Weather Data Generation via Messaging Activity Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing content sharing platforms struggle to accurately determine whether media content is generated indoors or outdoors, leading to inaccurate weather data and irrelevant creative tools, as existing methods do not account for varying geographic and environmental conditions.
Innovation Solution
A system that uses machine learning models trained on user-provided and device-collected data to predict whether media content is generated indoors or outdoors, incorporating GPS, sensors, and user input to determine location and temperature, allowing for more accurate weather data generation and relevant creative content application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If creative tools use generic weather assumptions, then implementation is simple, but accuracy of weather data deteriorates
Solution Approach 1:
The patent introduces machine learning models as intermediary components that process messaging system activity data to infer weather conditions and location information. These models act as mediators between raw user activity data and the creative tools, translating indirect signals (message metadata, timestamps, user behavior patterns) into accurate weather determinations without requiring direct weather sensor integration in each device.
Solution Approach 2:
The system implements feedback loops where user interactions with creative tools and corrections to weather data are fed back into the machine learning models. This continuous feedback refines the models' ability to accurately determine weather conditions from messaging activity patterns, progressively improving weather data accuracy while maintaining system efficiency.
2Measurement precision
If the system collects detailed user data for accurate weather determination, then weather data accuracy improves, but user privacy concerns increase
Solution Approach 1:
The patent extracts only the necessary minimal features from user messaging data required for weather determination, such as timestamps, message frequency patterns, and location metadata already present in the messaging system. It deliberately excludes sensitive personal information, achieving accurate weather inference while minimizing privacy intrusion by taking out only what is essential.
Solution Approach 2:
The machine learning models process and interpret user data automatically within the messaging system infrastructure, eliminating the need for separate data collection mechanisms. The system serves itself by utilizing existing messaging metadata that users already generate during normal communication, converting this existing data into weather information without additional user burden or privacy exposure.
3Measurement precision
If the system uses multiple data sources (GPS, sensors, user input), then measurement accuracy improves, but device complexity increases
Solution Approach 1:
The patent makes the messaging system infrastructure multi-functional by enabling it to serve both its primary communication purpose and secondary weather data collection purpose. The existing messaging infrastructure, already handling user data transmission and storage, is extended to also collect and process location and temperature information, eliminating the need for separate dedicated weather collection systems.
Solution Approach 2:
The system merges multiple data collection functions (location tracking, temperature sensing, user input collection) into a unified processing pipeline handled by the machine learning models. These models integrate data from GPS, device sensors, and user inputs simultaneously, combining multiple information sources into a single coherent weather determination process that reduces overall system complexity.
Data Source
AI summary
Systems and methods are provided for analyzing messages generated by a plurality of computing devices associated with a plurality of users in a messaging system to generate training data to train a machine learning model to determine a probability that a media content item was generated inside an enclosed location or outside, receiving a media content item from a computing device, analyzing the media content item using the trained machine learning model to determine a probability that the media content item was generated inside an enclosed location or outside, determining, based on the probability generated by the trained machine learning model, that the media content item was generated inside an enclosed location, and determining an inside temperature associated with the venue based on messages generated by a plurality of computing devices in a messaging system comprising media content items and temperature information for the venue or a similar venue type.