Behavioral Pairing for Multistage Task Assignment

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Solution Overview

Problem

Multistage task assignment systems face challenges in optimizing task assignment across multiple stages, particularly in systems like contact centers and prescription medication fulfillment, where tasks require sequential processing by different types of agents, leading to inefficiencies and increased handle times.

Innovation Solution

A behavioral pairing model is developed to determine the optimal sequence of agents for each task based on its characteristics, using a computer processor to analyze historical data and adjust strategies in real-time to minimize average total handle time across multiple stages.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If a single-stage task assignment system is used, then the system structure is simple, but tasks requiring multiple processing stages cannot be efficiently handled

Engineering Contradiction:

Improvecapability to handle multistage tasksVSAvoidsystem structure

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent segments the task assignment system into multiple stages, where each stage handles a specific portion of the task processing workflow. This allows complex multistage tasks to be broken down and assigned to appropriate agents at each stage, improving the system's capability to handle diverse task types while maintaining manageable complexity through modular stage-based architecture.

Inventive Principle:

Principle #1Segmentation

2Productivity

If FIFO or round-robin assignment strategies are used, then the assignment process is simple, but average total handle time increases

Engineering Contradiction:

Improveaverage total handle timeVSAvoidassignment strategy

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The patent changes the assignment parameters by considering multiple task characteristics (priority, type, complexity) and agent characteristics (skills, availability, performance history) rather than using simple positional parameters like queue position. This enables the system to optimize average total handle time by matching tasks with most suitable agents, while the complexity is managed through structured parameter evaluation frameworks.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent implements feedback mechanisms that use historical task completion data and agent performance metrics to continuously improve assignment decisions. By analyzing past performance patterns and adjusting future assignments based on this feedback, the system reduces average handle time while maintaining a manageable level of complexity through data-driven optimization.

Inventive Principle:

Principle #23Feedback

3Productivity

If performance-based routing prioritizes highest-performing agents, then individual task completion speed increases, but overall system efficiency decreases

Engineering Contradiction:

Improveoverall system efficiencyVSAvoidtask completion quality

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The patent applies local quality by matching specific task requirements with specific agent capabilities rather than uniformly assigning to highest-performing agents. Different stages of multistage tasks are assigned to agents with appropriate local expertise and skills, ensuring that each portion of the task is handled by the most suitable agent for that specific requirement, thereby maintaining both quality and overall system efficiency.

Inventive Principle:

Principle #3Local quality

4Productivity

If behavioral pairing is optimized for short-term performance, then immediate task completion improves, but long-term system performance decreases

Engineering Contradiction:

Improvelong-term system performanceVSAvoidhandle time

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent performs preliminary actions by pre-evaluating agent capabilities, task requirements, and historical performance data before making assignments. This advance preparation enables the system to make optimized assignment decisions that balance short-term handle time with long-term performance sustainability, avoiding burnout and maintaining consistent quality across multiple stages of task processing.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS12008494B2Techniques for behavioral pairing in a multistage task assignment system

Publication Date: 2024.06.11 AFINITI AI LTD

AI summary

Techniques for behavioral pairing in a multistage task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a multistage task assignment system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the multistage task assignment system, one or more characteristics of a task; determining, by the at least one computer processor and based at least on the one or more characteristics of the task, a sequence of agents; and pairing, by the at least one computer processor, the task with the sequence of agents.