Discovering technology opportunities of latecomers based on RGNN and patent data: The example of Huawei in self-driving vehicle industry

Abstract

Emerging technologies provide competitive opportunities for latecomers to catch up with leading giants. As most of the extant literature indicated, types of single-dimensional relations from patent data have been revealed in technology opportunity discovery (TOD) research. Still, few have been aware of the more complex characteristics extracted from higher-dimensional patent information such as the patentee-technology relation. To derive this valuable relation for more robust results, this article introduces a novel TOD method, utilizing a recursive graph neural network (RGNN) to transform this high-dimensional information into measures of heterogeneity as internal capability, and combining it with external challenges evaluated by the competitiveness index, to identify technological opportunities. Taking the self-driving vehicle (SDV) industry with 33,347 patent families from 2010 to 2021 as the initial dataset, it shows significant performance promotions compared to previous analogous TOD models. Meanwhile, tested by recent filing patent data, the predicted opportunities are consistent with Huawei and other enterprises. Upon illuminating the intense technological competition situation among the preeminent SDV firms worldwide as a case exploration, this research contributes theoretical and practical views to the TOD research and network analysis.