Al-enabled personalized digital twin system for predictive health monitoring and precision management

Abstract

The present disclosure provides a system for digital twin health monitoring that receives, via a network, feature data denoting physiological, behavioral, or environmental information, and dietary information denoting food consumption. The system implements a digital twin model configured to generate health predictions based on the feature data. The digital twin model includes a long-term predictive model configured to analyze multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state, and a real-time predictive model configured to predict a real-time physiological reaction. The system provides an interactive digital twin interface to deliver the forecasted temporal pattern and real-time physiological reaction to the user devices.

Description

BW Ref. No. 010301.00001Al-enabled Personalized Digital Twin System for Predictive Health Monitoring and Precision ManagementCROSS-REFERENCE TO RELATED APPLICATIONS:

[0001] This application claims the benefit of U.S. Provisional Application No. 63 / 751,850 filed on January 31, 2025. The above-referenced application is hereby incorporated by reference in its entirety.TECHNICAL FIELD

[0002] The present disclosure relates to a Digital Twin System for predictive modeling of patientspecific health indicators using artificial intelligence.SUMMARY:

[0003] The Al -enabled Personalized Digital Twin System for Predictive Health Monitoring and Precision Management integrates multimodal data — including dietary intake, physical activity, heart rate, medication usage, and compliance indicators — into predictive models for both longterm physiological trend and real-time physiological response. By generating a dynamic virtual replica of a patient’s health status, the system provides timely, tailored interventions and guidance. In some embodiments, the system includes an Al-driven nutrition engine configured to run interactive what-if simulations for proposed meals and / or activities through the real-time predictive model and to output a visual forecast of predicted physiological responses. The system also includes a digital twin language model configured to reason over engineered features and predictive outputs in combination with externally retrieved clinical and / or educational knowledge obtained via an information retrieval mechanism. This platform empowers patients with personalized insights, aids healthcare professionals in delivering targeted care, and enhances chronic disease management through proactive data-driven strategies.

[0004] Aspects are directed to a system, method, and non-transitory computer-readable medium for digital twin health monitoring. In one embodiment, a system for a digital twin health monitoring system comprises a processor and a memory that stores instructions that, when executed by the processor, cause the system to receive, from one or more user devices via a network, feature data that denotes physiological, behavioral, environmental, or dietary information of a user. The instructions further cause the system to implement a digital twin model configured to generate health predictions based on the feature data. The digital twin model comprises a long-term predictive model configured to analyze multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state of the user, and a real-time predictive model configured to analyze the dietary information to predict a real-time physiologicalBW Ref. No. 010301.00001 reaction of the user. The instructions further cause the system to provide an interactive digital twin interface configured to deliver, to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction.

[0005] In some embodiments, the instructions, when executed by the processor, cause the system to periodically receive, from the one or more user devices, updates to the feature data, and maintain the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates. In some examples,the feature data includes compliance feature data indicating whether the user has complied with a health recommendation provided by the interactive digital twin interface; and the instructions, when executed by the processor, cause the system to train the long-term predictive model or the real-time predictive model using the compliance feature data.

[0006] In some aspects, the instructions, when executed by the processor, cause the system to implement a feature engineering engine configured to generate engineered data sets from the feature data by normalizing the feature data to adjust for inter-patient variability, generating derived metrics from the feature data, time-aligning the feature data, aggregating the feature data into periodic summaries, or organizing the feature data into predetermined categories interpreted by the long-term predictive model. The long-term predictive model is configured to generate the forecasted temporal pattern of the health state of the user based on the engineered data sets. In some embodiments, the feature engineering engine is further configured to generate time-series feature data comprising one or more of lag feature data representing historical data points in the feature data at a predetermined duration prior to a baseline time, a rolling average to smooth shortterm fluctuations in the feature data, and event detection features identifying statistically distinguishable data occurrences in the feature data.

[0007] In some aspects, the instructions, when executed by the processor, cause the system to implement an Al-based nutrition input engine configured to parse the dietary information to extract meal components and portion details, wherein the dietary information comprises at least one of text data, speech data and image data, and generate a nutrition breakdown comprising caloric content, macronutrient composition, or micronutrient content. The real-time predictive model is configured to predict the real-time physiological reaction of the user based on the nutrition breakdown. In some embodiments, the Al-based nutrition input engine is configured to query the user via the one or more user devices to provide an additional detail about the meal components in the dietary information, and transform the dietary information into the nutrition breakdown based on food-nutrition data stored in a database. In some aspects, the Al-basedBW Ref. No. 010301.00001 nutrition input engine is configured to analyze an image of a meal to identify one of the meal components, estimate a portion size of the one of the meal components from the image, or extract text from the image to identify food products or ingredient lists.

[0008] In some embodiments, the instructions, when executed by the processor, cause the system to implement a nutritional insight engine configured to determine dietary habits of the user based on the nutrition breakdown, compare the dietary habits against nutritional guidelines and personalized health goals, and recommend alternative food choices based on at least one of a user preference, historical data, or a nutritional target.

[0009] In some aspects, the instructions, when executed by the processor, cause the system to implement a metabolic simulation engine configured to receive a projected meal from the one or more user devices, interface with the real-time predictive model to generate a simulated real-time physiological reaction to the projected meal, and transmit the simulated real-time physiological reaction to the one or more user devices. In some embodiments, the metabolic simulation engine is further configured to compare the simulated real-time physiological reaction to a user-defined performance benchmark, and generate a recommendation for adjusting a portion size or macronutrient ratio in the projected meal based on the comparison of the simulated real-time physiological reaction to the user-defined performance benchmark.

[0010] In some aspects, the feature data comprises data generated by at least one of a wearable device, a health monitoring device, a continuous glucose monitor, or a blood pressure cuff.

[0011] In some embodiments, the instructions, when executed by the processor, cause the system to implement an interactive digital twin language model configured to receive a user query from the one or more user devices, generate a retrieval query by extracting one or more parameters from the user query, extract health information from a knowledge base based on the one or more parameters, wherein the knowledge base comprises structured data derived from health data of the user, the digital twin model, and clinical guidelines, generate a response to the user query based on the health information, and transmit the response to the one or more user devices.

[0012] In one embodiment, a method for digital twin health monitoring comprises receiving, by a processor, from one or more user devices via a network, feature data that denotes physiological, behavior, environment, or dietary information of a user. The method further comprises implementing, by the processor, a digital twin model that generates health predictions based on the feature data. The digital twin model comprises a long-term predictive model that analyzes multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state of the user, and a real-time predictive model that analyzes the dietary information to predict a real-time physiological reaction of the user. The method further comprises providing, by theBW Ref. No. 010301.00001 processor to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction.|0013| In some aspects, the method further comprises periodically receiving, from the one or more user devices, updates to the feature data, and maintaining the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates.

[0014] In some embodiments, the method further comprises generating engineered data sets from the feature data by normalizing the feature data to adjust for inter-patient variability, generating derived metrics from the feature data, time-aligning the feature data, aggregating the feature data into periodic summaries, or organizing the feature data into predetermined categories interpreted by the long-term predictive model. The long-term predictive model generates the forecasted temporal pattern of the health state of the user based on the engineered data sets.

[0015] In some aspects, the method further comprises parsing, with an Al-based nutrition input engine, the dietary information to extract meal components and portion details, wherein the dietary information comprises at least one of text data, speech data and image data, and generating, with the Al-based nutrition input engine, a nutrition breakdown comprising caloric content, macronutrient composition, or micronutrient content. The real-time predictive model predicts the real-time physiological reaction of the user based on the nutrition breakdown. In some embodiments, the method further comprises determining dietary habits of the user based on the nutrition breakdown, comparing the dietary habits against nutritional guidelines and personalized health goals, and recommending alternative food choices based on at least one of a user preference, historical data, or a nutritional target.

[0016] In some aspects, the method further comprises receiving a projected meal from the one or more user devices, interfacing with the real-time predictive model to generate a simulated realtime physiological reaction to the projected meal, and transmitting the simulated real-time physiological reaction to the one or more user devices.

[0017] In some embodiments, the method further comprises implementing an interactive digital twin language model that receives a user query from the one or more user devices, generates a retrieval query by extracting one or more parameters from the user query, extracts health information from a knowledge base based on the one or more parameters, wherein the knowledge base comprises structured data derived from health data of the user, the digital twin model, and clinical guidelines, generates a response to the user query based on the health information, and transmits the response to the one or more user devices.

[0018] In some embodiments, a non-transitory computer-readable medium stores instructionsBW Ref. No. 010301.00001 that, when executed by one or more processors of a system, cause the system to receive, from one or more user devices via a network, feature data that denotes physiological, behavior, or environment information of a user, and dietary feature data that denotes food consumption information of the user. The instructions further cause the system to implement a digital twin model configured to generate health predictions based on the feature data and the dietary feature data. The digital twin model comprises a long-term predictive model configured to analyze multiday sequences of the feature data and the dietary feature data to generate a forecasted temporal pattern of a health state of the user, and a real-time predictive model configured to analyze the feature data and the dietary feature data to predict a real-time physiological reaction of the user. The instractions further cause the system to provide an interactive digital twin interface configured to deliver, to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction, periodically receive, from the one or more user devices, updates to the feature data and the dietary feature data, and maintain the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates.DESCRIPTION OF THE DRAWING:

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, show certain aspects of the subject matter disclosed herein and, together with the description, help explain some of the principles associated with the disclosed implementations.

[0020] Like labels are used to refer to the same or similar items in the drawings.

[0021] Fig. 1 A illustrates a functional block diagram of a digital twin system for predictive health monitoring, according to aspects of the present disclosure.

[0022] Fig. IB illustrates a digital twin system network for health monitoring and data communication, according to aspects of the present disclosure.

[0023] Fig. 2 illustrates a block diagram of an Al-based nutrition input engine and associated components, according to aspects of the present disclosure.

