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題名 專利法關於人工智慧發明重要議題之研究
A study on the Core Issues of Invention Related to Artificial Intelligence in Patent Law
作者 鄭禕寧
Cheng, Yi-Ning
貢獻者 沈宗倫
Shen, Chung-Lun
鄭禕寧
Cheng, Yi-Ning
關鍵詞 人工智慧
軟體專利
專利適格性
權利主體
先前技術
進步性
Artificial intelligence
Software patent
Patent eligibility
Subject of rights
Inventive step
Non-obviousness
日期 2019
上傳時間 5-Sep-2019 16:31:34 (UTC+8)
摘要 從人臉、語音識別到自動駕駛汽車，人工智慧已經跳脫科幻電影的虛構，逐漸走入並且遍佈我們的生活。雖然就目前已知的技術來說，人工智慧尚未具備思考能力，然而科學家不斷地希望能夠突破限制，讓人工智慧不僅能夠成為生活上的輔助、協助人類進行研究發明，甚至是由人工智慧獨立完成發明。
為了鼓勵創作與發明，我國制定了智慧財產權相關法律如專利法、商標法及著作權法，惟因其保護之前提為精神上創作，目前的保護對象即權利歸屬主體，皆僅限於自然人。隨著人工智慧越來越接近人類，開始能夠進行創作甚至思考，進而可能成為真正有貢獻之發明人時，現有的智慧財產權相關法制規範，將會面臨包含權利主體、專利要件等適用上之困境，。
事實上，人工智慧之本質與電腦軟體極為相似，其對於現行法制造成的衝擊，除了上述權利歸屬即要件認定問題外，另一方面則再次喚起了電腦軟體於智慧財產法上的適格性議題。就此，本文將討論範圍限縮在專利法，先從技術角度剖析介紹人工智慧之內涵與發展，再從專利法立法目的切入，綜合各國規範見解與近期發布之相關審查指南分析其發明適格性，進一步討論當由人工智慧產生衍生之發明時，應如何進行權利主體之認定，以及其對於相關產業造成之利弊影響。
Artificial intelligence (A.I.) such as face recognition, voice recognition, autopilot, etc., has become not only the sci-fi movie plots but been all over our lives. Although A.I. hasn’t been capable of thinking like a real human-being yet according to the technology known so far, scientists still keep working on breaking through the restrictions, making the artificial intelligence become an important assistant role in human life. It helps people during the research process, and can even become the independent inventor.
Intellectual property law such as patent law, trademark law, and copyright law are enacted to encourage creation and invention. While the premise of the law protection above currently should be mental activity of human, in other words, “natural person” is the only subject of rights of creator and inventor. It may cause problems when A.I. starts to act like human and even be able to make the substantive features of the invention. Thus, it is essential to decide whether to enable the eligibility of subject of right of artificial intelligence in advance.
In fact, the essence of A.I. is very similar to computer software. Besides the problems of subject of right mentioned above, the impact of A.I. on the current law system also evoked the issue of eligibility of computer software on intellectual property law once again. In this regard, this article limits the scope of discussion to the patent law, and introduce the connotation and development of A.I. from the technical point of view. After that, it will cut through the legislative purpose of the patent law, analyze the opinions and guidelines issued recently in different countries, and also the identification of the subject of rights and the pros and cons of related industries.
參考文獻 壹、 中文資料（按作者姓氏筆劃排序）
一、 書籍
1. 三宅陽一郎、森川幸人，從人到人工智慧：破解AI革命的68個核心概念，2017年5月。
2. 李友專，AI醫療大未來：台灣第一本智慧醫療關鍵報告，2018年9月。
3. 楊智傑，專利法，3版，2014年9月。
4. 認識著作權（二版），經濟部智慧財產局，2011年11月。
5. 鄭捷，自然語言處理：用人工智慧看懂中文， 2018年1月。
6. 謝銘洋，智慧財產權法，8版，2018年9月。
二、 專書論文
1. 沈宗倫，人工智慧科技與智慧財產權法制的交會與調和，載：人工智慧相關法律議題芻議，頁177-214，2018年11月。
三、 期刊論文
1. 曲建仲，機器是如何學習與進步？人工智慧的核心技術與未來，科學月刊，580期，2018年4月。
2. 宋皇志，專利法中「發明所屬技術領域中具有通常知識者」之法實證研究，政大法學評論，146期，2016年9月。
3. 沈宗倫，專利新穎性之先前技術範圍界定與比對評最高行政法院九十八年度判字第一四九號行政判決，月旦法學雜誌，197期，2011年10月。
4. 洪振盛，Alice案後美國電腦軟體專利適格性之發展，智慧財產權月刊，211期， 2016年7月。
5. 徐瑞甫、陳聖，由智慧財產法院判決探討我國專利請求項中功能性用語之相關爭議問題（II）--據以實現／明確性要件及功能性子句智慧財產權月刊，195期，2015年3月。
6. 莊智惠，進步性判斷方式及論理之探討―以發明專利進步性審查基準修訂為例，智慧財產權月刊，225期，2017年9月。
7. 陳龍昇，「自然法則」運用與個人化醫療診斷方法專利適格性判斷—從美國Mayo v, Prometheus案判決談起，高大法學論叢，11卷1期，2015年9月。
8. 陳龍昇，由美國Bilski v. Kappos案探討商業方法發明之專利適格，臺北大學法學論叢，84期，2012年。
9. 謝銘洋，專利新穎性之認定—智慧財產法院行政判決98年度行專訴字第123號解析，法令月刊，61卷9期，2010年9月。
10. 謝銘洋、李素華，專利權訴訟中之進步性與均等論，台灣法學雜誌，218期，2013年2月。
四、 學位論文
1. 邱亮儒，由美國Alice v. CLS Bank案探討電腦軟體相關發明之專利適格性，國立中興大學科技法律研究所碩士論文，2016年6月。
2. 陳昭妤，論人工智慧創作與發明之法律保護—以著作權與專利權權利主體為中心，國立政治大學科技管理與智慧財產研究所碩士學位論文，2017年1月。
五、 法院判決
1. 智慧財產法院101年度民專上更(二)字第5號民事判決。
2. 智慧財產法院102年度民專上字第23號民事判決。
3. 智慧財產法院102年度民專上字第25號民事判決。
4. 智慧財產法院105年度行專更(一)字第4號行政判決。
5. 智慧財產法院106年度民專訴字第60號民事判決。
6. 智慧財產法院106年度行專訴字第76號行政判決。
7. 智慧財產法院98年度民專上字第39號民事判決。
8. 最高行政法院103年度判字第406號判決。
9. 最高行政法院105年度判字第503號行政判決。
六、 官方文件
1. 入出國自動查驗通關系統績效評估之研究－以臺灣桃園國際機場為例，內政部入出國及移民署自行研究報告，101年8月。
2. 中華民國專利法逐條釋義（103年9月版）。
3. 經濟部智慧財產局，2014年版專利審查基準彙編。
4. 經濟部智慧財產局，人工智慧技術專利分析報告，https://pcm.tipo.gov.tw/PCM2010/PCM/commercial/03/AI.aspx?aType=3&Articletype=1&aSn=613 (最後造訪日：2019/04/26)。
5. 經濟部智慧財產局，著作權基本概念，https://www.tipo.gov.tw/ct.asp?xItem=219594&ctNode=7561&mp=1
6. 經濟部智慧財產局全球專利檢索系統，https://gpss.tipo.gov.tw/gpsskmc/gpssbkm (最後造訪日：2019/04/21)。
七、 網路資料
1. 「靠臉走遍天下」的時代到了？，雷鋒網，2019年1月16日，https://www.leiphone.com/news/201901/YnfJas5bLXPdiHUS.html506/（最後造訪日：2019/05/20）。
2. 「臉部解鎖」誰最安全？《富比士》公佈 5 款旗艦手機測試結果，自由時報，2018年12月18日，https://3c.ltn.com.tw/news/35369（最後造訪日：2019/05/20）。
3. AI 入門必備懶人包：圖解 27 種神經模型，讓你秒懂差在哪，科技報橘，2018年1月24日，https://buzzorange.com/techorange/2018/01/24/neural-networks-compare/（最後造訪日：2019/05/20）。
4. AI換臉黑產業賣偽明星色情片，中時電子報，2019年7月19日，https://www.chinatimes.com/newspapers/20190719000150-260301?chdtv（最後造訪日：2019/08/05）。
5. AI 權威 Yann LeCun：機器人 Sophia 不過就是場騙局！，INSIDE，2018年1月6日，https://www.inside.com.tw/article/11618-facebook-ai-yann-lecun-sophia-robot-bullshit（最後造訪日：2019/05/20）。
6. AI專利申請50強企業：中國反超美國，2019年3月13日，https://zh.cn.nikkei.com/industry/scienceatechnology/34657-2019-03-13-05-00-30.html （最後造訪日：2019/04/27）。
7. AI應用導入5大醫療專業領域，智慧應用，2018年11月27日，https://www.digitimes.com.tw/iot/article.asp?cat=158&id=0000547354_eyd4xz0344hkdt518tgh3（最後造訪日：2019/05/20）。
8. NEC為玉山銀行提供ATM人臉辨識系統，iThome，2019年2月26日，https://ithome.com.tw/pr/128943（最後造訪日：2019/05/20）。
9. 人工智慧「學」不會的東西，以及「人」無法被取代的理由，數位時代，2016年4月1日，https://www.bnext.com.tw/article/39094/BN-2016-04-01-135646-178（最後造訪日：2019/04/21）。
10. 人工智慧的病理切片系統，台灣癌症防治網，http://web.tccf.org.tw/lib/addon.php?act=post&id=4363（最後造訪日：2019/05/20）。
11. 中央研究院，人工智慧再進化，開啟電腦新「視」界，研之有物，http://research.sinica.edu.tw/computer-vision-liao-hong-yuan/（最後造訪日:2019/06/06）。
