本論文以文字探勘之技術與機器學習的方法，建立預測電腦軟體適格性與專利價值之模型。由於現今電腦軟體性質的演算法所包含之抽象概念（abstract ideas），於美國的專利法下屬於專利法適格標的之例外，由於2014年 美國Alice v. CLS 等案例否定電腦軟體之適格性，美國最高法院期望釐清此一抽象概念的界線，導致目前電腦軟體之專利申請，在歷年來的判例下，可能被排除於專利保護領域之外。但由於至今商業方法及資訊科技之蓬勃發展，電腦軟體產業之專利申請，對所有相關產業亦有關鍵影響，如何適度保護電腦軟體專利，且如何界定抽象概念與可專利之技術界線，則成為近年來專利法領域重要之議題。因此本研究，針對電腦軟體專利之專利範圍，利用文字探勘之技術，與機器學習之方法，建立專利適格性預測模型，並加以整合專利之申請流程紀錄，與專利文件之資料分析之特徵值，利用訴訟之軟體專利進而建立專利價值之預測模型，研究成果在預測專利適格性之成果可達80%準確率，預測軟體專利性更可達90%的準確率，最後在預測軟體專利價值的準確率在五年內可達88%，以利未來電腦軟體專利申請與軟體專利價值之預測。

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a precise definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants.   In this research, we address the software patent eligibility issues by proposing an effective model to determine patent claim eligibility and examine the patent examination process to predict patentability. Furthermore, with patent claim features and important prosecution events, we attempt to identify important indicators to valuable patents.
We collect patent claims, patent examination records, and patent litigation data of software patents from USPTO website, USPTO PAIR, Google Patents, and MaxVal's Patent Litigation Databank. The experiment results show our patent claim eligibility model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model. Using sequence learning on patentability, our patentability predictive model can achieve around 90% accuracy based on our time-duration features. With the value indicators identified by previous models and prior studies, the accuracy of our patent value model can reach up to 88%.

Table of Contents
Chapter 1 Introduction 1
Chapter 2 Background Information on Patents 12
2.1. Subject matter eligibility (SME) 12
2.2. Patent Examination at United States Patent and Trademark Office (USPTO) 16
2.3. The USPTO Patent Assignment 19
Chapter 3 Related Work 22
3.1. Patent Analysis 22
3.2. Patent Examination 28
3.3. Patent Value 31
3.4. Machine Learning Classifiers 40
Chapter 4 Methodology 43
4.1. Research Framework for Patent Predictive model 43
4.2. Data Collecting and Data Processing 45
4.3. Patent Claim Eligibility Model 50
4.4. Patent Prosecution Model 62
4.5. Patent Value Model 67
Chapter 5 Evaluation 77
5.1. Patent Claim Eligibility Model 77
5.2. Patent Prosecution Model 88
5.3. Patent Value Model 96
Chapter 6 Conclusion 121
Chapter 7 Limitations and Future Research 125
References 126

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