Abstract: Implementations include receiving an image of an object; obtaining a reconstructed image by processing the image through a ML model; obtaining a gradient difference image by comparing the image to the reconstructed image; generating an output image at least partially by suppressing non-significant regions representing non-significant anomalies from the gradient difference image using a non-significant suppression (NSS) map; determining whether an anomaly is depicted in the output image; and in response to determining that an anomaly is depicted in the output image, sending an alert indicating that the object is defective.

Abstract: The present disclosure relates to a technique for addressing an issue to be resolved associated with an electronic document. The method discloses accessing an actionable portion associated with a particular knowledge domain of the electronic document and associated context. Further, retrieve data from data sources to provide additional information related to the particular knowledge domain and the associated context. Then structuring the retrieved data to produce a subset of organized data and determine the issue to be resolved related to the electronic document. Further, generate data elements associated with the issue to be resolved and map dependency relationships between data elements. Also, determine test goals associated with the issue to be resolved based on the dependency relationships.

Abstract: Implementations are directed to receiving a first set of images included in a first video captured by a camera that monitors a human performing a task; processing the first set of images using a first machine learning (ML) model to determine whether the first set of images depicts a gesture that is included in a predefined set of gestures; in response to determining that the first set of images depicts a gesture included in a predefined set of gestures, processing a second set of images included in the first video using a second ML model to determine a first gesture type of the gesture; comparing the first gesture type with a first expected gesture type to determine whether performance of the task conforms to a standard operating procedure (SOP) for the task; and providing feedback representative of a comparison result in a user interface.

Abstract: Implementations include receiving an image of an object; obtaining a reconstructed image by processing the image through a ML model; obtaining a gradient difference image by comparing the image to the reconstructed image; generating an output image at least partially by suppressing non-significant regions representing non-significant anomalies from the gradient difference image using a non-significant suppression (NSS) map; determining whether an anomaly is depicted in the output image; and in response to determining that an anomaly is depicted in the output image, sending an alert indicating that the object is defective.

Abstract: Implementations are directed to receiving a first set of images included in a first video captured by a camera that monitors a human performing a task; processing the first set of images using a first machine learning (ML) model to determine whether the first set of images depicts a gesture that is included in a predefined set of gestures; in response to determining that the first set of images depicts a gesture included in a predefined set of gestures, processing a second set of images included in the first video using a second ML model to determine a first gesture type of the gesture; comparing the first gesture type with a first expected gesture type to determine whether performance of the task conforms to a standard operating procedure (SOP) for the task; and providing feedback representative of a comparison result in a user interface.