12. 中國監控新疆「天網」資料庫有漏洞，超過 250 萬人資料及詳細路徑、座標可能外洩，科技新報，2019年2月22日，https://technews.tw/2019/02/22/chinese-company-leaves-muslim-tracking-facial-recognition-database-exposed-online/（最後造訪日：2019/05/20）。
13. 未來疾病檢測，AI 技高一籌?，2018年1月15日，https://geneonline.news/index.php/2018/01/15/ai-is-better/（最後造訪日：2019/05/20）。
14. 目前技術進展仍以「弱人工智慧」為主，資策會產業經濟研究所，2017年10月24日，http://mic.iii.org.tw/scholar/GraphDtl.aspx?docid=128178 （最後造訪日：2019/05/18）。
15. 印象派繪畫，國立台灣大學網路教學課程，http://vr.theatre.ntu.edu.tw/fineart/th9_1000/open-34-broadcast.htm（最後造訪日：2019/06/05）。
16. 自然語言處理頂會NAACL最佳論文出爐！谷歌BERT獲最佳長論文，2019年4月12日，https://kknews.cc/tech/yvpzxgb.html（最後造訪日: 2019/05/13）。
17. 別再誤會了，特斯拉根本無法全自動駕駛，2018年10月22日，https://www.bnext.com.tw/article/50994/tesla-full-self-driving-option-gone-musk-autopilot（最後造訪日：2019/05/19）。
18. 周秉誼，淺談Deep Learning原理及應用，國立台灣大學計算機及資訊網路中心電子報，38期，2016年9月，http://www.cc.ntu.edu.tw/chinese/epaper/0038/20160920_3805.html（最後造訪日：2019/05/20）。
19. 阿里巴巴成立全球科研項目「達摩院」 投逾千億元人民幣於科技創新，2017年10月11日，https://www.alibabanews.com/alibabachengliquanqiukeyanxiangmudamoyuan-touyuqianyiyuanrenminbiyukejichuangxin/ （最後造訪日：2019/05/19）。
20. 活體偵測技術介紹，https://tw.nec.com/zh_TW/solutions/security/liveness_detection.html（最後造訪日：2019/05/20）。
21. 馬偉雲，斷開中文的鎖鍊！自然語言處理 (NLP)，研之有物，http://research.sinica.edu.tw/nlp-natural-language-processing-chinese-knowledge-information/（最後造訪日：2019/06/18）。
22. 國際頂級人工智慧協會AAAI 2019年度大會隆重開幕，2019年1月31日，https://kknews.cc/tech/l3b6ne9.html（最後造訪日：2019/04/22）。
23. 陳秉訓，新穎性優惠期修法後智財局之挑戰，北美智權報，177期，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Industry_Economy/IPNC_170125_0705.htm（最後造訪日：2019/06/26）。
24. 華為／三星／小米手機都中箭 一張照片就攻破臉部解鎖，中時電子報，2019年1月7日，https://www.chinatimes.com/realtimenews/20190107003216-260412?chdtv（最後造訪日：2019/05/2）。
25. 葉雲卿，《Enfish, LLC v. Microsoft Corp》案對於美國軟體專利適格性判斷之影響 ─ 抽象概念之判斷，北美智權報，175期，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Infringement_Case/IPNC_161228_0502.htm （最後造訪日：2019/06/11）。
26. 監督式學習？增強學習？聽不懂的話，一定要看這篇入門的機器學習名詞解釋！，INSIDE，2017年7月19日，https://www.inside.com.tw/article/9945-machine-learning（最後造訪日：2019/05/20）。
27. 蔡炎龍，函數、神經網路與深度學習，科學月刊，2019年2月26日，http://scimonth.blogspot.com/2018/03/blog-post_8.html（最後造訪日：2019/06/24）。
28. 機器人是台灣長照的解藥嗎？，2019年2月11日，https://www.bnext.com.tw/article/52017/robot-ai-long-term-care（最後造訪日：2019/05/20）。
29. 機器學習裡資料預處理及特徵工程總結，https://codertw.com/%E7%A8%8B%E5%BC%8F%E8%AA%9E%E8%A8%80/457901/（最後造訪日：2019/06/24）。
30. 臉部辨識技術快速過安檢的背後，你付出了什麼代價？，科技新報，2019年3月26日，https://technews.tw/2019/03/26/cost-of-face-recognition-fast-security-check/（最後造訪日：2019/05/20）。
31. 臉部辨識將成智慧手機標配？2020 年估達 10 億支導入，科技新報，2018年2月9日，https://technews.tw/2018/02/09/more-than-one-billion-smartphones-to-feature-facial-recognition-in-2020/ （最後造訪日：2019/05/20）。
貳、外文資料（按作者首字母排序）
I. Books
1. Bonner, Anthony (2007). THE ART AND LOGIC OF RAMON LLULL: A USER’S GUIDE. Leiden, Netherlands: Brill Academic.
2. Buchanan, Bruce G. & Edward H. Shortliffe (1984). RULE BASED EXPERT SYSTEM- THE MYCIN EXPERIMENTS OF THE STANFORD HEURISTIC PROGRAMMING PROJECT. Boston, MA: Addison-Wesley.
3. CRAIG, JOHN J. (2009). INTRODUCTION TO ROBOTICS: MECHANICS AND CONTROL. New York, NY: Pearson Education
4. Crevier, Daniel (1993). AI: THE TUMULTUOUS SEARCH FOR ARTIFICIAL INTELLIGENCE. New York, NY: Basic Books.
5. Domingos, Pedro (2018). THE MASTER ALGORITHM: HOW THE QUEST FOR THE ULTIMATE LEARNING MACHINE WILL REMAKE OUR WORLD. New York, NY: Basic Books.
6. Fausett, Laurene (1994). FUNDAMENTALS OF NEURAL NETWORKS: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS. New York, NY: Pearson.
7. Gardener, Martin (1958). LOGIC MACHINES AND DIAGRAMS. New York, NY: McGraw-hill.
8. Goldstein, Herman (1972). THE COMPUTER FROM PASCAL TO VON NEUMANN. Princeton, NJ: Princeton University Press.
9. Goodfellow, IAN & BENGIO, YOSHUA & COURVILLE, AARON (2016). DEEP LEARNING. Cambridge, MA: MIT Press.
10. Hebb, D.O. (2002). THE ORGANIZATION OF BEHAVIOR: A NEUROPSYCHOLOGICAL THEORY. London: Psychology Press.
11. Hegel, G.W.F. (1821). PHILOSOPHY OF RIGHT (S.W Dyde trans, Batoche Books 2001). Kitchener, ON: Batoche Books.
12. Kumar, Ela (2008). ARTIFICIAL INTELLIGENCE. India: I.K. International Publishing House.
13. Locke, John (2015). THE SECOND TREATISE ON CIVIL GOVERNMENT. Canada: Broadview Press.
14. Mandal, Durbadal & Kar, Rajb & Das, Swagatam & Panigrahi, Bijaya Ketan (2015). INTELLIGENT COMPUTING AND APPLICATION: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ICA. New York, NY: Springer.
15. McCarthy, J. & Minsky, M.L. & Rochester, N. & Shannon, C.E. (1955). A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECTON ARTIFICIAL INTELLIGENCE. Hanover, NH: Dartmouth College.
16. McCorduck, Pamela (2ed. 2004). MACHINES WHO THINK. Boca Raton, FL: CRC Press.
17. Minsky, Marvin L. & Papert, Seymour A. (2017). PERCEPTRONS: AN INTRODUCTION TO COMPUTATIONAL GEOMETRY. Cambridge, MA: MIT Press.
18. Pearl, Judea (1984). HEURISTICS: INTELLIGENT SEARCH STRATEGIES FOR COMPUTER PROBLEM SOLVING. Boston, MA: Addison-Wesley.
19. Pearl, Judea (1988). PROBABILISTIC REASONING IN INTELLIGENT SYSTEMS: NETWORKS OF PLAUSIBLE INFERENCE. San Francisco, CA: Morgan Kaufmann.
20. Plotkin, Robert (2009). THE GENIE IN THE MACHINE: HOW COMPUTER-AUTOMATED INVENTING IS REVOLUTIONING LAW & BUSINESS. Stanford, CA: Stanford Law Books.
21. Russel, Stuart & Norvig, Peter (3ed. 2018). ARTIFICIAL INTELLIGENCE: A MODERN APPTOACH. New York, NY: Pearson Education.
22. Schaeffer, Jonathan (2ed. 2008). ONE JUMP AHEAD: CHALLENGING HUMAN SUPREMACY ICHECKERS. New York, NY: Springer.
23. SIMON, HEBERT A. (1965). THE SHAPE OF AUTOMATION FOR MAN AND MANAGEMENT. New York, NY: Harper & Row.
24. SUTTON, RICHARD S. & BARTO, ANDREW G (2ed. 2015). REINFORCEMENT LEARNING: AN INTRODUCTION. Cambridge, MA: The MIT Press.
