Expertise-Based Command Sequence Model Recalibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users of knowledge-based applications face difficulties in diagnosing new or infrequently encountered problems due to the vast amount of knowledge required to navigate and find relevant information in time-of-error data dumps, especially when they are not experts in the field.
Innovation Solution
A method that differentiates between average and expert users by generating baseline command sequence models based on entered commands and frequencies, allowing expert users to input confidence levels and supporting evidence for divergent commands, which are then used to recalibrate and update the models, incorporating expertise levels and command sequence frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If baseline command sequence models are generated based on command frequencies for given tasks, then average users can follow standardized procedures, but expert users cannot efficiently apply their specialized knowledge to diagnose new or infrequently encountered problems
Solution Approach 1:
The system dynamically adapts command sequence models by incorporating expert user feedback and divergent commands. When expert users deviate from baseline sequences, the system learns from these deviations and updates the models accordingly, making the system flexible and adaptable to new problem types while maintaining ease of operation for common tasks.
Solution Approach 2:
The system implements feedback mechanisms where expert user actions and divergent commands are captured and used to recalibrate baseline command sequence models. This feedback loop enables the system to continuously improve by learning from expert expertise, resolving the contradiction between standardized ease of use and adaptability to new situations.
2Measurement precision
If extensive time of error data is provided for comprehensive problem analysis, then diagnostic accuracy is improved, but processing time and resource requirements increase significantly
Solution Approach 1:
The system extracts and utilizes expert user knowledge and divergent commands from the data stream to recalibrate command sequence models. By focusing on the most valuable insights from expert interactions rather than processing all available data uniformly, the system achieves high diagnostic accuracy while minimizing processing time and resource requirements.
3Adaptability or versatility
If baseline command sequence models are frequently updated to incorporate new expertise, then system adaptability improves, but system complexity and recalibration requirements increase
Solution Approach 1:
The system performs self-calibration by automatically incorporating expert user feedback and divergent commands into updated command sequence models. This self-service capability enables the system to adapt to new situations without requiring manual recalibration or increasing operational complexity, as the update process is automated and integrated into normal system operation.
Data Source
AI summary
A method, system, and computer program product are disclosed for implementing enhanced expertise and evidence based decision making in knowledge-based applications. Expertise and evidence based decision making operations include differentiating between an average user and an expert user, and using real time feedback from expert users to update and embed expert knowledge into a predefined baseline command sequence model for a given task or problem.