Adoption-Weighted Technology Function Matrix for Big Data Patent Analytics in Smart Elderly Care

Patent databases represent a large-scale, heterogeneous form of Big Data that captures technological evolution, diffusion, and commercialization potential of technologies. Traditional systematic mapping approaches such as the Technology Function Matrix (TFM) are widely used to correlate technical solutions with functional needs of society. However, the nature of the data that is utilized in the mapping process lacks indicators of market adoption which portrays the ground level potential of technological innovation. Therefore, such mappings do not fairly represent the technologies that have been widely adopted and cloud the judgement of stakeholders. Addressing this, we propose an Adoption-weighted Technology Function Matrix (ATFM), an analytics framework that integrates functional mapping with adoption-oriented indicators such as forward citations, grant rate, family size, and claims. Using a large dataset of 15,807 patents grants (2015–2025), we systematically showcase the procedure to apply ATFM to a case study of China's smart elderly care sector. The framework reveals not only technology-function linkages but also differential adoption patterns across domains. Our results highlight adoption hotspots in IoT-based sensing, low-latency 5G communication, and AI-driven health management, while identifying gaps in privacy protection and predictive diagnostics. Compared to conventional TFM, ATFM provides a more grounded, scalable and data-driven methodology that enriches patent analytics with adoption insights, offering value for both academic research in large scale dataset and practical decision-making in emerging industries and government stakeholders.