PSAP Call Routing Anomaly Detection for Faster Corrections

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Solution Overview

Problem

Existing cellular networks face errors in emergency call routing, leading to incorrect routing of calls to the wrong public service answering points (PSAPs), which can result in delayed response times and overloading of PSAPs, posing a risk to lives and degrading emergency response efficiency.

Innovation Solution

A PSAP call routing issue detection tool is implemented to identify and correct routing problems by providing analysis data and suggesting or automatically reconfiguring call routing to more efficient PSAPs, utilizing machine learning and artificial intelligence to optimize emergency call handling.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional call routing methods are used, then calls can be routed to PSAPs, but routing errors occur leading to incorrect PSAP selection

Engineering Contradiction:

Improverouting accuracyVSAvoidresponse time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system implements feedback by continuously monitoring call routing data, analyzing routing efficiency metrics, and using machine learning models to detect anomalies and suggest corrections. The feedback loop compares actual routing outcomes against expected performance, enabling the system to learn from past routing decisions and improve future routing accuracy, thereby reducing both routing errors and response time delays.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system performs self-service through automated anomaly detection and self-correction capabilities. The machine learning model automatically identifies routing inefficiencies and generates correction suggestions without requiring manual intervention. The system can autonomously reconfigure routing rules based on detected anomalies, enabling it to service itself and continuously improve routing accuracy without external assistance.

Inventive Principle:

Principle #25Self-service

2Reliability

If manual routing correction is used, then routing errors can be identified, but the process is time-consuming and delays emergency response

Engineering Contradiction:

Improverouting accuracyVSAvoidcorrection speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system replaces manual mechanical analysis with automated machine learning-based anomaly detection. Instead of human operators manually reviewing routing data and identifying errors, the system uses AI algorithms to automatically analyze routing patterns, detect anomalies, and suggest corrections. This substitution dramatically increases correction speed while maintaining or improving routing accuracy, directly addressing the contradiction between reliability and productivity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Quantity of substance

If PSAPs receive all routed calls, then call volume increases, but PSAPs can become overloaded degrading response quality

Engineering Contradiction:

Improvecall volumeVSAvoidresponse quality

Core Design Contradiction:

Quantity of substanceVSReliability

Solution Approach 1:

The system applies local quality by optimizing routing decisions for each individual call based on specific criteria such as PSAP capacity, call type, and location. Rather than uniformly distributing all calls, the system analyzes local conditions at each PSAP and routes calls to the most appropriate facility. This ensures that PSAPs receive call volumes matched to their capacity and expertise, maintaining response quality while managing overall call volume distribution.

Inventive Principle:

Principle #3Local quality

4Reliability

If routing reconfiguration is performed frequently, then routing accuracy improves, but system stability decreases

Engineering Contradiction:

Improverouting accuracyVSAvoidrouting system stability

Core Design Contradiction:

ReliabilityVSStability of the object's composition

Solution Approach 1:

The system implements dynamics by making routing configuration adaptive rather than static. The machine learning model continuously monitors routing performance and dynamically adjusts routing rules based on changing conditions such as PSAP capacity, call patterns, and detected anomalies. This dynamic approach allows the system to improve routing accuracy over time while maintaining stability through controlled, data-driven adjustments rather than frequent arbitrary changes.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20250324346A1Public service answering point call routing issue detection and resolution

Publication Date: 2025.10.16 AT&T INTELLECTUAL PROPERTY I L P

AI summary

The described technology is generally directed towards public service answering point (PSAP) call routing issue detection and resolution. A PSAP call routing issue detection tool is disclosed. The PSAP call routing issue detection tool can be configured to identify PSAP call routing problems in a cellular network. The PSAP call routing issue detection tool can present the PSAP call routing problems for further analysis and correction by cellular network engineers. The PSAP call routing issue detection tool can furthermore identify and suggest call routing corrections to address identified PSAP call routing problems. The PSAP call routing issue detection tool can optionally also automatically correct identified PSAP call routing problems according to suggested call routing corrections.