Personalized Weather Forecasting via User Profile Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing weather forecasting technologies fail to provide personalized forecasts tailored to individual users' preferences and sensitivities, leading to inadequate communication of weather considerations that may be critical to specific individuals.
Innovation Solution
A method and system that collect user data and weather conditions, extract relevant features, and use machine learning models to determine personalized weather forecasts. This system includes feedback loops to adjust models based on user feedback and improve forecast accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional weather forecast models are used to obtain high frequency future weather forecast data for a plurality of locations, then weather forecast coverage and frequency are improved, but personalization and user-specific relevance deteriorate
Solution Approach 1:
The patent applies local quality by customizing weather forecast parameters according to individual user characteristics. Different users receive different forecast parameters (e.g., temperature thresholds, humidity levels, air quality concerns) based on their personal preferences, health conditions, and sensitivity profiles, while maintaining high-frequency updates across multiple locations.
Solution Approach 2:
The patent segments the generic weather forecast into personalized components by dividing users into different profiles with specific sensitivities and preferences. Each user segment receives tailored forecast parameters and alerts relevant to their individual needs, allowing simultaneous high-frequency forecasting for multiple segmented user groups.
2Area of stationary object
If weather forecasts are generalized for location-based predictions, then coverage area is improved, but user-specific sensitivity and preference alignment worsen
Solution Approach 1:
The system maintains broad geographic coverage while applying local quality by adjusting forecast parameters for each user's specific location and personal characteristics. Users receive location-based weather data customized to their individual sensitivities, health conditions, and preferences, achieving both wide coverage and precise personalization.
Solution Approach 2:
The patent implements dynamics by making forecast parameters adaptive and changeable based on user feedback and evolving preferences. The system dynamically adjusts temperature thresholds, alert sensitivities, and forecast focus areas for each user, allowing the personalized forecast to evolve while maintaining broad location coverage.
3Reliability
If multiple weather parameters are monitored for comprehensive forecasting, then forecast completeness is improved, but data processing complexity and computational requirements worsen
Solution Approach 1:
The patent extracts and focuses on only the weather parameters most relevant to each user's specific needs and sensitivities. Instead of processing all possible weather data uniformly, the system extracts and prioritizes parameters such as temperature, humidity, air quality, or precipitation based on individual user profiles, reducing computational complexity while maintaining forecast completeness for each user.
Solution Approach 2:
The system changes parameters by dynamically adjusting which weather parameters are monitored and emphasized for each user based on their preferences and sensitivities. This selective parameter monitoring reduces data processing complexity while ensuring comprehensive coverage of relevant weather conditions for each personalized forecast.
4Adaptability or versatility
If personalized weather forecasts are generated for each user, then user-specific relevance is improved, but system complexity and data requirements worsen
Solution Approach 1:
The patent applies universality by implementing a standardized framework and algorithm structure that handles personalization for multiple users simultaneously. The system uses a universal processing architecture that can accommodate different user profiles, sensitivities, and preferences without requiring separate complex systems for each user, thereby reducing overall system complexity while maintaining high personalization levels.
Data Source
AI summary
The exemplary embodiments disclose a method, a computer program product, and a computer system for determining a personalized weather forecast. The exemplary embodiments may include collecting data of a user and weather conditions of a location, extracting one or more features from the collected data, and determining a personalized weather forecast of the location for the user based on the extracted one or more features and one or more models.