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: 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: A first vehicle testing tuple comprising a plurality of first testing parameters and a second vehicle testing tuple comprising a plurality of second testing parameters can be obtained. The plurality of first testing parameters can be determined to be associated with an evaluated operating condition. The first tuple can be appended to a first portion of a plurality of portions of a vehicle testing knowledge structure. A second testing parameter can be determined to be associated with an unevaluated operating condition. The unevaluated operating condition can be evaluated. A second portion comprising the second vehicle testing tuple can be generated for the vehicle testing knowledge structure.

Abstract: A breath capture and analysis system includes a breath inlet for capturing a user's breath. The breath is drawn into the system and retained for analysis. The system includes a membrane that allows volatile organic compounds contained in the breath to pass therethrough. The volatile organic compounds pass into a vacuum chamber that includes a residual gas analyzer that is configured to analyze the volatile organic compounds. The analyzed breath is then vacated from the system.

Abstract: A laminate includes a first glass article having a first thickness, a first annealing point (TA1), and a first softening point (TS1), a second glass article having a second thickness, a second annealing point (TA2), and a second softening point (TS2), and an interlayer disposed between the first glass article and the second glass article. The first thickness is greater than the second thickness, the second annealing point (TA2) is less than or equal to the first annealing point (TA1), and the second softening point (TS2) is greater than the first softening point (TS1).

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: A percussive therapy device that includes a housing, an electrical source, a motor positioned in the housing, a switch for activating the motor, a push rod assembly operatively connected to the motor and configured to provide reciprocating motion in response to activation of the motor, and a massage attachment secured to a distal end of the push rod assembly. The reciprocating motion of the push rod assembly has a user-adjustable amplitude.

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: Pre-connected analyte sensors are provided. A pre-connected analyte sensor includes a sensor carrier attached to an analyte sensor. The sensor carrier includes a substrate configured for mechanical coupling of the sensor to testing, calibration, or wearable equipment. The sensor carrier also includes conductive contacts for electrically coupling sensor electrodes to the testing, calibration, or wearable equipment.

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: 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: 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: 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: 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 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: In an aspect, an apparatus for entity data aggregation is presented. An apparatus includes at least a processor and a memory communicatively connected to the at least a processor. A memory contains instructions configuring at least a processor to generate a web harvester. A web harvester is configured to extract entity data from an external database as a function of an extraction criterion. At least a processor is configured to classify extracted entity data to an entity data category. At least a processor is configured to aggregate extracted entity data into an entity profile as a function of an entity data category. At least a processor is configured to generate an entity search index as a function of aggregation of entity data.