Distributed resource-aware data fusion engine (DRADFE) data processing system

Abstract

System for generating context-specific interventions based on user data comprising: Multi-Domain Data Acquisition Module MDDA 101 to ingest, structure and integrate data streams including biometric, e

Description

Field of the Invention

[0001] The present invention relates to computer-implemented data processing systems enabling real-time personalised adaptive inference across multiple diverse domains of user data. More particularly, the invention provides technical improvements enabling the dynamic integration, synthesis, and prioritisation of multimodal, cross-domain data streams from extensive sources, including biometric sensors, comprehensive personal health records, individual medical history, family medical history, genomic (DNA) profiles, microbiome (gut biome) data, environmental monitors, behavioural logs, psychological and cognitive assessments, economic indicators, social interactions, and additional external digital data sources. The invention generates precise, context-aware interventions designed to optimise measurable aspects of individual wellbeing, such as health parameters, cognitive performance, productivity metrics, financial decision-making, nutritional management, and environmental comfort. These capabilities are realised through advanced computational methods within a unified and scalable computational framework capable of integrating future developments in Al-driven data ingestion technologies. Background of the Invention

[0002] Existing adaptive personal assistance and decision-support systems typically operate within narrowly defined domains, focusing on isolated user factors and limited data types. Conventional systems commonly analyse basic biometric indicators or single-source sensor inputs but lack comprehensive integration of extensive datasets increasingly accessible from loT devices, wearable technologies, comprehensive health records, detailed medical and family medical histories, genomic (DNA) profiles, microbiome (gut biome) data, and diverse external digital services. For example, known systems such as US 2022 / 0036554 Al integrate voice and visual data to infer emotional or physical wellbeing but do not incorporate critical broader data sources, such as comprehensive medical histories, genomic profiles, microbiome data, detailed environmental sensor data, extensive behavioural records, economic indicators, and nutritional information. Consequently, prior art solutions provide fragmented and incomplete representations of the user's overall state, significantly limiting their accuracy, precision, and effectiveness in generating personalised, contextually relevant interventions.

[0003] Another critical limitation of prior art technologies involves inadequate computational resource management. Traditional personal analytics and adaptive assistance systems typically operate at fixed computational capacities regardless of actual demand due to the absence of dynamic resource optimisation. This inflexibility results in unnecessary CPU utilisation, rapid depletion of mobile device batteries, undue hardware stress, and increased environmental impacts caused by excessive power consumption. Thus, the lack of adaptive computational resource optimisation represents a substantial technical shortcoming in current decision-support and personal analytics systems.

[0004] Specific prior art clearly demonstrating these limitations includes: • US 2022 / 0036554 Al: Discloses multimodal wellness monitoring using voice and visual data but lacks integration of essential broader contextual information, such as comprehensive medical or family medical histories, genomic data, microbiome information, detailed environmental metrics, or extensive behavioural logs. • US 11,183,304 B2: Describes cognitive load-based alerts yet fails to incorporate essential emotional urgency indicators or contextual data beyond basic physiological metrics. • CA 3,230,610 Al: Focuses narrowly on sensory processing thresholds for neurodiverse individuals, excluding broader integration of comprehensive crossdomain datasets. • US 2024 / 0416067 Al: Employs multimodal emotional analytics but does not include adaptive weighting algorithms for balancing cognitive load with neurodiversity profiles, detailed medical histories, genomic data, microbiome factors, or other essential contextual parameters.

[0005] Therefore, no existing technology currently provides a unified, real-time inference system capable of holistically synthesising and prioritising diverse, multidimensional user data streams—including comprehensive health records, medical and family histories, genomic (DNA) information, microbiome (gut biome) profiles, detailed behavioural data, economic contexts, nutritional information, and environmental inputs—while simultaneously managing computational resources dynamically and efficiently to minimise environmental impact.

[0006] Consequently, a substantial technical challenge remains: developing a data processing system capable of comprehensively integrating multidimensional user data, specifically including real-time emotional states, cognitive load indicators, extensive behavioural patterns, comprehensive individual and family medical histories, genomic predispositions (DNA), gut microbiome information, economic factors, nutritional data, and social interactions, within a single integrated computational architecture. Such a system must dynamically prioritise data relevance, generate accurate, context-aware recommendations or automated actions, continuously adapt through real-time user feedback, and efficiently manage computational resources to achieve sustainable energy consumption and minimal environmental impact. The present invention addresses this technical challenge, establishing a robust, scalable, and expandable computational framework explicitly designed for deep, adaptive, and comprehensive personal inference applications. Summary of the Invention

[0007] The present invention addresses the limitations outlined above by introducing a Distributed Resource-Aware Data Fusion Engine (DRADFE), a modular, real-time inference system designed as a comprehensive computational framework for adaptive personal lifemanagement applications. The DRADFE dynamically integrates, synthesises, and prioritises extensive multimodal and cross-domain data streams, generating precise, personalised, context-specific interventions, such as tailored recommendations and automated adjustments. Concurrently, the invention provides advanced computational resource optimisation, proactively managing energy consumption and environmental impact.

[0008] The DRADFE comprises multiple interoperating modules structured within an adaptive data processing pipeline (see Figure 1). A Multi-Domain Data Acquisition Module (MDDA) (101) continuously ingests and preprocesses diverse data inputs from a comprehensive range of internal and external sources. These inputs include biometric sensor readings (e.g., heart rate variability, galvanic skin response, sleep patterns), detailed personal health data, complete individual and family medical histories, genomic (DNA) profiles, microbiome (gut biome) information, environmental sensor metrics (e.g., air quality, noise levels, ambient light), behavioural and activity logs, cognitive and psychological assessments, and further data aggregated via external APIs or wearable devices. Incoming data streams are normalised and integrated within a unified data repository designated as the Digital Life Twin Data Store (DLTDS) (106), which maintains an evolving composite data model representing the user’s state. The DLTDS continuously integrates new data points into a cohesive, time-indexed dataset, ensuring real-time and historical contextual insights inform all subsequent analyses.

[0009] An Adaptive Data Fusion Module (ADFM) (102) applies an advanced dynamic weighting algorithm to integrated data obtained from the DLTDS. The ADFM continuously evaluates several real-time contextual parameters reflecting the user’s immediate situation. These parameters include: • Emotional Urgency (Et): indicating the immediacy or intensity of emotional states, derived from biometric measurements or sentiment analysis of user communications; • Cognitive Load (Ct): estimating current mental workload or focus levels, based on interaction patterns and physiological indicators; • Neurodiversity Profile (Nu): detailing cognitive or sensory processing traits, such as autism spectrum conditions or attention deficit hyperactivity disorder (ADHD), influencing responses to interventions; • Behavioural Context (Bt): reflecting recent and long-term behavioural patterns, routines, schedules, and habits; and • Genomic Priority (Gu): highlighting genetic or biological predispositions relevant to the user’s immediate context, including specific genetic markers associated with health risks or requirements.

