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Title:	企業導入生成式AI的智財管理與資訊安全對策_以製鞋產業鏈為例 Intellectual Property Management and Information Security Measures for Enterprises Implementing Generative AI: A Case Study of the Footwear Industry Supply Chain
Authors:	曾蕙瑜 Tseng, Hui-Yu
Contributors:	宋皇志 Sung, Huang-Chih 曾蕙瑜 Tseng, Hui-Yu
Keywords:	生成式AI 製鞋產業智慧財產權資訊安全資料治理 Generative AI Footwear industry Intellectual property rights Information security Data governance
Date:	2024
Issue Date:	2024-09-04 13:52:02 (UTC+8)
Abstract:	本研究以製鞋產業為例，探討企業在導入生成式AI過程中的智慧財產權保護與資訊安全對策。透過技術-組織-環境-績效(TOE-P)框架、價值鏈分析、利益相關者分析以及風險-價值矩陣，結合文獻分析、深度訪談、案例研究等質性方法，多維度檢視生成式AI在製鞋產業鏈的應用現況、智慧財產管理風險與資料保護的挑戰。研究聚焦產業鏈中上下游尤其是製鞋代工企業在AI模型訓練資料取得、生成內容智慧財產歸屬、資料共享授權等方面的實務難題與因應之道。發現顯示，製鞋代工企業普遍意識到生成式AI帶來的智慧財產權與個人資料隱私風險，但尚缺乏系統性的應對舉措。企業內部跨部門協作、供應鏈資料共享機制有待完善，員工智慧財產權保護與資訊安全意識和能力亟需提升。外部法律環境變化與產業標準缺乏也為製鞋代工企業AI治理帶來更多不確定性。本研究根據案例分析，提出製鞋產業生成式AI智財管理與資安的整體治理框架，建議企業建立專責AI治理的跨部門協作機制，制定資料生命週期管理制度，運用同態加密、聯邦學習等隱私運算技術保護商業敏感資料，並積極參與產業智財政策與標準制定。研究深化了TOE-P框架、價值鏈分析、利益相關者分析以及風險-價值矩陣等，在生成式AI場域的理論應用，豐富了製造業AI治理的實務知識。研究結論可供同業企業在智慧財產權保護與資訊安全實踐上參考，助力製造業在智慧轉型中驅動創新並控管風險。 This study uses the footwear industry as an example to explore strategies for intellectual property protection and information security as enterprises introduce generative AI. Through the Technology-Organization-Environment-Performance (TOE-P) framework, value chain analysis, stakeholder analysis, and risk-value matrix, combined with qualitative methods such as literature review, in-depth interviews, and case studies, the research examines from multiple dimensions the current applications of generative AI in the footwear industry chain, the risks of intellectual property management, and the challenges of data protection. The research focuses on practical issues and coping strategies in the industry chain, especially for footwear OEM companies, regarding AI model training data acquisition, intellectual property ownership of generated content, and data sharing authorization. Findings indicate that footwear OEM companies are generally aware of the intellectual property and personal data privacy risks brought by generative AI, but lack systematic countermeasures. Internal cross-departmental collaboration and supply chain data sharing mechanisms need improvement, and there is an urgent need to enhance employees' awareness and capabilities in intellectual property protection and information security. Changes in the external legal environment and the lack of industry standards also bring more uncertainties to AI governance for footwear OEM companies. Based on case analysis, this study proposes an overall governance framework for generative AI intellectual property management and information security in the footwear industry. It recommends that companies establish cross-departmental collaborative mechanisms dedicated to AI governance, formulate data lifecycle management systems, use privacy computing technologies such as homomorphic encryption and federated learning to protect commercially sensitive data, and actively participate in the formulation of industry intellectual property policies and standards. The research deepens the theoretical application of the TOE-P framework, value chain analysis, stakeholder analysis, and risk-value matrix in the field of generative AI, enriching practical knowledge of AI governance in manufacturing. The research conclusions can serve as a reference for peer companies in intellectual property protection and information security practices, helping the manufacturing industry drive innovation and control risks during intelligent transformation.
