  Recent works in the information science literature have presented cases of
using patent databases and patent classification information to construct
network maps of technology fields, which aim to aid in competitive intelligence
analysis and innovation decision making. Constructing such a patent network
requires a proper measure of the distance between different classes of patents
in the patent classification systems. Despite the existence of various distance
measures in the literature, it is unclear how to consistently assess and
compare them, and which ones to select for constructing patent technology
network maps. This ambiguity has limited the development and applications of
such technology maps. Herein, we propose to compare alternative distance
measures and identify the superior ones by analyzing the differences and
similarities in the structural properties of resulting patent network maps.
Using United States patent data from 1976 to 2006 and International Patent
Classification system, we compare 12 representative distance measures, which
quantify inter-field knowledge base proximity, field-crossing diversification
likelihood or frequency of innovation agents, and co-occurrences of patent
classes in the same patents. Our comparative analyses suggest the patent
technology network maps based on normalized co-reference and inventor
diversification likelihood measures are the best representatives.
