Contextual OCR Tagging for Structured LLM Data Extraction

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Solution Overview

Problem

Conventional OCR and LLM-based methods face limitations in extracting structured data from unstructured documents with inconsistent formats, multilingual content, and complex layouts, leading to inefficiencies and inaccuracies.

Innovation Solution

A scalable system integrating advanced pre-processing, contextual tagging, and customized prompt engineering, leveraging knowledge graphs and machine learning models to enhance data extraction accuracy and flexibility, including Recursive X-Y cut and Voronoi-based segmentation, data enrichment, and post-processing validation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional OCR methods are used for data extraction, then processing speed is maintained, but extraction accuracy deteriorates due to inconsistent document formats and complex layouts

Engineering Contradiction:

Improvedata extraction accuracyVSAvoidsystem complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the document processing task into multiple specialized modules: layout analysis module, text recognition module, data extraction module, and validation module. Each module handles specific aspects of the processing pipeline, allowing the system to maintain high accuracy for complex documents while keeping individual components manageable in complexity

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces an intermediary structured representation layer between OCR and final data extraction. This intermediate structure captures document layout, hierarchy, and semantic relationships, enabling accurate extraction from complex formats without requiring the entire system to be overly complex

Inventive Principle:

Principle #24Intermediary (Mediator)

2Reliability

If LLM-based methods are used for data extraction, then handling of complex layouts improves, but processing time increases and scalability deteriorates

Engineering Contradiction:

Improveextraction reliabilityVSAvoidprocessing throughput

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The patent divides the processing workload by segmenting documents into structured regions and elements that can be processed independently and in parallel. This segmentation enables reliable extraction of complex layouts while improving throughput by allowing concurrent processing of multiple document sections

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent performs preliminary layout analysis and structure identification before main data extraction. This preliminary action prepares the document in a standardized format that enables faster, more reliable extraction in subsequent steps, improving both reliability and throughput

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If multi-layer document structures are processed, then contextual information is preserved, but processing complexity increases

Engineering Contradiction:

Improvecontext preservationVSAvoidprocessing complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent segments multi-layer document structures into hierarchical elements while maintaining relationships between layers. This segmentation preserves contextual information by explicitly modeling layer relationships without requiring the entire multi-layer structure to be processed as a single complex unit

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent transforms multi-layer spatial relationships into a structured dimensional representation that preserves context through explicit layer hierarchy modeling. This dimensional transformation maintains contextual integrity while simplifying processing by organizing complexity in a structured framework

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

4Productivity

If document formats are standardized, then processing efficiency improves, but adaptability to various document types deteriorates

Engineering Contradiction:

Improveprocessing efficiencyVSAvoidformat flexibility

Core Design Contradiction:

ProductivityVSAdaptability or versatility

Solution Approach 1:

The patent implements a universal processing framework that can handle multiple document formats and types through a common architecture. This universal system maintains high processing efficiency by using standardized internal representations while adapting to various external document formats through format-agnostic layout analysis

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260056924A1Enhanced OCR data processing through data enrichment and contextual tagging for llms

Publication Date: 2026.02.26 ETON SOLUTIONS LP

AI summary

A method and system are disclosed for improving the accuracy, efficiency, and scalability of data interpretation and extraction of structured data from unstructured documents utilizing Large Language Models (LLMs). Applicable in finance, healthcare, legal, and government contexts, the disclosed invention addresses limitations of conventional Optical Character Recognition (OCR), machine learning, and LLM-based methods. In particular, the system and method incorporate feedback loops for continuous learning and leverage pre-processing, contextual tagging, customized prompt engineering, and post-processing to achieve robust data extraction. By integrating data enrichment techniques, the invention manages the inherent complexities of multilingual documents and evolving content standards.