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: The present disclosure provides systems and methods for creating and updating vehicle listings using crowdsourced images. An exemplary embodiment of the disclosed systems and methods includes at least one processor and at least one non-transitory computer-readable medium containing instructions. The instructions, when executed by the at least one processor, cause the system to perform operations. The operations include communicating with a listing system to create a listing for a vehicle and receiving, from an identification application executing on a mobile device distinct from the listing system, an identifier for the vehicle and an image of the vehicle. The operations further include identifying the listing using the received identifier, determining one or more parameters associated with an image quality for the received image, and updating the listing for the vehicle to include the received image based on the determined one or more parameters for the received image.

Abstract: Aspects described herein may allow for vehicle access in a retail environment with limited human intervention. Systems and methods described herein may associate a vehicle with a physical location, and/or may enable a user to use a mobile device to select a vehicle for a test drive. The system may provide access to (e.g., unlock) the vehicle, and may track test drive information across one or more users.

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: Aspects described herein may allow a user to test drive a vehicle with limited human interaction. In one method, a server may receive a user request from a mobile device of a user to access a vehicle secured by a vehicle key. The server may determine that the vehicle key is stored within a storage compartment of a smart key storage device. The server may provide an unlock instruction to the smart key storage device. The unlock instruction may be provided in response to and/or as part of a request, from the mobile device, to unlock the storage compartment. The server may receive, from the smart key storage device, an indication that the vehicle key has been removed from the storage compartment based on a key sensor detecting removal of the vehicle key. This may prompt the server to update a user record to indicate removal of the vehicle key.

Abstract: Aspects described herein may allow for vehicle access in a retail environment with limited human intervention. Systems and methods described herein may associate a vehicle with a physical location, and/or may enable a user to use a mobile device to select a vehicle for a test drive. The system may provide access to (e.g., unlock) the vehicle, and may track test drive information across one or more users.

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: 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: 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: 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.

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: 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: 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.