This study examined Japanese patents in terms of the quantitative characteristics of application documents that resulted in the acquisition of rights in order to clarify the relationship between the features and paten...This study examined Japanese patents in terms of the quantitative characteristics of application documents that resulted in the acquisition of rights in order to clarify the relationship between the features and patentability of applications. The groups of approved applications and those that had not been approved were compared for 12 variables： publication time lag; numbers of inventors, classifications, pages, figures, tables, claims, priority claims, countries for priority claims, cited patents, and cited non-patent documents; and median of citation age. Furthermore, the authors carried out the experiments in which patent applications were automatically classified into two groups by the machine learning method, random forests. As a result, statistically significant differences between the two groups were observed for the following variables （p 〈 .001）： the numbers of inventors, pages, figures, claims, priority claims, and countries for priority claims were significantly larger in the group of approved applications, while the time lag until publication was smaller. In particular, the publication time lag and the numbers of inventors, pages, and figures were variables representing the features that largely contribute to discriminating approved applications in the classification using random forests, which implies that these have relatively strong relationships with patentability.展开更多
文摘This study examined Japanese patents in terms of the quantitative characteristics of application documents that resulted in the acquisition of rights in order to clarify the relationship between the features and patentability of applications. The groups of approved applications and those that had not been approved were compared for 12 variables： publication time lag; numbers of inventors, classifications, pages, figures, tables, claims, priority claims, countries for priority claims, cited patents, and cited non-patent documents; and median of citation age. Furthermore, the authors carried out the experiments in which patent applications were automatically classified into two groups by the machine learning method, random forests. As a result, statistically significant differences between the two groups were observed for the following variables （p 〈 .001）： the numbers of inventors, pages, figures, claims, priority claims, and countries for priority claims were significantly larger in the group of approved applications, while the time lag until publication was smaller. In particular, the publication time lag and the numbers of inventors, pages, and figures were variables representing the features that largely contribute to discriminating approved applications in the classification using random forests, which implies that these have relatively strong relationships with patentability.
