Capital One Services, LLC

Micah Price

Qiaochu Tang

Geoffrey Wayne Dagley

Jason Richard Hoover

Stephen Michael Wylie

Avid Ghamsari

Sunil Subrahmanyam Vasisht

Staevan Alan Duckworth

Chi-san Ho

Yue Duan

Olalekan Awoyemi

Habeeb Hooshmand

Aamer Charania

Kristen Przano

Raman Bajaj

Stewart Michael Youngblood

Arjun Dugal

Sean Reddy

Stephen Paul Anderson

Jeremy Edward Goodsitt

Sanjiv Yajnik

Melissa A Crawford

Bryan Joseph Tamayo

Benjamin Skylar Radcliffe

Amit Ramit Deshpande

Rebecca Hummel

Dinesh Sundaram

Jayson Sellers

Krista Rister

Praveen Kumar Gadipelly

Staeven Duckworth

Brian McGill

Chi-San Ho

Kasey Greuner

Sandeep Manepalli

Sripavan Sanka

Chaofeng Xu

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)

A computer-implemented method for providing a personalized interface to a user based on whether the user is serious about making a purchase may include: obtaining customer identification data and customer input data a customer, wherein the customer input data comprises a request from the customer; determining a request status of the customer based on the customer identification data and the customer input data; obtaining customer interface activity data of the customer based on the request status; obtaining customer purchasing data of the customer based on the request status; generating a prediction model based on the customer interface activity data and the customer purchasing data; training the generated prediction model by classifying the customer based on the customer interface activity data and the customer purchasing data; obtaining user identification data and user interface activity data of a user via a user device, the user interface activity data indicating interactive activities between the user and a user interface displayed on the user device; determining a rating of the user to purchase a product based on the user identification data, the user interface activity data, and the prediction model; and providing, to the user, an updated user interface on the user device based on the determined rating.

Methods and systems for updating a user interface based on level of user interest

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

Disclosed embodiments provide systems, methods, and computer-readable storage media for enhancing a vehicle identification with preprocessing. The system may comprise memory and processor devices to execute instructions for receiving an image depicting a vehicle. The image may be analyzed and first predicted identity and first confidence value may be determined. The first confidence value may be compared to a predetermined threshold. The processors may further select a processing technique for modifying the image and further analyze the modified image determining a second predicted identity of the vehicle. And a second confidence value may be determined. And the system may further compare the second confidence value to the predetermined threshold to select the first or second predicted identity for transmission to a user.

Systems and methods for enhancing real-time image recognition

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

Computer-implemented techniques and systems for generating vehicle profiles may include scanning a plurality of vehicles, each vehicle located at a respective vehicle location, identifying respective vehicle locations for each of the plurality of vehicles, based on scanning the plurality of vehicles, associating a vehicle identification tag for each vehicle with its respective vehicle location, interpreting a signal to determine a user location, determining that the user location is within a threshold distance from a first vehicle location of a first vehicle from the plurality of vehicles, providing a first vehicle identification tag corresponding to the first vehicle based on determining that the user location is within the threshold distance, generating a first vehicle profile based on the first vehicle identification tag, and providing the first vehicle profile to a user device.

User-based vehicle determination

A device may determine a price of a product based on first information concerning attributes of the product, and provide the price for display by a user device. The device may generate first hash values based on the first information, where a particular first hash value is associated with a particular attribute, and generate a first bit-string that includes the plurality of first hash values. The device may generate second hash values based on second information concerning the attributes of the product, where a particular second hash value is associated with the particular attribute. The device may generate a second bit-string that includes the second hash values, and determine a change to the particular attribute based on the first bit-string and the second bit-string. The device may determine a new price of the product based on the change, and provide the new price for display by the user device.

Growing community of inventors

Plano, TX, United States of America

Micah Price