1713results about "Nutrition control" patented technology

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

The invention relates to the technical field of health management systems, and discloses a health management scheme recommendation system based on big data analysis. The system comprises a health data acquisition module, a health characteristic quantification module, a health state identification module, a health scheme prediction module and a health parameter coupling module. The health data acquisition module synchronously acquires three types of time sequence data of physiological indexes, health behaviors and environmental exposure of a user and performs timestamp alignment; a health feature quantification module extracts features from the aligned data and generates corresponding feature matrixes and vectors; the health state recognition module classifies the feature data according to a preset rule and generates a health state label set; the health scheme prediction module is combined with a health intervention measure knowledge base to generate an initial scheme set through an association rule mining algorithm; and the health parameter coupling module corrects the intervention intensity parameter of the initial scheme based on the mapping relationship between the human physiological response and the behavior intervention parameter. According to the system, multi-dimensional health data can be integrated, and an accurate personalized health management scheme is generated.

The invention relates to a medical video and elderly user adaptation degree evaluation method and system based on multi-modal fusion, and the method comprises the steps: obtaining medical video data and elderly user data, inputting the data into a trained adaptation degree evaluation model, and outputting the adaptation degree of a medical video to an elderly user; the adaptation degree evaluation model adopts a three-level architecture of shallow attention weighting, deep attention interaction and double-feature fusion, fine matching of video features and user features is achieved, and the accuracy and robustness of video-user adaptation degree prediction can be remarkably improved.

The invention relates to a weight loss intervention method and device for chronic renal failure and obesity patients based on an individualized prescription. The method comprises the following steps: constructing a multi-modal dynamic time sequence feature vector of a patient; identifying and quantifying potential causal effects of different weight loss intervention measures on physiological outcomes of renal functions, electrolyte balance, anemia states, bone mineral metabolism and weight indexes of the CKD patient according to a dual robust estimator; dynamically constructing a weight loss intervention measure causal model according to the potential causal effect of the physiological outcome of each patient and the physiological and pathological background; and inputting the multi-modal dynamic time sequence feature vector of the patient into a weight loss intervention measure causal model, and generating a daily diet scheme, an exercise scheme and corresponding high-confidence risk early warning and causal explanation of the patient. The method overcomes the limitation that the traditional method only pays attention to correlation and cannot clearly determine the intervention effect, ensures that the causal atlas and the intervention scheme are optimized according to the real-time change state of the patient, and realizes a real dynamic prescription.

Disclosed herein are apparatuses and methods that can provide a delayed bolus calculator for use in closed loop diabetes therapy that can account for additional factors not taken into account in regular meal bolus calculations when a user wants to deliver a meal bolus a period of time after a meal was consumed. A delayed bolus calculator can enable the user to enter a period of time since the meal was consumed in addition to the number of carbohydrates consumed in a meal. This enables the system to account for the amount of increased insulin from the closed loop algorithm in response to the meal and / or the time since the meal to ensure that the risk of hypoglycemia is mitigated while still reducing hyperglycemia.

Systems, devices and methods are provided for determining a medication dose for a patient or user. The dose determination can account for recent and / or historical analyte levels of the patient or user. The dose determination can also take into account other information about the patient or user, such as physiological information, dietary information, activity, and / or behavior. Many different dose determination embodiments are set forth, pertaining to a wide array of different aspects of the system or environment in which the embodiments can be implemented.

The invention belongs to the technical field of medical health management, and particularly relates to an AI personalized health education system for elderly patients with head and neck cancer, which comprises a content generation and management module, a personalized push engine, an interaction and artificial triage module, an elderly friendly interaction module and a data monitoring and safety module, by adopting an AI + manual hierarchical response mechanism and combining elderly friendly interactive design and operation training support, the participation obstacles that elderly patients are inconvenient to move and poor in informatization ability are broken through, and the intervention compliance and sustainability are improved; according to the intervention method, strict double-group random control test design is adopted, multi-dimensional and multi-time-point effect evaluation is combined with a scientific statistical analysis method, the improvement effect of AI-assisted health education on cancer recurrence, fear, psychological state and life quality of a patient can be accurately verified, and a solid evidence-based basis is provided for clinical popularization.

