Requirement Management System Using Fuzzy Logic for Uncertainty
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Solution Overview
Problem
Current requirement, compliance, and resource management systems lack the ability to effectively handle inherent approximation, variability, and uncertainty in process steps, limiting their scalability and real-time collaboration capabilities.
Innovation Solution
The methodology incorporates mathematical algorithms such as fuzzy logic, statistics, and weighting logic to manage approximation and uncertainty, optimizing process steps, resources, and constraints, and enables near real-time collaboration through a scalable and modular framework with integration of fuzzy logic, statistical algorithms, and weighting logic modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional requirement management systems are used, then basic requirement tracking is achieved, but the system cannot handle inherent approximation, variability and uncertainty in process steps
Solution Approach 1:
The patent transforms qualitative requirement attributes into quantitative parameters by introducing fuzzy logic variables (e.g., importance, urgency, confidence levels) that can take continuous values. This allows the system to model uncertainty and approximation mathematically, enabling more reliable handling of incomplete or ambiguous requirement information while maintaining scalability.
Solution Approach 2:
The patent replaces traditional binary (yes/no, met/not met) requirement verification mechanisms with mathematical algorithms including fuzzy logic, statistical analysis, and weighting systems. This substitution enables the system to process degrees of compliance and uncertainty, improving reliability in handling real-world complexity without sacrificing adaptability.
2Productivity
If detailed process optimization is implemented, then process efficiency improves, but real-time collaboration capability is limited
Solution Approach 1:
The patent implements dynamic requirement specifications that can be modified in real-time through a standardized interface. The system allows stakeholders to update requirements, priorities, and statuses continuously, with automatic propagation of changes throughout the system. This dynamic capability enables both detailed process optimization and real-time collaboration by making the requirement model adaptive rather than static.
Solution Approach 2:
The patent incorporates automated feedback mechanisms where the system continuously monitors requirement status, process compliance, and resource allocation. Mathematical algorithms analyze this feedback data in real-time and provide recommendations for optimization, enabling continuous improvement without delaying collaborative decision-making processes.
3Ease of operation
If comprehensive resource allocation is performed, then resource management improves, but system complexity increases
Solution Approach 1:
The patent segments the resource allocation problem into distinct, manageable components: requirement-level resource needs, process-step resource requirements, and stakeholder-specific allocations. Each segment can be analyzed and optimized independently using mathematical models, then integrated into a comprehensive allocation plan. This segmentation reduces system complexity by breaking down the overwhelming whole into tractable parts while maintaining ease of operation through standardized interfaces.
Data Source
AI summary
A system and/or a method based on a scalable requirement, compliance and resource management methodology for designing a product/service, optimizing relevant processes and enhancing real time and/or near real time collaboration between many users is disclosed. The requirement, compliance and resource management methodology is further integrated with a fuzzy/neuro-fuzzy logic algorithm module and/or statistical algorithm module and/or weighting logic algorithm module and enhanced with a graphical user interface.