Platform Optimization
AI Driven Decisioning - Multivariate Optimization System
Dec. 2025 – Washington DC
Max Cybersecurity, LLC today announced the next phase of its intellectual property expansion strategy, including the filing of a Consolidated Continuation-in-Part (CIP) application integrating advanced technologies across AI-driven decisioning, multivariate optimization, orchestration engines, reinforcement learning, micro segmentation, and autonomous experience assembly systems.
This expanded patent family builds upon the company’s foundational U.S. Patent 9,378,505, which covers automated multivariate testing, behavioral feedback loops, and intelligent routing systems widely used across today’s digital and operational platforms.
The new CIP integrates emerging architectures for:
LLM-based content and decision variant generation
Autonomous sequence and workflow assembly
Reinforcement learning feedback systems
Cross-channel optimization, including email, SMS, API, and operational interfaces
AI-driven micro segmentation for OT and digital systems
Continuous drift detection and governance layers
Supply chain and logistics optimization pathways
“As AI-powered platforms continue to drive optimization across digital, marketing, supply chain, and operational technology systems, it is essential that innovators protect the underlying methods that enable this intelligence,” said CEO Michael A. Echols.
“Our CIP portfolio expansion ensures that Max Cybersecurity remains at the forefront of securing and defining the next generation of automated, adaptive experience systems.”
The expanded filings are designed to support:
future licensing programs,
collaborative innovation,
industry standardization efforts, and
responsible commercialization of AI technologies in enterprise environments.
Organizations leveraging automated optimization, experience orchestration, or reinforcement-learning-driven decision systems are encouraged to follow future updates as the expanded portfolio moves through examination.
For more information, please contact:
info@maxcybersecurity.com
Expanding Patent Pending Portfolio
1. Optimization Systems and Large Language Model Technologies The invention relates to systems and methods for automated optimization using large language models (LLMs), reinforcement learning, multi-variant generation, prompt engineering, governance filtering, multi-model arbitration, and continuous policy updating. The invention further encompasses adaptive experience assembly, cross-channel orchestration, content scoring, semantic-diversity enforcement, and reward-driven optimization loops for digital experiences, workflows, and communications.
2. Operational Technology (OT), Cyber Governance, and Micro-Segmentation
The invention also relates to systems for segmentation, behavioral drift detection, micro-zoning, and adaptive policy enforcement within operational technology (OT), industrial control systems (ICS), and critical-infrastructure environments. This includes dynamic segmentation (e.g., ZoneIQ™), OT device fingerprinting, zero-trust micro-zones, anomaly detection, predictive policy changes, and governance engines that enforce safety, compliance, bias control, and drift suppression across digital and physical systems.
3. Digital Twin Modeling, Supply-Chain Optimization, and Predictive Response
The invention relates to digital-twin simulation environments that support predictive modeling, supply-chain routing optimization, logistics forecasting, quantum-assisted decision modeling, and reinforcement-learning-driven operational decisions. This includes the integration of digital-twin data with LLM optimization engines, governance layers, and micro-segmentation systems to produce predictive routing, anomaly detection, resource allocation, and automated operational responses.
The Max Portfolio Difference
Max Cybersecurity’s present invention differs from prior MVT systems by integrating LLM-driven variant generation, real-time reinforcement signals, adaptive experience assembly, segmentation linked to behavioral drift, OT micro-zoning, digital-twin predictive optimization, and governance-safety enforcement into a continuous, interdependent optimization loop.
Unlike prior systems, which perform isolated A/B or multivariate comparisons, the unified architecture described herein allows every module to influence and update the others, producing real-time optimized experiences and operational policies. This cross-domain interdependence is not taught or suggested by any known prior art.
Traditional multivariate testing and personalization engines cannot autonomously:
• generate content variants at scale,
• reason across multiple channels or steps,
• create multi-path workflows,
• adapt to real-time behavioral changes,
• manage LLM drift or bias,
• optimize supply-chain or OT digital twins,
• execute predictive segmentation,
• modify prompts based on reward signals, or
• enforce coherent governance and safety constraints.
Existing systems operate as disconnected optimization islands. The filed provisional applications independently addressed components of this problem but lacked:
• a unified architecture,
• consistent terminology,
• centralized governance,
• a consistent priority structure,
• or a consolidated set of definitions and mechanisms.