The identification of promising inventions is an important task in technology planning practice. Although several studies have been carried out using patent-based machine learning techniques, none of these have used the quality of knowledge accumulation as an input for identifying promising inventions, and have simply considered the number of backward citations as the link with previous knowledge. The current study therefore aims to fill this research gap by predicting promising inventions with patent-based machine learning, using the quality of knowledge accumulation as an important input variable. Eight criteria and 17 patent indicators are used as input variables, and patent forward citations are employed as the output variable. Six machine learning techniques are tested on 363,620 G06F patents filed between January 1990 and December 2009, and the results show that the quality of knowledge accumulation is the most important variable in predicting emerging inventions.

中文翻译：

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考虑到知识积累的质量，识别有前途的发明：一种机器学习方法

识别有前途的发明是技术规划实践中的一项重要任务。尽管已经使用基于专利的机器学习技术进行了多项研究，但没有一项研究将知识积累的质量用作识别有前途的发明的输入，而只是将反向引用的数量视为与先前知识的联系。因此，当前的研究旨在通过使用基于专利的机器学习预测有前途的发明来填补这一研究空白，并将知识积累的质量作为重要的输入变量。8个标准和17个专利指标作为输入变量，专利前向引用作为输出变量。在 1990 年 1 月至 2009 年 12 月期间提交的 363,620 项 G06F 专利中测试了六种机器学习技术，