[0010] These parameters are algorithmically combined in real-time, enabling the ADFM to prioritise and weight incoming data streams appropriately. Specifically, the ADFM selectively filters, amplifies, or attenuates data signals based on their current contextual relevance. For example, when Emotional Urgency (Et) indicates heightened stress, data related to recent social interactions or specific biometric signals may receive increased priority, while less immediately relevant information—such as routine financial data—may temporarily be assigned lower priority. Conversely, during periods of elevated Cognitive Load (Ct), the system dynamically suppresses non-urgent recommendations to reduce cognitive burden. This adaptive fusion process generates a weighted composite dataset (see Figure 3, element 307), highlighting the most contextually significant data. By allocating computational resources primarily to high-priority data and excluding low-priority signals, the system enhances the accuracy, responsiveness, and efficiency of subsequent predictive modelling processes. Predictive Modelling and Intervention Generation Module (PMIG)

[0011] A Predictive Modelling and Intervention Generation Module (PMIG) (103) receives the weighted dataset from the Adaptive Data Fusion Module (ADFM) and applies sophisticated predictive analytics to generate targeted intervention outputs. The PMIG employs advanced machine learning models, including neural networks, decision trees, Bayesian networks, and expert rule-based systems, to analyse patterns within the prioritised data, identifying immediate user needs and opportunities for proactive improvement. Intervention outputs generated by the PMIG comprise personalised recommendations or alerts delivered directly to the user, as well as automated adjustments implemented through connected devices or integrated services.

[0012] The PMIG also includes an intervention delivery queue, managing the optimal timing and dissemination of intervention outputs to the user or associated devices. Since the data inputs have been prioritised by the ADFM, the PMIG efficiently allocates computational resources to evaluate the most relevant factors. This targeted computational approach enables sophisticated predictive analytics to operate effectively in real-time, avoiding unnecessary resource expenditure on irrelevant data streams. The interventions produced by the PMIG are inherently holistic and cross-domain; for example, a single intervention plan may simultaneously target the user’s health parameters, workplace productivity, and social interactions if analysis reveals significant interconnections among these domains (see illustrative example provided in Figure 5). Real-Time Feedback and Model Refinement Module (RFMR)

[0013] A Real-Time Feedback and Model Refinement Module (RFMR) (104) captures user feedback data following the delivery of interventions. This feedback consists of objective biometric responses, such as changes in heart rate, activity levels, or physiological indicators subsequent to the intervention, as well as direct user input or ratings regarding perceived effectiveness. The RFMR analyses this feedback to assess intervention outcomes, enabling iterative refinement of internal system models and parameters based on observed user responses.

[0014] Specifically, the RFMR updates weighting parameters within the Adaptive Data Fusion Module (ADFM) and initiates retraining or updates of predictive models within the Predictive Modelling and Intervention Generation Module (PMIG). By continuously incorporating real-world feedback, the DRADFE ensures ongoing adaptation aligned with the user's evolving behaviours and contexts. This continuous feedback-driven refinement prevents intervention recommendations from becoming outdated or misaligned over time, maintaining high levels of relevance and effectiveness without requiring manual system adjustments. Computational and Environmental Optimisation Module (CEOM)

[0015] In parallel with the user-focused inference pipeline described above, the invention incorporates a Computational and Environmental Optimisation Module (CEOM) (105) that manages system performance and resource efficiency. The CEOM continuously monitors the computational resource usage of the system in real-time, dynamically adjusting processing to minimise energy consumption and environmental impact while maintaining high-quality service delivery.

[0016] The CEOM comprises a Computational Load Monitoring Engine (401) (see Figure 4) that tracks performance metrics such as CPU and GPU utilisation, memory usage, network bandwidth, and device battery levels across all system components. Additionally, the module computes an Environmental Impact Score (EnvIS) (402), quantifying the environmental costs associated with current system operations. These metrics include total energy consumption, estimated carbon footprint, processing power source (battery or mains power), and device temperatures serving as proxies for energy efficiency.

[0017] Utilising these monitored inputs, the CEOM employs Dynamic Adjustment Logic (403) to make informed decisions regarding the scaling or throttling of various system components, thereby optimising resource utilisation. This logic seeks to preserve critical system performance, eliminating unnecessary computational activity. For example, during periods when the user is inactive or asleep, the CEOM may shift the system into a low-power operational mode by reducing the sampling frequency of specific sensors, pausing or significantly slowing non-critical analysis tasks, and deferring intensive data processing tasks to periods of lower resource demand or offloading them to more energy-efficient computational infrastructure.

[0018] Conversely, if an urgent scenario is identified—for instance, detection of a critical medical or safety condition—the CEOM temporarily permits the system to utilise increased computing resources, even at the expense of higher battery consumption, to immediately resolve the urgent issue. Upon resolution, the system swiftly returns to normal or energysaving operations. Furthermore, the CEOM intelligently redistributes computational tasks between devices; for example, if a user's mobile device battery is low, resource-intensive tasks may be offloaded to an available plugged-in home device or cloud-based server, extending battery life while ensuring continuity of critical functions.

[0019] The CEOM does not operate independently; instead, it provides optimisation feedback signals (see Figure 4, arrows 411-414) to other modules, such as the Multi-Domain Data Acquisition Module (MDDA) and Adaptive Data Fusion Module (ADFM). For example, during periods of elevated system load or increased environmental impact without corresponding user benefit, the CEOM may instruct the MDDA to reduce data collection frequency or selectively filter data to essential streams. Similarly, the ADFM may be instructed to elevate its relevance thresholds, focusing computational resources exclusively on highly significant inputs.

[0020] These coordinated, dynamic adjustments ensure that during periods of increased load, the system concentrates exclusively on essential tasks, temporarily deferring or skipping non-essential processing to prevent performance degradation or unnecessary power consumption. Conversely, during periods of low system load, such as when a user's device is plugged in and idle, the CEOM opportunistically performs additional background tasks, such as predictive model training or data caching, thus enhancing system intelligence without negatively impacting the user experience.

[0021] Through these measures, the invention consciously manages computational resource utilisation, actively reducing environmental impact compared to traditional systems that lack dynamic resource optimisation. This approach enables sustainable continuous (24 / 7) operation, maintaining a reduced energy and environmental footprint, even as data complexity and system demands increase. Technical Advantages of the DRADFE

[0022] In summary, the DRADFE provides a novel combination of technical capabilities addressing the limitations inherent in existing data processing technologies. It offers a unified real-time inference core capable of: • Comprehensively aggregating and integrating data from extensive and diverse life domains and sources; • Intelligently prioritising and fusing incoming data streams through context-aware adaptive weighting; • Utilising sophisticated predictive analytics to deliver highly personalised, contextually precise recommendations and automated interventions; • Continuously refining predictive models through real-time feedback from user interactions, ensuring sustained alignment with user needs; and • Proactively optimising computational resources and energy efficiency to support environmentally sustainable operation even under fluctuating system demands.

