“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.”
Three engines, one substrate. Patents, Research, and Bio each read their own corpus; the cross-engine layer the portfolio is building toward reads the structural patterns across all three. Built on the LEF Engine’s cross-engine topological dynamics.
The Living Profile of a research corpus — where the field came from, where it’s converging, where the gaps are. Domain Cognitive Foresight Network. Pulls papers from six sources, builds a typed concept graph, runs five cognitive operations across it. Detects entropy gaps, surfaces independent researchers converging without citing each other, grounds every hypothesis in a structural gap.
The Living Profile of the biotech research corpus — preprints, journals, clinical trial registries, mechanism-of-action papers. Same engine as DCFN - Research, dropped into life-sciences literature instead of generic scientific output. Surfaces where independent labs are converging on the same mechanism without citing each other, where the field is structurally ready to translate, and where the next move is missing rather than missing-from-search. Aimed at translational researchers, biotech founders, and funders deciding where to place bets.
The engine pointed at the federal mission, and the portfolio’s primary funding target. 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. 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.
The engine pointed at the hard-science frontier, where independent labs converge on the same pathway without citing each other and the next breakthrough is one composition or integration step away. In build.
The seed dropped into the energy research corpus — DOE OSTI papers, NREL reports, ARPA-E publications, materials energetics, grid integration studies. Reads where independent labs are converging on the same energy pathway without citing each other, where the field is structurally ready to translate, and where the next move is missing rather than missing-from-search. Built for the DOE program manager, energy R&D lead, or OEM evaluating where to place the next bet — and for SBIR/ARPA-E proposal teams structuring the technical narrative.
The seed dropped into the materials science corpus — NIST data, DOE materials research, university materials engineering output, industrial R&D publications. Reads where novel-material pathways are converging across labs that don’t read each other’s work, where structural breakthroughs are one composition away, and where the gap maps to a fundable proposal. Built for NIST researchers, DOE lab leads, and materials-startup founders deciding where the field’s structural readiness aligns with their roadmap. Shares ingestion infrastructure with DCFN - Energy.
The seed dropped into the semiconductor corpus: device-physics papers, fabrication-process literature, materials-integration research, and the technical disclosures around them. Reads where independent groups are converging on the same device or process pathway without citing each other, where the next architecture is one integration step away, and where the structural gap maps to a fundable program. Built for semiconductor R&D leads, fab process engineers, and CHIPS-era program evaluators deciding where the field is structurally ready to move.
LEF • Education
Feature Board • Private Preview • part of the 10-patent LEF Ai Engine portfolio
REQUEST ACCESS →LEF • Operational Intelligence
Development Hold • part of the 10-patent LEF Ai Engine portfolio
DEVELOPMENT HOLDInstitutional 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
READ 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
READ 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
READ 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
READ ABSTRACT →Provisional • Filed 03/10/2026
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.
Filed 03/10/2026 • Cross-Ref: 63/993,278 & 63/993,317
READ 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
READ ABSTRACT →Supplemental Provisional • Filed 04/18/2026
App. No. 64/043,294 • Patent Center #75356812 • Supplements all prior six provisionals
READ ABSTRACT →Supplemental Provisional • Filed 04/21/2026
App. No. 64/045,185 • Patent Center #75384870 • Supplements all prior seven provisionals
READ ABSTRACT →Supplemental Provisional • Bundle A
App. No. 64/061,710 • Supplements all prior eight provisionals
READ ABSTRACT →Supplemental Provisional • Bundle B
App. No. 64/061,715 • Supplements all prior nine provisionals
READ ABSTRACT →All patents filed by Zontonnia Moore on behalf of Living Eden Frameworks LLC • Micro Entity • Henderson, NV
Every contribution fuels the next framework, the next partnership, the next bridge between what exists and what's possible.
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