Closed-loop precision-nutrition platform with hybrid digital-twin modelling and blockchain-verified supply-chain orchestration

Abstract

The invention captures multimodal biometric, clinical, and IoT-logistics data inside a privacy-preserving enclave, builds a hybrid digital twin that unites mechanistic metabolism with machine-learning and taste-genomics embeddings, and—every time physiology, medication, or stock conditions change—computes a drug-safe, nutrient-balanced, culturally palatable meal. It then auto-mints a non-fungible smart-contract purchase order, selects the lowest-carbon, highest-reliability vendor via a self-retraining Deep-Q network, tracks temperature, humidity, and shock in minute-level cold-chain telemetry, and reroutes or reorders within ten seconds of any excursion, all events anchoring to a blockchain ledger that passes quarterly zero-knowledge audits. Post-delivery metrics feed back to the graph, triggering on-the-fly model retraining and instant reformulation on new HL7-FHIR medication events, so clinical efficacy and ingredient integrity are maintained end-to-end without human intervention.

Description

Closed-Loop Precision-Nutrition Platform with Hybrid Digital-Twin Modelling and Blockchain-Verified Supply-Chain Orchestration

[0001] The present invention encompasses three synergistic inventions—an Integrated closed-loop precision-nutrition system, a Hybrid digital-twin and graph-based profiling method, and an Event-driven supply-chain orchestrator—each directed to a distinct layer of delivering “food as medicine.” The first invention automates diet‐personalisation for individual users by securely ingesting biometric, clinical and taste data, computing drug-safe nutrient prescriptions, and issuing blockchain-backed smart-contract purchase orders. The second invention provides the computational core: a hybrid model that fuses mechanistic metabolism with self-retraining machine learning while continuously updating a multi-layer graph of genomics, microbiome, lifestyle and supplier attributes to generate dual scores for metabolic benefit and logistical feasibility. The third invention extends the platform into commerce and logistics; it ranks vendors with a carbon-aware reinforcement-learning policy, monitors IoT-equipped cold-chain containers, rejects non-compliant batches, and automatically reroutes or re-orders ingredients—every event anchoring to a tamper-evident blockchain ledger. Together these inventions capture, compute and deliver clinically verified meals that adapt in real time to physiological change and supply-chain shocks, enabling healthcare providers, food-service operators and end users to achieve reliable, safe and transparent precision nutrition at industrial scale.

[0002] G06F, G06N, H04L, G06Q, A61B, A23L

[0003] US 2021 / 0241918 A1 – Computer-Implemented System and Methods for Predicting the Health and Therapeutic Behavior of Individuals Using AI, Smart Contracts, and Blockchain.

[0004] This published patent application proposes a healthcare platform that uses personal health data (including diet / nutrition intake, medications, genetics, and medical history) to recommend optimized nutrition or medication plans for an individual. The system integrates blockchain-based smart contracts and tokens: for example, it creates “Smart Healthcare contracts” on a blockchain to predict medication usage and manage healthcare costs, even rewarding patients for sharing anonymized data. In essence, the invention focuses on a blockchain-backed digital health record and recommendation system that tailors therapy (including nutritional guidance) based on a patient’s real-time data and predictive models.

[0005] While US 2021 / 0241918 A1 uses personal factors and even considers nutrition in a predictive health model, it primarily aims at overall therapy cost predictions and patient behavior, incentivized via blockchain tokens. In contrast, our system is a closed-loop precision nutrition platform that not only computes personalized meal plans from health data but also automates the procurement and cold-chain logistics of ingredients. Unlike the reference, our invention actively issues purchase orders for meals and monitors ingredient shipping conditions, adjusting the plan on the fly if clinical readings or shipment quality deviate. This level of integrated supply chain control and automatic reflex adjustment (e.g. rerouting shipments or updating meal prescriptions within minutes) is beyond the scope of the cited patent, which does not manage physical food supply or IoT-monitored delivery conditions.

[0006] US 11,195,015 B2 – IoT-Based Farming and Plant Growth Ecosystem.

[0007] This granted patent describes a comprehensive agriculture and food supply-chain system that leverages IoT sensors, robotics, and blockchain for tracking and optimizing crop production. It discloses that blockchain entries can be created at each stage of cultivation and delivery – for example, logging every irrigation cycle or farm operation as an immutable record to prove origin and quality. The system uses smart contracts and even cryptocurrency tokens to facilitate transactions among farmers, suppliers, shippers, and consumers in a farm-to-table network. Notably, it suggests using blockchain and IoT to monitor conditions like soil nutrients and to track produce through transport (including a mention that a farmer could automatically hail a delivery truck and update the blockchain as goods move to the buyer). Overall, this patent focuses on secure food provenance and automated supply chain actions using blockchain and AI in agriculture.

[0008] US 11,195,015 B2 is primarily an agricultural supply-chain innovation ensuring transparency and quality of food products, whereas our invention operates at the clinical nutrition and individual level. Our system’s emphasis is on personalizing meal plans for a specific individual using biomedical data and then orchestrating procurement and delivery specific to that person’s prescription. The cited patent does not address personal medical data, nutrient-drug interactions, or dynamic meal optimization; it tracks generic food batches and farm operations. In contrast, our solution combines the personal health domain with supply chain control – for example, selecting vendors based on carbon footprint and using health triggers (like a new medication order) to instantly reformulate a person’s diet. These closed-loop feedback features (meal plan adjustments from health or delivery feedback) and fine-grained interactions with electronic health records are unique to our system and not present in the farming ecosystem patent.

