Machine Learning Backup Strategy Automation

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Solution Overview

Problem

Current backup strategies for large volumes of dynamic data from various sources are inefficient and inflexible, requiring significant time, power, and storage space, and do not effectively adapt to changing data parameters.

Innovation Solution

A system utilizing machine learning, comprising a data analysis engine and a learning engine, updates a frequency database, evaluates file uniqueness, categorizes files into logical types, and forms backup strategies based on uniqueness, categorization, importance, recovery time, and confidentiality, incorporating secure, instant, distributed, and local backup methods as needed.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If regular backup of large amounts of data is performed, then data security and recovery capability are improved, but time consumption and power consumption increase significantly

Engineering Contradiction:

Improvedata securityVSAvoidbackup time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system dynamically changes backup parameters including frequency, copy type (full/incremental), timing, and storage location based on data characteristics analyzed by machine learning models. This allows optimization of backup strategies for different data types and changes in data importance over time.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

Different backup strategies are applied to different files or data portions based on their specific characteristics. Critical data receives more frequent and secure backup treatment, while less important data uses simpler backup methods, optimizing overall resource usage.

Inventive Principle:

Principle #3Local quality

2Reliability

If regular backup of large amounts of data is performed, then data security and recovery capability are improved, but storage space requirements increase significantly

Engineering Contradiction:

Improvedata securityVSAvoidstorage space

Core Design Contradiction:

ReliabilityVSQuantity of substance

Solution Approach 1:

The system dynamically adjusts storage location parameters (local vs. cloud), backup type (full vs. incremental), and retention policies based on data characteristics and changing requirements, optimizing storage space utilization while maintaining security.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system extracts and identifies only the essential portions of data that require backup, using machine learning to determine which files are unique, important, or changing. This reduces the volume of data that needs to be backed up while maintaining security for critical information.

Inventive Principle:

Principle #2Taking out (Extraction)

3Adaptability or versatility

If backup parameters are modified to adapt to varying data characteristics, then backup effectiveness is improved, but system complexity increases

Engineering Contradiction:

Improvebackup adaptabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system uses machine learning models to automatically analyze data characteristics and self-determine optimal backup parameters without human intervention. The system serves itself by making intelligent decisions about backup strategies based on patterns it has learned from data behavior.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system continuously monitors data changes, backup outcomes, and system performance, using this feedback to refine its machine learning models and adjust backup parameters dynamically. This closed-loop approach improves adaptability while keeping manual configuration minimal.

Inventive Principle:

Principle #23Feedback

4Productivity

If machine learning is used to form backup strategies, then backup efficiency and adaptability are improved, but computational resources and processing time increase

Engineering Contradiction:

Improvebackup efficiencyVSAvoidcomputational energy

Core Design Contradiction:

ProductivityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary analysis of data characteristics using machine learning models before executing backup operations. By pre-evaluating data importance, uniqueness, and change patterns, the system avoids unnecessary backup computations and focuses resources only on critical data.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies machine learning analysis selectively to determine which files require detailed analysis and which can use simpler backup rules. Not all files undergo the same level of computational analysis, reducing overall energy consumption while maintaining efficiency for critical data.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS12174706B2System and method for automating formation and execution of a backup strategy

Publication Date: 2024.12.24 MIDCAP FINANCIAL TRUST

AI summary

Disclosed herein are systems and method for forming and executing a backup strategy. In one aspect, an exemplary method comprises forming a respective backup strategy for each respective file of a plurality of files stored in a data source based on a frequency of occurrence, a desired recovery time, and a criticality of data loss for the respective file. The method further comprises executing the respective backup strategy for the respective file.