Generative AI Search Engine With Dynamic Intent-Aware Webpages
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Solution Overview
Problem
Existing search engines struggle with accurately reflecting user intent and often require users to sift through numerous results for relevant content, leading to a suboptimal user experience.
Innovation Solution
Utilizing large language models (LLMs) to determine user intent, modify queries contextually, and dynamically generate personalized webpages by specifying data sources and content types, allowing for enhanced content generation and enrichment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engines are used to provide results based on inputted keywords, then the search process is straightforward and automated, but the results may not accurately reflect user intent and require users to traverse many results to find desired content
Solution Approach 1:
The patent introduces an intermediary AI assistant that acts as a mediator between the user's query and the search engine results. The AI assistant analyzes user intent, refines queries, and synthesizes results, thereby improving accuracy while reducing the time users spend traversing results. This intermediary layer transforms the direct keyword-to-results process into an intelligent, intent-aware search workflow.
Solution Approach 2:
The system performs preliminary actions by pre-analyzing user intent and pre-refining queries before actual search execution. The AI assistant prepares contextualized queries and anticipates user needs in advance, which allows for more accurate results retrieval and reduces the subsequent time required for users to find relevant content among search results.
2Productivity
If search engines rank results according to their own criteria, then the search process is automated and efficient, but the results may not align with the underlying intent of the query
Solution Approach 1:
The patent implements feedback mechanisms where the AI assistant continuously monitors and analyzes the alignment between search results and user intent. Based on this feedback, the system dynamically adjusts query formulations and result selection criteria, creating a closed-loop system that maintains both automation and intent alignment. The AI learns from user interactions to improve future result relevance.
Solution Approach 2:
The system dynamically changes search parameters such as query weighting, result filtering criteria, and contextual relevance thresholds based on detected user intent. Rather than using fixed ranking criteria, the AI assistant adapts search parameters in real-time to better align results with what the user actually seeks, while maintaining automated processing.
3Reliability
If multiple search results are provided to ensure comprehensive coverage, then the search is thorough, but the user experience deteriorates due to the need to sift through numerous results
Solution Approach 1:
The patent extracts and prioritizes only the most relevant information from comprehensive search results. The AI assistant filters out redundant or less relevant content and presents only the essential findings to the user, thereby maintaining thoroughness while dramatically improving ease of operation. This extraction process eliminates the need for users to sift through numerous results.
Solution Approach 2:
The system segments comprehensive search results into organized, thematic groups with clear headings and summaries. Rather than presenting a flat list of results, the AI assistant structures information hierarchically, allowing users to quickly scan and navigate to relevant sections. This segmentation maintains comprehensiveness while making the information much easier to consume.
4Ease of operation
If dynamic webpage generation is implemented to personalize results, then user experience is improved through intent alignment, but system complexity increases due to AI model integration
Solution Approach 1:
The patent employs a universal AI assistant architecture that performs multiple functions: intent analysis, query refinement, result synthesis, and webpage generation. This multi-functional approach consolidates what could be separate complex systems into a single versatile platform, improving personalization while managing overall system complexity through functional integration rather than proliferation of specialized components.
Data Source
AI summary
Large language models (LLMs) are leveraged in order to dynamically generate webpages and to modify pre-existing webpages. The LLMs determine the intent of queries and modification requests and obtain relevant content using differently defined page generation strategies. Related apparatus, systems, techniques and articles are also described.