Alert Generation System Using Risk Probability Thresholds
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Solution Overview
Problem
Current data transmission and network optimization systems lack effective mechanisms to provide timely alerts to users based on their progress through content programs, failing to adequately address the risk of users not achieving desired outcomes.
Innovation Solution
A system comprising a content management server, user device, and supervisor device connected via a communication network, which generates and sends alerts when a calculated risk probability exceeds a threshold, utilizing location determining features and model functions to identify potential interventions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system continuously monitors user progress and calculates risk probabilities, then user outcome achievement improves, but system complexity and computational resources increase
Solution Approach 1:
The system pre-calculates and stores model functions for different content programs and user profiles before runtime. These model functions are prepared in advance based on historical data and program characteristics, allowing the system to quickly retrieve and apply appropriate models without performing complex calculations in real-time during user monitoring
Solution Approach 2:
The system creates simplified copies of complex risk assessment models in the form of pre-computed model functions. These functions represent distilled versions of complex predictive models that can be efficiently evaluated during runtime, balancing accuracy with computational efficiency
2Productivity
If the system sends alerts to supervisor devices when risk exceeds threshold, then user engagement improves, but information transmission load increases
Solution Approach 1:
The system extracts only the essential alert information (user identifier, risk probability, content program identifier, supervisor device identifier) from the comprehensive user data, transmitting only what is necessary for supervisor action rather than complete user profiles or detailed progress data
Solution Approach 2:
The system transforms complex user progress data into a simplified risk probability parameter that can be compared against a threshold. This parameter transformation reduces information complexity while preserving the essential risk assessment needed for alert generation
3Measurement precision
If the system uses location determining features to identify supervisor devices, then alert delivery accuracy improves, but device functionality requirements increase
Solution Approach 1:
The system uses location determining features that are already built into modern mobile devices (GPS, cellular triangulation, Wi-Fi positioning) rather than requiring specialized tracking hardware. This approach leverages existing multi-functional device capabilities to achieve precise location-based supervisor identification without adding dedicated functionality
Data Source
AI summary
Systems and methods for providing an alert to a user device based on generated parameters are disclosed herein. The system can include: a content management server; and a memory communicatingly connected to the content management server via a communication network. The memory can include: a content library database; and a user profile database. The system can include a user device and a supervisor device. The system can include a content management server that can calculate a risk probability and can generate and send an alert to the supervisor device when the risk probability exceeds a threshold level.