Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this s...
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Autor*in:	Mirae Kim [verfasserIn] Youngjung Geum [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Technology transfer patent transaction patent assignment patent machine learning

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 8(2020), Seite 188833-188843
Übergeordnetes Werk:	volume:8 ; year:2020 ; pages:188833-188843

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2020.3030960

Katalog-ID:	DOAJ068413149

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callnumber-first	T - Technology
author	Mirae Kim
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topic_title	TK1-9971 Predicting Patent Transactions Using Patent-Based Machine Learning Techniques Technology transfer patent transaction patent assignment patent machine learning
topic	misc TK1-9971 misc Technology transfer misc patent transaction misc patent assignment misc patent misc machine learning misc Electrical engineering. Electronics. Nuclear engineering
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title	Predicting Patent Transactions Using Patent-Based Machine Learning Techniques
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title_sort	predicting patent transactions using patent-based machine learning techniques
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title_auth	Predicting Patent Transactions Using Patent-Based Machine Learning Techniques
abstract	Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this study aims to suggest a predictive model for patent transaction considering a wide range of decision variables. For this purpose, this study highlighted two considerations-technological impact of technology donor and technological proximity in previous patent transactions. Six factors are employed for developing our predictive model-technological strength, knowledge accumulation, technological protection scope, technological jurisdiction, technological strength of companies, and previous history of patent transfers. Five machine learning techniques are employed. As a result, we find that technological strength of companies and previous transfer history significantly affect technology transfer. This study is expected to be used in practice where the technology buying decision is very complicated and comprehensive.
abstractGer	Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this study aims to suggest a predictive model for patent transaction considering a wide range of decision variables. For this purpose, this study highlighted two considerations-technological impact of technology donor and technological proximity in previous patent transactions. Six factors are employed for developing our predictive model-technological strength, knowledge accumulation, technological protection scope, technological jurisdiction, technological strength of companies, and previous history of patent transfers. Five machine learning techniques are employed. As a result, we find that technological strength of companies and previous transfer history significantly affect technology transfer. This study is expected to be used in practice where the technology buying decision is very complicated and comprehensive.
abstract_unstemmed	Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this study aims to suggest a predictive model for patent transaction considering a wide range of decision variables. For this purpose, this study highlighted two considerations-technological impact of technology donor and technological proximity in previous patent transactions. Six factors are employed for developing our predictive model-technological strength, knowledge accumulation, technological protection scope, technological jurisdiction, technological strength of companies, and previous history of patent transfers. Five machine learning techniques are employed. As a result, we find that technological strength of companies and previous transfer history significantly affect technology transfer. This study is expected to be used in practice where the technology buying decision is very complicated and comprehensive.
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