[0023] By emphasising measurable technical outcomes—such as improved computational efficiency, reduced latency in delivering interventions, adaptive loT device management, and minimised power consumption—the DRADFE significantly enhances the quality, responsiveness, and environmental sustainability of data-driven interventions. These technical advancements directly translate into tangible user benefits across multiple life domains, including improved health outcomes, enhanced productivity, safer environments, and increased overall quality of life. Brief Description of the Drawings

[0024] Embodiments of the invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0025] Figure 1 illustrates the overall system architecture of the Distributed Resource-Aware Data Fusion Engine (DRADFE). The figure depicts core interconnected modules, including the Multi-Domain Data Acquisition Module (MDDA, 101), Adaptive Data Fusion Module (ADFM, 102), Predictive Modelling and Intervention Generation Module (PMIG, 103), Real-Time Feedback and Model Refinement Module (RFMR, 104), and Computational and Environmental Optimisation Module (CEOM, 105). Additionally shown are the Digital Life Twin Data Store (DLTDS, 106) and external data sources accessed via APIs or devices (107). Arrows (111-115) indicate data and control signal flows among these components, including data acquisition, data fusion, intervention generation, feedback loops, and resource optimisation pathways.

[0026] Figure 2 presents a flowchart detailing the operational data flow process of the DRADFE. The depicted operational steps (201-206) include initiating multi-domain data acquisition (201), initial data structuring and validation (202), real-time integration of external data streams (203), aggregation and preparation of data for adaptive fusion analysis (204), routing processed data into the Adaptive Data Fusion Module (ADFM) for prioritisation and weighting (205), and finally outputting a structured and weighted dataset for predictive modelling (206). Arrows (211-213) illustrate the progression through these sequential steps.

[0027] Figure 3 illustrates the internal operational logic of the Adaptive Data Fusion Module (ADFM). The figure details key contextual parameters processed by the dynamic weighting algorithm engine (306), including Emotional Urgency (Et, 301), Cognitive Load (Ct, 302), Neurodiversity Profile (Nu, 303), Behavioural Context (Bt, 304), and Genomic Priority (Gu, 305). The adaptive weighting algorithm (306) integrates these parameters in real-time, producing a weighted output dataset (307) provided to the Predictive Modelling and Intervention Generation Module (PMIG). Arrows (311-313) depict the flow of information from contextual inputs through the weighting computation to the final output dataset.

[0028] Figure 4 outlines the operational logic of the Computational and Environmental Optimisation Module (CEOM). Illustrated components include a Computational Load Monitoring Engine (401), Environmental Impact Score calculation module (EnvIS, 402), Dynamic Resource Allocation Logic (403), and Computational Resource and Energy Optimisation Engine (404). Feedback loops from the CEOM to other modules, such as the MDDA and ADFM, are indicated by arrows (411-414), showing how computational resource usage data informs adjustments in data collection frequency, processing intensity, and resource allocation.

[0029] Figure 5 depicts an illustrative multi-domain use case scenario demonstrating the integrated analytical and intervention capabilities of the DRADFE. In this example, data simultaneously gathered from a user's Work Productivity domain (computer usage statistics and calendar information, 501), Health domain (sleep quality metrics from wearable devices, 502), and Relationship / Social domain (communication logs and stress indicators derived from message sentiment analysis, 503) are analysed collectively. The Cross-Domain Analysis Engine (504), part of the PMIG, identifies interrelationships among these domains—such as recognising poor sleep quality as a contributor to reduced cognitive performance at work and heightened irritability in social communications. Consequently, the system formulates and delivers a coordinated intervention plan (505), addressing root causes across all three domains. Examples include recommending an earlier bedtime or relaxation routine for improved sleep, automatically adjusting evening environmental settings to support restful sleep, and scheduling brief morning planning activities to improve work focus and reduce communication stress. Arrows (511-513) indicate data flow from initial domain inputs through integrated analysis to delivered intervention outputs. Detailed Description of the Invention

[0030] The Distributed Resource-Aware Data Fusion Engine (DRADFE) is structured as a modular, integrated data processing system that synthesises diverse data streams, dynamically adapts to real-time contexts, and delivers targeted personalised interventions across multiple life domains. The detailed description below outlines major components and processes of the system, referring to the drawings where appropriate. Overall Architecture and Data Flow (Figures 1 and 2)

[0031] Referring initially to Figure 1, the DRADFE comprises interconnected modules (101-107) working collaboratively within a comprehensive adaptive inference pipeline. The operational flow begins with the Multi-Domain Data Acquisition Module (MDDA) (101). As illustrated in Figure 2 (step 201), the MDDA systematically ingests diverse data streams from a wide range of internal and external sources. These sources include wearable and embedded biometric sensors capturing physiological signals such as heart rate, skin conductivity, physical activity levels, and sleep patterns; genomic databases and personal medical records providing genetic predisposition data, lab results, and microbiome information; ambient environmental sensors monitoring air quality, temperature, humidity, noise, and light levels around the user; detailed behavioural and activity logs documenting the user’s routines, device usage patterns, calendar events, and historical behavioural trends; emotional and cognitive assessments derived from analyses of text input, speech tone, facial expressions, or cognitive performance metrics indicating mood or mental state; and extensive external data accessed via APIs or connected services (107), such as weather conditions, traffic and location data, financial market updates, health alerts, news feeds, social media content, nutritional databases, and data from smart-home or loT devices used by the individual.

[0032] Upon ingestion, the MDDA conducts initial data structuring and verification (step 202, Figure 2) to ensure the incoming data aligns with the system’s defined schema and is suitable for further processing. The MDDA then dynamically integrates external real-time data streams (step 203), combining these with internally generated user data. Next, the MDDA aggregates these multi-domain inputs into a unified, time-synchronised dataset (step 204), routing this consolidated data into the subsequent module for adaptive fusion processing (step 205). Throughout data acquisition, preprocessing tasks—including noise filtering, data normalisation (to allow meaningful comparisons and fusion across data types), and anomaly detection (flagging values outside typical ranges or sensor errors)—ensure consistent data quality and reliability.

[0033] The consolidated data is continuously updated and maintained within a unified repository designated as the Digital Life Twin Data Store (DLTDS) (106). The DLTDS acts as a comprehensive virtual representation or digital twin of the user's state, aggregating both real-time data and historical contextual insights. Structurally, the DLTDS may be implemented as an in-memory database or a high-speed data store organising multi-domain data into timestamped, layered formats. For instance, the DLTDS maintains separate time-indexed data streams for biometric signals, environmental metrics, behavioural events, and other relevant domains, each new data entry being tagged with its timestamp and domain source.

