Global nutrient deep customization platform

Abstract

The invention discloses a global nutrient deep customization platform, and belongs to the field of health management. The platform mainly comprises a user terminal interface module used for collecting multi-dimensional original data of global users, including genes, real-time physiology, diet logs and culture preferences; the global nutrition knowledge base and rule engine module is used for storing and dynamically updating nutrition data and biological effect rules covering different race, regional laws and regulations and diet culture; an analysis engine is deeply customized, multi-objective optimization calculation is carried out on the fused deep health portrait of the user and a global knowledge base through artificial intelligence, and a comprehensive nutrition scheme which cooperatively considers individual genes, real-time states, long-term objectives and regional feasibility is generated; and the global supply chain docking and product customization module is used for converting the scheme into an entity nutrient product conforming to local regulations and completing distribution. According to the method, full-link deep customization from multi-source data deep fusion, globalized knowledge driving to cross-regional product accurate delivery is realized.

Description

Technical Field

[0001] This invention relates to health management, and more particularly to a global nutrient deep customization platform. Background Technology

[0002] With increasing health awareness and the popularization of precision medicine, personalized nutrition services have become an important development direction in the field of health management. Various forms of nutrition customization platforms or systems have emerged in the current technology landscape. For example, some platforms provide users with general dietary advice or nutritional supplement recommendations based on questionnaire information and basic nutritional knowledge. Other more advanced systems attempt to integrate data from more dimensions, such as incorporating genetic testing results to provide so-called "genetically customized nutrition"; or connecting to wearable device data to achieve dynamic adjustments based on real-time physiological indicators. Furthermore, some solutions have proposed business models that combine online assessment with offline product delivery.

[0003] However, these existing technological solutions still have significant limitations in achieving truly global and in-depth nutrient customization. First, in terms of data, most systems only integrate a limited number of data types (such as questionnaires, genetic data, or device data), lacking deep integration and standardized processing of multi-source heterogeneous data such as user dietary culture, regional ingredients, and real-time intake images, resulting in a one-sided user profile. Second, at the knowledge base and rule level, the nutritional models of existing systems are mostly based on dietary standards of a single country or region, lacking a continuously updated dynamic knowledge base covering different ethnicities, regional regulations, and dietary cultures globally, making it difficult to serve global users or handle accurate cross-cultural dietary conversions. Third, in terms of customization logic, existing solutions often focus on optimizing a single goal (such as supplementing a nutrient deficiency), failing to systematically and collaboratively optimize multiple constraints such as individual genetic metabolic characteristics, real-time physiological state, long-term health goals, and regional dietary feasibility. Finally, in terms of service loop, most platforms stop at generating suggested solutions, failing to seamlessly connect with a global compliant supply chain and physical product customization production, making it difficult to translate personalized solutions into accessible physical nutrient products. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a global nutrient deep customization platform, realizing a full-chain nutrient deep customization platform from deep integration of multi-source data and global knowledge-driven approach to precise cross-regional product delivery.

