New Semantic Digest Search Patent Cites, Learns, and Explains—Memory-First

Key Takeaways

• David Bynon's new patent introduces a 'Memory-First' approach that could eliminate AI hallucinations by providing structured, citation-backed knowledge.
• The Semantic Digest system ranks results based on source authority, glossary alignment, and user preferences rather than traditional keyword density.
• TrustPublishing.com is pioneering a new framework for AI-ready content that prioritizes memory over traditional SEO metrics.
• MedicareWire.com will transition to a non-commercial model in 2025, becoming a prototype for trustworthy AI data retrieval.
• The complete patent stack includes five interconnected technologies that enable AI systems to 'remember' information sources and context.

'Memory-First' Approach Solves AI Hallucination Problem

AI hallucinations aren't a flaw in the technology—they're the result of systems that were never taught how to properly remember information. David Bynon's groundbreaking patent application presents a solution through a 'Memory-First' approach to content structuring and retrieval. This innovative system could fundamentally transform how AI systems access, process, and deliver information to users. As reported by Barchart.com, TrustPublishing.com has been developing this framework that focuses on creating structured knowledge objects AI can reliably reference.

"AI isn't broken. It hallucinates because we never taught it how to remember," explains Bynon, who has developed a comprehensive system to address this fundamental issue. His recently filed provisional U.S. patent application, titled "System and Method for Context-Aware Vertical AI Retrieval from Structured Semantic Digest Endpoints," represents the culmination of years of work on AI-ready content structures.

The patent completes a multi-year intellectual property strategy and delivers memory-aware, citation-backed answers using structured knowledge objects and persistent user context. This approach stands in stark contrast to today's AI systems that often generate plausible but incorrect information without proper sourcing.

How the Semantic Digest Patent Works

Matching Queries Against Structured Knowledge Objects

Unlike conventional search engines that prioritize keyword density to return links, Bynon's system uses Semantic Digests—structured, machine-ingestible representations of real-world entities. These digests contain comprehensive information about specific topics, from Medicare plans to legal services, formatted specifically for AI consumption and accurate retrieval.

The system processes user queries by matching them against these structured knowledge objects rather than scanning for keywords across unstructured content. This approach improves accuracy by ensuring AI systems access information that's been deliberately structured for machine comprehension.

Trust-Based Ranking System

The Semantic Digest patent introduces a new approach to ranking information. Rather than prioritizing popularity or keyword frequency, results are ranked based on three critical factors: source authority, glossary alignment, and user-defined trust and preference memory.

Source authority evaluates the credibility and reliability of information sources, ensuring that only high-quality content gets prioritized. Glossary alignment matches information against standardized terminology definitions, reducing ambiguity and misinterpretation. The user-defined trust and preference memory aspect is particularly innovative—it allows the system to adapt to individual user preferences and their established trust relationships with different information sources.

User-Defined Memory and Context Persistence

Traditional search and AI systems typically treat each query as an isolated event. Bynon's patent recognizes that real human information seeking is contextual and builds upon previous knowledge. The system maintains persistent user context across sessions, remembering previous interactions and preferences to deliver increasingly relevant results over time.

This persistence of memory allows for more natural and effective information retrieval that mirrors how humans actually learn and process information—through context, connection, and continuity.

Citation-Backed Answer Delivery

Perhaps the most powerful aspect of the system is its ability to deliver answers with transparent citation. Unlike black-box AI systems that generate responses without clear sourcing, Bynon's patent enables automatic citation of information sources. This transparency addresses one of the most significant concerns with current AI—its tendency to hallucinate information without accountability.

When an AI system using this technology provides an answer, it simultaneously offers the specific sources from which that information was derived, allowing users to verify claims and understand the basis for conclusions.

The Complete Memory-First Patent Stack

1. Semantic Digests Generation Using Structured Datasets

The foundation of Bynon's patent stack begins with the creation of Semantic Digests—structured data objects that represent real-world entities, their properties, and relationships. These digests are specifically formatted for machine consumption and understanding.

Unlike traditional web content optimized for human readers, Semantic Digests are designed from the ground up to be processed by AI systems. They provide context, relationships, and structured definitions that machines can reliably reference.

2. W3C PROV Metadata for Source-Level Trust Scoring

The second layer of the patent stack addresses the critical issue of trust by implementing W3C PROV—a standardized format for capturing provenance information. This component traces where information originated, who verified it, when it was last updated, and other critical metadata that establishes trustworthiness.

