Virtual Assistant Server Call Routing via Predictive Model
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Solution Overview
Problem
Existing IVR systems and virtual assistants face challenges in managing call traffic effectively, leading to poor customer experience due to long hold times, incorrect navigation, and lack of standardization, which results in dropped calls and strained relationships between enterprises and customers.
Innovation Solution
A virtual assistant server that receives call parameters from an IVR system and uses a predictive model to route calls to intelligent communication modes such as chat or voice, overriding routing rules based on previous call fulfillment parameters to provide tailored responses and improve customer experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If callers are routed through traditional IVR menus, then call structure and security validation are maintained, but caller experience deteriorates due to navigation difficulty and long hold times
Solution Approach 1:
The patent introduces an intermediary system that sits between the IVR menu and the caller, analyzing caller inputs in real-time and providing intelligent suggestions for menu navigation. This intermediary processes the caller's intent and guides them through the IVR menu more efficiently, reducing both navigation difficulty and hold time without compromising the existing IVR structure.
Solution Approach 2:
The system enables callers to self-serve by providing them with contextualized menu options and predictions based on their intent. The caller receives real-time guidance that allows them to navigate the IVR menu independently and more quickly, reducing reliance on human agents and decreasing hold times while maintaining ease of operation.
2Ease of operation
If human agents are deployed to handle all queries, then caller experience improves through personalized attention, but operational cost and scalability worsen
Solution Approach 1:
The patent segments query resolution into multiple levels: routine queries are handled by an intelligent virtual assistant, while complex queries are escalated to human agents. This segmentation allows the system to maintain high service quality for simple issues without requiring extensive human agent deployment, thereby improving scalability while preserving personalized attention where needed.
Solution Approach 2:
An intelligent virtual assistant acts as an intermediary that handles initial query resolution and triage. This intermediary processes common customer service tasks, freeing human agents to focus on complex issues that require emotional intelligence and nuanced judgment, thus improving overall system productivity and scalability without sacrificing query resolution quality.
3Productivity
If virtual assistants are used for query resolution, then scalability and consistency improve, but ability to handle complex queries worsens
Solution Approach 1:
The system segments query complexity levels and routes accordingly. The virtual assistant handles standardized, high-volume queries with consistency and scalability, while complex, nuanced queries are automatically escalated to human agents. This segmentation allows the virtual assistant to maintain its scalability advantages while ensuring complex queries receive the adaptability they require.
Solution Approach 2:
The patent replaces mechanical rule-based IVR systems with an AI-powered virtual assistant that uses natural language processing and machine learning. This substitution enhances the system's ability to handle diverse query types while maintaining scalability, as the AI can adapt to new query patterns without requiring manual reconfiguration of rigid menu structures.
4Ease of operation
If priority routing is implemented for certain callers, then service quality for priority customers improves, but system complexity and fairness perception worsen
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors query complexity, caller history, and agent availability to dynamically adjust routing decisions. This feedback loop allows the system to prioritize callers based on multiple factors including query urgency and agent expertise, rather than simple static priority levels, reducing perceived unfairness while maintaining service quality through transparent, data-driven routing.
Solution Approach 2:
The system changes routing parameters dynamically based on real-time conditions such as agent workload, query type, and caller preferences. Rather than using fixed priority levels, the routing system adjusts multiple parameters simultaneously to optimize service distribution, reducing system complexity while maintaining fairness and query resolution speed through adaptive, multi-factor decision-making.
Data Source
AI summary
A virtual assistant server receives a web request such as a HTTP request with one or more call parameters corresponding to a call redirected from an interactive voice response server. The virtual assistant server inputs the received one or more call parameters to a predictive model, which identifies, based on the one or more call parameters, an intelligent communication mode to route the redirected call to. Subsequently, the virtual assistant server routes the redirected call to the intelligent communication mode.