Workload Routing With Dynamic Agent Capacity Allocation
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Solution Overview
Problem
Existing communication systems fail to dynamically adjust agent capacity assignments based on the varying complexity and difficulty of user interactions, leading to inefficiencies and potential overload in workload distribution.
Innovation Solution
Implementing an intelligence-driven workload model that analyzes multiple factors to predict and distribute communications among agents, dynamically adjusting slot assignments to match the identified workload measurement, thereby optimizing resource use and preventing system imbalances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If agents are assigned a predetermined number of slots to handle multiple user connections simultaneously, then agent productivity is improved, but the system cannot distinguish between different levels of conversation complexity leading to workload imbalance
Solution Approach 1:
The patent applies dynamics by transitioning from static predetermined slot assignments to dynamic slot adjustments based on real-time workload measurements. The system continuously monitors conversation complexity and adjusts the number of active slots for each agent accordingly, allowing the system to adapt to varying workload conditions while maintaining high productivity.
Solution Approach 2:
The patent changes the parameter of slot allocation from fixed to variable. Instead of assigning a predetermined number of slots to each agent, the system dynamically modifies the slot count based on measured workload parameters such as conversation complexity, duration, and resource consumption, thereby achieving precise workload distribution.
2Adaptability or versatility
If predetermined slot fractions are added or subtracted for simple or difficult conversations, then some workload adjustment is achieved, but the system still cannot identify predicted workload with precision
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors actual conversation characteristics and compares them against predefined thresholds. Based on this feedback, the system adjusts slot assignments in real-time, creating a closed-loop system that progressively improves workload prediction accuracy and adaptation.
Solution Approach 2:
The system performs preliminary analysis of conversation characteristics before finalizing slot assignments. By evaluating factors such as conversation type, complexity indicators, and expected duration in advance, the system pre-adjusts slot allocations to match the predicted workload, improving both adaptability and precision.
3Productivity
If agents handle multiple user connections simultaneously to maximize efficiency, then system productivity increases, but agents may be overwhelmed by complex conversations requiring more time and analysis
Solution Approach 1:
The system dynamically adjusts the number of active slots for each agent based on real-time workload assessment. When an agent is assigned a complex conversation requiring additional time and analysis, the system automatically reduces the number of active slots or reallocates resources, preventing agent overload while maintaining high system productivity.
Solution Approach 2:
The system continuously monitors agent performance and conversation complexity, using this feedback to adjust slot assignments. When an agent demonstrates signs of being overwhelmed or when a conversation exceeds expected complexity thresholds, the system responds by modifying the agent's capacity, thereby ensuring reliability while preserving productivity.
Data Source
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AI summary
Examples are described of smart capacity workload routing. One example involves storing a workload model in memory regarding a set of different factors associated with user communications, with each factor is associated with a measurement of workload. A received request including information regarding one or more of the factors is process and used in identifying a workload measurement for the requested user communication based on comparing the received request information to the stored workload model, and identifying an agent with capacity that is available to handle the requested user communication. A communication slot for the identified agent is activated and defined by the identified workload measurement. The request is then routed to the identified agent and updating available workload capacity in the system.