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研究生:陳明原
研究生(外文):Ming-Yuan Chen
論文名稱:應用貝氏理論及模糊邏輯進行專利分類及TRIZ方法改善之研究
論文名稱(外文):The Study of Developing Patent Classification and TRIZ Method Improving Using Bayes Theorem and Fuzzy Logic
指導教授:林榮慶林榮慶引用關係
指導教授(外文):Zone-Ching Lin
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:自動化及控制研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:166
中文關鍵詞:貝氏理論模糊物件導向模式專利分類TRIZ
外文關鍵詞:fuzzy object oriented modelpatent classificationBayes TheoremTRIZ
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本文著重於專利資料的分類管理,應用貝氏理論及模糊邏輯之相關技術,建構出一個專利資料分類系統,來協助使用者對於大量專利資料進行分類管理。首先透過專利資料庫搜集相關知識的專利資料內容,經由人工整理出其所屬的知識領域分類,和從其內容過濾出其相關代表詞彙,進一步建構成以本體論為精神的階層式分類模型,其分層概念從系統元件分類,到工程知識分類,再到技術詞分類,進而到功能詞分類,依據此模型將每一份專利文件依其所屬的知識領域進行加以分類。而本研究中以出現頻率為基礎建構出系統雛形,進而加入貝氏定理及模糊物件導向模式的概念處理出現頻率,以獲得系統在判別類別時的判別值,並透過專利資料的增加修正其分類結果的正確性。最後,本文亦利用分類出的技術詞及功能詞,建構出技術功能矩陣表的形式,透過此表的建立,可達到快速取得相關技術的專利資料及縮短研發的時間。
本文以前蘇聯學者阿舒勒(Genrich Saulovich Altshuller)所提出之TRIZ方法為基礎,進而提出利用其從大量專利推理而成的矛盾矩陣表中之特徵與發明法則,找出其相近似之物理意義的特徵群組與發明法則群組,並以統計出群組出現次數為基建構出系統雛形,而系統判別方式除出現次數外,再加入貝氏定理及模糊物件導向模式處理其統計出的出現次數,進而得到系統判別值,以此判別值做為思考設計問題時其群組之發明法則優先考慮次序的依據,加速找出該類專利設計解答,以建立出該類專利技術的設計突破點。並且以案例驗證本文所建構的改善TRIZ判讀性流程之可行性。進一步利用專利分類整理出的技術功能矩陣表運用TRIZ矛盾表的精神,依其「改善特徵」與「不欲惡化特徵」可對應到解決技術矛盾的發明法則之概念點,延伸到技術功能矩陣表依「技術」與「欲改善功能」加上「不欲惡化的功能」,進一步決定與技術功能矩陣表中技術相關的發明法則的優先順序,以提供使用者在考慮設計方法上不一樣的參考概念,進一步縮短研發時間。
Focusing on the classification management of patent information, this paper uses the related technology of Bayes Theorem and Fuzzy logic to build an patent information classification system, so as to assist users to undergo classification management of large quantities of patent information.First, by means of the patent database, content of the patent information of related knowledge can be acquired.The correlated knowledge scope category is collated manually, and from its content, the related representing terms are screened, so as to further construct a layer-based classification model under the principle of ontology.From the classification of system components to that of engineering knowledge, and further to that of technological term and functionality terms, the concept of layers classifies every piece of patent document based on its related knowledge scope.Based on the foundation of display frequency, this paper constructs the system prototype, and further adds in the concept of Bayes Theorem and fuzzy object oriented model to process the display frequency, so as to acquire the assessed value when the system is assessing the category, and modify the accuracy of the result of classification by adding the patent information.Finally, this paper uses classified technology terms and functional terms to construct the format of technological function matrix table.Through the establishment of this table, the patent information of related technology can be acquired speedily and the time spent on research and development can be reduced.
