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: A device detects that a user device is accessing a media stream that enables the user device to display, via a user interface, media content associated with a subject. The device receives a search query associated with the user device and the media stream, and determines, based on the search query, that a user associated with the user device has a threshold level of interest in a feature of the subject. The device receives feature information associated with the feature based on determining that the user has the threshold level of interest, and receives feature content data that is associated with feature content that includes the feature information. The device causes the feature content to be embedded into the media stream to cause the user device to display the feature content in relation to the feature when the feature is displayed in the media content via the user interface.

Abstract: A refuse vehicle includes a chassis, an energy storage device supported by the chassis and configured to provide electrical power to a prime mover, wherein activation of the prime mover selectively drives the refuse vehicle, a body for storing refuse therein supported by the chassis, a first hydraulic pump configured to convert electrical power into hydraulic power, a second first hydraulic pump configured to convert electrical power into hydraulic power, and a motor coupled to at least one of the body or the chassis and configured to drive the first hydraulic pump and drive the second hydraulic pump.

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: According to certain aspects of the disclosure, a computer-implemented method may be used for regulating vehicle stock. The method may include receiving one or more queries indicative of one or more characteristics of a vehicle for purchase by a user and determining based on the one or more queries indicative of the one or more characteristics of the vehicle, at least one vehicle available for purchase at a location of a merchant. The method may also include determining a quantity of the at least one vehicle purchased and assigning a value to the at least one vehicle based on the quantity of the at least one vehicle purchased and a quantity of received queries about the vehicle. The method may also include transmitting the value to the user, with a recommendation regarding the at least one vehicle available for purchase based on the value.

Abstract: Various embodiments are generally directed to techniques to provide specific vehicle recommendations to generic user requests. A method for providing the specific vehicle recommendation includes: receiving a generic automobile request from a user, applying a machine learning model (MLM) trained by a corpus of reviews to the received request, and generating, by the MLM, a recommendation for at least one specific automobile feature based on the generic automobile request.

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: 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: Embodiments disclosed herein generally relate to a method and system for generating a container image. A computing system receives a request from a remote computer to provision a container comprising a machine learning model. The computing system generates a first API accessible by the remote computer. The computing system receives one or more parameters for the container via the API. The one or more parameters include a machine learning model type. The computing system retrieves from a library of a plurality of machine learning models a machine learning model corresponding to a type of model specified in the one or more parameters. The computing system generates a container image that includes the machine learning model. The computing system provisions a container based on the container image.

Abstract: A device detects that a user device is accessing a media stream that enables the user device to display, via a user interface, media content associated with a subject. The device receives a search query associated with the user device and the media stream, and determines, based on the search query, that a user associated with the user device has a threshold level of interest in a feature of the subject. The device receives feature information associated with the feature based on determining that the user has the threshold level of interest, and receives feature content data that is associated with feature content that includes the feature information. The device causes the feature content to be embedded into the media stream to cause the user device to display the feature content in relation to the feature when the feature is displayed in the media content via the 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: A cable gland includes a body, a cover, and an insert. The body is configured to be received in an opening of a housing. The body has a tube defining a first cavity extending circumferentially about an axis. The cover is threadedly engaged with the tube and configured to seal the body to the housing. The insert is disposed in the first cavity and defines a second cavity configured to receive a cable. The insert includes a plurality of ribs engaged with the tube.

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: 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: 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.