Multi-Intent Query Retrieval with Tabbed Result Segmentation
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Solution Overview
Problem
Search retrieval systems often fail to provide relevant results for generic queries, requiring users to input additional keywords, leading to inefficiency and excessive resource consumption.
Innovation Solution
Implementing generative AI and vector similarity search to identify multiple intents in a query, generating a unified results page with organized sub-categories and clickable tabs for efficient navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the system provides generic search results for generic queries, then the system can return results without requiring additional user input, but the results are unlikely to include the specific types of information desired by the user
Solution Approach 1:
The patent segments the search results into multiple sub-categories (e.g., products, articles, videos) with clickable tabs, allowing the system to provide comprehensive generic results while enabling users to easily filter for specific information types. This segmentation resolves the contradiction by organizing diverse results into structured groups.
Solution Approach 2:
The system performs preliminary classification of search results into different sub-categories before presenting them to the user. By pre-organizing results with clickable tabs for different content types, the system anticipates user needs and prepares specific filters in advance, eliminating the need for users to perform additional search queries.
2Measurement precision
If the system requires users to input additional keywords to identify the type of item desired, then the system can provide more specific search results, but the process becomes time-consuming and frustrating for users
Solution Approach 1:
The system performs preliminary classification of search results into different sub-categories (products, articles, videos, etc.) with clickable tabs before the user completes their search refinement. This allows users to immediately see and select from organized result types without needing to perform additional search queries, significantly reducing search time while maintaining precision.
Solution Approach 2:
The patent introduces clickable tabs as an intermediary interface between the generic search results and the user's specific information needs. These tabs serve as a mediator that allows users to quickly filter and navigate to specific result types without formulating additional search queries, bridging the gap between generic queries and specific results.
3Adaptability or versatility
If the system provides comprehensive search results for generic queries, then users can access multiple types of information, but the results may be confusing and difficult to navigate
Solution Approach 1:
The patent segments comprehensive search results into distinct sub-categories with clickable tabs (e.g., products, articles, videos). This segmentation maintains the versatility and diversity of results while dramatically improving navigability by allowing users to quickly filter to their desired content type through intuitive tab selection rather than sifting through unorganized results.
4Adaptability or versatility
If the system processes generic search queries with multiple potential intents, then the system can accommodate various user needs, but excessive computing system resources are consumed
Solution Approach 1:
The system segments the processing of multi-intent queries by identifying and categorizing different intent types (product search, information search, video search, etc.) and processing results for each category separately with clickable tabs. This segmentation allows the system to accommodate various user needs while optimizing resource consumption by avoiding the need to deeply process all possible result types equally, instead providing a structured overview that users can filter as needed.
Data Source
AI summary
Examples provide a query intent-aware search retrieval system using generative artificial intelligence (AI) and vector similarity search. A customized profanity filter performs a customized profanity check to maintain search retrieval system integrity and prevent misuse by malicious actors. This safeguard ensures that inappropriate or offensive language is effectively detected and mitigated, contributing to a secure and user-friendly experience. A customized prompt generator provides pertinent recall queries for a specific intent query or scenario. By employing a tailored approach, the system effectively narrows down the search scope, thereby providing relevant results corresponding to multiple intents inherent in the user's query. A multi-use case query classifier determines whether each query is single intent or multi-intent and ascertains the intent behind each query. By effectively differentiating between these scenarios, the classifier ensures that the appropriate search methodology is utilized, resulting in a more efficient retrieval process and improved user satisfaction.