Deep Learning Data Extraction Model with Feedback Loop
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Solution Overview
Problem
Conventional automatic data extraction (ADE) systems using deep learning models face challenges in training, evaluation, and deployment due to their tedious and time-consuming nature, which involves intense computational processing and numerous manual user interactions, leading to human errors and substantial delays.
Innovation Solution
The system includes a database for storing documents and extracted data, a server with processors and memory for generating an interactive GUI, and real-time monitoring of model performance. It automatically updates and trains the extraction model using user correction information, reducing manual intervention and enhancing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional ADE training systems are used with manual user interactions, then model training can be performed, but the process becomes tedious and time-consuming with substantial delays
Solution Approach 1:
The system enables self-service by automatically performing document identification, collection, labeling, model building, evaluation, and deployment without requiring manual user interactions. The ADE system autonomously trains models using received documents and feedback, eliminating the need for users to manually guide each training step.
Solution Approach 2:
The system implements continuous feedback loops where model predictions are automatically evaluated against ground truth data, and performance metrics are used to trigger retraining when thresholds are exceeded. This automated feedback mechanism eliminates manual evaluation steps while maintaining model quality.
2Measurement precision
If manual document labeling and model building are performed, then model accuracy can be ensured, but human errors occur in labeling and model building processes
Solution Approach 1:
The system performs automatic document labeling through feedback from users or ground truth data, eliminating manual labeling by human operators. The automated labeling process consistently applies the same criteria without human fatigue or subjective interpretation errors.
Solution Approach 2:
The system replaces manual mechanical labeling processes with automated computational processes. Instead of humans manually labeling documents, the system uses algorithmic processing to assign labels based on model predictions and feedback, substituting human cognitive processes with automated systems.
3Reliability
If conventional model updating procedures are used, then model errors can be identified, but the process is prone to human error and substantial delay
Solution Approach 1:
The system continuously monitors model performance metrics in real-time rather than performing periodic manual checks. This continuous monitoring ensures model errors are detected immediately when they occur, eliminating delays associated with manual evaluation schedules.
Solution Approach 2:
The system implements automated feedback loops that continuously evaluate model predictions against ground truth data and trigger retraining when performance degradation exceeds predefined thresholds. This automated feedback mechanism eliminates manual error detection and response delays.
4Ease of operation
If numerous manual user interactions are required in training pipeline, then model development can be controlled, but device complexity increases
Solution Approach 1:
The system merges multiple separate manual operations (document identification, collection, labeling, model building, evaluation, deployment) into a single automated pipeline. This consolidation reduces the number of discrete user interactions required while maintaining comprehensive control over the entire model development process.
Solution Approach 2:
The system creates a universal automated training pipeline that handles multiple tasks (document processing, labeling, model training, evaluation, deployment) through a single integrated system. This multi-functional approach eliminates the need for separate manual processes for each task, reducing operational complexity.
Data Source
AI summary
Systems and methods for deep learning model development for data extraction using a feedback loop. A system generates an interactive graphical user interface (GUI) on one or more user devices for displaying a document with data extracted from the document by a data extraction model together with a user interaction tool allowing the user to correct the extracted data. The system receives, via the interactive GUI, correction information for the extracted data and monitoring performance characteristics of the extraction model in real-time based on the user correction information. The system automatically updates and trains the extraction model using the correction information responsive to detecting that the performance characteristics meet a predetermined performance reduction condition.