“Augmenting Human Intellect”
Douglas C. Engelbart · Stanford Research Institute · 1962
“It’s not about building a tool for Profit or something that imitates the current landscape; it’s about evolving the tools humanity has, creating the bridge to let humanity push past the hard edges.”
Zontonnia Moore · Founder, Living Eden Frameworks · 2026
Explore Our WorkPresence-based AI can't find a gap — and that's the whole point. Patent-analytics platforms index existing filings. Research-search tools rank existing papers. LLM assistants summarize existing content. All of them operate on presence, and a void has no document to return, no passage to summarize, no token to predict — so they are structurally incapable of finding it.
The LEF Engine reasons over the shape of the surrounding evidence rather than the evidence alone. It reads a field — scholarly literature, patent filings, a regulatory landscape, a school district — and surfaces the structural voids: positions ringed by dense, mature activity that themselves remain empty, where the field is structurally ready to advance next. The same apparatus, recalibrated rather than rebuilt, finds those gaps across very different domains. Per Engelbart's 1962 framing, this is augmentation: leverage at scale, decisions left in human hands.
Ten U.S. patents pending describe the substrate — cognitive traversal, ensemble optimization, structural discovery, cross-domain transfer, convergence detection, claim-element graph topology, and cross-engine topological dynamics. The engine is deterministic and self-calibrating. LLMs serve it as one tool among several, not as its substance.
One engine, two distinct types of application:
“Analysis is the work product, not the deliverable. The deliverable is the customer’s next action.”
The DCFN engine builds — six engines, one substrate. Patents, Research, and Bio read the civilian knowledge corpora; Energy, Materials, and Semiconductors read the hard-science frontier. Each holds on its own; the cross-engine layer the portfolio is building toward reads the structural patterns across all of them — deeper output when run cross-engine. Built on the LEF Engine’s cross-engine topological dynamics.
🔒 Deployable as a hardware-attested confidential enclave · Enclave Deployment →The Living Profile of a research corpus — where the field came from, where it’s converging, where the gaps are. Pulls from six sources, surfaces independent researchers converging without citing each other, grounds every hypothesis in a structural gap.
Reads the actual claims across hundreds of thousands of filings and surfaces the white space between them — the inventions no one has described yet, visible only because their absence shapes everything around them. Prior-art search run backwards.
The same engine dropped into life-sciences literature — preprints, journals, trial registries, mechanism-of-action papers. Surfaces where independent labs converge on the same mechanism without citing each other, and where the field is structurally ready to translate. For translational researchers, biotech founders, and funders.
The seed in the energy research corpus — DOE/OSTI, NREL, ARPA-E, grid integration. Reads where labs converge on the same pathway without citing each other and where the next move is structurally ready. For DOE program managers and SBIR/ARPA-E proposal teams.
The seed in the materials-science corpus — NIST, DOE, university and industrial R&D. Reads where novel-material pathways converge across labs that don’t read each other, structural breakthroughs one composition away. Shares ingestion with DCFN - Energy.
The seed in the semiconductor corpus — device physics, fabrication, materials integration. Reads where groups converge on the same device or process pathway without citing each other, the next architecture one integration step away. For CHIPS-era program evaluators.
The engine pointed at the federal mission — the portfolio’s primary funding target, and the LEF ISB. It is operational now: an agency-neutral, invite-gated demonstration where federal R&D evaluators (AFRL, IARPA, the broader IC) bring their own unclassified corpus and watch the engine read its structure, every gap cross-examined against reality before it surfaces. Where the DCFN builds surface the structural landscape of a domain, the ISB composes that read into a briefing-grade situational picture for institutional roles — agency officers, jurisdictional planners, board-facing leads — tied to the operator’s next decision, and across sources shows where a structure lived in one corpus but never another. The domain builds below point the same engine at specific federal corpora; those are in build.
The seed dropped into a federal program’s CQI corpus — performance reports, outcome data, case and operational records, and the governing CQI frameworks. Reads where intervention pathways are converging and where the data is structurally hiding a failed pathway. Surfaces the upstream cause of downstream performance failure: where the gap between policy intent and field practice is widening, not just reporting that outcomes are off. Built for the CQI specialist who has to defend a remediation plan to federal monitors.
