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作者姓名(中文):劉育哲
作者姓名(英文):Yu-Che Liu
論文名稱(中文):應用文件探勘技術於接觸式影像感測器專利分析之研究
論文名稱(外文):Application of Text Mining Techniques for Contact Image Sensor Patent Analysis
指導教授姓名(中文):郭中豐
指導教授姓名(英文):Chung-Feng Kuo
口試委員姓名(中文):蔡鴻文
葉雲卿
呂永和
口試委員姓名(英文):Hung-Wen Tsai
none
Yung-Ho Leu
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
學號:M10124002
出版年(民國):103
畢業學年度:102
學期:2
語文別:中文
論文頁數:115
中文關鍵詞:接觸式影像感測器本體論文件探勘集群分析分類分析
外文關鍵詞:Contact Image Sensor、Ontology、Text Mining、Clu
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我國為影像感測器的生產大國,在整體的製造與研發技術上亦居於全球領先地位。且國內外業者對智慧財產權的重視,積極對接觸式影像感測器技術進行專利佈局,每年皆有大量的專利申請案。針對這些大量的專利資料,實有必要以有系統的方式進行專利的管理及專利分析,以確保在未來的技術競爭中,獲得持續性的優勢。
目前的專利分析,研發人員或專利工程師若想透過專利來掌握技術動態,首先要經過專利的檢索。經專利檢索後所獲得的資料,往往需要花費大量的時間進行專利閱讀,才能瞭解專利資料中的技術內容。若能以文件探勘的方法,來掌握專利資料中的技術關鍵字,便能夠有效率的對資料進行解析,並且透過定義的本體論關鍵詞來快速的對專利資料進行技術分析,如此一來便能夠加快大量資料的處理時間。
本研究檢索接觸式影像感測器之美國專利,並且應用專利地圖來探討接觸式影像感測器領域的技術發展趨勢。將所檢索之專利資料以文件探勘技術提取出該技術領域之關鍵字,以建立接觸式影像感測器之本體論架構。進而利用此本體論對接觸式影像感測器專利進行解析,分別使用集群分析之連續信息瓶頸法及分類分析之貝氏分類法兩種分析角度進行探討。在應用上集群分析屬非監督式的分析方法,將樣本專利經演算法運算後共分成5個集群,以可視化的圖形介面來表示,並分別探討各集群之技術分類。而分類分析為監督式的分析方法,透過資料的處理後,利用熵(Entropy)之運算方法對TF-IDF加權處理後的特徵值進行篩選,將經過熵之運算後最佳化的特徵值結果,在精確率(Precision)上達90.4%,其召回率(Recall)達91.7%,而在調和平均數(F-Measure)上達91.1%。本研究利用文件探勘方法分析接觸式影像感測器專利,運用此分析方法在大量資料的處理時,能夠節省研發人員或專利工程師對專利文件的處理時間,進而提高專利分析的準確度及效率。
Taiwan is a major image sensor producing region, and its overall manufacturing and R&D technologies are in a leading position of the world. Companies have paid attention to the intellectual property, and patented their contact image sensor technologies actively. There are numerous patent applications annually. For the mass patent data, it is necessary to manage and analyze patents systematically, so as to guarantee persistent advantages in the future technology competition.
In the present patent analysis, the R&D personnel or patent engineers need to retrieve patents first in order to master technology conditions from patents. It is time-consuming to read through the patents, in order to learn the technical content in the patent data obtained from patent retrieval. If the technical keywords in patent data can be known by text mining, the data can be analyzed efficiently, and the patent data are analyzed technically and rapidly by the defined ontology keywords. The processing of mass data can be accelerated.
This study retrieved the U.S. patents for contact image sensor, and used patent map to discuss the technical development trend of contact image sensor field. The keywords of the technical field were extracted from the retrieved patent data by using text mining technology. The ontological framework of contact image sensor was built, and the contact image sensor patents were analyzed using this ontology. The sequential information bottleneck method of cluster analysis and the Bayes Classifier of classification analysis were used for discussion. In terms of application, the cluster analysis is a non-supervised analysis method, and the sample patents are divided into 5 clusters after algorithmic calculation, represented as visual graphic interface, and the technical classes of various clusters are discussed respectively. The classification analysis is supervised analysis method. After data processing, the eigenvalues of the weighted TF-IDF are screened by entropy calculation method, the precision of the optimized eigenvalue result after entropy calculation is 90.4%. The recall is 91.7%, and the F-Measure is 91.1%. The text mining method is used in this study to analyze the contact image sensor patents, using this analysis method to process mass data can shorten the patent document processing time for the R&D personnel or patent engineers, so as to increase the accuracy and efficiency of patent analysis.
摘 要I
ABSTRACTII
目 錄IV
圖目錄VI
表目錄VIII
第一章 緒論1
1.1 研究背景1
1.2 研究動機和目的2
1.3 文獻探討3
1.4研究架構6
第二章 接觸式影像感測器技術7
2.1 影像感測器種類7
2.2 接觸式影像感測器構造9
2.2.1 光源11
2.2.2 導光體13
2.2.3 透鏡組14
2.2.4 感測器16
2.2.5 線型感測器比較18
2.3 接觸式影像感測器產業鏈20
第三章 研究分析方法22
3.1 專利資訊22
3.1.1 專利地圖23
3.1.2 專利檢索27
3.2 文件探勘30
3.3 關鍵詞的擷取32
3.4 特徵值的權重33
3.5 向量空間模型35
3.6 本體論36
3.7 文件分類分群38
3.7.1 連續信息瓶頸法39
3.7.2 貝氏分類法40
第四章 專利分析42
4.1 接觸式影像感測器檢索42
4.2 接觸式影像感測器專利地圖44
第五章 專利文件探勘59
5.1 分析流程59
5.2 文件探勘60
5.3 本體論的建置69
5.4 特徵值擷取72
5.5 專利集群分析74
5.6 專利分類分析84
第六章 結論與未來展望93
6.1 結論93
6.2 未來展望97
參考文獻98
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全文檔公開日期:2019/08/05 (本校及區域網路)
全文檔公開日期:不公開 (校外網際網路)
全文檔公開日期:不公開 (國家圖書館)
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