[0009] US 2021 / 0196195 A1 – Precision Treatment with Machine Learning and Digital Twin Technology for Optimal Metabolic Outcomes.

[0010] This patent application (assigned to Twin Health, Inc.) discloses a “patient health management platform” that creates a digital twin of a patient’s metabolism to guide personalized interventions. It uses continuous sensor data (e.g. wearables, lab results) and patient-recorded inputs (diet, symptoms, activities) to train multiple machine-learned models which predict the patient’s metabolic state. The system updates the digital metabolic model in real time and compares predicted vs. actual outcomes (e.g. blood glucose) to flag inconsistencies. A recommendation engine then suggests tailored treatments – such as dietary changes or activity adjustments – to maintain metabolic health. Essentially, this invention focuses on AI-driven continuous personalization of diet / therapy: it monitors a patient’s data and provides feedback like meal recommendations or alerts if the patient’s logged diet is causing undesired glucose spikes, all without needing centralized data aggregation.

[0011] The Twin Health digital twin platform is similar to our work in its use of multi-modal personal data and adaptive dietary recommendations. However, our invention extends beyond recommendation into automated execution in the supply chain. Notably, our system doesn’t just tell the patient what to eat – it automatically orders the appropriate ingredients via smart contracts and ensures their quality delivery. The cited application has no teaching of integrating a procurement engine, blockchain-based purchase orders, or IoT cold-chain monitoring. Moreover, our invention incorporates additional layers like federated learning in secure enclaves for privacy and multi-objective optimization that balances metabolic goals with real-time inventory and carbon footprint. These features – for example, issuing an ERC-721 token for each ingredient batch and autonomously rerouting shipments if a temperature excursion occurs – are unique to our closed-loop nutrition system and go beyond the scope of the digital twin patent (which focuses on patient monitoring and advice, not end-to-end food supply orchestration.

[0012] WO 2019 / 168795 A1 – Blockchain-Based System and Method for Supply Chain Control.

[0013] This international patent application presents a blockchain-driven approach to monitoring cold-chain logistics. It features “smart labels” with sensors (e.g. temperature loggers) attached to packages, which continuously record storage conditions during transit. The sensor data (e.g. any temperature excursions beyond a threshold) are hashed and uploaded to a blockchain ledger for an immutable, time-stamped record. The system allows selective data visibility to different parties via permissioned blockchain access – for example, a shipper, receiver, or consumer can verify if a food or pharmaceutical shipment stayed within required conditions throughout the journey. It also contemplates automated comparisons of intended vs. actual conditions and can flag or isolate products that breached the cold-chain requirements. In summary, this invention ensures provenance and quality compliance in supply chains by combining IoT sensor data with blockchain for transparency and trust.

[0014] The WO 2019 / 168795 A1 system is focused on generic cold-chain compliance and traceability, whereas our invention ties such supply-chain monitoring directly into a personalized nutrition feedback loop. A key novelty of our approach is that a detected shipment anomaly (like temperature deviation) doesn’t just trigger an alert – it actually causes an automatic replanning of the individual’s meal prescription and immediate reordering from a backup supplier. The cited PCT does not describe any integration with personal health management or dynamic order revision; it is concerned with recording and auditing shipments for quality. Our system, by contrast, uses a similar blockchain / IoT backbone but uniquely bridges it with clinical efficacy: e.g. our logistics subsystem can reject a produce shipment that fell out of range and within seconds procure a replacement via a smart-contract, all while the feedback engine adjusts the patient’s nutritional plan accordingly. This seamless propagation of supply-chain events to patient-specific nutrition adjustments is a differentiating aspect not addressed in the prior art.

[0015] Khan, P. Waqas, Yung-Cheol Byun, and Namje Park (2020). “IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning.” Sensors 20(10): 2990.

[0016] This academic article proposes a framework combining blockchain with IoT sensors and deep learning to improve food supply chain provenance and efficiency. The authors note that blockchain provides a transparent and secure way to trace food products, while IoT devices collect real-time data (e.g. environmental conditions during transport). They develop a hybrid deep learning model (using LSTM and GRU networks optimized by a genetic algorithm) to analyze the large volume of data generated in this “Industry 4.0” food system, with the goal of predicting and preventing issues in the supply chain. The system is intended to help supply chain managers make data-driven decisions – for instance, to anticipate spoilage or logistics delays – and thereby ensure food safety and freshness.

[0017] Khan et al.’s work is oriented toward general food industry supply chains rather than personalized nutrition. It emphasizes optimization of provenance and logistics at an aggregate level, using AI to enhance transparency and efficiency for manufacturers and retailers. In contrast, our invention integrates similar technologies but in service of an individual’s clinical nutrition plan. We are not only tracking food quality via blockchain / IoT (as their system does) but also closing the loop with personal health requirements – our meal personalization engine uses biometric and medical inputs, and the entire pipeline (from recommendation to automated delivery) is tailored to one person. Additionally, Khan et al. do not incorporate factors like drug-nutrient interactions, patient taste preferences, or dynamic re-ordering based on health feedback. Our system’s multi-modal data ingestion (e.g. HL7-FHIR medical data, wearable biosignals) and its ability to automatically trigger supply-chain actions (like issuing a new purchase order if a shipment fails) distinguish it from the more infrastructural and predictive approach in the Sensors paper.

[0018] Lodhi, Aminah B., et al. (2023). “PNRG: Knowledge Graph-Driven Methodology for Personalized Nutritional Recommendation Generation.”

