US 11,961,109 B2
Multi-objective customer journey optimization
Lei Zhang, Fremont, CA (US); Jun He, Fremont, CA (US); Tingting Xu, Boston, MA (US); Jalaj Bhandari, New York, NY (US); Wuyang Dai, Santa Clara, CA (US); and Zhenyu Yan, Cupertino, CA (US)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by ADOBE INC., San Jose, CA (US)
Filed on Jan. 14, 2020, as Appl. No. 16/742,386.
Prior Publication US 2021/0217047 A1, Jul. 15, 2021
Int. Cl. G06Q 30/00 (2023.01); G06F 18/20 (2023.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06Q 10/107 (2023.01); G06Q 30/0207 (2023.01)
CPC G06Q 30/0239 (2013.01) [G06F 18/295 (2023.01); G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06Q 10/107 (2013.01)] 13 Claims
OG exemplary drawing
 
9. A system comprising:
a database storing user information for a plurality of users, the user information including user interaction data indicating an interest level of a user at a point in a user journey;
a message generation component configured to generate messages having a message type selected from a plurality of message types, wherein the plurality of message types correspond to interest levels representing the user journey;
a decision making component comprising a neural network that is trained based on user responses to the plurality of message types, wherein the neural network represents a policy function of a Markov Decision Process (MDP) model, and 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;
a message transmission component configured to transmit a message to a user among the plurality of users determined by the decision making component; and
a data collection component configured to identify a user interaction in response to the transmitted message, update the user information based on the user interaction, and determine whether to transmit a subsequent message during a subsequent time period based on the updated user information, wherein:
the decision making component is further configured to:
identify a user subgroup for new behavior exploration;
determine that the customer belongs to the user subgroup;
identify a probability for a random delivery schedule based on the determination; and
identify a random message based at least in part on the probability;
and wherein the message transmission component is further configured to:
transmit the random message to the customer;
identify a result of the random message; and
update the neural network, the user information, or both based on the result.