AI Search Carousel Layout for Absolute Relevance Ranking
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Solution Overview
Problem
Existing systems struggle to efficiently retrieve and arrange relevant content items in a user interface based on natural language inputs, lacking effective methods to convert relative relevance scores into absolute scores and optimize content placement.
Innovation Solution
A generative AI-based search and retrieval system that generates relevancy-ranked output listings, adjusts content item positions, and organizes content into display groupings using machine learning models to enhance content arrangement and presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a generative AI-based search and retrieval system is used to retrieve relevant content items, then the relevance and accuracy of retrieved content is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The patent introduces an intermediary component that converts relative relevance scores into absolute scores. This mediator layer transforms the output of the generative AI model into a standardized format that can be more efficiently processed and displayed, reducing the computational burden while maintaining high relevance accuracy
Solution Approach 2:
The system is divided into distinct functional modules: a content retrieval module that uses generative AI to fetch relevant items, a score conversion module that transforms relative scores to absolute scores, and a display module that presents results. This segmentation allows each component to be optimized independently, managing overall system complexity
2Ease of operation
If content items are organized into display groupings with optimized placement, then the user interface efficiency and content presentation quality are improved, but the processing time and computational effort increase
Solution Approach 1:
The system performs preliminary actions by pre-converting relative relevance scores to absolute scores and pre-organizing content items into display groupings before the user actually views them. This preparation work is done in the background, so when users interact with the interface, the content is already optimally arranged, reducing perceived processing time
Solution Approach 2:
The patent changes the parameter scale of relevance scores from relative to absolute values. This parameter transformation enables more efficient sorting and grouping operations, as absolute scores provide a clearer hierarchy for organizing content into display groupings, improving UI efficiency without requiring complex real-time calculations
3Measurement precision
If the system converts relative relevance scores to absolute scores and optimizes content placement, then the content retrieval relevance and display quality are improved, but the computational processing required increases
Solution Approach 1:
A score conversion intermediary is introduced that efficiently transforms relative relevance scores into absolute scores using a standardized formula. This mediator performs the computationally intensive transformation in a optimized manner, maintaining high content relevance accuracy while reducing the overall computational burden on the system
Solution Approach 2:
The system applies parameter changes by transforming the score metric from relative to absolute values. This transformation is performed using an efficient mathematical conversion that preserves the relevance information while enabling more straightforward sorting and filtering operations, thereby improving content relevance without proportionally increasing computational power requirements
Data Source
AI summary
Methods and systems provide content searching and retrieval using generative artificial intelligence (AI) Models. The system is configured to receive a user search for content, media or item listings. The system receives a natural language-based input associated with a client device of a user. The system generates a search criterion for the received natural language-based input. The system, via the generative AI-bases search and retrieval system, generates a relevancy-ranked output listing of content items. The relevancy-ranked output listing content items responsive to the generated search criterion content items having an associated content identifier and a content description. The system generates a carousel display structure definition of the relevancy-ranked content items. The system transmits the carousel display structure definition of the relevancy-ranked content items and the content items to the client device. The client device displays, via a user interface of the client device, relevancy-ranked content items according to the carousel display structure definition.