I
ifically, deviations away from zero in these coefficient functions equate to an increased positive or negative weighting for the associated patent indicator count at that moment in time, within the determination of the predicted mode of substitution. As such it can be seen from Fig. \ref{822351} that any patent indicator counts at t = 0 for the number of non-corporates by priority year (assuming these are present) will have a more significant influence on the predicted classification than at any other point in the emergence phase. Equally, Fig. \ref{822351} would suggest that the impact of non-corporates activity next peaks around 40% of the way through the emergence phase (potentially corresponding to the hype effect suggested by Fig. \ref{413726}), and again at the end of the emergence phase. For the number of cited references by priority year, this regression model suggests that the times of greatest impact on the mode of substitution are at the very beginning and at the very end of the emergence stage respectively. Whilst these coefficient plots gives some indication of the relative weighting applied to patent indicator counts as time progresses, the cumulative nature of the inner products used in functional linear regression means it is not possible to visually infer from these plots alone which mode the technology under evaluation is currently converging towards. For this it is also necessary to include the corresponding patent indicator count values that these coefficient terms are multiplied by for the specific technology being assessed.