靠后期提交的证据；如 果可以根据申请确认问题的确得到解 决，那么后期提交的证据只能用来证实 专利申请中的实验结果。
2
For example, a well thought-out training dataset and reward function may lead you to a machine of remarkable utility without the inventor ever truly understanding why the numbers underlying that particular machine are so successful. Attorneys working in chemistry have long dealt with the problem of an inventor not knowing why composition A has better properties than composition B, or why yield of a particular reaction product X increases when you change reaction variable Y, etc. – nevertheless lots of experiments may show that such an invention works. Although it may be relatively easy for a chemist to make a new composition as such, it is much more difficult to make new compositions that have a desired new or improved result. Essentially, the outcome of a new chemical reaction is unpredictable. These experiences in chemistry have parallels in the field of AI. For example, consider an image-classification AI machine learning method. The machine’s focus is getting to the right answer as opposed to understanding the problem itself. This means that it may not be apparent why there is such a strong weighting associated with a particular combination of pixels in a particular stage of a neural network. But by testing the machine, and checking the results independently, the inventor can be sure that their AI machine is a good one, in just the same way that a drug which results in increased survival is a good drug, even if the precise mechanism underlying its efficacy is not known. Therefore, we now recommend using experimental data and comparative tests in applications relating to AI, as is already common in chemistry applications. According to EPO examination guidelines and case law, it is better to include both experimental data and comparative tests in the patent application itself. Later-filed evidence can www.hlk-ip.cn
be taken into consideration, even if it was not included in the original application, but only under certain conditions. Later-filed evidence may not serve as the sole basis to establish that the application solves the problem it sets out to solve; it can only be used to back up findings in the patent application, if it is already credible from the application that the problem is indeed solved. Data In the field of chemistry, these requirements have led to the practice of ensuring experimental data is included in patent applications. This may include experimental data to demonstrate how the invention is carried out and to demonstrate the result which is achieved, and it may also include comparative data to demonstrate that the result in an improvement in the art. This standard practice in the world of chemical drafting is likely to be useful to the AI field.
数据 在化学领域，这些要求已形成确保专利 申请涵盖实验数据的做法，包括用于证 明发明实施方法和所得结果的实验数 据，以及用于证明所得结果改善了所属 领域的比较数据。化学领域的这种标准 申请起草做法在 AI 领域可能也会奏效。 例如，要想让针对新化学组合物提出的 特定专利申请满足充分性要求，就必须 在专利申请中提供充分的信息，确保技 术人员能够生产出这种组合物。通常需 要包含至少一种特定产品生产方法的完 整实验细节，例如起始材料、设备和反 应条件。对 AI 发明来说，这些细节相当 于描述数据集中所含数据的原理，以及 3
Consider, for example, that in order for a particular patent application directed to a new chemical composition to meet the requirement of sufficiency, the patent application must provide enough information for the skilled person to be able to produce the composition. It is often necessary to include full experimental details of at least one way of producing a particular product, for example, including starting materials, apparatus and reaction conditions. For AI inventions, this is comparable to describing the principles underlying the data included in the dataset, and any assumptions or parameters built into the AI model. Consider another scenario which concerns a chemical application where a particular result is limiting on the scope of the claims. For example, the claims may be directed to a method that provides a particular result, or to use of a composition for a particular purpose. In such cases, in addition to explaining how the method or use is carried out, it is generally necessary to provide data to prove that the claimed www.hlk-ip.cn
