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

Claim 5:
6. A system for predicting changes in customer demand, the system comprising:
a bus system;
a storage device connected to the bus system, wherein the storage device stores program instructions; and
a number of processors connected to the bus system, wherein the number of processors execute the program instructions to:
collect 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;
determine any changes in customer bundle subscriptions during a given time interval;
determine 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 model, 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

predict, via machine intelligence comprising predictive algorithms according to the simultaneously model, types and timing of the changes in the customer bundle subscriptions based on customer service activities, wherein in simultaneously model the plurality of bundle subscription change events and time-to-event, the number of processors execute the program instructions to:
train a recurrent neural network using the metrics for defined customer tasks as training data;
predict, with the recurrent neural network, subscription change events for the number of customers according to a timestamp sequence of customer activity data;
compute, 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
calculate a weighted average of the probability density functions.