Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

Kim, Juhwan; Jun, Sunghae; Jang, Dong-Sik; Park, Sangsung

doi:10.3390/su10010115

Cited by 16 publications

(16 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They extracted the technological keywords from the searched patent documents and analyzed them for technology forecasting [15]. Kim et al (2018) analyzed the IPC codes extracted from the patent documents related to AI [16]. They also used Bayesian regression and social networks analysis for the patent IPC codes analysis [16].…”

Section: Patent Analysis and Technology Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

2020

Self Cite

View full text Add to dashboard Cite

Technology analysis is one of the important tasks in technology and industrial management. Much information about technology is contained in the patent documents. So, patent data analysis is required for technology analysis. The existing patent analyses relied on the quantitative analysis of the collected patent documents. However, in the technology analysis, expert prior knowledge should also be considered. In this paper, we study the patent analysis method using Bayesian inference which considers prior experience of experts and likelihood function of patent data at the same time. For keyword data analysis, we use Bayesian predictive interval estimation with count data distributions such as Poisson. Using the proposed models, we forecast the future trends of technological keywords of artificial intelligence (AI) in order to know the future technology of AI. We perform a case study to provide how the proposed method can be applied to real areas. In this paper, we retrieve the patent documents related to AI technology, and analyze them to find the technological trend of AI. From the results of AI technology case study, we can find which technological keywords are more important or critical in the entire structure of AI industry. The existing methods for patent keyword analysis were depended on the collected patent documents at present. But, in technology analysis, the prior knowledge by domain experts is as important as the collected patent documents. So, we propose a method based on Bayesian inference for technology analysis using the patent documents. Our method considers the patent data analysis with the prior knowledge from domain experts.

show abstract

Section: Patent Analysis and Technology Forecastingmentioning

confidence: 99%

“…Kim et al (2018) analyzed the IPC codes extracted from the patent documents related to AI [16]. They also used Bayesian regression and social networks analysis for the patent IPC codes analysis [16].…”

Section: Patent Analysis and Technology Forecastingmentioning

confidence: 99%

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The research network for PM was also analyzed in the present study using NetMiner, which allows exploratory analysis and the visualization of data to understand the patterns and structures of a network [24][25][26]. Network analysis was thus used to detect collaborative relationships between different countries or institutes.…”

Section: Network Analysismentioning

confidence: 99%

Understanding the Global Status of Particulate Matter with Respect to Research Topics and Research Networks

et al. 2019

View full text Add to dashboard Cite

Atmospheric pollution caused by particulate matter (PM), which has been identified as a carcinogen, is a major concern in South Korea, with various PM emission sources identified, including vehicles and power plants. The Korean government recently announced plans to increase research and development (R&D) expenditure to reduce PM levels in the atmosphere. In this study, the research topics and research networks for PM technology are summarized to help policymakers to more efficiently plan future R&D investment. A topic model is implemented to identify the main keywords from Science Citation Index (SCI) papers related to PM, and social network analysis is used to understand research collaboration between different countries or institutions. The results reveal that China and the USA published the largest number of SCI papers related to PM research, while South Korea was ranked in the top five globally. Unlike China and South Korea, developed countries such as the USA, Germany, and France have been carrying out PM research on climate change, ozone, NH3, nanoparticles, and the health effects on pregnant women. The primary research networks are centered on China and the USA, while South Korea has weak collaboration with other countries or institutions. More effort is thus required to improve strategic R&D investment and the research network within Korea.

show abstract

“…In the MOT field, many companies and institutes have tried to find their sustainable technologies. Many research results on sustainable technology analysis have been published in academia [1,2,4,10,11]. Sustainable technology analysis has been made using diverse analytical methods in various technological fields.…”

Section: Sustainable Technology and Patent Analysismentioning

confidence: 99%

“…Many academics, research institutes, and companies have studied sustainable technologies. Recently, Kim et al (2018) published a statistical method for sustainable technology analysis [4]. They considered Bayesian inference and social network analysis for the proposed method and applied their research to the technology domain related to artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Bayesian Count Data Modeling for Finding Technological Sustainability

Jun

2018

Sustainability

Self Cite

View full text Add to dashboard Cite

Technology developments change society, and society demands new and innovative technology developments. We analyze technology to understand society and technology itself. Much research related to technology analysis has been introduced in various fields. Most of it has been on patent analysis. This is because detailed and accurate results of research and development are patented. In this paper, we study a new patent analysis method based on the count data model and Bayesian regression analysis. Using the count data model, we analyzed the technological keywords extracted from the collected patent documents. We used the prior distribution of Bayesian statistics to reflect the experience and knowledge of the relevant technological experts in the analysis model. Moreover, we applied the proposed model to find sustainable technologies. Finding and developing sustainable technologies is an important activity for companies and research institutes to maintain their technological competitiveness. To illustrate how our modeling could be applied to real domains, we carried out a case study using the patent documents related to artificial intelligence.

show abstract

Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

Cited by 16 publications

References 14 publications

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Patent Data Analysis of Artificial Intelligence Using Bayesian Interval Estimation

Understanding the Global Status of Particulate Matter with Respect to Research Topics and Research Networks

Bayesian Count Data Modeling for Finding Technological Sustainability

Contact Info

Product

Resources

About