Capital One Services, LLC

Brian McClanahan

Cruz Vargas

Joshua S Edwards

Lin Ni Lisa Cheng

Phoebe Atkins

Max Miracolo

Alexander Lin

Rajko Ilincic

Bryant Alan Yee

Michael Bakr Mossoba

Kevin Grant Osborn

Louis Buell

Systems and methods of the present disclosure enable database search. The systems and/or methods may include receiving a search query that includes an input document having text. Word embeddings are generated within the input document, where the word embeddings include vector representations of words in the text of the input document. An average input document word embedding vector is determined for the word embeddings of the input document. A set of stored documents is accessed, where each stored document includes a stored text has a particular average stored document word embedding vector. A similarity model is used to determine a similarity metric measuring the similarity between the input document and each stored document based on the average input document word embedding vector and the particular average stored document word embedding vector of each stored document.

Systems for database searching and database schemas management and methods of use thereof

A method and system performed by a processor includes receiving from a verified user, an authentication answer for identity-authentication questions. An authentication answer embedding vector in a textual embedding space is generated by inputting each authentication answer into an embedding engine and stored. An unverified-user authentication answer is received, in response to posing to the unverified user, a specific identity-authentication question of the verified user. An unverified-user authentication answer embedding vector is generated using the embedding engine. An embedding space distance is computed between the unverified-user authentication answer embedding vector and the authentication answer embedding vector for the specific identity-authentication question of the verified user posed to the unverified user. A similarity score based on the embedding space distance is computed. The unverified user is identified as the verified user when the similarity score is higher than a predefined threshold score.

Computer-based systems applying textual embedding space software engines to authentication question and answers for identifying a user and methods of use thereof

In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving an input text data retrieved from a transcription associated with a previously recorded audio data file between a user of a plurality of users and an agent associated with a call center; identifying personal information associated with the user of the plurality of users from the input text data by inputting the input text data into a trained machine learning model; determining at least one key term within the personal information associated with the user of the plurality of users; automatically determining a confidence positivity score associated with the at least one key term; automatically extracting a plurality of tuples from the input text data; storing the plurality of tuples in an external database; and automatically generating a call script for conducting a subsequent call with the user.

Computer-based systems configured for automated extraction of data from inputs and methods of use thereof

A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Feedback control for automated messaging adjustments

The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.

Systems and methods for data aggregation and cyclical event prediction

In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

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Silver Spring, MD, United States of America

Brian McClanahan