Concurrent Communication Multiplicity Control by AI Complexity Monitoring
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Solution Overview
Problem
Contact centers face inefficiencies in managing concurrent communications due to idle periods and delays in interactions, particularly with real-time communications like voice calls, leading to underutilization of agent and computing resources.
Innovation Solution
Implement a system that monitors communication content for complexity indicators using neural networks to dynamically adjust the maximum number of concurrent communications, transferring complex interactions to other agents or pausing them to optimize resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents handle more concurrent communications to maximize resource utilization, then productivity increases, but communication quality and agent performance deteriorate due to complexity management issues
Solution Approach 1:
An automated agent acts as an intermediary between customers and human agents, monitoring communication content in real-time and dynamically adjusting multiplicity settings. The automated agent analyzes complexity indicators through neural networks and transfers complex communications to appropriate human agents, thereby enabling higher overall productivity while maintaining quality through intelligent mediation.
Solution Approach 2:
The system dynamically adjusts the maximum number of concurrent communications (multiplicity) based on real-time complexity indicators detected in communication content. Rather than using fixed multiplicity limits, the automated agent continuously monitors communications and adapts resource allocation, allowing the system to optimize productivity while responding to changing complexity conditions.
2Reliability
If automated agents monitor all communication content for complexity indicators, then communication quality improves, but computing resources and system complexity increase
Solution Approach 1:
The automated agent performs self-monitoring of communication content using integrated neural networks, automatically detecting complexity indicators without requiring external human review. The system serves itself by autonomously analyzing communications, determining when complexity thresholds are exceeded, and initiating appropriate actions such as transferring communications to human agents.
Solution Approach 2:
Manual monitoring of communication content by human supervisors is replaced with automated neural network-based analysis. The neural networks process communication content and detect complexity indicators algorithmically, substituting mechanical human review with automated computational analysis, thereby improving consistency while reducing long-term operational complexity.
3Productivity
If the maximum number of concurrent communications is increased, then resource utilization improves, but wait times for complex communications increase
Solution Approach 1:
The automated agent preliminarily processes incoming communications by analyzing content for complexity indicators before routing to human agents. By pre-screening and identifying complex communications early in the workflow, the system can prepare appropriate routing decisions in advance, reducing wait times for complex cases while maintaining high resource utilization through parallel processing of simpler communications.
Solution Approach 2:
The system implements continuous feedback loops where the automated agent monitors communication outcomes and complexity indicators, then adjusts multiplicity settings and routing decisions in real-time. This feedback mechanism ensures that when complex communications are detected, the system can dynamically reduce multiplicity for those specific cases, preventing excessive wait times while maintaining overall high resource utilization.
Data Source
AI summary
Systems and methods are disclosed for determining whether a number of concurrent communications comprise content that is exceptionally complex or, conversely, exceptionally non-complex. The concurrent communications may be evaluated by an automated agent, such as an artificial intelligence (AI) that determines complexity indicators for the communications. If the complexity is too high, then the agent handling the concurrent communications may be excluded from adding additional concurrent communications or even have one or more existing concurrent communications transferred away, such as to another agent. If the complexity is exceptionally low, then additional communications may be provided to the agent, thereby improving customer response times and maximizing networking component utilization.