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研究生:林家銘
研究生(外文):Chia-Ming Lin
論文名稱:整合灰關聯分析與機器學習方法辨識LED專利競爭力
論文名稱(外文):Integrating Grey Relational Analysis and Machine Learning Methods to Identify the Competitiveness of LED Patents
指導教授:管中徽管中徽引用關係
指導教授(外文):Chung-Huei Kuan
口試委員:耿筠蘇威年
口試委員(外文):Yun KenWei-Nien Su
口試日期:2021-6-8
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:專利研究所
學門:法律學門
學類:專業法律學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:56
中文關鍵詞:專利訴訟灰關聯分析機器學習k-均值分群演算法類神經網路
外文關鍵詞:patent litigationgrey relational analysismachine learningk-meansneural network
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  • 下載下載:41
  • 收藏至我的研究室書目清單書目收藏:1
專利訴訟常發生於擁有高品質專利或在產品技術重疊性高的企業之間。擁有高品質專利之企業,在專利訴訟上常扮演主動訴訟之角色。反之,專利相對劣勢之企業則大多處於被告之位置。本文收集近幾年之訴訟新聞資料後,以灰關聯分析找出在專利訴訟上互動關聯較強之企業,為主要專利訴訟玩家群(常為原告及被告者)。本文發現近幾年之LED專利訴訟之主要玩家分別為Nichia(日亞化學)、Everlight(億光電子)、Seoul(首爾半導體)、及Epistar(晶元光電)。而主要玩家訴訟之專利標的代表該專利在產業中的重要性,也顯示訴訟企業彼此的技術能力差異。故透過專利訴訟可確定企業在相關產業技術中之專利定位及角色。最後,本文利用機器學習方法(k-均值分群演算法與類神經網路)提出一預測模型,以協助企業性更系統性地判斷專利在未來可能之訴訟角色。該預測模型可減少大量人力判斷之過程,且協助企業有更一致性的決策品質。
Patent litigation usually occurs in enterprises that have high quality patents or have high overlap in the product technology. Enterprises with high-quality patents often play the role of active litigation in patent litigation. In addition, enterprises with weak patents mostly in the position of defendants. After collecting news materials of litigation in recent years, we used grey relational analysis (GRA) to find enterprises with strong interaction in patent litigation, which are the key players of patent litigation (there are usually in the role of plaintiff and defendant). This thesis finds that the key players in patent litigation of LEDs in recent years are Nichia, Everlight, Seoul and Epistar. The objects of patent litigation of key players are often the important patents in the industry, which shows the differences in the technological capabilities of the litigation companies. Therefore, through patent litigation, the company's patent positions in related industrial technologies can be determined. Finally, we used machine learning methods including k-means clustering algorithm and neural network to propose a predictive model to comprehensively predict the litigation role of patents in the future. The proposed model can reduce a large number of human judgments and help companies have more consistent decision-making quality.
中文摘要 I
ABSTRACT II
誌謝 III
目 錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 2
第二章 文獻探討 3
2.1 灰關聯分析(Grey Relational Analysis,GRA) 3
2.2 企業角色及定位評估模式 9
2.3 k-平均分群演算法(k-means clustering algorithm) 11
2.4類神經網路(Artificial Neural Network,ANN) 12
第三章 研究方法 16
第四章 案例研究 21
4.1 專利訴訟新聞收集 21
4.2 訴訟次數分析 22
4.3 灰關聯分析 28
4.4 專利資料收集:Espacenet 34
4.5 專利分群 36
4.6 專利標籤設定 37
4.7 資料前處理 38
4.8 類神經網路建模及分析 39
第五章 結論與建議 41
5.1 結論 41
5.2 未來研究方向 42
參考文獻 44
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