Electronic Document Parsing for Insurance Estimates
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Solution Overview
Problem
Manual processing of large numbers of insurance claims is inefficient and prone to human error due to the need for manual recreation of estimates and the non-uniform format of submissions from adjusters.
Innovation Solution
An electronic document parsing system that receives an electronic document, generates an array of text, evaluates it using keyword lists and regular expressions, identifies sections, and extracts data values to create structured data objects for storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processing is used to recreate estimates, then human operators can handle complex document variations, but processing efficiency is low and human error occurs
Solution Approach 1:
The system performs self-service by automatically parsing, extracting, and validating data from insurance estimates without human intervention. The automated pipeline includes OCR processing, section identification, data extraction, and validation against expected formats, enabling the system to handle document variations independently while maintaining high accuracy and eliminating human error
Solution Approach 2:
The patent replaces the mechanical manual process of recreating estimates with an automated computational system. The mechanical action of manual data entry and document reconstruction is substituted with electronic document parsing, regular expression matching, and automated data validation, dramatically increasing processing speed while maintaining reliability
2Adaptability or versatility
If manual recreation of estimates is performed, then complex document variations can be handled, but considerable time is required for each file
Solution Approach 1:
The system achieves universality by designing a multi-functional automated pipeline that can handle multiple document formats and variations through a single unified process. The combination of OCR, flexible section identification, and adaptive data extraction enables the system to process diverse estimate formats without requiring separate manual procedures for each variation, reducing time per document while maintaining adaptability
Solution Approach 2:
The system handles format variations by dynamically adjusting parsing parameters and validation rules based on the specific document structure detected. Regular expressions and extraction criteria are modified according to the identified document type and section, allowing the system to adapt to different formats automatically without manual reconfiguration, thereby processing multiple variations at high speed
3Productivity
If automated parsing is implemented, then processing efficiency increases, but the system must handle non-uniform document formats
Solution Approach 1:
The system manages complexity by segmenting the document processing into distinct modular stages: OCR processing, section identification, data extraction, validation, and output generation. Each stage handles a specific aspect of the parsing task with dedicated algorithms and rules, making the overall complex system manageable and maintainable while achieving high processing throughput through parallel and sequential optimization
Data Source
AI summary
The systems and methods described herein provide for the parsing and evaluation of electronic documents. The electronic documents may be converted to a plurality of arrays, and these arrays may be parsed and split to generate structured data objects. The structured data objects may each be associated with a section of the electronic document. Sections may be identified by evaluating the array of text by comparing the delimited data of the array to one or more keyword lists of predetermined regular expressions.