Tree Structure Routing Model for Dynamic Attribute Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing routing systems are static and do not consider historical routing failures, leading to sub-optimal routing decisions and wastage of processing power, as they optimize in all instances without evaluating granular transaction attributes.
Innovation Solution
A system that analyzes historical routing data to identify optimization opportunities by determining processing volume and error rates for specific attributes, using a tree-based routing structure to rank attributes and select alternate routing means, thereby optimizing routing decisions based on historical data and model constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If routing systems optimize routing decisions in all instances, then routing optimization coverage is improved, but processing power waste increases
Solution Approach 1:
The system applies optimization only to routing decisions that meet specific criteria (high processing volume exceeding threshold and high error rate exceeding threshold), rather than optimizing all routing decisions. This partial action approach focuses computational resources on cases where optimization is most beneficial, avoiding waste on low-impact decisions.
Solution Approach 2:
The system changes the state of routing optimization from a universal constant operation to a conditional operation based on parameter thresholds. By monitoring processing volume and error rate parameters, the system dynamically determines when optimization should be applied, transforming the optimization process from exhaustive to selective.
2Device complexity
If routing systems consider only macro parameters, then system complexity is reduced, but routing decision precision deteriorates
Solution Approach 1:
The system segments routing decision-making into two distinct levels: macro-parameter-based initial routing decisions and micro-attribute-based optimization adjustments. This segmentation allows the system to maintain simplicity for core routing while adding precision through granular attribute analysis for specific optimization cases.
Solution Approach 2:
The system applies granular attribute analysis locally to specific routing decisions that meet optimization criteria, rather than applying it universally. This local quality approach maintains system simplicity for most decisions while enhancing precision where it matters most - for high-volume, high-error routing cases.
3Device complexity
If routing systems do not factor in historical routing failures, then system complexity is reduced, but routing reliability deteriorates
Solution Approach 1:
The system performs preliminary analysis of historical routing data to identify patterns of failures and sub-optimal decisions before making current routing decisions. By pre-processing historical data to establish baselines and thresholds, the system prepares optimization opportunities in advance, enabling more reliable routing without adding complex real-time analysis.
Solution Approach 2:
The system incorporates feedback from historical routing outcomes by continuously monitoring processing volumes and error rates, then using this feedback to identify and prioritize optimization opportunities. This feedback loop improves routing reliability by learning from past failures while maintaining system simplicity through threshold-based decision rules.
Data Source
AI summary
Systems and methods are disclosed for retrieving, from a database, over a network, historical routing data for multiple attributes and determining, for each attribute, based on its respective historical routing data, whether processing volume and processing error rates for each attribute exceed respective threshold. If both processing volume and error rate exceed their respective thresholds, the systems and methods describe herein calculate, for each qualifying attribute, a degree to which routing for each attribute can be improved. The systems and methods described herein output a ranking for each qualifying attribute based on their respective degrees to which routing can be improved for the respective attributes.