Geogenomix genomic & biomarker core fusion engine for real-time, privacy-preserving, and environmentally optimised health intelligence

Abstract

System comprising: Multi-Domain Data Acquisition Module MDDA 101 to capture, preprocess and integrate health data streams, Adaptive Data Fusion Module ADFM 102 to prioritise and fuse streams with a we

Description

LifeMaitrix Fortress - PATENT 3 Title GeoGenomix Genomic &Biomarker Core Fusion Engine for Real-Time, Privacy-Preserving, and Environmentally Optimised Health Intelligence Inventors Alice Dyson and Christopher Daw Technical Field

[0001] The present invention relates to real-time health intelligence systems. Specifically, it concerns computer-implemented methods and architectures for securely integrating genomic, microbiome, biomarker, nutritional, and environmental data. These integrated data streams deliver adaptive, personalised health insights and interventions. The system employs privacypreserving computational methods, including homomorphic encryption (HE) and zeroknowledge proofs (ZKP), to protect sensitive data. It further incorporates dynamic computational resource optimisation to minimise ecological impact, particularly within digital health and personalised medicine applications. Background to the Invention

[0002] Advances in personalised medicine and digital health platforms have accelerated, with growing efforts to integrate diverse data types—genomic sequencing, microbiome profiling, real-time biomarkers, nutrition tracking, and environmental sensor data—aiming to provide predictive insights tailored to individual health conditions and wellness goals. However, current systems display significant technical limitations. Most existing solutions employ static data integration and basic aggregation methods, lacking adaptability and dynamic prioritisation according to user-specific health contexts. Additionally, prevailing health analytics platforms inadequately address privacy concerns and computational sustainability within their architectures.

[0003] Several prior art references highlight these limitations:

[0004] US9819650B2 (NantHealth) describes the application of homomorphic encryption (HE) to healthcare data, including genomic and environmental information. This reference primarily focuses on data security without providing adaptive, real-time integration across genomic, microbiome, biomarker, nutritional, and environmental data domains. Additionally, it does not address computational sustainability.

[0005] US20250005110 (Color Health, Inc.) outlines methods combining genetic variant and environmental sensor data to produce disease-risk metrics. However, this prior art restricts its scope to genetic and environmental domains, excluding microbiome data or real-time biomarkers. It further omits adaptive weighting and computational resource optimisation.

[0006] US2019 / 0180844 Al discloses genomic biomarker prediction using deep learning models. This prior art confines itself to genomic data, neglecting multi-domain integration, adaptive weighting, privacy preservation through cryptography, and environmental optimisation.

[0007] US2015 / 0031061 Al and US8,236,503B2 detail analytical methods for specific immunological biomarkers and nucleic acid sequencing used to monitor health conditions. These systems focus narrowly on isolated data domains without real-time fusion, dynamic weighting, or computational efficiency.

[0008] In contrast, the present invention integrates five distinct health data domains— genomic, microbiome, real-time biomarkers, nutritional data, and environmental inputs— within a holistic, adaptive, and real-time health intelligence engine (Fig. 1, components 101105). A notable advancement is the Adaptive Data Fusion Module (Fig. 3, component 301), dynamically prioritising and integrating diverse data streams based on real-time user health contexts and evolving user-specific profiles.

[0009] The invention further comprises a robust Privacy-Preserving Cryptographic Layer (Fig. 4, components 401-402), applying advanced HE and ZKP methodologies to ensure secure computation on sensitive user data without revealing raw inputs. This cryptographic approach addresses stringent global privacy regulations such as GDPR and HIPAA.

[0010] Moreover, the invention uniquely incorporates a Computational &Environmental Optimisation Module (Fig. 6, components 601-603), which addresses computational sustainability—a previously unconsidered challenge in health analytics. By dynamically monitoring and adjusting computational resource allocation according to an Environmental Impact Score (EnvIS), the invention significantly reduces ecological impact while enhancing performance efficiency. This environmental optimisation feature surpasses existing solutions.

[0011] The integration of adaptive multi-domain fusion, rigorous privacy-preserving mechanisms, and proactive environmental optimisation positions this invention substantially ahead of current real-time health intelligence technologies. Summary of the Invention

[0012] The invention provides a computer-implemented health intelligence engine—the GeoGenomix Genomic &Biomarker Core Fusion Engine—capable of securely integrating, dynamically prioritising, and adaptively analysing diverse health data streams. These data streams include genomic sequencing data, microbiome profiles, real-time biomarkers, nutritional tracking information, and environmental parameters. The system delivers personalised, context-sensitive, and actionable health recommendations and interventions.

