Categorizing influential patents using bibliometric analysis of patent citations network

Recently, research for network has been actively progressing. Internet, bio-metabolic, and coauthor networks of scientific thesis has a decreasing distribution to power law. However, researches on network utilizing patent information have not been very active. It suggests the method calculating large sparse matrix by supercomputer, examining patent network distribution by bibliographic coupling and co-citation, and identifying influential patents. The majority of studies, which are targeted to find Hub patents, are using the number of forward citation of patents. Yet the most recent technological convergences among different fields have been enforced, and the development of this fusion technology has been rapidly progressing. Therefore the forward citation of patent occurs in the applicable field of technologies as well as in other fields of technology; the method to find influential patents within the applicable field of technology is using only the number of forward citation of patents that may cause severe distortions. This study will explore three types of influential patents by minimizing the distortion phenomenon accompanied by the number of patent forward citation. To serve this purpose, the patent classification method is using bibliographic coupling and co-citation analysis used in knowledge search.