AI 模型中内置的任何假设或参数。 还有一类化学申请，这类申请中会有一 个特定的结果限制权利要求的范围。例 如，既可以针对一种提供特定结果的方 法主张权利要求，也可以针对出于特定 目的使用的组合物主张权利要求。在此 情况下，除了需要说明这种方法的操作 过程或者组合物的使用方法，通常还需 要提供数据来证明要求保护的结果的确 是通过该项发明获得的，这样才能满足 充分性要求。对 AI 发明来说，类似的 示例可以是设计用于更好地区分图像中 不同类型动物的机器。如果的确能够实 现这一目的，应当在申请中提供相关证 据。 充分性要求是指，发明可以在整个权利 要求保护领域得到实质性实施；在化学 领域，如果不清楚为什么新的化学组合 物比已知的组合物效果更好，会很难解 释权利要求中新组合物的宽泛定义。但 是，申请人可能会希望专利保护范围涵 盖预计能够表现出类似改进的类似组合 物。实际上，如果起草的化学权利要求 过于宽泛，审查员会提出异议，认为申 请人在仅证实某一特定组合物具备某一 技术效果的情况下主张权利要求范围内 的所有实施例都可以实现这一技术效果 是不合理的。在某些情况下，申请人可 以基于已知的科学原理来论证不同实施 例的合理性。但是，额外提供与权利要 求范围内多个实施例有关的实验数据， 可以有助于消除审查员的任何潜在顾 虑。 我们也可以将这些考量用于 AI 发明。例 如，区分图像中不同类型动物的分类器 可以使用训练数据集，包括猫和狗的图 片，以及包含一组假设的特定模型。在 这个例子中，所述模型在资源处理方面 不仅有效，而且快速、轻量。申请人想 要主张该模型本身为分类器，用于将任 意两个对象分为两组。但是，审查员可 能会认为，基于仅与猫和狗相关的数据 作出发明过于宽泛，不合理。例如，猫 和狗的图像可能有一些特殊之处，而其 他图像集（例如螺栓和螺钉或浮油和彩 虹）则不然。因此，申请中包含不同的 数据集示例可能有助于证实这类示例的 合理性。
4
result is actually obtained by the invention in order to satisfy sufficiency requirements. For AI inventions, a comparable example would be a machine designed to better distinguish between different types of animal in an image. If this result is indeed achieved, then the application should provide evidence of this. A requirement of sufficiency is that the invention can be performed across substantially the whole of the area claimed and, in chemistry, without clearly knowing why a new chemical composition works better than already known compositions, it can be difficult to justify a broad definition of the new composition in the claims. Nevertheless, the applicant may want to obtain patent protection covering similar compositions that are expected to show similar improvements. In practice, when chemical claims are drafted broadly, Examiners can object that it is not plausible that a technical effect that has been demonstrated for one particular composition would be obtained for all embodiments falling within the scope of the claims. In some circumstances, plausibility of different embodiments can be argued based on known scientific principles. However, additional experimental data relating to a number of embodiments across the scope of the claims can be helpful to overcome any concerns the Examiner may have. We can also apply these considerations to AI inventions. For example, a classifier which distinguishes between different types of animal in an image may use a training dataset including pictures of cats and dogs, and a particular model embodying a set of assumptions. In this example, the model is not just effective, but also fast and lightweight in terms of processing resources. The applicant would like to claim the model as a classifier per se, for categorising any two objects into two groups. However, an Examiner may not consider such a broad invention to be plausible based on data relating only to cats and dogs. For example, it is possible www.hlk-ip.cn
例如，EPO 审查的申请与某一生产线上 物理样本的处理图像有关，这些图像旨 在识别成像物理样本上的缺陷，并且权 利要求明确定义了执行生成器神经网络 的无监督训练：
that there is something special about cat and dog images which isn’t true of other sets of images, such as of bolts and screws or oil slicks and rainbows. Therefore, including diverse examples of datasets in the application may help to show plausibility in such an example. As an example, an application undergoing prosecution at the EPO relates to processing images of physical samples on a production line to identify defects on the imaged physical samples, and the claims explicitly define executing unsupervised training of a generator neural network:
Unsupervised training 无监督训练 S100
Acquire images 获取图像 S102
Generate reconstructed version of images 生成图像的重建版本 S104
Compare reconstructed images and acquired images 比较重建图像和获取的图像 S106
Identify defects 识别缺陷
5
S108
www.hlk-ip.cn
在提交的说明书中载明比较测试数据， 可以证明该项发明比现有 AI 方法减少损 失的速度更快。这里的减少损失是指一 种方法，这种方法可以非常有效地训练 生成器神经网络，从而消除输入图像中 的缺陷。如下图所示：
Comparative test data was included in the specification as filed, demonstrating the ability of the invention to decrease losses faster than prior art AI methods. Reduced losses in this context refers to a method that is particularly effective at training the generator neural network to remove defects from input images. This is shown in the graph below:
10⁵
Case (i) 案例 (i) Case (ii) 案例 (ii)
Loss 损失
10⁴
0
20
40
60
80
100
Epoch 迭代轮次
6
这种申请一看就能很快得到批准。
The application looks set to grant shortly.