[0024] Fig. 3 illustrates a hierarchical structure for long-term predictive modeling within a digital twin system, according to aspects of the present disclosure.

[0025] Fig. 4 illustrates a block diagram of a real-time glucose predictive module, according to aspects of the present disclosure.

[0026] Fig. 5 illustrates a block diagram of a system for projecting glucose levels based on nutritional input, according to aspects of the present disclosure.

[0027] Fig. 6 illustrates a block diagram of a digital twin language model, according to aspectsBW Ref. No. 010301.00001 of the present disclosure.

[0028] Figs. 7A-7B illustrate a flow chart of a method performed by the digital twin system according to aspects of the present disclosure.

[0029] Fig. 8 illustrates a block diagram of a computing device configured to implement components of a digital twin system, according to aspects of the present disclosure.DETAILED DESCRIPTION:

[0030] The present disclosure introduces a digital twin system 100 that leverages predictive modeling to provide personalized health monitoring and lifestyle recommendations for precise chronic disease management. By aggregating and analyzing multimodal data — including dietary intake, physical activity, heart rate, medication adherence, and physiological sensor readings (e.g., glucose and blood pressure) — the system creates a dynamic, real-time digital twin. This virtual representation of an individual’s physiological state may be continuously updated with data from remote monitoring devices, wearable technology, patient-reported inputs, and an interactive digital twin interface. The system evolves the digital twin model as new information is ingested, providing actionable insights into the user’s health in both long-term and real-time contexts.

[0031] Fig. 1 A illustrates a functional block diagram of the digital twin system (DTS) 100, which integrates multimodal data collection, predictive modeling, and interactive user interfaces into a cohesive framework, enabling seamless data flow and adaptive modeling. DTS 100 may include multiple subsystems, such as a multimodal data collection system 101, a preprocessing system 102, a predictive modeling system 103, and an interactive digital twin interface 104, that work together to collect, process, and analyze data from diverse sources, generating insights that are both actionable and relevant to each patient’s health profile.

[0032] The multimodal data collection system 101 aggregates data from multiple sources, organized into four categories, to create a comprehensive representation of a patient's health status. Health feature data 105 comprises physiological measurements, such as blood pressure readings, blood glucose levels, heart rate measurements, heart rate variability, and oxygen saturation levels, collected from medical devices and wearable devices, including blood pressure cuffs, glucometers, pulse oximeters, continuous glucose monitors, activity trackers, and smartwatches. Lifestyle feature data 110 captures behaviors and habits that influence health outcomes, including sleep patterns, physical activity levels, and stress levels, which may be sourced from wearable devices, activity trackers, and mobile applications. Contextual feature data 120 encompasses demographic details such as age, biological sex, ethnicity, height, and weight; patient preferences; medication logs tracking prescribed regimens and dosing schedules. TheBW Ref. No. 010301.00001 compliance feature data tracks whether patients have followed previous lifestyle recommendations and captures feedback on patient experiences. Dietary feature data 130 includes information related to food consumption, such as food types, portion sizes, caloric intake, macronutrient breakdowns, and micronutrient content, which may be logged using multiple input modalities, including text descriptions, speech inputs, and images of meals.

[0033] The data from the multimodal data collection system 101 flows into the pre-processing system 102, which includes a feature engineering engine 135 and a nutrition input engine 140. The feature engineering engine 135 processes and normalizes the raw data to ensure seamless integration and usability. The Al-based nutrition input engine 140 utilizes multimodal inputs, including text, speech, and image data, to translate user-reported dietary feature data 130 into comprehensive nutritional insights.

[0034] Data from the pre-processing system 102 is fed into a predictive modeling system 103, which includes a digital twin model 150, and optionally, additional models tailored to specific health conditions. Digital twin model 150 hosts two predictive modeling components — a longterm predictive model 160 and a real-time predictive model 170 — that deliver a comprehensive monitoring and recommendation framework.

[0035] The long-term predictive model 160 analyzes extended time sequences of health and lifestyle data to identify temporal patterns and forecast health states over longitudinal periods, capturing trends that evolve over weeks, months, or longer, depending on the individual. In some examples, Long-term predictive model 160 is pretrained on one or more diverse multi -patient datasets to characterize health patterns. In some examples, the long-term predictive model 160 may be trained to discern specific health patterns that are correlated with patient information from a specific geographical region, specific ethnicity, specific age groups, and / or specific socioeconomic factors (e.g., education, income, etc.). Fine-tuning on patient-specific data ensures high precision and predictive accuracy of the digital twin model 150, enabling personalized long-term forecasts and intervention planning.

[0036] The real-time predictive model 170 processes inputs, such as dietary feature data, activity levels, and physiological markers, in real-time to monitor metabolic and other conditions and provide granular, immediate insights. The real-time predictive model 170 is designed as a general framework that can be tailored for, or supplemented with, one or more additional models for specific conditions, such as diabetes. For example, the real-time predictive model 170 may include a real-time predictive glucose monitoring (PGM) model that processes the inputs to predict short-term glucose responses with high accuracy, but other instances could focus on different physiological markersBW Ref. No. 010301.00001

[0037] The digital twin model 150 collaborates with the feature engineering engine 135 and nutrition input engine 140 to provide recommendations optimized for the user’s health trajectory. For instance, based on glucose trends, the system might suggest substituting high-carbohydrate meals with healthier alternatives — reviewed by professional dietitians — to reduce glucose spikes and improve overall glycemic control.

[0038] While the system provides personalized forecasts and intervention planning over a broad spectrum of health domains, glucose monitoring is provided as one example application due to the metabolic system’s sensitivity to lifestyle factors such as diet and exercise. While the longterm predictive model 160 remains broadly applicable across health domains, the real-time predictive model 170 can be trained to capture specific metabolic or physiological reactions, such as glucose prediction, making it an efficient tool for diabetes management and related metabolic conditions.

[0039] The interactive digital twin interface 104 receives information from multiple sources, including each data source in the multimodal data collection system 101, the feature engineering engine 135 and the nutrition input engine 140 in the pre-processing system 102, the long-term predictive model 160 and the real-time predictive model 170 in the predictive modeling system 103, and external data sources 215 (e.g., databases, cloud storage, Internet sources). External Data Sources 215 provide information such as clinical guidelines and / or educational knowledge (e.g., from authoritative medical and health organizations and government bodies). Based on the information from these sources, the interactive digital twin interface 104 delivers and synthesizes useful health information, which is delivered to users through multiple interactive components. Personalized recommendations engine 180 provides tailored health guidance based on the patient's health profile, predictive model outputs, and clinical guidelines. The digital twin language model (LM) engine 190 integrates patient data, predictive model outputs, and clinical guidelines to respond to user queries with personalized insights, educate users on health trends, and suggest targeted interventions. The metabolic simulation engine 200 enables users to simulate the impact of potential meals on health outcomes, such as glucose levels, before consumption, facilitating informed dietary decision-making. The nutritional insight engine 210 delivers dietary analysis and recommendations by comparing the user's dietary habits against nutritional guidelines and personalized health goals. It may recommend alternative food choices based on user preferences and historical data. Together, these components bridge the gap between complex data analytics and user-friendly applications, empowering users to explore long-term health trend forecasts, receive real-time alerts, and take an active role in managing their health and improving compliance with prescribed lifestyle changes.BW Ref. No. 010301.00001

[0040] Users can explore long-term health trend forecasts, simulate meal impacts (for example, using the metabolic simulation engine 200), and receive real-time alerts to optimize their diet, exercise, and medication adherence. By offering tailored insights, the system empowers users to take an active role in managing their health and improving compliance with prescribed lifestyle changes.

[0041] A robust feedback loop ensures model accuracy and adaptability. Continuous updates to both long-term models 160 and real-time models 170 are driven by new sensor readings, user interactions, and contextual data. This iterative calibration mechanism keeps predictions and recommendations relevant, even as user behaviors and health conditions evolve over time.

[0042] For example, the digital twin LM 190 provides a language-model-based reasoning layer that operates on engineered features rather than just raw sensor data. Raw feature data collected from health devices, lifestyle tracking systems, contextual sources, and dietary logs (from the multi-modal data collection system 101) are first processed by the feature engineering engine 135 to generate time-aligned, semantically meaningful features suitable for downstream modeling and interpretation. The digital twin LM 190 may be configured to reason over:• engineered health features, lifestyle features, dietary features, contextual features, compliance features, and / or dietary features:• outputs generated by the Long-Term Predictive Model 160 and / or the Real-Time Predictive Model 170; and / or• externally retrieved clinical, behavioral, and / or educational knowledge (e.g., from external data sources 215) obtained via a retrieval-augmented generation (RAG) mechanism (as further described below with respect to Fig. 6).

[0043] By reasoning over engineered features and model-derived summaries rather than just raw feature data streams, the digital twin LM 190 functions as an interpretive and explanatory interface. This interface is distinct from predictive models and is not configured merely to output numerical forecasts or ranked recommendations. Instead, it synthesizes feature-level information and retrieved knowledge to generate patient-specific explanations, contextualized insights, and rationale-driven guidance reflecting both short-term observations and longer-term patterns.

[0044] This feature-centric reasoning architecture improves interpretability, robustness, and system stability relative to approaches that directly expose raw sensor data or low-level prediction outputs to a language model.

[0045] In some examples, the digital twin system 100 provides further improvements by operating as a closed-loop learning architecture in which outputs of predictive models and user responses to delivered insights are continuously incorporated into subsequent processing stages.BW Ref. No. 010301.00001 Outputs generated by the Long-Term Predictive Model 160 and the Real-Time Predictive Model 170, including forecasts, short-term response estimates, and / or simulated what-if scenarios, are provided as contextual inputs for future feature engineering, modeling, and reasoning. For example, after delivering explanations or guidance through the interactive digital twin interface 104, the digital twin system 100 captures post-interaction feedback and compliance signals via the compliance feature data 125. Such compliance signals may include, by way of example: whether a user followed, partially followed, or declined a suggested action; timing and duration of behavioral changes after receiving an insight; deviations between expected and observed physiological responses following guidance; and explicit confirmations, corrections, or refusals provided through the user interface.