The seed dropped into the federal regulatory corpus — CFR sections, agency guidance, state implementation regulations, audit findings, OIG reports. Reads where the regulatory landscape has drifted, where state guidance contradicts federal CFR, and where compliance gaps cluster across agencies before they surface as findings. Built for the compliance officer who has to explain to a board why the gap exists and where to act. Pairs with DCFN - CQI under the ISB chassis for cross-domain regulatory + performance reads.
The seed dropped into the policy and legislative corpus — proposed legislation, regulatory impact assessments, congressional hearing records, state statute, GAO and CRS reports. Reads where statute is structurally incomplete, where proposed legislation contains internal contradictions that will surface during implementation, and where the regulatory record converges on a policy answer no one has authored yet. Built for Congressional Research Service analysts, GAO researchers, state legislative staff, and policy think tanks needing a structural map of the legislative landscape before drafting.
LEF Ai.E • Ed Continuum
Feature Board • Private Preview • part of the 10-patent LEF Ai Engine portfolio
REQUEST ACCESS →Institutional Credentials
Ten provisional patent applications protecting the LEF Ai Engine ecosystem — from deterministic diagnostics to autonomous AI self-optimization, population-scale behavioral profiling with context-conditioned pathway intelligence, cross-domain structural discovery and internal self-calibration, the structural composition geometry that binds all reasoning mechanisms into a single coherent engine across every deployment, and the cross-engine temporal/adversarial dynamics and portfolio-topology extensions that surface mechanism-level structure across mechanistically diverse portfolios.
Provisional • Filed 02/27/2026
Multi-phase diagnostic reasoning engine for competency-based education systems. Eight-phase sequential pipeline for root-cause analysis via DAG traversal with temporal decay modeling.
App. No. 63/993,278 • Patent Center #74663354
REQUEST ABSTRACT →Provisional • Filed 02/28/2026
Adaptive learning layer with cross-platform network effect for educational diagnostic systems. Entropy-driven recursive branching for autonomous AI self-upgrades.
App. No. 63/993,317 • Patent Center #74663931
REQUEST ABSTRACT →Provisional • Filed 03/01/2026
Hybrid AI-human optimization via qualitative input perturbation, decentralized hashed attribute aggregation, and entropy-driven synchronicity prediction.
App. No. 63/993,979 • Patent Center #74668095
REQUEST ABSTRACT →Provisional • Filed 03/01/2026
Unified multi-phase diagnostic reasoning engine and autonomous AI self-optimization ecosystem with cross-platform network learning and entropy-driven recursive branching.
App. No. 63/993,984 • Patent Center #74667530
REQUEST ABSTRACT →Provisional • Filed 03/11/2026
Five-operation cognitive traversal method for concept graphs with self-reflective optimization, constitutional runtime governance, qualia-based inter-agent context transfer, and bounded self-modification.
App. No. 64/002,205 • Patent Center #74802827
REQUEST ABSTRACT →Provisional • Filed 03/31/2026
Population-scale behavioral entity classification with context-conditioned academic pathway intelligence. Seven-dimensional longitudinal feature vectors, machine-derived profile types, and educator soft-sensor modeling with FERPA-compliant cross-institutional learning.
App. No. 64/023,988 • Patent Center #75105165
REQUEST ABSTRACT →Supplemental Provisional • Filed 04/18/2026
App. No. 64/043,294 • Patent Center #75356812 • Supplements all prior six provisionals
REQUEST ABSTRACT →Supplemental Provisional • Filed 04/21/2026
App. No. 64/045,185 • Patent Center #75384870 • Supplements all prior seven provisionals
REQUEST ABSTRACT →Supplemental Provisional • Bundle A
App. No. 64/061,710 • Supplements all prior eight provisionals
REQUEST ABSTRACT →Supplemental Provisional • Bundle B
App. No. 64/061,715 • Supplements all prior nine provisionals
REQUEST ABSTRACT →All patents filed by Zontonnia Moore on behalf of Living Eden Frameworks LLC • Micro Entity • Henderson, NV
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