ML-Based Electronic Document Completion System
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Solution Overview
Problem
The increasing complexity and volume of electronic documents require efficient automatic completion methods to reduce user input errors and burden, as manual linkage of fields across multiple documents is time-consuming and resource-intensive, especially with frequent updates in taxation laws and document types.
Innovation Solution
A machine learning (ML) model-based system that categorizes electronic documents, identifies common fields, and generates a dynamic form for user input, allowing for automatic completion and continuous learning from user inputs to optimize field identification and form updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual linkage of fields across multiple documents is performed, then document completion accuracy can be maintained, but user time consumption and operational burden increase significantly
Solution Approach 1:
The system performs automatic field linkage and document completion without requiring manual user intervention. The ML model autonomously categorizes documents, identifies fields, and populates data across multiple documents, allowing the system to serve itself rather than requiring continuous user oversight and manual field mapping.
Solution Approach 2:
The patent replaces the mechanical manual process of field linkage with an automated ML-based system. Instead of users manually identifying and linking fields across documents, the system uses machine learning models to automatically categorize documents, extract fields, and populate data, substituting human mechanical operations with automated computational processes.
2Productivity
If manual processing methods are used for document completion, then system complexity remains low, but productivity and processing efficiency decrease
Solution Approach 1:
The ML-based system performs multiple functions including document categorization, field identification, data extraction, and automatic population across various document types. This multi-functional approach increases processing efficiency without requiring separate manual processes for each task, as the unified ML system handles diverse document completion needs.
Solution Approach 2:
The system changes the operational parameters from manual human operations to automated ML-based processing. By transitioning from human cognitive and manual input processes to algorithmic classification and automatic population, the system achieves higher productivity despite increased computational complexity.
3Adaptability or versatility
If frequent updates in taxation laws and document types are accommodated through manual methods, then adaptability is limited, but extensive development resources are required
Solution Approach 1:
The ML-based system dynamically adapts to new document types and taxation law changes through continuous learning and retraining. Rather than requiring static manual reconfiguration, the system can be retrained on new data to accommodate updated document formats and requirements, providing dynamic adaptability without proportional increases in development resources.
Solution Approach 2:
The system performs preliminary document categorization and field identification using ML models trained on historical data. This preliminary automated processing prepares documents for completion before user input is required, allowing the system to proactively adapt to various document types and law changes that have already been encountered during training.
Data Source
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AI summary
Systems and methods for machine learning (ML) based electronic document completion are described. A system is configured to receive one or more electronic documents to be completed for a user and provide the one or more electronic documents to an ML model. The ML model is trained to categorize the one or more electronic documents based on previously categorized electronic documents. The system is also configured to: categorize, for each electronic document of the one or more electronic documents, the electronic document into an electronic document category by the ML model; identify one or more fields to be entered by the user based on categorizing the one or more electronic documents; generate a dynamic form including the one or more fields to be entered; and provide the dynamic form for display to the user. Identifying the one or more fields to be entered may be based on a statistical model.