AI Voice Call Throttling for High-Volume Priority Routing
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Solution Overview
Problem
Existing call management systems struggle to handle high call volume events efficiently, leading to unprocessed calls and unserviced high-priority callers during peak times.
Innovation Solution
An AI-based throttling engine using deep learning models predicts call intents and priorities, routing calls through multiple stages to optimize processing and direct them to appropriate resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a traditional call management system is used to handle high call volumes, then all calls can be routed through the system, but the system becomes overwhelmed and unable to provide support for every caller
Solution Approach 1:
The patent segments the call management system into multiple stages (first stage, second stage, third stage) with different processing depths. High-priority calls receive comprehensive multi-stage analysis while lower-priority calls are handled more efficiently with reduced processing, resolving the contradiction between handling all calls and maintaining support quality.
Solution Approach 2:
The system dynamically changes processing parameters based on call priority and system load. During high call volume events, the throttling engine adjusts the depth of intent prediction analysis and routing decisions, allowing the system to maintain reliability for critical calls while managing overall productivity across varying conditions.
2Measurement precision
If deep learning models are used to predict call intent and priority, then call routing accuracy is improved, but computing resources are consumed
Solution Approach 1:
The patent divides the deep learning processing into segmented stages where not all calls undergo the same level of analysis. The first stage performs basic routing, the second stage performs intent prediction, and the third stage performs priority determination. This segmentation reduces overall computing resource consumption while maintaining prediction accuracy for calls that require it.
Solution Approach 2:
The system applies partial action by performing deep learning-based intent prediction only on calls that require detailed analysis rather than all calls. The throttling engine selectively applies comprehensive processing to high-priority or complex calls while using simpler routing logic for straightforward calls, reducing total computing resource consumption.
3Productivity
If multiple stages of call routing are implemented to optimize processing, then call handling efficiency is improved, but system complexity increases
Solution Approach 1:
The patent implements segmentation by creating distinct routing stages (first stage, second stage, third stage) each with specific functions. This modular segmentation improves call handling efficiency by processing calls through appropriate stages while managing system complexity through clear stage boundaries and defined purposes.
Solution Approach 2:
The system performs preliminary actions in earlier stages to simplify subsequent processing. The first stage performs initial routing decisions, and the second stage performs intent prediction before the third stage makes final priority-based routing decisions. This preliminary action approach improves overall efficiency while organizing complexity into manageable sequential steps.
Data Source
AI summary
An Artificial Intelligence-based system for managing and directing voice calls during high call volume events. Incoming voice calls are routed to a first control unit. Call data is retrieved from the incoming call and inputted to a throttling engine to predict an intent of the voice call and determine a priority of the voice call. Based on the predicted intent and the priority of the call, a determination is made whether to route the voice call to a second control unit or to disconnect the voice call. Further, additional control units may be implemented to further determine whether to continue to route the call, based on the predicted intent and the verified priority and/or continue to route the call to a routing queue.