在知識經濟與工業4.0的世代下，全球產品生產成本競爭逐漸白熱化，公司如何發展關鍵技術，並發展相關的專利就變得非常重要，以藉此將專利商品化，進而獲取利益。但大量的專利資料會在企業辨識專利內容時耗費大量的時間，傳統質化的專利評價方法多仰賴專家來判斷專利價值，雖然此法可以明確地定義出適當的專利價值，但耗費的時間也長，同時也招致人為主觀的評估結果。因此本研究提出量化的專利評價方法，擬透過Python程式來建構一個專利品質分類系統，以進行專利評價的研究，藉由專利指標的選擇，將專利做價值評估，來探討哪些專利為企業的核心且應著重的部分，因為專利評價與研發評估具有密切的關聯，透過專利評價可以幫助管理者瞭解專利價值及辨識相關的商業機會，進而決定專利技術的發展方向。由於本研究是要幫助企業能更快速地辨識專利的價值，所以研究方法的流程主要包含三大步驟，首先，先定義專利來源與進行專利文件與專利指標數值資料的搜集；第二步為選擇專利指標的分析方法，本研究利用主成份分析法進行專利指標的選擇；第三步則是利用深度神經網路來進行專利品質的模擬。本研究選擇物聯網 (Internet of Things, IoT) 應用於製造業為案例，並透過深度神經網路進行專利品質分類評估以找出高價值專利，並以全球與台灣專利權人持有的高價值專利為分析標的，進而探討這些高價值專利的技術佈局，以提供台灣專利權人在物聯網技術研發方面的建議，作為台灣未來競爭優勢與技術研發策略擬定的參考依據。

In the era of knowledge economy and industry 4.0, global production costs for products have become increasingly competitive, how companies obtain benefits of patent commercialization by developing key technologies to make applications for related patents becomes very important. However, a large quantity of patent information requires much time for enterprises to identify patent values. Traditional evaluation methods of patent quality rely on experts to determine patent values, which requires long time spent and ends with subjective assessment results. Therefore, the research proposes a quantitative evaluation method of patent quality by constructing a Python-based system for patent quality classification to research on patent evaluations. Evaluation of patent values by taking patent indicators into consideration helps explore what kind of patents are fundamental and essential for enterprises. Patent evaluations help managers and R&D personnel understand patent values and identify related business opportunities, which further determines the directions of patented technology development. The process for the research method is divided into three parts. The first part is patent retrieval and patent indicators collections. Second part introduces Principal Component Analysis (PCA) to make key patent indicators selection. Third part ends with the Deep Neural Networks (DNN) analysis result. The research takes Internet of Things (IoT) applied in manufacturing industries as the example case, and find out high-value patents by using DNN to classify patent quality. Moreover, the research puts many efforts into both worldwide high-value patents and those of Taiwan to explore their portfolios, which provides useful suggestions for Taiwan assignees in the R&D strategies of IoT technologies.

中文摘要 I
Abstract II
誌謝辭 III
目錄 IV
表目錄 VII
圖目錄 X
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 4
1.3 研究流程圖 5
第二章 文獻探討 7
2.1專利指標訂定 7
2.1.1專利範圍 7
2.1.2專利核准時間 8
2.1.3專利發明人數量 8
2.1.4被引證次數 8
2.1.5引證次數 9
2.1.6專利申請範圍總項數 9
2.1.7專利家族數 9
2.1.8專利範圍文字長度 10
2.1.9引證非專利文獻次數 10
2.2專利評價模型 11
2.2.1質化方法 11
2.2.2量化方法 12
2.2.3質化與量化方法的優缺點比較 12
2.3分析方法 13
2.3.1主成份分析法 13
2.3.2 類神經網路 15
2.4 案例背景-物聯網 (Internet of Things, IoT) 19
2.4.1 物聯網技術 19
2.4.2 物聯網的技術標準 20
2.4.3 物聯網的標準必要專利 22
第三章 研究方法與流程 25
3.1 研究方法流程 25
3.2 資料來源與搜集 26
3.2.1專利檢索範圍說明 26
3.2.2主要專利權人分析 33
3.2.3專利資料指標擷取方式 34
3.3 專利指標選擇的分析方法 38
3.3.1適合度分析 38
3.3.2主成份分析法 38
3.4利用深度神經網路訓練專利指標的權重 42
3.5利用所建構的深度神經網路模型進行專利品質評估 46
第四章 案例研究 48
4.1 軟體需求 48
4.2 專利品質劃分依據說明 49
4.3 專利品質設計模式分析 49
4.3.1利用主成份分析擷取關鍵指標 49
4.3.2深度神經網路訓練分類模型 55
4.3.3專利品質評估-全球專利權人 66
4.4 案例解釋 72
4.4.1專利品質評估-台灣專利權人 72
4.4.2整體案例解析 76
第五章 結論與建議 79
參考文獻 81
附件A 高通的高價值專利清單 98
附件B 愛立信的高價值專利清單 108
附件C 宏達電的次高價值專利清單 110
附件D 工研院的次高價值專利清單 111
附件E 宏碁的次高價值專利清單 112
附件F 倒傳遞類神經網路的訓練分類結果 113

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