Generative AI Chatbot Transfer To Human Agents
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Solution Overview
Problem
Conventional chatbots are rudimentary and inefficient in handling customer service requests, often requiring users to wait for human agents who may be busy with multiple conversations, leading to delayed responses and inefficient information gathering.
Innovation Solution
Implementing a generative AI chatbot system that uses natural language processing and generative models to collect and organize information during conversations, allowing for seamless transfer of chat conversations to human agents with enhanced reports for efficient review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional chatbots use fixed directed graph of states with canned responses, then device complexity is reduced, but information gathering efficiency deteriorates and adaptability worsens
Solution Approach 1:
The patent replaces the mechanical fixed directed graph system with a generative AI-based natural language processing system. The chatbot uses large language models to generate contextually appropriate responses dynamically, substituting rigid pre-programmed state transitions with intelligent language understanding and generation capabilities, thereby improving information gathering efficiency while maintaining manageable complexity through cloud-based processing.
Solution Approach 2:
The patent changes the fundamental parameter of response generation from static canned responses to dynamic generative responses. By implementing a generative AI model that adapts its responses based on conversation context, user input, and information gathering needs, the system transforms the chatbot's operational parameters to enable efficient information collection without requiring complex local processing architecture.
2Ease of operation
If conventional chatbots provide only canned responses, then ease of operation is improved, but adaptability to specific service requests deteriorates
Solution Approach 1:
The patent substitutes the rigid canned response mechanism with a generative AI system that processes natural language inputs and generates contextually appropriate responses. This replacement maintains ease of operation through natural language interaction while dramatically improving adaptability by enabling the chatbot to handle diverse service requests through intelligent language understanding and dynamic response generation.
Solution Approach 2:
The generative AI chatbot implements multi-functionality by being capable of handling various types of service requests through a single unified system. Instead of requiring separate programmed responses for different scenarios, the chatbot uses natural language processing to adaptively respond to diverse user needs, making the system universally applicable across multiple service domains while maintaining simple user interaction.
3Productivity
If human agents handle multiple conversations simultaneously, then productivity is improved, but response time deteriorates
Solution Approach 1:
The patent introduces a generative AI chatbot as an intermediary between users and human agents. The chatbot handles initial information gathering, preliminary troubleshooting, and routine inquiries, allowing human agents to focus on complex issues requiring human judgment. This intermediary system reduces user wait times while maintaining high agent productivity by preventing agent overload with routine tasks.
Solution Approach 2:
The chatbot performs preliminary actions by gathering information, conducting initial diagnostics, and preparing context summaries before transferring conversations to human agents. This preliminary work reduces the time human agents need to spend on information gathering, enabling them to handle more conversations efficiently while providing users with faster initial responses and more focused human intervention when needed.
4Device complexity
If chat conversations are transferred without structured information, then device complexity is reduced, but information completeness deteriorates
Solution Approach 1:
The patent implements preliminary organization of conversation information by the generative AI chatbot before transfer to human agents. The system automatically structures gathered information, identifies key issues, and prepares context summaries in advance of transfer, ensuring information completeness without requiring complex post-transfer organization systems. This preliminary structuring enables efficient human agent onboarding to each conversation.
Solution Approach 2:
The system creates structured copies and summaries of conversation information that are transferred alongside the chat history to human agents. Instead of relying on agents to reconstruct context from raw chat logs, the chatbot generates organized information copies including key findings, user concerns, and recommended next steps, preserving information completeness while maintaining simple transfer mechanisms through standardized data formats.
Data Source
AI summary
Techniques for triggering a transfer of a chat conversation with a user from a chatbot to a human agent based on detection of transfer criteria are disclosed. The chatbot uses natural language processing and a generative model to collect and organize information from the chat conversation to present to the human agent in a report when the chat conversation is transferred to the human agent. The chat conversation is transferred to the human agent by presenting the report and a graphical chat interface to the human agent. The graphical chat interface displays messages from chat conversation between the human agent and the user and displays messages from chat conversations between the human agent and multiple other users. Transferring the chat conversation from the chatbot to the human agent includes presenting interface elements to the human agent for receiving user input from the human agent for transmission to the user.