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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏志平 | |
| dc.contributor.author | Guan-Ying Chen | en |
| dc.contributor.author | 陳冠穎 | zh_TW |
| dc.date.accessioned | 2021-07-11T14:42:27Z | - |
| dc.date.available | 2021-11-02 | |
| dc.date.copyright | 2016-11-02 | |
| dc.date.issued | 2016 | |
| dc.date.submitted | 2016-08-18 | |
| dc.identifier.citation | Abbas, A., Zhang, L., & Khan, S. U. (2014). A literature review on the state-of-the-art in patent analysis. World Patent Information, 37, 3-13.
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78107 | - |
| dc.description.abstract | 專利文件被廣泛地認為是評估技術發展趨勢和企業研發活動的一個重要來源，「科技發展」的實質意涵或許難以捕捉，但我們能將「專利文件」作為其代理資料，透過專利文件去剖析技術發展、預測科技的改變以及識別出有潛力的科技機會。基於「識別科技機會」的觀點，對於企業、學者而言，最主要的目標便是希望「識別出兼具新穎性和影響性的專利」，這些專利是特定技術領域中，相對有價值、有潛力的專利，也就是具有發展潛力的科技機會。
一旦企業識別出科技機會，便能夠掌握先機，做出具有相對競爭優勢的產品以便取得先進者優勢，並且擁有獨特的見解以制定企業研發活動的相關策略。而當企業針對自己所擁有的專利進行「識別科技機會」的相關分析時，便可以藉此捨棄排序在末端的專利，省下大筆專利維護費用。 本研究的主要目的便在於，以自動化的方式結合專利文件的書目資料和文本內容，在不需要專家介入的情況下，便能排序、評估專利的新穎性和影響性，藉此篩選出相對具有較高新穎性和影響性的專利，而這些「核心專利」便會被視為「最有可能的潛在科技機會」，以此協助科技機會的識別。研究結果也顯示我們所提出的方法之效能是優於前人的研究。 | zh_TW |
| dc.description.abstract | Patent data is an important proxy of technology trends, so people can predict the technological change and identifying technological opportunities via patent data. We propose a learning-to-rank approach, ‘‘Core Patent Identification (CPI)’’ method, that combines both bibliometric and content information of patents. This approach can automatically rank the novelty and impact of patents in a specific technology field and contribute to technological opportunity identification. The patents with relatively high novelty and influence are called core patents, which represent as probable technological opportunities. Our empirical evaluation results also suggest that our proposed CPI method (using Ranking SVM as the underlying learning-to-rank method) significantly outperforms the benchmark method.
This approach is beneficial for firms in several ways. Through finding novel and influential patents, firms would have insights in identifying technological opportunity and develop strategies for patent management. Furthermore, because the patents at the top of the ranking list are likely high novel and influential, firms can utilize the ranking result to make business decisions such as mergers and acquisitions. On the other hand, if firms use this approach to analyze own patents, firms can discard the patents that are at the tail of the ranking list to save the patent maintenance fee. In short, firms find out valuable patents which are regarded as technological opportunities in a specific technological domain, so firms can well utilize these patents to create competitive advantages. | en |
| dc.description.provenance | Made available in DSpace on 2021-07-11T14:42:27Z (GMT). No. of bitstreams: 1 ntu-105-R03725006-1.pdf: 2172873 bytes, checksum: ecbe06d46936408e48d208133e98ea7d (MD5) Previous issue date: 2016 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv TABLE OF CONTENTS v LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 1.1 Background 1 1.2 Structure of a Patent Document 2 1.3 Research Motivations and Objectives 3 Chapter 2 Literature Review 5 2.1 Technological Opportunity 5 2.2 Vacancy-based Technological Opportunity Identification 6 2.3 Novelty-based Technological Opportunity Identification 8 2.4 Another Thought about Technological Opportunity Identification 10 2.5 Prior Methods of Novelty-based Technological Opportunity Identification 11 2.5.1 Citation-based Analysis 11 2.5.2 Keyword-based Analysis 12 2.5.3 SAO-based Analysis 13 2.6 Research Gap 14 Chapter 3 Design of Our Proposed Core Patent Identification (CPI) Method 15 3.1 Data Preprocessing 17 3.2 Patent Document Representation 18 3.3 Patent Novelty and Impact Measurement 18 3.4 Patent Novelty and Impact Measurement: Bibliometric Analysis 19 3.4.1 Number of Backward Citations 20 3.4.2 Originality (USPC) 20 3.4.3 Science Linkage 21 3.4.4 Novelty Based on Similarity of Backward Citation Structure 22 3.4.5 Normalized Number of Patent Claims 24 3.5 Patent Novelty and Impact Measurement: Content-based Analysis 25 3.5.1 Semantic Vector Space 26 3.5.2 Content-based Variables: Novelty-based Measures 30 3.5.3 Content-based Variables: Impact-based Measure 33 3.6 Patent Novelty and Impact Measurement: Summary 35 3.7 Patent Novelty and Impact Ranking Model Construction 36 3.7.1 Learning-to-rank 37 3.7.2 Pairwise Approach: Ranking SVM 39 Chapter 4 Evaluation Design 42 4.1 Experimental Data 42 4.2 Evaluation Metrics 50 4.2.1 MAP (Mean Average Precision) 50 4.2.2 NDCG (Normalized Discounted Cumulative Gain) 51 4.3 Experimental Procedure 53 4.4 Parameter Tuning 54 4.4.1 Dimension Selection of LSA 55 4.4.2 Semantic Topic Selection of VSM, LSA, PLSA and LDA 58 4.5 Comparative Evaluation 61 4.5.1 Benchmark 61 4.5.2 Our Proposed Core Patent Identification (CPI) Method 62 4.5.3 Comparative Evaluation 62 4.6 Additional Evaluations 64 4.6.1 Experiment 1: Effects of Variable Selection 64 4.6.2 Experiment 2: Effects of Analysis Target 70 4.6.3 Experiment 3: Effects of Issue Date and Filing Date of a Patent 75 4.6.4 Experiment 4: Effects of Training Sizes 79 Chapter 5 Conclusion and Future Work 85 5.1.1 Conclusion 85 5.1.2 Future Work 87 References 88 | |
| dc.language.iso | en | |
| dc.title | 尋找核心專利以協助科技機會的識別 | zh_TW |
| dc.title | Finding Core Patents for Supporting Technological Opportunity Identification | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 104-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳彥良,楊錦生,吳怡瑾 | |
| dc.subject.keyword | 專利分析,專利新穎性,專利影響性,科技機會,排序學習, | zh_TW |
| dc.subject.keyword | Patent analysis,Patent novelty,Patent impact,Technological opportunity,Learning-to-rank, | en |
| dc.relation.page | 94 | |
| dc.identifier.doi | 10.6342/NTU201602646 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2016-08-18 | |
| 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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