[0019] In Digital Health Transformation, Smart Ageing, and Managing Disability (ICOST 2023), Lecture Notes in Computer Science vol. 14237, 230–238. – This conference paper presents a method for generating highly personalized nutrition advice using a multi-layer knowledge graph. The proposed system aggregates complex data about an individual – including health status, lifestyle factors, and dietary habits – into a graph representation. By reasoning over this knowledge graph, the system can tailor nutritional recommendations to the person’s specific medical needs (the paper uses chronic kidney disease as a case study) and personal preferences. Notably, the application integrates features such as medication reminders and accounts for drug–food interactions in its dietary guidance. The result is a continuously adapting diet recommendation engine that aligns with the individual’s health conditions and goals, demonstrating the feasibility of AI-driven personalization in nutrition.

[0020] The approach by Lodhi et al. shares our goal of fusing diverse personal data to improve nutrition planning, and it even acknowledges drug–diet interactions as an important factor. However, their implementation remains a software recommendation tool – essentially providing advice or meal suggestions within a mobile app for patient self-management. Our invention goes a step further by operationalizing the recommendations: we automatically formulate a meal plan and then handle the end-to-end execution (ordering ingredients, choosing suppliers, managing delivery conditions). The knowledge graph paper does not involve any supply chain or procurement component, whereas those are core to our closed-loop system. Additionally, our invention introduces elements like federated learning for privacy (not discussed by Lodhi et al.) and real-time logistic optimizations (e.g. carbon-aware routing, vendor ranking via reinforcement learning) that ensure the recommended nutrition is delivered safely and sustainably. In summary, Lodhi et al. focus on personalized nutrition reasoning, while our invention connects that reasoning to real-world actions and interventions without human intervention in the loop.

[0021] The invention introduces a cyber-physical precision-nutrition platform that transforms biometric streams into fully automated, clinically safe meals. Continuous-glucose, wearable, and HL7-FHIR data are normalised and tokenised inside a secure enclave, then fused with genotype, microbiome, taste-preference, and live supplier telemetry to populate a hybrid digital twin that combines constraint-based metabolism with self-retraining machine learning. For every eating occasion the twin forecasts biomarkers, checks 18 000+ drug- and nutrient-interaction rules, and scores candidate recipes on both metabolic impact and real-time stock-plus-carbon metrics, accepting only options that meet nutrient targets, keep medication exposure within ±5 %, and exceed a hedonic threshold. The chosen recipe is instantly converted into an ERC-721 smart-contract purchase order; a carbon-adaptive Deep-Q network selects the optimal vendor, and IoT-equipped refrigerated crates stream temperature, humidity, and shock data each minute to a logistics controller that can reroute or re-order within ten seconds of any excursion. All bids, sensor hashes, and custody transfers append immutably to a blockchain ledger that undergoes quarterly zero-knowledge audits. If a clinician adds a new drug, the system regenerates the prescription and issues fresh orders within sixty seconds, while post-meal glucose deviations trigger automatic model retraining. Taste-genomics alignment captures unspoken flavour preferences, and tele-dietitian notes update the graph in real time, ensuring adherence and clinical relevance. By uniting privacy-safe data capture, causal modelling, blockchain-backed provenance, and carbon-aware logistics, the platform delivers verifiably fresh, culturally appropriate meals that maintain therapeutic efficacy without human intervention, setting a new benchmark for personalised nutrition, supply-chain transparency, and healthcare integration.

[0022] Existing personalized-nutrition and food-delivery technologies, although increasingly data-driven, still suffer from a series of intertwined technical shortcomings that prevent them from delivering true “food as medicine” at scale. The invention addresses the following core problems:

[0023] Fragmented data ingestion and siloed analytics.

[0024] Current nutrition apps typically draw on only one or two sources—diet logs or continuous-glucose monitors—and seldom integrate clinical EHR data, genotypes, microbiome profiles, real-time wearable streams, or supply-chain telemetry in a single, time-aligned pipeline. This fragmentation prevents the construction of a continuously updated, mechanistically coherent model of each individual’s metabolic state.

[0025] Absence of a causal, safety-aware meal-design engine.

[0026] Most recommendation systems rely on statistical correlations between foods and outcomes; they do not fuse first-principles physiology with machine learning, nor do they screen every candidate meal against comprehensive drug–nutrient and nutrient–nutrient interaction rules. As a result, users with polypharmacy or micronutrient imbalances receive advice that can inadvertently upset medication pharmacokinetics or essential-nutrient ratios.

[0027] Lack of real-time, closed-loop procurement and logistics control.

[0028] Even the most advanced diet apps stop at generating a grocery list. They do not automatically convert a personalised prescription into supplier-specific purchase orders, track IoT-equipped cold-chain containers, reroute shipments on sensor excursions, or reformulate recipes when stock-outs occur. This gap forces manual intervention, introduces delays, and undermines nutrient integrity by the time food reaches the end user.

[0029] Limited personalisation of taste and cultural context.

[0030] Existing platforms seldom factor in individual flavour affinities, olfactory-genetic differences, or regional culinary norms when optimising nutrient targets. Consequently, adherence suffers because prescribed meals are nutritionally sound but organoleptically or culturally unappealing.

[0031] Static, calendar-driven model updates.

[0032] Predictive models behind current services are retrained on fixed schedules (weekly or monthly) rather than in response to real-time deviations between expected and observed metabolic responses. This rigidity leads to drift, especially when a user’s physiology or environment changes abruptly.

[0033] Inadequate security, privacy, and regulatory traceability.