[0013] The system architecture (Fig. 1) comprises five integrated yet distinct computational modules, dynamically interconnected through real-time data flow and adaptive control logic (Fig. 1, component 106):

[0014] Multi-Domain Data Acquisition Module (MDDA): This module continuously captures, preprocesses, and normalises diverse health-related data streams. The MDDA aggregates genomic data, microbiome profiles, real-time physiological biomarkers—such as glucose and inflammation indicators—detailed nutritional intake information, and environmental sensor inputs including air quality, temperature, and humidity. Data are harmonised and normalised in real-time, ensuring interoperability and consistency across subsequent modules.

[0015] Adaptive Data Fusion Module (ADFM): The ADFM dynamically prioritises and integrates heterogeneous data streams according to user-specific contexts and immediate health relevance. An adaptive weighting algorithm continuously recalculates data relevance in response to evolving user profiles, real-time health urgency, and emerging physiological or environmental inputs. This approach maintains personalised context-awareness, dynamically adjusting data integration priorities to ensure accurate and relevant outputs.

[0016] Privacy-Preserving Cryptographic Layer (PPCL): This layer ensures user privacy and secure processing of sensitive health data using advanced cryptographic methods, including Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP). These methods allow encrypted data to undergo complex analytical computations without exposing raw data to external systems or third-party services. The PPCL supports regulatory compliance with stringent privacy standards, including GDPR and HIPAA.

[0017] Predictive Health Insight &Recommendation Engine (PHIRE): The PHIRE module utilises machine learning algorithms, including deep neural networks and predictive analytics, to generate precise, context-sensitive health insights and recommendations. A continuous feedback loop incorporates user responses and emerging data into iterative model refinements, allowing predictive accuracy to evolve continually. Recommendations remain personalised, relevant, and responsive to the user's changing health trajectory.

[0018] Computational &Environmental Optimisation Module (CEOM): The CEOM proactively monitors computational load and evaluates the system’s ecological footprint via an Environmental Impact Scoring (EnvIS) algorithm. Based on real-time scores, the CEOM dynamically reallocates computational tasks and resources to minimise energy consumption and overall environmental impact. This environmental optimisation addresses a critical technical limitation absent from current health intelligence systems. Operational Method

[0019] The invention defines a cyclical, seven-step operational workflow:

[0020] 1. Real-Time Data Acquisition (MDDA): Continuous capture, preprocessing, and normalisation of multi-domain health data streams.

[0021] 2. Adaptive Data Integration and Fusion (ADFM): Real-time prioritisation and integration of data streams using adaptive weighting based on evolving user contexts and health urgency.

[0022] 3. Privacy-Preserving Cryptographic Processing (PPCL): Secure encryption and protection of integrated data using Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP) to enable private analytical computations.

[0023] 4. Predictive Insight Generation (PHIRE): Generation of personalised, predictive health insights and actionable recommendations through advanced machine learning and predictive analytics algorithms.

[0024] 5. Real-Time Feedback Integration (PHIRE Feedback Loop): Iterative refinement of predictive models informed by continuous user feedback, responses, and emerging data.

[0025] 6. Computational and Environmental Optimisation (CEOM): Ongoing monitoring of computational resource utilisation, with dynamic task reallocation guided by real-time Environmental Impact Scores (EnvIS) to minimise ecological impact and maximise efficiency.

[0026] 7. Secure Data Handling and Auditability: End-to-end encrypted handling, secure storage, and cryptographically verified auditability of all data transactions, computations, and operational actions. Key Technical Advantages

[0027] Adaptive, Real-Time Contextualisation: Continuous recalibration of data fusion priorities and predictive analytics according to real-time user health contexts and emerging data, significantly enhancing predictive accuracy and personalised relevance.

[0028] Rigorous Privacy Protection: Deployment of advanced cryptographic technologies (HE and ZKP) ensuring sensitive user health data remains secure and confidential throughout all analytical processes, fully compliant with global privacy regulations such as GDPR and HIPAA.

[0029] Computational and Environmental Sustainability: Dynamic computational resource monitoring and optimisation via real-time environmental impact assessment (EnvIS), significantly reducing ecological footprint—a substantial advancement beyond existing health analytics platforms.

[0030] Personal Prevention Index (PPI): Generation of a dynamic, individualised metric reflecting real-time health risks and recommended preventive actions, driven by comprehensive multi-dimensional data integration.

[0031] Visual Nutrition Intelligence (VNI) Engine: Real-time personalised nutritional guidance derived from visual analysis of dietary inputs (e.g., meal images or menus), intelligently integrated with genomic, microbiome, biomarker, nutritional, and environmental data to optimise dietary compatibility and health outcomes.