[0034] Cross-references between data points occurring simultaneously or at closely related timestamps enable the system to rapidly reconstruct a holistic view of the user’s state at any given moment. For example, the system can promptly retrieve a user's current heart rate alongside recent historical trends or today's environmental conditions compared to previous days. By providing a common, integrated contextual knowledge base, the DLTDS allows subsequent analytical modules to operate without isolation or redundancy. The DLTDS thus enables seamless, comprehensive cross-domain analytics and supports precise, context-aware decision-making throughout the DRADFE pipeline. Adaptive Data Fusion Module (ADFM) - Dynamic Weighting (Figure 3)

[0035] Once data is aggregated and stored in the Digital Life Twin Data Store (DLTDS), the Adaptive Data Fusion Module (ADFM) (102) initiates its processing. Central to the ADFM is a dynamic weighting algorithm engine (306), which evaluates incoming composite datasets according to key contextual parameters. These parameters include Emotional Urgency (Et), Cognitive Load (Ct), Neurodiversity Profile (Nu), Behavioural Context (Bt), and Genomic Priority (Gu). Each parameter is derived in real-time from user data and influences how incoming data streams are weighted and prioritised: • Emotional Urgency (Et): This parameter measures the intensity and immediacy of a user's emotional state. It is derived from biometric indicators (e.g., rapid increases in heart rate or blood pressure indicating stress or excitement) and sentiment analysis performed on user-generated text or voice communications. A high Et indicates significant emotional conditions, positively or negatively charged, requiring prioritisation of related data. • Cognitive Load (Ct): Represents current mental workload or cognitive strain, inferred through user interaction patterns (e.g., typing speed, frequency of task switching), error rates, interface response times, or physiological measures (such as eye-tracking metrics or EEG signals from wearable devices). High Ct levels indicate elevated cognitive demand, prompting the system to avoid additional cognitive burdens by filtering out non-essential information. • Neurodiversity Profile (Nu): Captures individual cognitive or sensory processing characteristics, especially in neurodivergent conditions such as Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), dyslexia, or similar traits. This parameter influences data weighting to ensure personalised interventions are appropriate to the user's cognitive style, considering both static traits and dynamic sensitivities. • Behavioural Context (Bt): Reflects recent and habitual user behaviours, daily routines, current activity context, and location information (home, work, commuting). Bt guides the weighting algorithm in assigning higher relevance to data matching current behaviour or activities. For instance, sleep data is prioritised at night, while work-related notifications may be deprioritised. • Genomic Priority (Gu): Highlights genetic predispositions or biological factors specific to the user's health context, derived from genomic data and family medical histories. For example, genetic markers indicating a predisposition to high blood pressure result in increased weighting of cardiovascular-related health data.

[0036] The ADFM algorithmically integrates these parameters in real-time, calculating relative weights for each incoming data stream or data type. This dynamic weighting process produces a prioritised composite dataset (307), which emphasises contextually significant data for the Predictive Modelling and Intervention Generation Module (PMIG). By filtering, amplifying, or attenuating data streams according to their immediate contextual relevance, the ADFM ensures subsequent predictive analyses are efficient, timely, and highly focused on the user's current needs.

[0037] The ADFM’s weighting process is adaptive, continuously recalibrating weights as the user's context evolves. If the user's emotional state calms or cognitive load decreases, or if a behavioural pattern shifts, the ADFM rapidly adjusts its weighting logic within subsequent data processing cycles. This adaptability ensures the composite dataset always accurately represents the user's present circumstances, enhancing the speed, accuracy, and responsiveness of subsequent intervention generation and delivery. Predictive Modelling and Intervention Generation (PMIG)

[0038] The Predictive Modelling and Intervention Generation Module (PMIG) (103) processes the weighted dataset provided by the Adaptive Data Fusion Module (ADFM) to generate personalised interventions designed to support and enhance the user's wellbeing across multiple life domains. Within the PMIG, sophisticated predictive analytics—such as neural networks, decision trees, Bayesian networks, and expert rule-based systems—analyse patterns and contextual indicators within the prioritised data. This analytical approach enables the system to identify immediate user needs and opportunities for proactive improvement efficiently and accurately. As the PMIG receives data that has already been filtered and prioritised by the ADFM, computational resources are efficiently focused on the most relevant factors, enabling real-time operation of complex predictive models without extraneous computations.

[0039] The PMIG is capable of generating multiple types of intervention outputs, which include: • Recommendations or Alerts to the User: These context-specific suggestions or prompts are delivered through user interfaces such as smartphone notifications, smart speakers, or on-screen messages. Typically structured as subtle prompts or "micronudges," these recommendations aim to gently guide user behaviour. Examples include prompting the user to take a brief rest break following extended periods of elevated cognitive load, recommending breathing exercises when heightened stress (high Et) is detected, or issuing reminders for upcoming tasks coupled with personalised suggestions—for instance, advising a coffee break or short mindfulness exercise if low sleep quality has been recorded. • Automated Adjustments of Connected Devices or Services: In many instances, interventions may involve automated actions executed directly by the system via integrated loT devices or software services. For example, in response to detected environmental noise combined with reduced user concentration (high Ct), the system might automatically activate noise-cancelling functions in headsets or reduce smartspeaker volume. Similarly, poor air quality measurements alongside identified health risks could trigger automatic activation of air purification systems. Additionally, if upcoming tasks indicate a requirement for sustained attention, the system can proactively adjust smart-lighting brightness or colour temperature to enhance user alertness. Such interventions are executed seamlessly through integrated environmental controls and actuators.

[0040] The interventions generated by the PMIG are inherently holistic, coordinated across multiple interconnected life domains. For instance, issues such as chronic fatigue may manifest across health (sleep patterns), productivity (work performance), and mood (emotional wellbeing) domains. Rather than addressing these as separate isolated issues, the DRADFE analyses and addresses them collectively. The PMIG would, in such scenarios, formulate a coordinated intervention set, such as recommending an earlier bedtime (addressing sleep health), automatically adjusting evening environmental controls such as room temperature and screen blue-light filters to facilitate restful sleep (environmental adjustments), and suggesting morning scheduling modifications or brief relaxation exercises to enhance productivity and reduce emotional stress in social interactions.

[0041] Following intervention delivery, outcomes are continuously monitored through the Real-Time Feedback and Model Refinement Module (RFMR), described subsequently. Real-Time Feedback and Model Refinement (RFMR)

[0042] The Real-Time Feedback and Model Refinement Module (RFMR) (104) continuously captures user responses to interventions, integrating these insights into the system's adaptive learning mechanisms. The RFMR collects real-time feedback comprising objective biometric responses (such as heart rate changes, skin conductance, physical movement data) and subjective user feedback (including user ratings or comments on specific recommendations). This feedback is analysed to assess the effectiveness and outcomes of each delivered intervention. For instance, if the system suggests a breathing exercise aimed at reducing stress, the RFMR subsequently evaluates biometric indicators, such as heart rate variability or galvanic skin response, to determine improvements. Additionally, the RFMR analyses user-submitted feedback, such as mood self-reports or satisfaction ratings, to refine system performance further.

[0043] Based on these analyses, the RFMR updates the system’s internal models accordingly. This involves adjusting weighting coefficients within the Adaptive Data Fusion Module (ADFM), highlighting which data signals most effectively predicted user outcomes, and refining or retraining predictive models within the Predictive Modelling and Intervention Generation Module (PMIG). By continuously integrating user feedback, the RFMR ensures ongoing alignment of the system's predictive models and weighting algorithms with evolving user behaviours and contexts. This continuous refinement process enhances the precision, accuracy, and relevance of subsequent interventions, maintaining the system's adaptability without necessitating manual recalibration or tuning. Computational and Environmental Optimisation Module (CEOM)

[0044] Complementing personalised interventions, the DRADFE also incorporates computational resource management through the Computational and Environmental Optimisation Module (CEOM) (105). The CEOM continuously monitors system performance metrics and environmental impact factors, dynamically adjusting operational parameters to optimise computational efficiency. The CEOM achieves this by monitoring system load, energy usage, battery status, and other related metrics, dynamically scaling or throttling system processes in response to real-time conditions.

