Command Model Training for Constrained Classification Devices
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Solution Overview
Problem
User devices with limited processing, storage, and communication capabilities have fixed functionality, leading to suboptimal user experiences.
Innovation Solution
A method and apparatus that utilize an intermediary device to receive classification device constraints, training data, and map it to constrained functionality, creating a command model that is transmitted to the classification device, optimizing its capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If user devices have fixed functionality during production, then manufacturing is simplified, but the user experience is not optimised due to limited processing, storage, and communication capabilities
Solution Approach 1:
The system divides functionality into two segments: fixed hardware capabilities (processed during manufacturing) and software-defined command models (trained and deployed post-manufacturing). The classification device is segmented into the physical device and the separate command model that can be independently trained and updated, allowing manufacturing to remain simple while functionality becomes adaptable through separate software deployment.
Solution Approach 2:
The command model is trained in advance on powerful intermediary devices with comprehensive processing capabilities, then deployed to the constrained classification device. This preliminary training action allows the device to gain advanced functionality without requiring complex manufacturing processes, as the intelligence is prepared beforehand and transferred to the simple hardware.
2Reliability
If training data is processed on the classification device itself, then data security is maintained, but the device's limited processing capabilities cannot optimise the training effectively
Solution Approach 1:
A secure intermediary communication channel is introduced between the powerful training environment and the constrained classification device. The intermediary device receives encrypted training data, processes it using advanced algorithms, and transmits only the essential command model parameters back to the classification device, maintaining security while enabling efficient training on powerful hardware.
Solution Approach 2:
The intensive training processing is extracted from the constrained classification device and performed on powerful intermediary devices. Only the essential trained parameters (command model) are extracted and transferred back to the classification device, allowing efficient use of powerful hardware while maintaining the security and simplicity of the original device.
3Measurement precision
If comprehensive training data is used, then model accuracy improves, but the device's storage and processing limitations are exceeded
Solution Approach 1:
Instead of storing and processing comprehensive training datasets on the constrained device, the system creates a distilled command model that captures the essential patterns from extensive training data. The intermediary device copies the learned knowledge into a compact model format that fits within the classification device's limited storage while maintaining high accuracy.
Solution Approach 2:
The training process transforms comprehensive training data into a different parameter representation (the command model) that is much more compact. By changing the parameters from raw training examples to distilled model weights and thresholds, the system achieves high accuracy with minimal storage requirements on the constrained device.
Data Source
AI summary
A method for training a classification device, the method comprising: receiving classification device constraints at an intermediary device; receiving training data at the intermediary device; matching the training data to the classification device constraints to provide constrained training data; mapping the constrained training data to classification device functionality to provide a command model; and transmitting the command model to the classification device.