[0005] Technical solution: A global nutrient deep customization platform, including: The user terminal interface module is used to receive and verify access requests from users in different regions around the world, provide multilingual interactive interfaces, and collect multi-dimensional raw data from users. The multi-dimensional raw data includes: basic personal information and health goals collected through standardized questionnaires, nutritional genomics data generated through authorized access gene testing reports, real-time physiological monitoring data from wearable devices synchronized through API interfaces, dietary logs uploaded by users and structured nutrient intake data after image recognition, as well as geographical location and dietary culture preference data of the user's region. The global nutrition knowledge base and rules engine module stores and dynamically updates databases across multiple dimensions, including: a global basic nutrient requirements database built based on regional dietary guidelines, clinical nutrition standards, and the latest research findings, covering benchmark values ​​for different races, ages, genders, and physiological states; a regional nutrient composition and compliance database integrating information on local specialty ingredients, food regulations, and supply chains; and a biological effect prediction rule base containing nutrient-gene interaction models, nutrient-disease association models, and nutrient-gut microbiome interaction models. The deep-customized analysis engine is based on an AI-driven main control unit. This main control unit performs the following operations: it integrates and standardizes multi-dimensional raw data from the user terminal interface module to generate a unified deep health profile of the user; it calls the global nutrition knowledge base and rule engine module, and combines the deep health profile of the user to perform multi-objective optimization calculations through a preset algorithm model. The algorithm model considers at least the user's individual nutritional gap, gene metabolic pathway efficiency, real-time physiological state fluctuations, long-term health goals, and regional dietary accessibility, and outputs a dynamic, personalized daily nutrient formula plan. This plan is accurate to the type, dosage, form, and recommended intake time of each macronutrient, micronutrient, prebiotic / probiotic, and functional phytochemical. The flexible manufacturing and supply chain collaboration module is used to transform the personalized daily nutrient formula into executable production instructions. This module includes: a formula analysis unit, which breaks down the formula into a specific raw material list and process parameters; a global supplier matching unit, which selects the optimal supply node from a pre-set global raw material supplier network based on the user's geographical location, raw material inventory, cost, and logistics timeliness; and a distributed micro-factory scheduling unit, which issues production instructions to on-demand production micro-factories in the user's region or the nearest region. These micro-factories are equipped with modular production lines and can automatically mix, dispense, and personalize small batches of various nutrient tablets, capsules, powders, or liquids according to instructions. The global logistics and delivery tracking module is responsible for receiving finished product information from the flexible manufacturing and supply chain collaboration module, integrating third-party logistics service provider interfaces, planning the optimal delivery route from the micro-factory to the user, providing real-time logistics tracking, and managing the periodic automatic repurchase and formula adjustment triggering process of subscribed users. The feedback and continuous optimization closed-loop module is used to collect user feedback submitted through the user terminal interface module, subsequent updated health data, and new monitoring data from wearable devices. This feedback data is compared and analyzed with the expected nutritional intervention effect. The algorithm parameters in the deep customization analysis engine are iteratively optimized using a machine learning model, thereby realizing the dynamic and gradual adjustment of the user's personal formula plan. The aggregated data is anonymized and used to optimize the models and rules in the global nutrition knowledge base and rule engine module.

[0006] Furthermore, the algorithm model in the deep customized analysis engine adopts a hierarchical decision-making framework, including: The first layer is the basic needs calculation layer, which calculates the range of basic nutritional needs based on the user's age, gender, weight, activity level, and health goals, with reference to the global basic nutrient needs database. The second layer, the gene and metabolism correction layer, is based on the user's nutritional genomics data and performs personalized corrections on the basic requirements calculated in the first layer. The correction factors include, but are not limited to, the impact of vitamin D receptor gene variations on vitamin D requirements and the impact of MTHFR gene variations on folic acid metabolism efficiency. The third layer, the real-time status adjustment layer, makes intraday or interday fine-tuning of nutrient formulations based on the most recent physiological monitoring data from wearable devices. For example, it dynamically adjusts the dosage of nutrients related to energy metabolism and nerve regulation, such as magnesium and B vitamins, according to the day's sleep quality, stress level, or exercise volume.

[0007] Furthermore, the distributed micro-factory scheduling unit in the flexible manufacturing and supply chain collaboration module is specifically implemented as follows: After receiving the formula, it automatically generates digital work instructions that conform to the specifications of the target micro-factory production line; The production line of the micro-factory adopts a modular design, including an independent raw material silo, a precision weighing module, a mixing module, a tablet / capsule filling / filling module, and a laser marking and packaging module; The scheduling unit performs load balancing calculations among multiple selectable micro-factories based on the complexity and urgency of the formula, and selects the factory with the shortest production queue or the lowest overall cost to issue instructions, ensuring that the process from formula generation to product delivery is completed within 48 hours.

