Bifurcated AI Document Evaluation for Accurate Data Extraction

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Solution Overview

Problem

Existing data evaluation processes for multiple datasets are inefficient, inaccurate, and resource-intensive, particularly when dealing with large volumes of data from diverse sources, leading to challenges in ensuring accurate data retrieval and processing.

Innovation Solution

A bifurcated artificial intelligence approach using machine learning engines for document recognition and data extraction, involving separate training processes for document identification and data value categorization, with preprocessing to remove numerical digits and special characters for enhanced accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional data evaluation processes are used for multiple datasets, then data extraction can be performed, but accuracy and efficiency deteriorate when dealing with large volumes of data from diverse sources

Engineering Contradiction:

Improvedata extraction accuracyVSAvoiddata processing efficiency

Core Design Contradiction:

Measurement precisionVSProductivity

Solution Approach 1:

The patent segments the data evaluation process into multiple specialized machine learning engines: one for document identification and classification, another for data extraction, and a third for validation. This segmentation allows each engine to specialize in specific tasks, improving both accuracy and processing efficiency for large volumes of diverse data sources

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system dynamically adjusts evaluation parameters and thresholds based on the type and volume of data being processed. Machine learning models adapt their parameters to handle different data sources and formats, maintaining high accuracy while optimizing processing speed for large datasets

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If manual verification processes are used to ensure data accuracy, then extraction precision can be maintained, but time consumption and resource requirements increase significantly

Engineering Contradiction:

Improvedata verification accuracyVSAvoidevaluation time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system implements self-service verification through machine learning models that automatically validate extracted data against multiple criteria, cross-reference with source documents, and identify inconsistencies without human intervention. This maintains high verification accuracy while eliminating the time loss associated with manual processes

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent incorporates feedback loops where extraction results are automatically verified and validated, with errors fed back into the system for continuous improvement. This automated feedback mechanism maintains high accuracy standards while significantly reducing the time required compared to manual verification

Inventive Principle:

Principle #23Feedback

3Reliability

If strict adherence to data source protocols is required, then data reliability can be improved, but process complexity and difficulty of operation increase

Engineering Contradiction:

Improvedata source complianceVSAvoidprotocol adherence difficulty

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system employs universal machine learning models capable of handling multiple data source protocols and formats simultaneously. These models automatically adapt to different protocols while maintaining compliance requirements, reducing operational complexity without sacrificing data reliability across diverse sources

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Measurement precision

If templates are used to improve data acquisition accuracy, then extraction precision can be enhanced, but adaptability to different data formats decreases and protocol strictness increases

Engineering Contradiction:

Improvedata extraction accuracyVSAvoiddata format flexibility

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The patent implements dynamic templates that can adapt their structure and criteria based on the input data format. The machine learning models adjust template parameters dynamically to maintain high extraction accuracy across different data formats without requiring strict adherence to fixed protocols, thereby enhancing both precision and adaptability

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250384707A1Method and appratus for dataset evaluation and database management

Publication Date: 2025.12.18 ONE DILIGENCE LLC

AI summary

A data uploader uploads a first plurality of data values associated with a transaction. A document uploader uploads digital images of a plurality of documents associated with the transaction, wherein a second plurality of data values are embedded in the documents. The digital images are stripped of numerical (or other) data values before being transmitted to a first machine learning engine to identify documents associated with the digital images. A second machine learning engine identifies categories of numerical data values (and other data values) that are included in the second plurality of data values and that are embedded in the documents. The first and second plurality of data values can be compared for accuracy and corrected. The first and second machine learning engine are sent bifurcated feedback regarding the respective identification performed by each to improve future accuracy.