[0046] Compliance feature data 125, including these post-interaction signals, is pre-processed to generate structured compliance features that reflect user adherence, engagement, and responsiveness. These compliance features are incorporated into the feature engineering pipeline and influence the interpretation of future data, the adjustment of model sensitivity, and the contextualization of subsequent reasoning outputs. By incorporating compliance feature data 125 into the feedback loop, the system adapts not only to observed physiological measurements but also to how individuals respond to the system’s guidance, enabling continuous personalization over time.

[0047] In summary, the digital twin system 100 combines multimodal data processing, predictive modeling, and interactive user interfaces into a cohesive framework, as shown in Fig. 1A. By integrating features such as the metabolic simulation engine 200 and the digital twin LM engine 190, it provides a highly adaptive and user-centric platform for precise chronic disease management. The system not only addresses immediate health challenges but also supports longterm wellness through proactive recommendations and dynamic, data-driven insights. Further, details of each component in the digital twin system 100 are provided below.

[0048] Fig. IB illustrates a digital twin system network 1000 configured to implement the functional components of the digital twin system 100 described with reference to Fig. 1A. The digital twin system network 1000 facilitates health monitoring and data communication through a distributed architecture that enables data collection, storage, processing, and delivery of personalized health insights.

[0049] The digital twin system network 1000 includes a network 1020 that serves as a communication link connecting various devices and storage systems. The network 1020 may comprise a distributed network infrastructure, such as the Internet, a local area network, a wide area network, a cellular network, or a combination thereof. The network 1020 enablesBW Ref. No. 010301.00001 bidirectional connectivity between all components of the digital twin system network 1000, supporting real-time data transmission and retrieval.|0050| Connected to the network 1020 are data storage and processing components that may implement portions of the predictive modeling system 103 and store data associated with the multimodal data collection system 101. Database 1005 is connected to server 1010, which together provide data management and processing capabilities for the digital twin system network 1000. The server 1010 communicates with the network 1020 via a bidirectional connection and may execute the digital twin model 150, including the long-term predictive model 160 and the real-time predictive model 170. The server 1010 may also execute the Al-based nutrition input engine 140, which processes multimodal dietary inputs received from the user device 1030 and user mobile device 1045 to generate nutrition breakdowns for use by the predictive modeling system 103. In some aspects, the server 1010 may further execute the feature engineering engine 135, which processes and normalizes raw data received from the multimodal data collection system 101 to ensure seamless integration and usability by the predictive modeling system 103. The database 1005 may store patient health records, nutritional information, predictive model parameters, historical data used for model training and validation, and other data collected by the multimodal data collection system 101. Cloud storage 1015 connects to the network 1020 and provides remote data storage functionality, enabling scalable storage of multimodal health data, model checkpoints, and user interaction logs.

[0051] On the user side, a user device 1030 and / or a user mobile device 1045 connects to the network 1020. The user device 1030 may comprise a desktop computer, laptop, or similar computing device configured to access the digital twin system network 1000. The user mobile device 1045 may comprise a smartphone, tablet, or similar portable computing device.

[0052] The user device 1030 may include user input and output devices such as a monitor and keyboard (e.g., for text entry). The user device 1030 may also be connected to peripheral input devices, including a microphone 1035 for audio input and a camera 1040 for visual input. The user mobile device 1045 may include the same or similar components to devices 1030, 1035, and 1040 and may have additional input devices, such as a touch screen. These input and output devices support the multimodal data collection capabilities of the multimodal data collection system 101 and interaction with the interactive digital twin interface 104. For example, using user device 1030 and / or user mobile device 1045, a user may log dietary information through text, speech, and image inputs as further described with reference to Fig. 2.

[0053] Digital twin system network 1000 may include additional devices for implementing multimodal data collection system 101 and interaction with the digital twin interface 104. ForBW Ref. No. 010301.00001 example, a wireless access point 1050 may connect to the network 1020, and provide wireless connectivity to the user device 1040, the user mobile device 1045, and / or health monitoring devices. The wireless access point 1050 may implement communication protocols such as BLUETOOTH®, WI-FI®, ZIGBEE®, or other wireless standards suitable for health device communication. For example, the wireless access point 1050 may facilitate communication with a health monitoring device 1055 and a wearable device 1060 as part of the multimodal data collection system 101.

[0054] The health monitoring device 1055 may comprise medical monitoring equipment configured to collect health feature data 105, such as a blood pressure cuff, glucometer, pulse oximeter, or a continuous glucose monitor. The health monitoring device 1055 collects physiological measurements and transmits collected data through the wireless access point 1050, user device 1030, user mobile device 1045, and / or network 1020 to server 1010 for processing by the predictive modeling system 103.

[0055] The wearable device 1060 may comprise a smartwatch, a fitness tracker, or a similar wearable computing device with health-monitoring capabilities. The wearable device 1060 collects data, such as lifestyle feature data 110, including physical activity levels, heart rate, sleep patterns, and stress indicators. In some aspects, the wearable device 1060 may also collect health feature data 105, such as heart rate variability and oxygen saturation. Data collected by the wearable device 1060 is transmitted through the wireless access point 1050 to the network 1020. The wearable 1060 device may be battery-powered and thus portable, or it may be a medical appliance that receives power through a power cord (e.g., plugged into an outlet).

[0056] The topology of the digital twin system network 1000 is illustrated in a star configuration, with the network 1020 serving as the central node through which all other components communicate. Data flows bidirectionally between the network 1020 and connected devices, enabling real-time data collection from the wearable device 1060, the health monitoring device 1055, and user inputs from the user device 1030 and the user mobile device 1045. The network 1020 also delivers data (e.g., personalized recommendations 180, projected nutrition breakdowns 200, and insights from the nutritional insight engine 210) back to users through their respective devices. While network 1020 is illustrated as a central node, it may be implemented in a distributed manner, for example, via multiple networks or point-to-point connections.

[0057] In some aspects, the digital twin system network 1000 may implement distributed processing, wherein portions of the predictive modeling system 103 execute on the server 1010 while other portions execute locally on the user device 1030 or user mobile device 1045. This distributed architecture may reduce latency for real-time predictions and enable offlineBW Ref. No. 010301.00001 functionality when network connectivity is limited. In some aspects, the server 1010 may comprise multiple servers operating in a distributed configuration, such as a server cluster or a cloud computing environment, to provide scalable processing capabilities and redundancy. Similarly, the database 1005 may comprise multiple databases distributed across different physical or virtual machines, which may be configured for data replication, load balancing, or partitioning of patient data across geographic regions. The cloud storage 1015 may also be implemented using distributed storage systems that replicate data across multiple storage nodes or data centers to provide fault tolerance and high availability. In some cases, the server 1010, database 1005, and cloud storage 1015 may be implemented using cloud-based infrastructure services that dynamically allocate computing and storage resources based on system demand.Data Collection

[0058] The multimodal data collection system 101 aggregates data from four primary streams: health feature data 105, lifestyle feature data 110, contextual feature data 120, compliance feature data 125, and dietary feature data 130. Features may include measurable attributes or characteristics of the patient’s health, behavior, or environment that contribute to the system’s modeling and analysis capabilities. These diverse inputs, as depicted in Fig. 1 A, allow the system to create a comprehensive and adaptive model of the patient’ s health, capable of delivering precise and individualized insights. Data is collected from multiple sources, including wearable devices, medical monitoring equipment, and mobile applications, and undergoes processing and normalization to ensure seamless integration and usability.

[0059] Health feature data 105 comprises physiological measurements collected from medical devices, such as a blood pressure monitor, a glucometer, a continuous glucose monitor (CGM), and an oxygen saturation monitor, as well as from wearable devices, such as a smartwatch (e.g., FITBIT®, APPLE WATCH®, or GARMIN®). These metrics provide real-time information on critical health indicators, including blood glucose levels, blood pressure, heart rate, and oxygen saturation. For example, blood pressure readings may include multiple timestamped measurements, allowing the system to identify systolic and diastolic trends over time. This data stream forms the foundation for understanding the patient’s current health status and predicting trends that can guide proactive interventions.

[0060] Lifestyle feature data 110 captures behaviors and habits that influence health, including sleep patterns, physical activity levels, and stress levels. This data may be sourced from the same or similar devices that capture health feature data 105, and from mobile applications designed to track lifestyle information. For instance, physical activity data may include step counts, duration of cardiovascular activity, and the number of stairs climbed.BW Ref. No. 010301.00001

[0061] Contextual feature data 120 includes demographic details, patient preferences, and medication logs. Demographic information, such as age, biological sex, ethnicity, baseline metrics like height and weight, and genetic factors such as family history and genetic markers, provide the system with personalization parameters. Medication adherence data tracks whether the patient has followed prescribed regimens for managing chronic conditions such as hypertension or diabetes.

[0062] Compliance feature data 125 extends this by tracking whether patients have followed previous lifestyle recommendations and capturing their feedback. For example, if a recommendation involves increasing daily step counts, compliance data measures activity levels before and after implementation to evaluate adherence. This feedback loop ensures that the system’s recommendations remain actionable and aligned with the patient’s capacity for change. Compliance feature data may include, by way of example: whether a user followed, partially followed, or declined a suggested action; timing and duration of behavioral changes after receiving an insight; deviations between expected and observed physiological responses following guidance; and explicit confinnations, corrections, or refusals provided through the user interface. Compliance Feature 125 processes these post-interaction signals to generate structured compliance features that reflect user adherence, engagement, and responsiveness.

[0063] Dietary feature data 130 includes information about a user's diet, including food types, portion sizes, caloric intake, macronutrient breakdowns, and sodium consumption.

