OCR Data Structuring with Confidence Validation
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Solution Overview
Problem
Large enterprises face challenges in accurately and efficiently accounting for unstructured data from multiple databases and documents, leading to errors and incomplete reporting due to manual operations and subjective interpretation.
Innovation Solution
A method and system for analyzing unstructured data by accessing electronic documents, generating data instances with defined fields, applying character recognition algorithms, assigning confidence factors, and storing the data in a structured format for comprehensive reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual operations are performed by agents to analyze unstructured data, then subjective interpretation and analysis can be applied, but errors increase and productivity decreases due to the large volume of data
Solution Approach 1:
The patent replaces manual mechanical analysis operations with an automated computer-implemented system that uses optical character recognition (OCR) algorithms and machine learning models to extract, validate, and structure data from unstructured documents, eliminating human error and scaling processing capacity
Solution Approach 2:
The system enables self-service data processing where the automated platform independently performs document ingestion, OCR extraction, data validation, confidence scoring, and structured storage without requiring manual human intervention for each document analysis task
2Productivity
If manual sampling is performed to analyze unstructured data, then some analysis can be completed, but reporting robustness decreases and data representation becomes incomplete
Solution Approach 1:
The patent implements continuous automated processing that analyzes all unstructured documents in the database rather than sampling, maintaining constant OCR extraction and validation operations to ensure complete data coverage and comprehensive reporting without interruption or manual batch processing
3Ease of manufacture
If unstructured data is stored without categorization, then data storage is simple, but data retrieval and analysis become difficult and time-consuming
Solution Approach 1:
The patent performs preliminary automated structuring actions during data ingestion by applying OCR algorithms to extract data fields, validating extracted values against confidence thresholds, and categorizing documents into structured formats before storage, so that data is immediately searchable and analyzable without requiring subsequent manual processing
Data Source
AI summary
The present disclosure describes devices and methods of providing a technology environment for analyzing unstructured data to generate structured data. A set of electronic documents, each electronic document associated with a type of product, may be accessed. A data instance for each of the documents may be generated. The data instance may include a plurality of data fields that are based on the type of product. The electronic documents may be analyzed to identify values for each of the plurality of data fields. Analyzing the electronic documents may comprise applying a respective character recognition algorithm to respective electronic documents, and assigning a confidence factor to each of the values. The data instances comprising the values for each of the plurality of data fields may be stored in a second database.