Automated Data Copy Verification Agent
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Solution Overview
Problem
Current computing systems face challenges in efficiently verifying the integrity of data copies due to the vast volume of data involved, leading to storage and processing complexities, and the risk of data corruption from cyber security threats.
Innovation Solution
An automated agent utilizing machine-learning based methods and systems for data copy verification, which includes a copy verification agent that employs deep learning models to assess data integrity and trigger corrective actions upon detecting compromised data copies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data copy verification methods are used, then data integrity can be assessed, but the storage and processing requirements become prohibitively complex due to the sheer volume of data
Solution Approach 1:
The patent extracts only the essential verification information from complete data copies by using checksums, hashes, or other condensed representations. Instead of storing and processing entire data copies for verification, the system extracts and stores only the verification metadata (checksums, hashes, signatures), dramatically reducing storage and processing requirements while maintaining verification capability
Solution Approach 2:
The patent changes the verification approach from examining complete data copies to examining derived parameters such as checksums, hash values, or other condensed representations of data integrity. This parameter transformation allows verification without handling the full volume of original data
2Reliability
If complete data copy verification is performed, then data corruption can be detected, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary verification actions by computing and storing checksums, hashes, or other verification parameters at the time of data copying. When verification is needed, the system compares these pre-computed values rather than重新 processing the entire data copy, significantly reducing verification time
Solution Approach 2:
The patent creates and verifies simplified copies (checksums, hashes, metadata) rather than working with complete data copies. These verification copies are much smaller and faster to process while containing all necessary information for integrity verification
3Ease of operation
If manual data integrity assessment is performed, then verification can be done with simple tools, but the sheer volume of data makes it an uncommon and impractical task
Solution Approach 1:
The patent implements automated verification systems that perform data integrity checks without human intervention. The system automatically computes verification parameters, compares them against stored values, and detects corruption, making the process both simple to operate and highly productive
Solution Approach 2:
The patent replaces manual verification processes with automated computational systems. Instead of human operators manually checking data, the system uses algorithms and software to automatically compute verification parameters and detect corruption, dramatically increasing throughput while maintaining simplicity
Data Source
AI summary
The implementation of an automated agent for data copy verification. Specifically, the implementation entails the execution of an intelligent, machine-learning based method and system for determining the integrity of data copies (i.e., for identifying whether data copies of a same data set have been impacted by malicious activities). Upon determining that data integrity is likely compromised, one or more corrective actions may be triggered. These actions may mitigate the spread of corruption and/or infection.