[0034] Many nutrition systems handle protected health information and food provenance data without end-to-end encryption, robust role-based access controls, or tamper-evident audit trails. Such weaknesses complicate compliance with GDPR, HIPAA, and emerging food-traceability mandates, and they erode user and payer trust.

[0035] Disconnection between clinical workflow and food supply chain.

[0036] There is no mechanism whereby a clinician’s medication adjustment instantly triggers a reformulated meal plan, automatic supplier change, or insurance-billable nutrition-therapy event. The clinical and logistical domains remain technologically decoupled, creating dangerous latencies and administrative overhead.

[0037] These technical gaps collectively highlight the need for an integrated, cyber-physical precision-nutrition system capable of unifying heterogeneous data, performing safety-aware optimisation, autonomously orchestrating procurement and logistics, adapting to each user’s metabolic and cultural context in real time, and providing regulator-grade security and provenance.Solution of problem

[0038] The invention delivers an autonomous “food-as-medicine” loop that gathers every signal relevant to an individual and finishes with a verifiably fresh, clinically safe, culturally familiar meal. It unites three engines—a closed-loop precision-nutrition system, a hybrid digital-twin modelling method, and an event-driven supply-chain orchestrator—to eliminate fragmentation, latency, and safety gaps found in existing personalised-nutrition offerings.

[0039] Solution 1 - Secure, multimodal data fabric

[0040] Continuous-glucose traces, smartwatch vitals, HL7-FHIR medication updates, genotype and microbiome files, grocery receipts, and per-minute cold-chain telemetry enter one ingestion layer. An ontology normaliser maps every code—drug, nutrient, or supplier SKU—onto a shared vocabulary. Inside a hardware secure enclave the payloads are tokenised so that only de-identified graph embeddings exit; raw identifiers remain sealed, enabling federated learning without privacy leakage. Millisecond time-stamps align physiological and logistical events, yielding a coherent decision canvas.

[0041] Solution 2 - Hybrid, safety-aware digital twin

[0042] Upon this fabric the personalisation processor constructs a two-layer metabolic model: a constraint-based core that respects mass balance, enzyme kinetics, and pharmacokinetics, wrapped by a machine-learning envelope tuned to the user’s historical food–biometric pairs. A multi-layer graph encodes genomics, microbiome taxa, medications, lifestyle patterns, taste preferences, and live supplier attributes, with edges weighted by pathway flux scores. Whenever new sensor data arrive, the twin forecasts biomarkers and re-evaluates every documented drug–nutrient and nutrient–nutrient interaction, recalculating medication exposure curves within sixty seconds of each five-minute CGM ping.

[0043] Solution 3 - Multi-objective recipe optimisation with hedonic safeguarding

[0044] Each candidate meal is scored for metabolic benefit and logistical feasibility derived from live safety-stock levels and route carbon footprints. Only recipes that keep medication AUC within ±5 percent, meet nutrient targets, and achieve a taste-preference score of at least 0.85 pass the gate. That hedonic fingerprint is embedded in the ensuing purchase order so any later substitution must preserve flavour. If observed post-prandial glucose deviates by more than one standard deviation from prediction, relevant graph embeddings update and the machine-learning envelope retrains automatically.

[0045] Solution 4 - Smart-contract procurement and provenance

[0046] Every accepted ingredient list is minted as a non-fungible smart contract containing batch identifiers, quality thresholds, hedonic metadata, and milestone-escrow rules. Supplier bids, custody transfers, and one-minute sensor hashes append immutably to a blockchain ledger, forming a tamper-evident provenance chain from producer to patient.

[0047] Solution 5 - Carbon-adaptive vendor selection and cold-chain quality control

[0048] A reinforcement-learning policy—retrained daily and re-seeded whenever route carbon intensity drifts—ranks suppliers by reliability, batch quality, and environmental impact. The chosen vendor ships in an IoT-instrumented crate whose temperature, humidity, and shock data stream every minute. If temperature strays more than two degrees Celsius for three consecutive readings, the batch is rejected, a backup vendor is selected within ten seconds, and a replacement smart contract is issued; the routing optimiser simultaneously replans to keep cold-chain compliance above 99 percent while minimising added emissions.

[0049] Solution 6 - Reflex clinical updates and closed-loop feedback

[0050] The feedback engine monitors the electronic health record. When a clinician prescribes a new drug, the digital twin regenerates the dinner prescription, the optimiser revises ingredient selections, and fresh purchase orders are dispatched—all within sixty seconds. Shipment status and ledger pointers flow back into the record as structured reports, ensuring nutrition therapy stays aligned with the active treatment plan.

[0051] Solution 7 - Continuous self-improvement and audit readiness

[0052] Forecast accuracy is protected by an event-triggered retraining cycle: whenever the gap between predicted and observed glucose exceeds one standard deviation, the machine-learning envelope fine-tunes itself with the newest de-identified embeddings and redeploys in minutes. Escrow releases only after blockchain verifiers confirm sensor compliance, and quarterly zero-knowledge proofs attest the ledger’s integrity without revealing proprietary supplier details.

[0053] Solution 8 - Taste-genomic intelligence for latent preference capture

[0054] A contrastive-learning routine aligns molecular fingerprints of flavour compounds with the user’s olfactory-receptor genotypes. If high affinity at, for example, OR7D4 indicates sensitivity to boar taint, the optimiser automatically lowers the hedonic weight of pork dishes even when the user has never flagged a dislike, preventing unpalatable meals from reaching the shortlist.

