Secure API Document Extraction for Unstructured Insurance Files

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Solution Overview

Problem

Insurance documents vary widely in structure and format, leading to inefficiencies in manual review and extraction of information by underwriters, resulting in significant gaps in underwriting and risk assessment.

Innovation Solution

A computerized method using a secure API to convert unstructured insurance documents to a structured format through text mining, data dictionary definition, and machine learning techniques, enabling automated data extraction and enrichment.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If manual review and data entry methods are used for unstructured insurance documents, then underwriters can review documents, but the process is extremely inefficient and time-consuming

Engineering Contradiction:

Improvedocument review efficiencyVSAvoidtime for manual review and data entry

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent replaces the manual mechanical process of reviewing and data-entry with an automated text mining system that uses software to extract information from unstructured documents, eliminating the need for manual underwriter intervention in data extraction tasks

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system enables documents to self-structure automatically through text mining operations that extract and organize data without human intervention, allowing the documents to serve themselves in the structuring process

Inventive Principle:

Principle #25Self-service

2Extent of automation

If unstructured documents are processed manually, then flexibility in handling various document types is maintained, but the lack of automation creates extreme inefficiencies in underwriting and risk review

Engineering Contradiction:

Improveautomation of information extractionVSAvoidcomplexity of document format variations

Core Design Contradiction:

Extent of automationVSDevice complexity

Solution Approach 1:

The patent creates a universal text mining framework that can handle multiple document types and formats through a single automated system, using data dictionaries that can be configured to work with various insurance document structures without requiring separate manual processes for each type

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Manufacturing precision

If manual data entry is used to convert unstructured documents to structured format, then data can be input into systems, but the process lacks efficiency and creates gaps in coverage assessment

Engineering Contradiction:

Improveaccuracy of data extractionVSAvoidspeed of document processing

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The patent replaces manual data entry operations with automated text mining software that extracts data directly from unstructured documents and converts it to structured format, maintaining accuracy while dramatically increasing processing speed

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The patent introduces data dictionaries as an intermediary layer between unstructured documents and structured data systems, enabling automated extraction while ensuring data quality and consistency through predefined extraction rules and validation

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS12561508B1Method and system of converting unstructured digital documents to a structure format using a secure API

Publication Date: 2026.02.24 YERRAMSETTY VENKATA SAI RAMAN

AI summary

In one aspect, a computerized method for document extraction workflow for unstructured documents includes the steps of implementing a text mining operation on a set of digital documents the incoming documents. This is done by defining a document type of each digital document. Based on the document type, the method defines a set of data dictionaries to extract any data from each digital document. The method uses the defined set of data dictionaries to extract any data from each digital document.