Technological Innovation for Sustainable Mobility : Identifying Green Technology Opportunities in the Railway and Automotive Industry through Patent Data Analytics = 지속가능한 모빌리티를 위한 기술혁신: 특허 데이터 분석을 통한 철도 및 자동차 산업의 녹색기술 기회 파악

국문 초록 (Abstract)

본 연구는 지속가능한 모빌리티를 위한 기술혁신을 목표로 철도 및 자동차 산업의 녹색기술 기회를 파악하기 위한 특허 데이터 분석을 수행한다. 철도 주변압기 기술, 철도 보조 전원 장치 기술, 강화된 유럽 배출가스 기준에 대응하는 친환경 자동차 기술 분야를 중심으로 분석을 수행한다.
첫 번째로, 철도 주변압기 기술 분야의 유망 공백 기술을 도출하고 잠재적인 기술 격차를 탐색하기 위해 특허 분석을 수행한다. 한국, 중국, 일본, 미국, 캐나다, 유럽의 특허청 데이터베이스에서 철도 주변압기 관련 특허 데이터 707개를 수집하였다. 수집된 데이터를 기반으로 특허-IPC 매트릭스를 구축하고 기술 매핑 분석, 시계열 분석, 소셜 네트워크 분석을 수행하여 유망 IPC 세부 기술 영역을 식별했다. 식별된 유망 IPC 세부 기술 영역에 해당하는 특허 데이터를 대상으로 GTM 분석을 수행하여 유망 공백 기술을 도출하였다. 분석 결과, 중국의 CRRC를 중심으로 특허 출원 비중이 증가하고 있으며, 기존 변압기의 냉각 방식인 팬을 제거하고 자연 냉각 방식을 사용하는 '블로워리스(Blowerless) 기술', 기존 변압기에 사용되는 절연유를 없애고 건식 절연 방식을 사용하는 '오일 프리(Oil-Free) 기술', 기존 변압기의 주요 구성 요소인 철심과 권선을 반도체 소자로 대체하는 '솔리드 스테이트(Solid-State) 기술'이 유망한 공백 기술로 식별된다. 이 기술들은 크기와 무게를 줄이고, 에너지 효율을 향상시키며, 소음을 감소시키고, 유지 보수 비용을 절감하는 효과가 있다.
두 번째로, 철도 보조 전원 장치 기술 동향 분석을 통해 기술 분류 체계를 구축하고 유망 기술을 파악한다. 기술과 관련된 특허를 추출하여 기술 분류 체계를 구축하기 위해 LDA 주제 모델링과 N-그램 분석을 통해 기술 주제를 분류하고, 의미를 파악하였다. 이후 시계열 분석과 기술 연관성 분석을 통해 세부 유망 기술 주제를 확인하였다. 마지막으로 특허 출원인의 서지학 및 소셜 네트워크 분석을 통해 유망 기술 주제 분야의 선도 기업과 연구 기관을 확인하였다. 분석 결과, 총 6개의 기술 주제가 확인되었으며, 컨버터(Converter)기술 관련 분야가 세부 유망 기술로 확인된다. 특히 ‘미쓰비시’, ‘히타치’, ‘도시바’ 등 일본 기업들이 이 분야의 R&D를 주도하고 있으며, CRRC를 대표로 하는 중국 기업들도 기술 경쟁력을 강화하고 있다. 추가적으로, 프랑스의 ‘알스톰’은 기술 경쟁력 지표만을 고려할 때 기술 경쟁력이 있다고 평가된다.
