Intent Management Tool for Query Classification
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Solution Overview
Problem
Current search engines face challenges in providing relevant responses due to the vast and varied ways users can request the same information, often requiring manual sorting through unrelated documents and necessitating extensive human resources for maintenance, leading to limited success in answering user queries effectively.
Innovation Solution
An intent management tool utilizing linguistic analysis to classify queries into common intent categories, allowing for the identification of new, obsolete, or refined categories, and providing associated intent responses, thereby reducing the number of unique queries and resources needed for maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If keyword-based search is used to locate information, then the search engine can return documents containing matching words, but the user must manually sort through unrelated documents which is time consuming
Solution Approach 1:
The patent introduces an intermediary classification layer between keyword matching and document retrieval. Query classification modules analyze the semantic intent of queries and route them to specialized classifiers, which then retrieve pre-classified documents. This intermediary step eliminates the need for users to manually sort through unrelated documents by ensuring that only relevant documents are returned based on query intent.
Solution Approach 2:
The system performs preliminary classification of both queries and documents before the actual search operation. Documents are pre-classified into categories during indexing, and queries are classified before retrieval. This preliminary action allows the system to quickly identify and retrieve only the most relevant documents, eliminating the time-consuming manual sorting process.
2Measurement precision
If the search engine classifies queries into information categories, then it can provide more targeted responses, but there are limitless ways users can request the same information requiring extensive human resources for maintenance
Solution Approach 1:
The patent implements dynamic query classification where the system adapts to new query patterns over time. Instead of maintaining a static, exhaustive list of all possible query formulations, the classification modules learn from incoming queries and dynamically adjust categories and routing rules. This dynamic approach allows the system to handle limitless query variations without requiring manual expansion of the classification schema.
Solution Approach 2:
The system uses universal classification categories that can handle multiple query formulations. Rather than creating separate categories for each possible way users might ask a question, the patent employs broad, flexible categories that can accommodate various natural language expressions through semantic analysis and routing logic, reducing the total number of categories needed.
3Adaptability or versatility
If more intent categories are created to cover all query variations, then more queries can be answered accurately, but more human resources are required to keep the search engine updated
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically maintains and updates its classification categories. Query classification modules analyze incoming queries, identify new patterns, and automatically create or update categories without human intervention. The system serves itself by continuously learning from query data and adapting its classification structure, eliminating the need for extensive human resources for category maintenance.
Solution Approach 2:
The system incorporates feedback loops where classification results are continuously evaluated and used to improve future classifications. User interactions, query patterns, and retrieval outcomes provide feedback that automatically refines the classification system. This feedback mechanism allows the system to maintain high adaptability while reducing manual maintenance requirements through automated learning and adjustment.
Data Source
AI summary
Linguistic analysis is used to identify queries from a plurality of users over a period of time that use different natural language formations to request similar information. Common intent categories are identified for the queries requesting similar information. Intent responses can then be provided that are associated with the identified intent categories. An intent management tool can be used for identifying new intent categories, identifying obsolete intent categories, or refining existing intent categories. The said intent categories are used in identifying ontologies associated with the intent categories to help in selecting concepts as ontology parameters.