[0055] Solution 9 - Human-in-the-loop refinement via tele-dietitian notes

[0056] During video consultations a dietitian tags observations—such as “early satiety” or “soft-food phase”—using structured SNOMED-CT terms. When the note arrives as a FHIR Observation, the corresponding graph node updates its embedding; the digital twin, optimiser, and vendor-ranking policy all inherit the new context in the next control cycle, letting expert insight propagate through modelling, procurement, and logistics without delay.

[0057] Together these nine operations match every element set out in the claims: they secure the data, model metabolism causally, optimise recipes under clinical and hedonic constraints, procure ingredients through carbon-aware smart contracts, guard cold-chain integrity, react instantly to medical changes, retrain themselves on deviation, infer hidden taste genetics, and incorporate practitioner feedback—delivering precision nutrition that is clinically effective, logistically flawless, and fully auditable.Advantage effects of invention

[0058] The present invention—a cyber-physical precision-nutrition platform—delivers decisive advantages over prior art by merging privacy-safe multimodal data capture, causal digital-twin modelling, taste-aware recipe optimisation, carbon-adaptive procurement, and blockchain-verified cold-chain logistics. Together these capabilities create a seamless, medically aligned “food-as-medicine” experience that current diet apps, meal-kit services, and supply-chain systems cannot approach.

[0059] A first major advantage is the creation of a panoramic, time-aligned view of each eater’s biological, clinical, and logistical context. By funnelling continuous-glucose readings, smartwatch vitals, HL7-FHIR medication updates, genotype and microbiome files, and live container telemetry into a single ontology and secure-enclave pipeline, the system eliminates the data silos that force today’s solutions to rely on guesswork or manual reconciliation.

[0060] A second advantage lies in its hybrid, safety-aware digital twin. Combining constraint-based physiology with machine-learning adaptation lets the platform forecast biomarkers with causal fidelity while instantly recalculating drug-nutrient interactions whenever new sensor data arrive. This prevents the dangerous mismatch between dietary advice and medication exposure that plagues correlation-only recommender engines.

[0061] Third, the platform moves beyond static grocery lists by auto-converting each approved meal into a non-fungible smart contract and routing it through a reinforcement-learning vendor network. Real-time carbon scoring, daily retraining, and rapid route re-seeding deliver ingredients that are not only clinically correct but also environmentally optimised—an integration unseen in conventional e-commerce or diet services.

[0062] Cold-chain integrity brings a fourth advantage. Minute-level temperature, humidity, and shock monitoring with instant rejection and ten-second reordering keeps nutrient quality intact and proves compliance to regulators. Escrow only releases once blockchain verifiers confirm that storage thresholds were never breached, giving payers and clinicians unprecedented confidence.

[0063] Fifth, the system maximises adherence by embedding a personalised hedonic fingerprint—derived from onboarding surveys and taste-genomics alignment—into every purchase order. This ensures substitute suppliers must match flavour profiles, solving the long-standing problem of nutritionally sound but unpalatable meal plans.

[0064] Clinical reflexivity constitutes a sixth advantage. Each new MedicationRequest triggers a full loop—meal redesign, procurement refresh, and EHR update—within one minute, turning nutrition from a static recommendation into a live adjunct to pharmacotherapy and unlocking direct reimbursement under nutrition-therapy codes.

[0065] Seventh, continuous self-improvement maintains accuracy and resilience. When observed glucose deviates beyond one standard deviation, the machine-learning envelope automatically retrains on the most recent de-identified embeddings and redeploys in minutes, preventing model drift without manual intervention.

[0066] An eighth advantage is robust privacy and auditability. Tokenisation inside hardware enclaves, on-chain provenance hashes, and quarterly zero-knowledge audits make the platform simultaneously GDPR / HIPAA compliant and tamper-evident, solving the dual challenge of protecting personal health data while satisfying regulators that every batch is genuine.

[0067] Finally, tele-dietitian graph updates provide a human-in-the-loop pathway for nuanced clinical insight. Structured SNOMED-CT annotations from a video consult flow directly into the graph embeddings, propagating clinician knowledge through modelling, procurement, and logistics without latency.

[0068] Collectively these advantages empower patients to receive meals that are safe, effective, tasty, timely, and ethically sourced; enable clinicians to prescribe nutrition with the same confidence as medication; and give suppliers a transparent, incentivised marketplace—all in a single, closed-loop architecture that marks a clear technological leap beyond existing personalised-nutrition and supply-chain solutions.

[0069] System-level architecture and data flow for the precision-nutrition platform, showing how multimodal data enter the secure ingestion layer, pass through the hybrid digital twin and optimisation stack, and drive smart-contract procurement, IoT cold-chain monitoring, blockchain provenance, and user- / clinician-facing interfaces.

[0070] Event-driven supply-chain orchestration sequence illustrating how a new clinical trigger (e.g., medication change) cascades through recipe reformulation, smart-contract minting, vendor re-ranking, cold-chain quality control, and blockchain escrow release, with automatic feedback to the electronic health record.

