Capital One Services, LLC

Staevan Alan Duckworth

Qiaochu Tang

Geoffrey Wayne Dagley

Micah Price

Jason Richard Hoover

Stephen Michael Wylie

Olalekan Awoyemi

Avid Ghamsari

Stewart Michael Youngblood

Jonathan Castaneda

Daniel Martinez

William Hardin

Bryan Joseph Tamayo

Susie Lee

Benjamin Skylar Radcliffe

Habeeb Hooshmand

Victoria Blacksher

Stephen Paul Anderson

Dinesh Sundaram

Jayson Sellers

Praveen Kumar Gadipelly

Kasey Greuner

Chaofeng Xu

Sripavan Sanka

Sandeep Manepalli

A device receives image data of a contractual document that includes an offer including terms of a proposed transaction, converts the image data to text data that identifies text within the contractual document, and receives preferences information for a recipient of the offer. The device identifies key terms within the contractual document by using term identification to analyze the text. The key terms may include a first key term that identifies subject matter of the proposed transaction and other key terms that are part of the offer. The device determines term scores that correspond to likelihoods of the other key terms being favorable to the recipient by using a data model to analyze the key terms and the preferences information. The device, based on the term scores, generates and provides another device with a recommendation to be used in determining whether the accept the offer.

Document term recognition and analytics

A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.

Visualization of damage on images

A device receives, from multiple cameras, streaming video data associated with multiple vehicles and provides the streaming video data to a user device associated with a user. The device receives, from the user device, a request to control a first camera and provides, to the user device, first streaming video data associated with the first camera. The device receives, from the user device, a camera control command for the first camera and causes the first camera to perform an action. The device receives updated first streaming video data from the first camera and provides, to the user device, the updated first streaming video data. The device receives, from the user device, information identifying a first vehicle in the updated first streaming video data and identifies first augmented reality vehicle information associated with the first vehicle. The device provides the first augmented reality vehicle information to the user device.

Utilizing machine learning to generate augmented reality vehicle information for a vehicle captured by cameras in a vehicle lot

A smart device is provided with an application program for displaying a video feed received from the smart device's camera. The application can determine the coordinates for an intersection point, which is a point on the ground where the smart device is pointing at. The application can display a target on the visual representation of the intersection point. Based on whether the smart device is at an appropriate distance from the intersection point, the user interface can superimpose an indicator on the video feed received from the camera. This can inform the user whether the smart device is at an optimal scan distance from the intersection point (or an object) so that the object can be identified by a machine learning model.

Method for determining correct scanning distance using augmented reality and machine learning models

According to certain aspects of the disclosure, a computer-implemented method may be used for determining one or more vehicle recommendations. The method may include receiving data pertaining to a user's internet browsing activity. The received data may be indicative of the user's automotive vehicle preferences. The method may include comparing the received data to a collection of stored vehicle qualities. The method also may include identifying, based on the received data and the comparison of the received data to the collection of stored vehicle qualities, a vehicle characteristic of interest to the user. Using the vehicle characteristic of interest, one or more vehicle recommendations may be determined. One or more vehicle recommendations may be communicated to the user.

Systems and methods for artificial intelligence-based personalized purchase recommendations

Systems and methods for providing a disposal recommendation for an owned vehicle are provided. The method includes generating a profile for the owned vehicle, the profile including age, mileage, and location of the owned vehicle, receiving monitoring data of the owned vehicle, and receiving sales data for non-owned vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the owned vehicle, based on the sales data and the monitoring data, and providing a recommended disposal time for the owned vehicle, based on the predicted depreciation data.

System and method for providing disposal recommendation for a vehicle owner

Disclosed herein are system, method, and computer program product embodiments for requesting in-person assistance with a purchase during a mobile device digital interaction (e.g., augmented reality) with a physical object of interest. Display of a real-time view includes the physical object image as well as interfaces for interacting with the image of the physical object. The interfaces include requesting in-person purchase assistance based on a mobile device location proximate to the physical object of interest.

