13 results about "Individual item" patented technology

The embodiment of the invention discloses an article loss alarm method and device for a self-service purchase scene, equipment and a medium. A specific embodiment of the method comprises the following steps: in response to a detected article code scanning event, synchronously reading code scanning article identification information and caching a video stream; performing code scanning area cutting processing on the cached video stream to obtain a code scanning area image sequence; performing object overlapping detection on the code scanning area image sequence to obtain an overlapping detection result; article main body positioning is carried out on the code scanning area image sequence to obtain position information of each article main body; performing article image segmentation on the code scanning area image sequence to obtain each article image; performing enhanced feature extraction processing on each article image to generate enhanced feature information of each article; determining the similarity of each article; and according to the similarity of each article, article loss alarm operation is executed through the self-service settlement equipment. According to the embodiment, the article loss prevention accuracy and the processing efficiency can be improved, so that the article loss is reduced.

A palletizing head (1) and a method for palletizing layers of individual items using a palletizing head (1) are disclosed. The palletizing head (1) has a support level (2) for each layer of individual items to be picked up and transferred from a receiving station to a destination. This support level (2) can be removed from under the layer of individual items at the destination. The placement direction (A) of the layer of individual items at the destination is oriented approximately perpendicular to a loading direction (B) at the receiving station. The layer of individual items can be stabilized and positioned within the palletizing head (1) both when picked up at the receiving station and when placed at the destination, in coordination with a positioning movement of the palletizing head (1) between the receiving station and the destination. The process involves picking up individual loads at a receiving station and transferring them onto a support level (2) of the palletizing head (1). The palletizing head (1) then deposits the loads at a destination location by removing the support level (2) from beneath the loads within the palletizing head (1). The discharge direction (A) of the loads at the destination is approximately perpendicular to the loading direction (B) of the loads at the receiving station. The loads are initially positioned within and relative to the palletizing head (1). As the palletizing head (1) moves to the destination, the loads are further advanced on the support level (2) in the loading direction (B). Upon reaching the destination, the support level (2) is removed from beneath the loads to be deposited.

A transaction system selects an individual item from an inventory associated with an online seller in an online e-commerce platform. The transaction system obtains, from a database of the online e-commerce platform, information associated with the individual item. The transaction system processes the information associated with the individual item by one or more large language models (LLMs) and one or more machine learning models to identify a target item that is associated with the individual item and generates a recommendation for presentation to the online seller to expand the inventory associated with the online seller to include the target item.

Exemplary embodiments relate to a machine-learning based approach to detecting individual items in a chaotic moving pick-and-place environment. In such an environment, objects may move relative to a robotic arm. As the objects move through the environment, their locations may change. A relatively more-processing-intensive procedure is employed once on an upstream side of the pick and place station in order to identify or initially segment objects in the environment. Identified items are then tracked using less intensive methods as the object moves through the environment. In order to provide rapid picks, the robot's vision system coordinates with the robot's gripper to image the target when the gripper is out of the image area. A pick location is transmitted back to the gripper within a few hundred milliseconds of the last pick.

Exemplary embodiments relate to a machine-learning based approach to detecting individual items in a chaotic moving pick-and-place environment. In such an environment, objects may move relative to a robotic arm. As the objects move through the environment, their locations may change. A relatively more-processing-intensive procedure is employed once on an upstream side of the pick and place station in order to identify or initially segment objects in the environment. Identified items are then tracked using less intensive methods as the object moves through the environment. Detection is performed once on an upstream side of the pick and place station and then identified items are tracked using less intensive methods as the object moves through the pick-and-place station.

Examples provide a system and method for dynamically filtering candidate item identifiers (IDs) from a pool of item IDs in real-time for automatic labeling of images for use as training data used to train computer vision (CV) models. Images of carts are paired with item receipts. Candidate item IDs are extracted from the receipts. Item recognition inference results generated by CV models are used to pair images of individual items with item IDs identifying the item in each item image. As each candidate item ID is assigned to an item image, the item ID is dynamically filtered. Any candidate item IDs remaining after filtering are assigned to any item images failing to pair with an item ID based on the infer results. The results are presented for review and status update via a user interface device for faster and more accurate auto-labeling of training data for CV models.