The invention relates to the technical field of nutritious food configuration, and discloses a system and a method for configuring personalized nutritious food, and the method for configuring personalized nutritious food comprises the following steps: collecting spectral characteristic data and chemical component data of a food material sample by adopting a hyperspectral imaging and mass spectrum combined technology, and obtaining holographic chemical fingerprint information; processing the collected spectral data by using a multi-scale feature extraction algorithm, extracting multi-level feature information of the food materials, and carrying out standardization and dimension reduction processing on the feature data; according to the method, high-precision prediction of the nutrient content of the food material is realized through combination of hyperspectral imaging and mass spectrometry technologies and a deep learning model, so that the nutrient content prediction accuracy and the configuration scheme accuracy are improved, the nutrient balance achievement rate is also improved, and meanwhile, the limitation of a static database is broken through; real-time nutritional ingredient analysis of any food material is achieved, the method can adapt to individual differences and freshness changes of the food materials, and accurate configuration based on actual food material characteristics is provided.

The invention discloses an interactive system fusing dynamic blood glucose monitoring data and personal health management behaviors, which belongs to the technical field of health management and comprises a data acquisition and preprocessing module, a feature extraction and analysis module, a personalized decision generation module, an interpretability feedback module, an interaction and compliance support module, a knowledge base and a patient end. The data acquisition and preprocessing module is used for acquiring blood glucose data and behavior information; the feature extraction and analysis module is used for analyzing the collected blood glucose data and behavior information; the personalized decision generation module is combined with the knowledge base and is used for generating a personalized intervention scheme according to an analysis result; the interpretability feedback module is used for displaying a causal chain; and the interaction and compliance support module is used for pushing a personalized intervention scheme in real time. According to the invention, multi-source data fusion, real-time risk analysis, personalized intervention suggestion and interpretable feedback can be realized, and refinement, intelligence and interpretability of individual management of diabetes mellitus are realized.

The invention belongs to the technical field of recipe optimization, and relates to a recipe optimization method and device based on nutrient intake and a storage medium. The method comprises the following steps: acquiring various food materials and initial dosages thereof in a recipe to be optimized; based on the content of each nutrient in each food material and the initial dosage of each food material, constructing a content equation of each nutrient in the to-be-optimized recipe; based on the content equation of various nutrients in the to-be-optimized recipe, constructing a nutrient type equation which does not meet the constraint in the to-be-optimized recipe by taking the condition that the content of each nutrient in the to-be-optimized recipe is greater than or equal to the minimum intake and less than or equal to the maximum intake as the constraint; constructing an optimization objective function of the to-be-optimized recipe by taking minimization of nutrient types which do not meet the constraint in the to-be-optimized recipe as an objective; and solving the optimization objective function to obtain the target dosage of each food material in the to-be-optimized recipe, so that the to-be-optimized recipe is optimized to generate a recipe conforming to the nutrition intake standard.

The invention relates to the field of intelligent toilets, and provides an intelligent toilet lid multi-dimensional health data fusion analysis and early warning method and device. The method comprises the following steps: collecting multi-modal health data of a user; performing real-time feature extraction and anomaly detection on the multi-modal health data through a lightweight convolutional network in an edge calculation layer, performing anonymization encryption processing on the multi-modal health data, and uploading the multi-modal health data to a cloud end; a multi-modal Transform fusion model is constructed on a cloud intelligent analysis layer, multi-modal health data is mapped to a unified feature space through a cross-modal attention mechanism for deep fusion analysis, and a family group health risk map is constructed based on an analysis result through a graph neural network; and dynamically predicting a user disease risk curve based on the family group health risk map, generating a personalized intervention scheme based on the user disease risk curve through a multistage early warning trigger mechanism, and feeding back the personalized intervention scheme to the user terminal. The accuracy of home auxiliary monitoring can be improved, and the early warning false alarm rate is reduced.

The invention provides a multi-source sensing integrated intelligent feeding scheduling and dieting control method and system for a cowshed, and relates to the technical field of intelligent agricultural control, and the method comprises the steps: collecting cattle physiology, cowshed environment, feed state and equipment operation parameters in real time through a distributed sensing network; a machine learning model fusing a time sequence convolutional network and a self-attention mechanism is utilized to predict an individualized feeding demand and a dieting threshold value, and a feeding-dieting risk map for quantifying health and production risks is generated; simulating an extreme scene based on a digital twinborn model to perform dual consistency check, and generating a robust detailed scheduling scheme; converting the scheme into a control instruction to drive execution equipment, and iteratively optimizing the model and the rule; the system comprises a multi-source sensing data acquisition module, an instruction execution module, a feedback optimization module and the like. According to the method, the accurate and self-adaptive feeding management efficiency of individual cattle in a group is improved, the feed utilization efficiency is effectively improved, and the metabolic disease risk is reduced.

