Dynamic Filter Generation for Data Protection Policy Configuration

Solution Overview

Problem

Manually setting filtering conditions for data protection policies is time-consuming and affects user experience, as it requires analyzing attributes of objects from scratch for each protection policy.

Innovation Solution

Automatically generating a dynamic filter based on user-selected objects using unsupervised clustering and supervised classification algorithms, such as decision trees, to simplify the process and reduce configuration time.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual configuration of filtering conditions is used for data protection policies, then protection accuracy can be ensured, but user time cost and operational complexity increase significantly

Engineering Contradiction:

Improveprotection accuracyVSAvoiduser time cost

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating filtering conditions through unsupervised clustering and supervised classification algorithms. The data protection system autonomously analyzes object attributes, clusters similar objects, and generates filtering conditions without requiring manual user intervention, thus reducing time cost while maintaining protection accuracy through algorithmic precision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of configuring filtering conditions is replaced by automated algorithmic systems. Unsupervised clustering algorithms automatically group objects based on attribute similarity, and supervised classification algorithms generate filtering conditions, substituting human manual configuration with automated computational processes that are both faster and consistently accurate

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Manufacturing precision

If manual analysis of object attributes is performed for each protection policy, then precise filtering conditions can be created, but operational complexity and user effort increase

Engineering Contradiction:

Improvefiltering condition precisionVSAvoiduser effort

Core Design Contradiction:

Manufacturing precisionVSEase of operation

Solution Approach 1:

The system performs self-service by automatically generating filtering conditions through unsupervised clustering and supervised classification algorithms. The data protection system autonomously analyzes object attributes, clusters similar objects, and generates filtering conditions without requiring manual user intervention, thus reducing time cost while maintaining protection accuracy through algorithmic precision

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The manual mechanical process of configuring filtering conditions is replaced by automated algorithmic systems. Unsupervised clustering algorithms automatically group objects based on attribute similarity, and supervised classification algorithms generate filtering conditions, substituting human manual configuration with automated computational processes that are both faster and consistently accurate

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If automatic filtering condition generation is implemented, then user effort and time are reduced, but system complexity increases

Engineering Contradiction:

Improvepolicy configuration efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The automatic filtering condition generation system is segmented into distinct functional modules: an unsupervised clustering module that groups objects by attribute similarity, and a supervised classification module that generates filtering conditions from clustered data. This segmentation allows each module to specialize in specific tasks, managing overall system complexity through modular design while maintaining high productivity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The unsupervised clustering algorithm serves as an intermediary between raw object attributes and the final filtering conditions. It transforms complex attribute data into clustered groups that are easier to process, acting as a mediator that simplifies the subsequent supervised classification task and reduces the overall computational complexity of the system

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS11494272B2Method, device, and computer program product for data protection

Publication Date: 2022.11.08 EMC IP HLDG CO LLC

AI summary

Embodiments of this disclosure relate to a method, a device and a computer program product for data protection. The method comprises determining objects selected by a user in a set of objects, and automatically generating one or more corresponding filtering conditions according to the objects selected by the user. The method further comprises automatically setting a predetermined protection policy for objects meeting the filtering conditions in the set of objects. In the embodiments of this disclosure, corresponding filtering conditions are automatically generated according to some protected objects selected by a user to form a dynamic filter, without manually setting the filtering conditions by the user, thereby improving the user experience of a data protection system.