Capital One Services, LLC

Geoffrey Wayne Dagley

Qiaochu Tang

Micah Price

Jason Richard Hoover

Stephen Michael Wylie

Avid Ghamsari

Sunil Subrahmanyam Vasisht

Staevan Alan Duckworth

Olalekan Awoyemi

Habeeb Hooshmand

Bryan Joseph Tamayo

Stewart Michael Youngblood

Kristen Przano

Benjamin Skylar Radcliffe

Raman Bajaj

Arjun Dugal

Sean Reddy

Stephen Paul Anderson

Sanjiv Yajnik

Amit Ramit Deshpande

Staeven Duckworth

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

A device detects that a user device is accessing a media stream that enables the user device to display, via a user interface, media content associated with a subject. The device receives a search query associated with the user device and the media stream, and determines, based on the search query, that a user associated with the user device has a threshold level of interest in a feature of the subject. The device receives feature information associated with the feature based on determining that the user has the threshold level of interest, and receives feature content data that is associated with feature content that includes the feature information. The device causes the feature content to be embedded into the media stream to cause the user device to display the feature content in relation to the feature when the feature is displayed in the media content via the user interface.

Platform to provide supplemental media content based on content of a media stream and a user accessing the media stream

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 computer-implemented method for using a machine-learning trained predictive engine to predict failures, comprising receiving, from a user using a graphical user interface, electronic transaction data comprising an item and candidate transaction terms, the candidate transaction terms comprising a first transaction term, the electronic transaction data being associated with a candidate transaction. The method may determine, by a machine learning predictive engine, a likelihood of success of the candidate transaction based on the electronic transaction data, and may determine, by the machine learning predictive engine, based on the first transaction term, a second transaction term that, together with the first transaction term, increases the likelihood of success of the candidate transaction above a predetermined threshold. An indication of the second transaction term and an indication of the likelihood of success of the candidate transaction may be displayed on the graphical user interface.

Systems and methods for using a predictive engine to predict failures in machine-learning trained systems for display via graphical user interface

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

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

Systems and methods for presenting an augmented reality view are disclosed. Embodiments include a system with a database for personalizing an augmented reality view of a physical environment using at least one of a location of a physical environment or a location of a user. The system may further include a hardware device in communication with the database, the hardware device including a renderer configured to render the augmented reality view for display and a controller configured to determine a scope of the augmented reality view authenticating the augmented reality view. The hardware device may include a processor configured to receive the augmented reality view of the physical environment, and present, via a display, augmented reality content to the user while the user is present in the physical environment, based on the determined scope of the augmented reality view.

Systems and methods for personalized augmented reality view

In one aspect, the present disclosure relates to a method for improved efficiency of an inventory management system. The method can include: performing a first inventory search of inventory data stored in a database using search criteria; receiving, from a beacon sensor, inventory data broadcast from a plurality of beacons; performing a second inventory search by correlating the inventory data broadcast from the plurality of beacons with results of the first inventory search; receiving image data from a camera; performing image classification on the image data to determine one or more class labels for an inventory item within the field-of-view of the camera; performing a third inventory search by correlating the class labels with results of the second inventory search; and presenting results of the third inventory search to a user.

Transitioning inventory search from large geographic area to immediate personal area

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.

Growing community of inventors

McKinney, TX, United States of America

Geoffrey Wayne Dagley