Capital One Services, LLC

Ponnazhakan Subramanian

Satish Chikkaveerappa

Liju Mathews

Sasi Kumar Unnikrishnan

Lokesh Vijay Kumar

Venkata Satya Parcha

Amit Ramit Deshpande

Mithra Kosur Venuraju

Renjith Pascas

Asutosh Pandya

Sahul Mohammed

Vishwanath Hiremath

Nishant Kumar Sinha

Chris Demchalk

Ryan M Parker

Amit Ramesh Deshpande

Lavika Aggarwal

Rhic Chowdhury

Nimish Mangal

Jacob Adair

A device may determine that a user has a threshold level of interest in a type of product. The device may receive an image of a face of the user, may perform a facial recognition analysis of the image, and may determine identification information of the user based on the facial recognition analysis. The device may identify account information associated with the user based on the identification information and may identify a product in an inventory that is associated with the type of product. The device may perform a preauthorization process associated with a transaction involving the product and the user based on the account information, and may perform an action associated with the transaction based on the preauthorization process.

Product analysis platform to perform a facial recognition analysis to provide information associated with a product to a user

A computer-implemented method for determining a reward associated with one or more transactions of a user may comprise obtaining travel data of the user via a device associated with the user, wherein the travel data includes travel dates of the user; obtaining, via one or more processors, exchange rate data based on the travel data of the user; determining, via the one or more processors, a value of the reward associated with the one or more transactions of the user during the travel dates based on the exchange rate data; transmitting, to the user, a notification indicative of the reward associated with the one or more transactions; and causing the reward associated with the one or more transactions to be directed to a financial account associated with the user.

Methods and systems for providing a vehicle recommendation

Aspects discussed herein may relate to methods and techniques for automatically generating a test drive schedule for a user based on known inventory, user preferences, demographic information, employee availability, and other factors, as well as detecting conflicts and rescheduling test drives in response to those conflicts. This may allow for easier access to test drives for the consumer, which may promote more test drives and higher resultant sales, as well as allowing for rescheduling in advance of conflicts, which may reduce lost consumers due to test drives that were conflicted.

Distributed system for test drive conflict rescheduling

Disclosed herein are system, method, and computer program product embodiments for identifying and managing emerging risks. Businesses, such as those participating in regulated industries, may need to regularly monitor enforcement actions that are cited in a variety of sources. Embodiments disclosed herein provide for scraping information from those sources and comparing it to existing risk events in order to determine possible discrepancies that should be assessed.

Compliance management for emerging risks

A device receives vehicle data associated with vehicles located at a vehicle dealership lot, and receives, from a user device, profile data identifying a user of the user device and data identifying a particular vehicle of the vehicles. The device compares the data identifying the particular vehicle and the vehicle data to determine particular vehicle data associated with the particular vehicle, and processes the particular vehicle data and the profile data of the user, with a first model, to determine purchase options for the particular vehicle and the user. The device provides, to the user device, the particular vehicle data and the purchase options for the particular vehicle to cause the user device to display the particular vehicle data and the purchase options for the particular vehicle.

Utilizing machine learning to generate vehicle information for a vehicle captured by a user device in a vehicle lot

A device receives, from a user device, a request to prune a primary database, and receives primary database information associated with the primary database and secondary database information associated with a secondary database that is different than the primary database. The device processes the primary database information and the secondary database information, with a machine learning model, to generate suggested pruning parameters, and provides the suggested pruning parameters to the user device. The device receives selected pruning parameters from the user device, where the selected pruning parameters are selected from the suggested pruning parameters or are input via the user device. The device removes pruned information from the primary database based on the selected pruning parameters, and provides the pruned information to the secondary database based on the selected pruning parameters.

Utilizing machine learning to determine data storage pruning parameters

Systems and methods are provided for depicting information about a vehicle in augmented reality. An exemplary method may include determining identification information of a vehicle based on input received from a client device and obtaining, from an information source, vehicle information based on the identification information. The method may also include analyzing the vehicle information using an artificial intelligence engine and extracting vehicle history data from the vehicle information based on the analysis. The method may also include identifying a portion of the vehicle associated with the vehicle history data and providing instructions, based on the vehicle history data, to an augmented reality-enabled device to superimpose an indicator on a visual representation of the vehicle at a location corresponding to the portion of the vehicle.

Systems and methods for depicting vehicle information in augmented reality

Methods and system for providing a vehicle recommendation

A system for generating an enhanced distributed online registry that utilizes an interoperable framework, and machine learning and natural language processing technologies to automatically provide compatible registry items. A persistent secure connection across distributed systems facilitates automatic synchronization of the generated online registry items across the distributed systems and devices accessing those systems. The online registry application processor utilizes machine learning and natural language technologies to generate an acquisition trending model which may be utilized to generate an enhanced distributed online registry that may determine and provide registry items that are compatible with the customer acquisition. Utilizing a persistent bi-directional connection, the online registry application processor may automatically synchronize the enhanced distributed online registry in real time as registry items are added and purchased.

System and method for generating enhanced distributed online registry

A device may receive third-party data associated with merchants and may receive customer interest data associated with customers of the merchants, wherein the customer interest data includes data identifying locations of the customers and birthdates of the customers. The device may train a machine learning model, with the third-party data and the customer interest data, to generate a trained machine learning model. The device may receive, from a user device, data identifying a location and a birthdate of a particular customer of a particular merchant, wherein the particular merchant is one of the merchants, and may process the data identifying the location and the birthdate of the particular customer, with the trained machine learning model, to determine a predicted interest of the particular customer. The device may perform one or more actions based on the interest of the particular customer.

Method, device, and non-transitory computer readable medium for utilizing a machine learning model to determine interests and recommendations for a customer of a merchant

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McKinney, TX, United States of America

Ponnazhakan Subramanian