Sigma-Based Glideslope Multi-Path Modeling for Dynamic Critical Paths
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Solution Overview
Problem
Existing monitoring systems struggle to accurately predict process completion times due to complexity, imperfect data, and computational burdens, leading to potential delays and inefficiencies.
Innovation Solution
A system models processes as a graph, decomposes them into paths, and uses Sigma-based and Glideslope modeling to predict path completion probabilities, identifying a dynamic critical path for timely mitigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track process completion, then the system can provide basic dashboards showing current state, but the system fails to accurately predict completion times due to process complexity and interdependent tasks
Solution Approach 1:
The patent segments the complex process into multiple paths, where each path represents a sequence of tasks from start to finish. By decomposing the process graph into discrete paths, the system can analyze and predict completion times for individual paths rather than attempting to model the entire complex process at once, thereby improving prediction accuracy while managing complexity.
2Measurement precision
If machine learning systems are used to learn from empirical data for predictions, then the system can make predictions on finish times, but the models produce skewed results when trained on imperfect data and require significant computational resources
Solution Approach 1:
The patent extracts only the necessary data elements required for path completion prediction, rather than utilizing comprehensive empirical datasets. By focusing extraction on critical path information and task durations, the system reduces computational burden and avoids the pitfalls of training on imperfect or excessive data while maintaining prediction accuracy.
3Reliability
If comprehensive process data is collected and modeled to account for all interdependencies, then the system can provide accurate predictions, but the computational complexity and data processing requirements become burdensome
Solution Approach 1:
The patent segments the process into discrete paths and further identifies the critical path among them. This segmentation allows the system to focus computational resources on the most important path(s) that determine overall process completion, rather than equally complexly modeling all possible task combinations and interdependencies.
Solution Approach 2:
The patent applies different analysis depths to different parts of the process model. The critical path receives detailed analysis with precise timing predictions, while non-critical paths receive less detailed treatment. This local quality approach ensures reliable predictions for the most impactful path without uniformly applying complex modeling to all process elements.
Data Source
AI summary
A system may use multi-path modeling to determine whether a process will complete on or before a specified time. For example, a system may access a process graph representing a process with multiple tasks, where each task is encoded as a node and each dependency between tasks is encoded as an edge. The system may traverse the process graph to discover pathway parameters. The system may decompose the process graph into multiple paths, each path including one or more tasks. The system may access a last known status for each task and, for each path, determine a probability of finishing on or before the specified time based on the pathway parameters and the last known status. The system may select a dynamic critical path based on the determined probabilities. The probability of completing the process on or before the specified time is determined based on the dynamic critical path.