Help Line Triage System Using ML for Risk-Based Call Routing

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Solution Overview

Problem

Help line response centers face challenges in training responders effectively, assigning calls based on intensity and responder preferences, and prioritizing users' requests according to their risk levels.

Innovation Solution

A system utilizing a database of previous interactions between help line users and responders, combined with machine learning modules, to simulate training scenarios, evaluate responder communications, provide recommended responses, and optimize the assignment of responders to users based on risk assessment and preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If real-time training interactions are provided to help line responders, then training effectiveness is improved, but user experience deteriorates due to negative interactions

Engineering Contradiction:

Improvetraining effectivenessVSAvoidnegative user experience

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system creates simulated users that replicate the communication patterns, language styles, and interaction behaviors of real help line users. These synthetic training partners allow responders to practice in realistic scenarios without exposing actual users to untrained responders. The simulated users generate training interactions based on historical data and behavioral patterns, enabling safe training experimentation.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system performs training interactions in advance before real users need assistance. Responders can practice and develop skills through multiple simulated interactions beforehand, ensuring they are properly trained before handling actual crisis situations. This preliminary training action prevents the need to train on real users and ensures readiness before actual deployment.

Inventive Principle:

Principle #10Preliminary action

2Ease of operation

If calls are assigned agnostic to intensity, then assignment simplicity is maintained, but crisis management effectiveness deteriorates

Engineering Contradiction:

Improveassignment simplicityVSAvoidcrisis management effectiveness

Core Design Contradiction:

Ease of operationVSReliability

Solution Approach 1:

The system dynamically changes the parameter of call intensity classification by analyzing communication patterns, language indicators, and interaction characteristics to assign intensity scores. Calls are categorized into different intensity levels (e.g., low, medium, high crisis) based on these parameters, enabling differentiated routing to appropriately trained responders while maintaining automated assignment simplicity.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system replaces manual subjective intensity assessment with automated machine learning analysis. The AI model objectively evaluates communication patterns and assigns intensity scores without human intervention, substituting the mechanical manual classification process with an automated intelligent system that maintains simplicity while improving accuracy.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Productivity

If calls are assigned agnostic to responder preferences, then assignment speed is improved, but responder effectiveness deteriorates

Engineering Contradiction:

Improveassignment speedVSAvoidresponder effectiveness

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system dynamically adjusts call assignment by considering both call intensity and responder preferences in real-time. The assignment algorithm flexibly balances automated routing speed with responder expertise matching, allowing rapid assignment while accounting for individual responder capabilities and preferences. This dynamic approach enables the system to adapt assignment criteria based on current conditions.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system incorporates responder preferences and performance feedback into the assignment algorithm. By collecting data on responder effectiveness, preferences, and outcomes, the system learns to make better assignment decisions that match callers with appropriately trained and willing responders, improving overall effectiveness while maintaining efficient assignment speeds through automated decision-making.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20250156980A1Systems and methods for triaging high risk messages

Publication Date: 2025.05.15 CRISIS TEXT LINE INC

AI summary

The present invention provides a system which comprises a database of previous interactions between help line users and help line responders. The database allows call response centers to review call interaction data and provide scores to individual users, real-time interactions, and call responders.