25. THE EDITORS OF TIME-LIFE BOOKS (1986), ARTIFICIAL INTELLIGENCE (UNDERSTANDING COMPUTERS).
26. WATERMAN, DONALD A. (1985). A GUIDE TO EXPERT SYSTEM. New York, NY: Pearson Education.
II. Book Chapters
1. Abbott, Ryan, Hal the Inventor: Big Data and Its Use by Artificial Intelligence, in BIG DATA IS NOT A MONOLITH, 187 (Cassidy R. Sugimoto, Hamid Ekbia & Michael Mattioli eds., 2016).
2. McCarthy, John. Programs with Common Sense, in COMPUTATION & INTELLIGENCE 479 (1995).
3. Rumelhart, D.E. & Hinton, G.E. & Williams, R.J., 1 Learning internal representations by error propagation, in PARALLEL DISTRIBUTED PROCESSING: EXPLORATIONS IN THE MICROSTRUCTURE OF COGNITION 318 (1985).
III. Conference Papers
1. Bahdanau, Dzmitry & Cho, Kyunghyun & Bengio, Yoshua, Neural Machine Translation by Jointly Learning to Align and Translate, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2015).
2. Bakker, Bram, Reinforcement Learning with Long Short-Term Memory, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (2002).
3. Baldi, Pierre, Autoencoders. Unsupervised Learning, and Deep Architectures, INTERNATIONAL CONFERENCE ON MACHINE LEARNING WORKSHOP ON UNSUPERVISED AND TRANSFER LEARNING (2012).
4. Bengio, Yoshua & Courville, Aaron & Vincent, Pascal, Representation Learning: A Review and New Perspectives, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013).
5. Ding, Yiming et al., A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain, 290 RADIOLOGY (2018).
6. Goodfellow, Ian et al., Generative Adversarial Nets, 2014 NEURAL INFORMATION PROCESSING SYSTEMS (2014).
7. Hochreiter, Sepp & Schmidhuber, Jürgen, Long Short-Term Memory, NEURAL COMPUTATION (1997).
8. Kurach, Karol et al., The GAN Landscape: Losses, Architectures, Regularization, and Normalization, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2019).
9. Lu, Chaochao & Tang, Xiaoou, Surpassing Human-Level Face Verification Performance on LFW with GaussianFace, PROCEDDING OF THE 29TH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (2014).
10. Mikolov, Tomas et al., Distributed Representations of Words and Phrases and Their Compositionality, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (2013).
11. Moore, Gordon E., Progress in Digital Integrated Electronics, INTERNATIONAL ELECTRON DEVICES MEETING 11 (1975).
12. Park, Taesung et al., Semantic Image Synthesis with Spatially-Adaptive Normalization, COMPUTER VISION AND PATTERN RECOGNITION (2019).
13. Ting, DSW et al., Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes, JAMA (2017).
14. Zhu, Jun-Yan et al., Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, COMPUTER VISION AND PATTERN RECOGNITION (2018).
15. Zoph, Barret & Le, Quoc V., Neural Architecture Search with Reinforcement Learning, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2017).
IV. Journal Papers
1. Abbott, Ryan, I Think, Therefore I Invent: Creative Computers and the Future of Patent Law, 57 B.C. L. REV. 4, 10979 (2016).
2. Abbott, Ryan, Patenting the output of autonomous inventive machine, 10 LANDSLIDE 16. (2017)
3. Darrach, Brad., Meet Shakey, the First Electronic Person, LIFE MAGAZINE 58 (1970).
4. Davies, Colin, An Evolutionary Step in Intellectual Property Rights - Artificial Intelligence and Intellectual Property, 27 COMPUTER LAW & SECURITY REVIEW 6, 601 (2011).
5. DeCosta III, Frank A., Intellectual Property Protection for Artificial Intelligence, 24 WESTLAWESTLAW J. INTELLNTELL. PROPROP. 1 (2017).
6. Denicola, Robert C., Ex machina: Copyright protection for computer-generated works, 69 RUTGERSUTGERS U. L. REVEV. 251 (2016)..
7. Fraser, Erica, Computers as Inventors – Legal and Policy Implications of Artificial Intelligence on Patent Law, 13 SCRIPTed 3, 305 (2016).
8. Gelernter, H. L. & Rochester N., Intelligent behavior in problem-solving machines, 2 IBM JOURNAL OF RESEARCH AND DEVELOPMENT 336 (1958).
9. Hattenbach, Ben & Glucoft, Joshua, Patents in an Era of Infinite Monkeys and Artificial Intelligence, 19 STAN. TECH. L. REV. 32 (2015).
10. Hodgkin, A. L. & Huxley, A. F., Action potentials recorded from Inside a nerve fibre, 144 NATURE 710 (1939).
11. Hopfield, John, Neural networks and physical systems with emergent collective computational abilities, 79 PROC. NAT’L ACAD. SCI. U. S. A. 2554 (1982).
12. Kohlhepp, Peter M., Note When the Invention Is an Inventor: Revitalizing Patentable Subject Matter to Exclude Unpredictable Processes, 93 MINN. L. REV. 795 (2008).
13. LeCun, Y. et al., Backpropagation applied to handwritten zip code recognition, 1 NEURAL COMPUTATION 541 (1989).
14. Lederberg, Joshua, How Dendral Was Conceived and Born, A HISTORY OF MEDICAL INFORMATICS 14 (1987).
15. Lee, Edward, Digital Originality, 14 VAND. J. ENT. & TECH. L. 940 (2012).
16. McCulloch, Warren S. & Pitts, Walter,. A Logical Calculus of the Ideas Immanent in Nervous Activity, 5 BULLETIN OF MATHEMATICAL BIOPHYSICS 115 (1943).
17. Mnih, Volodymyr et al., Human-Level Control through Deep Reinforcement Learning, 518 NATURE 529 (2015).
18. Pearlman, Russ, Recognizing Artificial Intelligence (AI) as Authors and Investors under U.S. Intellectual Property Law, 24 RICH. J. L. & TECH. 21 (2018).
19. Ravid, Shlomit Y. & Liu, Xiaoqiong, When artificial intelligence systems produce inventions: an alternative model for patent law at the 3A era, 39 CARDOZO L. REV. 2215 (2018).
20. Robinson, W. Keith & Smith, Joshua T., Emerging Technologies Challenging Current Legal Paradigms, 19 MINN. J.L. SCI. & TECH. 2, 355 (2018)..
21. Rosenblatt, Frank., The perceptron: A probabilistic model for information storage and organization in the brain, 65 PSYCHOLOGICAL REVIEW 386 (1958).
22. Samuelson, Pamela, Allocating Ownership Rights in Computer-Generated Works, 47 U. PITT. L. REV. 1185, 1207 (1985).
23. Schuster, W. Michael, Artificial Intelligence and Patent Ownership, 75 WASH. & LEE L. REV., 1945 (2018).
24. Searle, John,. Minds, Brains, and Programs, 3 BEHAVIORAL AND BRAIN SCIENCES 417 (1980).
25. Shannon, Claude E., A Symbolic Analysis of Relay and Switching Circuits, 57 ELECTRICAL ENGINEERING 471 (1937).
26. Silver, David et al., Mastering the game of Go with deep neural networks and tree search, 529 NATURE 484 (2016).
27. Slagle, James R., A heuristic program that solves symbolic integration problems in freshman calculus: symbolic automatic integrator, 10 JOURNAL OF THE ACM 507 (1963).
28. Smith, Gerald F., Beyond critical thinking and decision making: teaching business students how to think, 27 JOURNAL OF MANAGEMENT EDUCATION 24 (2003).
29. Stern, Richard H., Alice v CLS Bank: US Business Method and Software Patents Marching towards Oblivion?, 36 EUR. INTELL. PROP. REVEV. 619 (2014)..
30. Turing, Alan,. On Computable Numbers, with an Application to the Entscheidungs problem, 42 PROCEEDINGS OF THE LONDON MATHEMATICAL SOCIETY 230 (1937).
31. Turing, Alan., Computing Machinery and Intelligence, 49 MIND 433 (1950).
32. Vertinsky Liza & Rice, Todd M. (2002),. Thinking About Thinking Machines: Implications Of Machine Inventors For Patent Law, 8 B.U. J. SCI. & TECH. L., 574 (2002).
33. Weizenbaum, Joseph,. ELIZA--A Computer Program For the Study of Natural Language Communication Between Man and Machine, 9 COMMUNICATIONS OF THE ACM 36 (1966).
34. Wu, Andrew J. (1997),. From Video Games to Artificial Intelligence: Assigning Copyright Ownership to Works Generated by Increasingly Sophisticated Computer Programs, 25 AIPLA Q. J. 131 (1997).
35. Young, Tom et al., Recent Trends in Deep Learning Based Natural Language Processing, IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2018).
V. Cases
1. Alice Corp. v. CLS Bank Int`l, 134 S. Ct. 2347 (2014);.
2. BASCOM Global Internet Services v. AT&T MOBILITY, 827 F. 3d 1341 (Fed. Cir. 2016).
3. BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 107 F.Supp.3d 650 (N.D. Tex. 2015)
4. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
5. Berkheimer v. HP, Fed. Cir. No. 2017-1437 (May. 31, 2018);
6. Bilski v. Kappos, 130 S. Ct. 3221 (2010).
7. Blue Spike v. Google Inc., No. 2016-1054, 2016 U.S. App. LEXIS 20371 (Fed. Cir. 2016).
8. Burroughs Wellcome Co. v. Barr Labs., Inc., 40 F.3d 1223, 1228 (Fed. Cir. 1994).
9. CLS Bank Int’l v. Alice Corp., 685 F. 3d at 1352 (Fed. Cir. 2012).
10. CLS Bank Int’l v. Alice Corp., 717 F. 3d 1269 (Fed. Cir. (2013)
11. CLS Bank Int’l v. Alice Corp., 768 F. Supp.2d at 225 (2011).
12. Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d (Fed. Cir. 2018).
13. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014).
14. Enfish, LLC v. Microsoft Corp., 56 F.Supp.3d 1167 (C.D. Cal. 2014).
15. Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327 (Fed. Cir. 2016).
16. EPO Case Number T 0489/14 (Feb. 22, 2019).
17. EPO Case Number T 1358/09 (Nov. 21, 2014).
18. Finjan, Inc. v. Blue Coat Systems, Inc., 879 F.3d (Fed. Cir. 2018).
19. Glasswall Solutions Ltd. v. ClearSwift Ltd., 754 Fed. Appx. 996 (Fed. Cir. 2018).
20. Gottschalk v. Benson, 409 U.S. (1972).
21. Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1376 (Fed. Cir. 1986),
22. Iln re Bilski, 545 F.3d 943 (2008).
23. Interval Licensing LLC v. AOL, Inc., Fed. Cir. No. 2016-2502 (Jul. 20, 2018);
24. Mayo Collaborative v. Prometheus Labs., 132 S. Ct. 1289 (2012).
25. McRO, Inc. v. Bandai Namco Games America Inc., 837 F. 3d 1314 (2016)
26. Naruto v. Slater, 888 F.3d 418 (2018).
27. Parker v. Flook, 437 U.S. (1978).
28. PurePredictive, Inc., v. H2O.AI, Inc., Case No. 17-cv-03049-WHO. (N.D. Cal. Aug. 29, 2017).
29. Smart Systems Innovations, LLC v. Chicago Transit Authority, Fed. Cir. No. 2016-1233 (Oct. 18, 2017).
30. Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117 (Fed. Cir. 2018).
VI. Official Documents
1. 2018 European Patent Convention Guidelines for Examination.
2. European Patent Office, Case Law of the Boards of Appeal, 8th edition, 24, available at http://documents.epo.org/projects/babylon/eponet.nsf/0/5148B6F13CBE8990C1258017004A9EF6/$File/case_law_of_the_boards_of_appeal_2016_en.pdf (Last visited: 2019/06/17).
3. European Patent Office, Patenting Artificial Intelligence Conference Summary (May 30, 2018), available at https://e-courses.epo.org/pluginfile.php/23523/mod_resource/content/2/Summary%20Artificial%20Intelligence%20Conference.pdf (Last visited: 2019/03/11).
4. Japan Patent Office, Examination Handbook for Patent and Utility Model –Annex A, available at https://www.jpo.go.jp/e/system/laws/rule/guideline/patent/handbook_shinsa/document/index/app_a_e.pdf (Last visited: 2019/06/17).
5. United States Patent and Trademark Office, 2019 Revised Patent Subject Matter Eligibility Guidance, available at https://www.federalregister.gov/documents/2019/01/07/2018-28282/2019-revised-patent-subject-matter-eligibility-guidance
6. United States Patent and Trademark Office, Manual of Patent Examining Procedure, available at https://www.uspto.gov/web/offices/pac/mpep/index.html (Last visited: 2019/06/17).
7. United States Patent and Trademark Office, MEMORANDUM (Apr. 19, 2018), available at https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (Last visited: 2019/06/17).
8. United States Patent and Trademark Office, MEMORANDUM (June. 07, 2018), available at https://www.uspto.gov/sites/default/files/documents/memo-vanda-20180607.PDF (Last visited: 2019/06/17).
9. United States Patent and Trademark Office, MEMORANDUM, available at https://www.uspto.gov/sites/default/files/documents/memo-recent-sme-ctdec-20180402.PDF (Last visited: 2019/06/17).
10. United States Patent and Trademark Office, Subject Matter Eligibility Examples: Abstract Ideas (Jan. 7, 2019), available at https://www.uspto.gov/sites/default/files/documents/101_examples_37to42_20190107.pdf (Last visited: 2019/07/02).
11. United States Patent and Trademark Office, USPTO Performance and Accountability Report, 2, available at https://www.uspto.gov/sites/default/files/documents/USPTOFY18PAR.pdf (Last visited: 2019/04/22).
12. World Economic Forum, Artificial Intelligence Collides with Patent Law (Apr. 2018), available at http://www3.weforum.org/docs/WEF_48540_WP_End_of_Innovation_Protecting_Patent_Law.pdf (Last visited: 2019/06/28).
13. World International Property Organization, WIPO Technology Trends 2019: Artificial Intelligence, 42, available at https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf (Last visited: 2019/04/26).
VII. Internet Source
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3. Alan Turing: The codebreaker who saved `millions of lives` (June 19, 2012), available at https://www.bbc.com/news/technology-18419691 (Last visited: 2019/04/23).
4. AlphaGo Zero: Learning from scratch, available at https://deepmind.com/blog/alphago-zero-learning-scratch/ (Last visited: 2019/04/17).
5. Ana Ramalho, Patentability of AI-Generated Inventions: Is a Reform of the Patent System Needed? (February 15, 2018), available at http://dx.doi.org/10.2139/ssrn.3168703.
6. Apple Buys a Start-Up for Its Voice Technology (Apr. 2010), available at https://www.nytimes.com/2010/04/29/technology/29apple.html (Last visited: 2019/05/19).
7. Barrett, Bill Defensive Use of Publications in an Intellectual Property Strategy, BIOENTREPRENEUR, available at https://www.nature.com/bioent/2003/030101/full/nbt0202-191.html (Last visited: 2019/06/26).
8. China overtakes US in AI patent rankings (Mar. 10, 2019), available at https://asia.nikkei.com/Business/Business-trends/China-overtakes-US-in-AI-patent-rankings (Last visited: 2019/04/26).
9. Definition of: programmable computer, available at https://www.pcmag.com/encyclopedia/term/63683/programmable-computer (Last visited: 2019/06/05).
10. EPO ranked No. 1 for quality and service in largest-ever IAM Magazine survey (June 2018), available at https://www.epo.org/news-issues/news/2018/20180604.html (Last visited: 2019/04/27).
11. Evans, Johann, Data is everywhere, but not where it should be, available at https://www.itweb.co.za/content/WPmxVE7KRzyvQY85 (Last visited: 2019/05/19).
12. Face Recognition, available at https://www.eff.org/pages/face-recognition (Last visited: 2019/06/05)
13. Face Recognition: An Introduction for Beginners (Apr. 16, 2019), available at https://www.learnopencv.com/face-recognition-an-introduction-for-beginners/ (Last visited: 2019/06/05).
14. Facebook taps `deep learning` giant for new AI lab (Dec. 2013), available at https://www.wired.com/2013/12/facebook-yann-lecun/ (Last visited: 2019/05/19)
15. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems, available at https://www.fda.gov/news-events/press-announcements/fda-permits-marketing-artificial-intelligence-based-device-detect-certain-diabetes-related-eye (Last visited: 2019/05/20);
16. General Problem Solver, available at http://www.instructionaldesign.org/theories/general-problem-solver.html (Last visited: 2019/04/23).
17. Goldman, David ,Google will become Alphabet today (Oct. 2015), available at https://money.cnn.com/2015/10/02/technology/google-alphabet/ (Last visited: 2019/04/27).
18. Google buys UK artificial intelligence start-up DeepMind (Jan 2014), available at https://www.bbc.com/news/technology-25908379;
19. Google Has Bought A Startup To Help It Recognize Voices And Objects (Mar 2014), available at https://www.businessinsider.com/google-buys-dnnresearch-2013-3 (Last visited: 2019/05/19).
20. Google`s AlphaGo clinches series win over Chinese Go master, REUTERS (May 25, 2017), available at https://www.reuters.com/article/us-science-intelligence-go/googles-alphago-clinches-series-win-over-chinese-go-master-idUSKBN18L0LH.
21. How does facial recognition work?, available at https://us.norton.com/internetsecurity-iot-how-facial-recognition-software-works.html (Last visited: 2019/06/05).
22. Hussain, Shadab, Use cases of Different Machine Learning Algorithms, available at https://blog.usejournal.com/machine-learning-algorithms-use-cases-72646df1245f (Last visited: 2019/05/19).
23. IBM Services, https://www.ibm.com/services?lnk=hpmse_ts&lnk2=learn (Last visited: 2019/06/27).
24. Intellectual property and artificial intelligence: what does the future hold? (Feb. 2018), available at https://www.iam-media.com/litigation/intellectual-property-and-artificial-intelligence-what-does-future-hold (Last visited: 2019/04/25).
25. IPlytics, Who is patenting AI technology? (Apr. 2019), available at https://www.iplytics.com/wp-content/uploads/2019/03/IPlytics-AI-report.pdf (Last visited: 2019/06/28).
26. Korenberg, Alexander, Kilburn & Strode, Assessing the EPO’s new guidelines on AI (Dec. 03, 2018). available at https://www.ipstars.com/articles/assessing-the-epos-new-guidelines-on-ai/arjvptju. (Last visited: 2019/06/28).
27. Landau, Josh, Iancu’s First Hearing Answers Questions, Leaves More Open (Apr. 19, 2018), available at https://www.patentprogress.org/2018/04/19/iancus-first-hearing-answers-questions-leaves-more-open/ (Last visited: 2019/06/17).