[0045] The CEOM intelligently throttles back processing intensity during periods of low demand, offloads computational tasks to external devices or cloud services when local resources are constrained, and proactively balances system performance against energy consumption. Due to this continuous optimisation, the DRADFE maintains operational effectiveness even on devices with limited battery capacity, carefully balancing computational demands and energy consumption to minimise environmental impact.

[0046] By integrating computational efficiency optimisation directly into its operational logic, the CEOM ensures the system's sustained, practical, and energy-conscious operation in real-world settings. This dual emphasis—delivering highly relevant, personalised interventions and simultaneously managing computational resource usage—distinguishes the DRADFE from prior art systems that may rapidly deplete device batteries or prove impractical for continuous operation.

[0047] The detailed algorithms and pseudocode describing the adaptive processes within these modules are presented in subsequent sections, providing clarity on specific computational implementations. Detailed Algorithms and Pseudocode for Key Modules Adaptive Data Fusion Module (ADFM): Dynamic Weighting Algorithm

[0048] The Adaptive Data Fusion Module (ADFM) utilises a dynamic weighting algorithm to prioritise incoming data streams according to real-time context parameters. The detailed algorithm is presented as pseudocode below: lua CopyEdit function ComputeAdaptiveWeight(D, Et, Ct, Nu, Bt, Gu): for each data_point in D: weight[data_point] = (a x Et[data_point.type]) + (P x Ct[data_point.type]) + (y x Nu[data_point.type]) + (5 x Bt[data_point.type]) + (s x Gu[data_point.type]) if weight[data_point] >threshold: prioritised_data.add(data_point) else: deprioritised_data.add(data_point) return prioritised data

[0049] Parameter Explanation: In this pseudocode, a, P, y, 5, and a represent adjustable weighting coefficients derived from historical data and real-time feedback provided by the Real-Time Feedback and Model Refinement Module (RFMR). These coefficients quantify the relative influence of Emotional Urgency (Et), Cognitive Load (Ct), Neurodiversity Profile (Nu), Behavioural Context (Bt), and Genomic Priority (Gu), respectively. The "threshold" parameter represents a dynamically adjustable boundary for determining data significance, informed in real time by resource availability and computational context provided by the Computational and Environmental Optimisation Module (CEOM). Data points with computed weights exceeding the threshold are categorised into the prioritised dataset, while those below the threshold are categorised as deprioritised. This selective weighting facilitates efficient downstream processing by emphasising only contextually relevant data. Predictive Modelling and Intervention Generation Module (PMIG): Intervention Selection and Scheduling Algorithm

[0050] The Predictive Modelling and Intervention Generation Module (PMIG) employs a predictive algorithm to identify suitable user interventions based on the weighted data provided by the ADFM. The detailed algorithm is presented as pseudocode below: scss CopyEdit function Generatelnterventions(prioritiseddata): candidateinterventions = [] for each data_point in prioritiseddata: prediction = PredictUserNeed(data_point) intervention = SelectBestlntervention(prediction) intervention.priority = AssessUrgency(prediction) candidateinterventions.add(intervention) sortedinterventions = SortByPriority(candidateinterventions) for each intervention in sorted interventions: Schedulelntervention(intervention, OptimalTiming(intervention)) return InterventionQueue

[0051] Key Function Descriptions: • PredictUserNeed(data_point): Utilises trained machine learning models to forecast user needs or issues indicated by the given data point. For example, combined biometric and calendar data may predict the user's need for relaxation or rest. • SelectBestlntervention(prediction): Matches the predicted need to an optimal predefined intervention. This step may involve selecting the most appropriate recommendation from a repository of available interventions. For instance, a prediction indicating afternoon drowsiness may trigger a suggestion for a brief walk or power nap. • AssessUrgency(prediction): Determines the urgency or priority level for the predicted user need, based on severity, immediacy, and contextual relevance. For example, an urgent health concern results in a higher priority intervention. • SortByPriority(interventions): Orders candidate interventions by their assessed urgency, ensuring the highest-priority interventions are scheduled first. • Schedulelntervention(intervention, OptimalTiming): Allocates interventions into a delivery queue at times most suitable given the user's current state and context. For example, the system may delay non-urgent notifications until after user meetings or schedule automated interventions during user inactivity periods.

[0052] This algorithmic approach systematically processes prioritised data points to produce a scheduled queue of tailored interventions, optimising intervention effectiveness and minimising user disruption through intelligent timing and delivery management. Real-Time Feedback and Model Refinement Module (RFMR): Learning Algorithm

[0053] The Real-Time Feedback and Model Refinement Module (RFMR) incorporates a feedback-driven algorithm that continuously refines internal predictive models and weighting parameters based on user intervention outcomes. The algorithm's detailed operation is represented by the following pseudocode: php CopyEdit functi on UpdateModel s(feedb ackdata): for each intervention outcome in feedback data: efficacy score = EvaluateOutcome(interventionoutcome) UpdateWeightCoefficients(a, P, y, 5, 8, efficacy_score) if efficacy score <performance threshold: RetrainPredictiveModels(interventionoutcome.data) return UpdatedModelParameters

[0054] Explanation: Upon receiving feedback data, which may represent a batch of recent outcomes or real-time feedback streams, the algorithm evaluates each intervention outcome. The EvaluateOutcome function calculates an efficacy score by analysing objective metrics (e.g., improvements in heart rate variability or skin conductance following stress-reduction interventions) and subjective user feedback, such as user ratings or comments. Based on this score, the algorithm adjusts the weighting coefficients (a, P, y, 5, s) used by the Adaptive Data Fusion Module (ADFM), reinforcing the data signals that most accurately predict successful interventions. If the efficacy score falls below a defined performance threshold—indicating lower intervention effectiveness—the algorithm triggers retraining or updates to predictive models in the Predictive Modelling and Intervention Generation Module (PMIG), incorporating new feedback data or performing online parameter adjustments. Through this iterative feedback loop, the system continuously enhances predictive accuracy and personalisation, autonomously refining its performance without requiring manual intervention. Computational and Environmental Optimisation Module (CEOM): Optimisation Algorithm

[0055] The Computational and Environmental Optimisation Module (CEOM) utilises a resource management algorithm designed to dynamically optimise computational resource allocation and reduce environmental impact. The detailed operational logic is represented in the following pseudocode: php CopyEdit function OptimiseComputationalResources(current_load, envIS): if current load >load threshold or envIS >env threshold: reduce_sampling_rate(non_critical_sensors) simplify _data_processing(adaptive_weighting_threshold += A) defer_heavy_computation(off_peak_period) offload_computation(low_battery device, plugged in server) else: perform_background_optimisation_tasks() return AdjustedComputationalConfig

[0056] Explanation: The algorithm continuously evaluates the current computational load (e.g., CPU utilisation, memory usage, network bandwidth) and the Environmental Impact Score (envIS). If either the computational load surpasses a predefined threshold or the environmental impact approaches an undesirable level, the CEOM initiates resource optimisation actions. These actions include reducing the sampling rates of non-critical sensors to limit incoming data volume, simplifying data processing by raising adaptive weighting thresholds, deferring intensive computational tasks to off-peak periods, and offloading computationally intensive tasks from low-battery devices to available plugged-in servers or cloud resources. Collectively, these measures prevent system overload and excessive energy consumption. When computational load and environmental impact remain within acceptable limits, the CEOM opportunistically executes background optimisation tasks, such as predictive model training or data archiving, during idle periods. The output (AdjustedComputationalConfig) represents the optimised system configuration of sampling rates, processing schedules, and task distributions. This dynamic resource management approach ensures sustainable, efficient system operation while maintaining responsiveness to user needs.

