A Structured Representation Framework for TRIZ-Based Chinese Patent Classification via Reinforcement Learning

Theory of Inventive Problem Solving (TRIZ) -based [5] patent classification is essential for patent management and industrial analysis. However, the most research of patent classification are just on English texts and their methods just consider word order but do not use any parse structure, which rely on the word segmentation. In this paper, we propose a new structured representation [11] framework for TRIZ-based Chinese patent classification, which can discover structures automatically and measure structures Our framework uses structured representation model based on reinforcement learning to discover structures. Furthermore, the structured representation model we use lack attention in important structures which may have information memory loss when dealing with long sentences. Therefore, we use attention mechanisms to measure structures that contribute to the patent classification. On 4 TRIZ based classification tasks, our framework significantly outperformed all models in terms of area under curve (AUC) [23] and outperforms Hierarchically Structured LSTM (HS-LSTM) [11]. Moreover, we achieved absolute improvements of 10.08% in performance on "Innovation in product design" classification task in AUC score compared with the state-of-the-art model, bidirectional encoder representation from transformers (BERT) [8].