Systems and methods that allow a smartphone to be used as an imaging device for undercarriage inspection of a moving vehicle are provided. The method may include locating the smartphone on the ground via one or more sensors of the vehicle. The vehicle may generate a path for the vehicle to drive over the smartphone based on the location of the smartphone, and optionally display the path to facilitate manual driving of the vehicle by the driver over the smartphone. Alternatively, the vehicle may self-drive to follow the path. The smartphone may capture image data indicative of the undercarriage of the vehicle, inspect and analyze the image data to identify one or more issues of the undercarriage of the vehicle, and transmit the analyzed image data to the vehicle for display. The driver may confirm the one or more issues and transmit the data to an inspection professional for additional assistance if needed.

An apparatus for clinical evaluation of sensitivity of individuals to learning context provides a visual search task in which a target symbol to be located is associated with context images that cluster around the target symbol to varying degrees. Measurement of a rate of unconscious exploitation of the context symbols in finding the target symbols is used to sort the individuals into categories believed to be useful for characterizing their learning styles.

An apparatus for clinical evaluation of sensitivity of individuals to learning context provides a visual search task in which a target symbol to be located is associated with context images that cluster around the target symbol to varying degrees. Measurement of a rate of unconscious exploitation of the context symbols in finding the target symbols is used to sort the individuals into categories believed to be useful for characterizing their learning styles.

Apparatuses, systems, methods, and computer-readable media are provided for the preemptive logical configuration of vehicle control systems. A vehicle control computer may determine a location of a vehicle. The vehicle control computer may query a historical data source server for historical incident data corresponding to a first vicinity around the first location of the vehicle. Based on the historical incident data, the vehicle control computer may identify one or more driving danger areas in the first vicinity around the first location of the vehicle, wherein each of the one or more driving danger areas are associated with one or more driving hazards. The vehicle control computer may generate a configuration for vehicle operation in the first vicinity around the first location of the vehicle based on the one or more driving danger areas and may update driving logic of the vehicle with the configuration.

The disclosure provides a method for generating a three-dimensional virtual image. The method includes: obtaining a face image to be processed and a three-dimensional reference model; obtaining a three-dimensional face model, face attribute information and face image information by inputting the face image to be processed into a trained neural network; obtaining a three-dimensional image model by performing a deformation process on the three-dimensional reference model based on the three-dimensional face model; and obtaining a target virtual image by adjusting the three-dimensional image model based on the face attribute information and the face image information.

The disclosure provides a method for generating a three-dimensional virtual image. The method includes: obtaining a face image to be processed and a three-dimensional reference model; obtaining a three-dimensional face model, face attribute information and face image information by inputting the face image to be processed into a trained neural network; obtaining a three-dimensional image model by performing a deformation process on the three-dimensional reference model based on the three-dimensional face model; and obtaining a target virtual image by adjusting the three-dimensional image model based on the face attribute information and the face image information.

A user directed verbal interactive method and system for requesting a evaluation and obtaining a customized verbal therapy routine based on the evaluation obtained. The method and system allow users to interact with an artificial intelligence agent by answering a series of system directed questions that guides the users through evaluation and treatment of physical pain using a customized verbal interaction and delivery regimen. Users verbally engage with the artificial intelligence agent to create respective profiles. The system develops therapies based on their current physiological state and profile. The users are then delivered verbal therapy prompts through the system to implement the developed therapy routines.

A computer-implemented method in conjunction with mixed reality gear (e.g., a headset) includes imaging a real scene encompassing a user wearing a mixed reality output apparatus. The method includes determining data describing a real context of the real scene, based on the imaging; for example, identifying or classifying objects, lighting, sound or persons in the scene. The method includes selecting a set of content including content enabling rendering of at least one virtual object from a content library, based on the data describing a real context, using various selection algorithms. The method includes rendering the virtual object in the mixed reality session by the mixed reality output apparatus, optionally based on the data describing a real context (“context parameters”). An apparatus is configured to perform the method using hardware, firmware, and/or software.

Described herein are techniques for generating contextually rich plant images. A number of data captures of raw plant data are generated via a sensing unit configured to navigate a growing facility. Metadata is generated and assigned to the raw plant data including at least one of: plant location, timestamp, plant identification, plant strain, facility identification, facility location, facility type, health risk factors, plant conditions, and human observations. Images generated by the sensing unit are analyzed and pixel annotations are generated in the images based on their relation to one or more plant well-being features. Data tags are generated and assigned the data captures based on an analysis of the data captures. The data tags are text phrases linking a particular data capture to a specific threat to plant well-being.

Examples of the present disclosure describe systems and methods for detecting and preventing digital media piracy. In example aspects, a machine learning model is trained on a dataset related to digital media content. Input data may then be collected by a data collection engine and provided to a multimedia processor. The multimedia processor may extract multimedia features (e.g., audio, visual, etc.) and recognized patterns from the input data and provide the extracted multimedia features to a trained machine learning model. The trained machine learning model may compare the extracted features to the model, and a confidence value may be generated. The confidence value may be compared to a confidence threshold. If the confidence value is equal to or exceeds the confidence threshold, then the input data may be classified as pirated digital media. Remedial action response(s) may subsequently be deployed to thwart the piracy of the digital media.