Alert Generation via Risk Probability Calculation
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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 all user progress data and sends alerts for any deviation, then user outcome achievement improves, but system complexity and resource consumption increase
Solution Approach 1:
The system changes parameters by using model functions that take multiple user progress parameters (engagement level, time spent, completion rate) and transform them into a single risk probability score. This parameter transformation approach allows comprehensive monitoring without requiring complex multi-parameter alert rules, resolving the contradiction between thorough monitoring and system complexity
Solution Approach 2:
The risk probability calculation serves as an intermediary that mediates between raw progress data and alert generation. Instead of directly comparing multiple progress metrics against multiple thresholds, the system uses the risk probability as an intermediate representation that simplifies the decision-making process while maintaining comprehensive monitoring capability
2Measurement precision
If the system calculates risk probability using multiple model functions and parameters, then alert accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining model functions and their associated parameters before runtime. The model functions are prepared in advance with their mathematical formulations and parameter relationships established, allowing the system to quickly evaluate risk probability without performing complex setup or selection during real-time processing, thus reducing processing time while maintaining accuracy
Solution Approach 2:
The system efficiently handles multiple parameters by transforming them through model functions that accept multiple inputs (engagement, time, completion metrics) and produce a single integrated risk probability output. This parameter aggregation approach maintains measurement precision by considering multiple factors while reducing the computational burden of evaluating each parameter separately against multiple thresholds
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.