Electronic International Standard Serial Number (EISSN)
Patents provide valuable information to identify flows in the transfer of technical knowledge and assess the innovation capabilities of the actors involved in different industries. Patent citations are also recognized as a valid tool to measure the impact of innovations and to identify key influencers in diverse activity sectors.This study analyzes a collection of U.S. patents granted in the period between 1990 and 2012 for the subject "automatic document clustering and classification", a key technology within the Information Retrieval and Text Mining disciplines. The purpose of this research is to identify - using citation analysis- the most productive and influential companies and journals, and the patterns followed in the transfer and sharing of technical knowledge. The paper identifies the most productive organizations (those that have been granted a higher number of patents) and those with a higher impact (organizations whose patents have received a major number of citations), and compares the generated rankings with those obtained using traditional bibliometric indicators. The conclusions provide an overview of the innovation landscape in the area of study, and suggest to which extent bibliometric indicators match the conclusions obtained after analyzing productivity and impact using patent citation.