Medication Identification via Neural Network and Container Repositioning
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Solution Overview
Problem
Current medication identification and verification systems require complex devices with extensive processing resources and are limited to analyzing medication rather than the container and its components, such as the seal, necessitating the development of a more efficient and accurate method.
Innovation Solution
A medication management system that uses a camera device and an adjusting device to capture and reposition image data of medication within a container, processed via a neural network to identify and verify the medication based on prescription information, while also verifying the integrity of the container seal.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex devices with extensive processing resources are used for medication identification, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical/image processing systems with a neural network-based machine learning system. The neural network processes images of medication containers and labels to identify medications, eliminating the need for extensive traditional image processing algorithms and reducing computational resource requirements while maintaining high identification accuracy.
Solution Approach 2:
The system uses machine learning models that have been trained on large datasets of medication images and characteristics. These pre-trained models act as knowledge copies that can be deployed in various devices without requiring the devices to have extensive processing resources, as the intelligence has been captured in the trained neural network weights and parameters.
2Device complexity
If medication analysis is limited to the medication itself, then device complexity is reduced, but measurement precision deteriorates due to inability to verify container seal integrity
Solution Approach 1:
The neural network system is designed to perform multiple verification functions simultaneously: identifying the medication, verifying the container seal integrity, checking label information, and validating prescription details. This multi-functional approach allows comprehensive verification without requiring separate dedicated devices for each function, thus maintaining device simplicity while improving verification accuracy.
Solution Approach 2:
The patent combines multiple verification tasks (medication identification, seal verification, label recognition, prescription validation) into a single integrated neural network processing system. By merging these functions into one unified system, the patent achieves comprehensive verification accuracy without the complexity of multiple separate analysis systems.
Data Source
AI summary
In some implementations, a device may receive prescription information associated with a medication in a container. The device may cause a camera to capture first image data associated with the medication while the medication is in the container and the container is positioned on a receptacle. The device may cause an adjusting device to reposition the container on the receptacle. The device may cause the camera to capture second image data associated with the medication while the medication is in the container. The device may process, via a neural network, the first image data and the second image data to identify the medication based on depictions of individual units of the medication included in the first image data and the second image data. The device may verify the medication based on the prescription information and an identifier of the medication provided by the neural network.