Abstract: Systems and methods for presenting an augmented reality view are disclosed. Embodiments include a system with a database for personalizing an augmented reality view of a physical environment using at least one of a location of a physical environment or a location of a user. The system may further include a hardware device in communication with the database, the hardware device including a renderer configured to render the augmented reality view for display and a controller configured to determine a scope of the augmented reality view authenticating the augmented reality view. The hardware device may include a processor configured to receive the augmented reality view of the physical environment, and present, via a display, augmented reality content to the user while the user is present in the physical environment, based on the determined scope of the augmented reality view.

Abstract: A computer-implemented method for providing personalized purchasing information to a user may include: obtaining first identification data of the user from a first engine; verifying a registration status of the user based on the first identification data; obtaining second identification data of the user from a second engine based on the registration status of the user; comparing the first identification data of the user and the second identification data of the user; initiating a real-time credit monitoring session based on the comparison between the first identification data of the user and the second identification data of the user; generating the personalized purchasing information of the user based on the real-time credit score of the user; and demonstrating the personalized purchasing information on a display of a device associated with the user.

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: 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: A device receives transaction data associated with an individual that is participating in a transaction for a vehicle. The device identifies transaction term values relating to the transaction. The device determines scores indicating likelihoods of the transaction term values contributing to prevention of completion of the transaction. The device receives vehicle inventory data describing vehicles. The device identifies one or more recommended vehicles, of the vehicles, based on performing a vehicle search of the vehicles described by the vehicle inventory data, where the vehicle search identifies the one or more recommended vehicles based on one or more filters. The device provides vehicle offer data for the one or more recommended vehicles to one or more other devices to permit the one or more other devices to use the vehicle offer data as part of another transaction for a particular vehicle of the one or more vehicles.

Abstract: A device may determine a price of a product based on first information concerning attributes of the product, and provide the price for display by a user device. The device may generate first hash values based on the first information, where a particular first hash value is associated with a particular attribute, and generate a first bit-string that includes the plurality of first hash values. The device may generate second hash values based on second information concerning the attributes of the product, where a particular second hash value is associated with the particular attribute. The device may generate a second bit-string that includes the second hash values, and determine a change to the particular attribute based on the first bit-string and the second bit-string. The device may determine a new price of the product based on the change, and provide the new price for display by the user device.

Abstract: Systems and methods for preventing machine learning models from negatively affecting mobile devices are provided. For example, a mobile device including a camera, memory devices, and one or more processors are provided. In some embodiments, the processors may be configured to provide images captured by the camera to a machine learning model at a first rate. The processors may also be configured to determine whether one or more of the images includes an object. If one or more of the images includes the object, the processors may be further configured to adjust the first rate of providing the images to the machine learning model to a second rate, and in some embodiments, determine whether to adjust the second rate of providing the images to the machine learning model to a third rate based on output received from the machine learning model.

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: Systems as described herein may include authorizing the sharing of data and sharing data between a variety of systems. A request to share data may be provided to a first system. The system may create sharing session data on a distributed ledger accessible by a number of systems. Sharing session data may be stored using a transaction stored on a distributed ledger. A second system may obtain the sharing session account and verify the sharing session. On verification of the sharing session, a variety of data may be shared between the systems identified in the sharing session data. The sharing session data may be established between two or more systems. The distributed ledger may be maintained by the systems themselves and/or a distributed network system. In a variety of embodiments, encrypted data may be stored and/or obtained using the distributed ledger.

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: 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: In one aspect, the present disclosure relates to a method for improved efficiency of an inventory management system. The method can include: performing a first inventory search of inventory data stored in a database using search criteria; receiving, from a beacon sensor, inventory data broadcast from a plurality of beacons; performing a second inventory search by correlating the inventory data broadcast from the plurality of beacons with results of the first inventory search; receiving image data from a camera; performing image classification on the image data to determine one or more class labels for an inventory item within the field-of-view of the camera; performing a third inventory search by correlating the class labels with results of the second inventory search; and presenting results of the third inventory search to a user.

Abstract: Techniques for providing indirect local geo-positioning using AR markers are disclosed. A first moveable AR marker can be located or found by a computing device. A location of the first moveable AR marker can be known and shared with the computing device. The location of the first moveable AR marker can be based on distance between the first moveable AR marker and a fixed AR marker. A distance to the first moveable AR marker can be determined. Based on the known location of the first moveable AR marker and the distance to the first moveable 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: A computer-implemented method for using a machine-learning trained predictive engine to predict failures includes receiving electronic prior transaction data corresponding to a plurality of prior successful transactions and a plurality of prior unsuccessful transactions, and training a machine learning predictive engine based on the plurality of prior successful transactions and the plurality of prior unsuccessful transactions. Electronic transaction data may be received, the electronic transaction data being associated with a user, an item, and candidate transaction terms, the electronic transaction data being associated with a candidate transaction. The machine learning predictive engine may determine a likelihood of success of the candidate transaction based on the electronic transaction data, and display the likelihood of success of the candidate transaction.

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: A device receives image data of a contractual document that includes an offer including terms of a proposed transaction, converts the image data to text data that identifies text within the contractual document, and receives preferences information for a recipient of the offer. The device identifies key terms within the contractual document by using term identification to analyze the text. The key terms may include a first key term that identifies subject matter of the proposed transaction and other key terms that are part of the offer. The device determines term scores that correspond to likelihoods of the other key terms being favorable to the recipient by using a data model to analyze the key terms and the preferences information. The device, based on the term scores, generates and provides another device with a recommendation to be used in determining whether the accept the offer.

Abstract: A device may receive images of an object and information identifying the object, process the images using an artificial intelligence technique to identify parts of the object that are depicted in the images, and receive information identifying a location of damage on the object and information regarding the damage on the object. The device may process the information identifying the location of damage to identify a damaged part of the object, identify images depicting the damaged part, and identify, in the images, a location of the damaged part. The device may generate a first content item for display at the location of the damaged part in the images and generate a second content item for display with the images based on user interaction with the first content item, where the second content item includes information based on the information regarding the damage on the object.

Abstract: In some implementations, a system may receive an access request associated with an agent and the vehicle. The system may authenticate, based on an agent identification associated with the agent, the agent in association with providing the vehicle service. The system may send, based on authenticating the agent, an authorization request to a user associated with the vehicle, wherein the authorization request is sent to a user device associated with the user that enables the user to authorize the agent to access the vehicle in association with providing the vehicle service. The system may receive, from the user device, a user authorization that authorizes the agent to access the vehicle. The system may configure the vehicle to enable the agent to access the vehicle via a client device associated with the agent.

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.