Contact Center Configuration Automation via ML

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Solution Overview

Problem

Existing methods for configuring contact centers require significant expertise and time, as they rely on human estimation or standardized values, leading to inefficiencies and inaccuracies in determining the optimal number of scheduling units and skills needed.

Innovation Solution

A method and system using a computer processor to predict and select the optimal number of scheduling units and skills for contact centers based on the total number of agents and regions, employing machine learning algorithms to refine predictions from standard configurations and actual pre-existing sets, thereby automating the configuration process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If human experts estimate configuration values based on experience or use standardized values, then the configuration process is simple to perform, but the accuracy and precision of the configuration values are reduced

Engineering Contradiction:

Improveaccuracy of configuration valuesVSAvoidcomplexity of configuration process

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system enables self-service configuration by automatically determining scheduling unit requirements and skill configurations based on input parameters (number of agents, regions, contact types). The automated configuration engine replaces human expert judgment with algorithmic calculations, eliminating the need for experienced administrators while maintaining or improving accuracy through consistent application of configuration logic.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the mechanical system of human expert estimation with an automated computational system. The configuration engine uses algorithms to calculate optimal scheduling unit requirements and skill configurations, substituting human cognitive processes with deterministic computational methods that provide consistent, reproducible results without variability in expert judgment.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Productivity

If human experts configure contact centers, then the configuration can be customized, but the time required for configuration is increased

Engineering Contradiction:

Improvespeed of configurationVSAvoidtime required for configuration

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary configuration actions by pre-calculating scheduling unit requirements and skill configurations based on standard relationships between agents, regions, and contact types. The configuration engine has pre-established rules and algorithms that automatically determine optimal settings, eliminating the need for time-consuming manual analysis and iteration that would otherwise be performed by human experts.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent transforms the configuration process from a manual, time-intensive procedure into an automated parameter-driven system. By accepting input parameters (number of agents, regions, contact types) and applying transformation algorithms, the system rapidly generates configuration values without requiring the sequential manual steps previously needed, thereby dramatically reducing configuration time while maintaining customization through parameter variation.

Inventive Principle:

Principle #35Parameter changes

3Productivity

If standardized values are used for configuration, then the configuration process is faster, but the accuracy and precision of configuration values are reduced

Engineering Contradiction:

Improvespeed of configurationVSAvoidaccuracy of configuration values

Core Design Contradiction:

ProductivityVSMeasurement precision

Solution Approach 1:

The configuration system segments the determination of scheduling unit requirements into distinct calculation components: base scheduling units derived from agent count, additional units from region distribution, and supplementary units from contact type requirements. This segmentation allows each factor to be calculated independently and then combined, providing both speed through systematic processing and accuracy through comprehensive consideration of all configuration factors.

Inventive Principle:

Principle #1Segmentation

4Ease of operation

If automated configuration is implemented, then the time and expertise required are reduced, but the complexity of the configuration system increases

Engineering Contradiction:

Improveease of configurationVSAvoidcomplexity of configuration system

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The automated configuration engine serves as an intermediary between the input parameters (agents, regions, contact types) and the output configuration values (scheduling units, skills). This intermediary layer encapsulates the complex configuration logic within a standardized interface, shielding users from system complexity while providing accurate, customized results. The engine translates simple input parameters into complex configuration settings through automated calculations.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS20250021909A1Systems and methods for automating configurations for tenant onboarding

Publication Date: 2025.01.16 NICE LTD

AI summary

Systems and methods are provided for configuring a set of one or more contact centers, the set of one or more contact centers associated with a total number of agents and a number of regions. The systems and methods may include predicting or selecting a number of scheduling units or an optimal number of skills for the set of one or more contact centers based on the total number of agents and the number of regions.