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  1. NTU Theses and Dissertations Repository
  2. 管理學院
  3. 創業創新管理碩士在職專班(EiMBA)
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98927
完整後設資料紀錄
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dc.contributor.advisor郭瑞祥zh_TW
dc.contributor.advisorRuey-Shan Guoen
dc.contributor.author蘇彥彰zh_TW
dc.contributor.authorYen-Chang Suen
dc.date.accessioned2025-08-20T16:18:56Z-
dc.date.available2025-08-21-
dc.date.copyright2025-08-20-
dc.date.issued2025-
dc.date.submitted2025-08-12-
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31. Hsieh, P.-Y., Chen, H.-W., & Lin, C.-C. (2025). Advances in full-color microdisplays based on MicroLED for AR and VR applications. Journal of the Society for Information Display, 33(1), 1-15.
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44. Park, J., Kim, J.-H., & Lee, G.-W. (2021). Topical review—MicroLED technologies and applications: Characteristics, fabrication, progress, and challenges. Journal of the Korean Physical Society, 78(7), 583–601. https://doi.org/10.1007/s40042-021-00100-3
45. Paul, S. (2020, June 9). Pruning in deep learning model. Medium. https://medium.com/@souvik.paul01/pruning-in-deep-learning-models-1067a19acd89
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48. Porter, M. E. (1979, March). How competitive forces shape strategy. Harvard Business Review, 57(2), 137–145.
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68. 工業技術研究院 (2024年12月4日)。戶外也可戴!工研院發表國內首支Micro LED AR眼鏡亮度十倍突破打造擴增實境新「視」界 [Press release]。https://www.itri.org.tw/ListStyle.aspx?DisplayStyle=01_content&SiteID=1&MmmID=1036276263153520257&MGID=113120410051946058
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98927-
dc.description.abstract本計畫提出一套專為台灣新創公司量身打造的完整商業計畫,目標在於開發並導入AI賦能的AR智慧眼鏡,應用於高價值的工業場域。計畫核心論點為:正值大型多模態模型(LMM)、MicroLED微顯示技術與光場光學三項關鍵技術交匯之際,技術成熟度與市場需求同步到位,形成突破AR應用長期瓶頸的最佳時機,為工業場域提供可行的解決方案。

本計畫採取「集中化差異策略」,迴避價格競爭激烈的消費性市場,聚焦於對資訊安全、操作精準性與長時佩戴舒適性具有高度要求的工業利基市場,如半導體製造與能源設施等。此策略的實施仰賴以下四項核心能力:

1. 硬體技術整合:整合光場顯示技術與MicroLED微顯示器,解決AR常見的視覺輻輳調節衝突(VAC)問題,並提升配戴舒適度,滿足工業現場長時間使用之需求。

2. 軟體在地部署架構:設計「端-伺服器雙AI代理人」在地部署模式,使資料處理與模型推論均可於企業內部環境中進行,強化資料主權與資訊安全,降低資安風險,符合高科技產業之需求。

3. AI應用功能:本計畫將AR裝置由被動的資訊顯示器升級為具備邊緣推論與即時互動能力的智慧代理系統。透過導入大型多模態模型(LLM/LMM)、AI代理人(AI Agents),以及檢索增強生成(Retrieval-Augmented Generation, RAG)架構,裝置可在地執行知識擷取、指令理解與語意生成,提供即時輔助與決策支援,全面提升人機協作效率與智能化水準。

4. 商業模式設計:採用「AI成長飛輪」商業模式,以低成本硬體降低導入門檻,搭配免費增值(Freemium)策略吸引早期用戶。後續營收來自軟體即服務(SaaS)與模型即服務(MaaS)訂閱。使用者在操作過程中產生的工業數據將回饋至模型訓練,優化AI效能,透過數據與模型之正向循環,強化產品價值、提升使用者黏著度,逐步建立資料與技術門檻,形成可持續的競爭優勢。

整體而言,本計畫不僅聚焦單一硬體產品的開發,更致力於打造結合人工智慧與AR裝置的工業智慧平台。藉由精準的市場定位,結合台灣於半導體與光電產業的既有優勢,再搭配數據驅動的商業邏輯,預期本計畫所描繪的新創公司,具備於全球工業AR市場中建立長期競爭優勢之潛力。
zh_TW
dc.description.abstractThis project presents a comprehensive business plan tailored for a Taiwanese startup, aiming to develop and implement AI-powered augmented reality (AR) smart glasses for high-value industrial applications. The core proposition is that the convergence of three critical technologies—large multimodal models (LMMs), MicroLED microdisplay technology, and light field optics—has created a unique inflection point where technological maturity meets market demand, enabling practical solutions to longstanding bottlenecks in AR adoption for industrial use.

