Virtual Assistant Knowledge Graph Parsing for Query Response
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Solution Overview
Problem
Existing virtual assistance systems are inefficient in providing accurate and timely responses to user queries due to reliance on exact keyword matches, static ontologies, and high human supervision, leading to cumbersome search processes and delayed issue resolution.
Innovation Solution
A method and system that determine the domain and problem category of a user query by parsing a predefined knowledge graph, providing open-ended or closed-ended questions to gather feedback, and generating responses based on problem sub-nodes, allowing for scalable and automated issue resolution without language constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing virtual assistance systems use exact keyword matching and static ontologies, then implementation is simple, but response accuracy and adaptability deteriorate
Solution Approach 1:
The patent transforms static ontologies into dynamic, evolving knowledge structures. The system continuously learns from user interactions and feedback, adapting the knowledge graph to improve response accuracy over time while managing complexity through automated learning mechanisms
Solution Approach 2:
The patent introduces a feedback mechanism as an intermediary between the user query system and the knowledge base. This feedback loop enables the system to learn from interactions and refine its responses, improving accuracy without requiring complete system redesign
2Productivity
If existing systems require maximum human supervision, then response quality can be maintained, but productivity and scalability deteriorate
Solution Approach 1:
The patent implements a self-learning virtual assistant that automatically improves through user feedback without requiring continuous human supervision. The system autonomously processes queries, learns from interactions, and refines its knowledge base, enabling high productivity while maintaining reliability through automated quality control mechanisms
Solution Approach 2:
The patent incorporates explicit feedback mechanisms where user responses are captured and used to continuously refine the knowledge graph and improve future responses. This feedback-driven approach maintains response quality while enabling autonomous operation and high scalability
3Loss of information
If users search through bulky documentation, then comprehensive information is available, but time consumption and user experience deteriorate
Solution Approach 1:
The patent extracts essential information from comprehensive documentation and structures it into a navigable knowledge graph. This allows the system to present only the most relevant information for each specific query, maintaining information completeness while dramatically reducing search time through targeted retrieval
Solution Approach 2:
The patent segments comprehensive documentation into structured knowledge units organized in a graph format. This segmentation enables the system to quickly assemble relevant information pieces for specific queries without requiring users to search through entire documentation sets, reducing time loss while preserving information completeness
Data Source
AI summary
Disclosed subject matter relates to virtual assistance that includes a method and system for automatically generating response to a user query without language constraints. A response generating system receives the user query from a computing device associated with an end user and determines whether the user query belongs to at least one domain to determine goal data and a problem category of the user query. Further, a problem node associated with the user query is detected from problem nodes by parsing a predefined knowledge graph based on the goal data and the problem category. Furthermore, questions are provided based on problem sub-nodes of the problem node to the computing device to receive a feedback. The response to the user query extracted from the one of the problem sub-nodes is displayed to the end user based on the feedback. The present disclosure is highly scalable, reusable and requires minimal human supervision.