[0008] Furthermore, the regional nutrient composition and compliance database in the global nutrition knowledge base and rules engine module also integrates an intelligent compliance verification sub-engine, which can: Real-time monitoring of the latest regulations, standards, and claims management policies regarding nutritional supplements in major target markets worldwide (such as the US FDA and EU EFSA); After the deep customization analysis engine generates the formula plan, it automatically conducts a compliance pre-review of the legality of all ingredients in the formula, the maximum allowable amount added, and the health claims to be used on the packaging, based on the user's registered location or delivery destination, and provides automatic modification suggestions for non-compliant items.

[0009] Furthermore, the platform also includes an enterprise service interface module for professional nutritionists or medical institutions, which provides: The institution's management backend allows nutritionists in partner institutions to manage their clients in batches and view their clients' in-depth health profiles and nutrient formulation plans. The professional intervention interface allows nutritionists to manually adjust, add notes, or pause a suggestion based on their professional judgment on the formula plan automatically generated by the system. The adjusted plan is still produced and delivered through the flexible manufacturing and supply chain collaboration module. Data dashboards and analysis reports provide partner organizations with aggregated analytical data on nutritional deficiency trends and intervention effectiveness statistics for the populations they serve.

[0010] Furthermore, the indicators used for effect comparison analysis in the feedback and continuous optimization closed-loop module not only include subjective user feedback, but also objective data obtained by encouraging or incentivizing users to periodically perform authorized specific biomarker tests. These biomarkers include, but are not limited to, blood vitamin levels, Omega-3 index, and gut microbiome diversity test results. These objective test results are used as high-weight training data for the optimization algorithm model.

[0011] Furthermore, the user terminal interface module employs a progressive trust acquisition strategy when collecting data: The initial phase only requires providing basic health information and goals to generate a basic formula; As users spend more time on the platform, through educational content and points incentives, users are gradually guided to authorize access to more in-depth genetic data, continuous blood glucose monitoring data, or gut microbiota testing data. This enables the platform to provide more in-depth customized solutions, creating a positive cycle between user stickiness and data depth.

[0012] Furthermore, the overall architecture of the platform adopts a microservice design. The user terminal interface module, the global nutrition knowledge base and rule engine module, the deep customization analysis engine, the flexible manufacturing and supply chain collaboration module, the global logistics and delivery tracking module, and the feedback and continuous optimization closed-loop module are all deployed as independent microservices and communicate with each other through well-defined APIs to achieve elastic scaling, independent upgrades, and high availability of the system. Beneficial effects

[0013] (1) The platform has achieved a revolutionary improvement in the degree of customization by building a deeply integrated multi-dimensional user profile and a dynamic nutrition knowledge base covering the globe. It breaks down data silos and unifies the processing of heterogeneous data such as genes, real-time physiology, dietary behavior, and cultural preferences, making the analysis foundation more comprehensive and solid. At the same time, the global knowledge rule engine ensures that the nutrition advice not only conforms to the individual's biological characteristics, but also respects the dietary culture and regulatory standards of the region where the individual is located, truly achieving "deep" customization.

[0014] (2) The platform’s core analysis engine employs a multi-objective optimization algorithm, which can intelligently balance and meet users’ multiple needs, such as immediate nutritional supplementation, long-term health promotion, gene metabolism adaptation, and lifestyle feasibility, to generate highly personalized and logically consistent comprehensive nutrition plans. This avoids the one-sidedness or conflict that may result from single-dimensional recommendations, and improves the scientific nature, safety, and user compliance of the plans.

[0015] (3) The platform seamlessly integrates digital customization solutions with the global physical supply chain. By automatically matching compliant raw material suppliers and manufacturers in the user's location and driving flexible customized production lines, the virtual nutrition plan is ultimately transformed into personalized nutrient products that can be directly consumed (such as customized multivitamin tablets, nutritional powders, etc.), achieving a complete closed loop from "suggestion" to "product". This not only greatly enhances the user experience and the practical value of the service, but also provides a scalable business model for the health industry. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the platform process of the present invention. Detailed Implementation

[0017] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Example

[0018] The global nutrient customization platform in this embodiment is a software system based on cloud computing and microservice architecture. Its core server cluster can be deployed in multiple data centers globally, providing users with low-latency access through load balancing. The platform provides services to global users through three forms: web interface, mobile app, and open API. The backend system consists of multiple functional modules working collaboratively, with data flow and business logic as follows: Figure 1 As shown.

