摘要
基于incoPat专利文献数据库研究了全球近二十年的人工智能产业中机器学习等核心技术的发展趋势,分别对机器学习技术专利的申请与授权数据进行科学计量分析。首先,系统探索该技术在国内外的发展历程、区域分布及其关键核心驱动要素;然后,从国民经济以及产业分布角度分析其核心关键技术与应用的衔接及其主要取得的成果;最后,根据专利相关信息的语义相关度实现主题的聚类,深入挖掘机器学习技术现阶段的研究热点分布以及创新状况,为我国人工智能产业发展战略决策提供参考依据。数据分析表明,近二十年来我国人工智能产业尽管发展迅速,但相比美国、日本等工业发达国家,仍处于知识引入到知识输出的转型阶段,需要进一步增强自主技术创新实力。
Based on the incoPat patent literature database,the development trend of machine learning and other core technologies in the artificial intelligence industry in the world in the past two decades is studied,and the application and authorization data of machine learning technology patents are analyzed scientifically.Firstly,explore the development process,regional distribution and key core driving factors of the technology at home and abroad;then,from the perspective of national economy and industrial distribution,analyze the connection between core key technologies and applications and the main achievements;finally,according the current research hotspot distribution and innovation status in the field of machine learning technique,provide a reference for the strategic decision-making of artificial intelligence industry development in China.Data analysis shows that although artificial intelligence industry of China has developed rapidly in the past two decades,compared with industrialized countries such as the United States and Japan,it is still in the transformation stage of knowledge introduction to knowledge output,and it is necessary to further enhance the strength of independent technological innovation.
作者
陈利芳
杜宝林
林珠
CHEN Lifang;DU Baolin;LIN Zhu(Guangdong Electric Power Design and Research Institute Co.,Ltd.,China Energy Engineering Group,Guangzhou 510663;Guangdong Science&Technology Infrastructure Center,Guangzhou 510663)
出处
《中国发明与专利》
2023年第10期48-55,共8页
China Invention & Patent
基金
广东省重点领域研发计划“面向智慧公路的人工智能应用创新体系及技术平台研究”(编号:2022B0101070002)
广州市基础研究计划项目“科研绩效评估视角下基于深度学习的多源融合知识图谱构建研究”(编号:202002030300)资助。