세 번째로, 유럽 특허청(EPO)에 출원된 친환경 자동차 기술의 특허 데이터를 분석하여 강화된 유럽 배출가스 기준에 대응하는 글로벌 선도 자동차 기업을 파악하고 기술 기회를 파악하였다. 기술 주제를 파악하기 위해 LDA(Latent Dirichlet Allocation) 기반 토픽 모델링을 사용하였으며, 이를 해석한 N-gram 분석을 통해 기술 기회를 파악하였다. 그리고 글로벌 선도 자동차 기업을 파악하기 위해 기술 매핑 분석을 사용하였다. 분석 결과 기술의 성숙 단계에 해당하는 9개의 기술 주제가 도출되었다. 기술 맵 분석을 통해 파악된 156개의 글로벌 선도 기업 중에는 도요타 자동차, 닛산 자동차, 마쓰다 자동차, 히타치 등의 일본 기업이 다수 포함되어 있다. 자동차 산업은 유럽의 엄격한 환경 규제를 해결하기 위해 수소 연료 전지 자동차, 전기 자동차, 자율 주행 기술 등과 같은 친환경 혁신 기술에 대한 R&D 투자를 더욱 확대할 것으로 예상된다.
마지막으로, 특허 데이터 분석의 한계를 보완하고 결과의 신뢰성을 높이기 위해 최신 학술 논문 및 신문 기사 데이터를 추가적으로 활용했다. 이를 통해 녹색 기술 시장 분석의 정확도와 깊이를 향상시켰다. 나아가, 특허, 논문, 뉴스 데이터 분석 결과와 주요 경제 지표를 PEST (Political, Economic, Social, Technological) 분석 틀에 통합하여 녹색 기술 시장에 영향을 미치는 다양한 요인을 심층적으로 분석했다. 각 데이터 및 경제 지표는 녹색 기술 시장의 기술적, 학문적, 정치적, 경제적, 사회적 측면에 대한 종합적인 정보를 제공했고, 이를 통해 녹색 기술 투자 및 정책 결정에 필요한 핵심 정보를 확보했다. 특히, 저탄소 사회 구축 및 미래 사회의 지속 가능한 발전을 위한 녹색 기술 개발 및 확산 노력은 특허, 논문, 뉴스 데이터 분석 결과에서 공통적으로 확인되어, 현재의 시대적 흐름을 뒷받침했다. 이를 통해 녹색 기술 투자 결정, 정책 수립, 연구 개발 방향 설정뿐만 아니라 녹색 기술 시장의 지속 가능한 성장 전략 수립에도 활용될 수 있었다. 특히, 시장 규모 및 성장률 분석은 녹색 기술 시장의 전반적인 투자 매력도를 평가하는 데 유용했고, 기업 가치 비교 분석은 유망 기업 발굴 및 투자 포트폴리오 구성에 효과적으로 활용될 수 있었다.
추가로, 녹색 기술 시장 분석의 정확도와 깊이를 향상시키기 위한 방안을 모색하고, 철도 및 자동차 산업의 지속 가능한 성장을 위한 녹색 기술 로드맵 수립 및 실행 전략 마련의 중요성을 강조한다. 특히, 지속 가능한 모빌리티를 위한 특허 데이터 분석의 중요성을 부각하며, 특허 데이터가 녹색 기술 시장 분석에 있어 시장 규모, 성장률, 성숙도 등 핵심 정보를 제공함을 밝혔다. 그러나 전통적인 특허 데이터 분석 방법은 특허 공개 지연으로 인해 최신 기술 동향 파악에 어려움이 있으며, 특허 정보 접근 제한 및 분석 도구 부족 등의 문제점이 존재한다. 이에 대한 해결책으로, 미공개 특허 데이터 예측을 위한 AI 기술 도입과 AI 기반 오픈 액세스 (OA) 특허 분석 플랫폼 구축을 제안한다. 더불어, 녹색 기술 관련 지식 공유를 위한 국가 및 기업 간 협력 강화, 개발도상국의 녹색 기술 도입 지원 확대, 지속 가능한 이동성 확보를 위한 국제적 규제 및 기준 마련 등의 필요성을 제기하며, 지속 가능한 모빌리티 구현을 위한 다각적인 노력의 중요성을 강조한다.
본 연구는 특허 데이터 분석을 통해 철도 및 자동차 산업의 기술 동향을 심층적으로 파악하고 미래 녹색 기술 기회를 선제적으로 포착했다. 이러한 연구 결과는 지속 가능한 모빌리티를 위한 연구 개발(R&D) 전략 수립 및 정책 수립에 기여할 뿐만 아니라, 철도 및 자동차 산업의 녹색 기술 개발 및 활용에 중요한 시사점을 제공하여 궁극적으로 지속 가능한 모빌리티 실현에 기여할 것으로 기대된다.

다국어 초록 (Multilingual Abstract)

In this study, we conduct patent data analytics to identify green technology opportunities in the railway and automotive industry with the aim of technological innovation for sustainable mobility. Our analytics focuses on three specific fields: '(1) Railway Main Transformers', '(2) Railway Static Inverters', and '(3) Eco-Friendly Automotive Technology aligned with Stricter European Emission Standards'.
Firstly, we conduct a patent analytics to identify promising vacancy technologies and explore potential technological gaps within the field of railway main transformers technology. Patent data related to railway main transformers technology was collected from patent databases in KIPO (Korea), CNIPA (China), JPO (Japan), USPTO (USA), CIPO (Canada), and EPO (Europe). A total of 707 patent data was collected. Based on the collected data, a patent-IPC (International Patent Classification) matrix was constructed, and further analytics was conducted through technology mapping, time series analytics, and social network analytics. This process aimed to identify promising detailed technology areas within the IPC classification. GTM (Generative Topographic Mapping) analytics was then performed on the patent data corresponding to the identified promising detailed IPC technology areas to derive promising vacancy technologies. As a result of the analytics, an increasing proportion of patent applications were centered on CRRC, a Chinese company. Additionally, promising vacancy technologies were identified, including: "Blowerless technology" which eliminates fans in transformer cooling, "Oil-Free technology" which eliminates insulating oil, and "Solid-State technology" which replaces core components with semiconductors. These technologies offer benefits such as reduced size and weight, improved energy efficiency, lower noise levels, and reduced maintenance costs.
Secondly, to identify promising technologies in railway static inverters, we established a technology classification system through patent data analytics and technology trend analytics. Through LDA thematic modeling and N-gram analytics, we classified technology topics and grasped their meaning. Subsequently, time series analytics and technology relevance analytics helped identify detailed promising technology areas. Finally, bibliographic and social network analytics of patent applicants revealed leading companies and research institutes in these promising areas. Our analytics identified a total of 6 technology topics, with converter technology emerging as the most promising area. Notably, Japanese companies like Mitsubishi, Hitachi, and Toshiba are leading R&D efforts, while CRRC from China is also strengthening its technological competitiveness. Additionally, Alstom in France stands out in terms of technological competitiveness based solely on the technology competitiveness index.
Thirdly, we analyzed patent data of eco-friendly automotive technology filed with the European Patent Office (EPO) to identify global leading automakers responding to stricter European emission standards and to uncover technology opportunities. LDA (Latent Dirichlet Allocation)-based topic modeling was used to identify technology topics, while N-gram analytics interpreted these topics, and technology life cycle (TLC) analytics helped us identify technology opportunities. Then, technology mapping analytics was employed to identify global leading automakers. The analytics resulted in nine technology topics corresponding to the maturity stage of the technology. Many of the 156 global leading companies identified through technology map analysis are Japanese companies such as Toyota Motor, Nissan Motor, Mazda Motor, and Hitachi. To address stricter environmental regulations in Europe, the automotive industry is expected to further increase R&D investment in eco-friendly innovative technologies like hydrogen fuel cell vehicles, electric vehicles, and autonomous driving technologies.
