Abstract: Aspects described herein may allow for displaying augmented reality directions utilizing physical markers. Systems and methods described herein may allow a device to scan to provide augmented reality directions using one or more physical markers as reference points. One or more physical markers may be left along a path, and directions may be provided from one physical marker to the next. A device, such as a mobile device, may be used to scan the physical markers and/or display the augmented reality directions on a display overlaid over a depiction of the surrounding environment.

Abstract: Systems and methods are disclosed for providing purchase recommendations. According to some examples, a method may include: determining respective frequency distributions of a plurality of vehicle attributes, the frequency distributions being determined based on occurrences of values of the plurality of vehicle attributes in a set of vehicles; selecting a vehicle attribute from the plurality of vehicle attributes based on the frequency distributions of the plurality of vehicle attributes; transmitting, to a user device, an inquiry for user preference regarding the selected vehicle attribute; receiving, from the user device, a response indicating the user preference; and presenting, to the user device, a recommendation of one or more vehicles determined based on the received response.

Abstract: This disclosure relates generally to vehicle diagnostics, and more particularly to systems and methods for vehicle tracking and service prediction. In one embodiment, a processor-implemented vehicle tracking and prediction method is disclosed. The method includes sending an electronic signal to activate in-vehicle data collection by an on-board data aggregator disposed in communication with each of an on-board vehicle computer, via an on-board diagnostic port, and one or more user devices. The method further includes receiving aggregated in-vehicle data from the on-board data aggregator, predicting a future vehicle service event based on the aggregated data, and generating a vehicle valuation metric based on the aggregated data. The method further includes sending a notification to at least one of the on-board vehicle computer and the one or more user devices, where the notification is based on the predicted future vehicle service event and the generated vehicle valuation metric.

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: Aspects described herein may allow for displaying augmented reality directions utilizing physical markers. Systems and methods described herein may allow a device to scan to provide augmented reality directions using one or more physical markers as reference points. One or more physical markers may be left along a path, and directions may be provided from one physical marker to the next. A device, such as a mobile device, may be used to scan the physical markers and/or display the augmented reality directions on a display overlaid over a depiction of the surrounding environment.

Abstract: 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.

Abstract: Systems and methods are disclosed for providing purchase recommendations. According to some examples, a method may include: determining a set of vehicle characteristics represented in a candidate set of vehicles; for each vehicle characteristic in the set of vehicle characteristics, determining a percentage of the candidate set possessing the respective vehicle characteristic; selecting, from the set of vehicle characteristics, a vehicle characteristic based on a proximity of the respectively determined percentage to 50%; transmitting, to a user device associated with a user, an inquiry for a user preference regarding the selected vehicle characteristic; receiving, from the user device, a response indicating the user preference regarding the selected vehicle characteristic; excluding vehicles from the candidate set based on the indicated user preference to obtain a reduced candidate set; and transmitting, to the user device, a recommendation of one or more vehicles from the reduced candidate set.

Abstract: Disclosed are methods, systems, and non-transitory computer-readable medium for filtering vehicle information. For instance, the method may include receiving a vehicle search request from a user device, the vehicle search request including conditions with corresponding weights. The method may also include, in response to receiving the vehicle search request, forming a search query based on the conditions and the weights of the vehicle search request; obtaining vehicle information; filtering the vehicle information based on the search query to obtain a result set and a partial match set; and transmitting a search result message based on the result set and the partial match set to the user device.

Abstract: An image error identification system retrieves an image associated with a vehicle listing and uses various machine learning models to classify the image and generate identification data that may include a vehicle make, model, trim level, and/or various features of the vehicle present in the image. The identification data is compared to the rest of the vehicle listing to detect a mismatch between the image and the vehicle listing. An alert is generated, when a mismatch is detected, indicating the one of the image or the data in the vehicle listing is incorrect.

Abstract: Techniques to leverage machine learning for search engine optimization (SEO). Some techniques are applicable to turnkey e-commerce solutions operating on the Internet as an online platform. These techniques use machine learning to gain insights into patterns/relationships between search terms and items for sale on the online platform. Depending on a relevancy or an accuracy of a search term to a particular item for sale, the online platform inserts the search term into the online platform such that a public search engine crawler increases a search engine result position for the online platform. Other embodiments are described and claimed.

Abstract: 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.

Abstract: An image information overlay system retrieves an image associated with a vehicle listing and uses machine learning models to classify the image, generating identification data that may comprise a vehicle make and model, a feature or part of the vehicle present in the image, and a location of the vehicle feature or part. The identification data or an individual identifier of the vehicle, such as a Vehicle Identification Number (VIN), may be used to retrieve overlay information related to the vehicle make and model, such as recalls or known maintenance issues or information specific to the vehicle, such as mileage, accident reports, or ownership history. The overlay information is displayed on the image as an overlay at the location of the vehicle feature or part corresponding to the overlay information.

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: This disclosure relates generally to vehicle diagnostics, and more particularly to systems and methods for vehicle tracking and service prediction. In one embodiment, a processor-implemented vehicle tracking and prediction method is disclosed. The method includes sending an electronic signal to activate in-vehicle data collection by an on-board data aggregator disposed in communication with each of an on-board vehicle computer, via an on-board diagnostic port, and one or more user devices. The method further includes receiving, aggregated in-vehicle data from the on-board data aggregator, predicting a future vehicle service event based on the aggregated data, and generating a vehicle valuation metric based on the aggregated data. The method further includes sending a notification to at least one of the on-board vehicle computer and the one or more user devices, where the notification is based on the predicted future vehicle service event and the generated vehicle valuation metric.

Abstract: 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.

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: 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.

Abstract: Aspects described herein may facilitate an automated vehicle tracking for readiness with limited human interaction.

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: Disclosed are methods, systems, and non-transitory computer-readable medium for filtering vehicle information. For instance, the method may include receiving a vehicle search request from a user device, the vehicle search request including conditions with corresponding weights. The method may also include, in response to receiving the vehicle search request, forming a search query based on the conditions and the weights of the vehicle search request; obtaining vehicle information; filtering the vehicle information based on the search query to obtain a result set and a partial match set; and transmitting a search result message based on the result set and the partial match set to the user device.