At step 504, process 500 (e.g., using one or more components in system 200 (FIG. 2)) determines a feature input based on the user action. For example, the system may determine, using control circuitry, a first feature input based on the first user action in response to receiving the first user action. The system may generate the feature input based on one or more criteria. For example, the system may generate the feature input based on a conversational detail or information from a user account of the user, a time at which the user interface was launched, and/or a webpage from which the user interface was launched.

At step 506, process 500 (e.g., using one or more components in system 200 (FIG. 2)) inputs the feature input into a first machine learning model. For example, the system may input, using the control circuitry, the first feature input into a first machine learning model, wherein the first machine learning model is trained to select an intent cluster from a plurality of intent clusters based on the first feature input and the first user action, wherein each intent cluster of the plurality of intent clusters corresponds to a respective intent of a user following the first user action.

In some embodiments, the system may receive a first labeled feature input, wherein the first labeled feature input is labeled with a known intent cluster for the first labeled feature input. The system may then train the first machine learning model to classify the first labeled feature input with the known intent cluster.