Abstract
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.
Highlights
A company with sustainable technology can maintain technological competitiveness in the marketplace [1,2]
We considered the Poisson probability distribution for the proposed statistical patent analysis model, because the count data of patent keywords are nonnegative integer values [9]
We carried out Bayesian count data modeling to find technological sustainability
Summary
A company with sustainable technology can maintain technological competitiveness in the marketplace [1,2]. 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). They used the IPC (international patent classification) codes extracted from patent documents as input data for sustainable technology analysis. We propose a statistical modeling using Bayesian count data analysis for understanding sustainability of a given technology domain. We considered the Poisson probability distribution for the proposed statistical patent analysis model, because the count data of patent keywords are nonnegative integer values [9]. In the Conclusions section, we conclude our research and describe our future work related to this paper
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