📝 SummarXiv

Abstract

Technology convergence integrates distinct domains to create novel combinations, driving radical innovation that reshapes markets and industries. However, most approaches rely on pairwise networks that cannot capture multi-technology interactions and suffer scale biases from heterogeneous patenting activity. To overcome these gaps, we propose a hypergraph-based framework that directly models multi-technology convergence and identifies statistically significant convergence via a probabilistic null model. Using four decades of USPTO patent data (1984-2023), we construct two hypergraphs: a co-classification-based hypergraph representing explicit inventive convergence and a co-citation-based hypergraph capturing implicit knowledge-flow convergence. Evolution analysis on both hypergraph types reveals a sustained growth in multi-technology and cross-domain convergence, with a marked shift from chemistry-led to computing-led convergence patterns. Building on these insights, we formulate the forecasting of technology convergence as a hyperedge prediction task. We implement random forest classifiers trained on two complementary feature sets derived from both hypergraphs: similarity features capturing structural and semantic similarities among technologies, and intrinsic features representing inherent attributes of technologies. Predictive results demonstrate that co-citation-based features exhibit stronger predictive power than co-classification-based ones, with their combination achieving optimal performance. Explainable AI analyses identify global knowledge-flow reachability and semantic similarity as dominant drivers of convergence, while intrinsic citation and economic values exhibit contrasting associations with convergence probability. This framework bridges evolution analysis and predictive modeling of multi-technology convergence, offering actionable insights for innovation strategy.