图书情报工作 ›› 2020, Vol. 64 ›› Issue (7): 94-102.DOI: 10.13266/j.issn.0252-3116.2020.07.011

• 情报研究 • 上一篇    下一篇

专利转让视角下技术转移特征指标体系研究

郑思远1,2, 王学昭1,2   

  1. 1 中国科学院文献情报中心 北京 100190;
    2 中国科学院大学经济与管理学院图书情报与档案管理系 北京 100190
  • 收稿日期:2019-09-18 修回日期:2019-12-16 出版日期:2020-04-05 发布日期:2020-04-05
  • 通讯作者: 王学昭(ORCID:0000-0001-8496-3354),副研究员,博士,硕士生导师,通讯作者,E-mail:wangxz@mail.las.ac.cn
  • 作者简介:郑思远(ORCID:0000-0001-5522-9230),硕士研究生。
  • 基金资助:
    本文系中国科学院战略研究和决策支持专项颠覆性技术研究——生命科学领域(项目编号:GHL-ZLZX-2018-41)和知识产权信息传播利用效能提升项目(国家知识产权局)(项目编号:横1923)研究成果之一。

Research on Technology Transfer Characteristics Indicator System from the Perspective of Patent Transfer Information

Zheng Siyuan1,2, Wang Xuezhao1,2   

  1. 1 National Science Library, Chinese Academy of Sciences, Beijing 100190;
    2 Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190
  • Received:2019-09-18 Revised:2019-12-16 Online:2020-04-05 Published:2020-04-05

摘要: [目的/意义] 构建技术转移特征指标体系,提前识别具有应用潜力的专利,为专利技术转移提供信息支撑,促进技术成果转移转化。[方法/过程] 基于专利转让行为的客观事实信息,界定转让和未转让数据集,分析比对两者特征差异,构建技术、法律、市场、主体4个维度的基于专利文献自身的技术转移特征指标体系,并进行指标统计学检验以及实证模型检验。[结果/结论] 基于专利文献自身的基础特征指标能够预判技术转移,各指标影响程度相对均衡且不存在主导性指标。实证SVM模型检验该指标体系的预测效度,初步实现能够应用于国家/地区层面、机构层面、专利个体层面的技术转移特征指标体系的构建与检验,该方法具有实用性。

关键词: 专利转让, 技术转移, 专利预测, 情报分析, SVM

Abstract: [Purpose/significance] This paper intends to construct a technology transfer characteristics indicator system to provide information supporting for patent technology transfer, help to identify patents with potential application in advance, and promote the transfer of scientific and technological achievements. [Method/process] This paper based on patent transfer behavior which has already happened. First, we defined the transferred and not transferred datasets, and analyzed the differences between two datasets. Then we constructed the technological transfer characteristic indicator system based on the patent documents at 4 perspectives of technology, law, market and subject. Finally, we conducted statistical tests and empirical model tests. [Result/conclusion] The basic characteristic indicators based on the patent documents can predict technology transfer, and the impact degree of each indicator is relatively balanced and there is no dominant indicator. The predictive effect of the indicator system was tested by the SVM model, which showed that it could be applied to national level, organization level and patent level analysis.

Key words: patent assignment, technology transfer, patent prediction, intelligence analysis, SVM

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