Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.

Abstract: The present invention relates to dispensing instruments and methods for introducing treatment material and fluid-like material with an embryo into a uterus. More specifically, the present invention relates to apparatuses for delivering a fertilized egg or embryo into a maternal uterine endometrium in humans or any other mammalian species and associated computer programs for controlling said apparatuses.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to categorize image text. An example apparatus includes region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions. The example apparatus also includes mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to train machine learning models to reduce categorization bias, the apparatus comprising: interface circuitry; machine readable instructions; and programmable circuitry to at least one of instantiate or execute the machine readable instructions to: calculate category information corresponding to samples based on a plurality of models; calculate task loss values associated with respective ones of the samples and respective ones of the plurality of models based on product category information; calculate gating loss values for a model gate based on category frequency information; and train the model gate based on a sum of the task loss and the gating loss, the training to derive weights corresponding to respective ones of the plurality of models.

Abstract: A wearable exoskeleton and methods of use are discussed herein. The exoskeleton comprises two shoulder bridges connecting a front harness and a back structural plate over shoulders of a user's body, and a back elastomeric element, connected to a tension cable system with shoulder clutches in which the tension cable can be reeled in or out, positioned to follow the back functional line of a user's body with a pre-tension cable system which all together form an artificial myofascial tension line in the exoskeleton. Other elastomeric elements may follow other user's body's myofascial lines to form the other artificial myofascial tension lines and to assist the user in handling objects.

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed for lexical analysis. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to at least one of instantiate or execute the machine readable instructions to encode tokens with token semantic characteristics, encode the tokens with token lexical characteristics to generate encoded tokens, respective ones of the encoded tokens including at least one of the semantic characteristics and at least one of the lexical characteristics, and identify a match between two of the encoded tokens.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to categorize image text. An example apparatus includes region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions. The example apparatus also includes mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: The present invention relates to dispensing instruments and methods for introducing treatment material and fluid-like material with an embryo into a uterus. More specifically, the present invention relates to apparatuses for delivering a fertilized egg or embryo into a maternal uterine endometrium in humans or any other mammalian species and associated computer programs for controlling said apparatuses.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve model training efficiency comprising block circuitry to: generate a first blocking corresponding to first ones of first data samples retrieved from a first data source, the first ones of the first data samples including a first heuristic; and generate a second blocking corresponding to second ones of the first data samples that include a second heuristic; match circuitry to: retrieve a second data sample from a second data source and determine a match of the first blocking or the second blocking; and assign respective ones of the first data samples from the match one of a first designation type or a second designation type; and batch circuitry to: combine the first designation type and the second designation type into a machine learning input batch.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for processing an image using visual and textual information. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to detect regions of interest corresponding to a product promotion of an input digital leaflet, extract textual features from the product promotion by applying an optical character recognition (OCR) algorithm to the product promotion and associating output text data with corresponding ones of the regions of interest, determine a search attribute corresponding to the product promotion, generate a first dataset of candidate products corresponding to the product in the product promotion by comparing the search attribute against a second dataset of products, and select a product from the first dataset of candidate products to associate with the product promotion, the product selected based on a match determination.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: The present disclosure relates to a residual film winder for a packaging machine, wherein the residual film winder comprises a reception mandrel, a reception sleeve, and at least one film spreader. The reception sleeve is configured to be arranged onto the reception mandrel. The film spreader has a first end and a second end, wherein the first end extends out of the reception sleeve, and the first end has an outer contour extending along its long axis. The outer contour of the film spreader has at least two sections, a first section and a second section, wherein the first and second sections are not parallel to each other.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to categorize image text. An example apparatus includes region detection model training circuitry to identify candidate regions in an input image that include text, and generate bounding boxes around respective ones of the identified candidate regions. The example apparatus also includes mask application circuitry to improve optical character recognition (OCR) by applying a mask to the input image, wherein the mask removes content of the input image except for portions of the input image within the bounding boxes, and OCR circuitry to perform OCR on the masked input image to obtain text data within the bounding boxes.

Abstract: Example methods, apparatus, and articles of manufacture to classify labels based on images using artificial intelligence are disclosed. An example apparatus includes a regional proposal network to determine a first bounding box for a first region of interest in a first input image of a product; and determine a second bounding box for a second region of interest in a second input image of the product; a neural network to: generate a first classification for a first label in the first input image using the first bounding box; and generate a second classification for a second label in the second input image using the second bounding box; a comparator to determine that the first input image and the second input image correspond to a same product; and a report generator to link the first classification and the second classification to the product.

Abstract: The present disclosure relates to a residual film winder for a packaging machine, wherein the residual film winder comprises a reception mandrel, a reception sleeve, and at least one film spreader. The reception sleeve is configured to be arranged onto the reception mandrel. The film spreader has a first end and a second end, wherein the first end extends out of the reception sleeve, and the first end has an outer contour extending along its long axis. The outer contour of the film spreader has at least two sections, a first section and a second section, wherein the first and second sections are not parallel to each other.