Conversational Interface Using Domain-Trained Semantic Matcher
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Developing artificial intelligence applications that support natural language-based interactions for users to access web content is challenging, particularly in generating dialog flows, which are often costly and limited to narrow subjects, relying on human-authored and retrieval-based models.
Innovation Solution
A conversational system that uses a domain-trained semantic matcher to determine user intent and generate queries across multiple knowledge sources, ranking results based on domain-specific knowledge, allowing for a more dynamic and adaptive natural language interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human-authored dialog flows are used, then the system can provide accurate responses in narrow subjects, but the development cost and time increase significantly
Solution Approach 1:
The system uses automated machine learning models to generate dialog flows and responses without human authoring. The model trains on domain-specific data and autonomously creates the conversation logic, eliminating the need for manual dialog flow development while maintaining response accuracy through domain-adapted semantic understanding.
Solution Approach 2:
The patent replaces the mechanical process of human-authored dialog flow creation with an automated machine learning system. The system uses trained models to generate responses and dialog logic automatically, substituting human labor with computational processes that scale efficiently without proportionally increasing development time.
2Reliability
If retrieval-based models with predefined responses are used, then the system can answer questions in narrow subjects, but the system lacks adaptability to different domains
Solution Approach 1:
The system changes the parameter of domain specificity by training separate machine learning models on domain-specific data. Each domain (e.g., healthcare, finance, e-commerce) has its own trained model with adapted semantic understanding, allowing the system to maintain high reliability within each domain while being versatile across multiple domains through model selection or fine-tuning.
Solution Approach 2:
The patent creates a universal framework that can handle multiple domains by using domain-specific training data to adapt the same underlying architecture. The system maintains a core dialog management structure that works across domains, with domain-specific knowledge injected through training data, making the system both reliable in specific domains and versatile across domains.
3Ease of operation
If human help agents are used, then the system can provide comprehensive assistance, but the operational costs increase
Solution Approach 1:
The system enables users to get assistance through automated dialog interactions without requiring human help agents. The machine learning model handles user queries autonomously by generating appropriate responses based on domain-specific training, eliminating the need for human operators while maintaining ease of operation through natural language interaction.
Solution Approach 2:
The patent replaces the mechanical system of human help agents with an automated machine learning-based dialog system. The system uses trained models to understand and respond to user queries, substituting human labor with computational processes that have significantly lower operational costs while maintaining or improving assistance quality through consistent, scalable responses.
Data Source
AI summary
A method, apparatus and computer program product for presenting a user interface for a conversational system is described. A user input is received in a dialog between a user and the conversational system, the user input in a natural language. A domain trained semantic matcher is used to determine a set of entities and a user intent from the user input. One or more queries is generated to selected ones of a plurality of knowledge sources, the knowledge sources created from domain specific knowledge. The results from the one or more queries are ranked based on domain specific knowledge. A system response is presented in the dialog based on at least a highest ranked result from the plurality of knowledge sources.