Human-in-the-Loop Confidence Threshold Optimization
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Solution Overview
Problem
Administrators of document processing workflows face challenges in configuring the Human In The Loop (HITL) process to achieve targeted quality and determining the optimal number of users required for HITL, leading to inefficiencies or deficiencies in manpower.
Innovation Solution
A computer-implemented method and system that generate document processing insights by receiving a document insight request, obtaining a machine learning model trained on a training corpus of documents, and generating insights that include accuracy and user review rate targets, allowing administrators to optimize the HITL process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If machine learning models are used to automate document processing workflows, then processing efficiency is improved, but prediction accuracy deteriorates
Solution Approach 1:
The patent introduces a confidence threshold as an intermediary mechanism between machine learning predictions and human review. Documents with predictions below the threshold are automatically routed for human review, while those above the threshold are processed automatically. This intermediary filtering layer resolves the contradiction by allowing high-volume automated processing while maintaining accuracy through selective human intervention.
Solution Approach 2:
The system implements feedback by using human reviews of low-confidence predictions to retrain and improve the machine learning model. This feedback loop allows the model to learn from its mistakes and progressively improve accuracy, resolving the trade-off between automated processing volume and prediction accuracy.
2Measurement precision
If human in the loop feature is implemented to ensure targeted quality, then prediction accuracy is improved, but device complexity worsens
Solution Approach 1:
The system performs self-service by automatically calculating confidence thresholds and determining which documents require human review based on the model's own performance metrics. This self-determination mechanism reduces the need for complex external configuration and monitoring systems, thereby reducing overall system complexity while maintaining accuracy.
Solution Approach 2:
The patent uses parameter changes by dynamically adjusting the confidence threshold based on business requirements and model performance. This flexible parameter adjustment allows the system to optimize accuracy without requiring complex reconfiguration of the entire workflow, simplifying the system while maintaining targeted quality.
3Adaptability or versatility
If administrators manually configure HITL process parameters, then system adaptability is improved, but loss of time worsens
Solution Approach 1:
The system performs preliminary action by pre-calculating confidence thresholds and automatically determining HITL parameters based on the machine learning model's performance characteristics. This pre-computation eliminates the need for administrators to manually configure complex parameters, significantly reducing configuration time while maintaining adaptability through model-driven parameter generation.
Solution Approach 2:
The system uses copying by replicating optimal HITL parameters across different document types and scenarios based on the machine learning model's learned patterns. This parameter replication strategy allows rapid adaptation to new document types without requiring time-consuming manual reconfiguration, as the system copies proven parameter settings from similar scenarios.
Data Source
AI summary
A computer-implemented method includes receiving a document insight request that requests document insights for a corpus of documents. The document insight request includes the corpus of documents, a set of entities contained within each document of the corpus of documents, and document insight request parameters that includes a confidence value threshold. The method also includes generating the document insights for the corpus of documents based on the confidence value threshold. Here, the document insights include an accuracy target and a user review rate target. The method also includes transmitting the document insights to the user device causing a graphical user interface to display the document insights on the user device.