DocumentCode
3576396
Title
Recommending missing citations for newly granted patents
Author
Sooyoung Oh ; Zhen Lei ; Wang-Chien Lee ; Yen, John
Author_Institution
Dept. of Comput. Sci. & Eng., Pennsylvania State Univ., University Park, PA, USA
fYear
2014
Firstpage
442
Lastpage
448
Abstract
The U.S. recently adopted a post-grant opposition procedure to encourage third parties to challenge the validity of newly granted patents by providing relevant prior patents that are missed during patent examination (i.e., missing citations). In this paper, we propose a recommendation system for missing citations for newly granted patents. The recommendation system, based on the patent citation network of a newly granted query patent, focuses on paths that start with the references of the query patent in the network. Our approach is to identify the relevancy of a candidate patent to the query patent by its citation relationship (paths) that are distinguished based on the direction, topology and semantics of the paths in the network. We consider six different types of paths between a candidate patent and a query patent based on their citation relationship and define a relevancy score for each path type. Accordingly, we rank candidate patents via a RankSVM model learned by using those relevancy scores as features. The experimental results show our approach significantly improves the average precision and recall performance compared to two baseline methods, i.e., Katz distance and text similarity.
Keywords
citation analysis; patents; recommender systems; support vector machines; text analysis; Katz distance; RankSVM model; candidate patent; citation relationship; missing citation; newly granted patent; newly granted query patent; patent citation network; patent examination; patents; post-grant opposition procedure; recommendation system; relevancy score; text similarity; Collaboration; Context; Couplings; Educational institutions; Patents; Q measurement; Semantics;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Science and Advanced Analytics (DSAA), 2014 International Conference on
Type
conf
DOI
10.1109/DSAA.2014.7058110
Filename
7058110
Link To Document