[0032] The invention’s sophisticated integration of adaptive data fusion, advanced privacypreserving methods, proactive computational sustainability strategies, and highly personalised predictive analytics positions it distinctly ahead of current digital health platforms, establishing a novel standard for ethical, environmentally responsible, and deeply individualised health intelligence solutions. Brief Description of Drawings

[0033] Embodiments of the invention are illustrated in the accompanying drawings, with consistent component numbering to ensure clarity and ease of reference:

[0034] Figure 1 illustrates the overall system architecture of the GeoGenomix Genomic &Biomarker Core Fusion Engine, depicting integrated computational modules and real-time data interactions: • 101: Multi-Domain Data Acquisition Module (MDDA), capturing and normalising multi-modal health data. • 102: Adaptive Data Fusion Module (ADFM), dynamically prioritising and integrating data streams. • 103: Privacy-Preserving Cryptographic Layer (PPCL), employing Homomorphic Encryption (HE) and Zero-Knowledge Proofs (ZKP) for secure analytics. • 104: Predictive Health Insight &Recommendation Engine (PHIRE), generating adaptive, personalised health recommendations. • 105: Computational &Environmental Optimisation Module (CEOM), actively managing computational resources to minimise environmental impact. • 106: Real-Time Data Flow and Control Integration, coordinating data exchange among modules. • 107: External Data Source API Integration, securely interfacing with external databases. • 108: Secure Data Store and Audit Ledger, securely archiving data and operational logs.

[0035] Figure 2 provides a detailed view of the Multi-Domain Data Acquisition Module (MDDA), illustrating health-related data inputs and preprocessing mechanisms: • 201: Genomic Data Input (e.g., genome sequencing). • 202: Microbiome Data Input (gut microbiome analysis). • 203: Real-Time Biomarker Data Input (e.g., glucose, inflammation markers). • 204: Nutritional Intake Tracking (dietary logs and analysis). • 205: Environmental Parameter Sensors (e.g., air quality, temperature, humidity). • 206: Data Preprocessing and Normalisation Layer, preparing data for integration.

[0036] Figure 3 depicts the Adaptive Data Fusion Module (ADFM), highlighting data prioritisation and integration components: • 301: Adaptive Weighting Algorithm, dynamically adjusting data stream importance based on user context. • 302: Contextual Health Relevance Scoring Layer, assessing immediate data urgency. • 303: User-Specific Profile Integration, incorporating user preferences and historical health data. • 304: Dynamic Recalibration Engine, updating weighting parameters continuously.

[0037] Figure 4 details the Privacy-Preserving Cryptographic Layer (PPCL), illustrating secure analytic processing: • 401: Homomorphic Encryption (HE) Processing Module, performing secure data computations. • 402: Zero-Knowledge Proof (ZKP) Verification Module, validating computational integrity privately. • 403: Secure Data Analytics Layer, ensuring analytical operations maintain data confidentiality.

[0038] Figure 5 presents the Predictive Health Insight &Recommendation Engine (PHIRE), describing predictive analytics and recommendation processes: • 501: Machine Learning &Neural Network Algorithms, deriving predictive insights from integrated data. • 502: Predictive Analytics Module, forecasting health conditions and user needs. • 503: Context-Aware Recommendation Generator, delivering personalised health interventions. • 504: Continuous Feedback Loop and Model Refinement, enhancing predictive accuracy through user feedback.

[0039] Figure 6 illustrates the Computational &Environmental Optimisation Module (CEOM), emphasising resource efficiency mechanisms: • 601: Computational Resource Monitoring, assessing computational resource use in real-time. • 602: Environmental Impact Scoring (EnvIS) Module, quantifying the ecological footprint. • 603: Dynamic Resource Allocation &Energy Optimisation Layer, reallocating tasks to minimise ecological impact and optimise computational efficiency. Detailed Description of Invention

[0040] The GeoGenomix Genomic &Biomarker Core Fusion Engine (Fig. 1) provides a computational framework designed for secure, real-time, adaptive integration and analysis of genomic, microbiome, biomarker, nutritional, and environmental data. The invention dynamically generates predictive health insights, personalised interventions, and recommendations using the methods described herein.

[0041] Multi-Domain Data Acquisition Module (MDDA - 101): As depicted in Fig. 2, the MDDA continuously acquires and preprocesses diverse health-related data streams. Genomic data (201), microbiome profiles (202), real-time biomarkers (203), nutritional intake records (204), and environmental sensor inputs (205) undergo normalisation and standardisation. Normalisation uses the following standard formula: Xnorm = (X - Xmin) / (Xmax - Xmin) Where: Xnorm represents the normalised data value, X is the raw data value, Xmin and Xmax represent the respective minimum and maximum values within data-specific ranges.

[0042] Adaptive Data Fusion Module (ADFM - 102): Referring to Fig. 3, the ADFM prioritises data streams dynamically based on real-time context and individual user profiles. The adaptive weighting algorithm (301) calculates data stream priorities using: W(Di) = aGt + PMt + yBt + 5Nt + sEt Where: W(Di) denotes the weighted priority of data stream Di, Gt is genomic risk priority at time t, Mt is microbiome relevance score at time t, Bt represents real-time biomarker significance at time t, Nt indicates nutritional intake relevance at time t, Et signifies environmental context urgency at time t, a, P, y, 5, a are adaptive weighting parameters recalibrated through continuous user-contextual feedback (304).

