Before functional data objects can be generated from the B-spline basis systems the degree of curve smoothing to be applied has to be determined (i.e. the tightness of fit). Following the process outlined in \cite{Ramsay_2009} a 'functional parameter object' that allows smoothness to be imposed on estimated functional parameters is now created (see section 5.2.4 of \cite{Ramsay_2009}). Functional parameter objects extend the existing datasets, by storing additional attributes relating to the smoothness constraints that need to be respected in any B-spline curve fit. A functional data object is then created for the current model component using the new functional parameter object, along with an initial value of the smoothing parameter (\(\lambda\)). The degrees of freedom and generalised cross-validation criterion coefficient (see section 5.3 of \cite{Ramsay_2009}) can then be calculated for the current functional data object. By repeating this process for a range of \(\lambda\) values and plotting the results (not shown here) a suitable smoothing parameter can be identified that will be used in the final functional data object for each model component. Selection of a smoothing parameter in this fashion ensures that the functional data object generated will have the best chance of capturing the dynamics present in the current datasets, whilst also being more likely to be adaptable to future out-of-sample technologies. An example of a smoothed functional data object generated for the number of corporations associated with different technologies in a given priority year is illustrated in Fig. \ref{135605}.