Earticle

본 연구는 인공지능 분야의 공동연구 중요성을 인식하고 특허를 중심으로 산․학․연 기술협력 특성을 TES(Techno-Economic Segment) 분석으로 파악하였다. 이를 위해 2012년 이후 미국, 중국 등 5개국 특허청에 출원된 10년의 인공지능 특허 113,289건 가운데 7,062건을 공동연구 대상으로 하여 기업, 대학, 연구기관 등의 경제 주체를 식별하고, 기술협력 주제와 특성을 파악하였다. 분석결과 인공지능 분야 기술협력이 증가하는 가운데 전체 협력 가운데 산업계와 산업계(40%), 산업계와 대학(25.2%)의 협력이 상대적으로 높은 비중을 차지하였다. 그리고 자금과 분석데이터에 강점을 가진 산업계와 대학(9.8%), 우수한 인력을 보유한 대학 간 협력(1.9%) 비율이 증가하는 추세를 확인하였고, 이를 통해 대학의 역할이 강화되고 있음을 볼 수 있었다. 또한 토픽모델링과 네트워크 분석을 통해 협력특허 관심 분야와 연구 주체 간 협력 관계를 파악한 결과 협력 유형에 상관없이 유사한 관심 연구 주제가 도출되는 가운데, 자율주행, 엣지 컴퓨팅, 클라우드, 마케팅 및 소비자 행동 분석 등의 응용 영역으로 연구범위가 확대되고, 협력 주체는 다양해지며, 중국 대학이 중심이 되는 대규모 네트워크가 발현되는 현상을 확인할 수 있었다.

This study recognized the importance of joint research in the field of artificial intelligence and analyzed the characteristics of the industry-academic-research technological cooperation ecosystem focusing on patents from the perspective of the Techno-Economic Segment (TES). To this end, economic entities such as companies, universities, and research institutes within the ecosystem were identified for 7,062 joint research projects out of 113,289 artificial intelligence patents over the past 10 years filed in IP5 countries since 2012. Next, this study identified the topics of technological cooperation and the characteristics of cooperation. As a result of the analysis, technological cooperation is increasing, and the frequency of all types of cooperation was high in industry-to-industry (40%) and industry-to-university (25.2%) relationships. Here, this study confirmed that the role of universities is being strengthened, with an increase in the ratio of companies with strengths in funding and analytical data, industry and universities with excellent research personnel (9.8%), and cooperation between universities (1.9%). In addition, as a result of identifying collaborative patent research areas of interest and collaborative relationships through topic modeling and network analysis, overall similar research interests were derived regardless of the type of cooperation, and applications such as autonomous driving, edge computing, cloud, marketing, and consumer behavior analysis were derived. It was confirmed that the scope of research was expanding, collaborating entities were becoming more diverse, and a large-scale network including Chinese-centered universities was emerging.

최근 자동차 산업은 전기차와 자율 주행차의 등장으로 큰 변화를 겪고 있으며, 이에 따라 첨단 차량용 반 도체 기술에 대한 수요도 증가하고 있다. 이 연구에서는 완성차 제조 산업, 자동차 부품 산업, 차량용 반도체 산업, 종합 반도체 산업, 정보 기술 산업 등 5개 산업에 대해 차량용 반도체 기술의 특허 활동과 공동 개발 활동을 분석 하였다. 2010년부터 2021년까지 12년 동안 40개 기업의 단독 특허 421,802건과 공동 특허 22,342건을 분석한 결과 두 가지 중요한 사실을 밝혀냈다. 첫째는 공동 특허 분석을 통해 완성차 제조 산업과 정보기술 산업이 협업 활동 을 통한 공동 특허 확보에 적극적이라는 것을 알 수 있었다. 둘째는 12년간의 공동 특허 네트워크의 커뮤니티 분 석을 통해 기술 변화와 역량 보완의 필요에 따라 커뮤니티 내부의 기업의 협력 활동의 변화를 보여주는 것이 확인 되었다. 우리는 이번 연구를 토대로 특허 협업 전략에 관련해 기업의 협력 역량을 강화하거나 취약점 보완에 관한 의미있는 제안을 하였다.

The automotive industry is undergoing a major transformation with the advent of electric vehicles and autonomous vehicles, and the demand for advanced automotive semiconductor technologies is increasing. This study analyzes the patenting and joint development activities of automotive semiconductor technologies in five industries: automobile manufacturing, auto parts, automotive semiconductor, integrated semiconductor, and information technology. The study analyzed 421,802 single patents and 22,342 co-patents of 40 companies over the 12-year period from 2010 to 2021 and found two important findings. First, the analysis of joint patents shows that the automobile manufacturing industry and the information technology industry are active in securing joint patents through collaborative activities. Second, the community analysis of the co-patent network over the 12-year period shows that while technical cooperation relationships have been steadily maintained to secure changing automotive semiconductor technologies, the collaborative activities of companies within the community have changed according to technological changes and the need to complement capabilities. Based on this research, we proposed some collaboration strategies for either strengthening a company’s capability or compensating for weakness.

Purpose: This study proposes an engineering design model for a blockchain-enabled collaborative medical information platform that integrates patented AI imaging-diagnosis technology. Research design, data and methodology: Building upon Patent KR10-2604558, the platform incorporates a multi-layered architecture consisting of an AI diagnostic engine, a self-corrective learning loop (ADE), a PBFT-based blockchain integrity module, and an FMEA-driven risk-management framework. A synthetic dataset of 2,000 musculoskeletal ultrasound images was generated to evaluate the structural feasibility of the proposed model. The AI module, developed using a ResNet50 backbone and a four-class Softmax classifier, demonstrated stable self-correction through ADE, which autonomously identified false positives and false negatives and used them to construct a hard-case dataset for selective retraining. Results: The blockchain module?designed with ECC-256 encryption, SHA-256 hashing, and a seven-node PBFT network?successfully ensured immutability, tamper detection, and privacy preservation using Zero-Knowledge Encryption Exchange (ZKEE). FMEA analysis confirmed that risks related to AI misclassification, data integrity, consensus failure, and user input errors could be decomposed into modular risk structures, resulting in a 44.8% reduction in overall RPN.Conclusions: The findings demonstrate that the proposed design model can serve as a technically reliable architecture for AI-driven, legally robust, and privacy-preserving collaborative healthcare data platforms