Consistency and Trends of Technological Innovations: A Network Approach to the International Patent Classification Data

  • Yuan Gao
  • Zhen Zhu
  • Massimo Riccaboni
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 689)


Classifying patents by the technology areas they pertain is important to enable information search and facilitate policy analysis and socio-economic studies. Based on the OECD Triadic Patent Family database, this study constructs a cohort network based on the grouping of IPC subclasses in the same patent families, and a citation network based on citations between subclasses of patent families citing each other. This paper presents a systematic analysis approach which obtains naturally formed network clusters identified using a Lumped Markov Chain method, extracts community keys traceable over time, and investigates two important community characteristics: consistency and changing trends. The results are verified against several other methods, including a recent research measuring patent text similarity. The proposed method contributes to the literature a network-based approach to study the endogenous community properties of an exogenously devised classification system. The application of this method may improve accuracy and efficiency of the IPC search platform and help detect the emergence of new technologies.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.IMT School for Advanced Studies LuccaLuccaItaly
  2. 2.Department of Managerial Economics, Strategy and InnovationKatholieke Universiteit LeuvenLeuvenBelgium

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