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

Claim 0:
1. A method of training a neural network, the method comprising:
training a 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 interest levels representing a user journey, wherein the neural network is trained based on user responses to each of the plurality of message types;
identifying user information for a user, wherein the user information includes user interaction data indicating an interest level of the user at a point in the user journey;
determining a message type from the plurality of message types using the neural network, wherein an input to the neural network includes a state variable based on the user information, and an output of the neural network comprises a probability vector including a plurality of values corresponding to the plurality of message types, respectively;
selecting a message having the determined message type, wherein the message includes content appropriate for the interest level of the user;
transmitting the message to the user;
identifying a user interaction in response to the message;
updating the neural network using reinforcement learning based on the user interaction; and
identifying a user subgroup for new behavior exploration;
determining that the user 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.