Taking the chosen time series dimensions as a starting point, a functional data object must first be created for each of the patent indicators (or model components) included in the chosen subset. This is necessary in order to combine all of the different technology profiles being evaluated into two regression terms: one representing the number of non-corporates by priority year, and a second term representing the number of cited references by priority year. These terms, when multiplied by their respective regression coefficients (which are calculated in the subsequent regression analysis), provide the relationship between the predicted mode of substitution and the two selected measures of science and technology. However, as the Technology Life Cycle segments being combined will have a different number of observations for each case study technology, it is first necessary to resample the segmented time series based on a common number of resampling points. This ensures that even if one Technology Life Cycle stage spans 20 years in one time series, and spans 50 years in another, both time series will have 50 observations, which enables the two curves to be aligned relative to each other for the current Technology Life Cycle stage. Next a B-spline basis system is created for each model component based on the common number of resampling points defined, and at the same time for the regression coefficients (\(\beta_i\)) to be estimated by the functional linear regression analysis (see Eq. 1 and Eq. 3 in Appendix A, as well as sections 3.4.1, 3.4.2, 9.4.1 and 9.4.2 of (Ramsay 2009)), as illustrated in Fig. \ref{416597}.