[0064] Table 1 below summarizes each of these types of data, provides examples of source devices and applications for collecting the data, and indicates example frequencies at which data is collected.Table 1BW Ref. No. 010301.00001

[0063] The multimodal data collection system 101 may differentiate between first and second data collection devices. First data collection devices (e.g., 1055, 1060), such as blood pressure cuffs, activity trackers, and wearable monitors, are configured for direct data collection. For instance, a blood pressure cuff records systolic and diastolic readings along with timestamps, while an activity tracker logs step counts and heart rate data. Second data collection devices (e.g., 1030, 1045), such as smartphones, tablets, or laptops, function as aggregation and interaction hubs. These devices collect data from multiple first data collection devices and allow users to log additional contextual information, such as dietary intake or stress levels, via software applications or graphical interfaces. Second data collection devices facilitate bidirectional communication between patients and the digital twin system 100. For example, a patient can log blood pressure readings into a mobile application on a second data collection device, which aggregates data from other first data collection devices, such as glucometers and wearable trackers. This consolidated view enables users to monitor their health comprehensively. To ensure interoperability acrossBW Ref. No. 010301.00001 devices from different manufacturers, the system may employ middleware for data translation. Formats such as JSON or XML may be standardized into a unified data model through normalization techniques, preserving data integrity and consistency across the system.

[0064] Some examples of data collection performed by the multimodal data collection system 101 include:• A CGM device continuously monitors glucose levels, providing real-time data on glucose fluctuations throughout the day. This information can be combined with meal logs to understand the patient’s glycemic response to specific foods.• A patient uses a manual blood pressure cuff to measure systolic and diastolic pressure daily. These readings are logged into a mobile application (a second user device) via a graphical interface, along with timestamps.• A wearable device records physical activity levels, capturing heart rate, steps, and exercise duration. This data is synchronized with the system through BLUETOOTH®.• Dietary logs include meal composition and portion sizes, which are further analyzed by the system to assess their impact on health indicators like glucose or blood pressure. • Self-assessment questionnaires on a mobile application collect stress and mood ratings, providing additional context for physiological changes observed in the data.Feature Engineering

[0065] The raw feature data (e.g., health, lifestyle, contextual, and / or compliance feature data) collected from multiple sources undergoes pre-processing through feature engineering engine 135 (within the pre-processing system 102). This engine aligns timestamps, normalizes values and derives engineered feature data that are both time-aligned and actionable. The feature engineering process transforms raw multimodal data into meaningful inputs for predictive modeling. By organizing data into structured feature vectors / sets, the system ensures compatibility with machine learning algorithms and enhances the precision of predictions, forming the foundation for personalized recommendations and precise chronic disease management. Table 2 below summarizes some of the diverse data categories, signal types, and collection frequencies, sourced from a variety of devices that the feature engineering engine 135 correlates intoTable 2BW Ref. No. 010301.00001BW Ref. No. 010301.00001BW Ref. No. 010301.00001

[0066] The feature engineering engine 135 may aggregate minute-level data, such as step counts or heart rate variability, into daily summaries while retaining temporal context for analysis. Normalization is applied to features such as caloric intake or systolic blood pressure to adjust for inter-patient variability, ensuring consistency across the dataset. Additionally, derived metrics are generated from raw data; for example, combining "active energy burned" with "basal energy burned" calculates total daily energy expenditure. The feature engineering engine 135 employs algorithms to identify the most relevant features for predicting specific health outcomes, such as glucose or blood pressure trends. The feature engineering engine 135 may reduce the dimensionality of the data using techniques such as Principal Component Analysis (PCA) and Recursive Feature Elimination (RFE) to ensure that only the most impactful signals are considered, thereby improving model efficiency and interpretability. To capture temporal dependencies and patterns, the feature engineering engine 135 may generate time-series feature data. For example, lag feature data may be generated that represent historical data points, for example, glucose levels at a predetermined duration (e.g., one hour) prior to a baseline time at which other data is aligned. The feature engineering engine 135 may filter feature data, such as calculating rolling averages to smooth short-term fluctuations in metrics like heart rate or sleep duration. The feature engineering engine 135 may also perform event detection that identifies significant occurrences (e.g., statistically distinguishable data, e.g., data exceeding a predetermined threshold or meeting predetermined criteria), such as glucose spikes following high-carbohydrate meals, and encode the events as actionable features for processing by the predictive modeling system 103.Nutrition Data Processing

[0067] As noted above, the pre-processing system 102 includes the nutrition input engine 140, which utilizes multimodal inputs including text, speech, and image data to translate user-reported dietary feature data 130 into comprehensive nutritional insights. Fig. 2 illustrates further details of the Al-based nutrition input engine 140.

[0068] Nutrition input engine 140 interfaces with a data collection and user interface device 260 (e.g., such as user interface device 260 and / or user user mobile device 1045) to log meals using various modalities, including text inputs (via a keyboard or touchscreen), speech inputs (via microphone 1035 or integrated microphone in mobile device 1045), and image inputs (via with camera 1040 or integrated camera in user mobile device 1045) using interactive input refinement engine 220. For example, users can describe their meals manually or verbally, such as typing or saying, "grilled salmon with a quinoa salad." Interactive input refinement engine 220 parses this input to extract meal components and portion details. For image inputs, the interactive inputBW Ref. No. 010301.00001 refinement engine 220 uses computer vision algorithms and fine-tuned large language models (LLMs) to analyze photos of meals. When a user submits a photo of a plate containing rice, grilled chicken, and vegetables, the interactive input refinement engine 220 identifies individual items, estimates portion sizes such as " 1 cup of rice," "4 oz grilled chicken," and " 1 cup steamed broccoli. Interactive input refinement engine 220 may also capture text in images, such as product names or ingredient lists, to identify individual foods and portion sizes.

[0069] Interactive input refinement engine 220 also incorporates an interactive feedback algorithm to enhance data precision. If ambiguous entries like "pasta" are logged, the interactive input refinement engine 220 may query the user (e.g., via user mobile device 1045) for additional details such as preparation methods, portion sizes, or accompanying ingredients. The interactive input refinement engine 220 dynamically determines the number and type of interactions needed based on its understanding of the user and the input modality, minimizing interactions while achieving high accuracy. This adaptive approach ensures high-quality inputs that are critical for accurate modeling. Nutrition input engine 140 may further include a nutrition conversion engine 230, which transforms dietary inputs into detailed nutrient breakdown information 250. This nutrition conversion engine 230 incorporates fine-tuned LLMs optimized with techniques such as Low-Rank Adaptation (LoRA) for domain-specific tasks. These LLMs are trained on comprehensive datasets, stored within the food-nutrition database 240, of global food compositions, guidelines from authoritative nutrition bodies like the USDA and the WHO, and / or real-world data validated through expert-curated logs. Nutrition conversion engine 230 may validate the real-world data, for example, through cross-validation with user feedback, iterative refinement based on real-world usage scenarios, and periodic evaluation against benchmark datasets.

[0070] The nutrition breakdown information 250 may then be provided to the real-time predictive model 170 and / or nutritional insight engine 210 for further processing.Predictive Modeling

[0071] As described with respect to Fig. 1 A, the predictive modeling system 103 includes a digital twin model 150 comprising two main components: a long-term predictive model 160 and a realtime predictive model 170. Each component functions independently yet complements the other to provide a comprehensive and adaptive health monitoring solution. The real-time predictive model 170 may be further tailored for specific conditions, such as diabetes, by incorporating additional models, such as a real-time predictive glucose monitoring (PGM) model.Long-term Predictive Modeling

[0072] The long-term predictive model 160 analyzes multi -day sequences of health and lifestyleBW Ref. No. 010301.00001 data (either as raw data from the multimodal data collection system 101 or engineered feature data from the pre-processing system 102) to identify temporal patterns and trends, enabling accurate predictions of key health indicators such as blood glucose and blood pressure over extended periods. By anticipating changes in patient health, the system facilitates proactive and precise interventions that improve chronic condition management. Examples of personalized trends, trajectories, and projections for longitudinal physiological indicators generated by the long-term predictive model 160 may include: long-term glucose control metrics (e.g., estimated HbAlc trends, glucose variability, and time-in-range trajectories), blood pressure trajectories (e.g., projected systolic and diastolic trends under different lifestyle or medication scenarios), cardiometabolic risk estimates (e.g., risk scores associated with obesity, insulin resistance, hypertension, or cardiovascular disease), and other longitudinal physiological indicators derived from integrated health, lifestyle, and dietary feature data.

[0073] The system processes time-series data spanning multiple days, capturing dependencies between health indicators and lifestyle behaviors. Input data may include daily measurements of target disease metrics (e.g., glucose levels, blood pressure), heart rate, dietary intake, physical activity, and sleep patterns. To ensure seamless integration across diverse data formats and sources, the raw data may be preprocessed into lower-dimensional feature representations by the feature engineering engine 135 as described above, making the data compatible with various modeling architectures.

[0074] Long-term predictive model 170 may implement a wide range of time-series modeling architectures, including recurrent neural networks (RNNs), gated recurrent units (GRUs), long short-term memory (LSTM) networks, and attention-based models such as transformers. The long-term predictive model 160 utilizes historical data to account for the cumulative effects of past behaviors on current health indicators. For instance, the long-term predictive model 160 may analyze how consecutive days of high physical activity correlate to reducing blood pressure or how disrupted sleep paterns correlate to increasing blood pressure. Temporal dependencies are incorporated to provide forecasts that account for both daily and multi-day patterns, ensuring accurate predictions of health dynamics. To maintain chronological integrity, techniques such as causal masking and autoregressive processing may be applied, ensuring predictions are based solely on past data. This adaptable design accommodates advances in time-series modeling technologies while maintaining robust, context-aware predictions.