[0057] These algorithms provide clear technical mechanisms illustrating the DRADFE's adaptive processes, demonstrating concrete implementations that maximise efficiency, personalisation, and environmental sustainability. Illustrative Multi-Domain Use Case (Figure 5)

[0058] To illustrate integrated operation across multiple domains, consider a practical example scenario depicted conceptually in Figure 5, involving a user experiencing interconnected challenges in productivity, health, and social relationships. Suppose the user has reported decreased productivity at work, poor sleep quality, and increased tension in interpersonal communications. Traditional methods typically approach these issues separately—using distinct systems or applications for productivity management, sleep tracking, and social interaction support—often failing to recognise underlying connections. By contrast, the DRADFE simultaneously aggregates and analyses data across these domains, enabling comprehensive insight and intervention strategies.

[0059] In this illustrative scenario, the Multi-Domain Data Acquisition Module (MDDA) concurrently collects relevant data streams: work productivity information, including computer usage patterns, task completion rates, and scheduled calendar events (element 501); sleep quality metrics gathered through wearable devices, such as sleep stages and total sleep duration (element 502); and relationship or social data, including communication logs and sentiment analyses indicating emotional tone and stress levels in emails and messages (element 503). These diverse data streams are integrated simultaneously within the Digital Life Twin Data Store (DLTDS).

[0060] Subsequently, the Adaptive Data Fusion Module (ADFM) dynamically evaluates and prioritises these inputs, identifying patterns such as elevated Emotional Urgency signals coinciding with poor sleep and negative sentiment in recent communications, alongside increased Cognitive Load during work hours. The Cross-Domain Analysis Engine within the Predictive Modelling and Intervention Generation Module (PMIG) (504) analyses these patterns collectively, hypothesising that inadequate sleep quality may be the underlying cause of reduced productivity and heightened irritability in interpersonal interactions.

[0061] Instead of generating separate interventions for isolated issues, the PMIG formulates a coordinated, holistic intervention plan (505). This integrated strategy may involve recommending an earlier bedtime or evening relaxation routine to enhance sleep (addressing the health domain), automated adjustments to evening environmental conditions—such as dimming lights and reducing room temperature—to facilitate restful sleep (environmental domain), and a morning suggestion encouraging brief physical activity or exercise to improve cognitive focus and mood prior to engaging in work or social communications.

[0062] Following intervention implementation, the Real-Time Feedback and Model Refinement Module (RFMR) continuously monitors outcomes. In this example, it might detect improvements such as increased sleep duration, enhanced productivity metrics at work (higher task completion rates, improved focus periods), and more positive sentiment in communications. Such feedback reinforces the system’s predictive models, validating sleep quality as a critical leverage point for this user. If certain recommendations prove less effective—such as specific morning suggestions being disregarded—the RFMR identifies these patterns, prompting adjustments in future intervention strategies, timing, or communication style.

[0063] This illustrative use case demonstrates the unique capability of the DRADFE to identify cross-domain relationships and deliver comprehensive, coordinated interventions, distinct from conventional single-domain solutions. Furthermore, it highlights the efficient management of computational resources, ensured by the Computational and Environmental Optimisation Module (CEOM), which dynamically minimises non-essential processing during critical moments. Overall, the DRADFE provides significant advancements in adaptive life management by delivering measurable improvements across interrelated domains of health, productivity, and social wellbeing. Illustrative Use Cases

[0064] The following practical use cases illustrate diverse operational scenarios of the Distributed Resource-Aware Data Fusion Engine (DRADFE). Each scenario demonstrates the system’s capability to integrate multi-domain data streams and deliver precise, contextually appropriate interventions: Use Case 1: Integrated Health and Emotional Support

[0065] A user displays signs of increasing stress indicated by biometric measurements (elevated heart rate variability, galvanic skin response) combined with high Emotional Urgency scores derived from recent communications, alongside negative sentiment analysis of messages. The DRADFE integrates real-time biometric data, emotional-cognitive indicators, recent social interaction logs, environmental conditions (such as room temperature, air quality, and ambient noise levels), and pertinent medication or health history data. In response, the system formulates a coordinated, personalised intervention strategy— recommending brief mindfulness sessions or guided breathing exercises while simultaneously adjusting environmental settings, such as dimming lights or modifying air quality parameters, to provide immediate emotional relief. These integrated interventions directly guide the user and adjust ambient conditions concurrently, supporting overall emotional wellbeing. Use Case 2: Personalised Career Productivity and Sleep Optimisation

[0066] A user exhibits declining work productivity, identified through decreased task completion rates and increased cognitive load indicators (e.g., slower reaction times, heightened error frequency). Concurrently, the DRADFE identifies associated sleep disturbances via wearable sleep quality metrics and detects heightened stress indicators within recent communications. Integrating this multi-domain data, the system delivers an integrated intervention plan, recommending adjustments to sleep hygiene practices (such as modified bedtime routines or evening relaxation activities), issuing cognitive load management prompts (including reminders for strategic breaks and task prioritisation during work hours), providing financial reassurance derived from economic indicators if anxiety is noted, and tailoring communication strategies based on the user's Neurodiversity Profile. This coordinated intervention spans health, productivity, financial, and social domains, effectively reducing cognitive overload, improving sleep quality, and enhancing workplace performance. Use Case 3: Tailored Financial Decision Support

[0067] The system detects atypical financial behaviours through real-time financial data indicating unusual spending patterns, combined with biometric indicators suggesting financial stress (elevated heart rate or cortisol levels). Analysing financial information alongside cognitive processing characteristics (from the user's Neurodiversity Profile), recent economic trends, and individual financial goals, the DRADFE generates tailored micronudges. These interventions include simplified budgeting advice, timely investment recommendations aligned with the user's risk profile, and gentle reminders to reassess spending habits or financial plans. The interventions are crafted to alleviate financial anxiety, promote stable financial habits, and support long-term economic stability, exemplifying the system’s ability to integrate economic data contextually with personal stress indicators. Use Case 4: Relationship Management Enhancement

