Abstract: Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

Abstract: A device may process, after obtaining an image that includes image data concerning a product, first audio data obtained concerning a first utterance of a user of a user device, and first video data obtained concerning a first eye gaze direction of the user, to determine a first reaction of the user to the image. The device may process, after causing display of the image and an overlay superimposed on the image by the user device, second audio data obtained concerning a second utterance of the user, and second video data obtained concerning a second eye gaze direction of the user, to determine a second reaction of the user to the image and the overlay. The device may retrain a product recommendation model using the first reaction and the second reaction, and may generate and cause display of a product recommendation based on the product recommendation model.

Abstract: 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.

Abstract: A computer system receives a request to locate a vehicle within a facility. The computer system parses the request to identify one or more characteristics of the vehicle. The computer system identifies a sensor corresponding to the vehicle based on the one or more characteristics of the vehicle. The computer system identifies a most recent location of the vehicle based on the last registered position of the vehicle within the facility. The computer system transmits a signal to a parking spot sensor at the most recent location of the vehicle in the facility. The computer system receives the unique identification. The computer system determines that the unique identification of the vehicle sensor co-located with the parking spot sensor matches the sensor of the requested vehicle. The computer system reports to the user a current location of the vehicle within the facility based on a location of the parking spot sensor.

Abstract: 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.

Abstract: 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.

Abstract: A device having extended reality capabilities may capture a video feed including one or more video frames depicting an object that is visible in a field of view of the device. The device may provide the one or more video frames to a 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. The device may obtain positional tracking information that represents a position and an orientation associated with the object relative to a coordinate space that corresponds to the field of view of the device. The device may obtain a workflow including a sequence of content items for visually inspecting the object using the extended reality capabilities of the device. The device may render digital content associated with the workflow using the extended reality capabilities of the device.

Abstract: Various aspects described herein generally relate to visualizing a vehicle history using extended reality. In some implementations, an extended reality device may obtain image data corresponding to one or more vehicle parts that are visible in a field of view of the extended reality device. The extended reality device may identify one or more anchor points in a coordinate space corresponding to the field of view of the extended reality device and obtain a vehicle history report based on an identifier associated with the vehicle. The extended reality device may obtain digital content based on one or more records contained in the vehicle history report and an expected visual appearance of the one or more parts of the vehicle. The extended reality device may render the digital content on a display of the extended reality device. Various other aspects are provided.

Abstract: An artificial intelligence system for identifying attributes in an image. The system may include a processor in communication with a client device; and a storage medium. The storage medium may store instructions that, when executed, configure the processor to perform operations including: extracting first features; categorizing the first images in a first group or a second group; modifying first metadata associated with each image in the first images to include a binary label; calculating a classification function; classifying a second plurality of images using the classification function; extracting second features from the second images classified in the first group; categorizing the second images in the first group by attribute; calculating an attribute identification function that identifies attributes of the second images; and identifying at least one attribute associated with a client image using the attribute identification function, the client image being received from the client device.

Abstract: 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.

Abstract: Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. A method for providing the specific vehicle recommendation includes: receiving a generic automobile request from a user, applying a machine learning model (MLM) trained by a corpus of reviews to the received request, and generating, by the MLM, a recommendation for at least one specific automobile feature based on the generic automobile request.

Abstract: Techniques for providing indirect local geo-positioning using AR markers are disclosed. A first moveable AR marker can be located or found by a computing device. A location of the first moveable AR marker can be known and shared with the computing device. The location of the first moveable AR marker can be based on distance between the first moveable AR marker and a fixed AR marker. A distance to the first moveable AR marker can be determined. Based on the known location of the first moveable AR marker and the distance to the first moveable AR marker from the computing device, an estimate of the location of the computing device can be determined without having line-of-sight (LOS) to the fixed AR marker.

