Distributed Constraint-Based Routing for Contact Centers
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Solution Overview
Problem
Current routing systems in contact centers are inefficient and inflexible, often relying on centralized engines that struggle with complex scenarios, fail to account for real-time agent availability, and require complex maintenance, lacking the ability to make globally optimized routing decisions in a distributed architecture.
Innovation Solution
A distributed constraint-based optimized routing system that uses media servers, a routing database, and a routing server to execute a constraint-based optimization process, matching interactions with resources by computing wait times, costs, and optimizing interaction distribution across multiple resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a centralized routing engine is used, then routing decisions can be made uniformly, but the system becomes less scalable and flexible for complex scenarios
Solution Approach 1:
The patent divides the centralized routing engine into multiple distributed routing nodes, each capable of making independent routing decisions. This segmentation allows the system to handle complex scenarios with greater flexibility while maintaining scalability, as each node operates autonomously without requiring a single central authority.
Solution Approach 2:
The patent introduces a new dimension to routing by enabling agents to have multiple skills and interactions to be handled by multiple agents simultaneously. This transforms the traditional one-to-one routing model into a many-to-many model, increasing adaptability while distributing complexity across the network.
2Productivity
If real-time agent availability is considered in routing, then resource utilization improves, but the routing system becomes more complex
Solution Approach 1:
The patent performs preliminary actions by pre-defining agent skills and interaction types before routing occurs. This allows the routing system to quickly match interactions to agents based on predefined criteria without performing complex real-time analysis, thus improving resource utilization while keeping computation manageable.
Solution Approach 2:
The patent makes the routing system dynamic by allowing agents to acquire and lose skills over time and by enabling real-time updates to agent availability. This dynamic approach allows the system to adapt to changing conditions and improve resource utilization without requiring overly complex routing computations, as changes are handled through updates rather than complete re-evaluations.
3Adaptability or versatility
If a distributed architecture is implemented, then system scalability improves, but routing optimization becomes more difficult
Solution Approach 1:
The patent implements feedback mechanisms where routing decisions are monitored and results are fed back into the system. This allows distributed nodes to learn from previous routing outcomes and improve their decision-making over time, achieving global optimization effects without requiring centralized control. The feedback loop enables scalability while addressing the complexity of distributed optimization through iterative improvement.
4Measurement precision
If complex routing scenarios are handled, then routing accuracy improves, but system maintenance becomes more difficult
Solution Approach 1:
The patent enables the routing system to be self-service by allowing agents to self-manage their skills and availability, and by enabling automatic routing decisions based on predefined criteria. This reduces the need for manual maintenance and configuration, making the system easier to maintain while preserving routing accuracy through automated, rule-based decision-making that adapts to complex scenarios without human intervention.
Data Source
AI summary
A system for optimized routing of interactions, comprising media servers, a statistics server, a routing database, and a routing server. Upon receiving or initiating an interaction, a media server sends a route request message to the routing server, the statistics server receives event notifications from the media servers and computes one or more statistics, and the routing server executes, using statistical data from the statistics server and data from the routing database, a routing script comprising a constraint-based optimization process in response to the route request message.