IVR Tree Chatbot Mapping for Repeatable Zero-Shot Responses
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Solution Overview
Problem
Traditional IVR systems are tedious and time-consuming due to their structured navigation, while AI-driven chatbots lack repeatability and require extensive training for each IVR system, making them inefficient for replacing IVR systems.
Innovation Solution
A chatbot system that utilizes a trained neural model, such as a Large Language Model (LLM), to encode IVR trees and user prompts, generating business process information, intents, slots, and actions, enabling zero-shot capabilities without individual training for each IVR tree.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional IVR systems are used, then structured navigation and routine account actions can be performed, but the interaction becomes tedious and time-consuming
Solution Approach 1:
The patent replaces the mechanical navigation structure of traditional IVR systems with an AI-driven neural model that processes natural language. Instead of navigating through rigid menu trees via touch tones, users can communicate naturally, and the neural model interprets their intent to navigate the IVR tree dynamically, eliminating the tedious structured navigation while maintaining functional capabilities.
2Ease of operation
If AI-driven chatbots are used to replace IVR systems, then natural language interaction is enabled, but the chatbots lack repeatability and require extensive training for each IVR system
Solution Approach 1:
The patent introduces an intermediary layer consisting of the neural model that acts as a mediator between the user's natural language input and the IVR tree structure. This intermediary processes the natural language, identifies intents and slots, maps them to IVR tree nodes, and executes the corresponding actions, thereby enabling reliable and repeatable responses while maintaining natural language interaction capabilities.
3Adaptability or versatility
If specialized chatbots are trained for each IVR system, then domain-specific functionality can be achieved, but the training process is time-consuming and must be performed for each system
Solution Approach 1:
The patent creates a universal neural model that can handle multiple IVR systems through a single training process. The model is trained on diverse IVR tree structures and natural language patterns, enabling it to adapt to different domain-specific IVR systems without requiring separate training for each one. This universal model processes various intents and slots across different domains, achieving both domain-specific functionality and time efficiency.
Data Source
AI summary
A chatbot system includes a computer hardware system for implementing a chatbot and a hardware processor configured to initiate the following executable operations. A user prompt associated with a user and directed to the chatbot is received from a client device. An interactive voice response (IVR) tree associated with the user prompt is identified. The user prompt and the IVR tree are encoded into an encoded input. The encoded input is consumed by a trained neural model, and the neural model generates, using the encoded input, business process information. The trained neural model generates, using the business process information, an answer, and the answer is provided to the client device.