In this study, we propose a novel approach to analyze big data related to patents in the field of smart factories, utilizing the Latent Dirichlet Allocation (LDA) topic modeling method and the generative artificial intelligence technology, ChatGPT. Our method includes extracting valuable insights from a large data-set of associated patents using LDA to identify latent topics and their corresponding patent documents. Additionally, we validate the suitability of the topics generated using generative AI technology and review the results with domain experts. We also employ the powerful big data analysis tool, KNIME, to preprocess and visualize the patent data, facilitating a better understanding of the global patent landscape and enabling a comparative analysis with the domestic patent environment. In order to explore quantitative and qualitative comparative advantages at this juncture, we have selected six indicators for conducting a quantitative analysis. Consequently, our approach allows us to explore the distinctive characteristics and investment directions of individual countries in the context of research and development and commercialization, based on a global-scale patent analysis in the field of smart factories. We anticipate that our findings, based on the analysis of global patent data in the field of smart factories, will serve as vital guidance for determining individual countries' directions in research and development investment. Furthermore, we propose a novel utilization of GhatGPT as a tool for validating the suitability of selected topics for policy makers who must choose topics across various scientific and technological domains.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.

Explainable AI (XAI) is an approach that leverages artificial intelligence to support human decision-making. Recently, governments of several countries including Korea are attempting objective evidence-based analyses of R&D investments with returns by analyzing quantitative data. Over the past decade, governments have invested in relevant researches, allowing government officials to gain insights to help them evaluate past performances and discuss future policy directions. Compared to the size that has not been used yet, the utilization of the text information (accumulated in national DBs) so far is low level. The current study utilizes a text mining strategy for monitoring innovations along with a case study of smart-farms in the Honam region.

The study aims at quantifying the effect of nano technology in the fields of economics and social aspects by using the methodology of system dynamics. A case study which using selenium oxide nanoparticles as additive agent in order to enhance fuel efficiency was selected as an example of nano technology in economic and societal benefits. Additionally, models for exhaust gas from combustion of fuel (diesel) and related issues are developed to evaluate real-time assessment of the effect of nano technology. It was found that the selenium oxide nanoparticles increase fuel efficiency, and it also affects on the amount of exhaust gas and the respiratory disease related issues. The results of this study which give quantitative value for the effect of nano technology can be used as objective references in development of national policy.

It is difficult to make an accurate estimate of the economic value and effects on societal economy of Nano-technologies. This research measures an economic value of Nano-technologies quantitatively and analyzes its influences on societal economy. This paper chooses some major industries as analysis targets and adapts the DEFRA comparative methodology model which has been developed in the UK and recommended by OECD. For this reason, some industries which are in range of economic value assessment were investigated and related data were collected. Also, the economic value and societal influences of Nano-technologies were calculated, through the procedure of the model. In addition, this study conducts a questionnaire to experts for the validity of measurement results and procedures. This paper suggests a guideline for economic value and effects on societal economy of Nano-technologies assessments through quantitative Defra comparative methodologies.