28. Logic Theorist, available at https://en.wikipedia.org/wiki/Logic_Theorist#cite_note-FOOTNOTEMcCorduck2004167-3 (Last visited: 2019/04/28).
29. Lohr, Jason, Artificial intelligence drives new thinking on patent rights (Jul. 15, 2016), available at https://www.lexology.com/library/detail.aspx?g=cfb71b99-e4ac-4a13-96cf-7c1fd6e98543 (Last visited: 2019/06/28).
30. Lytvyn, Andriy, The Obviousness Standard in the United States Patent System, SMITH & HOPEN (June 4, 2012), available at http://www.smithhopen.com/news_detail/557/Obviousness_in_the_U.S._Patent.
31. Lytvyn, Andriy, The Obviousness Standard in the United States Patent System, SMITH & HOPEN (June 4, 2012), available at http://www.smithhopen.com/news_detail/557/Obviousness_in_the_U.S._Patent (Last visited: 2019/06/26).
32. Merges, Rob ,Go ask Alice — what can you patent after Alice v. CLS Bank? (Jun. 20, 2014), available at http://www.scotusblog.com/2014/06/symposium-go-ask-alice-what-can-you-patent-after-alice-v-cls-bank/ (Last visited: 2019/03/07).
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描述 碩士
國立政治大學
法律科際整合研究所
1056520011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G1056520011
資料類型 thesis
dc.contributor.advisor 沈宗倫zh_TW
dc.contributor.advisor Shen, Chung-Lunen_US
dc.contributor.author (Authors) 鄭禕寧zh_TW
dc.contributor.author (Authors) Cheng, Yi-Ningen_US
dc.creator (作者) 鄭禕寧zh_TW
dc.creator (作者) Cheng, Yi-Ningen_US
dc.date (日期) 2019en_US
dc.date.accessioned 5-Sep-2019 16:31:34 (UTC+8)-
dc.date.available 5-Sep-2019 16:31:34 (UTC+8)-
dc.date.issued (上傳時間) 5-Sep-2019 16:31:34 (UTC+8)-
dc.identifier (Other Identifiers) G1056520011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125696-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 法律科際整合研究所zh_TW
dc.description (描述) 1056520011zh_TW
dc.description.abstract (摘要) 從人臉、語音識別到自動駕駛汽車，人工智慧已經跳脫科幻電影的虛構，逐漸走入並且遍佈我們的生活。雖然就目前已知的技術來說，人工智慧尚未具備思考能力，然而科學家不斷地希望能夠突破限制，讓人工智慧不僅能夠成為生活上的輔助、協助人類進行研究發明，甚至是由人工智慧獨立完成發明。
為了鼓勵創作與發明，我國制定了智慧財產權相關法律如專利法、商標法及著作權法，惟因其保護之前提為精神上創作，目前的保護對象即權利歸屬主體，皆僅限於自然人。隨著人工智慧越來越接近人類，開始能夠進行創作甚至思考，進而可能成為真正有貢獻之發明人時，現有的智慧財產權相關法制規範，將會面臨包含權利主體、專利要件等適用上之困境，。
事實上，人工智慧之本質與電腦軟體極為相似，其對於現行法制造成的衝擊，除了上述權利歸屬即要件認定問題外，另一方面則再次喚起了電腦軟體於智慧財產法上的適格性議題。就此，本文將討論範圍限縮在專利法，先從技術角度剖析介紹人工智慧之內涵與發展，再從專利法立法目的切入，綜合各國規範見解與近期發布之相關審查指南分析其發明適格性，進一步討論當由人工智慧產生衍生之發明時，應如何進行權利主體之認定，以及其對於相關產業造成之利弊影響。		zh_TW
dc.description.abstract (摘要) Artificial intelligence (A.I.) such as face recognition, voice recognition, autopilot, etc., has become not only the sci-fi movie plots but been all over our lives. Although A.I. hasn’t been capable of thinking like a real human-being yet according to the technology known so far, scientists still keep working on breaking through the restrictions, making the artificial intelligence become an important assistant role in human life. It helps people during the research process, and can even become the independent inventor.
Intellectual property law such as patent law, trademark law, and copyright law are enacted to encourage creation and invention. While the premise of the law protection above currently should be mental activity of human, in other words, “natural person” is the only subject of rights of creator and inventor. It may cause problems when A.I. starts to act like human and even be able to make the substantive features of the invention. Thus, it is essential to decide whether to enable the eligibility of subject of right of artificial intelligence in advance.
In fact, the essence of A.I. is very similar to computer software. Besides the problems of subject of right mentioned above, the impact of A.I. on the current law system also evoked the issue of eligibility of computer software on intellectual property law once again. In this regard, this article limits the scope of discussion to the patent law, and introduce the connotation and development of A.I. from the technical point of view. After that, it will cut through the legislative purpose of the patent law, analyze the opinions and guidelines issued recently in different countries, and also the identification of the subject of rights and the pros and cons of related industries.		en_US
dc.description.tableofcontents 第一章 緒論 1
第一節 研究動機與目的 1
第二節 研究範圍與方法 2
第一項 研究範圍 2
第二項 研究方法 3
第三節 研究架構 4
第二章 人工智慧技術背景介紹 6
第一節 歷史發展 6
第一項 導論 6
第二項 演進 10
第三項 小結 22
第二節 技術領域 23
第一項 神經網路簡介 23
第二項 神經網路分支領域 26
第三項 小結 31
第三節 現今應用 31
第一項 資訊安全 32
第二項 醫療與照護 33
第三項 資料分析與決策 35
第四項 小結 37
第四節 人工智慧專利申請現況分析 37
第一項 檢索方式與對象 37
第二項 資料分析 38
第三項 小結 51
第三章 人工智慧相關發明之專利適格性判斷 53
第一節 美國人工智慧發明專利適格性 53
第一項 美國專利法與專利審查基準相關規範 53
第二項 美國專利適格性之相關判決介紹 54
第三項 近期司法實務發展 68
第四項 小結 81
第二節 歐洲人工智慧發明專利適格性 82
第一項 歐洲專利公約與專利審查基準相關規範 82
第二項 近期司法實務發展 84
第三節 我國人工智慧發明專利適格性 89
第一項 我國專利法與專利審查基準相關規範 89
第二項 近期司法實務發展 90
第四節 小結 94
第四章 人工智慧衍生發明之專利權權利歸屬 97
第一節 現行專利法之權利歸屬認定 97
第一項 專利法立法意旨 97
第二項 權利主體 97
第二節 人工智慧衍生發明之權利歸屬認定 102
第一項 人工智慧衍生發明之類型 102
第二項 人工智慧對現行法制之衝擊 106
第三項 權利歸屬認定之可能因應方案與建議 109
第四項 小結 128
第五章 人工智慧衍生發明之專利要件判斷 132
第一節 現行專利制度之專利要件判斷 132
第一項 產業利用性 132
第二項 新穎性 134
第三項 進步性 135
第二節 人工智慧衍生發明之專利要件判斷 138
第一項 先前技術與新穎性 139
第二項 進步性 141
第六章 結論 145
參考文獻 149		zh_TW
dc.format.extent 3384823 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G1056520011en_US
dc.subject (關鍵詞) 人工智慧zh_TW
dc.subject (關鍵詞) 軟體專利zh_TW
dc.subject (關鍵詞) 專利適格性zh_TW
dc.subject (關鍵詞) 權利主體zh_TW
dc.subject (關鍵詞) 先前技術zh_TW
dc.subject (關鍵詞) 進步性zh_TW
dc.subject (關鍵詞) Artificial intelligenceen_US
dc.subject (關鍵詞) Software patenten_US
dc.subject (關鍵詞) Patent eligibilityen_US
dc.subject (關鍵詞) Subject of rightsen_US
dc.subject (關鍵詞) Inventive stepen_US
dc.subject (關鍵詞) Non-obviousnessen_US
dc.title (題名) 專利法關於人工智慧發明重要議題之研究zh_TW
dc.title (題名) A study on the Core Issues of Invention Related to Artificial Intelligence in Patent Lawen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 壹、 中文資料（按作者姓氏筆劃排序）
一、 書籍
1. 三宅陽一郎、森川幸人，從人到人工智慧：破解AI革命的68個核心概念，2017年5月。
2. 李友專，AI醫療大未來：台灣第一本智慧醫療關鍵報告，2018年9月。
3. 楊智傑，專利法，3版，2014年9月。
4. 認識著作權（二版），經濟部智慧財產局，2011年11月。
5. 鄭捷，自然語言處理：用人工智慧看懂中文， 2018年1月。
