Remote Communication Device with Edge AI and Multi-Protocol Connectivity
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Solution Overview
Problem
Existing remote communication systems face challenges in adapting to diverse environments with varying hardware and software requirements, leading to inefficiencies in processing and transmission of data, and the need for flexible and adaptable devices that can perform AI and machine learning functions locally.
Innovation Solution
A remote communication device equipped with multiple network interfaces, AI chips, and network communication processors that can connect to and control various external devices, process data on-site, and adapt to different hardware and software protocols, while minimizing hardware requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If data is transmitted to remote devices for processing, then centralized processing capability is improved, but transmission time and bandwidth consumption increase
Solution Approach 1:
The system divides processing tasks between edge devices and centralized servers. Edge devices perform local processing for time-sensitive operations, while centralized servers handle complex analysis tasks, segmenting the processing workload to reduce transmission time for critical data.
Solution Approach 2:
Different processing capabilities are distributed to different locations. Edge devices with AI chips perform local processing for immediate responses, while remote servers provide sophisticated processing for less time-critical tasks, creating local quality variations in processing capability.
2Productivity
If AI hardware is added to perform local processing, then processing speed is improved, but device complexity increases
Solution Approach 1:
The edge device is designed with multi-functionality, incorporating AI chips, network interfaces, and processing units that can perform various tasks including local AI processing, data transmission, and device control, reducing the need for separate specialized hardware.
Solution Approach 2:
The system can dynamically adjust processing parameters by switching between local and remote processing modes, allowing the same hardware to operate at different complexity levels depending on the task requirements and network conditions.
3Adaptability or versatility
If the device connects to multiple types of external devices, then adaptability is improved, but compatibility issues increase
Solution Approach 1:
The device acts as an intermediary between various external devices and the processing system. It includes network interfaces and communication protocols that mediate connections to different types of external devices, translating between different protocols to ensure reliable compatibility.
4Loss of energy
If data processing is performed at the periphery, then bandwidth consumption is reduced, but processing capability is limited
Solution Approach 1:
The edge device performs partial processing locally for time-critical operations, while excessive or more complex processing is delegated to remote servers. This partial action approach reduces bandwidth consumption for critical data while maintaining the option for more sophisticated processing when needed.
Data Source
AI summary
Remote communication devices, systems, and methods for facilitating communication between remote users is described herein. The remote communication devices may be installed in any suitable location. For example, the remote communication devices and systems may be installed in a medical environment to enable one or more medical functions. In some embodiments, the remote communication devices and systems may facilitate remote patient monitoring, teleconsulting, analyzing one or more data inputs to determine a patient event, or any other suitable medical function.