AI Data Readiness and Metadata-Driven Taxonomy Framework

Patent: 63/948,511 • Year: Patent Pending (U.S. Provisional Patent Application Filed, 2025)

Official Patent Title Systems and Methods for Determining and Enforcing Artificial Intelligence Data Readiness Using Metadata-Driven Taxonomies and Governance Controls Overview This invention introduces a metadata-driven framework for determining and enforcing artificial intelligence (AI) data readiness within enterprise environments. It provides a structured approach to evaluate, classify, and govern data before it is used in AI systems, ensuring compliance, quality, and reliability. Key Innovations * AI Data Readiness Scoring Engine
Evaluates datasets using metadata attributes such as completeness, lineage, quality, and governance status. * Metadata-Driven Taxonomy Framework
Uses hierarchical taxonomies to classify and organize enterprise data for AI consumption. * Governance Enforcement Mechanism
Applies policy-driven controls to ensure that only compliant and properly classified data is used in AI workflows. * Enterprise Data Integration
Designed to integrate with modern data ecosystems including data lakes, warehouses, and governance platforms. Technical Impact This invention enables organizations to operationalize AI responsibly by ensuring that only high-quality, compliant, and well-governed data is used for AI processing. It addresses key challenges in enterprise AI adoption, including data trust, governance, and regulatory compliance. Filing Information Filed with the United States Patent and Trademark Office as a provisional patent application in 2025. 🌐 Relevance to AI Governance and Enterprise Systems This invention contributes to advancing enterprise AI governance by enabling structured, metadata-driven evaluation of data readiness. It supports responsible AI adoption by ensuring compliance, improving data quality, and strengthening trust in AI-driven systems.

Privacy-Preserving Local AI-Assisted File Comparison

Patent: 64/007,514 • Year: Patent Pending (U.S. Provisional Patent Application Filed, 2026)

Official Patent Title Systems and Methods for Privacy-Preserving Local AI-Assisted File Comparison Overview This invention introduces a privacy-preserving system for comparing digital files using artificial intelligence within a fully local execution environment. Unlike conventional AI-based solutions that require transmitting sensitive data to external servers, the system ensures that all processing remains on-device, eliminating risks associated with data exposure and third-party access. Key Innovations * Local-First AI Processing
All file comparison and AI analysis are executed within a secure local environment, ensuring that sensitive data never leaves the user’s control. * Selective AI on Difference Segments
The system extracts only modified portions of files and applies AI analysis selectively, minimizing data exposure while improving computational efficiency. * Privacy Enforcement Architecture
Built-in controls such as network isolation, sandboxed execution, and policy-based governance prevent unauthorized data transmission. * Hybrid Comparison Pipeline
Combines deterministic comparison algorithms with AI-driven contextual interpretation to provide meaningful insights into file changes. Technical Impact This invention enables secure AI-assisted document and file analysis in environments where data privacy and regulatory compliance are critical, including legal, healthcare, financial, and government sectors. Filing Information Filed with the United States Patent and Trademark Office as a provisional patent application in 2026. 🌐 Relevance to AI Governance and Privacy Engineering This invention contributes to advancing privacy-preserving AI systems by addressing key challenges in secure data processing and regulatory compliance. It supports responsible AI adoption by ensuring that sensitive information is processed within controlled environments, aligning with evolving standards in data protection, cybersecurity, and enterprise AI governance.