The strategy adopted in this plan is a focused differentiation strategy, deliberately avoiding the highly competitive consumer market and instead targeting industrial niche sectors with high demands for data security, operational precision, and long-duration wearability, such as semiconductor manufacturing and energy infrastructure. Successful implementation of this strategy relies on four key capabilities:

1. Hardware Integration: By combining light field display technology with MicroLED microdisplays, the system addresses the common issue of vergence-accommodation conflict (VAC) in AR and enhances wearing comfort, making it suitable for prolonged use in industrial environments.

2. Localized Software Deployment Architecture: A dual-agent architecture involving device-side and server-side AI agents is proposed, enabling all data processing and model inference to be executed within the enterprise's internal environment. This design ensures data sovereignty and reduces cybersecurity risks, aligning with the security requirements of the high-tech industry.

3. AI Application Capabilities: The AR device is transformed from a passive information display into an intelligent agent equipped with edge inference and customization capabilities. Leveraging large multimodal models (LLM/LMM), AI agents, and retrieval-augmented generation (RAG), the system supports on-device knowledge retrieval, command interpretation, and real-time semantic generation. This architecture enhances field task support, decision-making assistance, and human-machine collaboration efficiency.

4. Business Model Design: An "AI growth flywheel" model is employed to lower the entry barrier through low-cost hardware and to attract early users via a freemium strategy. Subsequent revenues are generated through Software-as-a-Service (SaaS) and Model-as-a-Service (MaaS) subscriptions. Industrial data generated during usage feeds back into model training, continuously optimizing AI performance. This positive feedback loop between data and models enhances product value, strengthens user retention, and gradually builds data and technology barriers, establishing a sustainable competitive advantage.

