Deep Learning Allergen Mapping System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective solutions for accurately detecting and mapping allergens, particularly aeroallergens, which pose significant health and economic challenges due to their widespread impact and difficulty in prevention.
Innovation Solution
A deep learning-based system that analyzes online content data to detect allergen content with geographic location, tagging it with quality and intensity indicators, and generates an allergen map to visualize and predict allergen distribution, aiding in prevention and planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional methods are used to detect and map allergens, then the system complexity remains low, but the measurement precision and reliability of allergen detection are insufficient
Solution Approach 1:
The patent replaces traditional mechanical or manual allergen detection methods with a deep learning-based computational system. The deep learning model processes online content data (images, text, videos) to automatically detect and map allergens, substituting manual analysis with automated AI-driven processing to achieve higher measurement precision.
Solution Approach 2:
The patent introduces an intermediary processing layer between data collection and allergen detection. The deep learning model acts as an intermediary that processes raw online content data, extracts relevant features, and generates allergen maps, thereby improving detection accuracy without directly increasing system complexity at the user interface level.
2Productivity
If manual allergen mapping methods are used, then the ease of operation is high, but the productivity and coverage area are limited
Solution Approach 1:
The system performs self-service by automatically processing online content data and generating allergen maps without requiring manual intervention. The deep learning model autonomously detects allergens, determines their locations, and creates visualizations, thereby increasing productivity while maintaining ease of operation through automated functionality.
Solution Approach 2:
The patent enables continuous allergen mapping by processing online content data in real-time as it becomes available. The system continuously updates allergen maps without interruption, maintaining productive operation while requiring minimal user input, thus resolving the contradiction between productivity and ease of operation.
3Reliability
If comprehensive allergen data collection is implemented, then the reliability of allergen mapping is improved, but the loss of information from unprocessed data increases
Solution Approach 1:
The patent implements feedback mechanisms where the deep learning model continuously learns from and adjusts to the quality and quantity of available online content data. This feedback loop ensures that the system maximizes the utility of collected data, improving reliability while minimizing information loss through adaptive processing capabilities.
Data Source
AI summary
An entry on an allergen map may be generated by a computer system where a deep learning model is trained using online content data. Allergen content data which contains geographic data may be detected from the online content data. The allergen content data may be analyzed by the computer system and tagged with a quality and intensity indicator. Based on the tagging and the geographic location, an allergen map may be generated.