Load Balancing Server for Intelligent Agent Assignment
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Solution Overview
Problem
Conventional messaging systems fail to effectively manage load balancing in asynchronous messaging scenarios, leading to delays in response time due to improper assignment of conversations between users and agents, as they do not consider the activity status of conversations and agents, resulting in inefficient communication processing.
Innovation Solution
A computer-implemented process that distinguishes active from inactive conversations and intelligently assigns active conversations from one active agent to another within an agent group based on the number of active conversations each agent is handling, using a load balancing server to monitor agent activity and conversation status for efficient load distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If conventional load balancing methods (round robin or basic load balance) are used to assign conversations to agents, then the system can distribute conversations among available agents, but the response time is delayed due to improper assignment in asynchronous messaging scenarios
Solution Approach 1:
The system changes the parameter of conversation status by introducing active/inactive states. Conversations are marked as active when users or agents are unresponsive, and this status change triggers reassignment to different agents, optimizing response times in asynchronous messaging scenarios
Solution Approach 2:
The load balancing system dynamically adjusts conversation assignment based on real-time agent availability and conversation status. Instead of static round-robin assignment, the system continuously monitors and reassigns conversations, making the assignment process adaptive and dynamic to changing conditions
2Adaptability or versatility
If the system continuously monitors and manages conversations in real-time session-based chat, then users and agents must stay connected for immediate communication, but this approach is not suitable for asynchronous messaging where users may not respond within predefined periods
Solution Approach 1:
The system segments the messaging functionality into two distinct modes: real-time session-based chat and asynchronous messaging. Each mode has its own conversation status management, allowing the system to adapt to different communication needs without forcing continuous online presence
Solution Approach 2:
The load balancing server is designed to handle multiple messaging modes universally. It can manage both real-time conversations and asynchronous messages through the same infrastructure, using conversation status monitoring to determine appropriate assignment strategies for each mode
3Productivity
If the system assigns conversations without considering conversation activity status, then the assignment process is simple, but the communication processing time is slowed due to improper agent assignment
Solution Approach 1:
The system implements feedback mechanisms where conversation status (active/inactive) is continuously monitored and fed back to the load balancing server. This feedback loop enables the system to make informed assignment decisions, improving processing speed despite the added complexity of status monitoring
Data Source
AI summary
A process for balancing loads among active agents within an agent group. The process may include distinguishing, by a load balancing server, an active conversation from an inactive conversation. The process may also include assigning, by the load balancing server, the active conversation from one active agent to another active agent depending on a number of active conversations assigned to each of the plurality of active agents within the agent group.