Neural Network Agent Logging System for Automated Call Documentation
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Solution Overview
Problem
The existing customer service systems face inefficiencies in documenting interactions, as generating agent logs is time-consuming and prevents agents from attending to other users, especially in complex communication scenarios involving multiple tasks and criticalities across various communication channels.
Innovation Solution
An agent logging system utilizing neural networks generates a merged agent log based on caller utterances during communication sessions, including member context, tasks, start and end times, and success values, to streamline customer service processes and enhance agent productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If agents manually document interactions in customer service systems, then complete interaction records are generated, but agent productivity decreases and time is lost
Solution Approach 1:
The system enables self-service logging by automatically generating agent logs from audio signals of communication sessions. The neural network processes the audio data and creates structured logs with tasks, time stamps, and success values without requiring manual agent intervention, thus eliminating the trade-off between documentation completeness and productivity
Solution Approach 2:
The manual mechanical process of agents writing down interaction details is replaced by an automated electronic system using neural networks. The system transforms audio signals directly into structured log data, substituting the manual documentation mechanism with an automated intelligence-based system that maintains information completeness while freeing agent time
2Manufacturing precision
If agents spend time manually creating detailed agent logs, then accurate interaction records are produced, but time for serving other users is reduced
Solution Approach 1:
The system performs preliminary action by automatically capturing and processing interaction data during the communication session itself. The neural network analyzes audio signals in real-time or near-real-time, generating accurate logs before agents need to document anything, thus eliminating the time loss while maintaining precision through automated audio-based recording
3Loss of information
If traditional manual logging methods are used, then agents can document interactions, but the process is time-consuming and prevents agents from attending to other users
Solution Approach 1:
The logging system performs self-service by automatically generating complete interaction records from audio signals without requiring agent time investment. The neural network independently processes the audio data and creates comprehensive logs, eliminating both the time loss and information loss that occur with manual logging
Solution Approach 2:
The audio signal serves as an intermediary that captures interaction data automatically. Instead of agents directly documenting interactions, the system uses audio recording as a mediator that the neural network then processes into structured logs, thereby capturing complete information without consuming agent time
Data Source
AI summary
A system for generating a merged agent log starts with a processor receiving an audio signal of a communication session between a member-related client device and an agent client device. Processor processes the audio signal to generate caller utterances and generates identified tasks based on the caller utterances. The processor then generates caller utterance data including the identified tasks and a start time of the caller utterances and an end time of the caller utterances. The processor groups the caller utterances based on the identified tasks, and for each of the identified tasks, the processor generates an agent log using an agent logging neural network. The agent log is based on the caller utterances. Other embodiments are disclosed herein.