[0071] In this embodiment a Multimodal Ingestion Gateway (101) receives HL7-FHIR clinical data, CGM streams, wearable vitals, genotype files, microbiome profiles, and per-minute logistics telemetry. A Secure Enclave (102) tokenises identifiers and passes only de-identified embeddings to an Ontology Normaliser (103) that maps disparate codes onto a shared vocabulary. The Hybrid Digital-Twin Engine (104) fuses a constraint-based metabolic core with a self-retraining ML envelope and outputs nutrient targets plus interaction-safety scores. A Recipe Optimiser (105) generates meal prescriptions that meet those targets and a hedonic threshold. The Smart-Contract Generator (106) mints an ERC-721 purchase order containing flavour metadata and cold-chain limits, while a Reinforcement-Learning Vendor Module (107) selects the lowest-carbon, highest-reliability supplier. Container telemetry streams through the Cold-Chain IoT Hub (108) to a Quality-Control Processor that can reroute or reorder on excursions; all events append to the Blockchain Ledger (109). A Feedback Engine (110) writes nutrition reports and ledger pointers back to the EHR and updates mobile and web User Interfaces (111) for patients, dietitians, and payers.

[0072] In this embodiment a Clinical trigger (201)—for example, a newly posted HL7-FHIR MedicationRequest—reaches the Feedback engine, which immediately interrogates the Hybrid digital twin (202). The twin recomputes nutrient targets and drug-interaction risk and forwards an updated nutrient vector to the Recipe optimiser. The optimiser then issues a Replacement smart contract (203), encoding the refreshed ingredient list, interaction limits and flavour metadata. The Carbon-aware deep-Q vendor ranking (204) evaluates available suppliers on lead-time reliability, batch quality and route carbon intensity; the top-ranked vendor receives the contract and initiates Supplier dispatch. Goods leave in an IoT-equipped container (205) that streams temperature, humidity and shock data every minute.

[0073] Cold-chain quality control (206) compares each sensor packet to the specified thresholds. When three successive readings show a two-degree Celsius over-temperature, the module triggers the dashed excursion loop: data flow diverts to the Backup supplier selector (207), which cancels the compromised lot, mints a new smart contract and hands off to the Routing optimiser (208). The optimiser replans a delivery path that maintains at least 99 % cold-chain compliance while minimising added emissions, and a replacement container is dispatched—closing the quality-control loop in under ten seconds.

[0074] If no excursion occurs, or after a successful replacement run, the container arrives within specification; the sensor hash chain satisfies the conditions of the Sensor-audited escrow on the blockchain ledger (209), which releases payment automatically to the vendor. Simultaneously the Feedback engine compiles a Diagnostic report containing the ledger pointer and uploads it to the patient’s electronic-health record. Clinicians therefore see a complete, cryptographically verifiable chain of custody—from prescription change to meal delivery—without manual reconciliation.Examples

[0075] To demonstrate the full functionality of the invention, eleven illustrative examples follow. They trace a single user (Sara) and the supporting back-end through routine and edge-case scenarios; they are illustrative, not limiting.

[0076] Example 1 Onboarding and graph construction

[0077] Sara installs the app, grants OAuth to her Dexcom CGM, Fitbit, and hospital portal, and uploads genotype and microbiome files. The ingestion layer tokenises all identifiers inside the secure enclave; de-identified embeddings enter a multi-layer graph whose vertices represent genes, microbiome taxa, medications, lifestyle patterns, taste answers, and live supplier attributes. A hybrid digital twin initialises from this graph, setting baseline nutrient targets and safety thresholds.

[0078] Example 2 Morning closed-loop breakfast

[0079] At 06:55 Sara’s overnight glucose trend and HRV land in the model. The twin predicts a morning spike and designs a low-GI smoked-salmon rye wrap that keeps warfarin exposure within ±5 %, meets macro / micronutrient goals, and achieves a hedonic score of 0.89. An ERC-721 smart-contract purchase order is minted; a Deep-Q vendor policy selects the lowest-carbon fishmonger and dispatches a chilled crate whose sensors hash data to the blockchain every minute.

[0080] Example 3 Real-time drug-interaction reflex

[0081] At 14:12 her cardiologist adds amiodarone in the EHR. Within sixty seconds the feedback engine regenerates dinner, replacing grapefruit segments with blueberries, withdraws the old order, issues a fresh smart contract, and posts a nutrition-therapy DiagnosticReport back to the chart for reimbursement.

[0082] Example 4 Cold-chain excursion and rapid replacement

[0083] En-route, container temperature rises three degrees for three readings. The logistics controller rejects the batch, selects a backup vendor in ten seconds, and sends a replacement order; the routing optimiser replans to keep ≥99 % cold-chain compliance while adding only 0.4 kg CO₂e.

[0084] Example 5 Out-of-stock and vendor re-ranking

[0085] Later in the week a preferred kale farm reports a blight-driven stock-out. The vendor-ranking policy, retrained each night, downgrades that supplier and promotes a greenhouse source; the optimiser shifts to baby kale micro-greens without changing flavour metadata.

[0086] Example 6 Taste-genomics preference discovery

[0087] Genotype analysis shows high sensitivity at receptor OR6A2 (soapy-cilantro trait). The taste–genomics routine lowers coriander’s hedonic weight; future salsas automatically swap in parsley, preventing dissatisfaction without explicit user feedback.

[0088] Example 7 Tele-dietitian annotation

[0089] During a video consult the dietitian records “soft-food phase—early satiety” via SNOMED codes. The graph node updates; the twin biases toward puréed textures and smaller portions for the next ten meals.

[0090] Example 8 Performance-adaptive retraining

[0091] One evening Sara’s measured post-prandial glucose overshoots prediction by 1.2 σ. The ML envelope automatically fine-tunes on the latest embeddings and redeploys in five minutes, restoring forecast accuracy above 95 %.

