Remote Resource Connector Interface for Adaptive Threat Detection

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing threat mitigation systems struggle to effectively address the increasing complexity of computer attacks due to their reliance on predefined rules and signature-based detection, which are limited in identifying new and evolving threats.

Innovation Solution

Implementing an AI/ML process that learns from data and adapts over time to identify unusual patterns and behaviors, enhancing the detection and response to security events across multiple computing systems and subsystems.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If predefined rules and signature-based detection are used, then the system is simple to implement, but it cannot effectively identify new and evolving threats

Engineering Contradiction:

Improvethreat detection effectivenessVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces traditional mechanical rule-based detection systems with AI/ML-based automated detection. The system uses machine learning models to automatically analyze network traffic patterns, user behaviors, and system events, substituting the manual configuration and maintenance of predefined rules with adaptive, self-learning algorithms that continuously improve threat detection without proportional increases in operational complexity

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The AI/ML process performs self-service by automatically learning from data and adapting to new threats without human intervention. The system continuously trains on new security events, automatically updates detection models, and improves its capabilities over time, reducing the need for manual rule updates and expert configuration while maintaining high detection effectiveness

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If AI/ML processes are implemented to adapt to new threats, then the system becomes more adaptable, but it requires more computational resources and data processing

Engineering Contradiction:

Improveadaptability to new threatsVSAvoidcomputational resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary action by pre-training AI/ML models on extensive security datasets before deployment. Detection models are pre-computed and optimized, allowing the system to make rapid threat assessments during operation without requiring excessive real-time computational resources. Historical security patterns are analyzed in advance to build robust detection capabilities that can be applied efficiently to new threats

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by focusing computational resources on analyzing only the most relevant and suspicious data points rather than processing all network traffic equally. The AI/ML models prioritize analysis of anomalous behaviors and high-risk patterns, applying excessive computational effort only where needed to detect potential threats while maintaining efficiency for normal traffic flows

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20260025436A1Threat Mitigation System and Method

Publication Date: 2026.01.22 RELIAQUEST HOLDINGS LLC

AI summary

A computer-implemented method, computer program product and computing system for accessing interface rules for a remote resource; generating a connector interface based, at least in part, upon the interface rules; and accessing the remote resource via the connector interface.