Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making processes. In this paper, we propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) to provide human-understandable explanations for predictions. We train several DNN models, including Bi-LSTM, CNN, and CNN-BiLSTM, and propagate the predictions backward from the output layer up to the input layer of the model to identify the relevance of words for individual predictions. Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class. Experimental results on two datasets comprising two-million patent texts demonstrate high performance in terms of various evaluation measures. The explanations generated for each prediction highlight important relevant words that align with the predicted class, making the prediction more understandable. Explainable systems have the potential to facilitate the adoption of complex AI-enabled methods for patent classification in real-world applications.

翻译：近期技术进步催生了大量跨领域专利，使得人类专家难以有效分析与管理。当前多标签专利分类的先进方法依赖深度神经网络（DNN），这类模型因其不透明的决策过程被视作黑箱。本文通过引入逐层相关性传播（LRP）方法，提出一种新型可解释深度学习专利分类框架，为预测结果提供人类可理解的解释。我们训练了包括双向长短期记忆网络（Bi-LSTM）、卷积神经网络（CNN）及CNN-BiLSTM在内的多种DNN模型，通过将预测结果从输出层反向传播至输入层，识别各单词对单个预测的贡献度。基于相关性得分，我们利用可视化预测专利类别中相关词的方式生成解释。在包含两百万专利文本的两个数据集上的实验表明，该方法在多项评估指标上均表现优异。为每项预测生成的解释突出显示了与预测类别一致的关键相关词，使预测过程更易理解。可解释系统将有助于推动基于复杂人工智能方法的专利分类在现实场景中的实际应用。