[0019] (1) User terminal result module: This module is the entry point for users to interact with the platform and is jointly implemented by the front-end application layer and the access gateway layer.

[0020] Multilingual and Regional Adaptation: The front-end application has a built-in internationalization framework, supporting more than 15 languages ​​including Chinese, English, Spanish, French, German, and Japanese. Upon first visit, the system recommends an initial language based on the user's IP address or browser language settings and displays a privacy agreement and informed consent form for data collection that comply with the laws and regulations of their region (such as the EU, the US, and China). The interface layout, colors, and icons are also subtly adjusted according to regional cultural preferences (e.g., the Asian version may place greater emphasis on page information density).

[0021] Multi-dimensional Raw Data Collection: Standardized Questionnaire: An adaptive questionnaire engine is used. Users first fill in basic information (age, gender, height, weight) and health goals (such as "muscle gain," "improved sleep," and "pregnancy nutritional support"). Subsequently, the engine dynamically pushes more detailed questions based on the initial answers. For example, users who select "improved gut health" will answer further detailed questions about bowel movement frequency, food tolerance, etc. All questions are calibrated by clinical nutrition experts and mapped to the parameter dimensions required by the subsequent analysis engine; Nutritional Genomics Data Access: The platform has established OAuth 2.0 authorization interfaces with several certified third-party genetic testing institutions (such as 23andMe, AncestryDNA, and compliant domestic genetic companies). After user authorization, the platform retrieves the user's raw genetic data files (such as .vcf format) through a secure API. The platform's built-in gene interpretation submodule parses the file, identifies key SNP sites related to nutrient metabolism (such as the MTHFR gene and folate metabolism, the FADS1 gene and fatty acid conversion, and the AMPD1 gene and exercise recovery), and converts them into structured tags such as "high / medium / low metabolic efficiency" or "increased / decreased demand," storing them in the user profile. Wearable device data synchronization: The platform offers access options to mainstream health data platforms such as Apple HealthKit, Google Fit, and Huawei Health. After user authorization, the system synchronizes data such as heart rate variability (HRV), resting heart rate, sleep stage (deep sleep, light sleep, REM), daily steps, and activity energy at a set frequency (e.g., hourly). This data is cleaned and aggregated to assess the user's real-time stress level, recovery status, and daily activity expenditure. Dietary logs and image recognition: Users can upload their diet through text records or photos. The image recognition function is based on a pre-trained deep learning model (such as a convolutional neural network CNN), trained using millions of labeled food images, capable of recognizing common global ingredients and dishes and estimating their portion sizes. The identification results are linked to a vast global food nutrient database, automatically converting them into specific nutrient intake amounts (e.g., XX grams of protein, YY milligrams of vitamin C). For dishes that cannot be identified, the system provides a manual selection and portion adjustment interface. Regarding geographical location and cultural preferences: the system automatically records the user's country and city codes. Additionally, the questionnaire includes a dedicated section asking about dietary restrictions (e.g., religious fasting, veganism), taste preferences (sweet or salty), preferred cooking methods, and weekly frequency of dining out; this data is encoded into a "cultural preference vector."

[0022] (2) Global Nutrition Knowledge Base and Rule Engine Module: The module is the knowledge brain of the platform, consisting of multiple databases, knowledge graphs and rule bases.

[0023] The Global Basic Nutrient Requirements Database is based on the Dietary Reference Intakes (DRIs) published by the Food and Agriculture Organization of the United Nations (FAO), the World Health Organization (WHO), and national health departments (such as the US National Institutes of Health). However, it is not a simple list; rather, it constructs a multidimensional parameter model. The model's independent variables include: age (accurate to the year), sex, and physiological status (e.g., ordinary adult, pregnant women in early / mid / late stages, lactating women, athletes). For each combination, the model stores parameters such as the estimated average requirement (EAR), recommended intake (RNI), and tolerable upper intake level (UL) for various nutrients. The database has a maintenance backend, updated by a team of nutrition scientists, and is promptly synchronized when authoritative organizations publish new standards (e.g., the EU updates its vitamin D recommendations).