Abstract: Disclosed are systems and methods for determining fuel economy of a vehicle. The method may include: receiving, by a processor, travel route information for the vehicle and fuel payment information for the vehicle from a first user device through a network; determining, by the processor, an amount of fuel used by the vehicle based on the received fuel payment information; determining, by the processor, fuel economy of the vehicle based on the received travel route information and the determined amount of fuel used by the vehicle; and transmitting, by the processor, the determined fuel economy of the vehicle to a second user device through the network.

Abstract: The present disclosure relates to systems and methods for generating real-time quotes using machine learning algorithms. The system may include a processor in communication with a client device, and a storage medium storing instructions that, when executed, cause the processor to perform operations including: receiving an image of a vehicle from the client device, extracting one or more features from the image, based on the extracted features and using a machine learning algorithm, identifying one or more attributes of the vehicle, based on the identified attributes of the vehicle, determining a make and a model of the vehicle, obtaining comparison information based at least in part on the determined make and model, estimating a quote for the vehicle based on the comparison information; and transmitting the estimated quote for display on the client device.

Abstract: A credit history blockchain wallet of the user account in one or more blocks of a credit history blockchain may be searched to identify a plurality of credit event records associated with a user account responsive to a loan request. A first loan offer may be received for the user account based on the plurality of credit event records in the credit history blockchain. First information associated with the first loan offer may be recorded in the credit history blockchain wallet, the first information associated with the user. An acceptance of the first loan offer may be received from the user account. Second information associated with the accepted first loan offer may be recorded in the credit history blockchain wallet of the user account in the credit history blockchain.

Abstract: A session continuity platform may detect a user action during a first user session associated with a first user device, wherein the first user session is associated with a first platform. The session continuity platform may determine identification information of a user associated with the user session. The session continuity platform may determine a continuity record identifier associated with the identification information. The session continuity platform may obtain, based on the continuity record identifier, activity data from a plurality of continuity records of a distributed ledger. The session continuity platform may determine, based on the activity data, activity information associated with the user action and the second user session. The session continuity platform may cause the first user device to present the activity information via a display of the user device.

Abstract: Various embodiments may be directed to systems, devices, apparatuses to perform techniques such as determining vehicle data for a plurality of vehicles, wherein the vehicle data may be based on detections made by input devices of a plurality of mobile devices. Embodiments may further include determining vehicle attributes for the plurality of vehicles and environmental data. Embodiments also include training a machine-learning model with the vehicle data, the vehicle attributes, and the environmental data, and the machine-learning model may be trained to detect anomalies associated with vehicles having the at least one of the vehicle attributes. Embodiments may also include a trained machine-learning model to data to detect anomalies associated with a vehicle.

Abstract: 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.

Abstract: Embodiments may generally be directed components and techniques to receive and process an indication of a web-based search performed for an item of a plurality of items and determine a co-occurrence matrix comprising rows and columns having entries associated with the plurality of items. Each of the entries of the co-occurrence matrix may indicate a number of co-occurrences of two of the plurality items. Embodiments also include techniques to determine related pairs of the plurality of items based on the number of co-occurrences for each entry and a relationship threshold value indicating two of the items are related and provide an indication of each item of the plurality of items that is related to the item searched based on the related pairs determination.

Abstract: A vehicle receives sensor data that includes image data of frames that depict one or more individuals outside of the vehicle, and identifies, by analyzing the sensor data using one or more attribute recognition techniques, a set of attributes of an individual of the one or more individuals. The vehicle determines a set of scores indicating a set of likelihoods of a set of vehicle configurations being a preferred vehicle configuration for the individual, based on a data model performing a machine-learning-driven analysis of attribute data identifying the set of attributes, and/or location data identifying a location of the individual relative to the vehicle. The vehicle selects a particular vehicle configuration based on a score that indicates a likelihood of the particular vehicle configuration being the preferred vehicle configuration and provides an instruction to cause a vehicle component to implement the particular vehicle configuration by updating a configurable value.