Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector

• Published in: PloS one Vol. 16; no. 9; p. e0257086
• Main Authors: Eom, Haneul, Choi, Sungyun, Choi, Sang Ok
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 13.09.2021; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Customer satisfaction; Data mining; Dirichlet problem; Electric industries; Engineering and Technology; Ensemble learning; Fuzzy sets; Laws, regulations and rules; Learning algorithms; Machine learning; Methods; Modelling; Physical Sciences; Prediction models; Predictions; Regeneration; Regression analysis; Research and Analysis Methods; Semantics; Social Sciences; Statistical analysis; Technical information; Valuation; United States; United States > US; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257086

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.