Abstract: Disclosed are a system and techniques for identity and electronic signature verification that utilizes blockchain technology. An enterprise system enables computing devices to engage the enterprise and prospective users for the purposes of executing a document or a smart contract. Users may obtain a computer application from an enterprise system and may utilize the computer application to retrieve a document or select a smart contract. The identity of all users who execute the document may be verified based on an authentication by a trusted independent system. Information related to the respective signers, the document or smart contract, and the authentication may be stored as transactions in a blockchain. The transactions may be stored in the blockchain under a user's address, a document or smart contract address, or a digital wallet, if available.

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: Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. Various techniques, methods, systems, and apparatuses include utilizing one or more tags generated by application of a machine learning model to a data source, where the data source may include generic and specific language with respect to one or more automobiles or vehicles, to provide a recommendation for a particular automobile in response to a user request for a suggestion.

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: A device receives an image including image data of a scale model of a vehicle, and processes the image data, with a model, to identify a make, a model, and a year represented by the scale model. The device determines augmented reality (AR) vehicle information based on the make, the model, and the year represented by the scale model of the vehicle, and provides the AR vehicle information to enable a user device to associate the AR vehicle information with the image of the scale model of the vehicle. The device receives an input associated with the AR vehicle information, and determines updated AR vehicle information based on the input associated with the AR vehicle information. The device provides the updated AR vehicle information to enable the user device to associate the updated augmented reality vehicle information with the image of the scale model of the vehicle.

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: Techniques for providing indirect local geo-positioning using AR markers are disclosed. A first movable AR marker can be located or found by a computing device. A location of the first movable AR marker can be known and shared with the computing device. The location of the first movable AR marker can be based on distance between the first movable AR marker and a fixed AR marker. A distance to the first movable AR marker can be determined. Based on the known location of the first movable AR marker and the distance to the first movable AR marker from the computing device, an estimate of the location of the computing device can be determined without having line-of-sight (LOS) to the fixed AR marker.

Abstract: An autonomous vehicle vendor lot may include a plurality of vehicle selection indicators, a visual navigation system, and a control system. The indicators may be distributed in the lot, associated with a respective parking spot in the lot, and operable to generate a visual indication indicative of a selection of a vehicle in the respective parking spot. The navigation system may be operable to generate a visual indication of a route between various locations in the lot. The control system may be operatively connected to the indicators and the navigation system, and configured to: receive a vehicle search query; identify at least one vehicle in the lot corresponding to the query; operate each vehicle selection indicator corresponding to each identified vehicle; identify a respective location in the lot for each identified vehicle; and operate the visual navigation system to generate a visual indication of a route to at least one identified vehicle.

Abstract: According to certain aspects of the disclosure, a computer-implemented method may be used for determining one or more vehicle recommendations. The method may include receiving data pertaining to a user's internet browsing activity. The received data may be indicative of the user's automotive vehicle preferences. The method may include comparing the received data to a collection of stored vehicle qualities. The method also may include identifying, based on the received data and the comparison of the received data to the collection of stored vehicle qualities, a vehicle characteristic of interest to the user. Using the vehicle characteristic of interest, one or more vehicle recommendations may be determined. One or more vehicle recommendations may be communicated to the user.

Abstract: Aspects described herein may allow for auto-locating a user in a parking lot and/or automatically directing a user to recommended inventory. For example, a customer may capture an image of a vehicle, or a marker associated with a vehicle, in a parking lot. The image may be processed locally or at a server. A vehicle may be identified based on analysis of the image. Further, information regarding the user may be processed to determine another vehicle appropriate for a recommendation. For example, a prompt on a mobile device may suggest a popular model similar to the vehicle being viewed. The system may then provide directions (e.g., augmented reality directions) to the user to direct the user to the vehicle.

