Document Template Matching for Large-Scale Data Review Batching

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Manual data review processes for identifying compromised information in cybersecurity breaches are inefficient, slow, and prone to errors due to the increasing complexity and volume of modern data ecosystems, particularly in unstructured data environments.

Innovation Solution

An automated pipeline that identifies unique templates in documents, performs pairwise comparisons to group similar files into clusters, and processes them as batches for tasks like data analysis and mining, using pixel key points, descriptors, and algorithms like ORB and brute force k-nearest neighbors to enhance efficiency and consistency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If manual data review processes are used to identify compromised information, then human judgment and flexibility can be applied, but the process becomes slow, error-prone, and inefficient when handling large volumes of diverse data

Engineering Contradiction:

Improveaccuracy of identifying compromised informationVSAvoidprocessing speed and efficiency

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent introduces template identification and matching algorithms as an intermediary between raw document data and human analysis. The system automatically identifies document templates, extracts structured data according to template patterns, and presents processed results for review, thereby filtering and organizing data before human judgment is applied

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces manual mechanical review processes with automated computer-based template matching and data extraction systems. Optical character recognition, template identification algorithms, and automated data extraction replace the manual sifting through documents, significantly increasing processing speed while maintaining reliability through consistent application of extraction rules

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If automated template identification and pairwise comparison is implemented to group documents into clusters, then processing efficiency and consistency are improved, but system complexity and computational resources increase

Engineering Contradiction:

Improvebatch processing efficiencyVSAvoidalgorithmic and system complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent segments the complex task of document processing into distinct stages: template identification, feature extraction, pairwise comparison, and cluster formation. Each stage handles a specific aspect of the problem, making the overall system more manageable and efficient despite the complexity of individual components

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms documents into standardized parameter representations through template identification and feature extraction. By converting diverse document formats into uniform structural parameters and features, the system enables efficient comparison and clustering while reducing the complexity of handling raw heterogeneous data

Inventive Principle:

Principle #35Parameter changes

3Adaptability or versatility

If diverse data types, languages, and formats are processed manually, then human adaptability can handle various formats, but the process becomes arduous and error-prone

Engineering Contradiction:

Improveability to handle diverse data formatsVSAvoidaccuracy and consistency of processing

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent implements universal template identification algorithms that can recognize and adapt to multiple document formats, languages, and structures through a single unified system. The template matching approach is language-agnostic and format-flexible, enabling the same system to process diverse data types consistently without requiring format-specific manual intervention

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS12511927B1Template identification and matching for data analysis in large sets of documents

Publication Date: 2025.12.30 ACTIVE FORENSICS LLC

AI summary

In one aspect, a computer-implemented method includes accessing, by one or more processors, a plurality of documents stored in one or more databases; converting, by the one or more processors, each page of each document of the plurality of documents into a respective image that comprises a plurality of pixel key points and corresponding descriptors; identifying, by the one or more processors, based upon the plurality of pixel key points and corresponding descriptors for each page of each document of the plurality of documents, a plurality of unique templates corresponding to each document of the plurality of documents; and batching, by the one or more processors, a first list of documents based upon matching a first unique template of the plurality of unique templates included in a first document of the plurality of documents with other documents of the plurality of documents.