Despite the usefulness of machine learning approaches for the early screening of potential breakthrough technologies, their practicality is often hindered by opaque models. To address this, we propose an interpretable machine learning approach to predicting future citation counts from patent texts using a patent-specific hierarchical attention network (PatentHAN) model. Central to this approach are (1) a patent-specific pre-trained language model, capturing the meanings of technical words in patent claims, (2) a hierarchical network structure, enabling detailed analysis at the claim level, and (3) a claim-wise self-attention mechanism, revealing pivotal claims during the screening process. A case study of 35,376 pharmaceutical patents demonstrates the effectiveness of our approach in early screening of potential breakthrough technologies while ensuring interpretability. Furthermore, we conduct additional analyses using different language models and claim types to examine the robustness of the approach. It is expected that the proposed approach will enhance expert-machine collaboration in identifying breakthrough technologies, providing new insight derived from text mining into technological value.

本研究解决了早期筛选潜在突破技术中模型不透明的问题，提出了一种可解释的机器学习方法，利用专利特定的层次注意力网络模型（PatentHAN）来预测专利文本的未来引用次数。通过对35376项药品专利的案例研究，验证了该方法在早期筛选潜在技术方面的有效性，同时确保了模型的可解释性。

增强可解释性的潜在突破技术早期筛选：一种专利特定的层次注意力网络模型