Abstract: One or more embodiments are generally directed to techniques to provide specific vehicle recommendations. Various techniques, methods, systems, and apparatuses include utilizing user web-traffic and/or one or more tags generated by application of a machine learning model to a data source, where the data source may include language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile.

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: 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.

Abstract: Various embodiments are generally directed to object tracking using laser emitters and sensors. Light emitting devices may emit laser light that is received by sensor devices including photosensors. The sensor devices may determine a location of the respective sensor device based on the received laser light. The sensors devices may form a wireless mesh network that allows each sensor device to transmit the determined location to a base station.

Abstract: The present disclosure relates to interactive projection systems. In one implementation, an interactive projection system for a vehicle window may include a film having a plurality of sensors embedded therein, the plurality of sensors configured to detect local pressure disturbance on the film; a projector configured to project a user interface onto the film; and at least one processor. The at least one processor may be configured to perform operations, including receiving a detection of a local pressure disturbance from at least one of the plurality of sensors, based on a location of the detected local pressure disturbance, determining a change in the user interface, and transmitting a command to the projector to modify a projected user interface according to the determined change.

Abstract: Disclosed embodiments provide devices, methods, and computer-readable storage media for determining information of an object from a captured image. Further, the disclosed device responds to a user input by capturing, with the camera, image data representing an object. The device then transmits, via a messaging application through a network interface to a server, the image data and a request for information relating to the object. The object information is received via the messaging application through the network from the server and is displayed on the display screen.

Abstract: A computer-implemented method for processing images may include obtaining at least one image for analyzing; inputting the at least one image to at least one of a plurality of image plugins; analyzing the at least one image via the at least one of the plurality of image plugins; determining metadata related to the at least one image based on the at least one of the plurality of image plugins; filtering the at least one image based on one or more rule sets to generate at least one filtered image; sorting the at least one filtered image to generate at least one sorted image; displaying the at least one sorted image based on an organizational sequence of a webpage; displaying navigation controls via the webpage; and displaying the at least one sorted image according to a user interaction with the navigation controls on the webpage.

Abstract: A computer-implemented method for providing personalized purchasing information to a user may include: obtaining first identification data of the user from a first engine; verifying a registration status of the user based on the first identification data; obtaining second identification data of the user from a second engine based on the registration status of the user; comparing the first identification data of the user and the second identification data of the user; initiating a real-time credit monitoring session based on the comparison between the first identification data of the user and the second identification data of the user; generating the personalized purchasing information of the user based on the real-time credit score of the user; and demonstrating the personalized purchasing information on a display of a device associated with the user.

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: Example embodiments of systems and methods for data delegation and control through the use of tokenized data are provided. In an exemplary method of data delegation and control, a data device may store private information associated with a user and generate an access token, comprising tokenized data identifying the data device. The data device may transmit the access token to a user device through a front channel and receive an information request from a merchant device comprising the access token through a secure back channel. Upon authenticating the access token, the data device may transmit a portion of the private information to the merchant device through the secure back channel.

Abstract: A device receives transaction data associated with an individual that is participating in a transaction for a vehicle. The device identifies transaction term values relating to the transaction. The device determines scores indicating likelihoods of the transaction term values contributing to prevention of completion of the transaction. The device receives vehicle inventory data describing vehicles. The device identifies one or more recommended vehicles, of the vehicles, based on performing a vehicle search of the vehicles described by the vehicle inventory data, where the vehicle search identifies the one or more recommended vehicles based on one or more filters. The device provides vehicle offer data for the one or more recommended vehicles to one or more other devices to permit the one or more other devices to use the vehicle offer data as part of another transaction for a particular vehicle of the one or more vehicles.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.

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.