In summary, this project goes beyond the development of a single hardware product, aiming instead to build an industrial intelligence platform that integrates AI and AR technologies. By leveraging Taiwan's existing strengths in the semiconductor and optoelectronics industries, combined with a data-driven business logic and precise market positioning, the startup described in this plan has the potential to establish long-term competitiveness in the global industrial AR market.
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dc.description.tableofcontents感言與誌謝 i
中文摘要 iv
ABSTRACT vi
目次 ix
圖次 xiii
表次 xv
第一章、計畫背景與時機 1
1-1、背景 1
1-2、計畫優勢和獨特性 3
第二章、痛點與機會辨識 5
2-1、現有解決方案之產業痛點 5
2-2、AI賦能AR帶來之潛在效益 8
第三章、解決方案 11
3.1、維修輔助模式演進與服務層級 12
3.2、解決方案總覽:回應產業核心痛點 13
3.3、核心硬體平台:奠定卓越體驗之基石 17
3.3.1、光場顯示系統:根除視覺輻輳調節衝突 17
3.3.2、MicroLED微顯示器:實現全天候輕量化設計 19
3.3.3、整合感測與運算單元 20
3.4、軟體資訊安全架構:絕不妥協的數據主權 21
3.4.1、「端-伺服器雙AI代理人」在地化架構 22
3.4.2、邊緣運算與模型壓縮技術 24
3.5、AI賦能服務:從顯示器到智慧協作夥伴 25
3.5.1、大型多模態模型(LMM)應用核心 25
3.5.2、檢索增強生成(RAG)於私有知識庫之應用 26
3.5.3、應用場景:AI維修助理與訓練教練 27
3.6、整合性價值主張與策略綜效 28
第四章、市場分析與競爭策略 30
4-1、外部分析 30
4-1-1、PESTLE分析 30
4-1-2、五力分析 32
4-1-3、國家競爭力分析(波特鑽石模型) 34
4-2、內部分析 36
4-2-1、團隊簡介 36
4-2-2、核心能力 (VRIO分析) 40
4-2-3、策略定位與競爭優勢 42
4-3、綜合策略分析 43
4-3-1、STP分析 43
4-3-2、企業綜合評價(SWOT分析) 45
第五章、商業模式 48
5-1、願景、任務與價值 48
5-2、目標客戶與價值主張 49
5-2-1、目標客戶 49
5-2-2、價值主張 50
5-3、營運模式 54
5-3-1、通路 54
5-3-2、顧客關係 55
5-3-3、收益流 56
5-3-4、關鍵資源 57
5-3-5、關鍵活動 60
5-3-6、關鍵合作夥伴 60
5-4、持續性 61
5-4-1、成本結構 61
5-4-2、營收模式 61
第六章、公司策略 64
6-1、策略擬定 64
6-2、空間策略:集中差異化之實踐 67
6-3、時間策略:分階段成長路徑 68
6-3-1、第一階段:市場滲透與驗證 69
6-3-2、第二階段:市場開發與產品開發 70
6-3-3、第三階段:全球化與平台化多角化 71
6-4、保齡球道策略:跨越鴻溝的執行藍圖 72
6-4-1、設計攻擊路徑 72
6-4-2、戰略意涵與成功關鍵 74
第七章、財務規劃與可行性分析 76
7-1、市場規模分析 (TAM, SAM, SOM) 76
7-2、商業模式 78
7-2-1、收入結構 78
7-2-2、獲利邏輯 80
7-3、財務預估之關鍵假設 80
7-3-1、收入預測假設 81
7-3-2、支出結構假設 81
7-4、綜合財務預測與分析 82
7-4-1、五年營收預測 83
7-4-2、預計損益表 84
7-4-3、預計現金流量表 85
7-4-4、預計資產負債表 86
7-4-5、關鍵績效指標(KPI)分析 87
7-5、募資規劃與時程 87
7-5-1、階段性募資藍圖 87
7-5-2、募資資金使用分配表 89
7-6、估值分析與投資回報 89
7-6-1、股權規劃與募資稀釋分析 89
7-6-2、估值合理性與投資回報再評估 90
7-7、投資人出場策略 90
第八章、風險分析與解決方案 92
8.1 技術風險 92
8.2 產品風險 93
8.3 商業風險 94
8.4 生態風險 95
8.5 風險應對 96
參考文獻 97
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dc.language.isozh_TW-
dc.subjectAI代理人zh_TW
dc.subject光場顯示zh_TW
dc.subjectAR擴增實境智慧眼鏡zh_TW
dc.subjectMicroLED微顯示器zh_TW
dc.subject在地部署人工智慧zh_TW
dc.subject大型多模態模型(LMM)zh_TW
dc.subjectAI成長飛輪商業模式zh_TW
dc.subjectMicroLED Microdisplayen
dc.subjectAI Growth Flywheel Business Modelen
dc.subjectLight Field Displayen
dc.subjectLarge Multimodal Model (LMM)en
dc.subjectAI Agentsen
dc.subjectAR Augmented Reality Smart Glassesen
dc.subjectOn-Premise Artificial Intelligenceen
dc.titleAI人工智慧賦能的AR智慧眼鏡導入產業應用之商業計畫– 以台灣新創公司為例zh_TW
dc.titleBusiness Plan for the Industrial Deployment of AI-Enabled AR Smart Glasses: The Case of a Taiwanese Startup Companyen
dc.typeThesis-
dc.date.schoolyear113-2-
dc.description.degree碩士-
dc.contributor.coadvisor陸洛zh_TW
dc.contributor.coadvisorLuo Luen
dc.contributor.oralexamcommittee孔令傑;吳學良zh_TW
dc.contributor.oralexamcommitteeLing-Chieh Kung;Hsueh-Liang Wuen
dc.subject.keywordAR擴增實境智慧眼鏡,光場顯示,MicroLED微顯示器,在地部署人工智慧,大型多模態模型(LMM),AI代理人,AI成長飛輪商業模式,zh_TW
dc.subject.keywordAR Augmented Reality Smart Glasses,Light Field Display,MicroLED Microdisplay,On-Premise Artificial Intelligence,Large Multimodal Model (LMM),AI Agents,AI Growth Flywheel Business Model,en
dc.relation.page102-
dc.identifier.doi10.6342/NTU202504107-
dc.rights.note同意授權(全球公開)-
dc.date.accepted2025-08-14-
dc.contributor.author-college管理學院-
dc.contributor.author-dept創業創新管理碩士在職專班-
dc.date.embargo-lift2025-08-21-
顯示於系所單位:創業創新管理碩士在職專班(EiMBA)

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