[0024] Regional Nutrient Composition and Compliance Database: This is a dynamically updated commercial database. It integrates: Ingredient Composition Data: Contains detailed nutritional information on tens of thousands of global ingredients, with their origin indicated (e.g., the difference in fatty acid composition between New Zealand grass-fed beef and US grain-fed beef); Food Regulatory Database: Includes lists of permitted uses, limits, and claims requirements for food additives, fortifiers, and health food ingredients from major markets (e.g., US FDA, EU EFSA); Supply Chain Information: Connects with the data systems of major global nutrient ingredient suppliers (e.g., DSM, BASF, and domestic raw material suppliers) to obtain real-time specifications, purity, price, certifications (e.g., organic, non-GMO), and inventory information for raw materials.

[0025] Biological Effect Prediction Rule Base: This base exists in the form of "IF-THEN" rules and probabilistic graphical models. Nutrient-Gene Interaction Rules: For example, "IF user MTHFR genotype is C677T homozygous mutation (TT type) THEN their metabolic efficiency for folic acid synthesis (e.g., folate) is reduced to 30%, it is recommended to prioritize supplementation with methylated form folate (5-methyltetrahydrofolate)." Nutrient-Disease Association Model: Built based on publicly available medical literature and clinical guidelines. For example, for users at risk of hypertension, the model associates evidence of the relationship between "sodium-potassium intake ratio" and blood pressure, generating optimized recommendations to limit sodium and increase potassium. Nutrient-Gut Microbiome Model: Integrates research consensus on the effects of dietary fiber, prebiotics, polyphenols, etc., on specific gut microbiota (e.g., Bifidobacteria, Prevotella), used to guide the design of programs aimed at improving gut microbiota.

[0026] (3) Deeply Customized Analysis Engine: This is the core computing unit of the platform, and its workflow is as follows: Step 1: Data Fusion and Deep User Health Profile Generation. The analysis engine receives all raw data from the interface module. First, it performs data cleaning (removing outliers and filling in reasonable missing values) and standardization (unifying all data units to international standard units). Then, it launches the "Profile Generator" submodule, which integrates the data into a unified user model containing the following layers: Static layer: demographic information, genetic tags; Dynamic layer: Recent average and trend physiological indicators (such as average sleep quality over the past week), baseline intake of macronutrients and micronutrients analyzed from current dietary patterns; Goals and Constraints Layer: User-declared health goals, dietary culture preference vectors, and geographic location.

[0027] Step 2: Multi-objective optimization calculation. The engine inputs the above user deep health profile into a multi-objective optimization algorithm (this embodiment uses the improved NSGA-II algorithm). The optimization problem is defined as: Decision variables: Recommended daily / weekly intake of various nutrients (a multidimensional vector); Objective function (to be optimized simultaneously): 1. Minimize the nutritional gap: Make the recommended intake as close as possible to or exceed the personalized RNI calculated based on the user's static and dynamic layers; 2. Maximize genetic fit: Based on genetic tags, prioritize the recommendation of nutrient forms or proportions with high metabolic efficiency (e.g., optimize the proportion of fat types for ApoE4 gene carriers). 3. Smooth physiological fluctuations: Based on dynamic layer data, increase the recommended intake of B vitamins and magnesium when the user's stress index is high; optimize the ratio of sleep-aiding nutrients (such as glycine and theanine) when sleep data is poor. 4. Approaching long-term health goals: Transform goals (such as "muscle gain") into specific nutritional constraints (such as protein intake of 1.6-2.2 grams per kilogram of body weight) and assign them high weight; 5. Compliance with regional and cultural feasibility: "Cultural preference vector" and "geographical location" are used as hard constraints or penalties. For example, a plan generated for Indian vegetarian users would prioritize protein sources from legumes and dairy products, rather than meat. Constraints: The recommended intake of all nutrients must be below their tolerable upper intake level (UL); the total cost of the program must be below the user's budget; and the ingredients / ingredients in the program must comply with the regulations of the user's region.

