Automated Data Utilization System for Document Field Extraction
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Solution Overview
Problem
Organizations face significant administrative burdens in manually extracting and correlating data from various sources due to the diverse formats in which data is received, leading to inefficiencies and missed opportunities for data assimilation into operations.
Innovation Solution
A computer-implemented system and method that automatically identifies, extracts, and correlates data from documents received in electronic format by scanning for data fields, proposing document definitions to users, and linking extracted data with existing database elements, enabling efficient storage and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual extraction methods are used to handle diverse data formats, then data can be processed with human judgment, but administrative resources and time consumption increase significantly
Solution Approach 1:
The system performs self-service by automatically scanning incoming documents, identifying data fields, and extracting relevant information without requiring manual intervention. The system serves itself by proposing document definitions and data field mappings based on its own analysis of the document structure and content.
Solution Approach 2:
The system changes parameters by dynamically adjusting document definitions and data field configurations based on the specific characteristics of each incoming document. It modifies extraction parameters automatically to adapt to different document formats while maintaining consistent data output standards.
2Reliability
If manual data extraction and correlation is performed, then data accuracy can be verified by human analysts, but organizational productivity and efficiency decrease
Solution Approach 1:
The system implements feedback by allowing users to review proposed document definitions and data field mappings, and to accept or modify them before final extraction. This feedback loop ensures data accuracy while maintaining automation, as users can correct any misidentifications without reprocessing the entire document manually.
Solution Approach 2:
The system performs preliminary action by automatically scanning documents and proposing document definitions and data field mappings before the actual extraction process. This preliminary analysis ensures that the extraction is based on pre-validated structures, improving both accuracy and efficiency.
3Productivity
If automated scanning and identification is implemented, then data extraction speed increases, but system complexity and development requirements increase
Solution Approach 1:
The system achieves universality by designing a multi-functional platform that can handle various document types (invoices, purchase orders, contracts, etc.) using a single automated scanning and identification engine. The system performs multiple functions including document classification, data field identification, extraction, and correlation within one integrated architecture, reducing the need for separate systems for each document type.
4Loss of information
If comprehensive data correlation with existing databases is performed, then data assimilation quality improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary action by pre-processing incoming data through automatic scanning and identification before correlation with existing databases. This preliminary structuring of data enables faster and more efficient correlation operations, as the data is already organized in a standardized format ready for matching with existing database records.
Data Source
AI summary
The invention relates to a computer-implemented system and method for automatically utilizing data from a document. The method may comprise the steps of: receiving a document; automatically scanning the document to identify at least one data field in the document; proposing a document definition to a user based on the scan; receiving an acceptance or modification of the document definition from the user through the user interface; automatically extracting at least one data element and at least one data field from the document using the document definition; automatically searching an existing database for at least one data element or data field that matches the data element or data field extracted from the document; and storing a link between at least one of the extracted data element or data field and a data element or data field in the existing database.