Generative AI Backup Restore Automation
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Solution Overview
Problem
Existing backup solutions for information systems require specialized user knowledge and are not user-friendly, leading to time-consuming and error-prone backup and restoration processes.
Innovation Solution
The implementation of a backup system that utilizes generative artificial intelligence (AI) and a conversational AI-trained model based on a large language model (LLM), allowing users to provide simple requests, such as "restore my browser," without needing specialized knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If users restore individual files manually, then restoration precision is improved, but ease of operation deteriorates and time consumption increases
Solution Approach 1:
The patent introduces an AI assistant as an intermediary between the user and the backup system. The AI assistant automatically identifies which files need to be restored based on user intent, eliminating the need for users to manually search and select files while maintaining high restoration precision through intelligent file identification.
Solution Approach 2:
The system enables self-service restoration where the AI assistant autonomously performs file identification and selection without requiring user expertise. The system automatically understands user needs, locates relevant files in backup images, and prepares restoration parameters without human intervention beyond the initial restoration request.
2Reliability
If users restore entire systems, then reliability of data recovery is improved, but loss of time increases and data loss risk increases
Solution Approach 1:
The patent extracts only the necessary files and data from the backup image based on AI-driven analysis of user intent, rather than restoring the entire system. This selective extraction approach maintains reliability by ensuring all needed files are restored while eliminating unnecessary restoration time and preventing overwriting of benign files that changed since the last backup.
Solution Approach 2:
The system performs partial restoration by restoring only the subset of files actually needed by the user, determined through AI analysis. This avoids the excessive action of restoring entire systems, reducing time loss while maintaining sufficient reliability for the user's specific restoration needs.
3Productivity
If backup solutions use AI/ML models for data identification, then productivity is improved, but device complexity increases
Solution Approach 1:
The AI assistant serves as an intermediary layer that manages the complexity of AI/ML models. Users interact with simple natural language commands rather than complex technical parameters, while the AI assistant translates these into detailed restoration operations. This shields users from device complexity while maintaining high productivity through intelligent automation.
4Manufacturing precision
If users select files for restoration manually, then restoration precision is improved, but loss of time increases
Solution Approach 1:
The AI assistant performs preliminary actions by automatically identifying and pre-selecting all files that need to be restored before the user confirms the restoration operation. This preliminary file identification and selection process maintains restoration precision while eliminating the time users would spend manually searching for and selecting each file.
Data Source
AI summary
Systems and methods for simplified software backup. Generative artificial intelligence (AI) based on a large language model (LLM) is utilized to determine a backup restore operation for a backup request for a target system using a metadata tracked during a previous backup of the target system, and execute the backup restore operation to satisfy the backup request.