Early identification of important patents: Design and validation of citation network metrics

Manuel Sebastian Mariani, Matúš Medo and François Lafond

Technological Forecasting and Social Change, 2019, vol. 146, issue C, 644-654

Abstract: One of the most challenging problems in technological forecasting is to identify as early as possible those technologies that have the potential to lead to radical changes in our society. In this paper, we use the US patent citation network (1926–2010) to test our ability to early identify a list of expert-selected historically significant patents through citation network analysis. We show that in order to effectively uncover these patents shortly after they are issued, we need to go beyond raw citation counts and take into account both the citation network topology and temporal information. In particular, an age-normalized measure of patent centrality, called rescaled PageRank, allows us to identify the significant patents earlier than citation count and PageRank score. In addition, we find that while high-impact patents tend to rely on other high-impact patents in a similar way as scientific papers, the patents' citation dynamics is significantly slower than that of papers, which makes the early identification of significant patents more challenging than that of significant papers. In the context of technology management, our rescaled metrics can be useful to early detect recent trends in technical improvement, which is of fundamental interest for companies and investors.

Keywords: Patent analysis; Citation networks; Significant patents; Technological forecasting; PageRank (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162517314312
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:146:y:2019:i:c:p:644-654

DOI: 10.1016/j.techfore.2018.01.036

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().