Patent citation recommendation for examiners

Tao Yang Fu, Zhen Lei, Wang-chien Lee

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

3 Citations (Scopus)

Abstract

There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.

Original languageEnglish (US)
Title of host publicationProceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
EditorsZhi-Hua Zhou, Charu Aggarwal, Hui Xiong, Alexander Tuzhilin, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages751-756
Number of pages6
ISBN (Electronic)9781467395038
DOIs
StatePublished - Jan 5 2016
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2016-January
ISSN (Print)1550-4786

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

Fingerprint

Search engines

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Fu, T. Y., Lei, Z., & Lee, W. (2016). Patent citation recommendation for examiners. In Z-H. Zhou, C. Aggarwal, H. Xiong, A. Tuzhilin, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015 (pp. 751-756). [7373384] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2015.151
Fu, Tao Yang ; Lei, Zhen ; Lee, Wang-chien. / Patent citation recommendation for examiners. Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. editor / Zhi-Hua Zhou ; Charu Aggarwal ; Hui Xiong ; Alexander Tuzhilin ; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 751-756 (Proceedings - IEEE International Conference on Data Mining, ICDM).
@inproceedings{0e7503adb528489eb76407a36afb722b,
title = "Patent citation recommendation for examiners",
abstract = "There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.",
author = "Fu, {Tao Yang} and Zhen Lei and Wang-chien Lee",
year = "2016",
month = "1",
day = "5",
doi = "10.1109/ICDM.2015.151",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "751--756",
editor = "Zhi-Hua Zhou and Charu Aggarwal and Hui Xiong and Alexander Tuzhilin and Xindong Wu",
booktitle = "Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015",
address = "United States",

}

Fu, TY, Lei, Z & Lee, W 2016, Patent citation recommendation for examiners. in Z-H Zhou, C Aggarwal, H Xiong, A Tuzhilin & X Wu (eds), Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015., 7373384, Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2016-January, Institute of Electrical and Electronics Engineers Inc., pp. 751-756, 15th IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, United States, 11/14/15. https://doi.org/10.1109/ICDM.2015.151

Patent citation recommendation for examiners. / Fu, Tao Yang; Lei, Zhen; Lee, Wang-chien.

Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. ed. / Zhi-Hua Zhou; Charu Aggarwal; Hui Xiong; Alexander Tuzhilin; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 751-756 7373384 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2016-January).

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

TY - GEN

T1 - Patent citation recommendation for examiners

AU - Fu, Tao Yang

AU - Lei, Zhen

AU - Lee, Wang-chien

PY - 2016/1/5

Y1 - 2016/1/5

N2 - There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.

AB - There is a consensus that U. S. patent examiners, who are responsible for identifying prior art relevant to adjudicationof patentability of patent applications, often lack thetime, resources and/or experience necessary to conduct adequateprior art search. This study aims to build an automatic andeffective system of patent citation recommendation for patentexaminers. In addition to focusing on content and bibliographicinformation, our proposed system considers another importantpiece of information that is known by patent examiners, namely, applicant citations. We integrate applicant citations and bibliographicinformation of patents into a heterogeneous citationbibliographicnetwork. Based on this network, we explore metapathsbased relationships between a query patent application anda candidate prior patent and classify them into two categories:(1) Bibliographic meta-paths, (2) Applicant Bibliographic metapaths. We propose a framework based on a two-phase rankingapproach: the first phase involves selection of a candidate subsetfrom the whole U. S. patent data, and the second phase usessupervised learning models to rank prior patents in the candidatesubset. The results show that both bibliographic informationand applicant citation information are very useful for examinercitation recommendation, and that our approach significantlyoutperforms a search engine.

UR - http://www.scopus.com/inward/record.url?scp=84963538262&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963538262&partnerID=8YFLogxK

U2 - 10.1109/ICDM.2015.151

DO - 10.1109/ICDM.2015.151

M3 - Conference contribution

AN - SCOPUS:84963538262

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 751

EP - 756

BT - Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015

A2 - Zhou, Zhi-Hua

A2 - Aggarwal, Charu

A2 - Xiong, Hui

A2 - Tuzhilin, Alexander

A2 - Wu, Xindong

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Fu TY, Lei Z, Lee W. Patent citation recommendation for examiners. In Zhou Z-H, Aggarwal C, Xiong H, Tuzhilin A, Wu X, editors, Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 751-756. 7373384. (Proceedings - IEEE International Conference on Data Mining, ICDM). https://doi.org/10.1109/ICDM.2015.151