Lastly, the latest academic papers and newspaper articles were incorporated to address the limitations inherent in patent data analysis and enhance the reliability of the results. This approach improved the accuracy and depth of the green technology market analysis. Moreover, a PEST (Political, Economic, Social, Technological) analysis framework was employed, integrating patent, paper, and news data with major economic indicators to comprehensively examine the factors influencing the green technology market. Each data source and indicator provided a multi-faceted view of the technical, academic, political, economic, and social dimensions of the market, yielding crucial information for green technology investment and policy decisions. Notably, the results consistently revealed a commitment to developing and disseminating green technologies for a low-carbon society and sustainable future, aligning with current global priorities. This information could be used not only for investment decisions, policymaking, and R&D direction but also for shaping sustainable growth strategies for the green technology market. Specifically, market size and growth rate analyses were valuable for assessing overall investment attractiveness, while comparative analyses of corporate value could aid in identifying promising companies and constructing investment portfolios.
Additionally, this study explores strategies to enhance the accuracy and depth of green technology market analysis and emphasizes the importance of establishing and implementing green technology roadmaps and execution strategies for the sustainable growth of the railway and automotive industries. It highlights the significance of patent data analysis for sustainable mobility, revealing that patent data provides essential information such as market size, growth rate, and maturity in green technology market analysis. However, traditional patent data analysis methods face challenges in identifying the latest technology trends due to delays in patent disclosure, as well as issues such as limited access to patent information and a lack of analysis tools. To address these challenges, the study proposes the introduction of AI technology for predicting undisclosed patent data and the establishment of an AI-based Open Access (OA) patent analysis platform. Furthermore, the study calls for strengthened cooperation between countries and companies to share knowledge related to green technology, expanded support for developing countries to adopt green technology, and the establishment of international regulations and standards to ensure sustainable mobility, emphasizing the importance of multifaceted efforts to achieve sustainable mobility.
In this study, we have provided a deep understanding of technology trends in the railway and automotive industries through patent data analysis and has proactively identified future green technology opportunities. These research findings are expected to contribute not only to the establishment of R&D strategies and policies for sustainable mobility but also to provide important implications for the development and utilization of green technology in the railway and automotive industries, ultimately contributing to the realization of sustainable mobility.

목차 (Table of Contents)

• ABSTRACT i
• 국문 초록 v
• ACKNOWLEDMENTS x
• TABLE OF CONTENTS xi
• LIST OF TABLES xvii
• LIST OF FIGURES xxvii
• NOMENCLATURE xxxiii
• CHAPTER 1. INTRODUCTION 1
• 1.1 Background 1
• 1.1.1 Importance and Challenges of Sustainable Mobility 1
• 1.1.2 Efforts of the Railway and Automotive Industry Towards Sustainability 3
• 1.2 Research Objectives 8
• 1.2.1 To Identify Green Technology Opportunities in the Railway and Automotive Industry 8
• 1.2.2 Leveraging Patent Data for Strategic Green Technology Innovation in the Railway and Automotive Industry 9
• 1.3 Research Methodologies 11
