Confidence Prediction Model for Document Extraction Routing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Automated document processing systems are not 100% accurate, requiring manual intervention and inefficiently distributing tasks between machines and humans, leading to delays in data delivery and reduced customer trust in automated systems.
Innovation Solution
A hybrid system using a machine learning ensemble architecture for confidence prediction in document processing, routing tasks based on predicted accuracy to optimize processing speed and accuracy by determining whether tasks should be automated or manually processed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated document processing is used to improve processing speed, then productivity increases, but accuracy decreases requiring manual intervention
Solution Approach 1:
The patent introduces a confidence prediction model as an intermediary component between the document processing system and the routing decision. This model predicts the confidence score of extraction results, enabling the system to automatically identify when manual review is needed without sacrificing processing speed. The intermediary model acts as a filter that routes only uncertain cases to human reviewers.
Solution Approach 2:
The system performs preliminary confidence prediction before final routing decisions are made. By predicting confidence scores in advance during the automated processing flow, the system can proactively identify documents requiring manual intervention rather than reacting after processing errors occur, thus maintaining both speed and accuracy.
2Reliability
If all documents are processed manually to ensure 100% accuracy, then reliability improves, but productivity decreases
Solution Approach 1:
Instead of applying manual review to all documents (excessive action), the system applies partial manual intervention only to documents with low confidence scores. The confidence prediction model identifies the subset of documents that actually need human review, eliminating unnecessary manual processing of high-confidence documents while maintaining accuracy for uncertain cases.
Solution Approach 2:
The system changes the parameter of review intensity based on confidence score thresholds. Documents above a certain confidence threshold are processed automatically, while those below the threshold are routed for manual review. This dynamic parameter adjustment optimizes the balance between automation efficiency and manual accuracy.
3Manufacturing precision
If manual review is performed on all documents to improve accuracy, then manufacturing precision improves, but loss of time increases
Solution Approach 1:
The patent segments the document population into two distinct groups based on confidence prediction: high-confidence documents processed automatically and low-confidence documents requiring manual review. This segmentation eliminates the need for universal manual review, reducing time loss while maintaining precision for the critical minority of uncertain cases.
Solution Approach 2:
The system extracts and isolates only the documents with low confidence scores for manual review, separating them from the bulk of high-confidence documents that can be processed automatically. This extraction approach minimizes the portion of documents subjected to time-consuming manual review while ensuring accuracy for the extracted subset.
4Productivity
If automated processing is used to increase productivity, then speed improves, but device complexity increases due to routing requirements
Solution Approach 1:
The confidence prediction model serves multiple functions within the system: it predicts extraction confidence, determines routing decisions, and identifies documents needing manual review. This multi-functionality reduces overall system complexity by consolidating multiple potential components into a single universal model.
Solution Approach 2:
The system uses the confidence prediction model to make autonomous routing decisions without requiring complex external oversight. The model self-evaluates extraction confidence and automatically directs documents to appropriate processing paths, reducing the need for complex manual routing logic and external intervention mechanisms.
Data Source
AI summary
A document extraction system executed by a processor, may process documents using manual and automated systems. The document extraction system may efficiently route tasks to the manual and automated systems based on a predicted probability that the results generated by the automated system meet some baseline level of accuracy. To increase document processing speed, documents having a high likelihood of accurate automated processing may be routed to an automated system. To ensure a baseline level of accuracy, documents having a smaller likelihood of accurate automated processing may be routed to a manual system.