This paper adopts the TRIZ Method proposed by the Russian scholar Genrich Saulovish Altshuller as a foundation to further propose a features and invention principle of contradiction matrix that is formed by the investigation of large quantities of patent information, so as to find the feature group and invention principle group that are similar to its physical meaning.Under the foundation of group display frequency calculated by statistics, the system prototype is constructed.Apart from the display frequency, the system assessment method adds in Bayes Theorem and fuzzy object oriented model to process the display frequency acquired by statistics, so as to further acquire the system assessment value.During the design process, the priority sequence of its invention principle group arrangement is made under this assessment value, which can speed up the solution of design problem of such kind of patent.This is a breakthrough in the design of such kind of patent technology.Also, case study is described herein to verify the feasibility of the improved TRIZ assessment procedures constructed in this paper.Furthermore, with the use of the principle of the TRIZ contradiction matrix, patent classification method is used to organize the matrix table of the technology function.Based on its “improved features” and “worsening features,” the concept point of the invention principle of the technological contradiction can be solved.When this theory is extended to the technological function matrix table, based on “technology” and “improved function” add on the “worsening function,” further decided invention principle group arrangement with the correlation technology in the technical function matrix table, providing a special reference concept to the users in times of design process and the time spent on research and development can be reduced.
摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 X
表目錄 XII
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 1
1-3 研究範圍 2
1-4論文架構 3
第二章 文獻回顧 5
2.1 專利分類 5
2.2 TRIZ方法 5
第三章 專利簡介 8
3.1 專利意義及重要性 8
3.2 專利資訊檢索 10
3.3 專利資料庫 12

3.4 專利分類 18
3.4.1 國際專利分類 18
3.4.2 美國專利分類(USPC) 19
3.4.3 日本專利分類(JPC) 20
3.4.4 歐洲專利分類(ECLA) 21
3.5 專利地圖 22
第四章 運用理論及方法介紹 24
4.1 本體論的介紹 24
4.2 貝氏理論 26
4.2.1 貝氏定理 26
4.2.2 貝氏分類 27
4.3 模糊理論 28
4.3.1 模糊理論簡介 28
4.3.2 模糊集合 29
4.3.3 模糊物件導向模式 29
第五章 化學機械研磨知識介紹 31
5.1 化學機械研磨之介紹 31
5.2化學機械拋光系統之移除率分析 31
5.3 傳統式化學機械研磨 32
5.4 補償式化學機械研磨 33
5.5 研磨墊 33
第六章 應用貝氏理論及模糊邏輯之專利分類建構 35
6.1 專利分類設計流程 35
6.2 專利資料的相關詞彙分析 37
6.3 專利分類模式的建構流程 38
6.3.1 出現頻率模式 38
6.3.2 貝氏機率模式 39
6.3.3 模糊值模式 43
6.3.4 模糊值結合貝氏機率模式 46
6.3.5 分類結果分析 53
6.4 階層式分類流程 57
6.5 分類評估標準 59
6.6 案例說明 60
6.6.1 案例之系統元件類別層及相關詞彙之建構 60
6.6.2 工程類別層及相關詞彙之建構 62
6.6.3 技術詞層及相關詞彙之建構 66
6.6.4 功能詞層及相關詞彙之建構 71
6.6.5 案例之階層式類別 73
6.6.6 系統分類效果評估 74
6.6.7 技術功能矩陣表的建立 79
第七章 改善TRIZ判讀性方法 81
7.1 TRIZ的簡介 81
7.1.1 TRIZ一般創研作法 82
7.1.2 予盾衝突矩陣表 83
7.1.3 三十九改善特徵 84
7.1.4 四十發明法則 88
7.2 改良TRIZ判讀性方法之建構流程 93
7.2.1 建立群組要素 95
7.2.2建構群組優先次序方法介紹 99
7.2.2.1 出現次數模式 99
7.2.2.2 貝氏機率模式 103
7.2.2.3 模糊值模式 108
7.2.2.4 模糊值結合貝氏模式 113
7.2.3 改善TRIZ判讀性系統之判讀流程 120
7.2.4 案例判讀流程說明 126
7.2.4.1 研磨頭之自動換墊裝置 126
7.2.4.2 其它案例說明 131
第八章 TRIZ概念應用於技術功能矩陣表 136
8.1 技術功能矩陣表及TRIZ矩陣表的相關性 136
8.2 TRIZ概念應用於技術功能矩陣表之設計說明 137
8.3 案例說明 138
第九章 結論及未來發展 142
9.1 結論 142
9.2 未來發展 143
參考文獻 144
附錄一 TRIZ矛盾矩陣表 152
附錄二 不欲惡化特徵出現次數表 158
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