Conversation Bot Voice Synthesis for Seamless Call Transitions
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Solution Overview
Problem
Automated call management systems face challenges in efficiently transitioning conversations from automated bots to human agents, leading to lost continuity and frustration for customers, as they often struggle to determine when to hand over calls effectively and may not utilize human agents' time optimally.
Innovation Solution
The system selects a conversation bot associated with a human agent, connects an audio call, generates audio based on the human agent's voice, and transitions the call to the appropriate human agent when criteria are met, such as high interest levels, using supplemental data and speech synthesis to ensure seamless handovers and optimize human agent involvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system uses automated conversation bots to handle initial customer interactions, then productivity is improved by reducing human agent workload, but reliability deteriorates due to loss of conversation continuity and customer frustration during handoff transitions
Solution Approach 1:
The system performs preliminary actions by training conversation bots using specific conversation data recorded during actual conversations conducted by human agents. This pre-training ensures that when bots handle initial customer interactions, they can maintain conversation continuity and seamlessly hand off to the appropriate human agent, thereby improving reliability while maintaining productivity benefits.
2Ease of operation
If the system transitions calls to human agents based on manual determination, then ease of operation is improved by simple control, but productivity deteriorates due to suboptimal utilization of human agent time
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring conversation data and analyzing when transition criteria are met. This automated feedback loop determines the optimal moment to transfer calls from bots to human agents, ensuring that human agent time is utilized efficiently for high-value interactions while maintaining simple operational control through automated decision-making.
3Device complexity
If the system uses generic automated responses, then device complexity is reduced by simplifying bot functionality, but adaptability deteriorates due to inability to personalize customer interactions
Solution Approach 1:
The system applies local quality by training each conversation bot using specific conversation data from a particular human agent. This creates personalized bot instances that reflect individual agent communication styles and expertise, enabling tailored customer interactions without requiring complex centralized control. Each bot adapts to local conversation patterns while maintaining overall system simplicity.
Data Source
AI summary
Systems and methods for managing a call between a contact, a conversation bot, and a human agent are disclosed. The method selects a conversation bot associated with a particular human agent from multiple conversation bots that are each associated with a different human agent. Each conversation bot can be a model trained using conversation data recorded during conversations conducted by the particular human agent with which it is associated. The method connects an audio call with a human contact, and generates audio during the call based upon a voice of the particular human agent. The method determines that a transition criterion is satisfied, and selects a selected human agent from amongst a plurality of available human agents. When the transition criterion is satisfied, the method enables a selected human agent to participate on the call, and continues the call between the selected human agent and the human contact.