[0092] Example 9 Cross-border travel scenario

[0093] Travel plans to Helsinki shift the routing optimiser to Finnish micro-fulfilment hubs and adjust macronutrient ratios for colder weather; carbon-aware routing reduces emissions 18 % versus air-freight fallback.

[0094] Example 10 Quarterly zero-knowledge audit

[0095] At quarter-end the ledger executes a zero-knowledge proof confirming every temperature hash and escrow release without exposing vendor secrets. The resulting certificate satisfies payer and regulator audit demands.

[0096] Example 11 Federated learning for population gain

[0097] Sara’s device participates in nightly actor-critic micro-experiments, sending encrypted gradient deltas to the cloud. Aggregated updates improve the shared policy for portion timing; Sara’s percentile dashboard shows she now beats 72 % of peers in glucose stability, validating collective progress without compromising privacy.

[0098] These scenarios collectively exhibit secure ingestion, causal modelling, taste-aware optimisation, carbon-adaptive procurement, cold-chain assurance, clinical reflexes, self-learning, privacy preservation, and regulatory transparency—the full operational scope of the invention.

[0099] The cyber-physical precision-nutrition platform described herein is fully susceptible of industrial applicability.

[0100] The system’s hardware—secure-enclave server blades, HL7-FHIR gateways, Bluetooth and NFC edge collectors, refrigerated containers with MEMS temperature–humidity–shock sensor boards, and blockchain-validated smart-contract nodes—can be manufactured and deployed with off-the-shelf components and established production lines. Standard processors (e.g., ARM-based edge SoCs), connectivity modules (Wi-Fi 6, BLE 5.2), sensor ICs (TI HDC2080, Bosch BME280, Maxim MAX31889), and distributed-ledger stacks (Hyperledger Fabric with ERC-721 extensions) all fit existing electronics, cloud-hosting, and cold-chain-equipment supply chains. The software layer leverages routine technologies such as Kubernetes micro-services, TensorFlow or PyTorch ML engines, Neo4j graph stores, React Native mobile clients, and FHIR-native APIs, ensuring straightforward integration into present-day DevOps workflows.

[0101] In the healthcare and tele-medicine industries the invention enables dietitians and physicians to prescribe verifiable “food doses” as confidently as pharmaceuticals, with automatic HL7-compliant billing artifacts. Hospitals, accountable-care organisations, and insurance payers can embed the platform into chronic-disease programmes for diabetes, cardiovascular disease, oncology cachexia, or post-operative recovery, reducing readmissions and drug-nutrient adverse events.

[0102] Food-service operators, meal-kit brands, and grocery chains gain an automated procurement and provenance layer that guarantees drug-safe, nutritionally precise, low-carbon meals. The reinforcement-learning vendor-ranking engine optimises supplier utilisation, while the blockchain ledger provides regulators and consumers with tamper-evident proof of origin, storage compliance, and sustainability scores—commercial advantages in premium and clinical-nutrition markets.

[0103] Logistics providers and cold-chain equipment manufacturers can license the minute-level excursion-detection and ten-second re-ordering logic to enhance reliability guarantees, lowering spoilage insurance premiums and opening new service tiers for high-value perishables such as biologics or specialty seafood.

[0104] Finally, the anonymised, federated-learning architecture makes the platform attractive for research entities studying nutrigenomics, microbiome–diet interactions, or climate-aware food supply. De-identified embeddings can feed large-scale studies without breaching privacy statutes, accelerating discovery while respecting regulatory frameworks such as GDPR and HIPAA.

[0105] Thus, across medical, food-service, logistics, and research sectors, the invention can be manufactured, deployed, and monetised using existing industrial capabilities, delivering clinically verified, environmentally responsible meals at scale.

