Unsupervised Audio Feature Model for Emergency Response Automation
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Solution Overview
Problem
Existing methods for providing assistance in emergency situations, such as cardiac arrest or acute injuries, rely on human dispatchers and may not provide timely or accurate recommendations for action.
Innovation Solution
A method utilizing a computer system with a sound recorder and processing unit, employing statistically learned models to analyze sound samples from an interviewee in real-time, to determine and present recommendations for assistance, such as dispatching emergency services or providing first aid instructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If human dispatchers are used to provide assistance in emergency situations, then the system can handle complex communication and provide personalized guidance, but the response time increases and timely recommendations cannot be provided
Solution Approach 1:
The system enables self-service by allowing the interviewee to describe their emergency situation in their own words, and the automatically trained model to independently analyze the audio, determine the problem type, and generate appropriate recommendations without requiring human dispatcher intervention for the initial assessment
Solution Approach 2:
The patent replaces the mechanical system of human dispatchers listening and analyzing emergency calls with an automated computer system that uses trained statistical models to process audio data, identify emergency types, and generate recommendations, thereby reducing response time while maintaining effectiveness
2Reliability
If automated systems are used to provide emergency assistance recommendations, then response time is reduced, but the accuracy and reliability of recommendations may decrease
Solution Approach 1:
The system performs preliminary action by pre-training statistical models on extensive datasets of emergency calls before actual use. These pre-trained models capture patterns and relationships from historical data, enabling them to quickly and accurately analyze new emergency situations without requiring time-consuming real-time learning or human review
Solution Approach 2:
The system incorporates feedback mechanisms where the trained models continuously learn from actual emergency call outcomes and adjust their recommendations. This feedback loop improves reliability over time by refining the models' understanding of emergency patterns and improving recommendation accuracy based on real-world results
3Productivity
If real-time audio analysis is performed during the interview, then timely recommendations can be provided, but the processing complexity and computational requirements increase
Solution Approach 1:
The processing system is segmented into distinct functional modules: audio recording, audio feature extraction, emergency type classification, and recommendation generation. Each module handles a specific aspect of the analysis independently, allowing parallel processing and reducing overall computational complexity while maintaining high productivity
Solution Approach 2:
The system manages computational complexity by dynamically adjusting processing parameters based on the situation. The trained models process audio at optimized sampling rates and the system adapts the level of analysis depth based on the urgency and complexity of the emergency description, balancing processing requirements with response speed
Data Source
AI summary
A method for determining and presenting a recommendation for assisting an interviewee party in a state in need of help solving a problem, such as experiencing a cardiac arrest or an acute injury or disease such as meningitis, during an interview between an inter-viewing party and said interviewee party. The method determines a recommendation as a function of the sound of the interviewee party without an automatic speech recognition routine, i.e. in the processing the sound of the interviewee party is not converted to text (text strings), but instead a feature model is used and the output of that goes direct into a recommendation model.