同时涉及 AI 和化学的申请
Applications Combining AI and Chemistry
如今，AI 在化学领域自身的应用日益增 多。例如，下图显示了 CPC 分类 C01 - C14（涉及传统化学）和 C21 - C30（ 涉及冶金）全球专利申请数量的增长情 况，这些申请在描述中也提到了“人工 智能”或“机器学习”。
AI is now also finding more and more applications in Chemistry itself. For example, the below figure shows the growth in the number of patent applications filed worldwide in the CPC classifications C01-C14 (which relate to traditional Chemistry) and C21-C30 (which relate to Metallurgy) and which also include the words “artificial intelligence” or “machine learning” in the description. www.hlk-ip.cn
4500
450 C01-C14
400
C21-C30
3500
350
3000
300
2500
250
2000
200
1500
150
1000
100
500
50
0
No. applications C21-C30 C21 - C30 申请数量
No. applications C01-C14 C01 - C14 申请数量
4000
0 2011
2012
2013
2014
2015
2016
2017
2018
2019
Filing year 提交年份
这类申请中的 AI 方法多用于识别具有期 望特性的现有或新型化学品、组合物或 材料。例如，AI 机器可以识别出用于治 疗某一特定疾病的现有药物是否适用于 治疗一种不相关的疾病。或者，可使用 AI 设计使机械或电气性能得到改善的新 材料。在这类情况下，必须确保专利申 请对发明的 AI 内容和化学内容都进行了 充分的描述。 举个能够说明上述潜在问题的例子，我 们确认某项申请涉及一种用于设计飞机 部件材料的 AI 方法。使用含有不同组合 物并且具备不同特性的合金结构的图像 训练数据集，训练某一神经网络，让该 神经网络能够将合金的结构特征与材料 特性（例如屈服强度）关联起来。 要求保护的方法使用神经网络来识别一 组结构特征，这些特征能够为特定应用 实现所需的特性组合。该项申请还试图 要求保护具有确定结构并因此具备所需 性能的合金的制造步骤。
7
In many of these applications, AI methods are used to identify existing or new chemicals, compositions or materials which have desirable properties. For example, an AI machine may identify existing drugs, known for treating one particular disease, as being suitable for use in the treatment of an unrelated disease. Alternatively, AI may be used to design new materials which have improved mechanical or electrical properties. In such cases, care must be taken to ensure that the patent application sufficiently describes both the AI and the chemical aspects of the invention. As an example that also illustrates the potential issues discussed above, one application we have identified concerns an AI method for designing a material for an aircraft component. A neural network is trained to correlate structural features of alloys with material properties, such as yield strength, using a training data set of images of alloy structures having varied compositions and properties. www.hlk-ip.cn
The claimed method uses the neural network to identify a set of structural features capable of achieving a desired combination of properties for a particular application. The application also attempts to claim a step of manufacturing an alloy having the identified structure and, thus, the desired properties.
申请人已在欧洲收到依据 EPC 第 83 条 对该项专利申请提出的充分性异议。首 先，审查员认为，将合金特性与结构特 征联系起来的通用理论模型通常不适用 于合金系统，也不适用于申请示例中提 供的特定类型的钛基合金。审查员还质 疑，所述的 AI 模型是否能够预测具备所 需性能的合金的优化微观结构。审查员 认为，AI 可用以分析未知合金结构的图 像并预测其特性，但这项任务与上述任 务截然不同。因此，审查员提出异议认 为，技术人员仍需通过大量的实验才能 确定一组给定特性的合适结构特征。其 次，EPO 审查员提出异议认为，该项申 请没有说明如何制造具备 AI 识别的特定 结构的合金。审查员指出，要想开发出 一种制造特定合金的方法，技术人员不 仅需要进行大量的实验，而且还要先考 虑一下 AI 输出的结构在化学或物理上是 否可行。 这个案例也凸显了审查同时涉及 AI 和化 学的申请期间可能遇到的困难。我们建 议在起草此类申请时，同时听取 AI 领域 代理律师和化学领域代理律师的意见， 并使用实验数据来支持申请的 AI 内容和 化学内容。 8
The patent application has received sufficiency objections in Europe under Article 83 EPC. First, the Examiner has objected that generic theoretical models linking alloy properties to structural features are not known for alloy systems in general, nor are they known for the specific type of titanium-based alloy provided in the Examples of the application. The Examiner has also doubted that the AI model described can predict optimized microstructures for an alloy having desired properties. Instead, the Examiner thinks that the AI could be used to analyse an image of a previously unknown alloy structure and predict its properties, which is a fundamentally different task. The Examiner has therefore objected that excessive experimentation would still be required for the skilled person to determine suitable structural features for a given set of properties. Second, the EPO Examiner has objected that the application does not explain how to manufacture an alloy having a particular structure identified by the AI. The Examiner notes that developing a manufacturing method for a particular alloy requires extensive experimentation and that the skilled person would also need to first consider whether any structure output by the AI was even chemically or physically feasible. This case also highlights the difficulties which can be encountered when prosecuting applications at the overlap between AI and Chemistry. We recommend that such applications are drafted with input from both AI and Chemistry attorneys, and that experimental data is used to support both AI and chemical aspects of the application. www.hlk-ip.cn
概述
Summary
为了降低收到充分性异议的可能性，建 议申请人在申请中提供申请人可以获得 的所有相关数据（包括实验和比较数 据），尤其是能够区分所述发明与发明 人知晓的现有技术的数据。
To decrease the chances of a sufficiency objection, we recommend that all relevant data (including experimental and comparative data) is included in the application where this is available to the applicant, particularly data that distinguishes the invention from the prior art known to the inventor.
联系我们
Contact us
周冠冲 合伙人 dchew@hlk-ip.com
Daniel Chew
利敏
Li Min
中国代表处首席代表 lmin@hlk-ip.com
9
Partner, Head of Asia Group dchew@hlk-ip.com
Chief Representative China Office lmin@hlk-ip.com
www.hlk-ip.cn