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作 者:曾闻 王曰芬[1] 周玜宇 ZENG Wen;WANG Yue-fen;ZHOU Hong-yu(Nanjing University of Science and Technology,Nanjing 210094,China)
机构地区:[1]南京理工大学,江苏南京210094
出 处:《情报科学》2020年第12期4-11,共8页Information Science
基 金:国家社会科学基金重大招标项目“面向知识创新服务的数据科学理论与方法研究”(16ZDA224)。
摘 要:【目的/意义】从专利申请状态分布与演化的视角,分析产业领域技术的整体发展态势及其变化。【方法/过程】借助数据分析方法与技术生命周期理论,构建研究框架。以人工智能领域为例,基于德温特专利数据库,通过综合调查构造检索式,采集1985-2019年相关专利数据,对获得的数据进行预处理与信息特征抽取;划分人工智能领域技术生命周期,并对该产业领域专利申请的年度趋势、国别分布、专利申请人、技术分类等特征进行分布和演化分析。【结果/结论】通过专利信息分析,人工智能领域技术的发展可以被划分为萌芽期、缓慢发展期、快速发展期和腾飞期四个阶段,其在不同的特征分布上都呈现出一定的特点与演化规律。【Purpose/significance】From the perspective of the distribution and evolution of patent application status,this is paper analyzes the overall development trend and changes of industrial technology.【Method/process】A research framework was built with the help of data analysis methods and technology life cycle theory. Taking the field of artificial intelligence industry as an example, using the Derwent patent database as a data source, a search formula was constructed through a comprehensive survey to collect relevant patent data from 1985 to 2019, and perform preprocessing and information feature extraction on the obtained data. Divide the technology life cycle of the artificial intelligence industry, and analyze the distribution and evolution of the characteristics of the industry’s patent applications such as annual trends, country distribution, patentees, and technical topics.【Result/conclusion】Through the analysis of patent information, the field of artificial intelligence industry can be divided into four stages: the budding period, the slow development period, the rapid development period, and the take-off period. They all show certain characteristics and evolution laws in different feature distributions. The research conclusions can provide references for decision-making suggestions.
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