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베이지안 네트워크에 기반한 특허 등록 예측 모형 구축

  • 강동진 (공학대학원 산업공학전공)

초록/요약

Recently the importance of intellectual property has been increased. There has been various ways of research on analysis of companies, forecast of technology and so on through patents and many investments of money and time. Unlike traditional method of patent analysis such as company analysis, forecasting technologies, this research is to suggest the ways to forecast registration and rejection of patents which help minimize the efforts to register patents. To do so, in- formation such as inventors, applicants, application date, and IPC codes were extracted to be used as input variables for analyzing Bayesian network. Especially, among various forms of Bayesian network, we used Tree Augmented NBN (TAN) to forecast registration and rejection of patent. This is because, TAN was assumed to have dependence between variables. As a result of this Bayesian network, it was shown that there are nearly more than 80% of accuracy to fore- cast registration and rejection of patents. Therefore, we expect the minimization of time and cost of registration by forecasting registration and rejection of R&D patent through this research. Keywords: Bayesian Network; Patent Registration; Tree Augmented NBN; Forecast

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목차

목 차

영문요약 ·······································································ⅳ
목 차 ········································································· vi
그림 목차······································································ viii
표 목차 ··········································································ix

제 1 장 서론 ·····················································1
1.1 연구의 배경 및 목적 ································1
1.2 관련연구 ·················································· 4
1.3 논문의 구성··············································· 5

제 2 장 연구방법 ·············································7
2.1 지식재산권 ··············································· 7
2.2 베이지안 네트워크 ··································11

제 3 장 특허데이터 ·········································15

제 4 장 실험 및 결과 ······································18
4.1 데이터 수집 ·············································18
4.2 변수선정 ··················································20
4.2.1 신규성 ············································· 21
4.2.2 기술의 권리범위 ······························ 22
4.2.3 연구개발의 협력성 ··························· 23
4.2.4 기술의 세분화 ································· 24
4.2.5 특허활동 지수 ································· 25
4.2.4 혁신성과의 기술적 중요성 ··············· 26
4.3 예측모형 구축 ········································28
4.4 결과 ·························································31

제 5 장 결론 ··················································38


참고 문헌 ························································40









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