The invention discloses a personalized diet recommendation method for enteritis patients, a medium and equipment. The method comprises the following steps: continuously collecting diet image data, physiological state time sequence data and subjective symptom feedback data; performing food material component deconstruction and cooking mode identification on the diet image through a convolutional neural network, and generating a structured diet vector; analyzing physiological state time sequence data by using a hidden Markov model, and constructing an individualized physiological rhythm map; establishing a symptom triggering condition probability matrix based on a multi-modal association mining algorithm; generating an exploratory diet scheme according to the real-time physiological state of the user and the conditional probability matrix; dynamically updating the conditional probability matrix through an online reinforcement learning mechanism; and finally, periodically outputting a personalized diet taboo list and a safety exploration list. According to the method, through the dynamic correlation analysis of fusing the time sequence physiological features and the dietary components, accurate matching of diet recommendation and individual intestinal tract state fluctuation is realized, and the individuation and adaptability of diet intervention are effectively improved.

The invention relates to the technical field of clinical medicine, and discloses an intertrochanteric fracture postoperative progressive rehabilitation system, which comprises an intertrochanteric fracture comprehensive data acquisition module, a dynamic data monitoring module, a rehabilitation scheme generation module, a rehabilitation execution module, a rehabilitation mode adjustment module, a diet structure adjustment module and a rehabilitation interaction feedback module, the intertrochanteric fracture comprehensive data acquisition module is responsible for acquiring static and semi-dynamic data of a rehabilitation full cycle, the dynamic data monitoring module monitors physiological and motion change data of a patient in daily activities in real time, the rehabilitation scheme generation module generates a personalized stage rehabilitation scheme, and the rehabilitation execution module executes a full-process rehabilitation action. The rehabilitation mode adjusting module is used for adjusting a rehabilitation exercise mode, the diet structure adjusting module is used for optimizing a diet structure, and the rehabilitation interaction feedback module is used for collecting feedback so as to correct the scheme.

Techniques disclosed herein relate to food analysis and glucose level management. In some examples, the techniques involve determining a consumption of a first food item by a user, receiving data indicative of a glucose level of the user, determining an effect of the first food item on the glucose level of the user based on a change of the glucose level of the user, and storing the effect of the first food item on the glucose level of the user to a database that includes data indicating effects of individual food items on the glucose level of the user. In some examples, the techniques also involve providing one or more recommendations for managing the glucose level of the user based on the data in the database and a food item consumed by the user.

A programmatically generated AI avatar includes a customizable personality module, acting as the embodied interface for a powerful AI “mind” that delivers personalized coaching to improve user health, well-being, and longevity. The system uses machine learning, large language models, and biometric modeling to synthesize real-time, multi-modal health data—including sleep, nutrition, glucose, mood, and activity—and generate forward-prescribed KHAs. Unlike human coaches, it continuously adapts based on context and behavior, targeting the root cause: metabolic dysfunction—namely by restoring healthy, sustainable body composition through the preservation or building of lean muscle mass and reduction of excess fat. KHAs can also be shared with friends or programmatically generated AI avatars, allowing for coordinated action, emotional support, and accountability through social connection—further reinforcing positive behavior and adherence. The system's reinforcement learning engine incorporates both individual response data and anonymized population-level insights to optimize recommendations over time, learning which interventions are most effective for users with similar physiological and behavioral profiles. First validated with Olympic athletes—resulting in measurable improvements and medal-winning outcomes—this system offers a scalable, emotionally intelligent coaching enginethat exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention relates to the technical field of intelligent medical treatment and clinical nutrition, and discloses a multi-dimensional sign sensing personalized enteral nutrition infusion regulation and control device which comprises a multi-parameter sign sensing module, an intelligent analysis control module, an adjustable infusion execution module and a man-machine interaction and early warning module. The intelligent nutrition management platform is in communication connection with the modules; the intelligent nutrition management platform is composed of a nutrition screening and evaluation unit and a statistical analysis unit. According to the method, a full link from nutrition risk automatic screening, individualized scheme intelligent generation, bedside safety execution and self-adaptive regulation and control to whole-process data statistical analysis is formed; information integration and initial decision making are automatically completed, manual operation links and cognitive loads are reduced, scientificity and normalization of a treatment scheme based on newest clinical guidelines and patient data are ensured, and medical workers are liberated from tedious transactional work and focus on higher-value clinical judgment.

