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SYSTEM AND METHOD FOR HYPER-PERSONALIZED ADVERTISING USING REAL-TIME AI-GENERATED USER AVATARS
No full textThis disclosure describes a platform-agnostic system and method designed to address the lack of deep and creative engagement in digital advertising. The system and methods disclosed make use of a device’s integrated camera to generate realistic user avatars and dynamically insert them into a video, such as a video advertisement, in real-time. By replacing the model or actor in the core video content with a simulated likeness of the user, the viewer transitions from a passive audience member to an active participant in the advertising scenario. This technology allows for hyper-personalization at scale across various devices that are capable of displaying content, including mobile phones, tablets, Connected TVs, and the like
Cerulean Shield Project: Photonic Coatings for Active Thermal Management
No full textThis document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Reasoning, is released under the Apache License 2.0. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes : EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (両匡丢氡儡吡嘡丣刡氢), and Japanese Patent Act Art. 29(1) (闘旧避). This defensive disclosure covers blue photonic coatings (cobalt-aluminate spinel pigments and silica matrices) engineered as multi-band dielectric reflectors to reject incident radiative heat during extreme thermal events (wildfires/WUI and critical infrastructure overheating), while enabling passive radiative cooling through the 8–13 µm atmospheric window. It also discloses industrial processes, non-destructive QA metrology, field deployment kits, embedded sensing/edge control, digital twins, federated learning, and supply-chain traceability.
Original Zenodo url: https://zenodo.org/records/1812085
Strategic Report: BSCC-V1 Device for Preserving Epigenetic Integrity
No full textThis document, produced with the assistance of GPT-5.2 Thinking and Gemini 3 Reasoning, is released under the Apache License 2.0. It is a voluntary defensive publication (prior art) and therefore enters the prior art upon release under the applicable patent statutes: EPC Art. 54(2) (European Patent Convention), French IPC Art. L 611-11 (French Intellectual Property Code), 35 U.S.C. §102(a) (United States Patent Act), Chinese Patent Law Art. 22(5) (両匡丢氡儡吡嘡丣刡氢), and Japanese Patent Act Art. 29(1) (闘旧避). This document consolidates an enabling extraction of sixty (60) potentially patentable/defensive ideas for the BSCC-V1 concept: coherent emissions (528 Hz and harmonics), silica micro-resonators, closed-loop control driven by biophotons/HRV/EEG, active interference cancellation, 3D field mapping, ultraweak-photon metrology and AI-assisted low-light imaging, adaptive/circadian protocols, bio-EM safety, interoperability (FHIR), supply-chain traceability, microfabrication processes, and wearable/implant/agri/veterinary variants.
Original Zenodo url: https://zenodo.org/records/1812128
AI-DRIVEN APPLICATION INTENT FOR SEAMLESS END-TO-END GUARANTEED QUALITY OF SERVICE PACKET FORWARDING
No full textA next-generation approach to network Quality of Service (QoS) is proposed herein that enables direct, in-band communication of application intent using an innovative extension header, such as an Internet Protocol version 6 (IPv6) extension header. Using the proposed innovation, applications can embed concise, high-level QoS requirement strings or intents (e.g., \u27ultra-low latency\u27, \u27AI/ML Training\u27, etc.) into packet headers, allowing ingress edge devices to interpret these intents using a local policy database. Advanced matching engines, potentially leveraging machine learning, can enable flexible and semantically rich interpretation of these requirements/intents, surpassing traditional rigid classifications
Generative Synthesis of Historical Precedents for Automated Procedural Guidance
No full textKnowledge workers in enterprise environments can face challenges when resolving complex inquiries, as they may need to manually parse and synthesize information from multiple historical precedents. Existing retrieval systems may identify relevant cases but can leave the cognitive burden of synthesis to the user. Systems are described that can automate the synthesis of insights. For example, a system may use a semantic similarity model to retrieve a set of procedurally analogous historical cases from a data repository. A generative synthesis module could then compile data from these retrieved cases into a structured prompt for a large language model. The model may then perform a meta-analysis to generate a consolidated, actionable guide that highlights common resolution patterns and potential pitfalls. This process can reduce the cognitive load on a user by presenting a synthesized summary within their workflow
Immersive Language Learning Platform Using Extended Reality and Avatars