Abstract: A valuation platform may receive, from a user device, video data associated with a vehicle, and may receive a vehicle history report of the vehicle based on a vehicle identification number of the vehicle. The valuation platform may receive, from the user device, feature data associated with the vehicle, and may process the video data, the vehicle history report, and the feature data, with a machine learning model, to determine one or more values for the vehicle. The valuation platform may determine a valuation for the vehicle based on the determined one or more values for the vehicle. The valuation platform may create a vehicle profile for the vehicle based on the video data, the vehicle history report, the feature data, the determined one or more values for the vehicle, and the valuation for the vehicle, and may perform one or more actions based on the vehicle profile.

Abstract: Aspects described herein may provide an interface and/or search functionality for a dealership to determine vehicles a customer is most likely to purchase. A recommender system may generate vehicle recommendations for a dealership to sell to a customer based on customer information, vehicle information, and dealership information. Machine learning may be used to generate the recommendations. The recommendations may be based on the vehicle preferences of a customer.

Abstract: A computer-implemented method for dynamically adjusting a landing page with a personalized recommendation to a user may include obtaining first image data of one or more vehicles via a device associated with the user; obtaining second image data of the one or more vehicles based on the first image data, wherein the second image data comprises at least a subset of the one or more images of the one or more vehicles; determining user preference data based on the second image data of the one or more vehicles via a trained machine learning algorithm, wherein the user preference data comprises one or more features of a user-preferred vehicle; determining the personalized recommendation to the user based on the user preference data, wherein the personalized recommendation comprises a personalized webpage showing information related to the user-preferred vehicle; and presenting, to the user, the personalized recommendation.

Abstract: A server device may receive a first request to utilize surplus parking spaces available at one or more locations from a first device associated with an organization having an inventory of vehicles to be stored in the surplus parking spaces. The server device may process, between the organization and one or more parking providers associated with the one or more locations, a transaction allocating the surplus parking spaces to the organization. The server device may receive, from a second device, a second request to use a vehicle in the inventory from a starting location. The server device may determine, from among the surplus parking spaces allocated to the organization, a storage location for the vehicle that differs from the starting location and transmit, to the second device, a notification indicating that the vehicle is ready for use based on the vehicle having been moved to the starting location.

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 device having extended reality capabilities receives a video feed including one or more video frames depicting an object. The device provides the one or more video frames to a first machine learning model that identifies the object and one or more parts of the object that are depicted in the one or more video frames, and obtains positional tracking information that represents a position and an orientation associated with the object depicted in the one or more video frames relative to a coordinate space. The device identifies a plurality of parts from the one or more parts of the object that are depicted in the one or more video frames and determines, using one or more second machine learning models, information regarding the plurality of parts and information regarding one or more replacement parts.

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: Aspects described herein may allow for auto-locating a user in a parking lot and/or automatically directing a user to recommended inventory. For example, a customer may capture an image of a vehicle, or a marker associated with a vehicle, in a parking lot. The image may be processed locally or at a server. A vehicle may be identified based on analysis of the image. Further, information regarding the user may be processed to determine another vehicle appropriate for a recommendation. For example, a prompt on a mobile device may suggest a popular model similar to the vehicle being viewed. The system may then provide directions (e.g., augmented reality directions) to the user to direct the user to the vehicle.

Abstract: A behavioral analytics platform may obtain a first data model that has been trained based on behavior data derived from historical sales data associated with a vendor and receive, from a user device, one or more interactions that request information related to one or more financing terms. The behavioral analytics platform may determine one or more financing preferences for a user of the user device based on a second data model that has been trained based on behavior data derived from the one or more interactions. The behavioral analytics platform may determine, based on the first data model and the second data model, a probability that the user of the user device and the vendor will be able to reach an agreement for an item available to purchase from the vendor and provide intelligence data related to the probability for display.

Abstract: Aspects described herein may provide an interface and/or search functionality for a dealership to determine a sales associate to sell a vehicle to a customer. A recommender system may recommend sales associates to the dealership based on a prediction of how successful the sales associates will be with a particular customer. The recommender system may learn from historical sales data which sales associate is better at selling a particular type of vehicle. Machine learning may be used to generate the recommendations.