Reference:	Adidas. (2024). Adidas Originals 推出首創的數位 Ozworld 體驗，上網日期2024年5月22日，檢自 adidas.com iThome. (2018a). 再傳大規模資料外洩！運動用品大廠愛迪達(Adidas)上周公佈自家美國網站遭駭，使數量不明的消費者個資，包括使用者名稱、密碼及聯絡資訊等外流，但有媒體報導受害人數高達數百萬，上網日期2024年5月22日，檢自https://www.ithome.com.tw/news/124246 iThome. (2018b). Nike旗下網站被爆有漏洞遲未修補，可能外洩密碼等敏感資訊，上網日期2024年5月22日，檢自https://www.ithome.com.tw/news/121655 MyMKC. (n.d.). Nike 利用 AI 技術深化數位經營，上網日期2024年5月22日，檢自https://mymkc.com/article/content/23503 Synergies. (2024). AI-driven scheduling and inventory management at Yuqi Group: 全球鞋業Top5:從3小時到10分鐘，分析效率提升40倍｜JarviX智能供應鏈做了什麽. 上網日期2024年4月22日，檢自https://www.synergies.com.tw/technical-article/321.html 李朋叡. (2023). Nike推出首款虛擬球鞋、還將空投海報NFT！元宇宙內是否能延續搶鞋熱潮？上網日期2024年5月20日，自 https://web3plus.bnext.com.tw/article/662 行政院. (2023). 使用生成式AI參考指引，上網日期2024年5月10日，檢自 https://www.nstc.gov.tw/folksonomy/list/c79bf57b-dc94-4aff-8d14-3262b5559cfc?l=ch 個人資料保護法. (2023). 個人資料保護法 (中華民國法律第123號) ，上網日期2024年5月10日，檢自 https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=I0050021 資通安全管理法. (2018). 資通安全管理法，上網日期2024年5月10日，檢自 https://law.moj.gov.tw/LawClass/LawAll.aspx?pcode=A0030297 數位發展部. (2024). 數據公益運作指引，上網日期2024年5月10日，檢自 https://moda.gov.tw/information-service/govinfo/administrative-directions/ad-plural-innovation/1419 數位發展部. (2024). 隱私強化技術應用指引，上網日期2024年5月10日，檢自 https://moda.gov.tw/information-service/govinfo/administrative-directions/ad-plural-innovation/1419 AI Expert Network. (2024). Case study: How Nike is leveraging AI across its operations. Retrieved April 20, 2024, from https://aiexpert.network/case-study-how-nike-is-leveraging-ai-across-its-operations/ Adidas. (2024). Annual report 2023. Retrieved April 20, 2024, from https://report.adidas-group.com/2023/en/group-management-report-financial-review/risk-and-opportunity-report/illustration-of-risks.html Adidas. (2018). GPriv-01 Global Privacy Management Policy. Retrieved April 20, 2024, from https://www.adidas-group.com/en/sustainability/transparency/policies Adidas. (n.d.). Security. Retrieved May 20, 2024, from https://adidas.gitbook.io/api-guidelines/general-guidelines/security Anderson, L. B., Kanneganti, D., Houk, M. B., Holm, R. H., & Smith, T. (2023). Generative AI as a tool for environmental health research translation. GeoHealth, 7(7), e2023GH000875. ASICS Corporation. (n.d.) Policy of Engagement/Supplier Code of Conduct. Retrieved April 20 2024, from https://corp.asics.com/en/p/asics-policy-of-engagement ASICS Corporation. (n.d.). Information Security Guiding Principles. Retrieved April 20, 2024, from https://corp.asics.com/en/investor_relations/management_policy/corporate_governance/information-security-guiding-principles Bajaj, Y., & Samal, M. K. (2023). Accelerating Software Quality: Unleashing the Power of Generative AI for Automated Test-Case Generation and Bug Identification. International Journal for Research in Applied Science and Engineering Technology, 11(7). Bi, Q. (2023). Analysis of the application of generative AI in business management. Advances in Economics and Management Research, 6(1), 36-36. BrainStation. (2021). Nike’s digital ecosystem paved the way for D2C transformation. Retrieved April 20, 2024, from https://brainstation.io Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. arXiv preprint arXiv:2005.14165. Deloitte. (n.d.). Deloitte Omnia. Retrieved April 22, 2024, from https://www2.deloitte.com/us/en/pages/audit/solutions/audit-technology-solutions.html Designboom. (2024). Explore NIKE A.I.R and its 13 new 3D printed sneakers made using AI, math and algorithms. Retrieved April 25, 2024, from https://www.designboom.com/design/nike-air-3d-printed-sneakers-ai-math-algorithms-interview-john-hoke-04-13-2024/ Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv. arXiv preprint arXiv:1810.04805. Dezeen. (2024). Nike developing AI model as part of design "step change". Retrieved May 20, 2024, from https://www.dezeen.com/2024/05/07/nike-ai-model-john-hoke/ Dhariwal, P., & Nichol, A. (2021). Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems, 34, 8780-8794. Epstein, Z., Hertzmann, A., Herman, L., Mahari, R., Frank, M., Groh, M., Schroeder, H., Smith, A., Akten, M., Fjeld, J., Farid, H., Leach, N., & Pentland, A. (2023). Art and the science of generative AI. Science, 381(6657), 158-161. European Commission. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. Retrieved May 20, 2024, from https://eur-lex.europa.eu Gatt, A., & Krahmer, E. (2018). Survey of the state of the art in natural language generation: Core tasks, applications, and evaluation. Journal of Artificial Intelligence Research, 61, 65-170. https://doi.org/10.1613/jair.5477 Geiger, C. (2024). Elaborating a Human Rights-Friendly Copyright Framework for Generative AI. IIC-International Review of Intellectual Property and Competition Law, 1-37. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27. Google. (2018). Google's AI Principles. Retrieved April 20, 2024, from https://ai.google/principles/ Gorian, E. (2020). Singapore’s cybersecurity act 2018: A new generation standard for critical information infrastructure protection. In Smart Technologies and Innovations in Design for Control of Technological Processes and Objects: Economy and Production: Proceeding of the International Science and Technology Conference" FarEastСon-2018" Volume 1 (pp. 1-9). Springer International Publishing. Hagendorff, T. (2020). The ethics of AI ethics: An evaluation of guidelines. Minds and Machines, 30(1), 99-120. Hong, M. K., Hakimi, S., Chen, Y. Y., Toyoda, H., Wu, C., & Klenk, M. (2023). Generative ai for product design: Getting the right design and the design right. arXiv preprint arXiv:2306.01217. Hu, Y., Zhang, D., & Quigley, A. (2023). GenAIR: Exploring design factors of employing generative AI for augmented reality. In Proceedings of the 2023 ACM Symposium on Spatial User Interaction. Institute of Electrical and Electronics Engineers. (2020). IEEE code of ethics. Retrieved April 20, 2024, from https://www.ieee.org/about/corporate/governance/p7-8.html Hypebeast. (2024, April). Nike showcases AI-designed sneakers in Paris. Retrieved April 20, 2024, from https://hypebeast.com/hk/2024/4/nike-showcases-ai-designed-sneakers-paris-info International Organization for Standardization. (2023). ISO/IEC 42001:2023 Information technology — Artificial intelligence — Management system. Geneva, Switzerland: ISO. Japanese Copyright Act. (2019). Amendment to the Copyright Act. Retrieved April 20, 2024, from http://www.bunka.go.jp Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494-514. Jiang, R. (2023). Research on the Digital Marketing Strategy of Adidas. Advances in Economics, Management and Political Sciences. https://doi.org/10.54254/2754-1169/54/20230945. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. Kohl, G., Chen, L. W., & Thuerey, N. (2023). Turbulent flow simulation using autoregressive conditional diffusion models. arXiv preprint arXiv:2309.01745. Kop, M. (2019). AI & intellectual property: Towards an articulated public domain. SSRN Electronic Journal. Lee, S. (2023). A Study on China’s Generative AI Regulations. Law Research Institute Chungbuk National University. https://doi.org/10.34267/cbstl.2023.14.1.115. Li, J., Jia, R., He, H., & Liang, P. (2018). Delete, retrieve, generate: A simple approach to sentiment and style transfer. arXiv preprint, arXiv:1804.06437. https://arxiv.org/abs/1804.06437 Li, J., Cai, X., & Cheng, L. (2023). Legal