Capital One Services, LLC

Md Arafat Hossain Khan

Arturo Hernandez Zeledon

Minh Le

Nathan Wolfe

Isha Chaturvedi

Mohammad Sorower

In some embodiments, intent labeling may be facilitated for machine learning model training. In some embodiments, a set of natural language inputs may be obtained. Based on an action set, content related to intent labeling may be presented on a user interface. Based on an entity hierarchy and user selection of an agent-side action of the action set, a first entity subset of entities associated with the agent-side action may be presented on the user interface. Based on the entity hierarchy and user selection of a first entity from the first entity subset, a second entity subset of entities associated with the first entity may be presented on the user interface. Based on user selection of a second entity from the second entity subset, the natural language input may be associated with an ordered intent label that includes the agent-side action, the first entity, and the second entity.

Systems and methods for facilitating intent labeling for machine learning model training

Methods and systems for generating training data for sequential conversational responses to alleviate the collection burden are disclosed herein. More specifically, the methods and systems describe generating determining dynamic conversational responses to generate training data for sequential conversation responses. For example, the system creates a special form of a random treatment control experiment to reduce the data collection while at the same time generating effectively adequate learning data for intents for the sequenced machine learning.

Systems and methods for generating training data for sequential conversational responses

Methods and systems are presented for generating real-time dynamic conversational responses during conversational interactions using machine learning models based on historic intents for a plurality of users and user-specific interactions. The machine learning models comprise a neural network trained to select a first intent from a plurality of intents based on historic data accumulated prior to the conversational interaction and a neural network trained to select a first interaction-specific intent from a plurality of interaction-specific intents based on interaction-specific data for a user.

Systems and methods for generating real-time dynamic conversational responses during conversational interactions using machine learning models

Described are methods and systems are for generating dynamic conversational queries. For example, as opposed to being a simply reactive system, the methods and systems herein provide means for actively determining a user's intent and generating a dynamic query based on the determined user intent. Moreover, these methods and systems generate these queries in a conversational environment.

Systems and methods for generating dynamic conversational responses based on predicted user intents using artificial intelligence models

Methods and systems disclosed herein collect user data in real-time and organize them (e.g., using a collaborative filtering matrix) into groups (e.g., cluster assignments). The system then determines statistical distributions of observed real-time intents (e.g., based on actual selections made by users) for each group. The system then merges this distribution with existing model predictions (e.g., a model trained on historical training data) to balance between historical and dynamically updated information.

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McLean, VA, United States of America

Md Arafat Hossain Khan