Patent ID: 11961109
Assignee: ADOBE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 12:
13. A method of training a neural network, the method comprising:
training a deep Q-learning neural network representing a policy function of a Markov Decision Process (MDP) model comprising an action set including times of day and a plurality of message types corresponding to a customer journey, wherein the deep Q-learning neural network is trained based on customer responses to the plurality of message types;
identifying user information for a customer, wherein the user information includes user interaction data;
determining a message type from the plurality of message types and a time of day using the deep Q-learning neural network and the user information;
selecting a message based on the determined message type;
transmitting the message to the customer at the determined time of day based on the selection;
identifying a customer interaction in response to the message;
updating the deep Q-learning neural network based on the customer interaction; and
exploring additional subgroups for new behavior, wherein the exploring additional subgroups comprises:
identifying a user subgroup for new behavior exploration;
determining that the customer belongs to the user subgroup;
identifying a probability for a random delivery schedule based on the determination;
identifying a random message based at least in part on the probability;
transmitting the random message to the customer;
identifying a result of the random message; and
updating the neural network, the user information, or both based on the result.