Systems and methods for generating dynamic conversational responses through aggregated outputs of machine learning models
AssigneeCapital One Services, LLC
InventorMinh LE
Life of this patent
Estimating the term…
Abstract
Methods and systems are described herein for generating dynamic conversational responses. For example, dynamic conversational responses may facilitate an interactive exchange with users. Therefore, the methods and systems used specialized methods to enriched data that may be indicative of a user's intent prior to processing that data through the machine learning model, as well as a specialized architecture for the machine learning models that take advantage of the user interface format.
Claims20
A system for generating dynamic conversational responses through aggregated outputs of machine learning models, the system comprising:
- storage circuitry configured to store:
- a first machine learning model, wherein the first machine learning model is trained using a multi-class cross entropy loss function;
- and
- a second machine learning model, wherein the second machine learning model is trained using a binary cross entropy loss function;
- control circuitry configured to:
- receive a first user action during a conversational interaction with a user interface;
- determine, based on the first user action, a first feature input for the first machine learning model;
- determine, based on the first user action, a second feature input for the second machine learning model;
- input the first feature input into the first machine learning model to generate a first output from the first machine learning model;
- input the first feature input into the second machine learning model to generate a second output from the second machine learning model;
- determine a third output based on a weighted average of the first output and the second output;
- and
- select a subset of the dynamic conversational responses from a plurality of dynamic conversational responses based on the third output;
- and
- input/output circuitry configured to:
- generate, at the user interface, the subset of the dynamic conversational responses during the conversational interaction.
A method for generating dynamic conversational responses through aggregated outputs of machine learning models, the method comprising:
- receiving a first user action during a conversational interaction with a user interface;
- determining, based on the first user action, a first feature input for a first machine learning model, wherein the first machine learning model is trained using a multi-class cross entropy loss function;
- determining, based on the first user action, a second feature input for a second machine learning model, wherein the second machine learning model is trained using a binary cross entropy loss function;
- inputting the first feature input into the first machine learning model to generate a first output from the first machine learning model;
- inputting the first feature input into the second machine learning model to generate a second output from the second machine learning model;
- determining a third output based on a weighted average of the first output and the second output;
- and
- selecting a subset of dynamic conversational responses from a plurality of dynamic conversational responses based on the third output;
- and
- generating, at the user interface, the subset of dynamic conversational responses during the conversational interaction.
A non-transitory computer-readable media for generating dynamic conversational responses through aggregated outputs of machine learning models, comprising of instructions that, when executed by one or more processors, cause operations comprising:
- receive a first user action during a conversational interaction with a user interface;
- determine, based on the first user action, a first feature input for a first machine learning model, wherein the first machine learning model is trained using a multi-class cross entropy loss function;
- determine, based on the first user action, a second feature input for a second machine learning model, wherein the second machine learning model is trained using a binary cross entropy loss function;
- input the first feature input into the first machine learning model to generate a first output from the first machine learning model;
- input the first feature input into the second machine learning model to generate a second output from the second machine learning model;
- determine a third output based on a weighted average of the first output and the second output;
- and
- select a subset of the dynamic conversational responses from a plurality of dynamic conversational responses based on the third output;
- and
- generate, at the user interface, the dynamic conversational responses during the conversational interaction.
Dependencies are inferred from the claim text.