AI Integration with IVR Systems for Agent Response Latency
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Solution Overview
Problem
Interactive Voice Response (IVR) systems face challenges in accurately discerning caller purposes or goals, leading to inefficient transitions from automated to human agent interactions, which can result in reduced caller satisfaction and increased response times.
Innovation Solution
Integration of Artificial Intelligence (AI) within IVR systems to analyze voice inputs, provide predictive recommendations, and generate machine-generated responses that can be used by human agents, allowing for concurrent management of multiple conversations and reducing response time latency by offering pre-formulated responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IVR system transfers caller to human agent when unable to discern purpose, then caller service quality improves, but system efficiency deteriorates due to loss of automated assistance
Solution Approach 1:
The patent introduces an AI engine as an intermediary that continues to analyze caller inputs and generate predictive recommendations even after transfer to human agent. This mediator bridges the automated IVR system and human agent, allowing the AI to secretly assist the human agent with predictive text suggestions, thereby maintaining system efficiency while ensuring reliable caller service through human intervention when needed.
Solution Approach 2:
The AI engine performs preliminary analysis of caller inputs before and during human agent interaction, generating predictive recommendations in advance. This preliminary action prepares potential responses that the human agent can quickly review and deploy, reducing the human agent's cognitive load and response time while maintaining the reliability of human judgment for complex caller issues.
2Ease of operation
If human agent exclusively attends to caller without automated assistance, then caller attention improves, but response time increases
Solution Approach 1:
The human agent is empowered with AI-generated predictive recommendations that enable them to quickly formulate responses without extensive manual analysis. The agent can review and deploy pre-analyzed suggestions, effectively using the AI's self-service analysis capabilities to reduce their own response time while maintaining full attention on the caller through the integrated chat interface.
3Measurement precision
If AI engine continues monitoring after transfer, then predictive accuracy improves, but system complexity increases
Solution Approach 1:
The patent merges the AI engine's monitoring function with the existing human agent interaction system. The AI continues to analyze caller inputs through the same communication channel, and its predictive recommendations are integrated into the human agent's interface. This merging approach improves predictive accuracy by maintaining continuous analysis while avoiding the need for separate complex monitoring systems.
4Productivity
If multiple conversations are managed concurrently, then agent productivity improves, but response quality may deteriorate
Solution Approach 1:
The AI engine performs preliminary analysis and generates predictive recommendations for each concurrent conversation independently. When a human agent manages multiple conversations, the AI provides pre-analyzed suggestions for each caller, allowing the agent to quickly review and respond to multiple callers without sacrificing response quality. The preliminary AI analysis ensures that even under multitasking conditions, each caller receives thoughtful, data-informed responses.
Data Source
AI summary
When a caller initiates an interaction with an interactive voice response (“IVR”) system, the caller may be transferred to a live agent. Apparatus and methods are provided for integrating automated tools into the interaction after the caller been transferred to the agent. The agent may determine which AI responses are appropriate for the caller. AI may be leveraged to suggest responses for both caller and agent while they are interacting with each other. Such human-computer interaction may shorten response time of human agents and improve efficiency of IVR systems.