專利分類號是審查人員詳細閱讀專利文獻後依照其所涉及的技術領域給予的分類代碼，它的應用層面廣泛，可以用於衡量企業的價值、計算專利的強度以及藉由過去申請的專利資料分析特定技術的發展史，進而做出如技術功效矩陣圖並找到未來潛在的研究方向等等。隨著2013年歐美正式啟用共同分類號(CPC)，專利技術的分類更加明確，有助於專利分類號分析的相關研究。
過去，專利分類號分析都是從待分析專利提取其專利分類號進行統計，由此可初步瞭解何種分類號的數量較多，判斷這些專利的技術領域，或藉由結合其他分析要件，比如依照不同申請國家、不同申請權人等分析面向等等非常多樣的分析方式。然而，當一件專利被賦予許多的分類號時，除了代表這件專利包含的技術領域廣泛以外，可能尚有另一層面的含意，即：是否需要兩個分類號或是多個分類號同時出現才能完整表達這件專利的技術內涵，而無法僅從單一分類就代表整個專利的技術特徵。
因此，本研究採用實證分析，選擇近幾年快速興盛的生物技術CRISPR做為研究標的，以美國公開資料庫所有與CRISPR有關的專利做為研究對象，探討在技術發展的過程中，相較於一般的專利分類號分析，是否有出現專利分類號共現的情形。除了從整體專利的角度切入外，還有切分成各年度進行觀察，藉此分析每年專利分類號共現組合的變化，也確實發現到研發方向的轉變或投入可以藉由共現組合數量的變化看出一些端倪。最後，再依照觀察到的結果對分類號共現情形做定義，如「依賴共現」的出現以及符合本研究理想中的共現型態。此外，針對一組共現組合對應的專利做人工分類，驗證在該共現組合下，專利的技術範疇大致相同。本研究有機會可供日後做為一種快速篩選的方式。

A patent classification is a system for examiners to categorize patent documents by its technical fields. We know that patent classification is extensively used to evaluate a company, calculate the strength of a patent and ,by inspecting the filed patent applications and documents, even the history of a specific technology. We may use these data to create a technology-function Matrix, which will help us to find some potential direction for research in the future. In 2013, Europe and America started using cooperative patent classification (CPC), enabling the examiners to give patent classifications more clearly by its technical field which has been very helpful for relevant researches of patent classification analysis.
In previous researches, in order to analyse the patents at interest, extracting out the classification numbers and coming out with a statistic result is required. So that knowing which one(s) of the classifications are much more than the others. Moreover, utilizing the analysis enables us to determine the technical fields of these patents. Furthermore, analysis can also be done with the combination of other elements, such as countries, patentees, and etc. However, when a patent is given many classifications (by the examiners), despite that it means the patent could have covered a more extensive scope of art, it may indicate otherwise. It could have been that the requirement of two or more classifications are necessary to express the information about its technique more profoundly, instead of being understandable from one classification.
As a result, we chose to case study about CRISPR, a popular and rapid-developing biological technique and analyze the related patents from the United State Patent and Trademark Office database. We want to find out, when a technique is developing, whether co-occurrence of patent classifications occurs, compare to the traditional patent classification analysis. In addition to looking at the results of the patents as a whole, there are also annual observations. We observe the number of classification group variation from year to year, and it does present some clues demonstrating the directions of future research. Finally, according to results of the research, we define some types of patent classification co-occurrence, for example, “dependent co-occurrence” and what match the theory of co-occurrence in the research. Furthermore, focusing on the patents corresponding to one classification group and manually distributing them, we verify them having the same technical field. As a result, the classification co-occurrence done by the research could be another way to filter, which could be helpful for reducing the time to search for patents.

指導教授推薦書 II
學位考試委員審定書 III
中文摘要 IV
ABSTRACT V
致謝 VII
目錄 VIII
圖表目錄 X
第1章 概論 1
1.1研究目的 1
1.2研究方法 3
1.3研究架構 9
第2章 文獻探討 10
2.1 CPC分類號的發展 10
2.2專利分類號的應用 13
2.3分類號的共現 21
第3章 分析方法 26
3.1專利檢索 26
3.2資料篩選 29
3.3資料分析方法 33
3.3.1 分類號處理方法 34
3.3.2資料呈現方式 39
3.4可視化工具選擇 41
第4章 研究結果 45
4.1資料概況 45
4.2 整體觀測結果 47
4.3各年度分析結果 52
4.4 共現性觀察 70
第5章 結論 78
5.1 研究總結 78
5.2 研究限制及未來方向 81
參考文獻 83
中文部份 83
英文部份 84
附錄 86

中文部份
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祁延莉、刘西琴（民105）。核心专利识别方法研究。情报理论与实践，第11期，5-9。
冯雪飞、何健、袁红梅（民107）。基于专利组合分析方法改进的企业技术竞争情报研究。情报杂志，第37卷第3期，79-86。
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张宪义（民102）。基于专利共类分析的技术领域关联研究。大連理工大學碩士論文。
陈立新、梁立明（民102）。技术领域的集成与整合研究——基于美国专利 IPC 的关联分析。情报杂志，第32卷第1期,，37-41。
林展逸（民103）。專利公開案與其對應公告案之專利分類號差異研究。國立台灣科技大學專利研究所碩士論文。
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英文部份
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