As discussed in sections \ref{218955} and \ref{814495} an unsupervised learning approach has been employed here based on applying Dynamic Time Warping and the 'PAM' variant of K-Medoids clustering on the relative distance measures calculated between time series. This is again implemented as a MATLAB script based on the DTW and K-Medoid functions made available by MathsWorks \cite{k-medoids_clustering,Dynamic_Time_Warping_Clustering}, which is provided in Appendix XX. The first step of this process involves generating a list of all the unique subsets that can be created from the ten patent indicator metrics considered in this study. Consequently, this produces 1,023 (i.e. \(2^{10}-1\)) possible combinations of the ten patent indicators to be tested, as illustrated by Fig. \ref{488951}: