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.

Original languageEnglish (US)
Title of host publicationDSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics
EditorsGeorge Karypis, Longbing Cao, Wei Wang, Irwin King
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages442-448
Number of pages7
ISBN (Electronic)9781479969913
DOIs
StatePublished - Mar 10 2014
Event2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014 - Shanghai, China
Duration: Oct 30 2014Nov 1 2014

Other

Other2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014
CountryChina
CityShanghai
Period10/30/1411/1/14

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Recommender systems
Semantics
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Citations
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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Information Systems
  • Information Systems and Management

Cite this

Oh, S., Lei, Z., Lee, W., & Yen, J. (2014). Recommending missing citations for newly granted patents. In G. Karypis, L. Cao, W. Wang, & I. King (Eds.), DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics (pp. 442-448). [7058110] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/DSAA.2014.7058110
Oh, Sooyoung ; Lei, Zhen ; Lee, Wang-chien ; Yen, John. / Recommending missing citations for newly granted patents. DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics. editor / George Karypis ; Longbing Cao ; Wei Wang ; Irwin King. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 442-448
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title = "Recommending missing citations for newly granted patents",
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.",
author = "Sooyoung Oh and Zhen Lei and Wang-chien Lee and John Yen",
year = "2014",
month = "3",
day = "10",
doi = "10.1109/DSAA.2014.7058110",
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booktitle = "DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
address = "United States",

}

Oh, S, Lei, Z, Lee, W & Yen, J 2014, Recommending missing citations for newly granted patents. in G Karypis, L Cao, W Wang & I King (eds), DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics., 7058110, Institute of Electrical and Electronics Engineers Inc., pp. 442-448, 2014 IEEE International Conference on Data Science and Advanced Analytics, DSAA 2014, Shanghai, China, 10/30/14. https://doi.org/10.1109/DSAA.2014.7058110

Recommending missing citations for newly granted patents. / Oh, Sooyoung; Lei, Zhen; Lee, Wang-chien; Yen, John.

DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics. ed. / George Karypis; Longbing Cao; Wei Wang; Irwin King. Institute of Electrical and Electronics Engineers Inc., 2014. p. 442-448 7058110.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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Oh S, Lei Z, Lee W, Yen J. Recommending missing citations for newly granted patents. In Karypis G, Cao L, Wang W, King I, editors, DSAA 2014 - Proceedings of the 2014 IEEE International Conference on Data Science and Advanced Analytics. Institute of Electrical and Electronics Engineers Inc. 2014. p. 442-448. 7058110 https://doi.org/10.1109/DSAA.2014.7058110