Speech Analysis Neural Network for Task Completion Probability
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Solution Overview
Problem
Existing customer service systems face challenges in providing equally timely, efficient, and enjoyable experiences across various communication channels, especially when handling tasks of varying complexity and criticality, and managing relationships between users and members, such as medical prescriptions, which require accurate task completion probability determination.
Innovation Solution
A customer service system that includes a relationship determination system, task completion probability system, and criticality system, utilizing speech analysis and neural networks to identify relationships, tasks, and criticality, and generating task completion probabilities based on communication data, including tone, pitch, and loudness analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If customer service is provided via multiple communication channels (audio call, electronic messaging) to increase user accessibility, then user access to agents is improved, but system complexity increases
Solution Approach 1:
The system implements a universal task completion probability determination mechanism that functions across multiple communication channels (audio calls, electronic messaging, chat). The neural network-based analysis system processes different communication types through a unified framework, enabling consistent task completion assessment regardless of the communication medium used.
2Measurement precision
If speech analysis with neural networks is used to determine task completion probability, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system replaces traditional rule-based or manual task completion assessment methods with neural network-based speech analysis. The neural networks process audio and text data to automatically determine task completion probability, substituting complex computational mechanisms for simpler but less accurate traditional approaches.
3Productivity
If tasks are prioritized based on complexity and criticality analysis, then productivity is improved, but device complexity increases
Solution Approach 1:
The system introduces new parameters (task complexity score, criticality score, task completion probability) to characterize and prioritize customer service tasks. By analyzing speech patterns, text content, and communication metadata, the system dynamically assigns these parameters to automatically prioritize tasks, enabling more efficient resource allocation without manual intervention.
Data Source
AI summary
Method starts with processing, by a processor, audio signal to generate audio caller utterance and transcribed caller utterance. Processor generates identified task based on transcribed caller utterance. Processor samples audio caller utterance to generate samples of audio caller utterance. Processor generates loudness result based on loudness values of samples using loudness neural network associated with identified task. Processor generates pitch result based on pitch values of samples using pitch neural network associated with identified task. Processor generates tone result for each word in transcribed caller utterance using tone neural network associated with identified task. Using task completion probability neural network associated with identified task, processor generates task completion probability result that is based on at least one of: loudness result, pitch result, or tone result. Other embodiments are disclosed herein.