Structured Report Generation from Unstructured Data
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Solution Overview
Problem
Manual review of unstructured documents for claim processing is time-consuming and expensive due to the lack of semantic context in existing data extraction solutions, which often rely on non-transparent and inflexible machine learning algorithms.
Innovation Solution
A system and method for generating a structured report from unstructured data using customizable template rules, where extraction rules can be dynamically modified to refine data extraction, providing semantic context and transparency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning algorithms are used for data extraction, then extraction accuracy is improved, but transparency and flexibility are lost
Solution Approach 1:
The patent introduces a rule-based extraction system as an intermediary between the unstructured documents and the final extracted data. This rule-based system provides transparency by allowing users to see and modify the extraction rules, while still achieving accurate extraction through structured rule definitions that capture complex extraction logic without the opacity of black-box machine learning algorithms.
Solution Approach 2:
The patent implements dynamic rule modification capabilities that allow extraction rules to be adjusted during the extraction process. Users can add, remove, and modify rules based on their specific needs, providing flexibility that contrasts with static machine learning models. The system adapts to different extraction scenarios by allowing rule customization while maintaining transparency in the extraction logic.
2Measurement precision
If manual review is used for document processing, then data accuracy is maintained, but time consumption and costs increase
Solution Approach 1:
The patent implements a self-service extraction system where users can independently configure and modify extraction rules without requiring extensive manual review or intervention. The system automatically applies the defined rules to extract data from documents, reducing the need for time-consuming manual verification while maintaining accuracy through user-defined rule logic.
Solution Approach 2:
The patent allows users to define and configure extraction rules in advance before processing documents. This preliminary configuration of extraction logic enables the system to automatically perform accurate data extraction without requiring time-consuming manual review during the actual processing phase, thus maintaining accuracy while reducing time consumption.
3Productivity
If extraction rules are hardcoded for automation, then productivity is improved, but adaptability and user modification capability are reduced
Solution Approach 1:
The patent implements dynamic rule modification capabilities that allow extraction rules to be adjusted during the extraction process. Users can add, remove, and modify rules based on their specific needs, providing flexibility that contrasts with static machine learning models. The system adapts to different extraction scenarios by allowing rule customization while maintaining transparency in the extraction logic.
Solution Approach 2:
The patent creates a universal rule-based extraction framework that can handle multiple extraction scenarios through a single configurable system. The same extraction engine can apply different rules for different document types and extraction needs, providing adaptability across various use cases while maintaining high productivity through automated processing.
Data Source
AI summary
The disclosed methods and systems provide computer-assisted guided review of unstructured data to generate a structured data output based on customizable template rules. In embodiments, an unstructured file is received, and a predefined template is selected. The predefined template includes a plurality of fields, each field corresponding to a field of the structured report. The predefined template also defines extraction rules for each field of the predefined template, and the extraction rules define parameters for identifying unstructured data relevant to the associated field. The extraction rules are applied to the unstructured file to identify data relevant to the field associated with the corresponding extraction rule, and the data identified as relevant is confirmed. Confirming the relevant data includes determining to refine the relevant data based on a condition, and modifying the extraction rule associated with the field to refine the relevant data.