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 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: Embodiments disclosed herein provide for a system and method for recalibrating an augmented reality experience in mobile devices using a plurality of physical markers. The system and methods provide for realigning the digital representation to the physical world using known physical locations associated with the physical markers that map directly to the digital representation.

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: 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: 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: 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: 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: A computer-implemented method for providing a vehicle recommendation to a user may include: obtaining one or more vehicle images via a device associated with the user; identifying one or more user-selected images of the one or more vehicle images based on user interaction with the one or more vehicle images performed by the user via a user interface; identifying one or more first-level attributes from the one or more user-selected images; obtaining one or more vehicle identifications from the one or more user-selected images; determining one or more second-level attributes based on the one or more vehicle identifications; determining a value of each of the one or more first-level attributes and the one or more second-level attributes; determining the vehicle recommendation based on the value; and transmitting, to the device associated with the user, a notification indicating the vehicle recommendation.

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 disclosed for providing a vehicle suggestion to a user based on image analysis. The method may include: receiving one or more vehicle images via website data associated with the user; identifying one or more first-level traits from the one or more vehicle images; obtaining one or more vehicle identifications from the one or more vehicle images based on the one or more first-level traits; determining a value of each of the one or more first-level traits and/or the one or more vehicle identifications via one or more algorithms; determining the vehicle suggestion based on the value of each of the one or more first-level traits and/or the one or more vehicle identifications; and transmitting, to a device associated with the user, a notification indicating the vehicle suggestion.

Abstract: A signal interpretation system includes a communication device, a memory storing instructions, and one or more processors configured to execute the instructions to perform operations including: receiving scanned information including a shape of at least one article scanned with a scanning device to identify the at least one article and identifying a geographic location of the scanning device, when a scan of the at least one article was performed, based on a location signal received from the scanning device. The operations also include evaluating a signal indicative of interest in the identified at least one article and transmitting, via the communication device, a notification based on the evaluated signal, the notification being indicative of the identified at least one article and the identified geographic location, the notification being configured to cause an update in at least one graphical element presented by the scanning device or another device.

Abstract: Disclosed are techniques and an apparatus for accessing a smart contract library that may include a number of templates of different legal contracts implementable as a smart contract between respective parties. Each template may include a number of sections having different contractual terms and conditions and fillable fields for specific contract terms. Each respective section of the number of sections includes programming code operable to enforce conformance with specific section-related contractual terms and conditions of the respective section and with any specific contract terms input to a fillable field of the respective section. A first user address generated based on cryptographic keys may be associated with a contract-creating computing device in a private blockchain. The first user address may be associated with the first user in the private blockchain. A finalized smart contract may have a smart contract address generated from the first user address and a nonce value.

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: 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: 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.