Program in Science & Technology Studies, Korea University, Seoul, South Korea.
School of Electrical Engineering, Korea University, Seoul, South Korea.
PLoS One. 2021 Sep 13;16(9):e0257086. doi: 10.1371/journal.pone.0257086. eCollection 2021.
Patent valuation is required to revitalize patent transactions, but calculating a reasonable value that consumers and suppliers could satisfy is difficult. When machine learning is used, a quantitative evaluation based on a large volume of data is possible, and evaluation can be conducted quickly and inexpensively, contributing to the activation of patent transactions. However, due to patent characteristics, securing the necessary training data is challenging because most patents are traded privately to prevent technical information leaks. In this study, the derived marketable value of a patent through event study is used for patent value evaluation, matching it with the semantic information from the patent calculated using latent Dirichlet allocation (LDA)-based topic modeling. In addition, an ensemble learning methodology that combines the predicted values of multiple predictive models was used to determine the prediction stability. Base learners with high predictive power for each fold were different, but the ensemble model that was trained on the base learners' predicted values exceeded the predictive power of the individual models. The Wilcoxon rank-sum test indicated that the superiority of the accuracy of the ensemble model was statistically significant at the 95% significance level.
专利估值对于激活专利交易至关重要，但要计算出消费者和供应商都能接受的合理价值却颇具难度。在应用机器学习时，基于大量数据进行定量评估成为可能，并且可以快速、廉价地进行评估，有助于激活专利交易。然而，由于专利的特点，获取必要的训练数据颇具挑战性，因为大多数专利都是私下交易的，以防止技术信息泄露。在本研究中，通过事件研究得出的专利可变现价值用于专利价值评估，并将其与基于潜在狄利克雷分配（LDA）的主题建模计算得出的专利语义信息进行匹配。此外，还采用了一种集成学习方法，将多个预测模型的预测值进行组合，以确定预测的稳定性。虽然对于每个折叠的预测，具有高预测能力的基学习器不同，但基于基学习器预测值训练的集成模型的预测能力超过了单个模型。Wilcoxon 秩和检验表明，在 95%的置信水平下，集成模型的准确性优势具有统计学意义。
