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This paper proposes a new method for decomposing a technological domain (TD). Specifically, the method identifies sub-TDs at the different levels of technological hierarchy within the TD based on the characteristics of patent co-classification and classification hierarchy. We defined the smallest class, named Minimum Overlapped Class (MOC), constructed by overlaps of sub-group IPC(s) and sub-class UPC(s), and sub-TD is basically identified as a set of the MOCs. In order to cluster the MOCs, technological distances among MOCs are calculated based on patent co-classification and hierarchical structure of patent classification systems. Technologically similar MOCs are grouped by using a hierarchical clustering and the identified clusters at the different level of hierarchy show the hierarchical structure of a TD. Detailed technological content for each sub-TD is represented by extracting representative keywords through a text-mining technique. The method is empirically tested by the solar photovoltaic technology and the results show that the identified sub-TDs are reasonably acceptable by qualitative analysis.

Purpose The purpose of this paper is to propose a quantitative method for identifying multiple and hierarchical knowledge trajectories within a specific technological domain (TD). Design/methodology/approach The proposed method as a patent-based data-driven approach is basically based on patent classification systems and patent citation information. Specifically, the method first analyzes hierarchical structure under a specific TD based on patent co-classification and hierarchical relationships between patent classifications. Then, main paths for each sub-TD and overall-TD are generated by knowledge persistence-based main path approach. The all generated main paths at different level are integrated into the hierarchical main paths. Findings This paper conducted an empirical analysis by using Genome sequencing technology. The results show that the proposed method automatically identifies three sub-TDs, which are major functionalities in the TD, and generates the hierarchical main paths. The generated main paths show knowledge flows across different sub-TDs and the changing trends in dominant sub-TD over time. Originality/value To the best of the authors’ knowledge, the proposed method is the first attempt to automatically generate multiple hierarchical main paths using patent data. The generated main paths objectively show not only knowledge trajectories for each sub-TD but also interactive knowledge flows among sub-TDs. Therefore, the method is definitely helpful to reduce manual work for TD decomposition and useful to understand major trajectories for TD.

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The blockchain is a technology with high growth potential that increases social benefits by streamlining procedures, reducing costs, and innovating the way we work. Considering the growth potential of blockchain technologies, countries around the world are attempting to graft into various fields such as finance, logistics, and healthcare, and actively promoting technology development. Tracing and analyzing the developmental trajectories of blockchain technology can give great insight for R&D direction and strategies. We developed an improved knowledge persistence-based main path approach to identify technological trajectories of the blockchain technology. In addition, future technological directions for each sub-technology under blockchain technology were identified by the knowledge unconventionality metric. The results show that the blockchain technology can be divided into five sub-technologies, and each subtechnology has evolved with high technological interactions among other sub-technologies. Based on the last knowledge streams of the main paths, this paper suggests potential future directions for each subtechnology in the blockchain technology.