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作者姓名(中文):杜維民
作者姓名(英文):Wei-Ming Tu
論文名稱(中文):應用分類號共現偵測美國癌症登月計畫相關發明演進之研究
論文名稱(外文):The study of applying co-occurrence of classification symbols in detecting the technology evolvement in US Cancer Moonshot related patents
指導教授姓名(中文):管中徽
指導教授姓名(英文):Chung-Huei Kuan
口試委員姓名(中文):劉顯仲
何秀青
口試委員姓名(英文):John S. Liu
Mei H.C, Ho
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:M10524911
出版年(民國):107
畢業學年度:106
學期:2
語文別:中文
論文頁數:103
中文關鍵詞:專利分類號共現分析癌症登月計畫共現矩陣
外文關鍵詞:Classification symbolco-occurrence analysisCancer Moonshotco-occurrence matrix
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科技發展是一個萌發與融合不斷循環的過程;隨著基礎科學的進展,不同學門的交流互動刺激了跨領域知識的誕生、隨時都有新的技術與突破發生,因此必需要有一個可靠的方法,可以準確的描繪知識與技術進展的圖譜。對於企業研發與創新而言,更必需要仰賴一個準確的評估方式以瞭解自身所處的位置,並能探知當下的技術研發焦點所在、正確的擬定發展策略。

專利分類號(classification symbol)是重要的專利分析資源。其係專利申請過程中,經由專業的審查人員,在經過閱讀、理解專利申請案的技術內涵後所決定的;因此分類號乃是最能反映專利技術內涵的書目資料、而分類號在實務與研究上的運用也非常豐富。當多個分類號共同出現在單一文獻中時,即反映了該項創作所具備的多元技術特徵;因此對特定技術領域之專利文獻其所具有的分類號,進行隨時間推演的共現(co-occurrence)分析,可描述特定技術領域發展的沿革與軌跡。分類號共現分析之定義為,對一份專利文獻中、每一分類號與另一分類號的組合進行整理與統計,利用數學、資訊科學和書目計量學的技術,挖掘隱藏於特定分類號共現組合中代表的意義。

本研究目的乃對特定技術主題所涵蓋的分類號進行分析。利用美國癌症登月計畫(Cancer Moonshot)中、由美國專利與商標局(United States Patent & Trademark Office, USPTO)所公佈之癌症相關發明專利資料,挑選其中特定技術主題之分類號做為分析標的。本研究試圖藉由對專利分類號進行共現分析,描繪特定時期的技術沿革及行進方向、並以圖形視覺化的方式呈現,以描述特定技術領域所獨具的演化轉變歷程。

研究結果發現,在USPTO所公佈的癌症治療相關藥物技術方面,與分類號A61K31相關聯的技術,一直是研發的焦點所在;在不同的年度間,不論既有或新出現的分類號,皆有以其為中心共現及斂聚的情況,而結合A61K47與 C07K16技術特徵的創作,則呈現逐年成長的趨勢、並成為技術主流;顯示在觀察該技術主題的研發趨勢時,聚焦在與此些分類號具有高共現情形的分類號群組,可具體地反映癌症用藥相關技術主題。

因此當企業或學研單位在擬定研發策略及決定資源投注時,可應用本研究所提出分類號共現分析方法,繪製技術網路圖譜,可確實的掌握特定技術主題的研發概況與趨勢。
Technology development is a circulation process combined with initiation and fusion; along with the advance of basic science research that various academic disciplines interact with each other and stimulate the birth of cross-field knowledge and emerging subjects prompted all time. Thus, there is a need to develop a reliable method to illustrate the profile of knowledge and technology precisely. To better research and development, that enterprise must rely on accurate evaluation for competitors, knowing its own location in the field and be award of the focus of R&D to help making decisions.

Classification symbol is essential patent analysis resource. It’s assigned by professional examiner after reading and realizing the connotation of patent application. Since classification symbol is the bibliographic data which reflect the connotation of technology, there are tremendous research applies both in theory or practice affairs. While multiple classification symbols being recorded in a single patent literature which reflect the various innovation features, that one can perform co-occurrence analysis combined with time factor to illustrate the history and trajectory of certain technology field.

In the study we analyze the classification symbol for Cancer Moonshot dataset released by USPTO. The definition of classification symbol co-occurrence analysis is that by performing the technic of statistic, computer science and bibliometrics, to mine the hidden connection between classification symbols in patent literatures. Our study tries to illustrate the unique evolve history and direction in a certain period of specific technology filed & present the procedure with data visualization.

In our study, we show that A61K31-related technology had always been the focus of R&D in cancer-drug treatment in the dataset; For both existing or emerging classification symbol, they are always center on A61K31, while the number of combination of A61K47 and C07K16 grow significantly; indicating that observing the field of drug treatment for cancer, focusing in the group that highly co-occurrence with it may reflect the technical trend. In the study we provide a method of utilizing classification symbol to help enterprise, research facility and school to keep abreast of new technology developments and making decision.
目錄
誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VIII
第一章 緒論 1
第一節 研究背景 1
第二節 傳統分類號分析的缺點 2
第三節 以共現分析描述分類號所代表技術主題的實質內涵 4
第二章 文獻探討 6
第一節 共現分析用於描述知識進展的軌跡 6
第二節 專利書目資料的應用 8
第三節 專利分類號系統及相關研究應用 9
第三章 研究方法 12
第一節 研究架構 12
第二節 研究資料介紹 13
第三節 案件選擇與資料清理流程 20
第四節 輪廓矩陣建立流程 24
第五節 視覺化處理 27
第六節 小結 29
第四章 研究結果 30
第一節 歷年前30大共現元素所對應之分類號統計 30
第二節 分類號共現頻率統計 31
第三節 歷年輪廓矩陣 32
第四節 技術分佈網路與密度圖 34
第五節 技術過渡成長圖呈現不同年度間的分類號相對變化 35
第五章 結論與建議 37
第一節 應用輪廓矩陣分析癌症登月計畫的藥物研發趨勢 37
第二節 研究限制 45
第三節 建議 46
第四節 未來展望 46
參考文獻 48
附錄(一) 本研究輪廓矩陣所收錄之分類號代碼意義 52
附錄(二) 2005-2015年專利分類號共現頻率統計 59
附錄(三) 輪廓矩陣分類號共現次數排序 60
附錄(四) 分類號網路與密度圖 72
附錄(五) 技術過渡變化圖 83


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全文檔公開日期:2021/08/18 (本校及區域網路)
全文檔公開日期:2021/08/18 (校外網際網路)
全文檔公開日期:2021/08/18 (國家圖書館)
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