A Smart Contract-Based Patent Value Assessment Model

To address issues in traditional patent valuation—such as subjective selection of dimensional metrics, weak sensitivity to high-dimensional transaction data noise, and insufficient correlation between evaluation indicators and dimensions—this study proposes a smart contract-based patent value assessment model. Firstly, existing patent valuation theories and techniques undergo systematic deconstruction and multidimensional efficacy assessment. Leveraging big data technology, a four-dimensional optimal framework integrating "technology-market-legal-risk" dimensions is constructed. Secondly, an enhanced non-negative matrix factorization algorithm (S-NMF) is designed. By incorporating diagonal matrices and fused regularization parameters, this algorithm maps the four-dimensional optimal framework into 14 quantifiable metrics using Hyperledger Fabric consortium blockchain transaction data. This addresses the core limitation of classical NMF algorithms—the inability to adjust dimension weights—enabling flexible weighting control to meet differentiated valuation needs across diverse patent application scenarios. Finally, performance analysis and simulation experiments were conducted on the patent value assessment model, comparing it with the traditional NMF algorithm. Results demonstrate that this model outperforms traditional models in both noise robustness and dimensional correlation, effectively supporting patent value assessment needs across multiple scenarios.