US 11,886,964 B2
Provisioning interactive content based on predicted user-engagement levels
Atanu R. Sinha, Bangalore (IN); Xiang Chen, San Jose, CA (US); Sungchul Kim, San Jose, CA (US); Omar Rahman, San Jose, CA (US); Jean Bernard Hishamunda, San Jose, CA (US); Goutham Srivatsav Arra, Dublin, CA (US); and Shiv Kumar Saini, Bangalore (IN)
Assigned to ADOBE INC., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on May 17, 2021, as Appl. No. 17/322,108.
Prior Publication US 2022/0366299 A1, Nov. 17, 2022
Int. Cl. G06N 20/00 (2019.01); G06F 3/0484 (2022.01); H04L 67/50 (2022.01)
CPC G06N 20/00 (2019.01) [G06F 3/0484 (2013.01); H04L 67/535 (2022.05)] 19 Claims
OG exemplary drawing
 
1. A method comprising:
accessing, by a content provider system, a machine-learning model configured to predict user-engagement levels of users in response to presentation of future interactive content of a targeted campaign initiated by the content provider system, 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; and
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, and 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;
receiving, by the content provider system, user-activity data of a particular user, wherein the user-activity data includes one or more user-device actions performed by the particular user in response to an interactive content of the targeted campaign;
applying, by the content provider system, the machine-learning model to the user-activity data to generate an output including a categorical value that represents a predicted user-engagement level of the particular user at a particular future time point in response to a presentation of the 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;
selecting, by the content provider system, a follow-up interactive content that is associated with the categorical value of the output, wherein resources allocated for generating the follow-up interactive content are determined in accordance with the outputted categorical value; and
transmitting, by the content provider system, the follow-up interactive content to a user device of the particular user, such that the user device displays the follow-up interactive content.