Patent ID: 11966927
Assignee: ADP, INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A computer-implemented method for predicting changes in customer demand, the method comprising:
collecting, by a number of processors, subscription data for a number of customers at specified time intervals, wherein each customer is subscribed to one of a number of defined bundles of services;
determining, by the number of processors, any changes in customer bundle subscriptions during a given time interval;
determining, by the number of processors, metrics capturing an amount of customer activity with regards to the defined bundles of services during the given time interval based on at least one of a number of clicks while using the defined bundles of services, a duration of using the defined bundles of services, and a number of page visits while using the defined bundles of services;
simultaneously modeling, by the number of processors via multimodal multi-task learning architecture comprising at least one recurrent neural network (RNN) including three layers and a plurality of fully connected neural networks (FCNNs) each associated with a plurality of bundle subscription change events, wherein the plurality of bundle subscription change events include an upgrade, downgrade, and termination, wherein the plurality of bundle subscription change events and time-to-event for each bundle subscription change comprising parameter sharing and density approximation for the metrics for defined customer tasks, wherein the plurality of FCNNs compute the density approximation for each bundle subscription change event; and
predicting, by the number of processors via machine intelligence comprising predictive algorithms according to the simultaneously modeling, types and timing of the changes in the customer bundle subscriptions based on customer service activities, wherein simultaneously modeling the plurality of bundle subscription change events and time-to-event comprises:
training a recurrent neural network using the metrics for defined customer tasks as training data;
predicting, with the recurrent neural network, subscription change events for the number of customers according to a timestamp sequence of customer activity data;
computing, with a plurality of fully connected neural networks operatively coupled to the recurrent neural network, a probability density function for each type of subscription change event predicted by the recurrent neural network; and
calculating a weighted average of the probability density functions.