近年來人工智慧的崛起，許多的領域都和人工智慧搭起橋樑，而其中的推薦系統 (Recommendation system)更是被相當廣泛的應用在各個領域，包含電子商務、音樂以及影視的領域，利用協同過濾 (Collaborative filtering)、內容為基 (Content-based)的等演算法來結合不同使用者或者商品的特徵來進行推薦，讓使用者能夠在瀏覽商品時，也能夠獲得潛在商品的推薦。現今智財文件有許多地用處，包括對公司未來的技術進行布局、迴避，甚至是與其他的公司進行侵權的訴訟，那麼如何精確地蒐集需要的智財文件資料來分析就顯得相當地重要了，現今的方式通常是以人工的方式，逐篇的閱讀並且人工的判斷是否有可以利用的價值，過程不僅費時且精準度不佳，常發生花了很多時間找尋的資料，卻只有很少部分真的符合需求。而本研究希望透過word2vec這個演算法的應用doc2vec，將專利文件向量化 (doc2vec)，並且透過餘弦相似度使用者搜尋過之文件進行推薦，本研究選擇智慧機械為案例，擬透過Python建構之推薦演算法，模擬不同使用者之使用紀錄，並對目標使用者進行推薦，並且透過比較目標專利以及起始專利之IPCs來驗證結果的好壞。

The recommendation system has been widely applied in many fields, e.g., e-commerce product search, audio and video digital content search, and so on. This research develops an intelligent recommendation system for smart patent search to provide researchers, engineers, and/or IP professionals an efficient e-discovery system when searching for relevant patents in global patent corpuses. The proposed recommendation system uses natural language process (NLP) algorithms, such as word-embedding and doc-embedding to conduct patent content analyses. Further, word2vec is adopted to extract keywords from initial target patents and, through the doc2vec to vectorization initial target patents. Thus, relevant patents are accurately and efficiently identified and recommended to the users.
In the era of pursuing Advanced Manufacturing (also called Industry 4.0), smart machinery related technologies (e.g., Internet of Things, IoT; Cyber Physical Systems, CPS; intelligent sensors; intelligent controllers; etc.) have become the critical technologies for the realization of Industry 4.0. The domain of smart machinery is defined as advanced machines with some degrees of artificial intelligence (AI). This research will develop a patent recommendation system and demonstrates its practical applications using the case of “smart machinery” patent search. The recommendation results provide companies and R&D teams accurate and relevant patents for precise patent analyses. The system benefits R&D teams by identifying prior arts in relevant patents, avoiding infringing on others’ patents, and protecting their own intellectual properties.

中文摘要 I
Abstract II
1. Introduction 1
1.1 Research Background 1
1.2 Research Motivation 2
1.3 Research Framework and Procedure 2
2. Literature Review 5
2.1 Recommendation System 5
2.1.1 Content-Based Filtering 5
2.1.2 Collaborative filtering 6
2.2 Deep learning 9
2.2.1 Recurrent neural network 9
2.2.2 Convolutional neural network 10
2.3 Semantic Analysis 11
2.3.1 Word embedding 13
2.3.2 Word2Vec 14
2.4 Patent Features 17
2.4.1 Patent search 17
2.4.2 Patent Search Platform 17
2.5 Smart machinery 19
2.5.1 Control intelligent 20
2.5.2 Decision making 22
2.5.3 Sensing intelligence 24
3. Methodology 27
4. Case Study 41
4.1. Case Background 41
4.2. Patent collection 41
4.3. Control intelligent level 43
4.3.1 Remote control 43
4.3.2 Control system and interfaces: 44
4.4. Decision making level 45
4.4.1 Path planning 46
4.4.2 Fault diagnosis: 47
4.5. Sensor intelligent level 48
4.5.1 Feature detection: 49
4.5.2 Data collection: 50
4.6. Validation 51
5. Conclusions 58
6. References 61
Appendix A – IPCs of Six Case Examples 70
Appendix B – IPCs of Six Case Results 72
Appendix C – Recommendation Results of Six Cases 77

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