Multimodal Communication Error Detection Normalization
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Solution Overview
Problem
Multimodal communication systems face challenges in accurately analyzing diagnostic data due to modality skewing, traffic volume variations, and user pattern differences, which can mask significant failures and generate noisy alerts, degrading user experience and operational efficiency.
Innovation Solution
A centralized diagnostic service normalizes detected errors based on modalities, traffic volumes, and user patterns to efficiently and accurately analyze failures across the communication system, using a real-time error detection algorithm that computes thresholds for alerting based on total volume, diagnostic volume, and user volume.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If diagnostic data is collected equally across all modalities, then comprehensive monitoring is achieved, but results are skewed by modality usage variations and traffic patterns
Solution Approach 1:
The patent applies parameter changes by transforming raw diagnostic data into normalized error rates through mathematical operations. Specifically, it divides the number of errors by total traffic volume and applies weighting factors based on modality importance, thereby changing the parameter representation from absolute error counts to normalized error rates that enable accurate cross-modality comparison and resolve the skewing effect of varying usage patterns
Solution Approach 2:
The patent segments the diagnostic data analysis by modality type, applying separate normalization and weighting procedures for each communication modality (voice, video, messaging, etc.). This segmentation allows the system to account for modality-specific traffic patterns and usage variations, preventing dominant modalities from skewing overall diagnostic results while maintaining comprehensive monitoring across all modalities
2Ease of operation
If error detection thresholds are set uniformly across all modalities, then simplicity is maintained, but failures in lesser-used modalities are masked by noise from high-traffic modalities
Solution Approach 1:
The patent applies local quality by assigning different weighting factors to different modalities based on their operational importance and traffic characteristics. Critical modalities receive higher weights in the normalized error rate calculation, ensuring that failures in these modalities have greater impact on overall system health assessment. This localized differentiation enables reliable failure detection across modalities with varying traffic volumes while maintaining a unified thresholding mechanism
Solution Approach 2:
The patent implements feedback through continuous monitoring of normalized error rates and dynamic adjustment of alerting decisions. The system calculates weighted error rates for each modality, compares them against thresholds, and generates alerts only when significant deviations are detected. This feedback mechanism filters out noise from high-traffic modalities while maintaining sensitivity to failures in lesser-used modalities, resolving the contradiction between uniform threshold simplicity and reliable failure detection
3Quantity of substance
If all errors are monitored with equal weight, then complete coverage is achieved, but persistent users generate false alarms that degrade operational efficiency
Solution Approach 1:
The patent applies dynamics by implementing time-based and user-based variability in error weighting. The system dynamically adjusts the weight assigned to errors based on user behavior patterns, identifying persistent users who generate repeated errors and reducing their impact on overall error metrics. This dynamic weighting maintains complete error monitoring coverage while preventing false alarms from persistent users from degrading operational efficiency, as the system adapts its sensitivity based on observed patterns rather than applying static equal weighting
Data Source
AI summary
Diagnostic data of a multimodal enhanced communication system is processed at a central diagnostic service by normalizing detected errors based on modalities, traffic volumes, and/or individual user patterns such that failures can be efficiently and accurately analyzed across the communication system. Configurable thresholds may be used for modality-specific logs, traffic volume normalized errors, and persistent user adjusted results to optimize alerts issued to administrators.