Monitoring System Optimizing Interaction Paths via Success Factors
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Solution Overview
Problem
In traditional client-server interactions, determining why certain interactions result in a desired outcome while others do not is challenging, as existing monitoring systems struggle to effectively analyze and correlate user interactions with application system outcomes.
Innovation Solution
A monitoring system that aggregates user interaction paths, calculates success factors by weighing prevalence and efficiency scores, and modifies the application system to increase the likelihood of desired outcomes by optimizing hardware resources, modifying interaction paths, or generating additional interactions based on correlation analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring systems are used to track user interactions, then interaction data can be collected, but the ability to effectively analyze and correlate interactions with outcomes is insufficient
Solution Approach 1:
The system implements continuous monitoring of user interactions and feeds this data back through correlation analysis to identify which interactions lead to desired outcomes. This feedback loop enables dynamic modification of interaction paths to improve outcome achievement rates based on measured correlations.
Solution Approach 2:
The patent replaces traditional mechanical monitoring approaches with data-driven correlation analysis using prevalence scores and efficiency scores. This substitution enables precise measurement of interaction-outcome relationships by calculating success factors based on aggregated interaction data.
2Productivity
If the application system is modified to increase desired outcomes, then outcome likelihood improves, but system complexity increases
Solution Approach 1:
The system dynamically modifies interaction paths based on calculated success factors rather than using static configurations. This allows the system to adaptively increase outcome achievement rates by adjusting which interactions are emphasized, without requiring complex permanent system changes.
Solution Approach 2:
The patent changes system parameters by adjusting the weighting of prevalence and efficiency scores to calculate success factors. These parameter changes enable controlled modification of interaction paths to improve outcomes while maintaining system manageability through quantitative adjustment rather than structural complexity.
3Measurement precision
If comprehensive interaction monitoring is implemented, then correlation accuracy improves, but data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from comprehensive interaction data by calculating prevalence scores and efficiency scores for specific interactions. This extraction process reduces the effective data volume needed for correlation analysis while maintaining measurement precision by focusing on key interaction metrics.
Solution Approach 2:
The patent segments comprehensive interaction data into discrete interactions with calculable prevalence and efficiency scores. This segmentation allows precise correlation measurement for individual interactions while managing data processing requirements through structured, modular analysis of interaction components.
Data Source
AI summary
Users of client devices can take any number of actions using applications of an application system to achieve an outcome. A monitoring system aggregates the interactions into a user interaction path. Over time, the monitoring system generates a large number of user interaction paths. The monitoring system analyzes the user interaction paths for correlation between interactions and outcomes. The monitoring system can correlate user interaction paths to generated interactions of a system interaction path. The monitoring system determines a correlation between interactions and outcomes by calculating a success factor based on an efficiency score and a prevalence score. The success factor is a measure correlation between a particular interaction of the application system and an outcome, the prevalence score is a measure of how often a particular interaction occurs, and the efficiency score is a measure of the application system performance for a particular interaction.