Continuously Trained Neural Network for Adaptive Detection
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Solution Overview
Problem
Existing detection and response systems using neural networks are limited by the initial training data and cannot be updated beyond the capabilities at the time of deployment, restricting their detection and response capabilities.
Innovation Solution
A continuously trained neural network system with a central server and remote devices, where sensor data is analyzed using an instanced copy of the neural network, and the training module updates the network based on validated detections, allowing for continuous retraining and improved detection and response capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a trained neural network is used for detection, then detection ability is improved, but the system becomes locked in initial capabilities and cannot adapt to new conditions
Solution Approach 1:
The patent implements a dynamic neural network system where the model is initially trained offline with substantial data, then continuously fine-tuned online using incoming sensor data. This transforms the static, locked neural network into a dynamic system that adapts over time while maintaining its core detection capabilities.
Solution Approach 2:
The system incorporates feedback loops where detection results and new sensor data are fed back into the neural network for continuous retraining. This allows the system to learn from its own operations and external data sources, improving both detection precision and adaptability simultaneously.
2Measurement precision
If substantial training data is used to improve neural network detection, then detection precision is improved, but the system requires substantial amounts of training data and is locked in once trained
Solution Approach 1:
The patent performs preliminary offline training of the neural network with substantial training data before deployment. This preliminary action establishes strong initial detection capabilities, allowing the system to achieve high precision without requiring continuous large-scale data input during operation.
Solution Approach 2:
The system implements continuous online learning where the neural network is continuously refined using incoming sensor data. This continuous useful action allows the system to maintain and improve detection precision over time using incremental data rather than requiring substantial batches of training data periodically.
3Device complexity
If static detection criteria are used in sensors, then system complexity is reduced, but detection capability is limited to rudimentary detection
Solution Approach 1:
The patent introduces a neural network as an intermediary layer between simple sensor detection and complex response systems. The neural network processes sensor inputs and generates detection results, enabling sophisticated detection capabilities while keeping the sensor hardware itself relatively simple and the overall system manageable.
Data Source
AI summary
A detection and response system includes a central server having at least one continuously trained neural network and at least one remote system connected to the central server. The at least one remote system includes a first sensor configured to provide an analyzable output corresponding to sensed information to an instanced copy of the continuously trained neural network, and a response system configured to generate a response to the instanced copy of the continuously trained neural network providing a positive detection. A training module is stored on the central server and is configured to update one of the continuously trained neural network and the instanced copy of the continuously trained neural network in response to receiving a data set including the positive detection.