Location-Based Risk Alert Processing System
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Solution Overview
Problem
Current risk assessment techniques lack precision in determining individual risks associated with geographical locations and movements, leading to inefficient resource utilization and inaccurate border policies, particularly during events like natural disasters or epidemics.
Innovation Solution
A location-based alert processing system that utilizes machine learning models to calculate user-specific risk scores based on geographical locations and movements, integrating data from multiple sources to provide personalized risk alerts and assistance while balancing user privacy and data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current risk assessment techniques use large location-based criteria (e.g., entire countries) for risk determination, then border policies can be implemented, but resource utilization becomes inefficient and risk determination accuracy decreases
Solution Approach 1:
The patent segments the risk assessment process by dividing location data into hierarchical levels (country, state, county, city, neighborhood) and processing only relevant segments based on event location. This allows precise risk determination for specific areas without processing entire countries, reducing computing resource waste while maintaining accuracy.
Solution Approach 2:
The system applies local quality by providing differentiated risk assessments for different geographical granularities. Instead of uniform country-level risk determination, the system evaluates and processes risk data at the specific local level where events occur, optimizing resource usage while improving precision.
2Reliability
If risk alerts are provided to all users in large geographical areas, then comprehensive coverage is achieved, but resource utilization is inefficient and unnecessary services are provided
Solution Approach 1:
The system segments the user notification process by identifying and alerting only users whose location data matches the event location hierarchy. This ensures comprehensive coverage for affected users while avoiding unnecessary notifications to users in unrelated areas, improving resource utilization efficiency.
Solution Approach 2:
Users control their own data sharing preferences and receive alerts only when relevant to their具体情况. The system allows users to self-determine their risk exposure levels by controlling data sharing, eliminating unnecessary service provision while maintaining reliability for those who need it.
3Measurement precision
If detailed location data is collected for accurate risk assessment, then precision is improved, but user privacy is compromised
Solution Approach 1:
The system applies local quality by collecting and processing location data only at the necessary hierarchical level for each event. Instead of collecting all possible user location data, it processes only the specific local area data relevant to the event, achieving precision while minimizing privacy intrusion.
Solution Approach 2:
Users control their own data sharing through explicit consent mechanisms. The system allows users to self-determine what location data is shared and with whom, enabling precise risk assessment for consenting users while preserving privacy for those who opt out.
4Measurement precision
If centralized data processing is used for risk assessment, then comprehensive analysis is possible, but system complexity and resource requirements increase
Solution Approach 1:
The system segments data processing by hierarchy level, processing location data at country, state, county, city, and neighborhood levels separately. This modular approach enables comprehensive analysis through hierarchical processing while reducing system complexity compared to processing all data centrally in a single system.
Data Source
AI summary
A location-based risk alerting system is disclosed. When information regarding a risk assessment situation is received, the location and time attributes of the risk assessment situation are extracted, a risk score is calculated and a risk alert is generated to include the attributes and the risk score upon validating the received information for risk. The risk alert is transmitted to client devices of subscribers where a user-specific risk score is calculated at each client device based on a comparison of the distance of the client device from the location attribute and the time at which the risk alert is received with the time attribute. The risk alert is displayed at the client device based on the user-specific risk score and information regarding the user is shared with trusted authorities upon receiving user consent.