IP Block Auditing via Machine Learning Prediction
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Solution Overview
Problem
The management of network addresses, particularly IPv4 and IPv6 addresses, is a time-consuming process due to the large inventory numbers and the need for efficient auditing and validation of IP blocks, which existing methods struggle to address effectively.
Innovation Solution
An internet numbers asset management system utilizing machine learning and artificial intelligence to predict which IP blocks need auditing, automate the validation process, and update historical data, supported by robotic process automation and a cloud-based architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If manual auditing and validation methods are used for IP blocks, then process simplicity is maintained, but time consumption increases significantly
Solution Approach 1:
The patent replaces manual mechanical auditing processes with automated electronic systems. Machine learning models predict which IP blocks require auditing, and robotic process automation executes validation tasks electronically, substituting human manual operations with automated computational processes that dramatically reduce time consumption while increasing automation level.
2Measurement precision
If comprehensive auditing of all IP blocks is performed, then validation accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies preliminary action by using machine learning models to predict and identify IP blocks that require auditing before the actual validation process. This pre-screening step filters the large inventory of IP blocks down to a manageable subset that needs comprehensive auditing, thereby maintaining high validation accuracy while reducing processing complexity by avoiding unnecessary auditing of all IP blocks.
Solution Approach 2:
The patent segments the IP block inventory into different categories based on machine learning predictions. High-priority IP blocks identified by the model undergo comprehensive auditing, while low-priority blocks are handled through streamlined processes. This segmentation allows the system to maintain high validation accuracy for critical blocks while reducing overall processing complexity through differentiated handling.
3Productivity
If manual management processes are used for IP address inventories, then system simplicity is maintained, but productivity decreases
Solution Approach 1:
The patent implements a universal automated management system that handles multiple IP block auditing and validation tasks through integrated machine learning models and robotic process automation. This multi-functional system consolidates prediction, prioritization, auditing, and validation operations into a single platform, dramatically improving productivity while managing system complexity through unified architecture rather than separate manual processes.
Data Source
AI summary
A system and method for internet numbers management is provided. Historical internet protocol (IP) block information associated with a plurality of IP blocks is received from a server database. The historical IP block information includes IP block size information and IP block type information. A machine learning model is trained based on the historical IP block information. Predictions are received from the trained machine learning model indicating a pool of IP blocks of the plurality of IP blocks to be audited. An electronic action is generated to obtain validation status information for each IP block of the pool of IP blocks. The historical IP block information in the server database is updated with the validation status information for each IP block of the pool of IP blocks.