Drivers of the decrease of patent similarities from 1976 to 2021

Author:

Filippi-Mazzola EdoardoORCID,Bianchi Federica,Wit Ernst C.

Abstract

The citation network of patents citing prior art arises from the legal obligation of patent applicants to properly disclose their invention. One way to study the relationship between current patents and their antecedents is by analyzing the similarity between the textual elements of patents. Many patent similarity indicators have shown a constant decrease since the mid-70s. Although several explanations have been proposed, more comprehensive analyses of this phenomenon have been rare. In this paper, we use a computationally efficient measure of patent similarity scores that leverages state-of-the-art Natural Language Processing tools, to investigate potential drivers of this apparent similarity decrease. This is achieved by modeling patent similarity scores by means of generalized additive models. We found that non-linear modeling specifications are able to distinguish between distinct, temporally varying drivers of the patent similarity levels that explain more variation in the data (R2 ∼ 18%) compared to previous methods. Moreover, the model reveals an underlying trend in similarity scores that is fundamentally different from the one presented previously.

Funder

Swiss National Science Foundation

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A stochastic gradient relational event additive model for modelling US patent citations from 1976 to 2022;Journal of the Royal Statistical Society Series C: Applied Statistics;2024-05-06

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