This chapter presents the methodological foundation for the patent factor decomposition analysis applied throughout the book. It first situates patent data analysis within the broader knowledge economy, explaining its utility for identifying R&D trends, benchmarking competitive positions, and informing strategic planning. The chapter then introduces three indicators—PRIORITY, ENVIRONMENT, and SCALE—which together decompose observed changes in patent application numbers for specific technology fields into theoretically distinct causal components. PRIORITY captures the strategic emphasis placed on a particular technology relative to its parent domain; ENVIRONMENT reflects the relative importance accorded to environmental technologies within the total patent portfolio; and SCALE represents the overall volume of R&D activity driven by macroeconomic and institutional conditions. The formal decomposition identity is derived and illustrated using a concrete numerical example involving wind power patents, demonstrating how a fourfold increase in applications can be attributed multiplicatively to each factor’s contribution. The Logarithmic Mean Divisia Index (LMDI) method is then introduced as the preferred decomposition technique, valued for its property of perfect decomposition without residual terms. The chapter concludes by noting that this framework enables nuanced interpretation of innovation trends—distinguishing genuine strategic reorientation from scale-driven expansion—and serves as the analytical backbone for the domain-specific analyses in Chapters 3 through 5 .