Capital One Services, LLC

Avid Ghamsari

Qiaochu Tang

Geoffrey Wayne Dagley

Micah Price

Jason Richard Hoover

Staevan Alan Duckworth

Stephen Michael Wylie

Rebecca Hummel

Souvik Das

Staeven Duckworth

Chi-san Ho

Chi-San Ho

Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. Various techniques, methods, systems, and apparatuses include utilizing one or more tags generated by application of a machine learning model to a data source, where the data source may include generic and specific language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile in response to a user request for a suggestion.

Natural language keyword tag extraction

In some implementations, a device may receive an indication of a code associated with granting authenticated access to a door. The device may transmit, to a user device, presentation information to cause an augmented reality image of an input pad to be displayed by the user device, wherein the augmented reality image is displayed over an area of an image of an exterior of the door via a user interface. The device may detect, via a camera device, one or more user inputs to the area of the exterior of the vehicle based on tracking a movement of a user. The device may identify an input code based on the one or more user inputs. The device may perform an action to cause the door to be unlocked based on the input code matching the code.

Augmented reality displayed keypad for authenticated keyless door access

Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.

Device, method, and computer-readable media for recommendation networking based on connections, characteristics, and assets using machine learning

The method, system, and non-transitory computer-readable medium embodiments described herein capture request data from requests transmitted on websites. In various embodiments, a server receives an HTML file including content of a first website from a web-component configured to extract the content of the first website. The content includes information about a search that was executed on the first website and returned a set of items. The server generates a profile of the user based on the information about the search in the HTML file and identifies a new item that corresponds to the profile. The server further detects authentication details of the user on a second website based on the user identifier and causes display of item data of the new item and the set of items on the second web site in response to detecting the authentication details of the user.

Method and system for capturing data from requests transmitted on websites

Aspects described herein may allow for vehicle tracking. Systems and methods described herein may allow a vehicle to automatically detect the presence of a physical marker at a parking space. An image of the physical marker may be processed to determine the location of the vehicle, which may be stored and/or output for display.

Automated system for vehicle tracking

A device receives user rendering data for a 3-D rendering of a user. The 3-D rendering is a proportional representation of the user and the user rendering data is available via an application. The device determines user characteristics of the user. The device receives an indication that a user device has submitted a vehicle search request. The device identifies vehicles to recommend to the user based on an analysis of: the user characteristics, and vehicle characteristics for a collection of vehicles being offered via the application. The device causes vehicle description data for the vehicles to be displayed via an interface of the application. The device receives user interaction data that indicates a user selection of vehicle. The device causes, based on receiving the user interaction data, the interface of the application to display a placement of the 3-D rendering of the user into a 3-D rendering of the vehicle.

Vehicle identification driven by augmented reality (AR)

The present disclosure is directed to techniques to facilitate online commerce by leveraging user activity, specifically search activity for goods/services on an online platform. As described herein, the online platform refers to a turnkey e-commerce solution that enterprises use to manage electronic transactions involving their goods/services. Some techniques are directed to an improved online platform operative to predict a user's intention behind a search query and after a consider number of search queries, to have that search query as a dynamic search filter for the online platform. Other embodiments are described and claimed.

Techniques to facilitate online commerce by leveraging user activity

In some implementations, a system may receive driving data representative of a driving pattern of a user. The system may identify a third party entity to facilitate a test drive by the user. The system may identify a geographic area within a threshold proximity of a location of the third party entity. The system may identify a plurality of waypoints within the geographic area, wherein one or more of the waypoints or one or more route segments defined by the waypoints satisfy one or more conditions based on the driving data. The system may obtain the test drive route starting and ending at the location of the third party entity and including the waypoints. The system may transmit data indicating the test drive route to at least one of a user device, an on-board computer of a test drive vehicle, or a third party device of the third party entity.

Generating a test drive route

Aspects described herein may provide an interface and/or search functionality for a dealership to determine vehicles a customer is most likely to purchase. A recommender system may generate vehicle recommendations for a dealership to sell to a customer based on customer information, vehicle information, and dealership information. Machine learning may be used to generate the recommendations. The recommendations may be based on the vehicle preferences of a customer.

Intelligent dealership recommendation engine

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

Computer-implemented methods, computer systems, and computer readable media for determining, sorting, or determining and sorting a set of items. The method includes receiving, via a user device, parameter values from a user and receiving parameter weights. The method further includes determining a sort score for each vehicle of a set of vehicles based on the received parameter values and parameter weights, and sorting the set of vehicles based on the sort score of each vehicle of the set of vehicles.

Item recommendations weighted by user-valued features

Aspects described herein may allow an automated generation of an interactive multimedia content with annotations showing vehicle damage. In one method, a server may receive vehicle-specific identifying information of a vehicle. Image sensors may capture multimedia content showing aspects associated with exterior regions of the vehicle, and may send the multimedia content to the server. For each of the exterior regions of the vehicle, the server may determine, using a trained classification model, instances of damage. Furthermore, the server may generate an interactive multimedia content that shows images with annotations indicating instances of damage. The interactive multimedia content may be displayed via a user interface.

Automatic annotation for vehicle damage

Aspects described herein may allow for auto-locating a user in a parking lot and/or automatically directing a user to recommended inventory. For example, a customer may capture an image of a vehicle, or a marker associated with a vehicle, in a parking lot. The image may be processed locally or at a server. A vehicle may be identified based on analysis of the image. Further, information regarding the user may be processed to determine another vehicle appropriate for a recommendation. For example, a prompt on a mobile device may suggest a popular model similar to the vehicle being viewed. The system may then provide directions (e.g., augmented reality directions) to the user to direct the user to the vehicle.

Augmented reality vehicle search assistance

A computer-implemented method for determining an actual cost of ownership of a purchase product may include determining a baseline value of the purchase product, and receiving usage information representative of usage of a user product from a computing device in communication with the user product. The purchase product and the user product may belong to a same class of products. The method also may include determining a user cost component based on the received usage information, and determining an actual cost of ownership of the purchase product based on at least the baseline value and the user cost component.

Method and system for determining an actual cost of ownership

An autonomous vehicle vendor lot may include a plurality of vehicle selection indicators, a visual navigation system, and a control system. The indicators may be distributed in the lot, associated with a respective parking spot in the lot, and operable to generate a visual indication indicative of a selection of a vehicle in the respective parking spot. The navigation system may be operable to generate a visual indication of a route between various locations in the lot. The control system may be operatively connected to the indicators and the navigation system, and configured to: receive a vehicle search query; identify at least one vehicle in the lot corresponding to the query; operate each vehicle selection indicator corresponding to each identified vehicle; identify a respective location in the lot for each identified vehicle; and operate the visual navigation system to generate a visual indication of a route to at least one identified vehicle.

Growing community of inventors

Frisco, TX, United States of America

Avid Ghamsari