[0068] Analysis of communication data reveals increasing relationship tension, identified by linguistic sentiment analysis (negative or abrupt messaging tone), recent interpersonal interaction patterns, personality assessment data, and elevated Emotional Urgency parameters. In response, the DRADFE formulates targeted micro-nudge interventions, offering empathetic communication prompts or specific dialogue suggestions to assist the user in managing social interactions. Additionally, the system may recommend shared activities or timely gestures selected based on personality profiles and contextual data, such as suggesting relaxing outdoor activities given favourable local weather conditions. These interventions aim to foster positive interpersonal engagement, providing immediate emotional support and demonstrating the system’s capability to enhance relationship quality through contextualised, data-driven recommendations. Glossary of Terms Adaptive Data Fusion Module (ADFM): An analytical module (102) employing a dynamic weighting algorithm (306) to prioritise and fuse multi-domain data inputs. Contextual parameters used include Emotional Urgency, Cognitive Load, Neurodiversity Profile, Behavioural Context, and Genomic Priority, producing a dynamically weighted dataset (307) for predictive analytics. Behavioural Context (Bt): A real-time parameter reflecting a user's recent and long-term behavioural patterns, routines, and current activity context. It guides the ADFM weighting algorithm in emphasising or suppressing certain data streams based on relevance to the user's activities or routines. Biometric Sensors: Wearable or embedded sensors capturing physiological user signals, including heart rate, skin conductivity (galvanic skin response), body movement (accelerometer data), temperature, blood oxygen level, and sleep phases. These data streams are integrated into the MDDA for further processing and analysis. Cognitive Load (Ct): A parameter quantifying the user's current mental workload or cognitive strain. Derived from factors such as frequency of task-switching, user interaction latency, error rates, or physiological indicators (e.g., pupil dilation, EEG patterns). Elevated Cognitive Load values prompt the ADFM to deprioritise non-essential data and interventions to avoid overloading the user. Computational and Environmental Optimisation Module (CEOM): A system module (105) continuously monitoring computational resource usage (CPU, memory, network, battery) and calculating an Environmental Impact Score (EnvIS) to quantify energy consumption and environmental footprint. Dynamically adjusts computational tasks—through throttling, scheduling, offloading, or reconfiguration—to reduce energy consumption and optimise performance. Provides feedback signals to modules such as MDDA and ADFM, modulating their operations based on real-time resource constraints. Digital Life Twin Data Store (DLTDS): A dynamic data repository (106) maintaining an integrated, time-indexed digital representation of the user's state across multiple life domains. Functions as a comprehensive "digital twin," storing real-time and historical user data in a unified model, providing contextually consistent data to inform system decisions and analytics. Emotional Urgency (Et): A numeric parameter reflecting the immediacy or intensity of a user's emotional state. Derived from biometric analyses (such as heart rate variability, hormone levels) and sentiment analysis on user communications (voice or text). High values prompt prioritisation of interventions targeting acute emotional needs. Environmental Impact Score (EnvIS): A computed metric estimating the environmental impact of the system’s current operations, considering factors such as energy consumption, processing load, battery usage, and related metrics. Used by the CEOM to determine when to scale down or optimise resource usage, minimising environmental footprint. External APIs / Data Sources: External application programming interfaces and data feeds (107) providing supplementary information beyond the user’s personal sensors. Examples include weather services, traffic conditions, financial markets, news feeds, public health alerts, social media data, nutritional databases, and smart-home or loT device data. Integrated via the MDDA to enhance system context awareness and data comprehensiveness. Genomic Priority (Gu): A weighting parameter derived from the user's genomic profile or identified medical predispositions. Applied in the ADFM to prioritise interventions or data streams relevant to genetically informed health risks or predispositions, such as indicators for diabetes or cardiovascular risk. Intervention Delivery Queue: A PMIG component managing scheduling and coordinated delivery of intervention outputs. Ensures interventions are prioritised according to urgency, delivered in a context-appropriate manner, preventing overload and ensuring suitable timing and mode of communication. Micro-Nudge: A context-sensitive, small-scale recommendation or automated action subtly influencing user behaviour or environment positively. Examples include prompts to rest, gentle environmental adjustments, or minor task reminders tailored to the user's immediate context. Generated within the PMIG. Multi-Domain Data Acquisition Module (MDDA): A module (101) responsible for collection, normalisation, and synchronisation of multidomain data streams from sensors, devices, external APIs, and user inputs. Conducts initial processing (format alignment, error checking) to ensure data quality and prepares aggregated data for subsequent adaptive fusion analysis. Neurodiversity Profile (Nu): A personalised profile reflecting individual cognitive and sensory processing characteristics, particularly relevant to users with neurodivergent conditions (e.g., Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), dyslexia). Guides adaptive data weighting and intervention delivery, adjusting communication styles and sensory input to accommodate user needs. Predictive Modelling and Intervention Generation Module (PMIG): An analytical decision module (103) processing weighted datasets from the ADFM to generate personalised, context-specific interventions. Utilises machine learning models, expert systems, and predictive analytics to produce tailored recommendations or automated actions across health, productivity, emotional, financial, and social domains. Real-Time Feedback and Model Refinement Module (RFMR): A continuous learning module (104) capturing user biometric and subjective feedback following intervention delivery. Analyses outcomes to iteratively refine weighting parameters within the ADFM and predictive models within the PMIG, ensuring sustained alignment and improving personalisation and accuracy over time. Weighted Output Dataset: The resultant dataset produced by the ADFM, wherein multi-domain information is annotated with dynamically calculated weights or filters highlighting contextually relevant data. Utilised by the PMIG to enhance precision, effectiveness, and computational efficiency of intervention generation.

Claims

System Claims1. A data processing system for generating context-specific adaptive interventions based on multi-domain user data, the system comprising:a) a Multi-Domain Data Acquisition Module (MDDA) configured to ingest, structure, and integrate data streams from multiple sources including biometric sensors, genomic and medical databases, environmental sensors, behavioural and activity logs, cognitive and emotional analysis tools, and external data sources accessible via application programming interfaces (APIs) or wearable devices;b) an Adaptive Data Fusion Module (ADFM) coupled to the MDDA, wherein the ADFM employs a dynamic weighting algorithm prioritising data inputs in real-time using context parameters including Emotional Urgency (Et), Cognitive Load (Ct), Neurodiversity Profile (Nu), Behavioural Context (Bt), and Genomic Priority (Gu), producing a dynamically weighted dataset reflective of the user’s current context;c) a Predictive Modelling and Intervention Generation Module (PMIG) configured to receive the dynamically weighted dataset from the ADFM and apply predictive analytics generating targeted intervention outputs, wherein the PMIG includes an intervention delivery queue managing timing and distribution of intervention outputs to the user or connected devices;d) a Real-Time Feedback and Model Refinement Module (RFMR) configured to capture user feedback data post-intervention delivery, including biometric response data and user input, and analyse such feedback to iteratively refine one or more of the weighting parameters used by the ADFM and predictive analytics models used by the PMIG, thereby continuously adapting system behaviour based on actual outcomes; ande) a Computational and Environmental Optimisation Module (CEOM) configured to monitor computational resource usage and calculate an Environmental Impact Score (EnvIS) associated with system operations, automatically adjusting resource allocation and processing strategies in response to monitored usage and the EnvIS to minimise computational load and energy consumption while maintaining system performance.

