Form Field Prediction Service Using ML for Automated Document Processing
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Solution Overview
Problem
Businesses face inefficiencies and human errors when manually populating multiple form fields in documents received from suppliers and customers, leading to a need for automated processing.
Innovation Solution
A system utilizing a machine learning model to identify and populate form fields in documents by analyzing similar validated documents, with an electronic processor configured to receive, analyze, and update the model based on user validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual population of form fields is used, then employees can process documents, but human error increases and processing efficiency decreases
Solution Approach 1:
The system enables self-service by allowing the machine learning model to automatically populate form fields without human intervention. The model learns from validated documents and independently extracts and populates data, eliminating the need for manual employee input while maintaining high accuracy through continuous learning from correct examples.
Solution Approach 2:
The patent replaces the mechanical manual process of employees copying and pasting data with an automated machine learning system. The ML model uses pattern recognition and natural language processing to extract information from source documents and populate form fields, substituting human manual operations with intelligent automated processing.
2Manufacturing precision
If more form fields are populated manually, then document completeness improves, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-training the machine learning model on a large dataset of validated documents before actual document processing. This preliminary training enables the model to learn correct population patterns in advance, allowing it to quickly and accurately populate form fields without requiring time-consuming manual verification during actual processing.
Solution Approach 2:
The patent implements continuity of useful action through the feedback loop where validated documents continuously update and retrain the machine learning model. Each validated document adds to the model's knowledge base, making the population process progressively more accurate and efficient over time, transforming a static system into a continuously improving one.
3Measurement precision
If machine learning model is updated frequently, then model accuracy improves, but system complexity increases
Solution Approach 1:
The system uses feedback by implementing a validation mechanism where user-corrected documents are fed back into the training dataset. The machine learning model periodically retrains on this enriched dataset, allowing it to learn from corrections and improve accuracy. This feedback loop automates the improvement process without requiring complex manual intervention for each update.
Solution Approach 2:
The patent applies parameter changes by adjusting the training parameters and update frequency of the machine learning model based on performance metrics. The system can modify hyperparameters such as learning rate, batch size, and update intervals to optimize the balance between accuracy improvement and system complexity, allowing flexible adaptation without over-engineering the update process.
Data Source
AI summary
Processing a first document using a first service that includes a first machine learning model. One embodiment provides a method that includes receiving, over a network, the first document including a first form field and identifying a first value associated with a first entity. The method also includes obtaining a first subset of documents from the first service, analyzing, using the first machine learning model, the first subset of documents to extract a second document including a first value and a second value, automatically populating the first form field with the first value or the second value, and providing, via a graphical user interface, the first document to a user to be validated. The method also includes saving a validated first document to a database, transmitting the validated first document to a second service, and updating the first machine learning model with the validated first document.