• 1.3.1 Overview of Patent Data Analytics Methods 11
• 1.3.2 Bibliographic Analytics 12
• 1.3.3 GTM (Generative Topographic Mapping) Analytics 13
• 1.3.4 LDA (Latent Dirichlet Allocation) Topic Modeling 15
• 1.3.5 N-gram Analytics 16
• 1.3.6 Social Network Analytics 17
• 1.3.7 Technology Life Cycle 19
• 1.3.8 Technology Mapping 20
• 1.3.9 Time Series Analytics 22
• 1.4 Research Content Overview 24
• 1.4.1 Railway Main Transformer Technology Forecasting: Identifying Potential Technological Gaps 24
• 1.4.2 Green Technology Opportunities in Railway Static Inverters: Insights from Patent Data Analytics 25
• 1.4.3 Global Leading Automakers' Response to Tightening European Emission Standards: Monitoring through Patent Data Analytics 26
• 1.4.4 Integrated Patent Data Analytics Framework and Expansion of Sustainable Mobility Technology Analysis Domains 27
• CHAPTER 2. RAILWAY MAIN TRANSFORMER TECHNOLOGY FORECASTING: A PATENT DATA ANALYTICS APPROACH 33
• 2.1 Introduction 33
• 2.1.1 Challenges and Drivers for Railway Main Transformer Innovation 33
• 2.1.2 Predicting Vacant Technology in Railway Main Transformers using Patent Analytics 36
• 2.2 Literature Review 39
• 2.2.1 The Value and Methodology of Patent Analytics for Technological Forecasting 39
• 2.2.2 A Review of Previous Patent Analytics Research on Railway Technologies 42
• 2.3 Methodology 43
• 2.3.1 Research Framework 43
• 2.3.2 Data Preprocessing and Patent-IPC Matrix Construction 45
• 2.3.3 Evaluating Technological Level of IPCs 46
• 2.3.4 Identifying Potential Vacant Technological Gaps using Generative Topographic Mapping (GTM) 48
• 2.4 Results 49
• 2.4.1 Patent Data Collection and Technology Trend Analytics 49
• 2.4.2 Evaluation of IPC Technological Level 52
• 2.4.3 Forecasting Potential Vacant Technological Gaps 55
• 2.5 Conclusions and Discussions 57
• 2.5.1 Identifying Vacant Technology Groups in Railway Main Transformers 57
• 2.5.2 Validating Findings and Potential Applications of Vacant Technologies 58
• 2.5.3 Patent Analytics Framework for Identifying Vacant Technologies 59
• 2.5.4 Limitations and Future Directions for Research and Application 60
• CHAPTER 3. IDENTIFYING GREEN TECHNOLOGY OPPORTUNITIES IN RAILWAY STATIC INVERTERS: INSIGHTS FROM PATENT DATA ANALYTICS 62
• 3.1 Introduction 62
• 3.1.1 The Importance and Evolution of Static Inverters in Railway Vehicles 62
• 3.1.2 Patent Data Analytics for Identifying Technology Opportunities and Innovation 65
• 3.2 Literature Review 66
• 3.2.1 Existing Patent Analytics Applications in Diverse Fields 66
• 3.2.2 Limited Knowledge on Railway Static Inverter Trends: Need for Patent-Driven Insights 70
• 3.3 Methodology 72
• 3.3.1 Research Framework 72
• 3.3.2 Data Acquisition and Preparation 73
• 3.3.3 Constructing a Document-Term Matrix (DTM) with TF-IDF Weighting 73
• 3.3.4 Identifying Technology Topics through Latent Dirichlet Allocation (LDA) and N-Gram Analytics 74
• 3.3.5 Multi-faceted Assessment of Technology Level to Determine Promising Areas 77
• 3.3.6 Identifying Key Players in Technology R&D via Bibliographic and Social Network Analytics 79
• 3.4 Results 80
• 3.4.1 Data Collection and Preprocessing Outcomes 80
• 3.4.2 Document-Term Matrix (DTM) with TF-IDF Weighting 81
• 3.4.3 Identified Technology Topics through LDA and N-Gram Analytics 81
• 3.4.4 Evaluation Outcomes for Determining Promising Technology Areas 82
• 3.4.5 Leading Companies and Research Institutes in Technology R&D 85
• 3.5 Conclusions and Discussions 89
• 3.5.1 Identifying Promising Technology Topic and Key Players in Railway Static Inverters Through Patent Data Analytics 89
• 3.5.2 Innovation Landscape: Promising Direction for Railway Static Inverters 90
• 3.5.3 Deriving the Key Implications through our Study 91
• CHAPTER 4. GLOBAL LEADING AUTOMAKERS' RESPONSE TO TIGHTENING EUROPEAN EMISSION STANDARDS: A PATENT DATA ANALYTICS 92
• 4.1 Introduction 92
