Generative AI Webpage Creation for Search Intent Accuracy
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Solution Overview
Problem
Search engines often fail to accurately reflect user intent and require users to traverse multiple results to find desired content, leading to a less than desirable 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 enriched content generation and interactive features like AI copilots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines are used to provide results responsive to inputted keywords, then information can be retrieved, but user experience deteriorates due to inaccurate reflection of user intent and requirement to traverse multiple results
Solution Approach 1:
The patent introduces an intermediary system between the user's keyword query and the search results. This intermediary uses large language models to interpret user intent, generate contextualized queries, and synthesize information from multiple sources into a unified response that accurately reflects what the user is seeking, rather than just matching keywords.
Solution Approach 2:
The patent replaces the traditional mechanical keyword-matching search engine system with an AI-driven system using large language models. This substitution enables the system to understand nuanced user intent, perform contextual query generation, and synthesize information in a way that goes beyond simple keyword comparison and result ranking.
2Productivity
If traditional search engines rank results according to their own criteria, then results can be organized, but accuracy of reflecting underlying query intent deteriorates
Solution Approach 1:
The system incorporates feedback loops where the large language model continuously refines its understanding of user intent based on the synthesized information and contextualized queries. The model adjusts its interpretation and information synthesis approach based on what it determines the user is truly seeking, creating a dynamic feedback mechanism that improves accuracy while maintaining organization.
3Quantity of substance
If multiple search results must be traversed to find desired content, then comprehensive information can be accessed, but time consumption increases
Solution Approach 1:
The patent merges information from multiple search results and data sources into a single synthesized response generated by the large language model. Instead of presenting users with multiple separate results to traverse, the system combines relevant information from various sources into one comprehensive answer that maintains information completeness while eliminating the need to navigate through multiple pages.
Solution Approach 2:
The system performs preliminary actions by pre-synthesizing information from multiple sources before presenting it to the user. The large language model proactively gathers, processes, and consolidates information from various data sources in advance, creating a ready-to-present comprehensive response that saves users the time of manually traversing multiple results.
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.