A method of tracking a container closure used to seal liquid in a container is provided, comprising applying a trackable indicia to a container closure, wherein the trackable indicia is unique to the container closure; recording an associated data set comprising a plurality of parameters related to the liquid in the container; correlating the associated data set with the trackable indicia; applying the container closure to the container to seal the liquid within the container; maintaining a database of the trackable indicia corresponding to the associated data set; and scanning the trackable indicia to retrieve the associated data set from the database. The method of the invention also includes utilization of a container closure having inherent natural imperfections and/or patterns on the top and/or sides of the closure and obtaining scanned images and data thereof which are analyzed and utilized for closure and container identification and likewise serve as scannable trackable indicia.

A production line (1) for making a covered box (8) comprises: a forming machine (2), configured to receive a cardboard box blank (6) and to fold it to form the box and including a plurality of folders (21) and a movement system configured to receive the box blank (6) oriented horizontally, and comprising a plurality of columns (23), extending along the vertical direction (V) and configured to abut against the base (6a) of the box blank (6) and to move it along a vertical direction (V); a gluing unit (3), configured to receive a covering blank (7) and to spread glue on the covering blank (7); a positioning device (4), configured to position the box on the covering blank (7); a covering machine (5), configured receive the box coupled to the respective covering blank (7) from the positioning device (4) and to fold the covering blank (7) into contact with the box in such a way as to cover the box.

An inspection tool comprises an imaging system configured to image a portion of a semiconductor substrate. The inspection tool may further comprise an image analysis system configured to obtain an image of a structure on the semiconductor substrate from the imaging system, encode the image of the structure into a latent space thereby forming a first encoding. the image analysis system may subtract an artifact vector, representative of an artifact in the image, from the encoding thereby forming a second encoding; and decode the second encoding to obtain a decoded image.

Provided is a cloud platform system for reading a medical image, the cloud platform system including: multiple image processing modules pre-programmed to perform preprocessing of the medical image and modularized; multiple artificial intelligence modules in which an artificial intelligence algorithm is pre-programmed and modularized; multiple layer modules in which layers applied to a configuration of the artificial intelligence algorithm are modularized by function; a learning model design unit providing a graphical user interface for designing an artificial intelligence-based learning model to a user terminal that has had access through a web browser; and a reading model generation unit generating a reading model by training the learning model designed by the learning model design unit.

Method and system for evaluating vehicle insurance claims. For example, the method includes collecting pre-accident images of a vehicle before one or more accidents, collecting telematics data of the vehicle during the one or more accidents, determining predicted post-accident images of the vehicle based upon the pre-accident images and the telematics data after the one or more accidents, receiving submitted post-accident images for vehicle insurance claims associated with the vehicle, comparing the predicted post-accident images with the submitted post-accident images, and determining whether frauds have been committed in the vehicle insurance claims based upon the comparison of the predicted post-accident images with the submitted post-accident images.

A method allows an estimator or other party to “Walk the Drawings.” An estimator can open one or more sets of drawings on a mobile device and walk those same electronic drawings as they actually walk or traverse the physical site itself. In other words, as the estimator physically moves across the construction site in the real world, their electronic icon (avatar) moves across the corresponding electronic drawings on their mobile device. Now the estimator can identify present and future such as features that need to be accessible being buried under asphalt. While walking the drawings, the estimator can label any challenges or features by simply clicking their avatar and it will post that geo-stamped location complete with corresponding notes and photos straight to the drawings for later review and analysis.

Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for training a classification neural network. The system generates, from a set of object-specific data, one or more meta-learning datasets for one or more respective initial training tasks. The system determines values for a set of meta parameters by performing meta-learning with a classification neural network on the one or more meta-learning datasets. The system obtains a set of labeled training examples for a characteristic-detection task. The system determines based at least on one of the values for the set of meta parameters and using the set of labeled training examples, target values for the network parameters for the classification neural network to perform the characteristic-detection task.

An inspection apparatus for detecting a difference between image data obtained by reading a sheet, and a reference image of the image data. The inspection apparatus obtains first image data by reading a sheet having a patch image printed thereon, obtains second image data from print data of the patch image, performs edge correction on a line included in the second image data by applying an edge correction parameter, and obtains an edge correction parameter that minimizes a difference between the density of the second image data subjected to the edge correction and the density of the first image data, and stores the obtained edge correction parameter.

An analysis device for visualizing an accuracy of a trained determination device includes an acquisition unit acquiring an image pair of a non-defective product image and a defective product image, an extraction unit extracting an image region of a defective part of the defective product, a generation unit generating a plurality of image regions of pseudo-defective parts, a compositing unit synthesizing each of the image regions of the plurality of pseudo-defective parts with the non-defective product image to generate a plurality of composite images having different feature quantities, an unit outputting the plurality of composite images to the determination device and acquiring a label corresponding to each of the plurality of composite images from the determination device, and a display control unit displaying an object indicating the label corresponding to each of the plurality of composite images in an array based on the feature quantities.

A system and method of using a tool assembly is provided. The system includes a body, a first camera and a second camera fixed to the body, and a controller. The controller is configured to receive data indicative of images of a reference feature from the first camera, determine data indicative of a first spatial position of the first camera based at least in part on the received data indicative of the images of the reference feature, and determine data indicative of a second spatial position of the second camera based on the first spatial position, a known spatial relationship between the first location and the second location, or both. Further, the controller may be configured to receive data indicative of images of a target feature using the second camera, derive dimensions of the target feature based on the images, and generate a three-dimensional representation of the target feature.