Disruptive technologies can reconfigure innovation trajectories and create new market opportunities, yet their early detection remains difficult because disruptive impact is uncertain and often becomes visible only years after invention. Building on the CD disruptiveness measure derived from citation-network dynamics, this study develops an interpretable machine-learning framework to screen and prioritize potentially disruptive patent candidates ex ante. Using IncoPat patent records in the artificial intelligence domain, we construct a multidimensional indicator system spanning technological, market, and legal signals. To ensure conceptual consistency with the CD framework, the main disruption label is defined as patents falling within the top 5% of the empirical CD distribution among patents with at least one backward citation. This definition allows disruptiveness to be assessed relative to identifiable prior art and reduces reliance on extreme CD values that may be sensitive to incomplete reference information. We train models on a time-split learning set covering patents filed from 2007 to 2021 and evaluate their ability to predict this stricter disruption label using early observable patent metadata. Among benchmarked classifiers, AdaBoost provides the most competitive screening performance after feature selection, reducing the feature space from 17 to 12 indicators. Under the main top-5% specification, the model achieves an accuracy of 0.914, a precision of 0.271, a recall of 0.431, and an F1 score of 0.333, indicating modest but nontrivial early-screening ability. Feature importance analysis highlights the predictive relevance of citation- and disclosure-related signals, including family citation activity, backward citations, and document length. The framework is best interpreted as a scalable candidate-generation tool for monitoring and expert review within the patented segment of AI innovation, rather than as a definitive classifier of realized disruptive impact.
Li et al. (Wed,) studied this question.