Abstract
Technological developments related to smart light emitting diode (LED) systems have progressed rapidly in recent years. In this paper, patent documents related to smart LED technology are collected and analyzed to understand the technology development of smart LED systems. Most previous studies of the technology were dependent on the knowledge and experience of domain experts, using techniques such as Delphi surveys or technology road-mapping. These approaches may be subjective and lack robustness, because the results can vary according to the selected expert groups. We therefore propose a new technology analysis methodology based on statistical modeling to obtain objective and relatively stable results. The proposed method consists of visualization based on Bayesian networks and a linear count model to analyze patent documents related to smart LED technology. Combining these results, a global hierarchical technology structure is created that can enhance the sustainability in smart LED system technology. In order to show how this methodology could be applied to real-world problems, we carry out a case study on the technology analysis of smart LED systems.
Highlights
Technology is one of the most important factors in national and company management
In addition to the statistical model, this study aims to develop a methodology for sustainable technology management using quantitative technology analysis
In our case study on smart light emitting diode (LED) technology, we show how the proposed method could be applied in reality
Summary
Technology is one of the most important factors in national and company management. Many companies have tried to research and develop innovative technologies to improve their technological competitiveness. We consider a visualization method based on Bayesian statistics, and add this visualization to the patent count data modeling. Howweevveerr, in the actual analysis process, the keyword and the IPC code data aarre iinnddeependently analyzed by ccoount rregression model and visualization based on Bayesian networks.
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