[0043] Reinforcement learning algorithms, integrated within the Predictive Health Insight &Recommendation Engine (PHIRE - 104, Fig. 5), dynamically recalibrate these weighting parameters. Initially, these algorithms use historical anonymised user data to determine baseline parameters. Parameters then continuously update in real-time in response to observed health outcomes, user feedback, and evolving biomarker trends, ensuring ongoing optimisation of data integration and priority accuracy.

[0044] Privacy-Preserving Cryptographic Layer (PPCL - 103): As shown in Fig. 4, the PPCL uses advanced cryptographic techniques to secure sensitive user data. The Homomorphic Encryption (HE) Module (401) encrypts data to enable secure analytical computation without exposing the raw data, represented mathematically as: Enc(ml + m2) = Enc(ml) © Enc(m2) Where Enc(m) represents encrypted data.

[0045] The Zero-Knowledge Proof (ZKP) Module (402) further ensures computational integrity without data disclosure. For a computation f(x), a prover P demonstrates to a verifier V that: P —> V : proof(f(x)) This verification process allows V to validate the accuracy of f(x) without direct knowledge of the input x.

[0046] Predictive Health Insight &Recommendation Engine (PHIRE - 104): As illustrated in Fig. 5, PHIRE employs advanced machine learning (501) and predictive analytics (502) algorithms to model user health outcomes. Health risk predictions and recommendations utilise neural network computations defined as: yhealth-risk=G()jwjhj+b)y_lhealth-risk} = \sigma \left(\sum_{j} wj hj + b\right) Where: • yhealth-risky_{health-risk} represents the predicted health risk probability, • hjhj denotes hidden layer nodes derived from integrated health data features, • wjwj signifies learned neural network weights, • bb is the bias parameter, • c\sigma is the activation function (e.g., sigmoid function for binary classification). Continuous refinement (504) iteratively updates parameters wjwj and bb through user feedback loops, integrating user outcomes and responses to enhance predictive accuracy.

[0047] PHIRE further computes a dynamic Personal Prevention Index (PPI), a personalised metric reflecting real-time health risks and prioritised preventive actions. The PPI integrates data from the Adaptive Data Fusion Module (ADFM - 102): PPIt=p(Gt)+v(Mt)+p(Bt)+G(Nt)+r(Et)PPI_t = \mu(G_t) + \nu(M_t) + \rho(B_t) + \sigma(N_t) + \tau(E_t) Where: • p,v,p,G,r\mu, \nu, \rho, \sigma, \tau are weighting factors calibrated through supervised learning on cohort data correlating integrated input patterns to established risk levels. These parameters are continually refined via user feedback and historical health data.

[0048] Computational &Environmental Optimisation Module (CEOM - 105): Depicted in Fig. 6, CEOM optimises computational efficiency and ecological footprint. The Environmental Impact Scoring (EnvIS) Module (602) calculates the environmental impact scores as: EnvISt=EmaxEmax-EtEnvIS_t = \frac{E_{max}}{E_{max} - E_t} Where: • EnvIStEnvISt represents the environmental impact score at time tt, • EmaxE_{max} is the predefined maximum acceptable energy threshold, • EtE t indicates the actual computational energy consumption at time tt.

[0049] The optimisation layer (603) dynamically reallocates computational resources by solving the optimisation problem: min: / o£t=lTEnvIStsubject toCt<Cmax,Rt>Rmin\min \sum_{t=l }A{T} EnvIS_t \quad \text{subject to} \quad C t\leq C_{max}, \quad R_t \geq R_{min} Here, computational load constraints CtC t and required system responsiveness RtR t are balanced to ensure minimal environmental impact and optimal performance.

[0050] Operationally, CEOM (603) provides real-time optimisation signals to the ADFM’s Dynamic Recalibration Engine (304). In response, the ADFM adjusts adaptive weighting parameters (a,P,y,5,c\alpha, \beta, \gamma, \delta, \epsilon), modulating data stream priorities based on resource availability and environmental impact. If computational demand or energy consumption surpasses a threshold (indicated by low EnvISEnvIS scores), the ADFM temporarily down-weights less critical data streams to conserve resources. Conversely, when resources are abundant (indicated by high EnvISEnvIS scores), ADFM increases the integration of all data streams for maximum precision. This bidirectional feedback loop ensures dynamic alignment of data prioritisation with computational efficiency objectives.

[0051] Real-Time Data Flow &Control Integration (106): This module coordinates data exchanges among modules, governed by latency LtL t and accuracy AtA t parameters, maintaining performance thresholds: Lt<Lmax,At>AminL_t \leq L_{max}, \quad A_t \geq A_{min} Ensuring optimal trade-offs between responsiveness, accuracy, and computational speed.

