Dynamic Bot Script Generation from Merged Transcripts
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Solution Overview
Problem
Current communication systems face challenges in categorizing user intent and identifying appropriate responses across various user inquiries, as bots may not have responses for all customer issues, leading to inefficient handling of complex or novel issues.
Innovation Solution
A system that generates merged transcripts of human and bot agent interactions, which are then used to automatically identify issues and integrate new solutions into bot systems, allowing human agents to handle complex issues while bots address known ones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated bot systems use decision tree scripts to handle user inquiries, then response efficiency is improved for known issues, but the system cannot handle complex or novel issues that require human agent intervention
Solution Approach 1:
The system captures feedback from human agent interactions with customers and uses this feedback to automatically generate updated decision tree scripts. The feedback loop allows the bot system to learn from human expertise and continuously improve its ability to handle various types of inquiries, including novel issues, thereby resolving the contradiction between response efficiency and adaptability.
Solution Approach 2:
The system performs self-updating by automatically generating new decision tree scripts from captured human agent interactions without requiring manual programming. This self-service capability enables the bot to autonomously expand its knowledge base and handle new issue types, improving adaptability while maintaining automated response efficiency.
2Adaptability or versatility
If human agents handle all customer issues, then adaptability to novel issues is improved, but response efficiency and scalability are reduced
Solution Approach 1:
The bot system automatically updates its own decision tree scripts by capturing and analyzing human agent interactions. This self-service mechanism enables the bot to independently acquire new knowledge and improve its handling capabilities, allowing it to manage diverse issues efficiently without requiring proportional increases in human agent resources.
Solution Approach 2:
By systematically capturing feedback from human agent resolutions and feeding it back into the script generation process, the system creates a continuous improvement cycle. This allows the bot to progressively handle more diverse issues with automated efficiency, reducing the need for human intervention in routine matters while maintaining adaptability.
3Adaptability or versatility
If bot systems are made more complex to handle a wider range of issues, then adaptability is improved, but system complexity and difficulty of maintenance increase
Solution Approach 1:
The system automatically generates and updates its own decision tree scripts from captured human interactions, eliminating the need for manual script creation and maintenance. This self-service approach allows the bot to handle a wider range of issues through automated script generation rather than increasing inherent system complexity, as the complexity is managed through autonomous learning rather than manual configuration.
Solution Approach 2:
The system merges multiple human agent interactions and their resolutions into unified decision tree scripts. By combining multiple sources of knowledge and experiences into consolidated automated rules, the system achieves high adaptability without proportionally increasing complexity, as the merging process creates efficient consolidated decision pathways.
Data Source
AI summary
The present disclosure relates generally to systems, methods, instructions, and other aspects describing automated transcription and associated script generation. In one aspect, a method includes facilitating a voice bot segment of a two-way communication session, where the voice bot segment is between a customer device and a non-human bot agent, and transfer of the session to a human agent device as part of a human voice segment of the two-way communication session, wherein the transfer occurs following a failure of the non-human bot agent to resolve a customer issue. Accessing survey data describing the two-way communication session, wherein the survey data is associated with successful resolution of the customer issue and automatically processing transcript data from the two-way communication with the survey data to identify language data from the transcript associated with resolution of the customer issue. The non-human bot agent is then dynamically updated using the language data.