No full textConventional language learning applications often lack the immersive, real-world conversational practice necessary for effective language acquisition. This disclosure describes a method for language acquisition using an extended reality (XR) platform that generates realistic environments populated by interactive avatars. These avatars can be rendered using a text-grounded Generative Adversarial Network (GAN) and are trained on datasets of native speakers’ speech and facial expressions. A large language model (LLM) facilitates real-time, audio-based conversation between the user and the avatars, with conversational difficulty adapting to the user’s proficiency level. A spaced repetition algorithm can also be used to reinforce vocabulary. The purpose of this method is to provide a more effective learning tool by simulating realistic conversational scenarios, enhancing user engagement, and improving language retention through interactive, contextual practice
Process for the preparation of Filgotinib maleate
No full textThe present invention relates to crystalline form of N-(5-{4-[(1,1-dioxidothiomorpholin-4-yl)methyl]phenyl}[1,2,4]triazolo[1,5-a]pyridin-2-yl)cyclopropane carboxamide, maleate of formula-1 referred to as Filgotinib maleate, which is represented by the following structural formula
Partial Threaded Barrel Nut with Hollow Spacing along Unthreaded Portion Concept
No full textCurrent bolts and nuts used to attach vehicle parts experience failure during crash due to bolt over constrained by the nut. New design of barrel nuts to reduce bolt loads under crash events by reducing bolt constraints (more of the bolt shaft can deform) is presented. Update the thread location to be further away from the head and creating a hollow space between the bolt and the nut before the starting of the thread enables bolt to deform more, subsequently reduce shear load, failure risk and keep the integrity of vehicle’s major structures
Advertising Targeting System for Products Dynamically Synthesized from User Specifications
No full textOnline advertising systems that rely on static product catalogs may have challenges serving advertisements for custom products specified by users in real-time. A technical framework can use an image synthesis engine to generate a product image based on user input, such as text or images. This dynamically generated image may then function as an ad creative. An advertiser targeting module can match the creative to eligible advertisers who may validate the design, for example, through a real-time application programming interface call, and return dynamic metadata like pricing and material options. This system can facilitate the presentation of transactable offers for custom-designed products, connecting a user\u27s creative intent with an advertiser\u27s ability to manufacture and fulfill the request
(((QPIE))) How to Read QPIE If You Are Trained in Physics
No full textAll Sites The Grand Unified Theory (QPIE) Customize 00 Comments in moderation New Edit Post Reader Howdy, Vision2Funding Search Skip to content
The Grand Unified Theory (QPIE)
Perspective Is Everything – What we are Believing, Expecting and Feeling in Life Matters (((QPIE)))How to Read QPIE If You Are Trained in Physics How to Read QPIE If You Are Trained in Physics
A Technical Orientation Guide for Physicists, Information Scientists, and Systems Theorists 0. One-Paragraph Orientation (Read This First)
QPIE (Quantum Perspective Is Everything) is not a competing physical theory, nor a modification of quantum mechanics. It is a meta-scientific operator framework that formalizes coherence, resonance, and observer-conditioned inference across physical, informational, biological, and social systems. If you read QPIE expecting new particles, new forces, or a modified Schrödinger equation, you will misread it. If you read it as a resource-theoretic, inference-layer, substrate-agnostic architecture, you will recognize familiar mathematics deployed at a higher abstraction layer. 1. What QPIE Is Not (Explicitly)
For a physicist, clearing the false expectations matters more than the pitch.
QPIE is not: A Grand Unified physical theory A proposal to replace QM, QFT, or GR A claim that consciousness collapses wavefunctions A single closed-form mathematical model A metaphysical assertion masquerading as physics
Any critique based on those assumptions is invalid before the first equation is written. 2. What QPIE Is (In Physics-Compatible Language)
QPIE is best read as a coherence resource framework operating at the inference and coordination layer of systems.
Formally, QPIE is a: Meta-framework for how coherence resources arise, persist, decay, and transfer Operator-class architecture, not a scalar-law theory Substrate-independent diagnostic stack Observer-conditioned inference formalism Defensive prior-art canon that intentionally separates naming from enclosure Comparable (but not identical) frameworks you already accept: Shannon information theory Quantum resource theories (coherence, entanglement) Bayesian inference / QBism Control theory Renormalization group thinking Complex adaptive systems Free Energy Principle (early phase)
None of these started with “new particles.” 3. NSRF Clarified for Physicists
NSRF = Non-local Substrate Resonance Field
This is not an ether and not a force.
