Capital One Services, LLC

Olalekan Awoyemi

Micah Price

Qiaochu Tang

Geoffrey Wayne Dagley

Stephen Michael Wylie

Jason Richard Hoover

Staevan Alan Duckworth

Sunil Subrahmanyam Vasisht

Dinesh Sundaram

Jayson Sellers

Praveen Kumar Gadipelly

Sandeep Manepalli

Kasey Greuner

Chaofeng Xu

Sripavan Sanka

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 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

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

A device having extended reality capabilities receives a video feed including one or more video frames depicting an object. The device provides the one or more video frames to a first machine learning model that identifies the object and one or more parts of the object that are depicted in the one or more video frames, and obtains positional tracking information that represents a position and an orientation associated with the object depicted in the one or more video frames relative to a coordinate space. The device identifies a plurality of parts from the one or more parts of the object that are depicted in the one or more video frames and determines, using one or more second machine learning models, information regarding the plurality of parts and information regarding one or more replacement parts.

Visualizing vehicle part replacement information using extended reality

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 device may determine that a user device, associated with a user, and a first object for comparison are within a threshold proximity of one another. The device may communicate with the user device to obtain a comparison profile, associated with the user, based on determining that the user device and the first object are within the threshold proximity of one another. The device may identify, based on the comparison profile, a set of desired attributes. The device may identify, based on the comparison profile, a set of attribute categories to be used to compare the first object and the set of desired attributes. The device may determine a first set of attributes of the first object for the set of attribute categories. The device may present, for display, information that identifies one or more attributes of the first set of attributes or the set of desired attributes.

Proximity-based vehicle comparison

Generating augmented reality vehicle information for a vehicle captured by cameras in a vehicle lot

A chatbot in the context of a chat group messaging is described. The chat group can include a plurality of users and a chatbot. A set of rules can be defined for the users of the group granting each user a privilege status. The chatbot can receive a request through a message transmitted to the chat group. The chatbot can discern a task associated with the message, and perform the task or ask another module to perform the task. Once the task is performed, the chatbot can report the results to the chat group. The chatbot can include a conflict resolution module which can resolve conflicts. The conflict resolution module can use each user's privilege status to resolve the conflicts.

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Prosper, TX, United States of America

Olalekan Awoyemi