Cognitive Framework for Non-Functional Requirement Disposition
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Solution Overview
Problem
Cognitive enterprises face challenges in adapting to diverse and heterogeneous environments to validate non-functional requirements (NFRs) quickly, especially with changes in hybrid-multi-cloud hosting technologies, requiring adaptive systems that align with business priorities and reduce regulatory and revenue impacts.
Innovation Solution
A method using a computing device to identify optimal solutions by overlaying prioritized NFRs across multiple candidate systems, employing a cognitive processing model with machine learning adaptability for extracting NFRs, and utilizing unbiased decision processing based on discord, exclusion, and similarity functions to classify and recommend systems that fulfill business requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a cognitive processing model with machine learning is used to extract and classify NFRs across multiple candidate systems, then the accuracy and objectivity of NFR fulfillment assessment is improved, but the system complexity and computational resources required increase
Solution Approach 1:
The patent replaces manual or traditional mechanical assessment methods with a cognitive processing model that uses machine learning algorithms to automatically extract, classify, and evaluate NFRs. This substitution of mechanical processes with intelligent systems enables more accurate and objective assessment while managing complexity through automation.
Solution Approach 2:
The patent introduces a cognitive processing model as an intermediary layer between the business requirements and the multiple candidate systems. This intermediary systematically processes and classifies NFRs, providing structured evaluation across all candidates without requiring direct complex interactions between all system components.
2Adaptability or versatility
If the system evaluates multiple candidate systems against prioritized NFRs to identify optimal solutions, then the adaptability to business requirements is improved, but the time required for assessment increases
Solution Approach 1:
The patent performs preliminary classification and prioritization of NFRs before the actual evaluation of candidate systems. By pre-processing and organizing the NFRs according to business priorities, the system prepares the evaluation framework in advance, enabling faster and more efficient assessment of multiple candidate systems against the structured criteria.
Solution Approach 2:
The patent implements a feedback mechanism where the cognitive processing model continuously refines its classification and evaluation based on the prioritized NFRs and the performance of candidate systems. This iterative feedback loop enables the system to adapt to changing business requirements while maintaining efficient assessment timelines through learned patterns and optimized evaluation paths.
3Reliability
If the system identifies sub-optimal solution spaces in absence of optimal solutions, then the robustness and flexibility of the system is improved, but the difficulty of detecting and measuring optimal solutions increases
Solution Approach 1:
The patent applies partial evaluation by identifying and analyzing sub-optimal solution spaces even when a perfect optimal solution is not immediately apparent. This approach allows the system to evaluate alternative paths and configurations in detail, building robustness by understanding near-optimal scenarios and their trade-offs, thereby improving the ability to detect and measure optimal solutions through comprehensive partial analysis.
Data Source
AI summary
A method of using a computing device to provide non-functional requirement (NFR) fulfilment based technical disposition including identifying, by the computing device, optimal solutions based on overlaying prioritized NFRs for at least one target system as supported by each candidate system of multiple candidate systems. Supportable technical NFRs are used for each combination of options under the multiple candidate systems. Identifying further includes generating a cognitive processing model using machine learning adaptability for extracting NFRs for options for existing system designs. Classification overlay of each individual candidate system is provided across the NFRs prioritized based on business requirement. Unbiased decision processing utilized based on discord, exclusion and similarity as functions of the cognitive processing model. A sub-optimal solution space and adaptability for the sub-optimal solution space in absence of an optimal solution is identified.