Systems and methods for dynamically updating machine learning models that provide conversational responses

A method for dynamically updating machine learning models that provide conversational responses, the method comprising:

• receiving, using control circuitry, a user action, at a user interface, during a conversational interaction with a user interface;
• determining, using the control circuitry, a feature input based on the user action in response to receiving the user action;
• inputting, using the control circuitry, the feature input into a machine learning model, wherein the machine learning model is trained to determine respective confidence levels for a plurality of specific intents, and wherein each of the respective confidence levels indicates a likelihood that a user has a corresponding specific intent;
• receiving, using the control circuitry, an output from the machine learning model, the output comprising the respective confidence levels for each of the plurality of specific intents;
• comparing, using the control circuitry, each of the respective confidence levels to a threshold confidence level;
• based on comparing each of the respective confidence levels to a threshold confidence level, retrieving, using the control circuitry, a configuration file, wherein the configuration file comprises a modification to rules used to select specific intents;
• selecting, using the control circuitry, a specific intent based on the configuration file, wherein the specific intent corresponds to a dynamic conversational response;
• and
• generating for display, at the user interface, the dynamic conversational response during the conversational interaction.

A non-transitory computer-readable medium for dynamically updating machine learning models that provide conversational responses comprising of instructions that, when executed by one or more processors, cause operations comprising:

• receiving a user action, at a user interface, during a conversational interaction with a user interface;
• determining a feature input based on the user action in response to receiving the user action;
• inputting the feature input into a machine learning model, wherein the machine learning model is trained to determine respective confidence levels for a plurality of specific intents, and wherein each of the respective confidence levels indicates a likelihood that a user has a corresponding specific intent;
• receiving an output from the machine learning model, the output comprising the respective confidence levels for each of the plurality of specific intents;
• comparing each of the respective confidence levels to a threshold confidence level;
• based on comparing each of the respective confidence levels to a threshold confidence level, retrieving a configuration file, wherein the configuration file comprises a modification to rules used to select specific intents;
• selecting a specific intent based on the configuration file, wherein the specific intent corresponds to a dynamic conversational response;
• and
• generating for display, at the user interface, the dynamic conversational response during the conversational interaction.