[0075] Fig. 3 illustrates an example of the hierarchical structure that may be used in long-term predictive model 160. Input data from diverse sources (Di, D2, ..., Dn) (e.g., from health feature data 105, lifestyle feature data 110, contextual feature data 120, compliance feature data 125,BW Ref. No. 010301.00001 and / or engineered feature data / set from feature engineering engine 135) is processed through an embedder 320 that transforms raw inputs into vector representations suitable for further analysis. These vector representations s are passed to the encoder 310, which captures temporal relationships and dependencies across multi-day sequences to generate encoded features (Ei, E2, En). The encoded features (Ei, E2, En) are then fed into a classifier 300, which outputs predictions for specific disease metrics (DM2, DM3, ..., DMnu), such as comorbidity risk scores, cardiovascular risk estimates, long-term metrics for physiological measures (e.g., blood pressure progression, heart rate variability trends), metabolic indices (e.g., insulin resistance scores), or other longitudinal health outcome scores used in clinical practice or research. This structure ensures efficient data processing while preserving critical temporal patterns that underpin accurate forecasts.

[0076] The long-term predictive model 160 incorporates a training process that may comprise two distinct stages: global pretraining and patient-specific fine-tuning. Global pretraining is performed on a large, aggregated dataset of anonymized data from multiple patients, establishing a foundational understanding of generalized patterns in health dynamics, such as the influence of consistent physical activity on blood pressure or the relationship between macronutrient intake and glucose trends. This phase enhances the model’s performance, accelerates convergence, and reduces the amount of data required from individual patients during fine-tuning.

[0077] During the patient-specific fine-tuning phase, the model is tailored to each patient's unique data, capturing personalized health and lifestyle characteristics. For example, the model adapts to individual glycemic responses to specific foods or the impact of their sleep routines on blood pressure. This phase ensures that predictions are precise and aligned with the patient’s unique health profile, providing highly personalized insights.

[0078] By combining global pretraining and patient-specific fine-tuning, the system offers both generalized and individualized predictions. Global pretraining ensures scalability and efficiency, while fine-tuning delivers specificity and relevance. For instance, the system can predict how a week of reduced physical activity might affect glucose levels or how consistent dietary changes influence long-term blood pressure trends. These actionable, patient-specific forecasts empower both patients and healthcare providers to make informed decisions and effectively manage chronic conditions.

[0079] Digital twin model 150 validates the long-term predictive model 160 through rigorous development and deployment processes. During the pretraining phase, the model is evaluated on large, anonymized datasets using performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). These metrics ensure the model captures long-term healthBW Ref. No. 010301.00001 patterns accurately while minimizing prediction errors. Cross-validation techniques are employed during this phase to assess model generalizability across diverse patient populations and datasets. |0080| Post-deployment, the digital twin model 150 continuously monitors model performance by comparing predicted long-term health trends with actual patient data. Discrepancies in predictions trigger recalibration using incremental learning algorithms, such as Online Gradient Descent and Incremental Support Vector Machines (SVM). These algorithms allow the model to adapt to evolving health and lifestyle patterns without requiring complete retraining. For example, if a patient begins experiencing changes in blood pressure trends due to a new fitness routine, the model adjusts its predictions dynamically to reflect these new patterns.

[0081] Incremental learning ensures the long-term predictive model 160 retains previously learned health trends while incorporating the latest data. To prevent overfitting to recent trends, techniques such as elastic net regularization and / or dropout are applied. Once updated, the model undergoes validation using recent data (for example, the most current data over a predetermined validation duration (e.g., 1 week)) to confirm that its predictions meet predefined thresholds for accuracy and reliability. This adaptive mechanism ensures that the long-term predictive model 160 delivers personalized, reliable forecasts that evolve as the patient’s health profile changes.Real-time Predictive Model and Predictive Glucose Monitoring (PGM)

[0082] Fig. 4 illustrates the modular design of the real-time predictive model 170 and its seamless integration with various data sources. The real-time predictive model 170 builds on the digital twin's capabilities by providing postprandial and full-day metabolic process predictions. The focus on postprandial monitoring is rooted in the fact that certain metabolic processes, such as glucose production, are correlated to nutritional intake, food consumption, and lifestyle factors. For example, postprandial glucose responses are highly predictable, making them suitable for real-time personalized modeling. The primary challenge lies in quantifying the impact of nutrition and lifestyle factors for each individual. By addressing this challenge, the real-time predictive model 170 provides actionable insights that empower patients to manage their glucose levels effectively.

[0083] During an initial training phase, the real-time predictive model 170 utilizes a continuous monitor 470 for measuring at least one health feature that is correlated to a metabolic process. The continuous monitor 470 may supply real-time data essential for understanding immediate fluctuations in the monitored health feature. For example, the health feature may be glucose that is monitored with a continuous glucose monitoring (CGM), which collects glucose levels periodically (e.g., every 10 minutes) over a predetermined duration (e.g., several weeks). The real-time predictive model 170 may also collect detailed logs of meals (and their nutritionBW Ref. No. 010301.00001 breakdown 250, including detailed macronutrient and caloric data) from dietary feature data 130 and nutrition input engine 140 as previously described. The real-time predictive model 170 may also capture lifestyle feature data 110, such as physical activity, sleep duration, and stress levels, offering a broader context for health feature predictions. This granular dataset enables the system to learn how specific factors, such as macronutrient composition, meal timing, and physical activity, influence metabolic processes, such as glucose generation (e.g., after meals). The initial training phase establishes a foundational understanding of the user's postprandial glucose dynamics.

[0084] The real-time predictive model 170 may model a response curve of a health feature that is continuously monitored by continuous monitor 470 (e.g., a glucose response curve) by identifying and predicting key parameters that characterize the postprandial patterns. These parameters include, but are not limited to:• Absolute Peak: The maximum level of the health feature (e.g., glucose) reached after a meal.• Relative Peak: The rise in levels of the health feature (e.g., glucose) from the baseline before the meal.• Time to Peak: The time interval from the start of the meal to the maximum level of the health feature (e.g., glucose).• Area Under the Curve (AUC): The total health feature (e.g., glucose) exposure over a defined period, for example, two hours post-meal.

[0085] The real-time predictive model 170 may utilize advanced machine learning algorithms, including Multi-task Learning Models, Gradient Boosting Machines, Attention-Based Models, and Ensemble Models, to predict these parameters. The real-time predictive model 170 may map dietary inputs (e.g., carbohydrates, proteins, fats from nutrition breakdown 250), physical activity levels, and physiological factors to corresponding health data (e.g., glucose) response parameters based on the health feature data from the continuous monitor 470. Once the parameters are predicted, the system reconstructs the entire health feature (e.g., glucose) response curve using computational models that simulate metabolic processes, such as glucose absorption, metabolism, and clearance dynamics. This parameter-based approach provides a detailed representation of postprandial responses while maintaining flexibility for incorporating additional predictive features.

[0086] The real-time predictive model 170 extends its functionality to full-day health feature (e.g., glucose) monitoring after establishing a robust postprandial prediction model. By integrating patterns derived from diet, exercise, and sleep, the system predicts fluctuations in theBW Ref. No. 010301.00001 health feature during fasting, physical activity, and rest. This capability captures the dynamics of the health features (e.g., changes and levels of glucose) beyond mealtimes, enabling comprehensive health feature management throughout the day. The system interacts with the digital twin interface 104, allowing patients to report new lifestyle data, such as dietary changes or exercise routines, which are used to continuously refine predictions.

[0087] The real-time predictive model 170 may employ robust validation and adaptive learning mechanisms to maintain accuracy and relevance in dynamic, short-term contexts. During initial training, the system validates its predictive models using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), focusing on postprandial dynamics (e.g., postprandial glucose dynamics). Validation is performed at both the parameter level and the reconstructed curve level. At the parameter level, predicted health feature response parameters — such as Absolute Peak, Relative Peak, Time to Peak, and Area Under the Curve (AUC) — are assessed to ensure alignment with observed data from the continuous monitor 470. At the curve level, the reconstructed response curve (e.g., glucose response curve) is evaluated for accuracy in capturing the overall dynamic interplay of factors, such as glucose absorption, metabolism, and clearance. Split-sample validation further evaluates the model’s ability to generalize across different meals, exercise routines, lifestyle features, and / or patient scenarios.

[0088] Within the real-time predictive model 170, interconnected components collaborate to generate precise health feature (e.g., glucose) predictions. The real-time predictive model 170 may include machine learning algorithms 410 that map input features to predicted health feature (e.g., glucose) parameters, employing advanced computational methods. A postprandial model 420 may be included that characterizes health feature (e.g., glucose) dynamics triggered by meals, predicting metrics such as Absolute Peak and Area Under the Curve (AUC). Complementing this, a non-prandial response model 440 (also referred to as an all-day model) may be included that captures health feature (e.g., glucose) variations throughout the day, considering lifestyle feature data 110 such as fasting periods, physical activity, and rest. The health feature response model 430 (also referred to as a parametric model) synthesizes all these predictions (e.g., glucose predictions) into a unified physiological response curve (e.g., glucose response curve), simulating the interplay of health feature factors. In an example of glucose monitoring, health feature factors may include glucose absorption, metabolism, and clearance. Collectively, models 410, 420, 430, and 440 generate health feature prediction 480 based on cun-ent or recent data from nutrition breakdown 250 and lifestyle feature data 110.)