6. 謝銘洋，智慧財產權法，8版，2018年9月。
二、 專書論文
1. 沈宗倫，人工智慧科技與智慧財產權法制的交會與調和，載：人工智慧相關法律議題芻議，頁177-214，2018年11月。
三、 期刊論文
1. 曲建仲，機器是如何學習與進步？人工智慧的核心技術與未來，科學月刊，580期，2018年4月。
2. 宋皇志，專利法中「發明所屬技術領域中具有通常知識者」之法實證研究，政大法學評論，146期，2016年9月。
3. 沈宗倫，專利新穎性之先前技術範圍界定與比對評最高行政法院九十八年度判字第一四九號行政判決，月旦法學雜誌，197期，2011年10月。
4. 洪振盛，Alice案後美國電腦軟體專利適格性之發展，智慧財產權月刊，211期， 2016年7月。
5. 徐瑞甫、陳聖，由智慧財產法院判決探討我國專利請求項中功能性用語之相關爭議問題（II）--據以實現／明確性要件及功能性子句智慧財產權月刊，195期，2015年3月。
6. 莊智惠，進步性判斷方式及論理之探討―以發明專利進步性審查基準修訂為例，智慧財產權月刊，225期，2017年9月。
7. 陳龍昇，「自然法則」運用與個人化醫療診斷方法專利適格性判斷—從美國Mayo v, Prometheus案判決談起，高大法學論叢，11卷1期，2015年9月。
8. 陳龍昇，由美國Bilski v. Kappos案探討商業方法發明之專利適格，臺北大學法學論叢，84期，2012年。
9. 謝銘洋，專利新穎性之認定—智慧財產法院行政判決98年度行專訴字第123號解析，法令月刊，61卷9期，2010年9月。
10. 謝銘洋、李素華，專利權訴訟中之進步性與均等論，台灣法學雜誌，218期，2013年2月。
四、 學位論文
1. 邱亮儒，由美國Alice v. CLS Bank案探討電腦軟體相關發明之專利適格性，國立中興大學科技法律研究所碩士論文，2016年6月。
2. 陳昭妤，論人工智慧創作與發明之法律保護—以著作權與專利權權利主體為中心，國立政治大學科技管理與智慧財產研究所碩士學位論文，2017年1月。
五、 法院判決
1. 智慧財產法院101年度民專上更(二)字第5號民事判決。
2. 智慧財產法院102年度民專上字第23號民事判決。
3. 智慧財產法院102年度民專上字第25號民事判決。
4. 智慧財產法院105年度行專更(一)字第4號行政判決。
5. 智慧財產法院106年度民專訴字第60號民事判決。
6. 智慧財產法院106年度行專訴字第76號行政判決。
7. 智慧財產法院98年度民專上字第39號民事判決。
8. 最高行政法院103年度判字第406號判決。
9. 最高行政法院105年度判字第503號行政判決。
六、 官方文件
1. 入出國自動查驗通關系統績效評估之研究－以臺灣桃園國際機場為例，內政部入出國及移民署自行研究報告，101年8月。
2. 中華民國專利法逐條釋義（103年9月版）。
3. 經濟部智慧財產局，2014年版專利審查基準彙編。
4. 經濟部智慧財產局，人工智慧技術專利分析報告，https://pcm.tipo.gov.tw/PCM2010/PCM/commercial/03/AI.aspx?aType=3&Articletype=1&aSn=613 (最後造訪日：2019/04/26)。
5. 經濟部智慧財產局，著作權基本概念，https://www.tipo.gov.tw/ct.asp?xItem=219594&ctNode=7561&mp=1
6. 經濟部智慧財產局全球專利檢索系統，https://gpss.tipo.gov.tw/gpsskmc/gpssbkm (最後造訪日：2019/04/21)。
七、 網路資料
1. 「靠臉走遍天下」的時代到了？，雷鋒網，2019年1月16日，https://www.leiphone.com/news/201901/YnfJas5bLXPdiHUS.html506/（最後造訪日：2019/05/20）。
2. 「臉部解鎖」誰最安全？《富比士》公佈 5 款旗艦手機測試結果，自由時報，2018年12月18日，https://3c.ltn.com.tw/news/35369（最後造訪日：2019/05/20）。
3. AI 入門必備懶人包：圖解 27 種神經模型，讓你秒懂差在哪，科技報橘，2018年1月24日，https://buzzorange.com/techorange/2018/01/24/neural-networks-compare/（最後造訪日：2019/05/20）。
4. AI換臉黑產業賣偽明星色情片，中時電子報，2019年7月19日，https://www.chinatimes.com/newspapers/20190719000150-260301?chdtv（最後造訪日：2019/08/05）。
5. AI 權威 Yann LeCun：機器人 Sophia 不過就是場騙局！，INSIDE，2018年1月6日，https://www.inside.com.tw/article/11618-facebook-ai-yann-lecun-sophia-robot-bullshit（最後造訪日：2019/05/20）。
6. AI專利申請50強企業：中國反超美國，2019年3月13日，https://zh.cn.nikkei.com/industry/scienceatechnology/34657-2019-03-13-05-00-30.html （最後造訪日：2019/04/27）。
7. AI應用導入5大醫療專業領域，智慧應用，2018年11月27日，https://www.digitimes.com.tw/iot/article.asp?cat=158&id=0000547354_eyd4xz0344hkdt518tgh3（最後造訪日：2019/05/20）。
8. NEC為玉山銀行提供ATM人臉辨識系統，iThome，2019年2月26日，https://ithome.com.tw/pr/128943（最後造訪日：2019/05/20）。
9. 人工智慧「學」不會的東西，以及「人」無法被取代的理由，數位時代，2016年4月1日，https://www.bnext.com.tw/article/39094/BN-2016-04-01-135646-178（最後造訪日：2019/04/21）。
10. 人工智慧的病理切片系統，台灣癌症防治網，http://web.tccf.org.tw/lib/addon.php?act=post&id=4363（最後造訪日：2019/05/20）。
11. 中央研究院，人工智慧再進化，開啟電腦新「視」界，研之有物，http://research.sinica.edu.tw/computer-vision-liao-hong-yuan/（最後造訪日:2019/06/06）。
12. 中國監控新疆「天網」資料庫有漏洞，超過 250 萬人資料及詳細路徑、座標可能外洩，科技新報，2019年2月22日，https://technews.tw/2019/02/22/chinese-company-leaves-muslim-tracking-facial-recognition-database-exposed-online/（最後造訪日：2019/05/20）。
13. 未來疾病檢測，AI 技高一籌?，2018年1月15日，https://geneonline.news/index.php/2018/01/15/ai-is-better/（最後造訪日：2019/05/20）。
14. 目前技術進展仍以「弱人工智慧」為主，資策會產業經濟研究所，2017年10月24日，http://mic.iii.org.tw/scholar/GraphDtl.aspx?docid=128178 （最後造訪日：2019/05/18）。
15. 印象派繪畫，國立台灣大學網路教學課程，http://vr.theatre.ntu.edu.tw/fineart/th9_1000/open-34-broadcast.htm（最後造訪日：2019/06/05）。
16. 自然語言處理頂會NAACL最佳論文出爐！谷歌BERT獲最佳長論文，2019年4月12日，https://kknews.cc/tech/yvpzxgb.html（最後造訪日: 2019/05/13）。
17. 別再誤會了，特斯拉根本無法全自動駕駛，2018年10月22日，https://www.bnext.com.tw/article/50994/tesla-full-self-driving-option-gone-musk-autopilot（最後造訪日：2019/05/19）。
18. 周秉誼，淺談Deep Learning原理及應用，國立台灣大學計算機及資訊網路中心電子報，38期，2016年9月，http://www.cc.ntu.edu.tw/chinese/epaper/0038/20160920_3805.html（最後造訪日：2019/05/20）。
19. 阿里巴巴成立全球科研項目「達摩院」 投逾千億元人民幣於科技創新，2017年10月11日，https://www.alibabanews.com/alibabachengliquanqiukeyanxiangmudamoyuan-touyuqianyiyuanrenminbiyukejichuangxin/ （最後造訪日：2019/05/19）。
20. 活體偵測技術介紹，https://tw.nec.com/zh_TW/solutions/security/liveness_detection.html（最後造訪日：2019/05/20）。
21. 馬偉雲，斷開中文的鎖鍊！自然語言處理 (NLP)，研之有物，http://research.sinica.edu.tw/nlp-natural-language-processing-chinese-knowledge-information/（最後造訪日：2019/06/18）。
22. 國際頂級人工智慧協會AAAI 2019年度大會隆重開幕，2019年1月31日，https://kknews.cc/tech/l3b6ne9.html（最後造訪日：2019/04/22）。
23. 陳秉訓，新穎性優惠期修法後智財局之挑戰，北美智權報，177期，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Industry_Economy/IPNC_170125_0705.htm（最後造訪日：2019/06/26）。
24. 華為／三星／小米手機都中箭 一張照片就攻破臉部解鎖，中時電子報，2019年1月7日，https://www.chinatimes.com/realtimenews/20190107003216-260412?chdtv（最後造訪日：2019/05/2）。
25. 葉雲卿，《Enfish, LLC v. Microsoft Corp》案對於美國軟體專利適格性判斷之影響 ─ 抽象概念之判斷，北美智權報，175期，http://www.naipo.com/Portals/1/web_tw/Knowledge_Center/Infringement_Case/IPNC_161228_0502.htm （最後造訪日：2019/06/11）。
26. 監督式學習？增強學習？聽不懂的話，一定要看這篇入門的機器學習名詞解釋！，INSIDE，2017年7月19日，https://www.inside.com.tw/article/9945-machine-learning（最後造訪日：2019/05/20）。
27. 蔡炎龍，函數、神經網路與深度學習，科學月刊，2019年2月26日，http://scimonth.blogspot.com/2018/03/blog-post_8.html（最後造訪日：2019/06/24）。
28. 機器人是台灣長照的解藥嗎？，2019年2月11日，https://www.bnext.com.tw/article/52017/robot-ai-long-term-care（最後造訪日：2019/05/20）。
29. 機器學習裡資料預處理及特徵工程總結，https://codertw.com/%E7%A8%8B%E5%BC%8F%E8%AA%9E%E8%A8%80/457901/（最後造訪日：2019/06/24）。
30. 臉部辨識技術快速過安檢的背後，你付出了什麼代價？，科技新報，2019年3月26日，https://technews.tw/2019/03/26/cost-of-face-recognition-fast-security-check/（最後造訪日：2019/05/20）。
31. 臉部辨識將成智慧手機標配？2020 年估達 10 億支導入，科技新報，2018年2月9日，https://technews.tw/2018/02/09/more-than-one-billion-smartphones-to-feature-facial-recognition-in-2020/ （最後造訪日：2019/05/20）。
貳、外文資料（按作者首字母排序）
I. Books
1. Bonner, Anthony (2007). THE ART AND LOGIC OF RAMON LLULL: A USER’S GUIDE. Leiden, Netherlands: Brill Academic.
2. Buchanan, Bruce G. & Edward H. Shortliffe (1984). RULE BASED EXPERT SYSTEM- THE MYCIN EXPERIMENTS OF THE STANFORD HEURISTIC PROGRAMMING PROJECT. Boston, MA: Addison-Wesley.
