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

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 an...

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Autores principales:	Haneul Eom, Sungyun Choi, Sang Ok Choi
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4aa35b8e6546464e97040f8ef4b190b7
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spelling	oai:doaj.org-article:4aa35b8e6546464e97040f8ef4b190b72021-12-02T20:08:17ZMarketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.1932-620310.1371/journal.pone.0257086https://doaj.org/article/4aa35b8e6546464e97040f8ef4b190b72021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257086https://doaj.org/toc/1932-6203Patent 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.Haneul EomSungyun ChoiSang Ok ChoiPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257086 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Haneul Eom Sungyun Choi Sang Ok Choi Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
description	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.
format	article
author	Haneul Eom Sungyun Choi Sang Ok Choi
author_facet	Haneul Eom Sungyun Choi Sang Ok Choi
author_sort	Haneul Eom
title	Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
title_short	Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
title_full	Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
title_fullStr	Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
title_full_unstemmed	Marketable value estimation of patents using ensemble learning methodology: Focusing on U.S. patents for the electricity sector.
title_sort	marketable value estimation of patents using ensemble learning methodology: focusing on u.s. patents for the electricity sector.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4aa35b8e6546464e97040f8ef4b190b7
work_keys_str_mv	AT haneuleom marketablevalueestimationofpatentsusingensemblelearningmethodologyfocusingonuspatentsfortheelectricitysector AT sungyunchoi marketablevalueestimationofpatentsusingensemblelearningmethodologyfocusingonuspatentsfortheelectricitysector AT sangokchoi marketablevalueestimationofpatentsusingensemblelearningmethodologyfocusingonuspatentsfortheelectricitysector
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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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