Patent Analysis as a Contemporary Research Method in the Context of Innovation and the Circular Economy

Abstract

Rapid digitalization, intensifying innovation competition, and the turn toward sustainability challenge traditional research designs. There is growing demand for hybrid methods that combine the rigor of quantitative analytics with the contextual depth of qualitative inquiry. In this landscape, patent analysis is gaining prominence as a data-rich, systematically reproducible approach to study technological change.

The article explores the place and role of patent analysis within modern research methods and substantiates its potential to (i) identify technological trends, (ii) assess innovation dynamics, and (iii) support the transition toward circular economy (CE) models. Building on a structured review of recent scholarship at the intersection of intellectual property, responsible innovation, and CE, the paper conceptualizes patent analysis as a mixed-method technique. It outlines individual and group patent analyses; discusses analytical lenses (e.g., patentability potential, performance, technology and value assessments, claim profiling); and positions patent analytics alongside qualitative methods, life-cycle thinking (LCA), and indicators of technological maturity (TRLs). Patent information enables mapping of innovation trajectories, identification of “white spaces,” and monitoring of sectoral transitions. In the CE context, patent analytics performs informational, prognostic, analytical, and strategic functions: revealing emergent eco-technologies, estimating their prospective impact, and informing IP strategy, R&D prioritization, and technology transfer. At the same time, the review highlights persistent gaps: limited data-driven empirical studies linking patent metrics to sustainability outcomes; insufficient integration with LCA/SDG indicators; and institutional frictions where conventional IP regimes may slow diffusion of circular innovations. Advancing CE requires integrated, evidence-based analytics that fuse patent data with sustainability metrics and maturity assessments, and that experiment with more open/shared IP models to balance incentives and diffusion. Such synthesis can underpin public policy, corporate strategy, and academic agendas oriented to regenerative growth. The paper frames patent analysis as a hybrid, evidence-oriented method tailored to the needs of circular transformation and proposes an agenda for embedding it in data-driven, responsibility-aware innovation research.