Template-Free Data Extraction Using Context Rules
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Solution Overview
Problem
Variations in document layouts and designs hinder efficient automatic extraction and transfer of data from documents, requiring manual input into applications, especially when dealing with diverse business and healthcare documents.
Innovation Solution
A system processes text from documents by applying rules to determine context, extracting data without templates, and enabling its use across applications without manual input, allowing for user modifications and rule updates to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If template-based data extraction is used, then data extraction accuracy is improved for standardized documents, but adaptability to varied document layouts deteriorates
Solution Approach 1:
The system performs self-learning by automatically analyzing document layouts and extracting data patterns without requiring manual template creation. The machine learning model trains itself on input documents, enabling adaptive data extraction across varied layouts while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts extraction parameters and model configurations based on the specific document layout characteristics detected during analysis, allowing it to adapt to different document types while maintaining high extraction accuracy through parameter optimization.
2Reliability
If manual data entry is required, then data extraction reliability is improved through user verification, but productivity deteriorates due to time-consuming manual input
Solution Approach 1:
The system implements feedback mechanisms where extracted data is verified against multiple sources and cross-checked using machine learning confidence scores. User corrections are fed back into the system to continuously improve extraction accuracy, maintaining reliability while reducing the need for manual verification.
Solution Approach 2:
The patent replaces manual mechanical data entry with automated machine learning-based extraction systems, substituting human operators with intelligent algorithms that can process documents at high speed while maintaining reliability through multiple verification layers.
3Measurement precision
If custom templates are created for each document type, then data extraction precision is improved, but device complexity increases due to template management
Solution Approach 1:
The system employs a universal machine learning model that can handle multiple document types and layouts through a single unified framework. This multi-functional approach eliminates the need for separate custom templates for each document type, reducing management complexity while maintaining extraction precision through adaptive learning.
Solution Approach 2:
The extraction system dynamically adapts its parameters and model structure based on the input document characteristics, transitioning from static template-based approaches to dynamic machine learning models that automatically adjust to different document types without requiring separate template configurations.
Data Source
AI summary
The disclosed embodiments provide a system that processes data. One example embodiment is a computer-implemented method for processing data. The computer-implemented method includes obtaining text from a document associated with a user, wherein the document was generated based on a template and, with the obtained text intact, applying a set of rules to each term in the obtained text to determine a broad category of a plurality of terms associated with the term. The computer-implemented method further includes applying an additional set of rules to refine the broad category associated with the term to a refined category of fewer terms based on a location in the document of at least one term in the broad category of the plurality of terms, extracting a term from the obtained text using template-independent code developed to process documents generated based on a plurality of templates and enabling use of the term with an application.