바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

  • P-ISSN1013-0799
  • E-ISSN2586-2073

특허인용 예측모형 구축에 관한 연구

A Study on Developing a Prediction Model of Patent Citation Counts

정보관리학회지, (P)1013-0799; (E)2586-2073
2010, v.27 no.4, pp.239-258
https://doi.org/10.3743/KOSIM.2010.27.4.239
유재복 (한국원자력연구원)
정영미 (연세대학교)

  • 다운로드 수
  • 조회수

초록

이 연구에서는 특허의 인용에 영향을 미치는 주요 변수들을 토대로 특허의 피인용횟수를 예측하기 위한 모형을 제시하였다. 이를 위해 미국특허를 대상으로 5개 주제분야에 걸쳐 특허의 피인용횟수와 일정 수준 이상의 상관관계, 즉 5% 이상의 설명력을 갖는 것으로 밝혀진 페이지 수, 청구항 수, 참고문헌 평균 피인용횟수, 서지결합도, 문헌간유사도 등 5개 변수들을 토대로 다중회귀분석을 실시하였다. 연구결과에 따르면, 제시된 5개 주제분야의 특허인용 예측모형의 설명력은 주제분야에 따라 58.3%~89.6%로 나타났으며, 예측변수로 사용된 5개의 독립변수 중 특허 피인용횟수에 가장 영향력이 높은 변수는 ‘문헌간유사도’로 나타났다. 또한 이 연구에서 추정된 주제분야별 예측모형을 토대로 산출한 특허 피인용횟수에 대한 예측값과 실제값을 비교한 결과 이들 예측모형은 5개 주제분야에서 모두 적합한 것으로 나타났다.

Abstract

The purpose of this study is to develop a prediction model of patent citation counts based on major factors which affect patent citation. To this end, we performed multiple regression analysis between the patent citation counts and five explanatory variables such as the number of pages, the number of claims, the reference-average-citation rate, the strength of bibliographic coupling, and the document similarity proved as having 5% or more standardized variances(r2) with patent citation counts, with a test dataset of U.S. patents in five subject fields. As a result, our prediction models showed 58.3% to 89.6% predictability depending on subject fields and revealed the document similarity has the highest impact on citation counts among the five predictive variables in all the subject fields. The result of comparison between the predicted citation counts and the actual ones confirmed the usefulness of the citation prediction models built for each subject field.

참고문헌

1

노경란. (2006). 특허분석을 통한 과학기술자의 과학논문 인용행태에 관한 연구.

2

박종용. (2008). 특허인용 관계를 이용한 기술지식 흐름의 실증분석: 산업수준의 네트워크 분석과 클러스터 분석.

3

유재복. (2010). 특허 인용에 영향을 미치는 요인 분석. 정보관리학회지, 27(1), 103-118.

4

Albert, M. B.. (1991). Direct validation of citation counts as indicators of industrially import patents. Research Policy, 20(3), 251-259.

5

Ashton, W. B.. (1989). Using patent information in technology business planning. Research Technology Management, 32(1), 36-42.

6

Carpenter, M. P.. (1981). Citation rates to technologically important patents. World Patent Information, 3(4), 160-163.

7

Carpenter, M. P.. (1983). Validation study: patent citations as indicators of science and foreign dependence. World Patent Information, 5(3), 180-185.

8

Chakrabarti, A. K.. (1991). Competition in high technology: analysis of problems of US, Japan, UK, France, West Germany and Canada. IEEE Transactions on Engineering Management, 38(1), 78-84.

9

Fung, M. K.. (2001). Measuring the intensity of knowledge flow with patent statistics. Economic Letters, 74, 353-358.

10

Hall, B. H.. (2000). Market value and patent citations: a first look. National Bureau of Economic Research.

11

Hall, B. H.. (2001). The NBER patent citation data file: lessons, insights and methodological tools. National Bureau of Economic Research.

12

Hall, B. H.. (2005). Market value and patent citations. RAND Journal of Economics, 36(1), 16-38.

13

Harhoff, D.. (1999). Citation frequency and the value of patented inventions. Review of Economics & Statistics, 81(3), 511-515.

14

Huang, M. X.. (2003). Evaluation of national competitiveness from the view of patent technology. Paper of China Society of Library, 70, 18-30.

15

Iversen, E. J.. (2000). An excursion into the patent-bibliometrics of Norwegian patenting. Scientometrics, 49(1), 63-80.

16

Jaffe, A. B.. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly Journal of Economics, 108(3), 577-598.

17

Jaffe, A. B.. (1996). Flows of knowledge from universities and federal, abs: modeling the flow of patent citations over time and across institutional and geographic boundaries. National Bureau of Economic Research.

18

Jaffe, A. B.. (1999). International knowledge flows: evidence from patent citations. Economics of Innovation and New Technology, 8(1), 105-136.

19

Jaffe, A. B.. (2000). Knowledge spillovers and patent citation: evidence from a survey of inventors. American Economic Review, 90(2), 215-218.

20

Karki, M. M. S.. (1997). Patent citation analysis: a policy analysis tool. World Patent Information, 33(4), 269-272.

21

Lanjouw, J. O.. (1998). How to count patents and value intellectual property: uses of patent renewal and application data. The Journal of Industrial Economics, 46(4), 405-432.

22

Lanjouw, J. O.. (2004). Patent quality and research productivity: measuring innovation with multiple indicators. The Economic Journal, 114(495), 441-465.

23

Yong-Gil Lee. (2006). An Analysis of Citation Counts of ETRI-Invented US Patents. ETRI Journal, 28(4), 541-544.

24

Lin, B. W.. (2007). Predicting citations to biotechnology patents based on the information from the patent documents. International Journal of Technology Management, 40(1), 87-100.

25

Narin, F.. (1984). Technological performance assessments based on patents and patent citations. IEEE Transactions on Engineering Management, 36(2), 172-183.

26

Narin, F.. (1997). The increasing linkage between US technology and public science. Research Policy, 26(3), 317-330.

27

Narin, F.. (1987). Patents as indicators of corporate technological strength. Research Policy, 16(2), 143-155.

28

Porter, M. F.. (1980). An algorithm for suffix stripping. Program, 14, 130-137.

29

Trajtenberg, M.. (1990). A penny for your quotes: Patent citations and the value of innovations. The Rand Journal of Economics, 21(1), 172-187.

30

Verspagen, B.. (1997). Measuring intersectoral spillovers: estimates from the European and US patent office databases. Economic Systems Research, 9(1), 47-66.

31

Yang, Z. K.. (2008). Top ten highly cited patents in USPTO (-). Proceedings of WIS 2008.

정보관리학회지