Exemplary embodiments relate to a machine-learning based approach to detecting individual items in a chaotic moving pick-and-place environment. In such an environment, objects may move relative to a robotic arm. As the objects move through the environment, their locations may change. A relatively more-processing-intensive procedure is employed once on an upstream side of the pick and place station in order to identify or initially segment objects in the environment. Identified items are then tracked using less intensive methods as the object moves through the environment. In order to provide rapid picks, the robot's vision system coordinates with the robot's gripper to image the target when the gripper is out of the image area. A pick location is transmitted back to the gripper within a few hundred milliseconds of the last pick.

This invention discloses an electronic prescription review method and system for smart pharmacies, relating to the field of smart pharmacy transaction control technology. It addresses the problem of how to achieve an electronic prescription review method that can adaptively adapt to inventory status, intelligently match prescriptions with physical medications, and accurately manage the risks of substitute dispensing. The method includes: obtaining the set of individual items to be dispensed corresponding to the electronic prescription to be reviewed and the set of actually scanned individual items obtained from actual scanning; generating an inventory constraint identifier for each individual item based on the real-time inventory status of the medication corresponding to each individual item in the set of individual items; determining the optimal mapping relationship between each individual item in the set of individual items and the individual items in the set of actually scanned individual items; determining the compliance risk value of each individual item based on the optimal mapping relationship and the inventory constraint identifier; and executing corresponding control instructions for the current transaction based on the compliance risk values ​​of all individual items in the set of individual items.

1. Name of the product in this design: Packaging Kit (Floral Design for Individual Item Packaging). 2. Purpose of this design: For holding face cream. 3. The key design elements of this product are the combination of shape, pattern, and color. 4. The image or photograph that best illustrates the design points: 3D model of Kit 2. 5. The design for which protection is sought includes color. 6. Other situations requiring explanation: Package description: Packages 1, 3, 5, 7, 9, 11, 13, and 15 are cylindrical inner packages, each containing one of eight different scented face creams; Packages 2, 4, 6, 8, 10, 12, 14, and 16 are square outer packages, which, in sequence, package packages 1, 3, 5, 7, 9, 11, 13, and 15 and the face creams they contain; Package 17 is a square package placed between the various inner and outer packages; This package is sold and used simultaneously.

A method for individually handling articles capable of managing the risk of simultaneous picking of more than one article, comprising the steps of transporting articles on a first collective movement unit (CONV); acquiring an image of a picking zone (PickZ) of the articles using a first image sensor (CAM1); analyzing the image and locating an article (IT); commanding an individual handling unit (MA) equipped with a second image sensor (CAM2) and positioning the second sensor above the located article; acquiring an image (IMG2) of this article; analyzing this image (IMG2) and assessing the risk of multiple picking if the article is picked up by the individual handling unit (MA); then commanding the individual handling unit (MA) to pick up the article and move it to a destination (INS, RET, REJ) chosen from at least one reject bin (REJ) and a second collective movement unit (INS). Figure to be published with the abbreviation: Fig. 2

A method of operating a storage and retrieval system comprising a plurality of storage containers. The method comprises, for each storage container, storing a contents record comprising, for each item previously determined to be contained in the storage container, an item type and a bounding box. Upon receiving an indication that an item of a specified item type has been added to plurality of contents of a selected storage container: capturing one or more images of the plurality of contents; processing the images to detect individual items and generate a current bounding box for each; identifying one of the current bounding boxes as relating to the added item; and updating the contents record to include, for the added item, the specified item type and the identified current bounding box.

Exemplary embodiments relate to a machine-learning based approach to detecting individual items in a chaotic moving pick-and-place environment. In such an environment, objects may move relative to a robotic arm. As the objects move through the environment, their locations may change. A relatively more-processing-intensive procedure is employed once on an upstream side of the pick and place station in order to identify or initially segment objects in the environment. Identified items are then tracked using less intensive methods as the object moves through the environment. Detection is performed once on an upstream side of the pick and place station and then identified items are tracked using less intensive methods as the object moves through the pick-and-place station.