[0028] Step 3: Generate a personalized nutrition plan. The optimization algorithm outputs one or more Pareto optimal solutions. The engine then selects the best solution using a ranking model (considering user compliance predictions) and transforms it into a readable personalized nutrition plan. This plan includes not only daily nutrient supplementation recommendations (such as "Vitamin D3 2000 IU"), but also specific dietary adjustment guidelines (such as "Increase salmon intake twice a week, 150 grams each time"), and clearly indicates the portions that can be met through diet and the "gaps" that need to be filled through supplements.

[0029] (4) Global supply chain collaboration and product customization module: This module is responsible for transforming virtual solutions into physical products.

[0030] Formula Mapping and Compliance Verification: The product customization submodule receives the portion of the "Personalized Nutrition Plan" that requires supplementation. It first filters all available and compliant raw materials from the "Regional Nutrient Composition and Compliance Database" based on the user's geographical location. Then, it runs a formula algorithm to convert nutrient requirements (e.g., "Vitamin D3 2000 IU, Magnesium 200 mg (glycine magnesium form)") into specific raw material combinations and ratios, while considering the stability and compatibility of the raw materials (to avoid antagonistic reactions) and the process requirements of the final dosage form (capsules, tablets, powder).

[0031] Production Order Generation: The final formula, along with the user-selected dosage form, packaging specifications (e.g., 30-day supply), and label language requirements, is automatically assembled into a standardized "production work order." This work order is then sent in real-time via the EDI (Electronic Data Interchange) system to contract manufacturing plants certified by the platform and located in the user's region (e.g., orders from European users are sent to cGMP-certified plants within the EU, and orders from Asian users are sent to plants in the Asia-Pacific region).

[0032] Supply chain status tracking: After the factory receives a work order, the system initiates the tracking process. Users can view the order status on the platform: "Formula Confirmation in Progress," "Raw Material Procurement in Progress," "Production in Progress," "Quality Inspection in Progress," and "Shipped." The system integrates with the factory's MES (Manufacturing Execution System) and logistics provider systems to update key node information.

[0033] (5) Interactive feedback and continuous optimization module: This module realizes closed-loop optimization.

[0034] Feedback Collection: After users receive the product, the platform will conduct lightweight feedback surveys periodically (e.g., every two weeks) via push notifications or emails, inquiring about their product usage experience (e.g., taste, gastrointestinal reactions), subjective feelings (e.g., energy levels, degree of sleep improvement), and adherence (whether the product was taken on time). Simultaneously, continuously synchronized wearable device data constitutes objective physiological feedback.

[0035] Model Iteration: Anonymized feedback data, compliance data, and changes in physiological indicators from all users are aggregated into the platform's machine learning training pool. Periodically (e.g., quarterly), this new data is used to retrain and fine-tune the weight model of the multi-objective optimization algorithm in the "Deep Customization Analysis Engine" and the parameters of the image recognition model in the "User Terminal Interface Module," thereby enabling the platform's customization capabilities to continuously evolve as the number of users served increases.

[0036] User A is a 35-year-old male software engineer living in Berlin, Germany. His health goal is to "relieve chronic fatigue caused by prolonged sitting and improve concentration".

[0037] 1. Data Input: A logged in using the German interface. He filled out a questionnaire, indicating that he sits for more than 10 hours a day and has a mild caffeine dependence. He authorized the platform to access his 23andMe gene data (showing a COMT gene Val158Met mutation, possibly related to dopamine clearance rate) and Apple Health data (showing low HRV and insufficient sleep depth). He uploaded photos of his meals over several days, and the system identified that his diet lacked sufficient intake of deep-sea fish and green vegetables.

