806 results about "Dieting" patented technology

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.

The utility model discloses an intelligent straw which is characterized in that an induction electrode is coated and attached in the pipe wall of a hollow pipe along the length direction of the hollow pipe to collect the data of the amount of liquid passing through the hollow pipe, the hollow pipe can be made of food-grade materials such as silica gel and food-grade plastics, and the intelligent straw has the function of digitizing drinking water. The system can transmit data to a mobile phone or a cloud end, carries out health analysis through the data, achieves recording reminding, affects healthy diet of young people from behaviors, serves as an intelligent terminal of the Internet of Things, builds a digital connection between a merchant and a user, and further optimizes customer services better.

The invention discloses a system and a method for intelligently monitoring blood sugar of a patient before and after diet based on food components, and belongs to the technical field of blood sugar monitoring, and the system comprises a food component quantification module which is used for converting a food image or description into accurate macro nutrient and micronutrient data; the multi-modal data fusion module is used for preprocessing and fusing the multi-modal data of the patient; the patient blood sugar prediction module is used for constructing a patient blood sugar prediction model to analyze the multi-modal fusion data of the patient and predicting the blood sugar of the patient; and the difference analysis optimization module is used for comparing actual blood glucose with predicted blood glucose after meal and analyzing difference reasons. The problems that blood glucose of a patient before and after eating cannot be intelligently monitored, and accurate quantification and prediction of the relation between diet and blood glucose cannot be achieved are solved. The system can intelligently monitor the blood glucose of the patient before and after eating, can help the diabetic to realize accurate quantification and prediction of the relationship between diet and blood glucose, and can effectively control the blood glucose and reduce the risk of complications.

The invention relates to an AI personalized nutrition evaluation and guidance method based on individual continuous diet data. The AI personalized nutrition evaluation and guidance method comprises the following steps: (1) calculating individual single meal food and nutrient intake; individual health indexes are collected, the individual daily energy demand amount is calculated, and the daily food and nutrient demand amount is determined; (2) carrying out instant personalized nutrition evaluation on food and nutrients ingested by an individual in a single meal according to a domestic public recommendation standard; (3) on the basis of individual nutrition and health data continuously recorded in the longitudinal direction of time, establishing a personalized nutrition intake-health model taking an individual as a unit, and carrying out personalized nutrition evaluation and health risk prompting; and (4) simulating the idea of nutrition guidance of professionals to create an AI nutrition guidance algorithm, and generating AI guidance. By adopting the AI personalized nutrition evaluation and guidance method based on the individual continuous diet data, a sustainable AI-assisted diet self-management solution suitable for daily life scenes is effectively provided.

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.

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 relates to the technical field of artificial intelligence, and discloses a system of a nutrition recipe recommendation system based on artificial intelligence in chronic disease intervention management and an application method. Comprising a data acquisition module, a data preprocessing and feature engineering module, a knowledge base management module, a personalized nutritional requirement modeling module, a recipe generation and optimization module, a user interface module and a model training and updating module which are in communication connection through a network. According to the method, personalized health data such as physiological indexes, chronic disease characteristics, diet preference, allergy and intolerance information of a user are collected in multiple dimensions, a nonlinear relation between the characteristics of the user and nutritional requirements is deeply mined in combination with a deep learning model, and an accurate personalized nutritional requirement model is generated according to specific illness conditions and body indexes of different chronic disease patients; therefore, recipe recommendation conforming to individual differences is provided, and the risk of aggravating the illness state due to improper diet is reduced.

The invention belongs to the technical field of medical treatment, and discloses a chronic fatigue syndrome screening and diet-exercise intervention method which has the advantages of being high in individuation degree, low in rehabilitation cost, flexible in intervention implementation field, flexible in intervention training time and small in space requirement. And the compliance of the patient in completing the exercise and diet combined intervention rehabilitation task is greatly improved. The method can effectively cope with patient low compliance challenges caused by infrastructure deficiency and motivation disorder, significantly improves training enthusiasm and participation willingness of CFS patients, has significant effect on promoting rehabilitation of the CFS patients, enables the patients to obtain efficient medical services without going to medical rehabilitation institutions personally, and has good application prospects. Therefore, the current situation that basic medical resources are extremely deficient is relieved to a certain extent, and favorable conditions are created for implementation and popularization of exercise and diet combined intervention training.

The invention belongs to the technical field of medicines, and particularly relates to a composition for improving obesity and promoting cardiovascular health as well as a preparation method and application thereof. The composition is prepared from the following components in parts by weight: 5 to 20 parts of glycine betaine, 1 to 5.5 parts of choline chloride, 0.5 to 4 parts of L-theanine, 1 to 15 parts of alpha-ketoglutarate calcium, 0.5 to 3 parts of black ginger extract, 0.05 to 0.25 part of black pepper extract, 0.5 to 3 parts of ginkgo leaf extract and 1 to 6 parts of black garlic juice powder. According to the invention, the eight components are selected and are scientifically combined in an optimal quantity ratio range, and the prepared composition can better reduce the inflammation level of a body, regulate abnormal blood fat, improve the lipid metabolism disorder state and effectively reduce impulsive diet behaviors, so that comprehensive improvement of obesity and cardiovascular related risks is realized, and the health-care effect of a human body is improved. The body is effectively assisted to maintain metabolic health.