[0089] The outputs of the real-time predictive model 170 are presented to the user through the digital twin interface 104 as further described below. This interface converts complex predictiveBW Ref. No. 010301.00001 data into actionable insights, empowering patients to make informed lifestyle and dietary adjustments for effective glucose management throughout the day.|0090| Over time, the real-time predictive model 170 reduces its reliance on continuous health feature data from continuous monitor 470 by leveraging established patterns and correlations to predict health feature (e.g., glucose) levels based on reported lifestyle activities. To maintain prediction accuracy and reliability, a calibration model 450 may be incorporated, which receives data readings intermittently (e.g., once a month) from a calibration sensor 460 to verify the health feature prediction 480 generated by models 410, 420, 430, and 440. For example, in the context of glucose monitoring, calibration sensor 460 may provide glucometer readings taken by the user at periodic (e.g., weekly) intervals. The real-time predictive model 170 may continuously compare predicted health features (e.g., glucose) values against real-time data from the continuous monitor 470 or the intermittent readings calibration sensor 460 to detect any discrepancies. When prediction accuracy declines, the system may trigger an incremental learning process to recalibrate its models (e.g., 410, 420, 430, and / or 440). For example, if a user adopts a new low-carbohydrate diet, the model may dynamically adjust its predictions to reflect the patient’s new health feature (e.g., glucose) response patterns. This recalibration ensures that the real-time predictive model 170 remains sensitive to real-time changes in diet, exercise, or medication. This reduces the need for invasive, continuous monitoring devices while still providing patients with continuous health features (e.g., glucose) insights.Multi-Horizon Interaction Between Long-Term and Real-Time Predictive Models

[0091] In some embodiments, the system aggregates the results of both the Long-Term Predictive Model 160 and the Real-Time Predictive Model 170, each operating over different temporal resolutions and interacting to support coherent modeling across time scales.

[0092] As discussed above, the Real-Time Predictive Model 170 processes near-real-time inputs to capture event-driven physiological dynamics, such as responses to meals, physical activity, stress, or medication intake. Outputs of the Real-Time Predictive Model 170 may be summarized, parameterized, or otherwise transformed into features characterizing short-term physiological response behavior. The Long -Tern Predictive Model 160 analyzes extended sequences of historical data and model-derived features to identify longitudinal trends, baselines, and gradual changes in health indicators.

[0093] The digital twin system 100 may aggregate and transform outputs or response characteristics produced by the Real-Time Predictive Model 170 into derived features, and provides such derived features as input features for training and / or updating the Long-Term Predictive Model 160, enabling the long-term model to incorporate repeated short-termBW Ref. No. 010301.00001 physiological response into its assessment of longer-term trajectories.

[0094] Conversely, outputs of the Long-Term Predictive Model 160, such as projected trends or baseline estimates, may inform configuration, calibration, or interpretation of the Real-Time Predictive Model 170. This bidirectional interaction aligns short-term predictions with longer-term context and supports consistent modeling across multiple time horizons.|0095| By utilizing an adaptive digital twin model with long-term and real-time (or short-term) prediction, the real-time predictive model 170 delivers a personalized, non-invasive solution for short-term health feature prediction and long-term health feature management.User Interactions

[0096] The digital twin system 100 seamlessly integrates the nutrition input engine 140, with the digital twin interface 104, including personalized recommendation engine 180, digital twin LM engine 190, metabolic simulation engine 200, and nutritional insights engine 210 to create a comprehensive, user-centric platform designed for precise health (e.g., chronic disease) management. This system empowers patients to engage with their health data, make informed lifestyle adjustments, and achieve personalized health goals. By combining actionable recommendations, dynamic meal and activity planning, and interactive insights, the platform addresses both immediate health needs and long-term objectives.

[0097] Features of the system include real-time guidance tailored to the user’s unique health profile, visual progress tracking to enhance engagement, and automated notifications to promote consistent self-management. The interface simplifies complex data into accessible visualizations, enabling users to understand how their behaviors directly impact health outcomes. For example, the system may present personalized trends and projections for long-term glucose control, blood pressure trajectories, cardiometabolic risk estimates, and other longitudinal physiological indicators. Together, these elements deliver an adaptive, supportive experience that fosters sustained behavior change and improved chronic disease management.User Interactions: Recommendations, Goal Setting, Progress Tracking

[0098] The personalized recommendation engine 180 enhances user engagement by providing personalized recommendations and actionable insights based on the predicted long-term health trends and / or the real-time health feature predictions output from the digital twin model 150. For example, a user with elevated blood pressure trends may receive tailored advice to reduce sodium intake or engage in moderate exercise, with compliance tracked via wearable data.

[0099] Personalized recommendation engine 180 may provide functionality such as goal setting, progress tracking, and customizable reminders that encourage consistent data logging and adherence to lifestyle recommendations. Notifications from the personalized recommendationBW Ref. No. 010301.00001 engine 180 may be delivered, for example, through mobile applications, SMS, or email, and prompt users to record health metrics, update lifestyle information, or participate in recommended activities. By simplifying complex data into intuitive visualizations (via a display in or attached to the user mobile device 1045 or user device 1030), the personalized recommendation engine 180 interface allows patients to understand trends and patterns in their health, fostering informed decision-making and encouraging proactive behavior changes.Metabolic simulation engine: Interactive Meal and Lifestyle Planning

[0100] Metabolic simulation engine 200 facilitates interactive decision-making to optimize dietary and lifestyle choices aligned with health objectives. As shown in Fig. 5, the metabolic simulation engine 200 interfaces with the real-time predictive model 170 to simulate how a proposed meal or activity might affect a user’s health feature (e.g., glucose) levels. Specifically, a proposed meal 550 input via user interface device 260 may be input to the nutrition input engine 140, which may generate a projected nutrition breakdown 250 of the meal. Data from the projected nutrition breakdown 250 is fed into the metabolic simulation engine 200, which passes the data to the real-time prediction model 170. Metabolic simulation engine 200 coordinates with the real-time prediction model 170 to process the projected nutrition breakdown 250 (as described above with respect to Fig. 4) and receive a simulated real-time physiological reaction from the real-time prediction model 170. The metabolic simulation engine 200 may then pass the simulated real-time physiological reaction to the user interface device 260. The metabolic simulation engine 200 may generate a visual forecast (e.g., a visually displayed plot) of trajectories for a projected health feature 560 based on the simulated real-time physiological reaction, such as glucose.

[0101] For instance, if a meal high in carbohydrates is proposed, the system might predict a postprandial glucose spike. The metabolic simulation engine 200 may then suggest specific interventions — such as reducing portion sizes, pairing the meal with fiber-rich foods, or adding a 20-minute walk — to mitigate the effect. By providing immediate, data-driven feedback, the metabolic simulation engine 200 helps users weigh the benefits of various meal and activity options before making a decision on the meal or activity, thereby reinforcing proactive selfmanagement.

[0101] Beyond real-time simulations, the metabolic simulation engine 200 also supports users in achieving customized health goals, such as maintaining health feature (e.g., glucose) levels within a certain target range. For example, after each meal or activity entry, metabolic simulation engine 200 may compare the predicted health feature (e.g., glucose) outcome to the user’s performance benchmarks and adapts subsequent recommendations accordingly. If the user struggles to stayBW Ref. No. 010301.00001 within the desired range, the metabolic simulation engine 200 may propose adjusting portion sizes or modifying macronutrient ratios to achieve improved health feature (e.g., glycemic) outcomes. By visually illustrating both immediate and long-term consequences of different choices, the metabolic simulation engine 200 empowers users to make informed decisions that balance shortterm satisfaction with sustained health improvements.Personalized Food Substitutes

[0102] The nutritional insight engine 210 may provide tailored dietary alternatives to enhance user compliance with nutritional goals. Using a human-nutritionist-in-the-loop framework, the nutritional insight engine 210 may employ fine-tuned LLMs to recommend substitutions based on historical meal data, personal preferences, and nutritional needs. The nutritional insight engine 210 may receive dietary feature data from the nutrition input engine 140 and suggest changes in food or eating habits. For example, the nutritional insight engine 210 might suggest replacing "white rice" with "quinoa" or "cream-based sauces" with "yogurt-based alternatives."

[0103] Recommendations may be validated by registered dietitians to ensure practicality and accuracy. Factors like reducing saturated fat or carbohydrate intake are balanced against userspecific constraints, ensuring the proposed alternatives are both effective and actionable.Digital Twin LM: Personalized Responses and Insights

[0104] As illustrated in Fig. 6, the digital twin interface includes a digital twin language model (LM) 190 that integrates inputs from multi-modal data collection system 101, pre-processing system 102 (e.g., engineered feature data), the digital twin model 150 (e.g., long-term predictive model 160 and / or real-time predictive model 170), and external data sources 215 (e.g., clinical guidelines). When a user asks a question — such as “How can I reduce post-meal glucose spikes?” — the question is first evaluated by the safety and integrity module 640. If deemed appropriate and relevant (e.g., is determined to be likely answerable based on data from 101, 102, 150, and / or 215), the question advances to the retrieval-augmented generation (RAG) engine 650, which generates a retrieval query to extract pertinent information from the knowledge base 660. The knowledge base 660 includes structured data from clinical guidelines 630, patient-specific physiological responses derived from patient data 610, and predictive outputs from the digital twin model 150.

[0105] Upon receiving the user’s query, the RAG engine 650 contextualizes the request by identifying key terms and relevant parameters, such as carbohydrate intake, postprandial glucose trends, and insulin sensitivity. The retrieval engine queries sources within the knowledge base 660 using a structured query format.

[0106] The retrieved data is then processed by the LM engine 670, a specialized language modelBW Ref. No. 010301.00001 fine-tuned for the user’s health data and personalized health insights. Unlike general-purpose large language models, which are designed for general tasks, the LM engine 670 may be distilled from general-purpose large language models but further trained on clinical best practices, interaction history, and patient-specific data and intervention strategies. This fine-tuning process ensures that responses are not only evidence-based but also highly tailored to an individual’s physiological profile, medical history, and behavioral patterns.

