Neural Network Scan Scheduling for Software Asset Compliance
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Solution Overview
Problem
Software asset management tools face challenges in balancing the frequency of system scans to maintain software compliance and avoid performance issues, as infrequent scans may miss installed or uninstalled software, while excessive scans consume system resources, impacting user experience and compliance.
Innovation Solution
A method and system using a neural network model to dynamically adjust the repetition frequency of system scans by calculating asset management factors from current and previous scans, treating computer systems as nodes and factors as synapses, and adapting weighing values through learning capabilities to optimize scan frequency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If system scan frequency is increased to maintain software compliance accuracy, then software license compliance monitoring is improved, but system resource consumption and user experience are worsened
Solution Approach 1:
The patent implements dynamic scan frequency adjustment by treating the scan system as an adaptive entity that automatically modifies its operation parameters. The neural network equivalent calculates asset management factors from current and previous scan results, then dynamically adjusts scan intervals based on detected changes in software installation patterns, system state, and compliance risk levels. This replaces static, empirically-set scan periods with dynamic, data-driven intervals that adapt to actual system conditions.
Solution Approach 2:
The core mechanism involves changing the time parameter (scan frequency) based on detected system state changes. The system monitors asset management factors such as software installation changes, system configuration modifications, and compliance status variations. When significant changes are detected, the scan frequency parameter is adjusted to increase monitoring intensity; when stability is detected, frequency is reduced to conserve resources.
2Productivity
If system scan frequency is decreased to reduce resource consumption, then system performance and user experience are improved, but software compliance monitoring accuracy is worsened
Solution Approach 1:
The system implements a feedback loop where scan results from current and previous operations are continuously compared. The neural network equivalent processes this feedback by calculating asset management factors that quantify changes in software asset states. This feedback mechanism enables the system to detect patterns and trends, adjusting future scan frequencies based on actual compliance risk rather than fixed intervals, thereby maintaining accuracy while optimizing performance.
Solution Approach 2:
The system performs preliminary analysis of asset management factors by comparing current scan results with historical data before determining the next scan frequency. This preliminary action allows the system to anticipate compliance risks and proactively adjust scan schedules, ensuring that high-risk periods receive intensified monitoring while low-risk periods benefit from reduced scanning, thus maintaining accuracy without excessive resource consumption.
3Ease of operation
If fixed empirical scan periods are used to simplify configuration, then ease of operation is improved, but adaptability to changing system conditions is worsened
Solution Approach 1:
The system implements self-service by automatically adjusting its own operation parameters without requiring manual reconfiguration. The neural network equivalent continuously learns from scan results and system state changes, autonomously determining optimal scan frequencies. This self-adjusting capability maintains configuration simplicity while dramatically improving adaptability, as the system serves itself by making intelligent decisions based on real-time data rather than requiring administrator intervention for each condition change.
Solution Approach 2:
The neural network equivalent acts as an intermediary between raw scan data and scan scheduling decisions. It processes asset management factors and translates them into optimized scan frequency recommendations. This intermediary layer enables the system to bridge the gap between simple configuration requirements and complex adaptive behavior, maintaining ease of operation while achieving high adaptability through intelligent data processing and pattern recognition.
Data Source
AI summary
An approach for updating of a repetition frequency of a system scan operation. The approach calculates values of asset management factors based on results of the asset management factors from a current and a previously performed system scan operation. Groups of the computer systems are treated as node equivalents, and the asset management factors are treated as synapse equivalents of the node equivalents. The approach also feeds values of the factors and weighing values as input for determining an update value for the repetition frequency as output. The weighing value is adaptable via the learning capability of the neural network equivalent. Finally, the repetition frequency is updated using the update value by an activation function.