Filtering Patent Maps for Visualization of Diversification Paths of Inventors and Organizations

TL;DR

This paper proposes a method to filter patent technology network maps by removing weak links, balancing clarity and explanatory power, to better visualize and analyze the technological diversification paths of inventors and organizations.

Contribution

It introduces an objective filtering method that optimally balances network clarity and explanatory power, improving visualization of innovation diversification paths.

Findings

The method effectively filters patent networks, enhancing visualization clarity.

It identifies optimal filtering thresholds that preserve explanatory power.

Application to inventors and organizations demonstrates practical utility.

Abstract

In the information science literature, recent studies have used patent databases and patent classification information to construct network maps of patent technology classes. In such a patent technology map, almost all pairs of technology classes are connected, whereas most of the connections between them are extremely weak. This observation suggests the possibility of filtering the patent network map by removing weak links. However, removing links may reduce the explanatory power of the network on inventor or organization diversification. The network links may explain the patent portfolio diversification paths of inventors and inventing organizations. We measure the diversification explanatory power of the patent network map, and present a method to objectively choose an optimal trade-off between explanatory power and removing weak links. We show that this method can remove a degree of arbitrariness compared with previous filtering methods based on arbitrary thresholds, and also identify previous filtering methods that created filters outside the optimal trade-off. The filtered map aims to aid in network visualization analyses of the technological diversification of inventors, organizations and other innovation agents, and potential foresight analysis. Such applications to a prolific inventor (Leonard Forbes) and company (Google) are demonstrated.