A system is provided.SOLUTION: The system includes means for acquiring physical data, means for transmitting the physical data to a server, means for calculating a metabolic rate, means for calculating a required calorie intake and an amount of exercise, means for generating voice advice, means for managing an exercise menu, and means for generating statistical data and utilizing the statistical data in a medical site.SELECTED DRAWING: Figure 1

The invention relates to the technical field of health management, in particular to an obese patient remote dynamic intervention system based on AI early warning, which comprises a multi-dimensional data acquisition module, an AI risk layering and early warning module, a personalized intervention scheme generation module, a remote collaborative management module and an effect evaluation and optimization module, the multi-dimensional data acquisition module is used for acquiring patient related data; the AI risk layering and early warning module constructs a multi-dimensional dynamic score card model to carry out risk scoring and layering, and obesity health risk early warning is generated; a personalized intervention scheme generation module constructs a multidisciplinary intervention strategy matrix, and dynamically generates an intervention scheme according to an early warning result and individual features of a patient; the remote collaborative management module realizes information interaction and collaborative intervention of multi-party resources; and the effect evaluation and optimization module tracks the intervention effect and optimizes the model and the intervention scheme. Therefore, the problems of data lag, intervention scheme solidification, untimely early warning and the like in obese patient management are solved.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

The invention relates to the technical field of health information, in particular to a dietary inflammation tendency analysis system based on a nutrition database, which comprises a nutrition data acquisition module, a user health data acquisition module, an inflammation tendency analysis engine and a personalized diet suggestion generation module, nutrient components and user health indexes are mapped to a unified inflammation semantic space through a multi-dimensional feature embedding technology, a dietary inflammation tendency index fusing food components, metabolic states and inflammation markers is dynamically calculated based on a deep learning model, and personalized analysis is realized in combination with cloud platform data synchronization and machine learning optimization. According to the scheme, accurate quantification and dynamic evaluation of the individualized dietary inflammation risk can be realized, and the problem of insufficient analysis precision caused by data isomerism and static rules in a traditional method is solved.

The invention relates to the technical field of diet health services, in particular to a diet service method and system based on human nutriology. The method comprises the steps of collecting multi-dimensional data for a user; on the basis of a human nutriology database and user full-amount data, a personalized nutrition target is determined through nutrition requirement calculation; in combination with personalized nutrition targets, user dietary habits and actual life scenes, food material recipes are matched to generate multiple sets of personalized dietary schemes; providing diet scheme execution guidance, and adjusting the diet scheme; the personalized diet scheme and the updating scheme are pushed to the user; the system comprises a user data acquisition module, a nutrition target determination module, a diet scheme generation module, a diet scheme guidance and dynamic adjustment module and a push module. According to the method, multi-dimensional adaptation of nutritional demand analysis to user data is realized, the disease state, exercise intensity and dietary habits of the user are fully combined, the nutrition target pertinence is improved, and the actual demand of the user is accurately matched.

The invention relates to the technical field of newborn clinical nutriology, and discloses a newborn parenteral nutrition evaluation method and system oriented by calorie, and the method comprises the steps: building a multi-dimensional energy level model, combining with enteral nutrition absorption rate difference correction, and precisely calculating a parenteral nutrition calorie target value by using a core balance formula 1 = EN / A + PN / B, and the composition proportion of glucose, amino acid and fat emulsion is dynamically adjusted in an optimized osmotic pressure safety range. The method provided by the invention effectively solves the clinical problems of extensive heat card supply, high liquid overload risk, insufficient osmotic pressure utilization, easy unbalance of nutritional structure and the like in the traditional method, remarkably promotes ideal growth of fat-free weight of the premature infant, and reduces the incidence rate of venous nutrition related complications.

A system for real-time generation of therapeutic plans based on predictive analytics of patient profile data including a processor of a therapeutic plan server (TPS) node configured to host a machine learning (ML) module and connected to at least one patient-entity node over a network and a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive the patient profile data including patient nutrients intake data and medications intake data from the at least one patient-entity node; parse the patient profile data to derive a plurality of key classifying features; query a local database to retrieve local historical patients-related data based on the plurality of key classifying features; generate at least one classifier feature vector based on the plurality of key classifying features and the local historical patients-related data; provide the at least one feature vector to the ML module coupled to an Artificial Neural Network (ANN); receive a plurality of nutrients-medications correlation parameters from a therapeutic plan predictive model generated by the ML module using outputs of the ANN based on the at least one feature vector; and generate a therapeutic plan for the at least one patient-entity node based on the nutrients-medications correlation parameters.

