Dynamic Data Extraction Model Updating via User Validation Feedback
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Solution Overview
Problem
Traditional document data extraction systems rely on manual processes, which are time-consuming and prone to errors, and existing prediction models struggle with accuracy when handling documents with varying types of data and layouts.
Innovation Solution
A system that generates data extraction predictions using a prediction model, allows user validation to correct predictions, and uses this validation to generate updated prediction models, improving efficiency and accuracy by creating training documents from user corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used for document data extraction, then the system can handle various document types, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical data extraction processes with an automated prediction model system. The system uses machine learning models to automatically extract data from documents, substituting human manual work with computational processes that are both faster and more accurate when properly maintained.
Solution Approach 2:
The patent implements a feedback mechanism where user corrections to extraction predictions are captured and used to retrain and update the prediction models. This continuous feedback loop improves model accuracy over time while maintaining automated processing speed, resolving the contradiction between accuracy and time efficiency.
2Productivity
If existing prediction models are used for data extraction, then automation is achieved, but accuracy deteriorates when handling documents with varying types and layouts
Solution Approach 1:
The patent makes the prediction model dynamic by implementing continuous updating mechanisms. Instead of using static models, the system dynamically adapts to varying document types and layouts by retraining models with new data, allowing it to maintain high accuracy across diverse document formats while preserving automation benefits.
Solution Approach 2:
The patent changes the parameters of the prediction models through continuous retraining with updated data from user corrections. By modifying model parameters based on real-world feedback, the system adapts to different document types and layouts, maintaining both productivity and precision.
3Measurement precision
If prediction models are continuously updated with user feedback, then accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically uses user feedback to retrain and update its own prediction models without requiring manual intervention. The system serves itself by automatically incorporating corrections into model improvements, reducing the operational complexity despite the sophisticated update mechanisms.
Data Source
AI summary
Techniques described herein relate to a method for performing data extraction for documents. The method includes obtaining a data extraction request associated with a document; in response to obtaining the request: generating a data extraction prediction using a prediction model and the document; providing the data extraction prediction to a user; obtaining a user validation associated with the data extraction prediction; making a determination that the user validation indicates that the data extraction prediction is not correct; in response to the determination: generating an updated data extraction prediction based on the user validation; and initiating performance of additional document processing using the document based on the updated data extraction prediction.