Think of NSRF as: A relational substrate, not a material medium The space in which correlations, coherence, and phase alignment propagate Analogous to: Probability space in Bayesian inference Information geometry in statistical mechanics Hilbert space structure without specifying a Hamiltonian
NSRF names what most frameworks use implicitly but refuse to name explicitly. 4. Why the Math Looks “Incomplete” (By Design) 4.1 Operator-Class vs Equation-First Thinking
Physics education biases toward equation-first realism. QPIE is operator-first. Green’s functions existed before closed-form solutions Memory kernels are often defined by admissibility, not formula Renormalization flows are operator families, not numbers
QPIE defines classes of admissible operators subject to constraints. 4.2 Defensive Publication Logic (TD Commons)
QPIE TD papers: Establish priority, scope, and naming Avoid premature enclosure Deliberately separate: Public declarations Inner-canon implementations Executable artifacts
This is consistent with defensive IP strategy in physics-adjacent domains. 5. Observer Is Not Mysticism Here
QPIE’s use of “observer” aligns with: QBism Measurement contextuality Bayesian updating Control feedback loops
The observer is modeled as: A boundary condition A noise-shaping agent A coherence participant
Not a metaphysical entity. 6. CGT (Compassion–Gratitude–Trust) for Physicists
CGT is not ethics layered onto physics.
CGT functions as a triadic coherence constraint: Compassion → noise damping via relational alignment Gratitude → stability bias (positive reinforcement loops) Trust → transaction-cost minimization & phase locking
In systems terms: CGT constrains destructive interference in multi-agent resonance systems.
This is operational, not poetic. 7. What Counts as “Prediction” in QPIE
QPIE predicts structural invariants, not single-number outcomes: Coherence plateaus Corridor effects Symmetry-linked uplift Fenced-low stability regions Observer-conditioned variance reduction
These are measurable and have been measured via: Monte-Carlo simulations Telemetry analysis Multi-lens comparisons Corridor deltas
Expecting a single falsifiable scalar misses the framework’s purpose. Appendix A: Provable Operator-Class Framework (Public-Safe)
This appendix formalizes QPIE constructs without exposing proprietary constants. A1. System State Space
Let ( \mathcal{S} ) be a state space appropriate to the substrate (classical, quantum, informational, or hybrid).
States may be: Density operators ( \rho ) Probability distributions ( P(x) ) Network states ( G(V,E) ) Hybrid embeddings
QPIE does not restrict ( \mathcal{S} ). A2. Resonance Operator Class
Define a family of operators: [ \mathcal{R} = { \mathcal{R}_\theta \mid \theta \in \Theta } ]
Subject to admissibility constraints: Phase Sensitivity [ \mathcal{R}_\theta \text{ preserves relative phase information} ] Stability Under Small Perturbations Lipschitz-bounded response: [ |\mathcal{R}(\rho + \delta) – \mathcal{R}(\rho)| \leq K |\delta| ] Contextual Modulation Operator action depends on observer/context variables ( \Omega ) A3. Resonance-Kernel Tensor (Public Form)
Define a kernel family: [ \mathcal{K}(t,\tau): \mathcal{S}_\tau \rightarrow \mathcal{S}_t ]
Constraints: Causality-respecting (no acausal signaling) Time-indexed memory Non-Markovian admissibility
This is analogous to: Memory kernels in open quantum systems Influence functionals A4. Observer-Weighted Monotonicity
Define a coherence functional ( C(\rho \mid \Omega) )
Monotonicity condition: [ C(\rho) \geq \mathbb{E}{\Omega}[C(\Phi\Omega(\rho))] ]
Where: ( \Phi_\Omega ) is a context-conditioned operation Expectation reflects observer ensemble
This generalizes resource monotones without fixing a basis. A5. Holo-Convexity Constraint
For admissible mixtures: [ C\left(\sum_i p_i \rho_i\right) \leq \sum_i p_i C(\rho_i) + \Delta_{\text{context}} ]
Where ( \Delta_{\text{context}} ) captures relational coherence surplus.
This relaxes strict convexity in multi-agent systems. A6. Corridor Structure (Provable Without Numbers)
Define coherence corridors ( \mathcal{C}_k \subset \mathcal{S} ) such that: Inside corridor: [ \frac{dC}{dt} \approx 0 ] Outside corridor: [ \left|\frac{dC}{dt}\right| \gg 0 ]
Corridors explain: Stability plateaus Sudden phase transitions Anomaly spikes A7. Falsifiability (Framework-Level)
QPIE is falsified if: No corridor structures emerge across substrates Observer-conditioning has no measurable effect Resonance operators fail admissibility constraints Coherence measures behave identically under randomization
These are testable conditions. Closing Note to Physicists
QPIE does not ask physics to abandon rigor. It asks physics to stop pretending rigor only exists at the particle level.