[0038] 2. Analysis and Customization: The platform generates a deep health profile for user A, combining their genetic tags (suggesting potential need for nutrients to support dopamine balance), physiological data (stress and insufficient recovery), and dietary gaps (Omega-3, magnesium, and B vitamin deficiencies). Through multi-objective optimization, a plan is generated: dietary recommendations include adding a handful of nuts daily (to supplement magnesium and vitamin E) and consuming mackerel twice a week (to supplement Omega-3); for supplements, a customized formula is generated: containing highly bioavailable magnesium (L-threonate magnesium), active B vitamins (especially B6, B9, and B12), and specific plant extracts adapted to their genetic characteristics to support cognitive function (such as purslane extract).

[0039] 3. Product Realization and Delivery: The customized formula has been verified by the system and complies with EU Regulation (EU) No 609 / 2013 (Food) and relevant German requirements. Production instructions are sent to the platform's contracted factory in Poland. The factory uses raw materials that meet EU standards for production and quality control, and finally delivers 30-day capsules with German labels to A via DHL.

[0040] 4. Feedback Optimization: After two weeks of use, user A reported that "afternoon fatigue has been slightly reduced." The platform recorded this positive feedback and used its anonymized data to optimize the weight of the "fatigue relief" target when generating solutions for users with similar profiles in the future.

[0041] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A global nutrient deep customization platform, characterized by: include: The user terminal interface module is used to receive and verify access requests from users in different regions around the world, provide multilingual interactive interfaces, and collect multi-dimensional raw data from users. The multi-dimensional raw data includes: basic personal information and health goals collected through standardized questionnaires, nutritional genomics data generated through authorized access gene testing reports, real-time physiological monitoring data from wearable devices synchronized through API interfaces, dietary logs uploaded by users and structured nutrient intake data after image recognition, as well as geographical location and dietary culture preference data of the user's region. The global nutrition knowledge base and rules engine module stores and dynamically updates databases across multiple dimensions, including: a global basic nutrient requirements database built based on regional dietary guidelines, clinical nutrition standards, and the latest research findings, covering benchmark values ​​for different races, ages, genders, and physiological states; a regional nutrient composition and compliance database integrating information on local specialty ingredients, food regulations, and supply chains; and a biological effect prediction rule base containing nutrient-gene interaction models, nutrient-disease association models, and nutrient-gut microbiome interaction models. The deep customization analysis engine is based on an AI-driven main control unit. This main control unit performs the following operations: it integrates and standardizes multi-dimensional raw data from the user terminal interface module to generate a unified user deep health profile; it calls the global nutrition knowledge base and rule engine module, and combines the user deep health profile to perform multi-objective optimization calculations through a preset algorithm model. The algorithm model considers at least the user's individual nutritional gap, gene metabolic pathway efficiency, real-time physiological state fluctuations, long-term health goals, and regional dietary accessibility, and outputs a dynamic, personalized daily nutrient formula plan. The flexible manufacturing and supply chain collaboration module is used to transform the personalized daily nutrient formula into executable production instructions. This module includes: a formula analysis unit, which breaks down the formula into a specific raw material list and process parameters; a global supplier matching unit, which selects the optimal supply node from a pre-set global raw material supplier network based on the user's geographical location, raw material inventory, cost, and logistics timeliness; and a distributed micro-factory scheduling unit, which issues production instructions to on-demand production micro-factories in the user's region or the nearest region. The global logistics and delivery tracking module is responsible for receiving finished product information from the flexible manufacturing and supply chain collaboration module, integrating third-party logistics service provider interfaces, planning the optimal delivery route from the micro-factory to the user, providing real-time logistics tracking, and managing the periodic automatic repurchase and formula adjustment triggering process of subscribed users. The feedback and continuous optimization closed-loop module is used to collect user feedback submitted through the user terminal interface module, subsequent updated health data, and new monitoring data from wearable devices. This feedback data is compared and analyzed with the expected nutritional intervention effect. The algorithm parameters in the deep customization analysis engine are iteratively optimized using machine learning models to dynamically and progressively adjust the user's personal formula plan. The aggregated data is anonymized to optimize the models and rules in the global nutrition knowledge base and rule engine module.

