Medical Image Data Packet Transmission Control via Trained Model
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Solution Overview
Problem
The transmission of high-volume medical image data over networks is prone to network overload and disruptions due to varying network capacities, which can lead to unreliable and insecure data transmission, especially in regions with limited network development.
Innovation Solution
A computer-implemented method using a trained model, such as a neural network, to generate a send command set that adapts data packet transmission based on current network characteristics, including breaking down packets and optimizing transmission timing to ensure reliable and secure data transfer even in overloaded networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If high-volume medical image data packets are transmitted over networks with varying capacities, then data transmission volume increases, but network overload and disruptions occur leading to unreliable transmission
Solution Approach 1:
The patent segments high-volume medical image data packets into smaller sub-packets for transmission. This segmentation allows the data to be transmitted in manageable units that are less likely to cause network overload, while maintaining complete data integrity through reassembly at the receiving end. The segmentation approach directly addresses the contradiction by reducing individual packet size (improving reliability) while still transmitting the complete high-volume dataset.
Solution Approach 2:
The patent implements dynamic adaptation of transmission parameters based on real-time network conditions. The system continuously monitors network capacity and adjusts transmission characteristics such as packet size, transmission rate, and routing decisions accordingly. This dynamic approach enables reliable transmission across networks with varying capacities by adapting to current network state rather than using fixed transmission parameters.
2Productivity
If data transmission is optimized for speed and volume, then productivity increases, but network disruptions occur in regions with limited network development
Solution Approach 1:
The patent changes transmission parameters dynamically based on network conditions and data characteristics. Different parameters such as packet size, compression level, and transmission protocol are adjusted according to the specific network environment and data type. This parameter adaptation allows the system to maintain high transmission efficiency on capable networks while ensuring stable transmission on limited infrastructure.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor transmission progress and network conditions in real-time. Based on this feedback, the system adjusts transmission strategies dynamically, such as retransmitting lost packets, changing routing paths, or modifying packet characteristics. This feedback loop ensures both productivity and reliability by continuously optimizing transmission based on actual network performance.
3Reliability
If a stable network infrastructure is deployed to ensure secure transmission, then transmission reliability improves, but device complexity and infrastructure requirements increase
Solution Approach 1:
The patent implements self-service mechanisms where the transmission system automatically adapts to network conditions without requiring complex external infrastructure. The system performs self-diagnosis, self-adjustment, and self-optimization based on embedded monitoring and control algorithms. This self-service capability reduces the need for complex network infrastructure while maintaining reliable transmission through intelligent software-based solutions.
Solution Approach 2:
The patent creates a universal transmission system that can operate reliably across diverse network infrastructures without requiring specialized hardware or complex configurations. The system uses software-based adaptation mechanisms that work across different network types and capacities, making the solution universally applicable from advanced to limited infrastructure environments without increasing device complexity.
Data Source
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AI summary
The present invention relates to the control of the transmission of medical data packets (p) over a network (NW). By accessing a trained model (M), an optimal time for data transmission is determined, taking into account the respective transmission requirements of the data packet (p) and the current network characteristics (kd). If necessary, the data packet (p) can be temporarily stored in a buffer memory (PS).