Structured Data Verification Using Element Segmentation

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Solution Overview

Problem

Determining whether structured data on a computing device includes malicious or unexpected code is challenging, especially when the data includes mutable elements that change over time, making it difficult to distinguish between expected changes and potential compromises.

Innovation Solution

A method and system for verifying structured data that involves deconstructing the data into elements, comparing these elements with standard elements, and analyzing element differences using a registry of comparisons and heuristic rules to determine if differences are expected or unexpected.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Device complexity

If structured data is compared directly with standard structured data to identify differences, then the verification process is simple, but the accuracy of identifying malicious code deteriorates due to expected mutable changes

Engineering Contradiction:

Improveverification process complexityVSAvoidmalicious code identification accuracy

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The patent segments structured data into individual elements and compares them separately against a registry of expected mutable elements. This allows the system to handle complexity by breaking down the verification process into manageable units while maintaining high accuracy through element-level analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary registry of expected mutable elements that acts as a mediator between the structured data and standard data. This registry contains heuristic rules and statistical information about which elements are expected to change, allowing the system to filter out benign variations before comparing with standard data.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If all element differences are flagged as unexpected, then malicious code detection sensitivity is high, but false positive rate increases due to expected mutable changes

Engineering Contradiction:

Improvemalicious code detection sensitivityVSAvoidfalse positive rate

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The patent uses feedback from statistical analysis of element comparisons to dynamically adjust the verification process. The system analyzes patterns of element differences across multiple structured data samples and uses this feedback to refine the registry of expected mutable elements, thereby reducing false positives while maintaining detection sensitivity.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The patent changes the parameters of element comparison by applying heuristic rules and statistical thresholds. Instead of treating all differences equally, the system adjusts the evaluation parameters based on the element type, frequency of occurrence, and deviation magnitude, allowing expected mutable changes to be distinguished from actual anomalies.

Inventive Principle:

Principle #35Parameter changes

3Measurement precision

If statistical analysis is performed on large numbers of structured data samples, then the accuracy of determining expected vs. unexpected differences improves, but the processing time and computational resources increase

Engineering Contradiction:

Improveexpected vs. unexpected difference determination accuracyVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary statistical analysis on structured data samples before the actual verification process. By pre-computing the registry of expected mutable elements and their statistical characteristics, the system prepares the necessary information in advance, reducing the computational burden during real-time verification while maintaining high accuracy.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250173325A1Verifying structured data

Publication Date: 2025.05.29 GOOGLE LLC

AI summary

A method for verifying structured data includes receiving structured data, deconstructing the structured data into corresponding elements and obtaining standard structured data having corresponding standard elements. The method also includes comparing the elements of the structured data with the standard elements of the standard structured data to identify any element differences. For each element difference, the method includes comparing the element difference against a registry of element comparisons, determining whether the element difference is expected or unexpected based on a heuristic or at least one rule, and when the element difference is unexpected, generating a signal indicating the presence of an unexpected element in the structured data.