A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II

Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the develo...

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Autores principales:	Min-Seung Kim, Chan-Ho Lee, Ji-Hye Choi, Yong-Ju Jang, Jeong-Hee Lee, Jaesik Lee, Tae-Eung Sung
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	KPAS II technology valuation Deep Neural Networks (DNN) intelligent system sales estimation discounted cash flow (DCF) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/72953631616047a3a8541a36a349cbde
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spelling	oai:doaj.org-article:72953631616047a3a8541a36a349cbde2021-11-25T19:03:06ZA Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II10.3390/su1322126662071-1050https://doaj.org/article/72953631616047a3a8541a36a349cbde2021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12666https://doaj.org/toc/2071-1050Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and the establishment of active R&D and commercialization strategies are essential factors in a company’s management sustainability, the activation of the technology market in practice is still in progress for its golden age. In this study, to promote a technology transfer-based company’s growth and to run technology-based various financial support activities, we develop and propose a new intelligent, deep learning-based technology valuation system that enables technology holders to estimate the economic values of their innovative technologies and further to establish a firm’s commercialization strategy. For the last years, the KIBO Patent Appraisal System (KPAS-II) herein proposed has been advanced by KIBO as a web-based, artificial intelligence (AI) and evaluation data applications valuation system that automatically calculates and estimates a technology’s feasible economic value by utilizing both the intrinsic and extrinsic index information of a patent and the commercialization entity’s business capabilities, and by applying to the discounted cash flow (DCF) method in valuation theory, and finally integrating with deep learning results based on the in-advance previously established patent DB and the financial DB. The KPAS-II proposed in this study can be said to have dramatically overcome the long-term preparation period and high levels of R&D and commercialization costs in terms of the limitations that the existing technology valuation method possesses by enhancing the reliability of approximate economic values from the deep learning results based on financial data and completed valuation data. In addition, it is expected that technology marketing coordinators, researchers, and non-specialty business agents, not limited to valuation experts, can easily estimate the economic values of their patents or technologies, and they can be actively utilized in a technology-based company’s decision-making and technologically dependent financial activities.Min-Seung KimChan-Ho LeeJi-Hye ChoiYong-Ju JangJeong-Hee LeeJaesik LeeTae-Eung SungMDPI AGarticleKPAS IItechnology valuationDeep Neural Networks (DNN)intelligent systemsales estimationdiscounted cash flow (DCF)Environmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12666, p 12666 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	KPAS II technology valuation Deep Neural Networks (DNN) intelligent system sales estimation discounted cash flow (DCF) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	KPAS II technology valuation Deep Neural Networks (DNN) intelligent system sales estimation discounted cash flow (DCF) Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Min-Seung Kim Chan-Ho Lee Ji-Hye Choi Yong-Ju Jang Jeong-Hee Lee Jaesik Lee Tae-Eung Sung A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
description	Technology finance, which has attracted worldwide attention for the successful business development of small-and-medium enterprises (SMEs) or start-ups, has advanced an innovation or stagnation way-out resolution strategy for companies in line with the low-growth economic trends. Although the development of new technologies and the establishment of active R&D and commercialization strategies are essential factors in a company’s management sustainability, the activation of the technology market in practice is still in progress for its golden age. In this study, to promote a technology transfer-based company’s growth and to run technology-based various financial support activities, we develop and propose a new intelligent, deep learning-based technology valuation system that enables technology holders to estimate the economic values of their innovative technologies and further to establish a firm’s commercialization strategy. For the last years, the KIBO Patent Appraisal System (KPAS-II) herein proposed has been advanced by KIBO as a web-based, artificial intelligence (AI) and evaluation data applications valuation system that automatically calculates and estimates a technology’s feasible economic value by utilizing both the intrinsic and extrinsic index information of a patent and the commercialization entity’s business capabilities, and by applying to the discounted cash flow (DCF) method in valuation theory, and finally integrating with deep learning results based on the in-advance previously established patent DB and the financial DB. The KPAS-II proposed in this study can be said to have dramatically overcome the long-term preparation period and high levels of R&D and commercialization costs in terms of the limitations that the existing technology valuation method possesses by enhancing the reliability of approximate economic values from the deep learning results based on financial data and completed valuation data. In addition, it is expected that technology marketing coordinators, researchers, and non-specialty business agents, not limited to valuation experts, can easily estimate the economic values of their patents or technologies, and they can be actively utilized in a technology-based company’s decision-making and technologically dependent financial activities.
format	article
author	Min-Seung Kim Chan-Ho Lee Ji-Hye Choi Yong-Ju Jang Jeong-Hee Lee Jaesik Lee Tae-Eung Sung
author_facet	Min-Seung Kim Chan-Ho Lee Ji-Hye Choi Yong-Ju Jang Jeong-Hee Lee Jaesik Lee Tae-Eung Sung
author_sort	Min-Seung Kim
title	A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
title_short	A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
title_full	A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
title_fullStr	A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
title_full_unstemmed	A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II
title_sort	study on intelligent technology valuation system: introduction of kibo patent appraisal system ii
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/72953631616047a3a8541a36a349cbde
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A Study on Intelligent Technology Valuation System: Introduction of KIBO Patent Appraisal System II

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