AI Chatbot Fallback to Live Agent for Unknown Queries
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Solution Overview
Problem
Chat-bots using artificial intelligence for online communications often fail to provide responses to user queries when the answers are not predefined, leading to user frustration and errors in text-based conversations.
Innovation Solution
A method and system that establish a connection between a user interface and a live agent interface when a chat-bot is unable to find a query answer, allowing a human operator to provide responses, which are then evaluated and stored for future use by the AI entity to improve its database of query answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a chat-bot uses predefined answers for automated responses, then the system can operate autonomously without human intervention, but the chat-bot cannot respond to queries outside its predefined database
Solution Approach 1:
The patent introduces a live agent interface as an intermediary when the AI entity cannot answer a query. The system detects when a query is unknown and automatically connects the user to a human operator, who then provides the answer. This mediator approach allows the system to maintain high automation for known queries while seamlessly handling unknown queries through human intervention.
Solution Approach 2:
The system implements a feedback mechanism where live agent answers are captured, evaluated, and stored in the AI entity's database. This creates a continuous learning loop where human-provided responses become training data, gradually improving the AI's ability to answer queries autonomously without requiring constant human intervention.
2Reliability
If a chat-bot connects users to live agents for unknown queries, then the system can provide accurate responses, but the complexity of the system increases due to multiple communication channels
Solution Approach 1:
The patent segments the response generation function into two distinct components: an AI entity for autonomous responses and a live agent interface for human-assisted responses. This segmentation allows each component to specialize in its strength while the system as a whole maintains reliability. The AI handles routine queries independently, while the live agent interface handles complex or unknown queries.
Solution Approach 2:
The system uses a communication manager as an intermediary that intelligently routes queries between the AI entity and live agent interface. This mediator component simplifies the overall system architecture by providing a unified interface to users while managing the complexity of multiple communication channels behind the scenes.
3Extent of automation
If the AI entity stores all possible query answers in its database, then it can respond to all queries autonomously, but the database size and maintenance complexity increase significantly
Solution Approach 1:
The system performs preliminary action by proactively connecting users to live agents when queries are unknown, rather than attempting to pre-store all possible answers. This approach avoids the need to maintain an exhaustive database of every potential query, as the system handles unknown queries through human intervention and learns from those interactions.
Solution Approach 2:
The AI entity serves itself by automatically learning from live agent responses and adding new knowledge to its database without requiring manual database updates. This self-service mechanism allows the system to expand its knowledge base organically through usage rather than requiring extensive pre-population.
Data Source
AI summary
A method for providing query responses to a user via online chat establishes a first communication connection for online chat between a user interface and an artificial intelligence (AI) entity comprising a processor and a memory element configured to store a database of query answers; receives a user input query transmitted via the first communication connection; performs a lookup in the database of query answers, to locate a query answer corresponding to the user input query; when unable to locate a query answer, establishes a second communication connection for online chat between the user interface and a live agent interface that transmits responses dynamically provided by a human operator; evaluates a chat between the user interface and the live agent interface; identifies an answer to the user input query, based on evaluating the chat; and stores the answer to be provided by the AI entity in the future.