3. CRAIG, JOHN J. (2009). INTRODUCTION TO ROBOTICS: MECHANICS AND CONTROL. New York, NY: Pearson Education
4. Crevier, Daniel (1993). AI: THE TUMULTUOUS SEARCH FOR ARTIFICIAL INTELLIGENCE. New York, NY: Basic Books.
5. Domingos, Pedro (2018). THE MASTER ALGORITHM: HOW THE QUEST FOR THE ULTIMATE LEARNING MACHINE WILL REMAKE OUR WORLD. New York, NY: Basic Books.
6. Fausett, Laurene (1994). FUNDAMENTALS OF NEURAL NETWORKS: ARCHITECTURES, ALGORITHMS, AND APPLICATIONS. New York, NY: Pearson.
7. Gardener, Martin (1958). LOGIC MACHINES AND DIAGRAMS. New York, NY: McGraw-hill.
8. Goldstein, Herman (1972). THE COMPUTER FROM PASCAL TO VON NEUMANN. Princeton, NJ: Princeton University Press.
9. Goodfellow, IAN & BENGIO, YOSHUA & COURVILLE, AARON (2016). DEEP LEARNING. Cambridge, MA: MIT Press.
10. Hebb, D.O. (2002). THE ORGANIZATION OF BEHAVIOR: A NEUROPSYCHOLOGICAL THEORY. London: Psychology Press.
11. Hegel, G.W.F. (1821). PHILOSOPHY OF RIGHT (S.W Dyde trans, Batoche Books 2001). Kitchener, ON: Batoche Books.
12. Kumar, Ela (2008). ARTIFICIAL INTELLIGENCE. India: I.K. International Publishing House.
13. Locke, John (2015). THE SECOND TREATISE ON CIVIL GOVERNMENT. Canada: Broadview Press.
14. Mandal, Durbadal & Kar, Rajb & Das, Swagatam & Panigrahi, Bijaya Ketan (2015). INTELLIGENT COMPUTING AND APPLICATION: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ICA. New York, NY: Springer.
15. McCarthy, J. & Minsky, M.L. & Rochester, N. & Shannon, C.E. (1955). A PROPOSAL FOR THE DARTMOUTH SUMMER RESEARCH PROJECTON ARTIFICIAL INTELLIGENCE. Hanover, NH: Dartmouth College.
16. McCorduck, Pamela (2ed. 2004). MACHINES WHO THINK. Boca Raton, FL: CRC Press.
17. Minsky, Marvin L. & Papert, Seymour A. (2017). PERCEPTRONS: AN INTRODUCTION TO COMPUTATIONAL GEOMETRY. Cambridge, MA: MIT Press.
18. Pearl, Judea (1984). HEURISTICS: INTELLIGENT SEARCH STRATEGIES FOR COMPUTER PROBLEM SOLVING. Boston, MA: Addison-Wesley.
19. Pearl, Judea (1988). PROBABILISTIC REASONING IN INTELLIGENT SYSTEMS: NETWORKS OF PLAUSIBLE INFERENCE. San Francisco, CA: Morgan Kaufmann.
20. Plotkin, Robert (2009). THE GENIE IN THE MACHINE: HOW COMPUTER-AUTOMATED INVENTING IS REVOLUTIONING LAW & BUSINESS. Stanford, CA: Stanford Law Books.
21. Russel, Stuart & Norvig, Peter (3ed. 2018). ARTIFICIAL INTELLIGENCE: A MODERN APPTOACH. New York, NY: Pearson Education.
22. Schaeffer, Jonathan (2ed. 2008). ONE JUMP AHEAD: CHALLENGING HUMAN SUPREMACY ICHECKERS. New York, NY: Springer.
23. SIMON, HEBERT A. (1965). THE SHAPE OF AUTOMATION FOR MAN AND MANAGEMENT. New York, NY: Harper & Row.
24. SUTTON, RICHARD S. & BARTO, ANDREW G (2ed. 2015). REINFORCEMENT LEARNING: AN INTRODUCTION. Cambridge, MA: The MIT Press.
25. THE EDITORS OF TIME-LIFE BOOKS (1986), ARTIFICIAL INTELLIGENCE (UNDERSTANDING COMPUTERS).
26. WATERMAN, DONALD A. (1985). A GUIDE TO EXPERT SYSTEM. New York, NY: Pearson Education.
II. Book Chapters
1. Abbott, Ryan, Hal the Inventor: Big Data and Its Use by Artificial Intelligence, in BIG DATA IS NOT A MONOLITH, 187 (Cassidy R. Sugimoto, Hamid Ekbia & Michael Mattioli eds., 2016).
2. McCarthy, John. Programs with Common Sense, in COMPUTATION & INTELLIGENCE 479 (1995).
3. Rumelhart, D.E. & Hinton, G.E. & Williams, R.J., 1 Learning internal representations by error propagation, in PARALLEL DISTRIBUTED PROCESSING: EXPLORATIONS IN THE MICROSTRUCTURE OF COGNITION 318 (1985).
III. Conference Papers
1. Bahdanau, Dzmitry & Cho, Kyunghyun & Bengio, Yoshua, Neural Machine Translation by Jointly Learning to Align and Translate, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2015).
2. Bakker, Bram, Reinforcement Learning with Long Short-Term Memory, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (2002).
3. Baldi, Pierre, Autoencoders. Unsupervised Learning, and Deep Architectures, INTERNATIONAL CONFERENCE ON MACHINE LEARNING WORKSHOP ON UNSUPERVISED AND TRANSFER LEARNING (2012).
4. Bengio, Yoshua & Courville, Aaron & Vincent, Pascal, Representation Learning: A Review and New Perspectives, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2013).
5. Ding, Yiming et al., A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using 18F-FDG PET of the Brain, 290 RADIOLOGY (2018).
6. Goodfellow, Ian et al., Generative Adversarial Nets, 2014 NEURAL INFORMATION PROCESSING SYSTEMS (2014).
7. Hochreiter, Sepp & Schmidhuber, Jürgen, Long Short-Term Memory, NEURAL COMPUTATION (1997).
8. Kurach, Karol et al., The GAN Landscape: Losses, Architectures, Regularization, and Normalization, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2019).
9. Lu, Chaochao & Tang, Xiaoou, Surpassing Human-Level Face Verification Performance on LFW with GaussianFace, PROCEDDING OF THE 29TH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE (2014).
10. Mikolov, Tomas et al., Distributed Representations of Words and Phrases and Their Compositionality, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS (2013).
11. Moore, Gordon E., Progress in Digital Integrated Electronics, INTERNATIONAL ELECTRON DEVICES MEETING 11 (1975).
12. Park, Taesung et al., Semantic Image Synthesis with Spatially-Adaptive Normalization, COMPUTER VISION AND PATTERN RECOGNITION (2019).
13. Ting, DSW et al., Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes, JAMA (2017).
14. Zhu, Jun-Yan et al., Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, COMPUTER VISION AND PATTERN RECOGNITION (2018).
15. Zoph, Barret & Le, Quoc V., Neural Architecture Search with Reinforcement Learning, INTERNATIONAL CONFERENCE ON LEARNING REPRESENTATIONS (2017).
IV. Journal Papers
1. Abbott, Ryan, I Think, Therefore I Invent: Creative Computers and the Future of Patent Law, 57 B.C. L. REV. 4, 10979 (2016).
2. Abbott, Ryan, Patenting the output of autonomous inventive machine, 10 LANDSLIDE 16. (2017)
3. Darrach, Brad., Meet Shakey, the First Electronic Person, LIFE MAGAZINE 58 (1970).
4. Davies, Colin, An Evolutionary Step in Intellectual Property Rights - Artificial Intelligence and Intellectual Property, 27 COMPUTER LAW & SECURITY REVIEW 6, 601 (2011).
5. DeCosta III, Frank A., Intellectual Property Protection for Artificial Intelligence, 24 WESTLAWESTLAW J. INTELLNTELL. PROPROP. 1 (2017).
6. Denicola, Robert C., Ex machina: Copyright protection for computer-generated works, 69 RUTGERSUTGERS U. L. REVEV. 251 (2016)..
7. Fraser, Erica, Computers as Inventors – Legal and Policy Implications of Artificial Intelligence on Patent Law, 13 SCRIPTed 3, 305 (2016).
8. Gelernter, H. L. & Rochester N., Intelligent behavior in problem-solving machines, 2 IBM JOURNAL OF RESEARCH AND DEVELOPMENT 336 (1958).
9. Hattenbach, Ben & Glucoft, Joshua, Patents in an Era of Infinite Monkeys and Artificial Intelligence, 19 STAN. TECH. L. REV. 32 (2015).