[0052] External Data Source API Integration (107): Secure external API integration employs authenticated and encrypted data transactions: APIaccess=Authenticate(Ut)®Encrypt(Dt)API_{access} = Authenticate(U t) \oplus Encrypt(Dt) Where: • Authenticate(Ut)Authenticate(U_t) denotes user verification at time tt, • Encrypt(Dt)Encrypt(D_t) represents encrypted incoming external data.

[0053] Secure Data Store &Audit Ledger (108): Data and operational activities are logged cryptographically into secure audit blocks using hash-linking for immutable traceability: Hasht=Hash(Dt||Hasht-l)Hash_t = Hash(D_t \parallel Hash_{t-1}) Where: • HashtHasht denotes the cryptographic hash at time tt, • DtDt represents the data at time tt, • ||\parallel indicates concatenation. This process ensures secure auditability for all analytical and operational activities.

[0054] Specific Examples and Use Cases

[0055] Example 1: Personalised Nutritional Guidance (Visual Nutrition Intelligence - VNI Engine) A user photographs a meal or menu item using a mobile device at a restaurant. The Visual Nutrition Intelligence (VNI) Engine, integrated within the Adaptive Data Fusion Module (ADFM - 102, Fig. 3), processes the visual input immediately through Optical Character Recognition (OCR) and ingredient identification algorithms: Dmenu=OCR_Extract(Image)®IngredientDetection(Image)D_{menu} = OCR\_Extract(Image) \oplus IngredientDetection(Image)Dmenu =OCR_Extract(Image)®IngredientDetection(Image) The ADFM integrates this nutritional data dynamically with genomic nutrient-sensitivity markers (201), microbiome profiles (202), real-time biomarkers (e.g., glucose) (203), historical dietary patterns (204), and environmental context data (205), computing a Nutrition Score as: NutritionScoret=f(Gt,Mt,Bt,Nt,Et,Dmenu)NutritionScore_t = f(G_t, Mt, B_t, N_t, E_t, D_{menu})NutritionScoret=f(Gt,Mt,Bt,Nt,Et,Dmenu) PHIRE (104) then generates real-time personalised dietary recommendations, including ingredient substitutions, glycaemic response guidance, and probiotic supplementation advice, delivered through an intuitive interface to enable practical decision-making.

[0056] Example 2: Adaptive Chronic Condition Management (Diabetes and Glucose Regulation) A diabetic user employs continuous glucose monitoring (CGM) technology, feeding real-time glucose readings (203) into the MDDA (101). Simultaneously, environmental factors (205) such as ambient temperature and humidity that affect insulin absorption are monitored. The ADFM dynamically integrates glucose trends with genomic insulin sensitivity markers (201) and microbiome-derived metabolic indicators (202), computing insulin recommendations through an integrated risk score: Rinsulin=a(Gt)+P(Mt)+y(Bt)+5(Et)R_{ insulin} = \alpha(G_t) + \beta(M_t) + \gamma(B_t) + \delta(E_t)Rinsulin=a(Gt)+P(Mt)+y(Bt)+5(Et) PHIRE (104, component 502) then forecasts short-term glucose variability, proactively recommending precise insulin adjustments to prevent glycaemic excursions. Secure computational methods within PPCL (103) employ homomorphic encryption (HE) and zeroknowledge proofs (ZKP) for complete privacy during sensitive data processing.

[0057] Example 3: Environmental Sensitivity &Allergy Management For a user with allergies, the GeoGenomix system dynamically integrates biomarker data (203, e.g., histamine levels), microbiome immune-response indicators (202), genomic predispositions to allergens (201), and real-time environmental sensor data (205, pollen, air quality). The ADFM calculates an Allergy Risk Score (ARS): ARSt=col(Gt)+co2(Mt)+co3(Bt)+co4(Et)ARS_t = \omega_l(G_t) + \omega_2(M_t) + \omega_3(B_t) + \omega_4(E_t)ARSt=col(Gt)+co2(Mt)+co3(Bt)+co4(Et) When ARS exceeds personalised thresholds, PHIRE proactively generates context-sensitive alerts and advice, such as recommendations to stay indoors or take antihistamines, demonstrating comprehensive multi-domain allergy management.

[0058] Example 4: Emotional and Mental Health Support (Stress Management) The system monitors real-time biomarkers (203), including cortisol and heart-rate variability, environmental conditions (205), genomic stress-resilience markers (201), and microbiome-derived stress-response indicators (202). The Predictive Health Insight &Recommendation Engine (PHIRE, 104) computes stress intervention strategies: Istress=arg: / o:min: / o:(StressRisk(Gt,Mt,Bt,Et))I_{stress} = \arg\min(StressRisk(G_t, Mt, B_t, E_t))Istress=argmin(StressRisk(Gt,Mt,Bt,Et)) Based on real-time emotional bandwidth assessments: Ebandwidth(t)=threshold(Gt+MtBt+Et)E_{bandwidth}(t) = threshold\left(\frac{G_t + M_t}{B_t + E_t}\right)Ebandwidth(t)=threshold(Bt+EtGt+Mt) PHIRE dynamically modulates intervention frequency and intensity during periods of acute stress or low emotional bandwidth to prevent cognitive overload, refining recommendations continuously based on immediate user feedback for sustained efficacy.