By embedding this provenance data directly into content, AI systems can evaluate the reliability of information before presenting it to users. This effectively creates a 'trust layer' for AI knowledge retrieval.

3. Glossary-Driven Explanation System Using DefinedTermSet

The third patent in the stack focuses on explanation capabilities through standardized terminology. By implementing DefinedTermSet schema, the system ensures consistent understanding of industry-specific terms.

This glossary-driven approach is particularly valuable in complex fields like healthcare, finance, or law, where precise terminology is essential for accurate information delivery. The system can automatically explain specialized terms or adjust explanations based on user expertise levels.

4. Retrieval Feedback Loop (AITO)

The fourth component introduces a feedback mechanism that reinforces accurate content retrieval through a system Bynon calls AITO (AI-Trained Optimization). This process tracks how content is cited, used, and validated across interactions, creating a self-improving ecosystem where the most accurate and useful information becomes increasingly visible to AI systems.

This feedback loop mimics how human knowledge communities naturally elevate reliable information and authorities through citation and reference patterns.

5. Context-Aware Vertical AI Retrieval

The fifth and final patent in the stack—the most recently filed—focuses on the retrieval interface itself. This component enables AI systems, chat interfaces, voice assistants, and background search processes to query and retrieve information from the structured Semantic Digest endpoints.

What makes this system unique is its ability to maintain context awareness throughout the retrieval process. The patent describes methods for preserving user intent, topic focus, and previous query history to deliver increasingly precise and relevant information over time. Unlike traditional search systems that handle each query in isolation, this approach builds a continuous understanding of user needs.

Real-World Implementation at MedicareWire.com

Transition to Non-Commercial Public-Benefit Model

To demonstrate the real-world application of his Memory-First patent stack, Bynon has announced that MedicareWire.com—one of the longest-operating Medicare plan directories in the U.S.—will transition to a non-commercial, public-benefit model on August 1, 2025.

This bold move involves removing all affiliate monetization in favor of pure data transparency and structured AI retrievability. The site will become both a public service platform for Medicare beneficiaries and a prototype implementation of the Memory-First AI publishing approach.

"We're putting principles before profits," Bynon explained. "The future of publishing isn't about maximizing page views—it's about maximizing memory integrity. By transitioning to a public-benefit model, we can demonstrate how trustworthy, structured content benefits both human users and AI systems."

Multi-Format Data Access for AI Systems

MedicareWire.com will provide AI systems—including major platforms like Google AI Overviews, GPT, Perplexity, and Claude—with real-time access to structured, verified Medicare information. The content will be available in multiple machine-readable formats:

• JSON-LD for semantic web compatibility and structured data embedding
• TTL (Turtle) for RDF graph databases and semantic reasoning
• Markdown for human-readable documentation with machine-parsable structure
• XML for legacy system integration and standardized data exchange
• W3C PROV for detailed provenance tracking and trust verification

This multi-format approach ensures that AI systems can access the information in whatever format best suits their retrieval and processing capabilities. The site's plan details, glossary terms, and FAQ content will serve as both human resources and machine-trainable endpoints.

David Bynon's Vision: 'Publish for AI Memory, Not SEO'

Bynon's patent stack represents a fundamental shift in how content is created, structured, and published. While traditional digital publishing has focused on search engine optimization (SEO) to maximize visibility, Bynon advocates for a new approach centered on AI memory integrity.

"We've spent the past decade publishing for SEO," Bynon noted. "Now it's time to publish for AI memory—because that's what really gets retrieved, cited, and trusted."

As the first known independent publisher to control the entire Memory-First AI publishing stack through patent protection, Bynon is positioned to influence how information is structured for the next generation of AI systems. His approach addresses the root cause of AI hallucinations by providing structured, provenance-tracked, context-aware content that machines can reliably remember and cite.

The patents collectively create a comprehensive system for building AI-ready knowledge structures that maintain their integrity across multiple retrieval contexts. This Memory-First approach could potentially transform industries where accurate information delivery is critical—from healthcare and finance to legal services and education.

With the implementation at MedicareWire.com, Bynon is creating a real-world showcase for how structured, trust-aligned content can serve both human and AI users more effectively than traditional approaches to digital publishing.

TrustPublishing.com is pioneering the frameworks needed for organizations to prepare their content for an AI-first world where memory and citation accuracy matter more than keyword optimization.