Dynamic Predictive Model Reconfiguration via User Feedback
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Solution Overview
Problem
Conventional workflow management systems using predictive analytics struggle to maintain efficient task assignment over longer periods due to model overfitting and failure to adapt to changing trends, leading to inefficient routing decisions.
Innovation Solution
A method that involves accepting a work item, routing it based on a predictive analytics model, validating the model, and reconfiguring it responsive to user feedback to ensure adaptability and accuracy, using validation and reconfiguration modules to detect outliers and adjust the model accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a predictive analytics model is used for workflow routing decisions, then routing efficiency is improved, but the model becomes prone to overfitting and failure to adapt to changing trends over time
Solution Approach 1:
The patent implements feedback loops where user corrections to routing decisions are captured and used to retrain and update the predictive analytics model. This continuous feedback mechanism allows the model to adapt to changing trends and prevent overfitting by incorporating new information, thereby maintaining both routing efficiency and adaptability over time.
Solution Approach 2:
The system dynamically updates the predictive analytics model by incorporating user feedback and retraining the model with new data. This dynamic approach allows the model to evolve and adapt to changing workflow patterns and trends, preventing stagnation and overfitting while maintaining routing efficiency.
2Measurement precision
If the predictive analytics model is continuously updated with user feedback, then model accuracy and adaptability are improved, but system complexity increases
Solution Approach 1:
The system automatically processes user feedback through automated retraining pipelines, where the model is retrained using new data without requiring manual intervention. This self-service approach maintains high model accuracy while minimizing the operational complexity of continuous updates.
Solution Approach 2:
The patent introduces intermediary components such as feedback collection modules and model retraining pipelines that mediate between user interactions and the core predictive model. These intermediaries simplify the overall system architecture by encapsulating complexity in modular components, making the system more manageable while maintaining accuracy.
3Reliability
If user feedback is collected and used for model reconfiguration, then model reliability is improved, but time consumption for validation and reconfiguration increases
Solution Approach 1:
The system implements periodic retraining cycles where the predictive model is updated at scheduled intervals or when triggered by accumulated feedback thresholds. This periodic approach maintains model reliability by incorporating user feedback systematically while minimizing continuous time consumption through batch processing rather than real-time updates.
Solution Approach 2:
The patent performs preliminary validation and preparation of user feedback before applying it to model reconfiguration. By pre-processing and validating feedback data in advance, the system reduces the time required for actual model retraining and deployment, maintaining reliability while reducing time loss.
Data Source
AI summary
Methods and arrangements for managing and enhancing workflow. A work item is accepted and the is routed from a first node to a second node based on a predictive analytics model. The predictive analytics model is validated, and is reconfigured responsive to the validating.