Patent ID: 11886964
Assignee: ADOBE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  H | IPC G  H

Claim 7:
8. A system comprising one or more processors and a memory having stored thereon instructions that, upon execution by the one or more processors, cause the one or more processors to perform one or more operations, the system further comprising:
a content generator configured to:
transmit, to a user device, interactive content for a user, wherein the interactive content is associated with a targeted campaign initiated by a content provider system;
receive, from a classifier subsystem, an output including a categorical value that represents a predicted user-engagement level of the user in response to a presentation of future interactive content associated with the targeted campaign;
select a particular follow-up interactive content from a set of follow-up interactive content by using the categorical value as input, wherein resources allocated for generating the particular follow-up interactive content are determined in accordance with the categorical value; and
transmit the particular follow-up interactive content to the user device, such that the user device displays the particular follow-up interactive content; and

the classifier subsystem configured to:
access user-activity data of the user, wherein the user-activity data includes a type of the interactive content and one or more user-device actions performed in response to the transmission of the interactive content to the user device;
apply a machine-learning model to the user-activity data to generate the output that includes the categorical value that represents the predicted user-engagement level of the user at a particular future time point in response to a presentation of a future interactive content of the targeted campaign, wherein the particular future time point is defined by a preconfigured duration of time elapsed from a time when a particular user-device action is performed in response to the interactive content of the targeted campaign, wherein the machine-learning model was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content associated with other campaigns, and wherein the machine-learning model was trained at least by:
identifying a time period within which the previous user-device actions were performed;
splitting the time period into a set of time windows;
training, for a subset of time windows of the set of time windows, the machine-learning model using a subset of the training dataset corresponding to a time window of the subset of time windows, wherein the subset of the training dataset includes previous user-device actions performed by at least one of the plurality of users and identified as being performed within the time window, wherein a duration of the time window is adjustable based on user-device actions in a corresponding subset of training dataset during training, wherein training the machine-learning model comprises creating a target label representing a known user-engagement level of a user associated with the subset of training dataset; and

transmit the output to the content generator to trigger selection of the particular follow-up interactive content.