欧州特許庁では、コンピュータ関連発明についてCOMVIKアプローチという手法がとられており、進歩性の判断においては「技術的な特徴」を有さないと進歩性の判断で考慮してもらえません。
今回下記のニューラルネットワークに関する発明が、「技術的特徴」を有さないと判断された事例です。
T 0702/20 (Sparsely connected neural network) of 7.11.2022
Catchword
A neural network defines a class of mathematical functions which, as such, is excluded matter. As for other "non-technical" matter, it can therefore only be considered for the assessment of inventive step when used to solve a technical problem, e.g. when trained with specific data for a specific technical task.
EPO - T 0702/20 (Sparsely connected neural network/MITSUBISHI) of 7.11.2022
ちなみに審査対象のクレームは、こんな形でかなり長くなってます。一方で、日本や米国では特許になっているそうなので、欧州の厳しさを物語っています。
EP3089081
A hierarchical neural network apparatus (1) implemented on a computer comprising
a weight learning unit (20) to learn weights between a plurality of nodes in a hierarchical neural network, the hierarchical neural network being formed by loose couplings between the nodes in accordance with a sparse parity-check matrix of an error correcting code, wherein the error correcting code is a LDPC code, spatially-coupled code or pseudo-cyclic code, and comprising an input layer, intermediate layer and output layer, each of the layers comprising nodes; and
a discriminating processor (21) to solve a classification problem or a regression problem using the hierarchical neural network whose weights between the nodes coupled are updated by weight values learned by the weight learning unit (20)
or comprising
a weight pre-learning unit (22) to learn weights between a plurality of nodes in a deep neural network, the deep neural network being formed by loose couplings between the nodes in accordance with a sparse parity-check matrix of an error correcting code, wherein the error correcting code is a LDPC code, spatially-coupled code or pseudo-cyclic code, and comprising an input layer, a plurality of intermediate layers and an output layer, each of the layers comprising nodes; and
a discriminating processor (21) to solve a classification problem or a regression problem using the deep neural network whose weights between the nodes coupled are updated by weight values learned by the weight pre-learning unit (22)
and
a weight adjuster (23) to perform supervised learning to adjust the weights learned by the weight pre-learning unit (22) by supervised learning; and wherein
the weights are learned by the weight pre-learning unit (22) by performing unsupervised learning; and
the weights between the nodes coupled are updated by weight values adjusted by the weight adjuster (23).
EPO - T 0702/20 (Sparsely connected neural network/MITSUBISHI) of 7.11.2022
Boardの意見として面白いと思ったところを抜き出していくと、出願人は「全結合層でなく疎結合部分を入れることで、メモリや計算量を減らせる」ことを主張していますが、一つのニューラルネットワークでもメモリや計算量を減らせるが機能は落ち、メモリや計算量を減らせること自体では技術的特徴を有さないとしています。
14.1 The Board notes that, while the storage and computational requirements are indeed reduced in comparison with the fully-connected network, this does not in and by itself translate to a technical effect, for the simple reason that the modified network is different and will not learn in the same way. So it requires less storage, but it does not do the same thing. For instance, a one-neuron neural network requires the least storage, but it will not be able to learn any complex data relationship. The proposed comparison is therefore incomplete, as it only focuses on the computational requirements, and insufficient to establish a technical effect.
EPO - T 0702/20 (Sparsely connected neural network/MITSUBISHI) of 7.11.2022
最後に示唆しているように、特定の用途に限定することが今回は求められていたのかなという気がします。今回のクレームでも分類や回帰に用いることができる旨の限定はりましたが、株価の予測のように、より具体的な効果を発揮できる用途での限定が示唆されているようにも思われます。
18. The claimed learning and use of the network "to solve a classification problem or a regression problem" (where classification is merely regression with discrete outputs corresponding to the classes), can use any data. The outputs of the neural network do not have therefore any implied "further technical use"; they may, for instance, be related to forecasting stock market evolution. In cryptography, the example provided by the Appellant, the situation is different: the encryption of digital messages was found to address the technical problem of increasing system security by preventing data access to parties not in possession of the decryption key (T 1326/06 reasons 6 and 7).
EPO - T 0702/20 (Sparsely connected neural network/MITSUBISHI) of 7.11.2022
一方で、機械学習の技術は汎用的に使えそうだから権利を取りに行くわけで、特定の場面に限定されると、それはそれで権利としては使えないものになりうるので、やはり欧州は厳しそうですね。