[0059] Example 5: Computational &Environmental Sustainability in Health Analytics During high computational demands (e.g., extensive genomic analysis), CEOM (105) evaluates Environmental Impact Scores (EnvIS) and computational resource usage: EnvISt=EmaxEmax-EtEnvIS_t = \frac{E_{max}}{E_{max} - E_t}EnvISt=Emax-EtEmax Real-time resource allocation optimisation solves: mini / ojXt= ITEnvISt,subject toCt<Cmax,Rt>Rmin\min \sum_{t=l}A{T} EnvIS_t, \quad \text{subject to} \quad C t\leq C_{max}, R_t\geq R jmin }mint=I^TEnvISt,subject toCt <Cmax,Rt>Rmin The system reallocates computational tasks to environmentally optimal resources (e.g., renewable-energy-based data centres), proactively communicating decisions and ecological impact reductions clearly to the user.

[0060] Example 6: Personal Prevention Index (PPI) for Proactive Health Management A user with genetic cardiovascular predispositions, suboptimal microbiome markers, and elevated inflammatory biomarkers receives a dynamic Personal Prevention Index (PPI) calculated within PHIRE (104): PPIt=p(Gt)+v(Mt)+p(Bt)+G(Nt)+r(Et)PPI_t = \mu(G_t) + \nu(M_t) + \rho(B_t) + \sigma(N_t) + \tau(E_t)PPIt=p(Gt)+v(Mt)+p(Bt)+G(Nt)+r(Et) When environmental conditions deteriorate (e.g., increased pollution), the system calculates an elevated PPI, triggering proactive recommendations such as immediate lifestyle modifications, targeted nutritional interventions, environmental adjustments, and prioritised medical follow-ups, clearly communicated via the user interface. Glossary of Terms Adaptive Data Fusion Module (ADFM, 102, Fig. 3): A computational module configured to dynamically prioritise, integrate, and recalibrate multiple data streams based on real-time user context, urgency, and evolving health profiles. Adaptive Weighting Algorithm (301, Fig. 3): A mathematical algorithm dynamically calculating weighting factors for incoming data streams, defined as: W(Di)=aGt+pMt+yBt+6Nt+cEtW(D_i) = \alpha G t + \beta M_t + \gamma B_t + \delta N_t + \epsilon E_t Computational &Environmental Optimisation Module (CEOM, 105, Fig. 6): A dedicated module actively monitoring computational resource usage and dynamically optimising computational load to reduce energy consumption and ecological footprint. Contextual Health Relevance Scoring (302, Fig. 3): Analytical method evaluating immediate relevance and urgency of integrated data streams, guiding real-time prioritisation decisions in the ADFM. Environmental Impact Scoring (EnvIS) (602, Fig. 6): Proprietary computational method quantifying the ecological impact of computational operations, calculated as: EnvISt=EmaxEmax-EtEnvIS_t = \frac{E_{max}}{E_{max} - E_t} External Data Source API Integration (107, Fig. 1): A secure computational interface integrating third-party and external data sources, enabling dynamic, real-time access to external databases including nutritional databases, environmental data feeds, medical records, and validated public and private data APIs. Data integration occurs securely through authenticated API calls, encrypted transmission, and realtime validation, enabling enriched adaptive data fusion within the GeoGenomix Core Fusion Engine. Homomorphic Encryption (HE) Module (401, Fig. 4): A cryptographic sub-module enabling secure computations on encrypted data, allowing sensitive analytical processes without revealing raw data. Machine Learning &Neural Network Algorithms (501, Fig. 5): Computational algorithms employed by the Predictive Health Insight &Recommendation Engine (PHIRE), utilising predictive analytics and deep learning to forecast health outcomes. Multi-Domain Data Acquisition Module (MDDA, 101, Fig. 2): Structured computational module capturing, preprocessing, and normalising health-related data, including genomic, microbiome, biomarker, nutritional, environmental, and external API data inputs. Personal Prevention Index (PPI): Dynamic metric representing personalised risk assessment and preventive intervention guidance, derived through adaptive multi-domain data integration within ADFM (102). Predictive Health Insight &Recommendation Engine (PHIRE, 104, Fig. 5): Computational module employing predictive analytics and machine learning to generate personalised, context-sensitive health insights and recommendations. Privacy-Preserving Cryptographic Layer (PPCL, 103, Fig. 4): Security module leveraging advanced cryptographic methods (Homomorphic Encryption and Zero-Knowledge Proofs) for secure health data analytics without data exposure. Real-Time Feedback Loop (504, Fig. 5): Continuous iterative refinement process integrating user responses and outcomes, dynamically recalibrating predictive algorithms for ongoing improvement of system outputs. Secure Data Store and Audit Ledger (108, Fig. 1): Encrypted data repository and cryptographically secured ledger maintaining verifiable logs of system operations, data handling, user interactions, API integrations, and computational allocations. Visual Nutrition Intelligence (VNI) Engine (within 102, Fig. 3): Specialised sub-module employing OCR and ingredient-recognition algorithms to analyse visual dietary inputs, dynamically integrating results with genomic, microbiome, biomarker, nutritional, environmental, and external nutritional API data inputs to provide real-time personalised nutritional insights and recommendations. Zero-Knowledge Proof (ZKP) Module (402, Fig. 4): Cryptographic verification module authenticating computational integrity without disclosing actual data processed, defined by: P—>V:proof(f(x)), V verifies f(x) without knowledge of xP \rightarrow V: proof(f(x)), \quad \text{ V verifies } f(x) \text{ without knowledge of} x Algorithmic Equations and Variables: W(Di): Adaptive weighting priority for data stream Di. Gt: Genomic risk priority at time t. Mt: Microbiome relevance at time t. Bt: Real-time biomarker significance at time t. Nt: Nutritional intake relevance at time t. Et: Environmental context urgency at time t. a, P, y, 5, a: Adaptive weighting coefficients. EnvISt: Environmental Impact Score at time t. Emax: Maximum permitted energy threshold. Et: Actual computational energy consumption at time t. Ct: Computational load at time t. Cmax: Maximum permissible computational load. Rt: System responsiveness or performance at time t. Rmin: Minimum required system responsiveness. Data Sources and Inputs (Fig. 2): Genomic Data (201): Whole genome or exome sequencing data, genetic risk markers. Microbiome Data (202): Microbial composition and diversity profiles, metabolic indicators. Biomarker Data (203): Physiological measurements (e.g., glucose levels, inflammation markers, cortisol, heart-rate variability). Nutritional Data (204): User dietary logs, food database information, visual nutrition analysis inputs. Environmental Data (205): Ambient sensor measurements (air quality, particulate matter, VOCs, temperature, humidity, noise). External API Data Streams (107, Fig. 1): Real-time integrations with external data sources such as medical records, public health databases, environmental data providers, and nutritional databases, via secure, authenticated APIs.

