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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏志平 | |
| dc.contributor.author | Yi-Min Huang | en |
| dc.contributor.author | 黃奕閔 | zh_TW |
| dc.date.accessioned | 2021-07-11T14:37:02Z | - |
| dc.date.available | 2022-08-30 | |
| dc.date.copyright | 2017-08-30 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-14 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/77902 | - |
| dc.description.abstract | 「跨業應用」是指企業在與自身不同的產業中為企業自身的核心技術尋找可能的應用。由於跨業應用會牽涉到技術轉移與專利的授權，因此跨業應用會幫助企業充分地利用自身現有的技術與資源獲得額外的收益，進而增加企業自身的競爭優勢。而對於被授權方來說，跨業應用也可以讓企業直接取得其他產業的專利知識進行生產，而免於投資大量時間與研發成本，以及研發失敗的風險。這樣雙贏的情況讓許多企業更有意願接受與尋找跨業應用。
傳統上尋找技術的跨業應用大都依賴領域專家的知識，但這樣通常會耗費企業大量的資源與時間，且專家通常沒有跨領域的知識，這對企業來說是很大的挑戰。因此，我們採用基於功能分析與書目耦合的概念提出一個系統化與全自動化的方法取代專家尋找技術的跨業應用。我們的想法是利用專利分析的方式來為焦點專利尋找功能相似與強書目耦合關係的其他專利，而這些專利的技術領域就可以視為焦點專利的跨業應用領域。 | zh_TW |
| dc.description.abstract | Cross-sectoral application of a technology is to explore the technology to different applications in industries different from the industry of the firm owning the technology. Cross-sectoral applications can help firms to enlarge their revenue streams because the firms can receive fees from technology transfer and/or licensing associated with possible cross-sectoral applications of their proprietary technologies. Besides economic returns and potential competitive advantage acquired by technology owners, the concept of cross-sectoral applications also benefits technology receiving firms. Such a win-win situation enhances the willingness of technology transfers or licenses between firms. Conventional approaches to identify potential application areas for a focal technology rely heavily on experts’ knowledge and intuition, which are costly (in terms of time and resources) and make most firms being limited in business resources and facing enormous challenges. Thus, in this research, we use the concept of function analysis and bibliographic coupling to propose a systematic and fully automatic approach to deal with the task of identification of cross-sectoral application efficiently. The main idea of our proposed method is that the application area in which most prior patents have similar functions and cite the same patents as the focal patent can be regarded as a potential cross-sectoral application area of the focal patent. Then we can recommend such cross-sectoral applications for the focal patent. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T14:37:02Z (GMT). No. of bitstreams: 1 ntu-106-R04725035-1.pdf: 2681976 bytes, checksum: 5430cdae1cc98459eaf1731d161877a7 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 致謝 i
中文摘要 ii ABSTRACT iii TABLE OF CONTENTS iv LIST OF FIGURES vi LIST OF TABLES viii Chapter 1 Introduction 1 1-1. Background 1 1-2. Research Motivation 3 1-3. Research Objective 4 Chapter 2 Literature Review 6 2-1. Identifying Characteristics of A Focal Technology 7 2-2. Identifying Reference Technologies of A Focal Technology 10 2-3. Mapping Reference Technologies to Application Areas 11 2-4. Rank Candidates of Identified Application Areas 14 2-5. Review Summary 15 Chapter 3 Our Proposed Approach 16 3-1. Overview 16 3-2. AO Structure Extraction 18 3-2-1. Natural Language Processing for Syntactic Analysis 18 3-2-2. Process of AO Structure Extraction 20 3-3. AO Structure Selection 23 3-3-1. Functional AO Structure Selection 24 3-3-2. AO Structure Grouping 25 3-4. Selecting Relevant Patents 27 3-4-1. Function-based Method 28 3-4-2. Bibliographic-coupling-based Method 28 3-4-3. Merge Method 29 3-5. Reference Patents Retrieval 29 3-6. Candidates of Application Areas Generation 30 3-7. Candidates Scoring and Ranking 30 Chapter 4 Empirical Evaluation 32 4-1. Golden Answer of Cross-sectoral Applications 32 4-2. Patent Data Collection 33 4-3. Evaluation Criterion 34 4-4. Performance Benchmark 35 4-5. Parameter Tuning Experiments 35 4-5-1. Tuning Quantity of AO Structures 36 4-5-2. Reference Patents Retrieval with Forward Citation Factor 37 4-5-3. Reference Patents Retrieval without Forward Citation Factor 41 4-5-4. Experimental Comparison 43 4-6. Evaluation Results 48 4-7. Effect of Candidates Scoring 50 Chapter 5 Conclusion and Future Work 55 REFERENCES 57 Appendix A: List of 44 Sectoral Fields 62 Appendix B: Concordance Matrix between Sectoral fields and Sub-classes of IPC Codes 63 | |
| dc.language.iso | en | |
| dc.subject | 功能分析 | zh_TW |
| dc.subject | 書目耦合 | zh_TW |
| dc.subject | 專利分析 | zh_TW |
| dc.subject | 自然語言處理 | zh_TW |
| dc.subject | 向量空間模型 | zh_TW |
| dc.subject | 跨業應用 | zh_TW |
| dc.subject | 跨業授權 | zh_TW |
| dc.subject | natural language processing | en |
| dc.subject | patent analysis | en |
| dc.subject | cross-sectoral applications | en |
| dc.subject | cross-sectoral licensing | en |
| dc.subject | function-based analysis | en |
| dc.subject | bibliographic coupling | en |
| dc.subject | vector space model | en |
| dc.title | 基於功能分析與書目耦合方法尋找專利跨業應用 | zh_TW |
| dc.title | A Function- and Bibliographic-coupling-based Approach for Identifying Cross-sectoral Applications of Patents | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳彥良,盧信銘 | |
| dc.subject.keyword | 專利分析,跨業應用,跨業授權,功能分析,書目耦合,向量空間模型,自然語言處理, | zh_TW |
| dc.subject.keyword | patent analysis,cross-sectoral applications,cross-sectoral licensing,function-based analysis,bibliographic coupling,vector space model,natural language processing, | en |
| dc.relation.page | 66 | |
| dc.identifier.doi | 10.6342/NTU201703197 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2017-08-15 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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