Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4968048 | Journal of Informetrics | 2017 | 19 Pages |
Abstract
Patent citation analysis is considered a useful tool for technology impact analysis. However, the outcomes of previous methods do not provide a fair reflection of a technology's future prospects since they are based on deterministic approaches, assuming that future trends will remain the same as those in the past. As a remedy, we propose a Hawkes process-based patent citation analysis method to assess the future technological impact and uncertainty of a technology in a time period of interest by employing the future citation counts of the relevant patents as a quantitative proxy. For this, we construct a citation interval matrix from the United States Patent and Trademark Office (USPTO) database, and employ a Hawkes process - a special case of path-dependent stochastic processes - as a method for patent citation forecasting. Specifically, the Hawkes process models the idiosyncratic and dynamic behaviours of a technology's evolution and obsolescence by increasing the likelihood of another subsequent citation by oneself (i.e., self-excitation) and decaying the likelihood back towards the initial level naturally. A case study of the patents about molecular amplification diagnosis technology shows that our method outperforms previous deterministic approaches in terms of accuracy and practicality.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Hyun Jin Jang, Han-Gyun Woo, Changyong Lee,