公司技術隨著網路的快速發展，也讓企業更加重視其技術和創新能力。專利權不僅可以為公司帶來商業信譽的提升，甚至可以為公司帶來收入的增長。然而，專利的申請過程非常的繁瑣且嚴謹，也因此通常取專利權需花費3-5年的時間。專利文件組成的複雜性，也讓增加了文本分析的複雜性。為了協助公司準確地做出技術投資決策，本文透過從不同方面的專利信息和財務數據中提取的指標，從不同的角度研究專利權資訊對公司績效的影響，並採用機器學習方法來預測公司績效。且提出了一種新興創新指標，進行深入探討每個專利技術主題的技術生命週期對公司績效的影響，經過實驗分析，專利主題指標對公司績效有一定影響，且在成長期的專利主題屬於重要指標之一。

With the rapid development of technology and the Internet, companies pay more attention to their technology and innovation capabilities. the patent can not only improve the company's business reputation but can even increase the company's revenue. However, the patent application process is very complicated and rigorous, so it usually takes 3-5 years to obtain a patent. The complexity of the composition of patent documents also increases the complexity of text analysis. To assist companies in making accurate technology investment decisions, this thesis uses indicators extracted from different aspects of patent information and financial ratios to study the impact of patent information on company performance from different perspectives and uses machine learning methods to predict company performance. And an innovation indicator is proposed to conduct an in-depth discussion on the impact of the life cycle of each patent topic on company performance. After experiments, the patent topic indicator has a certain impact on company performance, and the patent topic in the growth stage is one of the important indicators.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
CHAPTER 1 – Introduction 1
CHAPTER 2 – Related Works 3
2.1 Patent Analysis 3
2.2 Embedded Topic Model 4
2.3 Innovation Indicator 5
2.4 Indicators Impact on Firm Performance 7
2.5 Machine-Learning Ensemble Methods for Prediction 8
2.5.1 Random Forest 8
2.5.2 Boosting Method 8
2.5.2.1 AdaBoost 8
2.5.2.2 Gradient Boost 9
2.5.2.3 XGBoost 9
2.6 Technology Life Cycle 10
CHAPTER 3 – Methodology 12
3.1 Features Extraction 14
3.1.1 Features Related to Patent Meta Data 14
3.1.2 Features Related to Financial Ratio 16
3.1.3 Features Related to R&D Data 17
3.2 Predictors Related to Firm Performance 18
3.2.1 ROE (Return On Equity) 18
3.2.2 ROA (Return On Assets) 18
3.2.3 OCF (Operating Cash Flow) 18
3.3 ETM Model 19
3.4 Machine-Learning Ensemble Methods for Prediction 20
3.5 Model Evaluation 20
CHAPTER 4 – Experiments 22
4.1 Data Description Statistics 22
4.2 Indicator for Prediction Model 22
4.2.1 financial indicators & patent variables 22
4.2.2 Patent variables for ETM 23
4.3 Evaluation Metrics 27
4.4 The experiment results 27
4.4.1 Result of OCF for prediction 30
4.4.2 The Result of ROE for Prediction 32
4.4.3 The result of ROA for prediction 34
4.5 Using Technology Life cycle for Predictions 35
4.5.1 The Analysis of Logistic Curve 38
4.5.1.1 The TLC of Simulation/Optimization (Topic 1) 38
4.5.1.2 The TLC analysis of Storage Mechanism (Topic 3) 40
4.5.1.3 The TLC Analysis of Business System (Topic 7) 42
CHAPTER 5 – Discussion 44
CHAPTER 6 – Conclusions 49
Reference 51

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