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This invention describes a system that monitors a computer network's performance data using a set of analytical rules. It works by applying these rules to the network data, then getting feedback from human experts about any mistakes in its findings. Based on this feedback, the system automatically fine-tunes its rules to improve accuracy, especially for detecting unusual network behavior, and can alert human operators to specific patterns.
Why it matters: Filed before the maturation of advanced machine learning for adaptive systems. The automatic adjustment of rule parameters based on expert feedback could now be significantly enhanced by modern reinforcement learning or adaptive AI, making the tuning process more efficient and robust for complex network environments.
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