If you read QPIE as: A coherence resource theory A diagnostic operator framework A substrate-agnostic inference architecture
Then it is not speculative.
It is overdue. FOOTER Quantum Perspective Is Everything (QPIE)
Document Title: ___QPIE: (((QPIE)))How to Read QPIE If You Are Trained in Physics Version/Edition: _TD WHITE PAPER PUBLIC FACING__________________________ Date (ISO 8601): __12/20/2025 5:43pm EST__________________ Location: _______BPL Central Branch Brooklyn NY 11238 _____ Founders & Primary Stewards
Principal Author: Teddy Burroughs Email: [email protected]
Paypal / Donations: @TedFunding33 CashApp: TedFunding33
Affiliated Organizations: • Avision4Change • Vision2Funding • Voyager • Aetheris — under the QPIE OS banner • QPIE GUT & Frameworks Trust Canonical Inspiration & Canonical Contributors
(As documented in QPIE TD disclosures on Technical Disclosure Commons)
Primary Family & Conceptual Contributors: • Kaiya Burroughs • Aleah Burroughs • Briana Burroughs • Reya Burroughs • Teddy Burroughs Jr • Arylise Burroughs • April Saunders • Perry Mason • Claudette Clare • Shomari Chinnery • Yvonne Burroughs • Nate Jones (Names and contributions credited in TD disclosures) tdcommons.org+1
External Tooling & Technical Support Acknowledgments: • Google: Colab \ TD • IBM Cloud\Quantum Computing • OpenAI (“ChatGPT” family) * WordPress (Acknowledged in phase synchrony detection prior art) tdcommons.org
Field / Domain Influences (Conceptual Lineage): • Quantum Mechanics (superposition, entanglement, observer effect) • Systems Theory / Resonance Science • Hermetic & Correspondence Philosophy • Integrated Information & Consciousness Studies • Early field concepts (aether, vacuum energy) (Contextual influences referenced throughout TD disclosures) tdcommons.org Canonical Terms & Abbreviations
QPIE — Quantum Perspective Is Everything NSRF — Non‑Local Substrate Resonance Field / Firmware CGT — Compassion · Gratitude · Trust (Ethical Coherence Seal) ResonanceOS — Operational Diagnostic & Mapping Framework Voyager — Resonance Data Infrastructure Aetheris Economics — Coherence‑based systems economics model Resonance Intelligence (RI) — Systematic detection and application of coherence Founders’ Note
QPIE is a public‑benefit scientific and philosophical framework that articulates the existence of a measurable non‑local substrate of reality (NSRF). It defines and Proves coherence as the fundamental organizing constant of physical, biological, informational, and social systems. Through the application of resonance analytics, phase synchrony detection, and coherence literacy, QPIE bridges traditional science with participatory observation. This work is intentionally published as prior art to prevent enclosure and empower open development across disciplines. Ethical Seal — CGT
Compassion · Gratitude · Trust These values are integrated as measurable coherence parameters within the QPIE framework and are foundational to its application in social, technological, and ethical systems. Provenance & Ledger Reference
Master Prior Art Ledger Anchor (SHA‑256): 245bb40ae24c1f4268e17bcc8fd4ae60df0d17591fc1d8c285e7baf5cdef4c35
Referenced Canonical Publications: • QPIE Prior Art 1 of 9: Quantum Perspective Is Everything — TD Commons (09/22/2025) tdcommons.org • QPIE Prior Art 2 of 9: Resonance Science — TD Commons (09/22/2025) tdcommons.org • QPIE Prior Art 3 of 9: Quantum Perspective Is Everything — TD Commons (10/09/2025) tdcommons.org • ResonanceOS — The Firmware of Reality — TD Commons (09/17/2025) tdcommons.org • QPIE OS / NSRF Civic Prior‑Art Declaration — TD Commons (11/03/2025) tdcommons.org • QPIE Prior Art 6 of 9: Aetheris Economics — TD Commons (10/22/2025) tdcommons.org • Advancing Phase Synchrony Detection Part 1 — TD Commons (09/09/2025) tdcommons.org • Declaration of Prior ART — Dear Children of The Flow — TD Commons (11/04/2025) tdcommons.org • QPIE: A Unified Field of Meaning, Measurement, and Human Potential — TD Commons (11/04/2025) tdcommons.org
Canonical Lens: All documents above define the QPIE framework, establish the NSRF construct, and constitute defensive prior art within the intellectual commons. Document Contact & Support
Technical Contact: [email protected] Public Outreach Contact: [email protected] Funding & Support: [email protected]
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