2017
DOI: 10.2139/ssrn.2924387
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Long-Run Dynamics of the U.S. Patent Classification System

Abstract: Almost by definition, radical innovations create a need to revise existing classification systems. In this paper, we argue that classification system changes and patent reclassification are common and reveal interesting information about technological evolution. To support our argument, we present three sets of findings regarding classification volatility in the U.S. patent classification system. First, we study the evolution of the number of distinct classes. Reconstructed time series based on the current cla… Show more

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Cited by 11 publications
(10 citation statements)
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“…Moreover, an interesting property of codes is the fact that the classifications they are drawn from are not static, but rather change over time to keep up with the pace of technological change. In fact, recombination of existing knowledge appears as a distinguishing feature of innovation, a stylized fact that can be directly observed through changes of the classification system [43,44]. We envisage two main avenues for future research stemming from our study: first, an empirical analysis of autocatalytic sets at different scales of technological classification, to uncover possible fractal structures; second, a modelling framework that can reproduce and explain the statistical features of the empirical network of technology classes.…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, an interesting property of codes is the fact that the classifications they are drawn from are not static, but rather change over time to keep up with the pace of technological change. In fact, recombination of existing knowledge appears as a distinguishing feature of innovation, a stylized fact that can be directly observed through changes of the classification system [43,44]. We envisage two main avenues for future research stemming from our study: first, an empirical analysis of autocatalytic sets at different scales of technological classification, to uncover possible fractal structures; second, a modelling framework that can reproduce and explain the statistical features of the empirical network of technology classes.…”
Section: Discussionmentioning
confidence: 99%
“…Following common practice in the innovation literature, we measure Technological fertility as the mean number of citations received by the patents that are classified in the primary class in the given year. Additionally, we control for the yearly number of patents granted in each class ( Class size ) as previous studies found that citation growth rates are related to patent class size (Carnabuci, 2013; Lafond & Kim, 2019). Note that because the models presented include patent fixed effects, we do not need to include additional controls for factors that are constant for a given patent, such as the count of inventors, the count of claims, its degree of recombinativeness, the count of scientific references, or the nationality and size of the assignee.…”
Section: Studymentioning
confidence: 99%
“…characteristics. In addition, the usual presentation of these relationships may be biased towards particular technology types simply because of the skewed distribution of patent volume across these technologies (Lafond and Kim, 2019), which (by construction) is not a concern here.…”
Section: Renewalsmentioning
confidence: 99%