Predictive Data Protection System for Enterprise Backup

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Solution Overview

Problem

Current data backup systems lack predictive capabilities, leading to inefficient resource utilization and increased network congestion due to uncoordinated backups across enterprise networks, making it difficult to preemptively address data loss risks and maintain consistent backup policies.

Innovation Solution

A system and method for predictive data protection that selects data for backup based on predetermined schedules, collects device and user information, analyzes features to determine backup parameters, and generates a backup plan, including scheduling and destination storage parameters, to optimize data backup operations and resource allocation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If uncoordinated backups are performed across enterprise network, then data backup coverage is improved, but network congestion and resource over-utilization increase

Engineering Contradiction:

Improvedata backup coverageVSAvoidnetwork congestion

Core Design Contradiction:

ReliabilityVSObject-generated harmful factors

Solution Approach 1:

The system performs preliminary analysis of device features, access patterns, and risk factors to predict which data should be backed up and when. This predictive approach enables proactive scheduling of backup operations during off-peak hours or during low-network-traffic periods, preventing network congestion while ensuring comprehensive backup coverage.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The backup system dynamically adjusts scheduling parameters and backup strategies based on real-time network conditions, device features, and predicted risk factors. This dynamic adaptation allows the system to optimize backup timing and resource allocation, reducing network congestion during peak periods while maintaining reliable backup coverage.

Inventive Principle:

Principle #15Dynamics

2Reliability

If uncoordinated backups are performed across enterprise network, then data backup coverage is improved, but storage resource utilization becomes inconsistent

Engineering Contradiction:

Improvedata backup coverageVSAvoidstorage resource utilization

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system analyzes local device features, data access patterns, and risk factors to determine customized backup strategies for each device or data type. This localized approach ensures that backup resources are allocated efficiently based on actual needs rather than uniform policies, improving both backup coverage and storage resource utilization consistency.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system changes backup parameters such as frequency, timing, and data selection criteria based on predicted risk factors and device characteristics. By adjusting these parameters dynamically, the system optimizes storage resource utilization while maintaining comprehensive backup coverage across the enterprise network.

Inventive Principle:

Principle #35Parameter changes

3Ease of operation

If traditional backup scheduling is used, then backup operations are simple to implement, but data loss risks cannot be preemptively addressed

Engineering Contradiction:

Improvebackup implementation simplicityVSAvoiddata loss risk mitigation

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system performs self-service analysis of device features, access patterns, and risk factors to automatically generate optimized backup schedules and strategies. This automated predictive analysis reduces the need for manual configuration while enabling preemptive identification and mitigation of data loss risks, maintaining ease of operation alongside improved reliability.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS11126506B2Systems and methods for predictive data protection

Publication Date: 2021.09.21 ACRONIS INT

AI summary

Disclosed herein are systems and method for method for predictive data protection. In one aspect, an exemplary method comprises selecting data stored on a computing device for backing up, according to a predetermined schedule for performing a backup; collecting features associated with the computing device where the data for the backup is stored, the features comprising device information for the computing device, user information for a user of the data, and external information associated with a locale of the computing device; analyzing the features to determine a set of backup parameters for the backup, wherein the backup parameters comprise scheduling parameters and destination storage parameters; generating a backup plan based on the set of parameters for performing the backup; and performing the backup of the data according to the backup plan.