Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification

TL;DR

This paper introduces an integrated framework for patent classification that leverages hierarchical IPC code correlations, assignee historical patterns, and patent text semantics to improve accuracy and capture temporal and semantic dependencies.

Contribution

It proposes a novel method combining IPC code correlation learning, assignee historical pattern modeling, and text semantics for comprehensive patent classification.

Findings

Outperforms existing methods on real-world datasets.

Effectively captures temporal patterns of assignees.

Models semantic dependencies among IPC codes.

Abstract

Patent classification aims to assign multiple International Patent Classification (IPC) codes to a given patent. Recent methods for automatically classifying patents mainly focus on analyzing the text descriptions of patents. However, apart from the texts, each patent is also associated with some assignees, and the knowledge of their applied patents is often valuable for classification. Furthermore, the hierarchical taxonomy formulated by the IPC system provides important contextual information and enables models to leverage the correlations between IPC codes for more accurate classification. However, existing methods fail to incorporate the above aspects. In this paper, we propose an integrated framework that comprehensively considers the information on patents for patent classification. To be specific, we first present an IPC codes correlations learning module to derive their semantic representations via adaptively passing and aggregating messages within the same level and across different levels along the hierarchical taxonomy. Moreover, we design a historical application patterns learning component to incorporate the corresponding assignee's previous patents by a dual channel aggregation mechanism. Finally, we combine the contextual information of patent texts that contains the semantics of IPC codes, and assignees' sequential preferences to make predictions. Experiments on real-world datasets demonstrate the superiority of our approach over the existing methods. Besides, we present the model's ability to capture the temporal patterns of assignees and the semantic dependencies among IPC codes.