[0107] A working example of this synthesis is illustrated below:User Query:"How can I reduce post-meal glucose spikes?"RAG Retrieval Query:"SEUECT guidelines, clinical_studies, patient_data, digital_twin_predictions FROM KnowledgeB ase_660WHERE condition = 'postprandial glucose spikes'AND patient_ID = ’<USER_ID>’AND meal_context IN ('carbohydrate intake', 'macronutrient composition', 'exercise impact')ORDER BY relevance_score DESC LIMIT 5;"Retrieved Data from knowledge base 660:1. Clinical Guidelines:o Consuming fiber-rich foods before carbohydrates can reduce postprandial glucose spikes by 15-30%.o Physical activity within 30 minutes of eating can improve glucose regulation.2. Patient Data Insights:o User experiences an average glucose spike of 180 mg / dL after consuming high-GI carbohydrates.o Previous data indicates a 20% reduction in glucose spikes when a 15-minute post-meal walk is performed.3. Digital Twin Model Prediction:o If the user reduces simple carbohydrates by 20% and replaces them with protein, the predicted glucose peak is reduced to 150 mg / dL.o Engaging in resistance training twice a week may further stabilize glucose levels.Final Response Generated by LM engine 670:"To reduce post-meal glucose spikes, consider consuming fiber-rich foods before carbohydrates, as this has been shown to lower postprandial glucose spikes by up to 30%. Based on your historical glucose response, replacing 20% of high-GI carbohydrates with lean protein could reduce your glucose peakBW Ref. No. 010301.00001 from 180 mg / dL to 150 mg / dL. Additionally, incorporating a 15-minute walk after meals may lead to a 20% improvement in glucose regulation. Would you like meal or activity recommendations tailored to your schedule?"

[0108] Beyond generating immediate responses, the digital twin LM 190 educates users on health trends, explains the significance of key metrics, and suggests targeted interventions. Drawing on insights from the long-term predictive model 160 — possibly identifying how exercise duration or meal timing affects glucose variability — it can propose daily aerobic exercise or dietary adjustments to maintain stable glucose levels. When a patient repeatedly experiences elevated post-meal readings, for example, the system might recommend substituting certain carbohydrates with protein-rich alternatives or advising a moderate postprandial walk.

[0109] This framework blends short-term corrective actions with overarching health objectives, empowering users to address acute concerns while progressing toward sustained improvements. If someone experiences a sudden blood pressure spike, the system may suggest immediate stressreduction techniques alongside guidelines for longer-term lifestyle modifications. By continuously incorporating patient data 610, digital twin model 150 updates, and clinical best practices 630, the digital twin LM 190 bridges real-time decision-making with comprehensive and precise longitudinal health management. While glucose production has been used as an example of a metabolic process modeled and managed by the digital twin system 100, the system may be applied to model any health condition correlated to the data acquired by the multimodal data collection system 101 as described above.

[0110] Figs. 7A-7B illustrate a flow diagram 700 of a method performed by the digital twin system 100 according to aspects of the present disclosure. At block 710, the system receives, from one or more user devices via a network, feature data that denotes physiological, behavior, environment, or dietary information of a user. The feature data may include data generated by at least one of a wearable device, a health monitoring device, a continuous glucose monitor, or a blood pressure cuff. In some aspects, the feature data includes compliance feature data indicating whether the user has complied with a health recommendation provided by the interactive digital twin interface.

[0111] At block 720, the system implements a feature engineering engine that generates engineered data sets from the feature data. The feature engineering engine may normalize the feature data to adjust for inter-patient variability, generate derived metrics from the feature data, time-align the feature data, aggregate the feature data into periodic summaries, or organize the feature data into predetermined categories. The feature engineering engine may also generate time-series feature data comprising lag feature data representing historical data points at aBW Ref. No. 010301.00001 predetermined duration prior to a baseline time, rolling averages to smooth short-term fluctuations, and event detection features identifying statistically distinguishable data occurrences.

[0112] At block 730, the system implements an Al-based nutrition input engine that parses the dietary information to extract meal components and portion details, wherein the dietary information comprises at least one of text data, speech data, and image data. The Al-based nutrition input engine generates a nutrition breakdown comprising caloric content, macronutrient composition, or micronutrient content. In some aspects, the Al-based nutrition input engine queries the user via the one or more user devices to provide additional details about meal components, transforms the dietary information into the nutrition breakdown based on foodnutrition data stored in a database, analyzes images of meals to identify meal components, estimates portion sizes from images, or extracts text from images to identify food products or ingredient lists.

[0113] At block 740, the system implements a digital twin model configured to generate health predictions based on the feature data. The digital twin model comprises a long-term predictive model configured to analyze multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state of the user, and a real-time predictive model configured to analyze the dietary information to predict a real-time physiological reaction of the user based on the nutrition breakdown. The long-term predictive model may be trained using the compliance feature data.

[0114] At block 750, the system provides an interactive digital twin interface configured to deliver, to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction.

[0115] At block 760, the system implements a metabolic simulation engine that receives a projected meal from the one or more user devices, interfaces with the real-time predictive model to generate a simulated real-time physiological reaction to the projected meal, and transmits the simulated real-time physiological reaction to the one or more user devices. The metabolic simulation engine may compare the simulated real-time physiological reaction to a user-defined performance benchmark and generate a recommendation for adjusting a portion size or macronutrient ratio in the projected meal based on the comparison.

[0116] At block 770, the system implements an interactive digital twin language model that receives a user query from the one or more user devices, generates a retrieval query by extracting one or more parameters from the user query, extracts health information from a knowledge base based on the one or more parameters, generates a response to the user query based on the healthBW Ref. No. 010301.00001 information, and transmits the response to the one or more user devices. The knowledge base comprises structured data derived from health data of the user, the digital twin model, and clinical guidelines.

[0117] At block 780, the system periodically receives, from the one or more user devices, updates to the feature data and maintains the digital twin model in an updated state by retraining the longterm predictive model and the real-time predictive model dynamically in response to receiving the updates.

[0118] Fig. 8 illustrates a block diagram of a computing device 800 configured to implement any of the computing devices described with reference to the digital twin system network 1000 of Fig. IB, including the server 1010, the user device 1030, and the user mobile device 1045. The computing device 800 includes a processor 803 that executes instructions and manages computational operations within the system. The processor 803 may comprise one or more central processing units (CPUs), graphics processing units (GPUs), neural processing units (NPUs), or other processing elements suitable for executing the machine learning algorithms and predictive models described herein.

[0119] The computing device 800 includes RAM 805 for temporary data storage during active processing and ROM 807 for storing firmware and permanent instructions. The RAM 805 may be used to store intermediate computational results, model parameters during inference, and data being actively processed by the processor 803. The ROM 807 may store boot instractions, firmware, and other persistent data that remains available when the computing device 800 is powered off.

[0120] The computing device 800 includes a communications module 809 that facilitates data exchange between the computing device 800 and external devices. The communications module 809 may implement wired communication protocols, such as Ethernet or USB, and wireless communication protocols, such as BLUETOOTH®, WI-FI®, or cellular standards, to enable connectivity with the network 1020, wearable devices 1060, health monitoring devices 1055, and other components of the digital twin system network 1000. In some aspects, the communications module 809 may include multiple communication interfaces to support simultaneous connections with different device types.

[0121] The computing device 800 includes a user interface 811 that enables interaction between users and the system. The user interface 811 may comprise input devices, such as a keyboard, mouse, touchscreen, microphone 1035, or camera 1040, and output devices, such as a display, speakers, or haptic feedback mechanisms. The user interface 811 allows patients and healthcare providers to input data, receive health insights, and interact with the interactive user interfaceBW Ref. No. 010301.00001 system 103 (e.g., by receiving user queries and displaying texts, graphs, images, videos, and other visualizations).|0122| The computing device 800 includes a memory 815 that stores an operating system 817, applications 819, and a database 821. The operating system 817 manages hardware resources and provides services for the applications 819. The memory 815 may comprise non-volatile storage media, such as solid-state drives, hard disk drives, or flash memory, configured to persistently store data and software.

[0123] The applications 819 comprise functional modules that implement the capabilities of the digital twin system 100. A multimodal data collection module 819a gathers health feature data 105, lifestyle feature data 110, contextual feature data 120, compliance feature data 125, and dietary feature data 130 from various sources, including wearable devices 1060, health monitoring devices 1055, and user inputs received through the user interface 811. A data pre-processor module 819b implements the functionality of the pre-processing system 102, including the functionality of the feature engineering engine 135, processing and normalizing the collected data to ensure seamless integration and usability by the predictive modeling system 103. The data preprocessor module 819b may also implement the functionality of the nutrition input engine 140. A predictive modeling module 819c implements the digital twin model 150, including the long-term predictive model 160 and the real-time predictive model 170, to generate health predictions based on the processed multimodal data. A digital twin interface module 819d implements the interactive digital twin interface system 104, providing the interface through which users access personalized recommendations 180, interact with the digital twin LM 190, simulate lifestyle changes using the projected nutrition breakdown 200, and receive dietary analysis from the nutritional insight engine 210.

[0124] The database 821 stores patient health data, nutritional information from the foodnutrition database 240, predictive model parameters, and historical data used for model training and validation. In some aspects, the database 821 may synchronize with the database 1005 and cloud storage 1015 to maintain data consistency across the digital twin system network 1000.

