Automatic Chinese patent approval prediction is an emerging and valuable task in patent analysis. However, it involves a rigorous and transparent decision-making process that includes patent comparison and examination to assess its innovation and correctness. This resultant necessity of decision evidentiality, coupled with intricate patent comprehension presents significant challenges and obstacles for the patent analysis community. Consequently, few existing studies are addressing this task. This paper presents the pioneering effort on this task using a retrieval-based classification approach. We propose a novel framework called DiSPat, which focuses on structural representation learning and disentanglement to predict the approval of Chinese patents and offer decision-making evidence. DiSPat comprises three main components: base reference retrieval to retrieve the Top-k most similar patents as a reference base; structural patent representation to exploit the inherent claim hierarchy in patents for learning a structural patent representation; disentangled representation learning to learn disentangled patent representations that enable the establishment of an evidential decision-making process. To ensure a thorough evaluation, we have meticulously constructed three datasets of Chinese patents. Extensive experiments on these datasets unequivocally demonstrate our DiSPat surpasses state-of-the-art baselines on patent approval prediction, while also exhibiting enhanced evidentiality.

翻译：中国专利自动授权预测是专利分析领域一项新兴且具有重要价值的任务。然而，该任务涉及一个严谨且透明的决策过程，包括专利对比与审查，以评估其创新性与正确性。由此产生的决策证据性需求，加之复杂的专利理解，给专利分析领域带来了巨大的挑战与障碍。因此，现有研究鲜有涉足此任务。本文首次采用基于检索的分类方法对该任务进行探索。我们提出了一个名为DiSPat的新型框架，该框架聚焦于结构表示学习与解耦，以预测中国专利的授权情况并提供决策证据。DiSPat包含三个主要组件：基础参考检索，用于检索Top-k个最相似的专利作为参考基础；结构专利表示，利用专利中固有的权利要求层级结构来学习结构化的专利表示；解耦表示学习，学习解耦的专利表示，从而能够建立一个证据性的决策过程。为确保全面评估，我们精心构建了三个中国专利数据集。在这些数据集上进行的大量实验明确表明，我们的DiSPat在专利授权预测上超越了现有最先进的基线方法，同时展现出更强的证据性。