Dynamic Requirement Prediction for Network Request Compliance
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Solution Overview
Problem
There is a need for a system that can intelligently predict and implement dynamically changing requirements related to incoming network requests, particularly in entities such as financial institutions, to ensure compliance with standards like PCI DSS during transactions.
Innovation Solution
A system comprising a requirement prediction and implementation system that establishes a secure communication link with an entity system, extracts relevant parts of user requests, predicts requirements using a rule-based or machine learning model, evaluates these against a requirement catalog, and provides real-time solutions for deviations based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional static requirement evaluation methods are used, then system simplicity is maintained, but the system cannot adapt to dynamically changing requirements in real-time
Solution Approach 1:
The patent implements a dynamic requirement evaluation system that transitions from static pre-defined requirements to real-time adaptive requirement assessment. The system uses machine learning models and rule-based engines that continuously learn and adapt to new requirements patterns, enabling the system to dynamically adjust its evaluation criteria based on incoming requests and changing organizational standards.
Solution Approach 2:
The patent employs preliminary action by pre-training machine learning models with historical requirement data and pre-establishing rule-based evaluation frameworks. This allows the system to be prepared in advance for various requirement scenarios, enabling faster real-time adaptation without requiring complete system reconfiguration when new requirements emerge.
2Productivity
If manual requirement evaluation is performed, then accuracy can be maintained through human expertise, but processing speed and real-time response capability deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors requirement evaluation outcomes and uses this information to refine its machine learning models and rule-based engines. Historical evaluation data is fed back into the system to improve future prediction accuracy, creating a self-improving loop that maintains high precision while operating at automated speeds.
Solution Approach 2:
The patent replaces manual human expertise with automated machine learning models and rule-based evaluation systems. These computational systems process requirement evaluations at machine speed while incorporating sophisticated algorithms that capture the nuanced judgment previously performed manually, thereby maintaining accuracy while dramatically increasing processing throughput.
3Reliability
If comprehensive requirement catalogs are maintained, then coverage of all possible requirements is improved, but system complexity and data management burden increase
Solution Approach 1:
The patent segments the comprehensive requirement catalog into hierarchical categories and modular components. The requirement evaluation system processes requirements in structured layers, breaking down complex requirement sets into manageable segments that can be independently evaluated and maintained, reducing the overall management burden while preserving complete coverage.
Solution Approach 2:
The patent creates a universal requirement evaluation framework that handles multiple types of requirements through a single integrated system. The machine learning models and rule-based engines are designed to be multi-functional, capable of evaluating diverse requirement categories using unified processing logic, thereby simplifying catalog management while maintaining comprehensive coverage.
4Reliability
If real-time requirement prediction is implemented, then compliance with organizational standards is ensured, but computational resources and processing time are consumed
Solution Approach 1:
The patent applies partial action by implementing tiered requirement evaluation where not all requirements are fully processed for every incoming request. The system identifies and evaluates only the most critical requirements in real-time using streamlined processes, while less critical requirements receive deferred or reduced processing, thereby reducing computational overhead while maintaining essential compliance assurance.
Data Source
AI summary
Embodiments of the present invention provide a system for intelligent prediction and implementation of dynamically changing requirements relating to incoming network requests. The system is configured for determining that the entity system associated with an entity received a user request from a user, establishing a secure communication link with the entity system, extracting, via a requirement evaluator, through the secure communication link, at least a part of the user request that requires a requirement evaluation, predicting, via a requirement predictor, one or more requirements that are associated with the part of the user request, evaluating the one or more requirements and cataloged requirements stored in a requirement catalog, determining a real-time deviation between the one or more requirements and the cataloged requirements, and providing a solution for the real-time deviation.