Real-Time Utterance Modification for Polite Customer Responses
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Solution Overview
Problem
Customer service representatives experience increased stress and fatigue due to the inefficiency of manually inputting responses into generative AI models to maintain a polite tone, leading to high signaling overhead and response delays during customer interactions.
Innovation Solution
An utterance modification system that autonomously converts speech-based utterances to text, prompts a large language model (LLM) for polite responses, and converts back to speech, reducing manual input and delay.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If customer service representatives manually input responses into generative AI models to maintain a polite tone, then the quality of customer interactions is improved, but the stress and fatigue of representatives increase due to inefficiency
Solution Approach 1:
The system enables automatic response generation where the generative AI model autonomously creates polite responses based on customer inputs, eliminating the need for manual input by representatives. The system self-manages the tone adjustment process through automated prompt generation and response synthesis, allowing representatives to focus on customer service rather than manual text processing.
2Reliability
If customer service representatives manually input responses into generative AI models, then polite responses are generated, but signaling overhead and response delays increase
Solution Approach 1:
The system pre-generates response templates and maintains a cache of polite response patterns that can be quickly adapted to specific customer interactions. By preparing response frameworks in advance and using automated prompt generation, the system reduces the time required to produce polite responses while maintaining quality standards.
Solution Approach 2:
The system replaces manual mechanical input processes with automated speech-to-text conversion and AI-generated response synthesis. This substitution eliminates the need for representatives to manually type or dictate responses, significantly reducing signaling overhead and accelerating response delivery while preserving polite tone through AI-generated content.
3Productivity
If automated speech-to-text and text-to-speech conversion is implemented, then manual input is reduced and response time is improved, but system complexity increases
Solution Approach 1:
The system integrates multiple functions into a unified platform: speech-to-text conversion, prompt generation, AI response synthesis, and text-to-speech conversion all operate within a single system architecture. This multi-functionality reduces the need for separate systems and interfaces, managing complexity through integration while delivering comprehensive automated response capabilities.
Data Source
AI summary
An utterance modification system may receive a first utterance from a first user during an interactive conversation session between the first user and a second user. The utterance modification system may further receive a second utterance from the second user that is in a speech-based format. The utterance modification system may then transmit a prompt that includes the second utterance in a text-based format and a set of prompt parameters to a large language model (LLM). In response, the utterance modification system may receive a third utterance from the LLM that may be based on the second utterance and associated with a target user tone. Further, the utterance modification system may transmit the third utterance to the first user in a speech-based format.