Augmented Reality Detection for Loose Pharmacy Item Tracking
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Solution Overview
Problem
Pharmacy order fulfillment is labor-intensive and prone to human error due to manual retrieval and inventory management of bulk-stored items, which can be costly and resource-intensive.
Innovation Solution
An augmented reality system using a convolutional neural network to identify objects of interest, classify them, and update inventory lists, while providing guided access to pharmacy items through a wearable device, enhancing automation and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual retrieval and inventory management of bulk-stored items is used, then pharmacy orders can be fulfilled, but labor intensity and human error increase
Solution Approach 1:
The system enables self-service through automated image capture and processing. The image-capturing device automatically captures images of pharmacy items, and the processor automatically identifies, classifies, and updates inventory without human intervention in the detection process.
Solution Approach 2:
The patent replaces manual mechanical inventory management with an automated vision-based system. The image-capturing device and processor substitute for human visual inspection and manual record-keeping, using optical detection and automated image processing to identify and track pharmacy items.
2Productivity
If manual inventory management is used, then pharmacy operations can continue, but resource consumption increases
Solution Approach 1:
The system enables continuous automated monitoring of pharmacy items. The image-capturing device continuously captures images, and the processor continuously processes these images to update inventory status, eliminating interruptions and maintaining constant productivity without additional human labor.
Solution Approach 2:
The patent introduces an intermediary automated system between the pharmacy items and human workers. The image-capturing device and processor act as intermediaries that automatically detect, identify, and report inventory status, reducing the need for human labor in inventory management tasks.
3Measurement precision
If automated image recognition is implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the complex detection task into distinct functional modules: image capture by the image-capturing device, image processing and analysis by the processor, and result display by the display device. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The processor serves multiple functions within the system: it processes images from the image-capturing device, identifies pharmacy items, classifies detected objects, updates inventory lists, and provides information to the display device. This multi-functionality reduces the need for separate dedicated components for each task.
Data Source
AI summary
A method includes capturing, by an image-capturing device, a one or more images of at least a portion of a pharmacy workstation. The method also includes identifying, by a processor in communication with the image capturing device, objects of interest in a first image of the one or more images and classifying, by the processor, the detected objects of interest using a convolutional neural network associated with the processor. The method also includes identifying, by the processor, a boundary defining an opening of a container in a second image of the one or more images. The method also includes updating, by the processor, an objects in container list based on a determination that at least one of the classified objects passed the boundary.