Sustainable technology analysis of artificial intelligence using Bayesian and social network models

Juhwan Kim; Sunghae Jun; Dongsik Jang; Sangsung Park

doi:10.3390/su10010115

Sustainable technology analysis of artificial intelligence using Bayesian and social network models

Juhwan Kim, Sunghae Jun, Dongsik Jang, Sangsung Park

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

Abstract

Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

Original language	English
Article number	115
Journal	Sustainability (Switzerland)
Volume	10
Issue number	1
DOIs	https://doi.org/10.3390/su10010115
Publication status	Published - 2018 Jan 5

Bibliographical note

Funding Information:
Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1D1A1A01059742). This research was supported by Academic Research Fund Support Program through the Korea Sanhak Foundation. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03031152).

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1D1A1A01059742). This research was supported by Academic Research Fund Support Program through the Korea Sanhak Foundation. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03031152)

Publisher Copyright:
© 2018 by the authors.

Keywords

Artificial intelligence
Bayesian inference
Patent technology analysis
Social network analysis
Sustainable technology

ASJC Scopus subject areas

Geography, Planning and Development
Renewable Energy, Sustainability and the Environment
Environmental Science (miscellaneous)
Energy Engineering and Power Technology
Management, Monitoring, Policy and Law

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3390/su10010115

Cite this

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title = "Sustainable technology analysis of artificial intelligence using Bayesian and social network models",

abstract = "Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.",

keywords = "Artificial intelligence, Bayesian inference, Patent technology analysis, Social network analysis, Sustainable technology",

author = "Juhwan Kim and Sunghae Jun and Dongsik Jang and Sangsung Park",

note = "Funding Information: Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1D1A1A01059742). This research was supported by Academic Research Fund Support Program through the Korea Sanhak Foundation. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03031152). Funding Information: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT & Future Planning (NRF-2015R1D1A1A01059742). This research was supported by Academic Research Fund Support Program through the Korea Sanhak Foundation. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A3B03031152) Publisher Copyright: {\textcopyright} 2018 by the authors.",

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N2 - Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

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Sustainable technology analysis of artificial intelligence using Bayesian and social network models

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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