Patent ID: 6873979

Claim:
A method of building predictive models on transactional data, comprising: providing an aggregation module for each transactional record source; initializing output values of each aggregation module; inputting a first transactional record from each transactional record source into said corresponding aggregation module; calculating a first iteration of said output values for each aggregation module as: ƒ k i (1)= F (Ø(Σ t pq w i m ),0), where: φ is a neural network element function; F is a blending function that controls how fast a previous transactional record become obsolete; and W i m are weights of the neural network; inputting a next transactional record from each transactional record source into said corresponding aggregation module; updating said outputs values of each aggregation module as: ƒ k i ( r +1)= F (Ø(Σ t pq w i m ),ƒ k i ( r )); repeating the two prior steps until all transactional records are processed; and obtaining scalar values ƒ k i as scalar inputs for traditional modeling.