Structural Patent Classification Using Label Hierarchy Optimization (2025)

Abstract: Patent classification is a fundamental step in the patent examination process, directly impacting the efficiency and quality of substantive review. Existing methods mostly focus on general texts like titles and abstracts, thus ignoring the key technical content claims and the corresponding citation relationships. Meanwhile, these approaches treat labels as independent targets, failing to exploit the semantic and structural information within the label taxonomy. To address these problems, we propose a \textbf{C} laim \textbf{S} tructure based \textbf{P} atent \textbf{C} lassification model with \textbf{L} abel \textbf{A} wareness (CSPC-LA). The method first utilizes the citation relationship of patent claim texts to construct the citation graph and the co-reference graph. Then structural graph learning is used on both graphs to mine the internal logic of patent claims. Finally, we optimize the tree hierarchy of IPC labels and employ tree propagation learning to enhance the patent representation. Extensive experiments on the latest patent classification dataset from USPTO demonstrate that the proposed method is more effective than the state-of-the-art baselines.

## introduction with the rapid advancement of globalization and informatization, innovation has become a key driving force behind social progress and economic growth. as emerging technologies continue to flourish and industrial upgrading accelerates, the number of patent applications, an important indicator of innovation, has been increasing globally. although this trend reflects the vitality of technological development, it also places significant pressure on patent examination systems. among these processes, patent classification serves as a critical first step @xcite , whose accuracy and efficiency are crucial. ## conclusion in this work, we present cspc-la, a patent classification model that leverages the structural information of patent claims and the hierarchical organization of labels. by introducing modules for claim-structure graph learning and hierarchical label modeling, our approach captures fine-grained textual signals and strengthens the alignment between patent content and label semantics, resulting in improved classification performance. moving forward, we aim to explore entity-level structural relations within patents, viewing claim dependencies as semantic elaborations between entities in all types of claims, to further enhance the model's capacity in understanding complex patent structures.