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

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Název: Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
Autoři: Eom H; Program in Science & Technology Studies, Korea University, Seoul, South Korea., Choi S; School of Electrical Engineering, Korea University, Seoul, South Korea., Choi SO; Department of Public Administration, Korea University, Seoul, South Korea.
Zdroj: PloS one [PLoS One] 2021 Sep 13; Vol. 16 (9), pp. e0257086. Date of Electronic Publication: 2021 Sep 13 (Print Publication: 2021).
Způsob vydávání: Journal Article; Research Support, Non-U.S. Gov't
Jazyk: English
Informace o časopise: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
Imprint Name(s): Original Publication: San Francisco, CA : Public Library of Science
Výrazy ze slovníku MeSH: Electricity* , Patents as Topic*, Machine Learning/*economics , Marketing/*economics, Algorithms ; Data Mining ; Humans ; Neural Networks, Computer ; Regression Analysis ; United States
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
References: Proc Natl Acad Sci U S A. 2004 Apr 6;101 Suppl 1:5228-35. (PMID: 14872004)
Entry Date(s): Date Created: 20210913 Date Completed: 20211116 Latest Revision: 20211116
Update Code: 20250114
PubMed Central ID: PMC8437284
DOI: 10.1371/journal.pone.0257086
PMID: 34516562
Databáze: MEDLINE
Popis
Abstrakt: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.
ISSN:1932-6203
DOI:10.1371/journal.pone.0257086