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: 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 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 for providing a disposal recommendation for an owned vehicle are provided. The method includes generating a profile for the owned vehicle, the profile including age, mileage, and location of the owned vehicle, receiving monitoring data of the owned vehicle, and receiving sales data for non-owned vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the owned vehicle, based on the sales data and the monitoring data, and providing a recommended disposal time for the owned vehicle, based on the predicted depreciation data.

Abstract: The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving an indication of a first object entering an asset area and receiving data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on one or more of the plurality of captured images. The methods further include determining a first object location of the first object based at least in part on the object classification, and outputting an indication of the first object location.

Abstract: An electronic device may include logic to provide a trust credential for linking to a permissioned network over a local network, comprising a plurality of user devices; retrieve a document in an initial status for editing, corresponding to a first state of a distributed ledger, maintained by the permissioned network; generate a first change in the document, wherein the document is in a second status; send, to the permissioned network via the local network, the first change for storage in the distributed ledger, in a second state; retrieve the document in a third status, including a second change, performed subsequently to the first change, the second change being generated by a user device, external to the electronic device, and coupled to the local network; and link to the distributed ledger to retrieve a third state of the distributed ledger, the third state comprising the first change and the second change.

Abstract: Methods and systems for rendering virtual displays using cryptographic symbols are disclosed. In one aspect, a display device is disclosed that includes a processor and data storage including instructions that, when executed by the processor, cause the system to perform operations. The operations include emitting infrared light toward a cryptographic symbol associated with a surface, receiving a reflection of the emitted natural or man-made sunlight from the cryptographic symbol, and, based on the reflection, decrypting the cryptographic symbol to determine a unique identifier. The operations further include, based on the identifier, generating a first virtual display, and rendering the first virtual display on an image of the surface, through a heads up display, or projected onto the surface.

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

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: 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 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: Systems and methods for providing a disposal recommendation for an owned vehicle are provided. The method includes generating a profile for the owned vehicle, the profile including age, mileage, and location of the owned vehicle, receiving monitoring data of the owned vehicle, and receiving sales data for non-owned vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the owned vehicle, based on the sales data and the monitoring data, and providing a recommended disposal time for the owned vehicle, based on the predicted depreciation data.

Abstract: Various aspects described herein generally relate to visualizing a vehicle history using extended reality. In some implementations, an extended reality device may obtain image data corresponding to one or more vehicle parts that are visible in a field of view of the extended reality device. The extended reality device may identify one or more anchor points in a coordinate space corresponding to the field of view of the extended reality device and obtain a vehicle history report based on an identifier associated with the vehicle. The extended reality device may obtain digital content based on one or more records contained in the vehicle history report and an expected visual appearance of the one or more parts of the vehicle. The extended reality device may render the digital content on a display of the extended reality device. Various other aspects are provided.

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: 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 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 using smart contracts executed by a distributed network system. Sharing session data may be stored using a smart contract. A second system may obtain the sharing session data and verify the sharing session based on the execution of the smart contract. 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 systems and/or a number of systems. Smart contracts may provide a variety of functions for authorizing the sharing of data between systems. Additionally, encrypted data may be stored and/or obtained using a smart contract.

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: 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: The disclosed technology includes image-based systems and methods for object tracking within an asset area. Some exemplary methods include receiving an indication of a first object entering an asset area and receiving data indicative of a plurality of captured images. The methods also include performing, by at least one processor, object classification of the first object based on one or more of the plurality of captured images. The methods further include determining a first object location of the first object based at least in part on the object classification, and outputting an indication of the first object location.