[0125] The components of the computing device 800 are interconnected via one or more system buses or communication pathways that enable data transfer between the processor 803, RAM 805, ROM 807, communications module 809, user interface 811, and memory 815. The configuration of the computing device 800 may vary depending on its role within the digital twin system network 1000. For example, when implementing the server 1010, the computing device 800 may include multiple processors 803, expanded RAM 805, and larger memory 815 capacity to support concurrent processing of data from multiple users. When implementing the user device 1030 orBW Ref. No. 010301.00001 user mobile device 1045, the computing device 800 may include additional input peripherals and a display optimized for user interaction with the interactive user interface system 103.|0126| Various aspects of the disclosure are presented in the following numbered clauses: Clause 1. A digital twin-based system for health monitoring and lifestyle intervention, comprising:collecting multimodal health and lifestyle data from wearable devices, remote monitoring devices, and an interactive digital twin interface;constructing a digital twin model of a patient’s health by integrating real-time physiological, lifestyle, and contextual data;processing dietary data, including meal types, portion sizes, and nutrient content, using a generative Al-based input engine that supports text, speech, and image inputs; anddelivering personalized health recommendations and insights via an interactive interface, mobile application notifications, SMS, or email.Clause 2. The system of clause 1, wherein the system includes predictive modeling to:analyze long-term health trends using multi -day data sequences for forecasting health outcomes such as blood glucose or blood pressure; andprovide real-time health predictions, including post-meal glucose monitoring and full- day glucose forecasts, by capturing daily patterns in dietary habits, activity levels, sleep, stress, or a combination of these factors.Clause 3. The system of clause 1, comprising a dietary input engine that supports:multimodal inputs, including text, speech, and images, to log meals and extract detailed nutritional information; andan interactive mechanism that queries users to clarify ambiguous inputs to ensure accuracy of logged data, while dynamically minimizing user effort.Clause 4. The system of clause 1, further comprising a nutrition conversion engine configured to:transform the dietary data into detailed nutrient profiles, including caloric content, macronutrient breakdowns, and sodium levels, based on established nutritional guidelines and food composition databases.continuously validate nutrient profiles through user feedback and real-world data to maintain accuracy and reliability.Clause 5. The system of clause 1, wherein the interactive digital twin interface allows users to:log lifestyle data, including meals, exercise, and sleep, and receive real-time updates to their health model;BW Ref. No. 010301.00001 simulate an impact of potential lifestyle changes, such as dietary adjustments or exercise routines, on key health outcomes; andinquire about health-related scenarios and receive instant feedback supported by predictive modeling and evidence-based insights.Clause 6. The system of clause 1, further comprising an adaptive learning mechanism to:update predictive models dynamically based on new patient data, including changes in diet, activity levels, medication, or health conditions; andvalidate and recalibrate model predictions continuously using user feedback and intermittent health measurements, such as glucometer readings.Clause 7. The system of clause 1, further comprising a mechanism to enhance user engagement by:providing progress tracking and goal-setting tools integrated into the interactive digital twin interface; andoffering community -driven features, such as shared progress tracking and peer benchmarking, to foster a sense of belonging and mutual encouragement.Clause 8. The system of clause 1, wherein the digital twin leverages predictive modeling for:identifying key factors influencing health outcomes and delivering precise, targeted recommendations; andusing global pretraining on multi-patient datasets to capture universal health patterns, followed by patient-specific fine-tuning for personalized predictions.Clause 9. The system of clause 1, comprising a dietary input engine that provides real-time insights by:analyzing meal data to compare user dietary habits against nutritional guidelines or personalized health goals; andrecommending alternative food choices based on user preferences, historical data, and nutritional targets.Clause 10. The system of clause 1, further comprising a language model (LM) integrated into the digital twin model, wherein the LM:responds to user queries by synthesizing patient data, predictive model outputs, and clinical guidelines into personalized insights; andeducates users by explaining health trends, providing actionable recommendations, and addressing immediate and long-term health concerns.

Claims

BW Ref. No. 010301.00001 Claims1. A digital twin health monitoring system, comprising:a processor;a memory that stores instructions that, when executed by the processor, cause the system to: receive, from one or more user devices via a network, feature data that denotes physiological, behavior, environment, or dietary information of a user; implement a digital twin model configured to generate health predictions based on the feature data, the digital twin model comprising:a long-term predictive model configured to analyze multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state of the user; anda real-time predictive model configured to analyze the dietary information and at least one of physiological, lifestyle, or contextual feature data to predict a real-time physiological reaction of the user; andprovide an interactive digital twin interface configured to deliver, to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction.

2. The system of claim 1 , wherein the instructions, when executed by the processor, cause the system to:periodically receive, from the one or more user devices, updates to the feature data; and maintain the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates.

3. The system of claim 1, wherein:the feature data includes compliance feature data indicating whether the user has complied with a health recommendation provided by the interactive digital twin interface; and the instructions, when executed by the processor, cause the system to train the long-term predictive model or the real-time predictive model using the compliance feature data.

4. The system of claim 1, wherein the instructions, when executed by the processor, cause the system to implement a feature engineering engine configured to:generate engineered data sets from the feature data by:normalizing the feature data to adjust for inter-patient variability,BW Ref. No. 010301.00001 generating derived metrics from the feature data,time-aligning the feature data,aggregating the feature data into periodic summaries, ororganizing the feature data into predetermined categories represented as model input features interpreted by the long-term predictive model;wherein the long-term predictive model is configured to generate the forecasted temporal pattern of the health state of the user based on the engineered data sets.

5. The system of claim 4, wherein the feature engineering engine is further configured to generate time-series feature data comprising one or more of:lag feature data representing historical data points in the feature data at a predetermined duration prior to a baseline time;a rolling average to smooth short-term fluctuations in the feature data; andevent detection features identifying statistically distinguishable data occurrences in the feature data.

6. The system of claim 1, wherein the instructions, when executed by the processor, cause the system to implement an Al-based nutrition input engine configured to:parse the dietary information to extract meal components and portion details, wherein the dietary information comprises at least one of text data, speech data and image data; and generate a nutrition breakdown comprising caloric content, macronutrient composition, or micronutrient content, wherein the real-time predictive model is configured to predict the real-time physiological reaction of the user based on the nutrition breakdown.

7. The system of claim 6, wherein the Al-based nutrition input engine is configured to: query the user via the one or more user devices to provide an additional detail about the meal components in the dietary information; andtransform the dietary information into the nutrition breakdown based on food-nutrition data stored in a database.

8. The system of claim 6, wherein the Al-based nutrition input engine is configured to: analyze an image of a meal to identify one of the meal components;estimate a portion size of the one of the meal components from the image; orextract text from the image to identify food products or ingredient lists.BW Ref. No. 010301.00001 9. The system of claim 1, wherein the instructions, when executed by the processor, cause the system to implement a metabolic simulation engine configured to:receive a projected meal from the one or more user devices;interface with the real-time predictive model to generate a simulated real-time physiological reaction to the projected meal; andtransmit the simulated real-time physiological reaction to the one or more user devices.

10. The system of claim 9, wherein the metabolic simulation engine is further configured to: compare the simulated real-time physiological reaction to a user-defined performance benchmark; andgenerate a recommendation for adjusting a portion size or macronutrient ratio in the projected meal based on the comparison of the simulated real-time physiological reaction to the user-defined performance benchmark.

11. The system of claim 1, wherein the feature data comprises data generated by at least one of a wearable device, a health monitoring device, a continuous glucose monitor, or a blood pressure cuff.

12. The system of claim 1, wherein the instructions, when executed by the processor, cause the system to implement an interactive digital twin language model configured to:receive a user query from the one or more user devices;generate a retrieval query by extracting one or more parameters from the user query; extract health information from a knowledge base based on the one or more parameters, wherein the knowledge base comprises structured data derived from health data of the user, the digital twin model, and clinical guidelines;generate a response to the user query based on the health information; andtransmit the response to the one or more user devices.

13. A method for digital twin health monitoring, the method comprising:receiving, by a processor, from one or more user devices via a network, feature data that denotes physiological, behavior, environment, or dietary information of a user; implementing, by the processor, a digital twin model that generates health predictions based on the feature data, the digital twin model comprising:a long-term predictive model that analyzes multi-day sequences of the feature data to generate a forecasted temporal pattern of a health state of the user; andBW Ref. No. 010301.00001 a real-time predictive model that analyzes the dietary information to predict a real-time physiological reaction of the user; andproviding, by the processor to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction .

14. The method of claim 13, further comprising:periodically receiving, from the one or more user devices, updates to the feature data; and maintaining the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates.

15. The method of claim 13, further comprising generating engineered data sets from the feature data by:normalizing the feature data to adjust for inter-patient variability,generating derived metrics from the feature data,time-aligning the feature data,aggregating the feature data into periodic summaries, ororganizing the feature data into predetermined categories interpreted by the long-term predictive model;wherein the long-term predictive model generates the forecasted temporal pattern of the health state of the user based on the engineered data sets.

16. The method of claim 13, further comprising:parsing, with an Al-based nutrition input engine, the dietary information to extract meal components and portion details, wherein the dietary information comprises at least one of text data, speech data and image data; andgenerating, with the Al-based nutrition input engine, a nutrition breakdown comprising caloric content, macronutrient composition, or micronutrient content, wherein the real-time predictive model predicts the real-time physiological reaction of the user based on the nutrition breakdown.

17. The method of claim 16, further comprising:determining dietary habits of the user based on the nutrition breakdown;comparing the dietary habits against nutritional guidelines and personalized health goals; and recommending alternative food choices based on at least one of a user preference, historicalBW Ref. No. 010301.00001 data, or a nutritional target.

18. The method of claim 13, further comprising:receiving a projected meal from the one or more user devices:interfacing with the real-time predictive model to generate a simulated real-time physiological reaction to the projected meal; andtransmitting the simulated real-time physiological reaction to the one or more user devices.

19. The method of claim 13, further comprising implementing an interactive digital twin language model that:receives a user query from the one or more user devices;generates a retrieval query by extracting one or more parameters from the user query; extracts health information from a knowledge base based on the one or more parameters, wherein the knowledge base comprises structured data derived from health data of the user, the digital twin model, and clinical guidelines;generates a response to the user query based on the health information; andtransmits the response to the one or more user devices.

20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a system, causes the system to:receive, from one or more user devices via a network, feature data that denotes physiological, behavior, or environment information of a user, and dietary feature data that denotes food consumption information of the user;implement a digital twin model configured to generate health predictions based on the feature data and the dietary feature data, the digital twin model comprising: a long-term predictive model configured to analyze multi-day sequences of the feature data and the dietary feature data to generate a forecasted temporal pattern of a health state of the user; anda real-time predictive model configured to analyze the feature data and the dietary feature data to predict a real-time physiological reaction of the user; provide an interactive digital twin interface configured to deliver, to the one or more user devices, the forecasted temporal pattern of the health state and the real-time physiological reaction;periodically receive, from the one or more user devices, updates to the feature data and the dietary feature data: andBW Ref. No. 010301.00001 maintain the digital twin model in an updated state by retraining the long-term predictive model and the real-time predictive model dynamically in response to receiving the updates.

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