隨著人工智慧的快速發展，深度學習之神經網絡技術以已成為現今全球技術發展的重點之一，並將其技術運用在各產業領域中。本研究旨在探討深度學習中不同神經網路的技術發展趨勢與應用領域，並透過專利檢索與分析方法來評估其發展趨勢和影響力。通過TIPO全球專利檢索系統資料庫中大量專利數據的收集和分析，探討神經網路技術的歷年專利件數、領先國家別、領先公司別、技術發展現況等，透過專利檢索與技術生命週期分析方法，可以深入了解深度學習技術的應用範圍和為未來發展動向，為未來的研究和產業應用提供價值。總而來說，本研究旨通過專利分析方法深入探討深度學習基於訓練數據之神經網路與其八項神經網絡技術包含循環神經網絡 (Recurrent neural network, RNN) 、卷積神經網絡 (Convolutional Neural Network, CNN) 、生成對抗網絡 (Generative Adversarial Network, GAN) 、時序視覺網絡 (Temporal Segment Networks, TSN) 、自動編碼器 (Autoencoder, AE) 、深度置信網絡 (Deep Belief Network, DBN) 、深度轉移網絡 (Deep Transformation Networks, DTN) 、深度資訊最大化網絡 (Deep InfoMax, DIM)，為相關領域的研與應用提供一定程度的參考依據。

With the rapid development of artificial intelligence, deep learning, which involves neural network technologies, has become one of the focal points of global technological advancement. Its applications span across various industries. This study aims to explore the development trends and application domains of different neural network technologies within deep learning. Through patent retrieval and analysis methods, we assess their trends and impacts. By collecting and analyzing a vast amount of patent data from the Global Patent Search System (GPSS) provided by the Taiwan Intellectual Property Office (TIPO), we investigate the yearly number of patents, leading countries, leading companies, and current status of neural network technologies, including recurrent neural networks (RNN), convolutional neural networks (CNN), generative adversarial networks (GAN), temporal segment networks (TSN), autoencoders (AE), deep belief networks (DBN), deep transformation networks (DTN), and deep infomax (DIM). Through patent retrieval and technology lifecycle analysis methods, we gain insights into the application scope and future development trends of deep learning technology, providing valuable references for future research and industrial applications. Overall, this study aims to provide a certain level of reference for research and applications in related fields through patent analysis methods, focusing on deep learning based on training data and eight neural network technologies, including RNN, CNN, GAN, TSN, AE, DBN, DTN, and DIM.

中文文獻
世界智慧財產權組織（2019）。人工智能作為新的數字疆界,將對世界產生深遠的影響, 改變人們的工作和生活方式。 2019年產權組織技術趨勢 : 人工智能。
杜雨、張牧銘 （2023）。AI生成時代 : 從ChatGPT到繪圖、音樂、影片，利用智能創作自我加值、簡化工作，成為未來關鍵人才。台北市 : 英屬維京群島商高寶國際有限公司台灣分公司出版。
國家知識產權局 （2023年 2 月 13日）。
專利法 （2022 年 5 月 4 日）。
陳明琪、張有中、林逾先（2009）。 技術創新下不同風險型態之最適投資決策: 實質選擇權與產品生命週期之應用。管理學報，26（5），507-532。
黃嘉彥、劉冠廷 (2013)。運用複合式分區專利檢索策略與文字探勘分析雲端運算產業專利。 創新與經營管理學刊，4（2），105-118。
楊明德、蔡慧萍、許鈺群、曾信鴻（2018）。 人工智慧模型之建置與應用. 土木水利，45（5），59-66。
戴建耘（2020）。最新人工智慧概論－含AIL國際認證。新北市 : 台科大圖書出版。

