Capital One Services, LLC

Nimesh Bernard

Ricky Su

Remel Tucker

Alison Chi

Mahendher Govindasingh Krishnasingh

Anirudha Simha

Cosette Goldstein

Methods and systems are described herein for merging datasets from multiple content domains for training a prediction model to predict a solution for a request related to a specific content domain. A dataset for a content domain may include requests and solutions organized as groups. For example, a dataset for a first content domain may include a first group having (a) a first set of requests (e.g., questions or queries) related to a first topic, and (h) a solution (e.g., an answer) associated with the first set of requests. The datasets of different content domains are analyzed based on context-based vector representations of the requests or solutions to determine the groups that are similar and merge those similar groups into a single merged group. A prediction model is trained with the merged groups for obtaining a prediction of a solution to any given request.

Merging data from various content domains to train a machine learning model to generate predictions for a specific content domain

An API monitoring and validation tool that may be provided as part of a web-based application testing and deployment environment. The environment may enable developers to develop web-based applications, validate and test APIs used by the applications and deploy the applications. The API monitoring and validation tool may perform validation of a service endpoint used by an application. As part of the validation, a user may provide information regarding the form and content of a successful response to a call to the service endpoint is. The monitoring may measure performance metrics and obtain information regarding the causes of errors and failures. The monitoring can identify errors and failures that occur during API call chains to service endpoints.

API monitoring and validation for onboarded applications

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Aldie, VA, United States of America

Nimesh Bernard