Abstract：This paper analyzes the global patent application trend,patent geographical distribution,PCT patent layout of major countries,hot fields and future development trend of the AI industry based on Derwent innovative index database.The study finds that the United States and Japan are the dominant players in the global.China has been developing rapidly in recent years.The multinational giants in the field of artificial intelligence all attach importance to the strategy of patent globalization.Big data,cloud computing,deep learning,speech recognition,image recognition and human-computer interaction are the hot technologies.Six emerging technology themes,including intelligent robot,intelligent medical care,intelligent finance,intelligent driving,intelligent security and intelligent education,are closely interrelated with current hot technologies of artificial intelligence.Therefore,innovation institutions should pay more attention to these hot and emerging technologies.
王友发,罗建强,周献中. 基于专利地图的人工智能研究总体格局、技术热点与未来趋势[J]. 中国科技论坛, 2019(10): 80-89.
Wang Youfa,Luo Jianqiang,Zhou Xianzhong. Hotspots and Development Trend of Artificial Intelligence Technology Based on Patent Map. , 2019(10): 80-89.
[1]黄鲁成,王静静,李欣,等.基于文献计量的新兴趋势分析[J].情报杂志,2015,34(7):58-64. [2]张春博,丁堃,贾龙飞.国际人工智能领域计量与可视化研究——基于 AAAI年会论文的分析[J].图书情报工作,2012,56(22):69-76. [3]彭丽,叶充.基于 Web of Science 的人工智能研究计量分析[J].图书情报研究,2015,8(4):53-59. [4]喻国明,梁爽,程思琪.当前国际传媒领域人工智能研究的学术热点与框架——基于EBSCO数据库2016年英文文献的文献计量学方法[J].东南学术,2018(2):55-65. [5]ALFONSO I,PEDRO L,CONCHA B.Using Bayesian networks to discover relationships between bibliometric indices:A case study of computer science and artificial intelligence journal[J].Scientometrics,2016,89(2):523-551. [6]BARTNECK C.The end of the beginning:A reflection on the first five years of the HRI conference[J].Scientometrics,2010,86(2):487-504. [7]EOM S B.Decision support systems research:Reference disciplines and a cumulative tradition[J].Omega-international journal of management,1995,23(5):511-523. [8]EOM S B,LEE S M,KIM E B,et al.A survey of decision support system applications[J].Journal of the operational research society,2008,49(2):109-120. [9]EOM S B.Decision support systems research:Current state and trends[J].Industrial management & data systems,2012,99(5):213-221. [10]张振刚,黄洁明,陈一华.基于专利计量的人工智能技术前沿识别及趋势分析[J].科技管理研究,2018(5):36-42. [11]赵蓉英,李新来,李丹阳.专利引证视角下的核心专利研究——以人工智能领域为例[J/OL].情报理论与实践,2019,42(3)[2019-04-28].http://kns.cnki.net/kcms/detail/11.1762.G3.20181023.0924.002.html. [12]陈军,张韵君,王健.基于专利分析的中、美人工智能产业发展比较研究[J/OL].情报杂志,2019,38(1)[2019-03-24].http://kns.cnki.net/kcms/detail/61.1167.G3.20181113.1325.010.html. [13]沈君,高继平,滕立.德温特手工代码共现法:一种实用的专利地图法[J].科学学与科学技术管理,2012,33(1):12-16. [14]王友发,周献中.基于专利地图机器人产业知识产权发展对策[J].中国科技论坛,2015(9):31-37. [15]陈悦,谭建国,王智琦,等.专利视角下工业机器人领域的技术机会分析[J].科研管理,2018,39(4):144-156. [16]罗家德.社会网络分析讲义[M].北京:社会科学文献出版社,2005:134-146. [17]李德仁,姚远,邵振峰.智慧城市中的大数据[J].武汉大学学报,2014,39(6):631-640. [18]吴飞,阳春华,兰旭光,等.人工智能的回顾与展望[J].中国科学基金,2018(3):243-250. [19]张蔚敏,蒋阿芳,纪学毅.人工智能芯片产业现状[J].电信网技术,2018(2):67-71. [20]BRONSTEIN M M,BRUNA J,LECUN Y,et al.Geometric deep learning:Going beyond euclidean data[J].IEEE signal processing magazine,2017,34(4):18-42. [21]XIE Z,SUN Z,JIN L,et al.Learning spatial-semantic context with fully convolutional recurrent network for online handwritten Chinese text recognition[J].IEEE transactions on pattern analysis and machine intelligence,2018,40(8):1903-1917. [22]韩文静,李海峰,阮华斌,等.语音情感识别研究进展综述[J].软件学报,2014,25(1):37-50. [23]乔杨.专利计量方法在技术预见中的应用——以国内冶金领域为例[J].情报杂志,2013,32(4):34-37. [24]KLEINBERG J.Bursty and hierarchical structure in streams[J].Data mining and knowledge discovery,2003,7(4):373-397. [25]王田苗,陶永.智能机器人+人工智能创新创业的思考及建议[J].科技导报,2018,36(17):97-101.
2019, 