2. The system of claim 1, wherein the MDDA further integrates real-time external data via APIs and wearable devices, including economic indicators, social and communication data, nutrition and fitness metrics, and detailed environmental measurements, enriching the multi-domain dataset beyond immediate sensor readings.

3. The system of claim 1, wherein the ADFM dynamically adjusts the weighting algorithm parameters in real-time response to changes in the user's biometric, cognitive, emotional, behavioural, or environmental states, automatically recalibrating data stream prioritisation accordingly.

4. The system of claim 1, wherein the PMIG generates personalised micro-nudge recommendations or automated control actions designed to optimise measurable user metrics across multiple life domains, including physiological health, cognitive stress levels, productivity metrics, interpersonal communication quality, financial wellbeing, nutritional intake targets, and environmental comfort.

5. The system of claim 1, wherein the CEOM dynamically scales, schedules, or throttles processing tasks and modifies resource allocation strategies based on continuous monitoring of system load and calculated EnvIS, reducing energy usage andenvironmental footprint during operation without compromising critical real-time intervention capabilities.

6. The system of claim 1, wherein the CEOM provides optimisation feedback signals to one or more of the MDDA and ADFM, modulating data acquisition frequency or processing parameters based on current resource usage constraints to maintain operational efficiency under varying computational loads or power conditions.

7. The system of claim 1, wherein automated environmental adjustments are executed as interventions by interfacing with external environment-control devices to modify ambient lighting levels, screen blue-light filtering, air quality, temperature, humidity, or noise levels, triggered by analysis of biometric and environmental sensor inputs indicating improved user comfort or health is required.

8. The system of claim 1, wherein the RFMR uses iterative feedback cycles incorporating user biometric data and subjective feedback to continuously refine predictive models and weighting parameters, improving accuracy and personalisation of future interventions autonomously, without manual reconfiguration.

9. The system of claim 1, wherein interventions and data processing decisions are tailored according to individual neurodiversity profiles, adjusting data interpretation and intervention selection to accommodate cognitive or sensory processing characteristics associated with Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), or other neurodivergent conditions, adapting communication styles, sensory content, or scheduling accordingly.

10. The system of claim 1, wherein the PMIG generates nutritional and lifestyle interventions by integrating real-time dietary intake data, local food availability, userspecific dietary requirements or restrictions, economic affordability factors, and biometric feedback (such as blood glucose or activity levels), resulting in personalised, context-aware, and practically implementable dietary recommendations.

11. The system of claim 1, wherein the Digital Life Twin Data Store (DLTDS) continuously aggregates and updates comprehensive, time-indexed user state profiles, providing unified historical and real-time context accessible to the PMIG for crossdomain predictive analytics and integrated intervention generation.

12. The system of claim 1, wherein modules (MDDA, ADFM, PMIG, RFMR, CEOM) are deployed in a distributed computing environment comprising multiple network-connected computing devices, enabling shared responsibility for data acquisition, fusion, analysis, and feedback processing across multiple devices.

13. The system of claim 12, wherein the CEOM offloads or redistributes computational tasks among multiple computing devices based on current device resource availability, optimising overall system performance and energy usage by utilising more powerful or less utilised devices when available.

14. The system of claim 12, wherein the Digital Life Twin Data Store (DLTDS) is accessible to multiple computing devices, maintaining consistent, time-indexed user profiles across the distributed environment to ensure each device retrieves up-to-date user context for accurate analysis.

15. The system of claim 13, wherein the CEOM enables continuous 24-hour operation on battery-powered user devices by dynamically scaling down non-critical processes and offloading computationally intensive tasks to alternative plugged-in devices or cloud servers when the battery level falls below a predefined threshold, preserving battery life without service interruption.

16. The system of claim 12, wherein at least two modules selected from MDDA, ADFM, PMIG, RFMR, and CEOM execute on separate computing devices communicatingvia network connections, thereby distributing system computational load across multiple devices for balanced performance.Method Claims17. A computer-implemented method for delivering personalised adaptive interventions based on multi-domain user data, comprising:• ingesting and integrating diverse data streams by collecting, structuring, and normalising data from biometric sensors, genomic and health databases, environmental sensors, behavioural and activity logs, cognitive and emotional analysis tools, and external data sources via APIs or connected devices, forming a consolidated multi-domain dataset for an individual user;• dynamically weighting the integrated data by applying an adaptive weighting algorithm that prioritises data elements in real-time according to contextual parameters including Emotional Urgency, Cognitive Load, Neurodiversity Profile, Behavioural Context, and Genomic Priority, thereby producing a weighted dataset emphasising data relevant to the user's immediate context;• analysing the weighted dataset using predictive modelling algorithms to generate personalised intervention outputs, wherein such outputs comprise at least one of recommended user actions or notifications, and automated adjustments executed via connected devices or systems, tailored in content and timing to inferred user needs across multiple life domains;• capturing real-time feedback following intervention delivery by monitoring user biometric responses and obtaining user input, analysing feedback data to evaluate the effectiveness and outcomes of interventions; and• refining data processing parameters and predictive models based on analysed feedback while concurrently optimising computational resource usage by monitoring computational load, calculating environmental impact metrics associated with system operations, and dynamically adjusting resource allocation or processing strategies to reduce computational load and energy consumption without compromising responsiveness or intervention accuracy.

18. The method of claim 17, further comprising dynamically adjusting weighting algorithm parameters in response to detected shifts in user biometric, cognitive, emotional, behavioural, or environmental context, automatically recalibrating prioritisation of incoming data streams during the weighting step.

19. The method of claim 17, wherein generating personalised intervention outputs includes automatically issuing environmental control actions through communication with smart-environment devices, modifying ambient conditions including lighting levels, display settings, temperature, humidity, or noise levels based on current user biometric states and environmental sensor readings, thereby enhancing user comfort and wellbeing.

20. The method of claim 17, wherein adaptive weighting and predictive modelling incorporate the user's Neurodiversity Profile, adjusting data interpretation and intervention selection to optimise effectiveness for individuals with cognitive or sensory processing characteristics associated with Autism Spectrum Disorder (ASD), Attention Deficit Hyperactivity Disorder (ADHD), or other neurodivergent conditions.

21. The method of claim 17, wherein generating personalised nutrition and lifestyle interventions comprises analysing real-time dietary intake data, local food availability, user-specific dietary requirements or restrictions, economic affordability factors, and biometric feedback (such as blood glucose or activity metrics) to deliver tailored nutritional recommendations or meal guidance that are contextually appropriate and practically implementable.

22. The method of claim 17, further comprising automatically adjusting weighting algorithm parameters and predictive model parameters after each intervention based on captured user feedback, progressively enhancing predictive accuracy, relevance of interventions, and personalisation over multiple iterations without manual reconfiguration, enabling continuous autonomous refinement of system performance.

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