Claims

System ClaimsIndependent Claim1. A computer-implemented health intelligence system comprising:a) a Multi-Domain Data Acquisition Module (MDDA) configured to continuously capture, preprocess, normalise, and integrate diverse health data streams, including genomic data, microbiome profiles, real-time biomarkers, nutritional data, and environmental sensor inputs;b) an Adaptive Data Fusion Module (ADFM), operatively coupled to the MDDA, configured to dynamically prioritise and fuse integrated health data streams in realtime using an adaptive weighting algorithm responsive to context-specific user health conditions and evolving user profiles;c) a Privacy-Preserving Cryptographic Layer (PPCL), configured to securely process integrated data streams employing homomorphic encryption and zero-knowledge proofs to enable secure computation without exposing sensitive underlying data;d) a Predictive Health Insight &Recommendation Engine (PHIRE), configured to generate personalised predictive health insights and actionable recommendations derived from the fused data using advanced machine learning and neural network algorithms, dynamically updated through continuous user feedback; ande) a Computational &Environmental Optimisation Module (CEOM), configured to monitor real-time computational resource usage, evaluate environmental impact through an Environmental Impact Score (EnvIS), and dynamically reallocate computational tasks and resources to optimise energy efficiency and minimise ecological impact.Dependent Claims2. The system of claim 1, wherein the MDDA includes a preprocessing and normalisation layer configured to harmonise disparate incoming data streams via interpolation, normalisation, and validation to maintain interoperability.

3. The system of claim 1, wherein the adaptive weighting algorithm within the ADFM calculates data stream prioritisation according to the following equation: W(Di)=aGt+pMt+yBt+6Nt+cEtW(D_i) = \alpha G t + \beta M_t + \gamma B_t + \delta N_t + \epsilon E_tW(Di)=aGt+pMt+yBt+6Nt+cEtwhere parameters a,P,y,5,c\alpha, \beta, \gamma, \delta, \epsilona,P,y,5,c dynamically recalibrate based on real-time contextual feedback, including genomic data (GtG tGt ), microbiome data (MtM_tMt), biomarker data (BtB_tBt), nutritional data (NtN_tNt), and environmental data (EtE tEt).

4. The system of claim 1, wherein the PPCL further comprises a secure analytics submodule configured to conduct encrypted analytics compliant with global data privacy standards, including GDPR, HIPAA, and CCPA.