10. Hodgkin, A. L. & Huxley, A. F., Action potentials recorded from Inside a nerve fibre, 144 NATURE 710 (1939).
11. Hopfield, John, Neural networks and physical systems with emergent collective computational abilities, 79 PROC. NAT’L ACAD. SCI. U. S. A. 2554 (1982).
12. Kohlhepp, Peter M., Note When the Invention Is an Inventor: Revitalizing Patentable Subject Matter to Exclude Unpredictable Processes, 93 MINN. L. REV. 795 (2008).
13. LeCun, Y. et al., Backpropagation applied to handwritten zip code recognition, 1 NEURAL COMPUTATION 541 (1989).
14. Lederberg, Joshua, How Dendral Was Conceived and Born, A HISTORY OF MEDICAL INFORMATICS 14 (1987).
15. Lee, Edward, Digital Originality, 14 VAND. J. ENT. & TECH. L. 940 (2012).
16. McCulloch, Warren S. & Pitts, Walter,. A Logical Calculus of the Ideas Immanent in Nervous Activity, 5 BULLETIN OF MATHEMATICAL BIOPHYSICS 115 (1943).
17. Mnih, Volodymyr et al., Human-Level Control through Deep Reinforcement Learning, 518 NATURE 529 (2015).
18. Pearlman, Russ, Recognizing Artificial Intelligence (AI) as Authors and Investors under U.S. Intellectual Property Law, 24 RICH. J. L. & TECH. 21 (2018).
19. Ravid, Shlomit Y. & Liu, Xiaoqiong, When artificial intelligence systems produce inventions: an alternative model for patent law at the 3A era, 39 CARDOZO L. REV. 2215 (2018).
20. Robinson, W. Keith & Smith, Joshua T., Emerging Technologies Challenging Current Legal Paradigms, 19 MINN. J.L. SCI. & TECH. 2, 355 (2018)..
21. Rosenblatt, Frank., The perceptron: A probabilistic model for information storage and organization in the brain, 65 PSYCHOLOGICAL REVIEW 386 (1958).
22. Samuelson, Pamela, Allocating Ownership Rights in Computer-Generated Works, 47 U. PITT. L. REV. 1185, 1207 (1985).
23. Schuster, W. Michael, Artificial Intelligence and Patent Ownership, 75 WASH. & LEE L. REV., 1945 (2018).
24. Searle, John,. Minds, Brains, and Programs, 3 BEHAVIORAL AND BRAIN SCIENCES 417 (1980).
25. Shannon, Claude E., A Symbolic Analysis of Relay and Switching Circuits, 57 ELECTRICAL ENGINEERING 471 (1937).
26. Silver, David et al., Mastering the game of Go with deep neural networks and tree search, 529 NATURE 484 (2016).
27. Slagle, James R., A heuristic program that solves symbolic integration problems in freshman calculus: symbolic automatic integrator, 10 JOURNAL OF THE ACM 507 (1963).
28. Smith, Gerald F., Beyond critical thinking and decision making: teaching business students how to think, 27 JOURNAL OF MANAGEMENT EDUCATION 24 (2003).
29. Stern, Richard H., Alice v CLS Bank: US Business Method and Software Patents Marching towards Oblivion?, 36 EUR. INTELL. PROP. REVEV. 619 (2014)..
30. Turing, Alan,. On Computable Numbers, with an Application to the Entscheidungs problem, 42 PROCEEDINGS OF THE LONDON MATHEMATICAL SOCIETY 230 (1937).
31. Turing, Alan., Computing Machinery and Intelligence, 49 MIND 433 (1950).
32. Vertinsky Liza & Rice, Todd M. (2002),. Thinking About Thinking Machines: Implications Of Machine Inventors For Patent Law, 8 B.U. J. SCI. & TECH. L., 574 (2002).
33. Weizenbaum, Joseph,. ELIZA--A Computer Program For the Study of Natural Language Communication Between Man and Machine, 9 COMMUNICATIONS OF THE ACM 36 (1966).
34. Wu, Andrew J. (1997),. From Video Games to Artificial Intelligence: Assigning Copyright Ownership to Works Generated by Increasingly Sophisticated Computer Programs, 25 AIPLA Q. J. 131 (1997).
35. Young, Tom et al., Recent Trends in Deep Learning Based Natural Language Processing, IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE (2018).
V. Cases
1. Alice Corp. v. CLS Bank Int`l, 134 S. Ct. 2347 (2014);.
2. BASCOM Global Internet Services v. AT&T MOBILITY, 827 F. 3d 1341 (Fed. Cir. 2016).
3. BASCOM Global Internet Servs., Inc. v. AT&T Mobility LLC, 107 F.Supp.3d 650 (N.D. Tex. 2015)
4. Berkheimer v. HP Inc., 881 F.3d 1360 (Fed. Cir. 2018).
5. Berkheimer v. HP, Fed. Cir. No. 2017-1437 (May. 31, 2018);
6. Bilski v. Kappos, 130 S. Ct. 3221 (2010).
7. Blue Spike v. Google Inc., No. 2016-1054, 2016 U.S. App. LEXIS 20371 (Fed. Cir. 2016).
8. Burroughs Wellcome Co. v. Barr Labs., Inc., 40 F.3d 1223, 1228 (Fed. Cir. 1994).
9. CLS Bank Int’l v. Alice Corp., 685 F. 3d at 1352 (Fed. Cir. 2012).
10. CLS Bank Int’l v. Alice Corp., 717 F. 3d 1269 (Fed. Cir. (2013)
11. CLS Bank Int’l v. Alice Corp., 768 F. Supp.2d at 225 (2011).
12. Core Wireless Licensing S.A.R.L. v. LG Electronics, Inc., 880 F.3d (Fed. Cir. 2018).
13. DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245 (Fed. Cir. 2014).
14. Enfish, LLC v. Microsoft Corp., 56 F.Supp.3d 1167 (C.D. Cal. 2014).
15. Enfish, LLC v. Microsoft Corp., 822 F. 3d 1327 (Fed. Cir. 2016).
16. EPO Case Number T 0489/14 (Feb. 22, 2019).
17. EPO Case Number T 1358/09 (Nov. 21, 2014).
18. Finjan, Inc. v. Blue Coat Systems, Inc., 879 F.3d (Fed. Cir. 2018).
19. Glasswall Solutions Ltd. v. ClearSwift Ltd., 754 Fed. Appx. 996 (Fed. Cir. 2018).
20. Gottschalk v. Benson, 409 U.S. (1972).
21. Hybritech Inc. v. Monoclonal Antibodies, Inc., 802 F.2d 1367, 1376 (Fed. Cir. 1986),
22. Iln re Bilski, 545 F.3d 943 (2008).
23. Interval Licensing LLC v. AOL, Inc., Fed. Cir. No. 2016-2502 (Jul. 20, 2018);
24. Mayo Collaborative v. Prometheus Labs., 132 S. Ct. 1289 (2012).
25. McRO, Inc. v. Bandai Namco Games America Inc., 837 F. 3d 1314 (2016)
26. Naruto v. Slater, 888 F.3d 418 (2018).
27. Parker v. Flook, 437 U.S. (1978).
28. PurePredictive, Inc., v. H2O.AI, Inc., Case No. 17-cv-03049-WHO. (N.D. Cal. Aug. 29, 2017).
29. Smart Systems Innovations, LLC v. Chicago Transit Authority, Fed. Cir. No. 2016-1233 (Oct. 18, 2017).
30. Vanda Pharmaceuticals Inc. v. West-Ward Pharmaceuticals, 887 F.3d 1117 (Fed. Cir. 2018).
VI. Official Documents
1. 2018 European Patent Convention Guidelines for Examination.
2. European Patent Office, Case Law of the Boards of Appeal, 8th edition, 24, available at http://documents.epo.org/projects/babylon/eponet.nsf/0/5148B6F13CBE8990C1258017004A9EF6/$File/case_law_of_the_boards_of_appeal_2016_en.pdf (Last visited: 2019/06/17).
3. European Patent Office, Patenting Artificial Intelligence Conference Summary (May 30, 2018), available at https://e-courses.epo.org/pluginfile.php/23523/mod_resource/content/2/Summary%20Artificial%20Intelligence%20Conference.pdf (Last visited: 2019/03/11).
4. Japan Patent Office, Examination Handbook for Patent and Utility Model –Annex A, available at https://www.jpo.go.jp/e/system/laws/rule/guideline/patent/handbook_shinsa/document/index/app_a_e.pdf (Last visited: 2019/06/17).
5. United States Patent and Trademark Office, 2019 Revised Patent Subject Matter Eligibility Guidance, available at https://www.federalregister.gov/documents/2019/01/07/2018-28282/2019-revised-patent-subject-matter-eligibility-guidance
6. United States Patent and Trademark Office, Manual of Patent Examining Procedure, available at https://www.uspto.gov/web/offices/pac/mpep/index.html (Last visited: 2019/06/17).
7. United States Patent and Trademark Office, MEMORANDUM (Apr. 19, 2018), available at https://www.uspto.gov/sites/default/files/documents/memo-berkheimer-20180419.PDF (Last visited: 2019/06/17).
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11. United States Patent and Trademark Office, USPTO Performance and Accountability Report, 2, available at https://www.uspto.gov/sites/default/files/documents/USPTOFY18PAR.pdf (Last visited: 2019/04/22).
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13. World International Property Organization, WIPO Technology Trends 2019: Artificial Intelligence, 42, available at https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf (Last visited: 2019/04/26).
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dc.identifier.doi (DOI) 10.6814/NCCU201900818en_US