Multiple Regression Analysis between Citation Frequency of Patents and their Quantitative Characteristics

Abstract Many bibliometric studies have been conducted to examine the factors that influence citation frequency based on multiple regression analysis, targeting academic papers. As for patents, on the other hand, there are few studies in which citation frequency is explained/predicted as the response variable. This study executed a multiple regression analysis that explains citation frequency of patents with multiple feature values derived from the patent data set as explanatory variables, i.e., the numbers of inventors, classifications, pages, figures, tables, claims, priority claims, countries for priority claims, and classifications associated with backward citations (patents cited in the subject patent). The data on 5,253,614 patent applications were analyzed on the basis of the full text of the official gazette for patent applications published in Japan between 1993 and 2007. The results suggested that the influence of diversity of backward citations on citation frequency is large compared to those of the other factors.