5. The system of claim 1, wherein the PHIRE module includes a continuous feedback loop that integrates user responses and outcomes into iterative refinement of predictive algorithms and recommendation strategies, enhancing predictive accuracy and personalisation over time.

6. The system of claim 1, wherein the CEOM calculates the Environmental Impact Score (EnvIS) using the formula:EnvISt=EmaxEmax-EtEnvIS_t = \frac{E_{max}}{E_{max} - E_t}EnvISt=Emax-Et Emaxand dynamically reallocates computational resources according to: mini / ojXt=lTEnvISt,subject toCt<Cmax,Rt>Rmin\min \sum_{t=l }A{T} EnvIS_t, \quad \text{subject to} \quad C t \leq C_{max}, \quad R_t \geq R_{min}min£t=lT EnvlSt,subject toCt<Cmax,Rt>Rmin7. The system of claim 1, further comprising a Visual Nutrition Intelligence (VNI) submodule within the ADFM configured to analyse photographic images of meals or menus via optical character recognition (OCR) and ingredient identification algorithms, dynamically integrating nutritional data with genomic, microbiome, biomarker, and environmental data to provide personalised nutritional recomm endati ons.

8. The system of claim 1, further comprising an emotional bandwidth modulation submodule integrated within the PHIRE module, configured to adjust frequency, intensity, and timing of health interventions based on real-time physiological and emotional biomarker analysis, preventing cognitive overload and maintaining optimal user receptivity.

9. The system of claim 1, wherein the ADFM includes a contextual health relevance scoring sub-module configured to evaluate immediate relevance and urgency of integrated data streams to determine dynamic weighting adjustments.

10. The system of claim 1, wherein environmental sensors integrated within the MDDA include sensors for particulate matter, volatile organic compounds (VOCs), temperature, humidity, and ambient noise levels.

11. The system of claim 1, further comprising a Secure Data Store and Audit Ledger configured to cryptographically log consent decisions, ethical enforcement actions, predictive recommendations, user feedback, and computational resource allocations, ensuring immutable auditability and traceability.Method Claims12. A computer-implemented method for providing real-time adaptive health intelligence comprising:a) continuously capturing, normalising, and integrating genomic, microbiome, biomarker, nutritional, and environmental health data;b) dynamically prioritising integrated data streams according to user-specific health context and real-time adaptive weighting parameters;c) securely processing prioritised data streams using homomorphic encryption (HE) and zero-knowledge proofs (ZKP) to maintain confidentiality;d) generating personalised predictive health insights and actionable recommendations using machine learning and neural network algorithms;e) iteratively refining predictive algorithms and weighting parameters through continuous user feedback integration; andf) monitoring real-time computational resource usage, evaluating environmental impact via an Environmental Impact Score (EnvIS), and dynamically reallocating computational resources to optimise efficiency and minimise ecological footprint.

13. The method of claim 12, wherein dynamic prioritisation employs the adaptive weighting algorithm:W(Di)=aGt+pMt+yBt+6Nt+cEtW(D_i) = \alpha G t + \beta M_t + \gamma B_t + \delta N_t + \epsilon E_tW(Di)=aGt+pMt+yBt+6Nt+cEt where parameters recalibrate based on real-time contextual feedback.

14. The method of claim 12, wherein computational resource monitoring comprises calculating Environmental Impact Scores (EnvIS) defined as:EnvISt=EmaxEmax-EtEnvIS_t = \frac{E_{max}}{E_{max} - E_t}EnvISt=Emax-Et Emaxand reallocating computational resources by solving:mini / ojXt=lTEnvISt,subject toCt<Cmax,Rt>Rmin\min \sum_{t=l }A{T} EnvIS_t, \quad \text{subject to} \quad C t \leq C_{max}, \quad R_t \geq R_{min}min£t=lT EnvlSt,subject toCt<Cmax,Rt>Rmin15. The method of claim 12, further comprising computing and updating a dynamic Personal Prevention Index (PPI) according to:PPIt=p(Gt)+v(Mt)+p(Bt)+o(Nt)+T(Et)PPI_t = \mu(G_t) + \nu(M_t) + \rho(B_t) + \sigma(N_t) + \tau(E_t)PPIt=p(Gt)+v(Mt)+p(Bt)+o(Nt)+T(Et)to dynamically inform and prioritise personalised preventive health interventions.

16. The system of claim 1, comprising at least the MDDA, ADFM, PPCL, and PHIRE modules, providing adaptive personalised health interventions independently of the CEOM module.

17. The system of claim 1, comprising at least the ADFM, PPCL, and PHIRE modules, configured to securely generate personalised health recommendations from encrypted multi-domain data streams.

18. The system of claim 1, comprising at least the MDDA, ADFM, and PHIRE modules, adaptively generating personalised health insights from integrated multi-domain health data inputs.

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