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In this study, we propose an unconnected component inclusion technique (UCIT) for patent citation analysis. Our method generates a cluster solution that includes unconnected and connected components of a direct citation network, enabling a more complete analysis of the technology fields. Case studies of Internet of Things-related technologies were conducted to test the effectiveness of our proposed method. We observed that UCIT increased the number of nodes especially in relatively small networks. Additionally, we analyzed how the clusters changed by adding unconnected patents to the citation network and identified four types of clustering phenomenon. Our method can be used by patent officers, R&D managers, and policy makers when they want to understand the technology landscape better.

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The detection of emerging technologies is vital for R&D managers and policymakers; hence, the bibliometric approach to analyzing papers and patents was developed. In this study, we proposed a new method, the research classification schema (RCS). We used citation network analysis to classify technologies into four categories: change-maker, breakthrough, matured, and incremental. Each technology is then plotted on the RCS based on its publication profile. A case study in the field of antennas was undertaken to evaluate the relevance and effectiveness of the RCS. The RCS method demonstrates the usefulness of the identification process of promising technologies, and therefore, the convenience of target designing research projects in universities and companies. We also discussed the effect of the resolution limit of clustering algorithms on the RCS to improve reliability.