Claims

An integrated cyber-physical precision-nutrition system, comprising: (a) a multimodal data-ingestion layer configured to receive, normalise and cryptographically hash (i) biometric and clinical data compliant with HL7-FHIR, (ii) wearable-sensor streams, and (iii) logistics IoT telemetry; (b) a personalisation processor operatively coupled to the data-ingestion layer and configured to generate, for an identified individual, a meal prescription that satisfies nutrient targets while enforcing pre-defined drug–nutrient and nutrient–nutrient interaction thresholds; (c) a procurement engine configured, in response to the meal prescription, to automatically issue an electronic purchase order for required ingredients, said purchase order being instantiated as a smart-contract transaction on a distributed ledger; (d) a logistics control subsystem configured to (i) select a supplier and transport route on the basis of learned reliability and carbon-emission metrics, (ii) monitor refrigerated containers equipped with temperature, humidity and location sensors, and (iii) reroute or replace a shipment when a sensed parameter departs from a quality threshold; and (e) a feedback engine configured to update the meal prescription and the purchase order within a predetermined time window when either (i) newly received biometric or electronic-health-record data indicate that a clinical parameter is outside a target range or (ii) the logistics control subsystem detects a shipment excursion, whereby the system maintains both clinical efficacy for the individual and ingredient integrity without human intervention.The system of claim 1, wherein biometric and clinical identifiers are tokenised and processed in a hardware-based secure enclave such that only de-identified graph embeddings are exposed to downstream models, thereby enabling federated training without transferring personally identifiable information.The system of claim 1, wherein continuous-glucose-monitor measurements are received at intervals of five minutes or less and the drug–nutrient interaction engine recalculates predicted medication area-under-the-curve deviation within sixty seconds of each new measurement, maintaining medication AUC variation within ±5 percent.The system of claim 1, wherein the mixed-integer optimiser maximises a hedonic score of at least 0.85 and embeds the hedonic profile as metadata in the smart-contract purchase order, thereby ensuring any substitute supplier provides organoleptically equivalent ingredients.The system of claim 1, wherein each purchase order is instantiated as an ERC-721 non-fungible token that immutably links a batch identifier to sensor-data hash chains recorded on the distributed ledger.The system of claim 1, wherein supplier selection employs a Deep-Q-Network reinforcement-learning policy that (i) is retrained daily, (ii) re-seeds its exploration parameter in response to detected drift in transport-route carbon intensity, and (iii) applies a cost function that penalises vendors whose carbon intensity exceeds a predetermined threshold.The system of claim 1, wherein the logistics-control subsystem receives temperature, humidity and shock data at least once per minute and: (i) rejects a shipment when temperature deviates by more than 2 °C for three consecutive readings; (ii) selects a backup supplier via the policy of claim 6 within ten seconds of rejection; and (iii) re-issues the purchase order as the ERC-721 token of claim 5.The system of claim 1, wherein the feedback engine updates the meal prescription and re-issues the purchase order within sixty seconds of receiving an HL7-FHIR MedicationRequest resource indicating initiation of a new medication.A computer-implemented method for generating personalised nutrition recommendations, comprising: (a) receiving genomics data, microbiome data, medication data, lifestyle sensor data, taste-preference data and supplier attribute data for a user; (b) constructing a multi-layer graph representation in which vertices respectively encode said genomics, microbiome, medication, lifestyle, taste and supplier attribute data, and edges encode physiological, hedonic and logistical relationships; (c) executing, on a processor, a hybrid metabolic model that fuses (i) a constraint-based mechanistic core with (ii) a machine-learning layer trained on historical biometric-food pairs, the hybrid model being initialised with a node embedding of the graph representation; (d) for each candidate meal, generating (i) a first score representing predicted short-term metabolic benefit and (ii) a second score representing logistical feasibility based on current supplier stock and spoilage risk; (e) ranking the candidate meals by performing a multi-objective optimisation that maximises a Pareto frontier of the first and second scores; and (f) updating at least one node embedding of the graph representation in response to a post-prandial biometric deviation greater than a predetermined threshold, thereby enabling continuous adaptation of both metabolic predictions and supply-chain feasibility within a single modelling framework.The method of claim 9, wherein edges of the graph encode regulatory-pathway relationships weighted by in-silico flux-balance deviations of a constraint-based metabolic model.The method of claim 9, wherein the hybrid metabolic model comprises a constraint-based reconstruction and analysis (COBRA) core wrapped by a gradient-boosting ensemble or long-short-term-memory network.The method of claim 9, wherein the multi-objective optimiser ranks meals on a Pareto frontier defined by (i) predicted post-prandial glucose peak and (ii) expected ingredient-delivery latency derived from real-time supplier safety-stock levels and transport-route carbon-footprint scores.The method of claim 9, wherein the machine-learning layer is automatically retrained when measured post-prandial glucose deviates by more than one standard deviation from prediction.The method of claim 9, further comprising updating the graph representation in response to a tele-dietitian annotation encoded as a SNOMED-CT concept within a HL7-FHIR Observation resource.A supply-chain orchestration system for patient-specific nutrition, comprising: (a) a vendor-ranking module employing reinforcement learning to assign a reliability score to each of a plurality of ingredient suppliers using lead-time variance, batch-quality metrics and environmental-impact indicators; (b) a request-for-quotation (RFQ) generator configured to mint, upon receipt of a nutrition prescription trigger, a non-fungible-token-based RFQ smart contract that specifies ingredient type, quality parameters and delivery time; (c) a provenance ledger implemented on a blockchain and storing, as immutable records linked to the RFQ smart contract, (i) supplier bids, (ii) sensor data streamed from IoT-equipped transport containers, and (iii) custody transfers; (d) a quality-assurance processor configured to compare real-time sensor data against a storage-condition threshold and, when a breach is detected, (i) reject the in-transit batch, (ii) select a backup supplier via the vendor-ranking module, and (iii) issue a replacement RFQ smart contract; and (e) an interfacing module configured to transmit shipment-status updates and ledger pointers to a clinical nutrition system for automatic reformulation of future prescriptions, wherein every ingredient batch delivered to the patient is cryptographically linked to its production and transport history, and shipment anomalies automatically propagate to prescription revision without manual oversight.The system of claim 16, wherein the vendor-ranking module implements the Deep-Q-Network policy of claim 6 and dynamically re-seeds exploration when aggregate carbon intensity drift exceeds a predetermined threshold.The system of claim 16, wherein each RFQ smart contract includes an escrow that releases payment only upon verification, recorded on the provenance ledger, that in-transit temperature remained within the storage-condition threshold.The system of claim 16, wherein a batch is rejected when temperature deviates by more than 2 °C for three consecutive sensor readings or humidity exceeds 90 percent for ten consecutive readings, and a replacement RFQ is issued within ten seconds of the rejection.The system of claim 16, wherein the interfacing module transmits a supply-chain exception report as an HL7-FHIR DiagnosticReport resource that triggers reformulation of pending prescriptions.The system of claim 16, further comprising a routing optimiser that minimises expected carbon emissions while maintaining a probability of cold-chain compliance above 99 percent.The system of claim 16, wherein the provenance ledger undergoes a zero-knowledge-proof audit each quarter to verify authenticity of logistics events without revealing proprietary supplier identities.A non-transitory computer-readable medium storing instructions which, when executed by one or more processors, cause the processors to perform the method of any one of claims 9 through 15.

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