摘要
[目的/意义]对高校专利进行分级评估并采取有针对性的处置策略可以优化资源配置、避免无差别管理带来的资源浪费,提高专利转化效率。[方法/过程]以16所高校真实的专利转化数据为研究对象,通过4种机器学习算法对比选优,分别建立专利转化概率分类预测模型和转化金额分类预测模型,再以这两个模型为维度,构建高校专利四象限分级模型,并进行实证对比分析。[结果/结论]在对高校专利可转化性和转化金额进行分类评估时,基于随机森林算法的机器学习模型有效性更好;高校问题专利占比普遍较高;对不同象限的专利可采取不同的、有针对性的分级处置策略。
[Purpose/significance]Making a classification evaluation of university patents and taking targeted disposal strategies can optimize resource allocation,avoid resource wastage caused by indiscriminate management,and improve the efficiency of patent transformation.[Method/process]Taking the real patent transformation data from 16 universities as research objects,four machine learning algorithms are compared to establish patent transformation probability classification prediction model and transformation amount classification prediction model.Based on these two models,a four-quadrant classification model for university patents is constructed,and empirical comparative analysis is conducted.[Result/conclusion]When classifying and evaluating the patent transformability and transformation amount in universities,the machine learning model based on random forest algorithm is more effective.The proportion of problematic patents in universities is generally higher.Different and targeted classification and disposal strategies can be adopted for patents in different quadrants.
作者
魏太琛
韩闯
陈振标
Wei Taichen;Han Chuang;Chen Zhenbiao(University National Intellectual Property Information Service Center,Fuzhou University,Fuzhou Fujian 350108;Fuzhou University Library,Fuzhou Fujian 350108;Xiamen University Library,Xiamen Fujian 361005)
出处
《情报探索》
2024年第9期76-86,共11页
Information Research
基金
教育部人文社会科学研究一般项目“高校专利开放许可费定价模型构建及应用研究”(项目编号:23YJA870004)
福建省中青年教师教育科研项目“基于转化概率和转化金额预测的高校专利四象限分级管理模型研究”(项目编号:JAS23013)成果。
关键词
专利分级
专利转化
转化概率
转化金额
四象限
patent classification
patent transformation
transformation probability
transformation amount
four-quadrant
