# Adaptive Taxonomy Learning and Historical Patterns Modelling for Patent Classification

arXiv: https://arxiv.org/abs/2308.05385 · HTML: https://mlsanity.com/paper/2308.05385/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 53 references that are in the map.
- First posted on arXiv 10 Aug 2023.

## Builds on (most cited first, 20 of 20)

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- [Event-based Dynamic Graph Representation Learning for Patent Application Trend Prediction](https://mlsanity.com/paper/2308.09780/) (2023)
- [HIEN: Hierarchical Intention Embedding Network for Click-Through Rate Prediction](https://mlsanity.com/paper/2206.00510/) (2022)

## Abstract (arXiv)

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.