Personalized Pollen Allergy Prediction Using Symptom Diaries

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Solution Overview

Problem

Conventional pollen calendars do not provide personalized information on pollen concentration and allergic symptoms, failing to offer tailored predictions for individual users.

Innovation Solution

A method and server system that generates a personalized pollen allergy prediction by combining pollen calendar data with user-specific symptom records, calculating symptom indexes, and extracting allergy risk grades for each pollen species and tree type, to provide detailed forecasts for cities and counties.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Loss of information

If conventional pollen calendar information is used, then general pollen concentration data is provided, but personalized allergic symptom information for each user is not provided

Engineering Contradiction:

Improvepersonalized allergic symptom informationVSAvoidsystem complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The system segments the general pollen calendar information into personalized components by creating separate symptom diaries for each user. Each user's pollen exposure data is divided and correlated with their specific allergic symptoms, transforming generic pollen concentration data into personalized allergy risk assessments.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements feedback mechanisms by continuously collecting user-reported allergic symptoms and correlating them with pollen calendar data. This feedback loop allows the system to learn from each user's specific reactions and refine personalized predictions, ensuring that personalized information is provided without requiring completely new system architecture.

Inventive Principle:

Principle #23Feedback

2Measurement precision

If pollen calendar data is combined with personal symptom records, then personalized pollen allergy prediction is achieved, but data processing complexity increases

Engineering Contradiction:

Improvepollen allergy prediction accuracyVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-processing and organizing both pollen calendar data and user symptom records before correlation analysis. Data is structured and prepared in advance, with pollen information categorized by species, concentration, and geographic location, and symptom data organized by type, severity, and timing. This preliminary organization reduces the complexity of subsequent processing while maintaining high prediction accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system introduces intermediary processing layers that mediate between raw pollen calendar data and personal symptom records. These intermediaries include standardized data formats, correlation algorithms, and filtering mechanisms that simplify the integration process. The intermediary layer translates complex multi-source data into standardized formats that can be easily correlated, reducing overall processing complexity while improving measurement precision.

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If detailed symptom indexes and allergy risk grades are calculated, then personalized forecast accuracy improves, but computational requirements increase

Engineering Contradiction:

Improveallergy risk assessment precisionVSAvoidcomputational power

Core Design Contradiction:

Measurement precisionVSPower

Solution Approach 1:

The system applies partial action by calculating symptom indexes and allergy risk grades selectively rather than comprehensively for all users and all pollen types simultaneously. The system focuses computational resources on high-priority calculations such as users with severe allergies, peak pollen seasons, and high-concentration periods. This approach maintains high precision for critical assessments while reducing overall computational power requirements through targeted rather than universal processing.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20220183614A1Method of predicting personalized pollen allergy using pollen calendar and personal allergic symptom diary and server performing the same

Publication Date: 2022.06.16 NAT INST OF METEOROLOGICAL SCI

AI summary

A method of predicting a personalized pollen allergy includes generating a personal allergic symptom diary by recording a daily allergic symptom and daily drug taking information of a user, calculating a daily symptom index using a pollen calendar of a region corresponding to a location of the user and the daily allergic symptom, extracting allergy generation risk grades for each pollen generation species and allergy-sensitive tree species of the user by using the pollen generation species and a pollen generation grade extracted from the pollen calendar, and the daily symptom index, and generating a personalized pollen calendar based on the extracted information, and generating a personalized risk forecast for each city and county for the user by applying the allergy generation risk grades for each pollen generation species and the allergy-sensitive tree species to a Metrological Administration pollen forecast.