The invention provides a diabetic patient life management method and system in combination with traditional Chinese medicine knowledge, and relates to the field of medical health data processing, the method comprises the following steps: obtaining multiple groups of sample data, the sample data at least comprising personal information of sample patients and diabetic traditional Chinese medicine syndromes, and the personal information of the sample patients at least comprising diabetes index data; based on the multiple groups of sample data, multiple key traditional Chinese medicine syndrome discrimination units are determined, and a diabetes traditional Chinese medicine syndrome diagnosis scale is generated; based on the multiple groups of sample data, determining music types matched with different diabetes mellitus traditional Chinese medicine syndromes; determining the diabetic traditional Chinese medicine syndrome of the current user based on the personal information of the current user and the filling information of the diabetic traditional Chinese medicine syndrome diagnosis scale; based on the diabetic traditional Chinese medicine syndromes of the current user and the music types matched with different diabetic traditional Chinese medicine syndromes, music, traditional Chinese medicine health maintenance skills and traditional Chinese medicine recipes are recommended, and the method has the advantage of improving the quality of life management of diabetic patients.

An ingredient substitution system may include a shopper device associated with a given shopper that may have shopper profile data associated therewith. An ingredient substitution server is configured to store recipes. Each recipe may have ingredients associated therewith, and each ingredient may have an ingredient nutritional tag associated therewith. The server may determine a recipe from among the recipes based upon matching the ingredient nutritional tag to the corresponding shopper profile data and communicate the recipe to the shopper device. The server may also be configured to determine a substitute ingredient for a given ingredient of the recipe. The substitute ingredient may have a substitute ingredient nutritional tag, and the substitute ingredient may be determined based upon matching the substitute ingredient nutritional tag and the corresponding ingredient nutritional tag. The server may also generate a digital coupon redeemable toward the substitute ingredient and communicate the digital coupon to the shopper device.

Systems are described for measuring, recording, transmitting, accessing, and using an array of physical and physiological measurements that quantify states of complex adaptive systems, such as biological systems, more particularly the state is reflected in a metric designated health capacity. These measurements may be used, for example, for the pre-symptomatic detection and interception of disease states in a biological system. In one aspect, the system comprises a wearable device configured to measure, substantially simultaneously, an array of water-associated metrics, preferably at multiple loci on the biological system and as a function of time. The systems may further comprise using a scalable technology platform to identify, from the array of data across multiple systems, preferably compared to a training data set, utilizing machine readable instructions, to determine and / or predict health states, including the health capacity, of the biological system and to generate recommendations, including modification or nutrition, sleep, physical or mental inputs for the improvement of health for the biological system.

The invention discloses a diet data analysis method and device based on a large language model, and belongs to the technical field of diet data analys.The method comprises the steps that user basic information and taste preferences described by natural languages are collected through a user interface; unstructured data of food is captured regularly, and a healthy diet database is constructed after large language model cleaning and tagging processing; basic metabolic rate and daily total energy consumption are calculated in combination with the basic information of the user, and a nutrient intake target is determined; the big language model application layer generates a daily diet plan containing cooking suggestions; and receiving the diet recorded by the user photographing or natural language, analyzing the diet, comparing the analyzed diet with the nutrition target, and generating personalized feedback containing problems, reasons and adjustment suggestions. The device correspondingly comprises five functional modules. According to the invention, automatic maintenance and personalized analysis of diet data are realized, and intelligence and practicability of diet management are improved.

The invention discloses a diet management system for diabetic patients, which relates to the technical field of intelligent diet management and is technically characterized by comprising a data acquisition module, a nutrition analysis and suggestion generation module, a blood glucose prediction module, a feedback learning module, a personalized strategy recommendation module, an excitation and verification module and a communication module, the system automatically collects health and diet data of a user through a camera, a body fat scale, a glucometer and wearable equipment, identifies food types and estimates components by using a convolutional neural network, and realizes automatic calculation of energy and nutritional ingredients. The food-blood glucose response model constructed on the basis of individual historical data can predict postprandial blood glucose changes, sensitivity coefficients are dynamically updated through a regression correction mechanism, individualized blood glucose prediction is achieved, the system further generates intake suggestions through a component back-calculation method, and the intake suggestions and the intake suggestions are given to a user. And the result is visually displayed on the terminal in the form of a blood glucose curve and an energy distribution diagram, and real-time diet guidance is provided.

Provide a system. [Solution] a means for inputting the user's dietary goals, preferences, allergy information, and diet method; A means for validating and analyzing the entered information; A way to find the perfect recipe based on your dietary goals, preferences, allergy information, and diet method. A means for listing the ingredients and cooking equipment needed based on the recipe retrieved; a means for generating links to purchase the listed ingredients and cooking equipment; and a means for presenting recipe information, a list of ingredients and cooking equipment, and a purchase link to a user.