regulation of generative AI: A multidimensional construction. International Journal of Legal Discourse, 8, 365-388. Li, X., Wang, S., & Yang, Y. (2019). Anomaly Detection with Generative Adversarial Networks for Skin Disease Imaging. IEEE Transactions on Medical Imaging, 38(1), 20-28. https://doi.org/10.1109/TMI.2018.2865673 Liu, H., Zhang, Y., & Guo, J. (2020). Detection of Surface Defects on Leather Using Generative Adversarial Networks. Journal of Manufacturing Processes, 49, 92-101. https://doi.org/10.1016/j.jmapro.2020.02.003 Mantelero, A., Vaciago, G., Samantha Esposito, M., & Monte, N. (2020). The common EU approach to personal data and cybersecurity regulation. International Journal of Law and Information Technology, 28(4), 297-328. Mehri, S., Kumar, K., Gulrajani, I., Kumar, R., Jain, S., Sotelo, J., ... & Bengio, Y. (2017). SampleRNN: An unconditional end-to-end neural audio generation model. International Conference on Learning Representations. METI. (2019). Japan's Copyright Law Amendments and AI Development. Ministry of Economy, Trade and Industry. Retrieved April 20, 2024, from https://www.meti.go.jp Meurisch, C., Bayrak, B., & Mühlhäuser, M. (2020). Privacy-preserving AI Services Through Data Decentralization. Proceedings of The Web Conference 2020. Meurisch, C., & Mühlhäuser, M. (2021). Data protection in AI services. ACM Computing Surveys (CSUR), 54(4), 1-38 Microsoft. (2022). Microsoft responsible AI standard, v2. Retrieved April 25, 2024, from https://www.microsoft.com/en-us/ai/responsible-ai National Institute of Standards and Technology. (2022). AI risk management framework (AI RMF). Retrieved April 25, 2024, from https://www.nist.gov/itl/ai-risk-management-framework Nike. (n.d.). Acceptable use policy: Electronic communications and devices. Retrieved April 20, 2024, from https://www.nike.com NVIDIA. (2023). A perfect pair: adidas and Covision Media use AI, NVIDIA RTX to create photorealistic 3D content. Retrieved April 20, 2024, from https://blogs.nvidia.com/blog/covision-adidas-rtx-ai/ Ong, D. S., Chan, C. S., Ng, K. W., Fan, L., & Yang, Q. (2021). Protecting intellectual property of generative adversarial networks from ambiguity attacks. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3629-3638). Oord, A. v. d., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., ... & Kavukcuoglu, K. (2016). WaveNet: A generative model for raw audio. arXiv preprint, arXiv:1609.03499. https://arxiv.org/abs/1609.03499 Poland, C. M. (2023). Generative AI and US Intellectual Property Law. arXiv preprint arXiv:2311.16023. Prenger, R., Valle, R., & Catanzaro, B. (2019). WaveGlow: A flow-based generative network for speech synthesis. In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 3617-3621). https://doi.org/10.1109/ICASSP.2019.8683143 Radford, A., Metz, L., & Chintala, S. (2016). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint, arXiv:1511.06434. https://arxiv.org/abs/1511.06434 Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9. Ready Player Me. (2022). adidas Originals bring Ozworld avatars to Ready Player Me Retrieved April 20, 2024, from https://readyplayer.me/blog/adidas-originals-ozworld-3d-avatars-metaverse Reiter, E., & Dale, R. (2000). Building natural language generation systems. Cambridge University Press. https://doi.org/10.1017/CBO9780511519857 Samuelson, P. (2023). Generative AI meets copyright. Science, 381(6653), 158-161. https://doi.org/10.1126/science.adk3772 Shahriar, S., & Hayawi, K. (2022, March). NFTGAN: Non-fungible token art generation using generative adversarial networks. In Proceedings of the 2022 7th International Conference on Machine Learning Technologies (pp. 255-259). Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., ... & Wang, Y. (2018). Natural TTS synthesis by conditioning WaveNet on mel spectrogram predictions. In ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4779-4783). https://doi.org/10.1109/ICASSP.2018.8461368 Suessmuth, J., Fick, F., & Van Der Vossen, S. (2023). Generative AI for Concept Creation in Footwear Design. In ACM SIGGRAPH 2023 Talks (pp. 1-2). Sultan, F., Farley, J. U., & Lehmann, D. R. (1990). A meta-analysis of applications of diffusion models. Journal of Marketing Research, 27(1), 70-77. https://doi.org/10.1177/002224379002700107 Tang, B. (2016). Toward intelligent cyber-physical systems: Algorithms, architectures, and applications. ThroughPut. (2024). The Role of AI in Inventory Management. Retrieved April 25 2024, from https://throughput.world/blog/ai-in-inventory-management/ Tzirides, A., Saini, A., Zapata, G., Searsmith, D., Cope, B., Kalantzis, M., Castro, V., Kourkoulou, T., Jones, J., Silva, R., Whiting, J., & Kastania, N. (2023). Generative AI: Implications and Applications for Education. ArXiv, abs/2305.07605. https://doi.org/10.48550/arXiv.2305.07605 U.S. Congress. (2021). National AI Initiative Act of 2020. Public Law 116-283. Retrieved April 20 2024, from https://www.congress.gov/bill/116th-congress/house-bill/6216/text U.S. Patent and Trademark Office (USPTO). (2019). Artificial Intelligence and Intellectual Property Policy. Retrieved April 20 2024, from https://www.uspto.gov Wen, Q., Wang, B., Xu, Y., Li, Z., & Ma, H. (2021). CoST-GAN: A compound structure-aware GAN for multivariate time-series anomaly detection. arXiv preprint, arXiv:2107.02410. https://arxiv.org/abs/2107.02410 World Intellectual Property Organization (WIPO). (2019). WIPO technology trends 2019: Artificial intelligence. World Intellectual Property Organization. Xu, Y. (2023). Research on footwear industry marketing strategy in AI era. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis. Yang, L., Zhang, Z., Hong, S., Xu, R., Zhao, Y., Shao, Y., Zhang, W., Yang, M.-H., & Cui, B. (2022). Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56(1), 1-39. https://doi.org/10.1145/3522698 Yasar, A. G., Chong, A., Dong, E., Gilbert, T. K., Hladikova, S., Maio, R., ... & Zilka, M. (2023). AI and the EU Digital Markets Act: Addressing the Risks of Bigness in Generative AI. arXiv preprint arXiv:2308.02033. Yoon, J., Jarrett, D., & Van der Schaar, M. (2019). Time-series generative adversarial networks. Advances in neural information processing systems, 32. Zhang, L., Wang, S., & Liu, B. (2018). Deep learning for sentiment analysis: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), e1253. https://doi.org/10.1002/widm.1253 Zhang, P., & Kamel Boulos, M. N. (2023). Generative AI in medicine and healthcare: Promises, opportunities and challenges. Future Internet, 15(9), 286. https://doi.org/10.3390/fi15090286 Zhang, Y. (2023). Generative AI has lowered the barriers to computational social sciences. arXiv preprint arXiv:2311.10833. Zhong, H., Chang, J., Yang, Z., Wu, T., Mahawaga Arachchige, P. C., Pathmabandu, C., & Xue, M. (2023, April). Copyright protection and accountability of generative ai: Attack, watermarking and attribution. In Companion Proceedings of the ACM Web Conference 2023 (pp. 94-98). Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., & Zhang, W. (2021, May). Informer: Beyond efficient transformer for long sequence time-series forecasting. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 12, pp. 11106-11115).
Description:	碩士國立政治大學經營管理碩士學程(EMBA) 111932080
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0111932080
Data Type:	thesis
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