Gao, Yuan and Zhu, Zhen and Riccaboni, Massimo Consistency and Trends of Technological Innovations: A Network Approach to the International Patent Classification Data. In: Complex Networks & Their Applications VI. COMPLEX NETWORKS 2016 2017. Studies in Computational Intelligence. Studies in Computational Intelligence, 689 . Springer International Publishing, pp. 744-756. ISBN 978-3-319-72150-7 (2018)
Full text not available from this repository.Abstract
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.
| Item Type: | Book Section |
|---|---|
| Identification Number: | https://doi.org/10.1007/978-3-319-72150-7_60 |
| Subjects: | H Social Sciences > HA Statistics H Social Sciences > HD Industries. Land use. Labor |
| Research Area: | Computer Science and Applications |
| Depositing User: | Caterina Tangheroni |
| Date Deposited: | 28 Dec 2017 11:03 |
| Last Modified: | 28 Dec 2017 11:03 |
| URI: | http://eprints.imtlucca.it/id/eprint/3851 |
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