Measuring Technological Distance for Patent Mapping

TL;DR

This paper compares various distance measures for constructing patent technology network maps to identify the most effective ones, enhancing the accuracy of innovation analysis.

Contribution

It systematically evaluates 12 distance measures using US patent data to determine the most suitable for patent mapping applications.

Findings

Normalized co-reference and inventor diversification likelihood are the best measures.

The study clarifies how different measures influence network map structures.

Results guide better selection of distance metrics for patent analysis.

Abstract

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.