Patent ID: 11935080
Assignee: ADOBE INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 15:
16. One or more non-transitory computer storage media storing computer-useable instructions that, when executed by at least one hardware processor, cause the at least one hardware processor to:
determine an importance of a plurality of metrics by utilizing a reinforcement machine learning model having a machine learning algorithm that learns user behavior by automatically analyzing, a consumption pattern of a user, the consumption pattern corresponding to frequency, recency, and data query patterns of the user interacting with an analytics engine, according to an on -demand game, the on-demand game including:
choices, corresponding to the plurality of metrics, from a data duel, wherein a choice of a metric indicates that the metric is more critical to the user than another metric of the data duel and
application programming interface requests associated with the user interacting with the analytics engine;

learn, via the reinforcement machine learning model, similar user behavior of similar users by performing, unsupervised machine learning clustering based upon attributes that provide a maximum entropy, the similar user behavior being associated with the plurality of metrics;
based on determining the importance of the plurality of metrics and learning the similar user behavior of the similar users, provide suggested alerts to the user;
receive, from the user, an indication of selected alerts indicating which of the suggested alerts the user prefers and employ the reinforcement machine learning model to leverage user behavior feedback based on the indication of the selected alerts;
combine or deduplicate related alerts of the selected alerts, the related alerts representing a same macro event;
determine that a particular metric has changed in accordance with a threshold of one or more alerts of the selected alerts;
communicate to the user that the particular metric has changed;
determine, a statistical significance of respective dimensions associated with the plurality of metrics to rank contributing factors causing the change; and
provide, to the user in real time, contextual analysis based on the statistical significance of the respective dimensions.