2. The global nutrient deep customization platform according to claim 1, characterized in that, The algorithm model in the deep custom analysis engine adopts a hierarchical decision framework, including: The first layer is the basic needs calculation layer, which calculates the range of basic nutritional needs based on the user's age, gender, weight, activity level, and health goals, with reference to the global basic nutrient needs database. The second layer, the gene and metabolism correction layer, is based on the user's nutritional genomics data and performs personalized corrections on the basic requirements calculated in the first layer. The correction factors include, but are not limited to, the impact of vitamin D receptor gene variations on vitamin D requirements and the impact of MTHFR gene variations on folic acid metabolism efficiency. The third layer, the real-time status adjustment layer, makes intraday or interday fine-tuning of nutrient formulations based on the most recent physiological monitoring data from wearable devices. For example, it dynamically adjusts the dosage of nutrients related to energy metabolism and nerve regulation, such as magnesium and B vitamins, according to the day's sleep quality, stress level, or exercise volume.

3. The global nutrient deep customization platform according to claim 1, characterized in that, The distributed micro-factory scheduling unit in the flexible manufacturing and supply chain collaboration module is specifically implemented as follows: After receiving the formula, it automatically generates digital work instructions that conform to the specifications of the target micro-factory production line; The production line of the micro-factory adopts a modular design, including an independent raw material silo, a precision weighing module, a mixing module, a tablet / capsule filling / filling module, and a laser marking and packaging module; The scheduling unit performs load balancing calculations among multiple selectable microfactories based on the complexity and urgency of the recipe, and selects the factory with the shortest production queue or the lowest overall cost to issue instructions.

4. The global nutrient deep customization platform according to claim 1, characterized in that, The global nutrition knowledge base and rules engine module also integrates a regional nutrient composition and compliance database, as well as an intelligent compliance verification sub-engine, which can: Real-time monitoring of the latest regulations, standards, and claims management policies regarding nutritional supplements in major target markets worldwide; After the deep customization analysis engine generates the formula plan, it automatically conducts a compliance pre-review of the legality of all ingredients in the formula, the maximum allowable amount added, and the health claims to be used on the packaging, based on the user's registered location or delivery destination, and provides automatic modification suggestions for non-compliant items.

5. The global nutrient deep customization platform according to claim 1, characterized in that, The platform also includes an enterprise service interface module for professional nutritionists or medical institutions, which provides: The institution's management backend allows nutritionists in partner institutions to manage their clients in batches and view their clients' in-depth health profiles and nutrient formulation plans. The professional intervention interface allows nutritionists to manually adjust, add notes, or pause a suggestion based on their professional judgment on the formula plan automatically generated by the system. The adjusted plan is still produced and delivered through the flexible manufacturing and supply chain collaboration module. Data dashboards and analysis reports provide partner organizations with aggregated analytical data on nutritional deficiency trends and intervention effectiveness statistics for the populations they serve.

6. The global nutrient deep customization platform according to claim 1, characterized in that, The indicators used for effect comparison analysis in the feedback and continuous optimization closed-loop module include not only subjective user feedback, but also objective data obtained by encouraging or incentivizing users to regularly perform authorized specific biomarker tests. These biomarkers include, but are not limited to, blood vitamin levels, Omega-3 index, and gut microbiome diversity test results. These objective test results are used as high-weight training data for the optimization algorithm model.

7. The global nutrient deep customization platform according to claim 1, characterized in that, The user terminal interface module employs a progressive trust acquisition strategy when collecting data. The initial phase only requires providing basic health information and goals to generate a basic formula; As users spend more time on the platform, through educational content and points incentives, users are gradually guided to authorize access to more in-depth genetic data, continuous blood glucose monitoring data, or gut microbiota testing data. This enables the platform to provide more in-depth customized solutions, creating a positive cycle between user stickiness and data depth.

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