Abstract: In some implementations, a system may receive exchange information associated with a vehicle and a user. The system may identify, based on the exchange information, a type of the vehicle. The system may determine a characteristic of the user based on a user profile associated with vehicle usage by the user. The system may determine, using a service evaluation model and based on the type of the vehicle and the characteristic of the user, an evaluation score associated with the vehicle service. The system may generate, based on the evaluation score satisfying a threshold, a recommendation that the exchange include a service package associated with the vehicle. The system may provide, to the user, the recommendation, to permit the user to opt into receiving the vehicle service based on including the service package in the exchange.

Abstract: In some implementations, a system may input a set of values, for a corresponding set of features, to a machine learning model that is trained to predict a likelihood that a user will perform the action in connection with one or more entities. The set of values may be based on interactions of the user with one or more user interfaces associated with a plurality of entities. The system may execute the machine learning model on the set of values. The system may generate, based on executing the machine learning model on the set of values, an output indicative of the likelihood that the user will perform the action. The system may identify an entity, of the one or more entities, based on the output satisfying a threshold and may provide information to facilitate communication between the user and the entity.

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: 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 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: Systems and methods of determining changes in an individual's interaction patterns indicative of changed circumstances are disclosed. An exemplary method can include receiving a first set of interaction records relating to an individual and creating an interaction profile based on the first set of interaction records. The method can also include monitoring a second set of more recent interaction records, comparing the second set of interaction records to the interaction profile to identify one or more changes to the interaction patterns identified in the first set of interaction records, analyzing the one or more changes to the interaction patterns to determine if the one or more changes is indicative of a changed circumstance for the individual, and generating recommendations for programs to be presented to the individual based on the changes to the interaction patterns identified in the first set of interaction records.

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: 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: Disclosed herein are system, method, and computer program product embodiments for identifying an operational error on a website. In a given embodiment, a server may receive requests from a first website. The server identifies an attribute associated with the requests from the first website. The server may receive an HTML, file including content of the first website from a web-component. The server may identify an operational error with the first website based on the attribute and the content of the first website. Furthermore, the server may redirect an attempt to access the first website to a second website using the web-component.

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: 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: 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: Methods and systems are described herein for improvements for reducing client-server network traffic. For example, methods and systems allow for the reduction of client-server network traffic by altering search filters based on their respective rank and/or frequencies of being searched with other search filters included in a search query. In this way, users do not have to resubmit queries that are illogical or would return a null set of search results. For example, a first query may be received. A first search may be performed based on the first query. A first search filter may be selected based on a hierarchical tree structure of search filters. A second query may be generated comprising the first search filter and one or more other search filters, wherein the second query comprises fewer search filters than the first query, and a second search may be performed based on the second query.

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: In certain embodiments, a vehicle may obtain a video stream via a camera system of the vehicle. The video stream may be processed to extract one or more feature vectors from the video stream. Based on the feature vectors extracted from the video stream, vehicle description information related to one or more parked vehicles in the parking area may be determined. Space information indicating locations of the parked vehicles relative to available parking spaces in the parking area may also be determined based on the feature vectors extracted from the video stream. A mapping of the parking area may then be generated based on the vehicle description information and the space information.

Abstract: Disclosed herein are system, method, and computer program product embodiments for requesting in-person assistance with a purchase during a mobile device digital interaction (e.g., augmented reality) with a physical object of interest. Display of a real-time view includes the physical object image as well as interfaces for interacting with the image of the physical object. The interfaces include requesting in-person purchase assistance based on a mobile device location proximate to the physical object of interest.

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: Methods and systems are described herein for improvements for reducing client-server network traffic. For example, methods and systems allow for the reduction of client-server network traffic by altering search filters based on their respective rank and/or frequencies of being searched with other search filters included in a search query. In this way, users do not have to resubmit queries that are illogical or would return a null set of search results. For example, a first query may be received. A first search may be performed based on the first query. A first search filter may be selected based on a hierarchical tree structure of search filters. A second query may be generated comprising the first search filter and one or more other search filters, wherein the second query comprises fewer search filters than the first query, and a second search may be performed based on the second query.

Abstract: Embodiments are directed to systems, devices, methods, and techniques to determine words or word combinations of search engine queries and selected items corresponding to the words or word combinations. Embodiments also include applying a machine learning model to the word or word combinations and the selected items to determine a mapping between a word or word combination and a particular selected item, and updating attribute data in a web document with the word or word combination, wherein the web document is associated with the particular item such that a search including the word or word combination returns a result including the web document.

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.