Predicting commercialization in renewable energy patents: A hybrid multimodal framework integrating BERT and ELECTRA embeddings with gradient boosting

Predicting commercialization in renewable energy patents: A hybrid multimodal framework integrating BERT and ELECTRA embeddings with gradient boosting

Journal article

1 volume.

Accelerating the commercialization of renewable energy technologies is essential for closing the global climate investment gap. However, traditional models of patent valuation, which remain highly dependent on citation-based proxies, frequently fail to capture real-time market signals. To overcome this limitation, this study proposes a hybrid multimodal framework that integrates transformer-based semantic embeddings (BERT and ELECTRA) with gradient boosting algorithms (XGBoost, LightGBM, and CatBoost) to predict patent assignment frequency—a robust, behaviorally grounded indicator of actual commercialization activity. Based on a dataset of 33,570 renewable energy patents (CPC Y02E) issued by the USPTO between 2014 and 2023, we operationalized a predictive pipeline that fuses unstructured textual data (abstracts and full claims) with structured and relational metadata, including inventor/assignee identities and backward citations. The empirical results demonstrate that the hybrid architecture significantly outperforms single-modality baselines, achieving an score exceeding 0.95 and an RMSE as low as 0.62. A pivotal finding is the identification of a “performance crossover”: while ELECTRA combined with LightGBM exhibits superior stability across broad, heterogeneous datasets, BERT combined with XGBoost proves significantly more effective in high-selectivity regimes ( transfers), where a deep contextual understanding of complex technical claims is paramount. By bridging the methodological gap between deep semantic text extraction and structured market signals, this work provides policymakers, investors, and R&D managers with a scalable, data-driven mechanism for identifying high-potential green technologies.

Crossref

World Patent Information ; 86, September, 2026

World Patent Information

Oxford [England] : Elsevier Ltd., 2026.

eng

https://www.sciencedirect.com/science/article/abs/pii/S0172219023000108

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