• 4.1.1 EU Environmental Goals and Tighten Euro Emission Standards 92
• 4.1.2 Patents: Safeguarding Innovations and Competitive Advantage 94
• 4.1.3 Patent Data Analytics: Unveiling Technology Opportunities and Global Leading Automakers 97
• 4.2 Literature Review 110
• 4.2.1 Impact of EU Emission Standards on the Automotive Industry 110
• 4.2.2 Patent Data Analytics: A Tool for Identifying Technology Opportunities and Global Leading Automakers 103
• 4.2.3 Research Gaps and Proposed Study 107
• 4.3 Methodology 108
• 4.3.1 Data Acquisition and Preparation 110
• 4.3.2 Document-Term Matrix Construction with TF-IDF 111
• 4.3.3 Topic Extraction through LDA and Interpretation via N-gram Model 113
• 4.3.4 Technology Opportunity Identification using Technology Life Cycle(TLC) 115
• 4.3.5 Global Leading Automakers Identification using Technology Mapping(TM) 118
• 4.4 Results 120
• 4.4.1 Data Acquisition and Characterization 120
• 4.4.2 Document-Term Matrix Derivation via TF-IDF Analytics 125
• 4.4.3 Technology Topic Extraction using LDA and N-gram Model 126
• 4.4.4 Technology Opportunity Identification using TLC Analytics 130
• 4.4.5 Global Leading Automakers Identification using TM Analytics 132
• 4.5 Conclusions and Discussions 145
• 4.5.1 Identifying Technology Opportunities Global Leading Automakers through Patent Analytics 145
• 4.5.2 Technology Opportunities in Response to Tighten Euro Emission Standards 149
• 4.5.3 Contributions, Limitations, and Future Directions of the Study 151
• CHAPTER 5. PROPOSING AN INTEGRATED PATENT DATA ANALYTICS FRAMEWORK AND EXPANDING ANALYSIS DOMAINS FOR SUSTAINABLE MOBILITY TECHNOLOGY INNOVATION 155
• 5.1 An Integrated Patent Data Analytics Framework for Identifying Green Technology Opportunities in the Railway and Automotive Industry 155
• 5.1.1 Overview and Design of the Integrated Analytics Framework 155
• 5.1.2 Data Collection and Preprocessing 158
• 5.1.3 Detailed Technology Domain Classification 160
• 5.1.4 Promising Detailed Technology Domain Identification 162
• 5.1.5 Technology Trend and Opportunity Analysis for Promising Domains 165
• 5.1.6 Technological Innovation and Application in the Transportation Industry to Address Environmental and Regulatory Challenges 168
• 5.2 Multifaceted Validation of Green Technology Opportunities Identified through Patent Analytics Using Research Papers and News Data 171
• 5.2.1 Research Trends and Academic Development: Research Paper Data’s Analytics 171
• 5.2.2 Market Trends and Social Perception: News Data’s Analytics 176
• 5.2.3 Railway Main Transformers: Innovation for Maximizing Energy Efficiency 180
• 5.2.4 Railway Static Inverters: Key Components for Eco-friendly Energy Supply 197
• 5.2.5 Euro Eco-friendly Vehicles: Essential for Sustainable Mobility 213
• 5.3 Expansion and Convergence of Analysis Domains: PEST Analysis for Technology Innovation Using News, Research Papers, and Economic Indicators 232
• 5.3.1 Identifying Green Technology Innovation: PEST Analysis Integrating Patents, Research Papers, News Data, and Economic Indicators 232
• 5.3.2 PEST Analysis and Growth Prospects of the Green Technology Market in the Railway Core Electrical Equipment Sector 235
• 5.3.3 PEST Analysis and Growth Prospects of the Green Technology Market in the European Automotive Industry 246
• 5.4 Additional Suggestions for the Future of Patent Data Analysis and the Creation of an Innovative Research Environment 259
• 5.4.1 Strengthening Green Technology Roadmaps for Sustainable Growth in the Railway and Automotive Industries through Continuous Development of Analysis Methodologies 259
• 5.4.2 Revitalizing the Patent Information Ecosystem and Promoting Innovation through an AI-Powered Open Access Platform 265
• 5.4.3 Promoting Sustainable Mobility through Collaboration: Challenges, Solutions, and Policy Frameworks for Green Technology Adoption in Developing Countries 269
• REFERENCES 276