Dynamic Risk Analysis Engine for Multi-User Systems
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Solution Overview
Problem
Current dynamic risk analysis technologies for multiple users are inefficient in optimizing risk-based analysis and user interface dynamics, particularly in data collection and predictive modeling for single users.
Innovation Solution
A computer system and method that receives and stores input data from multiple users, calculates risk scores using a risk-based analytical engine, dynamically optimizes the user interface to reflect changes in risk analysis, and generates notifications based on optimization results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current dynamic risk analysis technologies are used for multiple users, then risk analysis can be performed, but the optimization of risk-based analysis and user interface dynamics is inefficient
Solution Approach 1:
The system segments risk analysis by creating separate risk profiles for different user roles (lenders, borrowers, investors, etc.), each with customized risk factors and thresholds. This allows parallel processing of multiple user-specific risk analyses without creating a monolithic complex system, improving efficiency while managing complexity through modular organization.
Solution Approach 2:
The patent implements a universal risk analysis engine that serves multiple user types and purposes through a single integrated system. The engine dynamically adapts to different user needs by loading appropriate risk factors and parameters, eliminating the need for separate analysis systems for each user type and improving overall productivity.
2Measurement precision
If data collection is performed for single users, then predictive modeling can be created, but optimization for multiple users is inefficient
Solution Approach 1:
The system merges data collection and predictive modeling capabilities into a unified multi-user framework. By combining individual user data with aggregate patterns from multiple users, the system maintains high measurement precision for each user while improving overall productivity through shared computational resources and cross-user pattern recognition.
Solution Approach 2:
The patent implements feedback mechanisms where risk analysis results from one user inform and refine the predictive models for other users. This continuous feedback loop allows the system to maintain accurate predictive modeling for each user while efficiently optimizing across the entire user base through shared learning.
3Ease of operation
If risk scores are calculated based on priority risk factors, then risks can be prioritized, but dynamic optimization requiring recalculation increases computational complexity
Solution Approach 1:
The system implements dynamic risk score calculation where priority risk factors are identified and weighted based on current conditions and user-specific parameters. The computational complexity is managed through dynamic adjustment of calculation intensity - full recalculation when conditions change significantly, and incremental updates when changes are minor, maintaining ease of operation while controlling computational complexity.
4Reliability
If user interface is dynamically optimized to reflect risk analysis changes, then decision-making is improved, but the system requires continuous monitoring and recalculation
Solution Approach 1:
The patent implements periodic updates of the user interface with risk analysis results rather than continuous real-time updates. The system monitors risk factors continuously in the background but refreshes the user interface at optimized intervals or when threshold changes occur, maintaining reliable decision-making information while reducing the time loss associated with constant monitoring and recalculation.
Data Source
AI summary
Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises receiving and storing input data from at least two users; calculating a risk score for each identified risk in the received data based on priority risk factors affecting respectively identified risks; dynamically optimizing a risk analysis of the received input for multiple users within a user interface of a computing device by recalculating risk scores based on the received data and identified risks; and generating a notification for the user interface of the computing device based on the dynamic optimization of the risk analysis of the received input.