Modeling Innovation by a Kinetic Description of the Patent Citation System

Gabor Csardi, Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences
Katherine J. Strandburg, DePaul University College of Law
Jan Tobochnik, Kalamazoo College
Peter Erdi, Kalamazoo College and Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences
Laszlo Zalanyi, Department of Biophysics, KFKI Research Institute for Particle and Nuclear Physics of the Hungarian Academy of Sciences

Abstract

This paper reports results of a network theory approach to the study of the United States patent system. We model the patent citation network as a discrete time, discrete space stochastic dynamic system. From patent data we extract an attractiveness function, A(k, l), which determines the likelihood that a patent will be cited. A(k, l) shows power law aging and perferential attachment, the exponent of the latter is increasing since 1993, suggesting that patent citations are increasingly concentrated on a relatively small number of patents. In particular, our results appear consistent with an increasing patent “thicket”, in which more and more patents are issued on minor technical advances.