AI Search Result Personalization for Intent-Aware Ranking
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Solution Overview
Problem
Current Internet search engines suffer from over-inclusive and under-inclusive search results, distorted rankings, and lack of higher-level categorization, leading to irrelevant data transmission and increased system load, while failing to provide actionable information.
Innovation Solution
An AI-driven system that personalizes search results by generating secondary queries, using AI servers like GPT-4 and embedding models, to categorize and rank results based on user-specific parameters, reducing data transmission and system load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If keyword searching is used to retrieve documents and websites, then the search engine can return results that accurately correspond to the search terms, but the search results may not reflect the user's underlying interests and goals
Solution Approach 1:
The patent segments the search process into multiple stages: initial keyword-based retrieval, followed by AI-driven analysis of user context, preferences, and behavior patterns. This segmentation allows the system to maintain keyword accuracy while layering additional personalization processing to recover lost information about user interests.
Solution Approach 2:
The patent introduces an AI system as an intermediary between the keyword search engine and the user. This intermediary analyzes both the search results and user characteristics, then personalizes the presentation of results to bridge the gap between accurate keyword matching and user intent understanding.
2Quantity of substance
If a large number of search results are returned to ensure completeness, then the user cannot review all of the results in a reasonable period of time
Solution Approach 1:
The patent applies partial action by using AI to selectively process and prioritize a subset of search results based on user preferences and relevance. Rather than presenting all results equally, the system performs sufficient analysis to identify and highlight the most valuable results, reducing review time while maintaining completeness for users who need it.
Solution Approach 2:
The patent performs preliminary AI-based filtering and ranking of search results before presenting them to the user. This preliminary action analyzes user profiles, query context, and result relevance in advance, organizing results in a personalized order that reduces the time users need to spend reviewing them.
3Productivity
If search results are ranked according to criteria applied by the search provider, then the rankings may be distorted by over-inclusion and highest bidder placement
Solution Approach 1:
The patent implements feedback loops where user interactions with search results (clicks, dwell time, corrections) are continuously analyzed by the AI system. This feedback refines the personalization model over time, allowing the system to learn from user behavior and improve ranking accuracy while maintaining efficient result presentation.
Solution Approach 2:
The patent changes the ranking parameters from traditional provider-based metrics (links, bids) to user-specific parameters including personal preferences, historical behavior, and contextual relevance. This parameter transformation enables the system to maintain ranking efficiency while eliminating distortion from commercial factors.
4Quantity of substance
If the amount of data transmitted to the user is increased to provide comprehensive results, then the relevant data is obscured in irrelevant data and system load increases
Solution Approach 1:
The patent applies local quality by personalizing the data transmission based on specific user characteristics and query context. Rather than uniformly transmitting all available data, the AI system analyzes user profiles and query specifics to selectively transmit only the most relevant results, improving signal-to-noise ratio while reducing overall data volume.
Data Source
AI summary
A computer server system and method are disclosed for personalization and customization of search results and search result rankings, such as for Internet searching. A representative server system comprises: a network interface to receive a primary query and transmit secondary queries and search results; and one or more processors configured to generate the secondary queries; to extract or transform responses into text variables; to use trained, supervised multi-class neural networks to classify the text variables to form initial classifications or categories and combine the initial classifications or categories to form resulting classifications or categories; to filter and rank the resulting classifications or categories; and to use the filtered and ranked resulting classifications or categories to generate and output the personalized search results and search result rankings, the personalized search results and search result rankings comprising one or more associated classifications or categories corresponding to the primary query.