Recognition Result Pages Using LLM Personalization and Key Info Extraction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing recognition applications fail to provide personalized and efficient display of recognition results, often requiring users to sift through extensive information and struggle to highlight key points relevant to individual user preferences.
Innovation Solution
A method utilizing a large language model to generate personalized recognition result pages based on user feature information, enhanced by multimodal models and AIGC models to adjust and supplement content, and structured with titles and dividing lines for clear presentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If recognition results are displayed with extensive information, then completeness of information is improved, but user ability to quickly grasp key points deteriorates
Solution Approach 1:
The recognition result page is segmented into multiple distinct modules including a title module, content module, and recommendation module. Each module presents specific types of information in a structured format, allowing users to quickly locate key information without being overwhelmed by unorganized extensive data.
Solution Approach 2:
The large language model extracts and highlights the most important recognition results and key information from the comprehensive analysis. The title module specifically extracts the core conclusion, while the content module presents detailed information in an organized manner, separating essential information from supplementary details.
2Adaptability or versatility
If recognition results are personalized according to user preferences, then user experience is improved, but system complexity increases
Solution Approach 1:
The large language model serves as an intermediary that receives both the recognition results and user preference information, then processes and integrates them to generate personalized content. This intermediary layer handles the complexity of personalization logic, keeping the overall system architecture clean while enabling sophisticated adaptive behavior.
Solution Approach 2:
The large language model performs multiple functions: it analyzes recognition results, understands user preferences, generates personalized titles, organizes content, and creates recommendations. This multi-functional component handles various personalization tasks within a single system element, reducing overall system complexity while maintaining high adaptability.
3Quantity of substance
If detailed content is provided in recognition results, then information completeness is improved, but information transmission efficiency deteriorates
Solution Approach 1:
Different modules of the recognition result page have different information densities and detail levels appropriate to their function. The title module provides concise summaries, the content module offers detailed information in an organized structure, and the recommendation module gives actionable suggestions. This local differentiation of information quality allows efficient transmission while maintaining completeness.
Data Source
AI summary
Disclosed are a method for processing recognition results and related devices. A method for processing recognition results includes: obtaining a species image from a user and feature information of the user; recognizing image features of the species image, and extracting content information from a content database based on the recognized image features; inputting the content information and the feature information to a large language model to generate a recognition result page from the content information based on the feature information; displaying the recognition result page provided by the large language model on a user interface.