AI Recycle Bin Workflow for Predictive Cloud Data Cleanup
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Solution Overview
Problem
Current data storage solutions lack efficient, cost-effective methods for identifying and managing unnecessary data in cloud environments, particularly for consumers and organizations, as existing technologies do not leverage AI and machine learning to proactively cleanse data and adhere to compliance requirements.
Innovation Solution
A cloud-based AI Recycle Bin (AiRB) service that utilizes AI and machine learning to automatically manage and cleanse data, featuring a subscription-based model with customizable rules and compliance integration, including a cloud-hosted SaaS platform, database, indexing engine, NLP classifiers, and machine learning algorithms for predictive data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional crawl technologies are used for data cleanup, then data management capability is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent replaces complex mechanical crawl technologies with AI/ML-based automated systems. The machine learning models automatically identify and classify unnecessary data without requiring complex manual crawling processes, thereby reducing system complexity while maintaining data cleanup capability.
Solution Approach 2:
The system performs self-service data management by automatically identifying, classifying, and managing unnecessary data without requiring manual intervention. The AI models continuously learn from user behavior patterns to autonomously make decisions about data retention and deletion.
2Measurement precision
If AI and machine learning are integrated into data management systems, then data identification accuracy is improved, but device complexity and implementation cost increase
Solution Approach 1:
The system implements feedback mechanisms where machine learning models continuously learn from user interactions and data patterns. User feedback on data classification decisions is used to retrain models, improving identification accuracy over time while the models handle complexity internally.
Solution Approach 2:
The patent segments the data management system into distinct functional modules: data collection, AI/ML processing, classification, and execution. This segmentation allows the complex AI components to be isolated and managed separately from the rest of the system, reducing overall implementation complexity.
3Productivity
If automated data cleansing services are implemented, then productivity and time efficiency are improved, but loss of information and data security risks increase
Solution Approach 1:
The system performs preliminary actions by creating a recycle bin that preserves data before deletion. All data is first copied to the recycle bin where it can be reviewed and verified, ensuring that only truly unnecessary data is permanently deleted and preventing accidental information loss.
Solution Approach 2:
The recycle bin acts as an intermediary layer between the original data storage and permanent deletion. This intermediate step allows for verification, recovery, and security checks before final data destruction, reducing the risk of information loss while maintaining automated cleanup efficiency.
Data Source
AI summary
A cloud-based AI Recycle Bin (AiRB) utilizes an autonomous predictive file management system. The method allows users to configure initial settings, select default templates, and set data processing workflows. Users can install scan agents on multiple devices, identify storage locations with necessary permissions, and designate target recycle bin locations in the AiRB cloud. The system schedules background scans to identify unnecessary data based on predefined rules. Approved results are copied to the recycle bin, prompting users to delete the corresponding source data. The method includes executing operational commands, applying rule patterns and search filters to scans, and configuring auditing and reporting dashboards for oversight. Additionally, it facilitates automated billing and account management, enhancing efficiency in data management and storage optimization.