英文部分
Abadi, H. H. N., & Pecht, M. (2020). Artificial intelligence trends based on the patents granted by the united states patent and trademark office. IEEE Access, 8, 81633-81643.
Aristodemou, L., & Tietze, F. (2018). The state-of-the-art on Intellectual Property Analytics (IPA): A literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data. World Patent Information, 55, 37-51.
Bacaër, N., & Bacaër, N. (2011). Verhulst and the logistic equation (1838). A short history of mathematical population dynamics, 35-39.
Chung, M. S., Jeong, S. H., & Lee, J. Y. (2018). Analysis of major research trends in artificial intelligence based on domestic/international patent data. Journal of digital convergence, 16(6), 187-195.
Bao, J., Kudo, M., Kimura, K., & Sun, L. (2023). Robust embedding regression for semi-supervised learning. Pattern Recognition, 145, 109894.
Downe‐Wamboldt, B. (1992). Content analysis: method, applications, and issues. Health care for women international, 13(3), 313-321.
Ernst, H. (1997). The use of patent data for technological forecasting: the diffusion of CNC-technology in the machine tool industry. Small business economics, 9, 361-381.
Fujii, H., & Managi, S. (2018). Trends and priority shifts in artificial intelligence technology invention: A global patent analysis. Economic Analysis and Policy, 58, 60-69.
Han, T., Liu, C., Yang, W., & Jiang, D. (2020). Deep transfer network with joint distribution adaptation: A new intelligent fault diagnosis framework for industry application. ISA transactions, 97, 269-281.
Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M., & Kuusk, A. (2022). Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities. Expert Systems with Applications, 119456.
Krestel, R., Chikkamath, R., Hewel, C., & Risch, J. (2021). A survey on deep learning for patent analysis. World Patent Information, 65, 102035.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.
Lee, M. (2020). An analysis of the effects of artificial intelligence on electric vehicle technology innovation using patent data. World Patent Information, 63, 102002.
Li, C., Zhang, J., & Yao, J. (2021). Streamer action recognition in live video with spatial-temporal attention and deep dictionary learning. Neurocomputing, 453, 383-392.
Li, Y. (2022). Research on neural network algorithm in artificial intelligence recognition. Sustainable Energy Technologies and Assessments, 53, 102691.
Li, Y., Peng, T., Hua, L., Ji, C., Ma, H., Nazir, M. S., & Zhang, C. (2022). Research and application of an evolutionary deep learning model based on improved grey wolf optimization algorithm and DBN-ELM for AQI prediction. Sustainable Cities and Society, 87, 104209.
Lim, S., Lee, S., Piao, Y., Choi, M., Bang, D., Gu, J., & Kim, S. (2022). On modeling and utilizing chemical compound information with deep learning technologies: A task-oriented approach. Computational and Structural Biotechnology Journal.
Lin, Z. C., Lin, M. H., & Hong, G. E. (2017). Analysis on Patent Categorization of LED Standing/Desk Lamp Patents with Considering the Position Weight. Journal of the Chinese Society of Mechanical Engineers, 38(2), 117-133.
Liu, Q., & Zhang, T. (2023). Deep learning technology of computer network security detection based on artificial intelligence. Computers and Electrical Engineering, 110, 108813.
Mehrish, A., Majumder, N., Bharadwaj, R., Mihalcea, R., & Poria, S. (2023). A review of deep learning techniques for speech processing. Information Fusion, 101869.
Miric, M., Jia, N., & Huang, K. G. (2023). Using supervised machine learning for large‐scale classification in management research: The case for identifying artificial intelligence patents. Strategic Management Journal, 44(2), 491-519.
Novoa-Paradela, D., Fontenla-Romero, O., & Guijarro-Berdiñas, B. (2023). Fast deep autoencoder for federated learning. Pattern Recognition, 143, 109805.
Pandey, B., Pandey, D. K., Mishra, B. P., & Rhmann, W. (2022). A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions. Journal of King Saud University-Computer and Information Sciences, 34(8), 5083-5099.
Parteka, A., & Kordalska, A. (2023). Artificial intelligence and productivity: global evidence from AI patent and bibliometric data. Technovation, 125, 102764.
Qiao, C., Gao, B., Liu, Y., Hu, X., Hu, W., Calhoun, V. D., & Wang, Y. P. (2023). Deep learning with explainability for characterizing age-related intrinsic differences in dynamic brain functional connectivity. Medical Image Analysis, 90, 102941.
Valliani, A. A. A., Ranti, D., & Oermann, E. K. (2019). Deep learning and neurology: a systematic review. Neurology and therapy, 8, 351-365.
Wikipedia. (2023). Logistic function. Retrieved from : https://en.wikipedia.org/wiki/Logistic_function
WIPO.(2019). BACKGROUND DOCUMENT ON PATENTS AND EMERGING TECHNOLOGIES. Standing Committee on the Law of Patents. Retrieved from : https://www.wipo.int/meetings/en/doc_details.jsp?doc_id=440251
WIPO.(2019). WIPO Technology Trends – Artificial Intelligence.Technology Trends – Artificial Intelligence. Retrieved from : https://www.wipo.int/tech_trends/en/artificial_intelligence/
Wlodarczak, P. (2019). Springer: deep learning in eHealth. Handbook of Deep Learning Applications, 319-331.