Prediction of Emerging Technologies Based on Analysis of the U.S. Patent Citation Network

TL;DR

This paper introduces a novel method for analyzing patent citation networks to identify and predict emerging technological clusters, aiding innovation forecasting and policy decision-making.

Contribution

It presents a new citation vector-based clustering approach that predicts the emergence of new technology clusters from patent citation data.

Findings

Successfully predicted a new patent cluster in agriculture and food technology.

Demonstrated the method's predictive accuracy using USPTO data from 1991 to 1997.

Provides a tool for policymakers to anticipate technological trends.

Abstract

The network of patents connected by citations is an evolving graph, which provides a representation of the innovation process. A patent citing another implies that the cited patent reflects a piece of previously existing knowledge that the citing patent builds upon. A methodology presented here (i) identifies actual clusters of patents: i.e. technological branches, and (ii) gives predictions about the temporal changes of the structure of the clusters. A predictor, called the {citation vector}, is defined for characterizing technological development to show how a patent cited by other patents belongs to various industrial fields. The clustering technique adopted is able to detect the new emerging recombinations, and predicts emerging new technology clusters. The predictive ability of our new method is illustrated on the example of USPTO subcategory 11, Agriculture, Food, Textiles. A cluster of patents is determined based on citation data up to 1991, which shows significant overlap of the class 442 formed at the beginning of 1997. These new tools of predictive analytics could support policy decision making processes in science and technology, and help formulate recommendations for action.