Traversal Path Optimization via Machine Learning Models
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Solution Overview
Problem
Existing systems face challenges in accurately and efficiently determining optimal traversal paths across a traversal network, particularly in real-time or near-real-time scenarios, due to high computational complexity and resource requirements.
Innovation Solution
The system employs a node-wise coverage region determination machine learning model and a traversal path optimization machine learning model to assess candidate paths, integrating node-wise feature data with link/edge-wise data to select the optimal path and perform prediction-based actions, such as alert notifications and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional path optimization algorithms are used to determine optimal traversal paths, then path optimization accuracy can be achieved, but computational complexity and resource requirements increase significantly
Solution Approach 1:
The patent replaces traditional mechanical/computational path optimization algorithms with machine learning models. The traversal path optimization machine learning model and node-wise coverage region determination machine learning model process path features and node data to determine optimal paths, substituting complex computational algorithms with AI-based prediction systems that achieve accuracy without proportional increases in computational complexity
Solution Approach 2:
The patent transforms the approach by changing from calculating paths based on explicit mathematical formulations to using learned parameters from training data. The machine learning models capture optimal path characteristics through trained parameters, allowing rapid inference without re-computing complex optimization formulas each time a path is needed
2Measurement precision
If comprehensive path evaluation is performed to ensure accuracy, then path optimization precision improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-training machine learning models on historical path data and features before actual path determination is needed. The traversal path optimization machine learning model and node-wise coverage region determination machine learning model are trained in advance to recognize optimal path patterns, enabling rapid inference during real-time path determination without performing comprehensive re-evaluation
Solution Approach 2:
The patent substitutes time-consuming comprehensive path evaluation algorithms with machine learning inference. Instead of systematically evaluating all possible paths using complex computational methods, the trained ML models quickly predict optimal paths by pattern recognition, dramatically reducing processing time while maintaining precision
3Measurement precision
If detailed node feature data and coverage region analysis are processed, then path determination accuracy improves, but computational resources increase
Solution Approach 1:
The patent replaces resource-intensive detailed analysis computations with machine learning inference. The node-wise coverage region determination machine learning model processes node feature data and determines coverage regions through learned patterns rather than exhaustive computational analysis, reducing computational resources while maintaining accurate path determination
Solution Approach 2:
The system changes the computational approach by transforming detailed node feature data and coverage region calculations into learned parameters. The machine learning models capture the relationship between node features, coverage regions, and optimal paths through training, allowing efficient inference that maintains accuracy without proportional increases in computational resources
Data Source
AI summary
Various embodiments of the present invention disclose techniques for traversal path optimization given a traversal network comprising a group of nodes comprising a plurality of navigation orchestration nodes and using a traversal path optimization machine learning model. In some embodiments, a path feature set is determined for each candidate traversal path of a plurality of candidate traversal paths. A traversal path optimization machine learning model is configured to generate path scores for each candidate traversal path based at least in part on the path feature set.