Mobile device with in-person assistance

The present disclosure provides systems and methods for creating and updating vehicle listings using crowdsourced images. An exemplary embodiment of the disclosed systems and methods includes at least one processor and at least one non-transitory computer-readable medium containing instructions. The instructions, when executed by the at least one processor, cause the system to perform operations. The operations include communicating with a listing system to create a listing for a vehicle and receiving, from an identification application executing on a mobile device distinct from the listing system, an identifier for the vehicle and an image of the vehicle. The operations further include identifying the listing using the received identifier, determining one or more parameters associated with an image quality for the received image, and updating the listing for the vehicle to include the received image based on the determined one or more parameters for the received image.

Systems and methods for updating listings

A computer-implemented method for providing personalized purchasing information to a user may include: obtaining first identification data of the user from a first engine; verifying a registration status of the user based on the first identification data; obtaining second identification data of the user from a second engine based on the registration status of the user; comparing the first identification data of the user and the second identification data of the user; initiating a real-time credit monitoring session based on the comparison between the first identification data of the user and the second identification data of the user; generating the personalized purchasing information of the user based on the real-time credit score of the user; and demonstrating the personalized purchasing information on a display of a device associated with the user.

Methods and systems for providing personalized purchasing information

Various embodiments are generally directed to techniques of overlaying a virtual object on a physical object in augmented reality (AR). A computing device may receive one or more images of the physical object, perform analysis on the images (such as image segmentation) to generate a digital outline, and determine a position and a scale of the physical object based at least in part on the digital outline. The computing device may configure (e.g., rotate, scale) a 3D model of the physical object to match the determined position and scale of the physical object. The computing device may place or overlay a 3D virtual object on the physical object in AR based on a predefined location relation between the 3D virtual object and the 3D model of the physical object, and further, generate a composite view of the placement or overlay.

Overlaying 3D augmented reality content on real-world objects using image segmentation

A valuation platform may receive, from a user device, video data associated with a vehicle, and may receive a vehicle history report of the vehicle based on a vehicle identification number of the vehicle. The valuation platform may receive, from the user device, feature data associated with the vehicle, and may process the video data, the vehicle history report, and the feature data, with a machine learning model, to determine one or more values for the vehicle. The valuation platform may determine a valuation for the vehicle based on the determined one or more values for the vehicle. The valuation platform may create a vehicle profile for the vehicle based on the video data, the vehicle history report, the feature data, the determined one or more values for the vehicle, and the valuation for the vehicle, and may perform one or more actions based on the vehicle profile.

Utilizing machine learning models and captured video of a vehicle to determine a valuation for the vehicle

A computer-implemented method for processing images may include obtaining at least one image for analyzing; inputting the at least one image to at least one of a plurality of image plugins; analyzing the at least one image via the at least one of the plurality of image plugins; determining metadata related to the at least one image based on the at least one of the plurality of image plugins; filtering the at least one image based on one or more rule sets to generate at least one filtered image; sorting the at least one filtered image to generate at least one sorted image; displaying the at least one sorted image based on an organizational sequence of a webpage; displaying navigation controls via the webpage; and displaying the at least one sorted image according to a user interaction with the navigation controls on the webpage.

Systems and methods for processing vehicle images based on criteria

Systems and methods for providing a disposal recommendation for a vehicle are provided. The method includes generating a profile for a first vehicle, the first vehicle being an income generating vehicle and the first vehicle profile including at least age, mileage, and location of the first vehicle, receiving monitoring data of the first vehicle, and receiving sales data for second vehicles, the second vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the first vehicle, based on the sales data and the monitoring data, generating a predicted profit data from the first vehicle, and providing a recommended disposal time for the first vehicle, based on the predicted depreciation data and the predicted profit data.

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The Colony, TX, United States of America

Staevan Alan Duckworth