IVR Tree to Chatbot Skill Generation via Intent Entity Analysis
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Solution Overview
Problem
Existing IVR systems are inefficient and user-unfriendly due to their rigid tree structure and requirement for specific grammar, leading to cumbersome interactions and frustration for users, while transforming IVR trees into chatbot skills is a challenging task.
Innovation Solution
A system and method that analyze an IVR tree to identify intent and entity nodes, assemble task-specific chatbot skills, and generate a chatbot capable of conducting natural language dialog with users, allowing for flexible and efficient user interactions by transforming IVR trees into task-oriented chatbot skills.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If IVR systems use a rigid tree structure with scripted questions and responses, then the system provides cost-effective automated service, but the user experience becomes impersonal and robotic
Solution Approach 1:
The patent transforms the static IVR tree structure into a dynamic chatbot system that adapts to user inputs. The chatbot uses natural language processing to understand user intent and generates context-appropriate responses, making the interaction flexible and human-like while maintaining full automation. This resolves the contradiction by making the automated system dynamic rather than rigid.
Solution Approach 2:
The patent changes the interaction parameters from structured touch-tone inputs to unstructured natural language text/speech inputs. By altering the input modality and processing approach, the system maintains automation while dramatically improving user experience. The chatbot can handle varied user expressions and responds in natural language, eliminating the robotic feel of traditional IVR.
2Measurement precision
If IVR systems require multiple question-answering turns to capture user intent, then the system can obtain necessary user inputs, but the interaction becomes tedious and time-consuming
Solution Approach 1:
The chatbot performs preliminary action by proactively gathering user intent and required information in fewer turns compared to traditional IVR. It uses natural language understanding to anticipate what information is needed and guides the user efficiently through the conversation, reducing the number of back-and-forth exchanges while ensuring all necessary inputs are captured.
Solution Approach 2:
The patent replaces the mechanical, step-by-step IVR questioning mechanism with an intelligent natural language processing system. The chatbot can understand user intent from incomplete or ambiguous inputs, infer missing information, and guide the conversation more efficiently, significantly reducing interaction time while maintaining precision in capturing user intent.
3Extent of automation
If IVR systems use scripted questions and responses, then the system can provide automated account servicing, but the system requires cumbersome authorization processes
Solution Approach 1:
The chatbot serves multiple functions including authentication, information gathering, and task execution within a single unified interface. It can handle various authorization scenarios (password verification, security questions, biometric authentication) and account servicing tasks (balance inquiry, transaction history, bill payment) through one adaptable system, reducing the perceived complexity for users.
Solution Approach 2:
The chatbot acts as an intermediary between the user and the backend authorization systems. It manages the authorization process by collecting credentials, validating them, and coordinating with backend services, thereby simplifying the user experience while maintaining security. The chatbot absorbs the complexity of authorization protocols, presenting a simple interface to users.
4Ease of operation
If enterprises transform IVR trees into chatbot skills, then users can interact using natural language, but the transformation process remains a challenging task
Solution Approach 1:
The patent implements self-service by enabling the chatbot to automatically analyze the IVR tree structure, extract intents and entities, and generate chatbot skills without extensive manual programming. The system uses natural language processing and machine learning to autonomously perform the transformation, significantly reducing the effort and expertise required compared to traditional chatbot development methods.
Solution Approach 2:
The patent uses copying by replicating the IVR tree's functional logic and flow in the chatbot system. Instead of manually recreating each interaction path, the system automatically copies the intent structure, dialogue flow, and business logic from the IVR tree to the chatbot, preserving the original system's functionality while enabling natural language interaction. This automated copying process makes transformation straightforward and scalable.
Data Source
AI summary
A method comprising: receiving an interactive voice response (IVR) tree configured to implement one or more tasks, each associated with one or more IVR node paths comprising a plurality of IVR nodes arranged in a hierarchical relationship; analyzing the IVR tree to identify one or more intent IVR nodes, each associated with one of the tasks; with respect to each of the intent IVR nodes, identifying a plurality of corresponding entity IVR nodes included within the IVR node path associated with the intent IVR node; assembling one or more task-specific chatbot skills, each comprising (i) one of the intent IVR nodes, and (ii) at least some of the plurality of corresponding entity IVR nodes, wherein each of the task-specific chatbot skills is configured to perform one of the tasks by conducting a dialog with a user; and generating a chatbot comprising at least one of the task-specific chatbot skills.