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 application discloses a kind of intelligent service systems of advanced cancer pain pain, including: analgesic device, massage piece, driving source and ice piece, ice piece is located inside the mounting seat side surface, ice piece is used to contact cooling massage ball;Push module is used to daily push the predetermined check quantization table to patient, check quantization table at least includes one or more of the following each item: diet condition, sleep condition, body pain condition, self cognitive ability and mental state;Evaluation module, evaluation module obtains patient state evaluation result according to the data of patient to check quantization table, and according to evaluation result, push management scheme to patient and adjust analgesic device analgesic scheme, the living condition of patient every day is obtained regularly by push module, and the individualized, centralized pain intervention scheme is formed by evaluation module, one aspect realizes the timely evaluation of patient life quality, while quantized mode is convenient for others to view specific change situation, to improve specific condition of patient in a targeted manner.

The invention belongs to the technical field of health information processing and management, and discloses a personalized health management service system based on multi-source data fusion, which comprises a data acquisition device access wearable device, a health application, a medical information system and a user terminal entry data, the fusion processing platform carries out missing completion, abnormity elimination, time alignment and weighted fusion on the data to form a weighted fusion sequence, the cross-domain correlation builder generates a correlation map of lifestyles and clinical indexes, and the risk modeling engine outputs individual health risks under the constraint of the map. The management scheme generator generates personalized diet, exercise, sleep and disease prevention schemes under the constraint of the rule base. The compliance evaluation system combines subjective feedback and objective monitoring to form scores, the scheme adjustment mechanism executes minimum disturbance update based on scores and atlas trends, and the auditing unit realizes whole-process data tracing and compliance guarantee.

The invention relates to the technical field of medical health, and discloses a pregnancy nutrition management method, device and equipment and a readable storage medium, and the method comprises the steps: obtaining pregnancy portrait data of a target user; generating an individualized nutrition intake target based on the pregnancy portrait data; generating a matched structured diet scheme according to the nutrition intake target and the diet taboo information; receiving an actual diet image uploaded by the user, and performing nutrition intake analysis on the actual diet image to obtain actual nutrition intake data; and dynamically adjusting the structured diet scheme according to the actual nutrition intake data. According to the method, personalized evaluation, intelligent catering and dynamic optimization of pregnancy nutrition management are realized, the scientificity, compliance and continuity of intervention are improved, pregnancy complications can be prevented, and the health of mothers and infants is promoted.

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 provides an application of streptococcus salivarius in fatty liver, in particular to an application of streptococcus salivarius in preparation of a product, and the product is used for at least one of the following applications: (a) losing weight or reducing body fat; (b) improving the glucose tolerance of the patient; (c) increasing the sensitivity of the patient to insulin; (d) treating, relieving and preventing liver injury; (e) treating, relieving and preventing non-alcoholic steatohepatitis; (f) fat deposition caused by high-fat and high-cholesterol diet is reduced; (g) reducing blood fat; and / or (h) improving the level of at least one of the following indexes in the liver of the mammal: total cholesterol, triglyceride, low density lipoprotein-cholesterol and free fatty acid.

The invention provides application of quinoa seedling acidic polysaccharide or modified acidic polysaccharide thereof in preparation of drugs for improving lipid metabolism disorder or health-care food beneficial to maintaining the health level of blood fat. The invention further provides the quinoa seedling modified acidic polysaccharide and a preparation method thereof. According to the invention, the quinoa seedling acidic polysaccharide is subjected to structural modification, and by comparing the physical and chemical structural characteristics of the quinoa seedling acidic polysaccharide before and after modification and the health effect of improving lipid metabolism disorder, the quinoa seedling acidic polysaccharide and the modified acidic polysaccharide thereof are found to be capable of remarkably improving the lipid metabolism disorder of mice induced by high fat diet; particularly, the quinoa seedling modified acidic polysaccharide prepared by a directional enzyme digestion technology has the best effect. A scientific theoretical basis and a key processing technology are provided for research and development of functional / healthy quinoa seedling food, and nutritional and high-value utilization of quinoa seedlings is facilitated.

The invention belongs to the field of biotechnology and nutritional medicine, relates to a maternal obesity intervention preparation, and particularly relates to a preparation method of a plant embryo fermentation product and application of the plant embryo fermentation product in blocking of trans-generation delivery metabolic diseases. FWG is prepared through a probiotic fermentation technology, obesity, glucose and lipid metabolism disorder and oxidative stress induced by high-fat diet of a parent body can be remarkably improved, and cross-generation transmission of metabolic diseases is blocked by adjusting composition of intestinal flora of the parent body and offspring and a hypothalamic PI3K-Akt signal channel. The invention reveals that maternal FWG intervention passes through a mechanism that a PI3K-Akt pathway is dominated and multiple pathways are coordinated on the transcriptome level for the first time, the HFD-induced offspring hypothalamus metabolism programming abnormality is reversed, and a new central regulation target is provided for cross-generation metabolic disease prevention. The FWG is a wheat processing byproduct, is free of chemical additives, is suitable for long-term dietary supplement, is a natural and safe nutrition intervention scheme, and is suitable for the field of maternal and infant health.

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.

The application belongs to the technical field of functional food, and particularly discloses a meal replacement powder for weight loss of obese patients and a preparation method thereof. The meal replacement powder comprises whey protein powder, composite dietary fiber (containing at least two kinds of konjac powder and the like), functional carbohydrate, functional fat (containing medium-chain fatty acid and the like), and composite vitamins, minerals, functional additives (L-carnitine and the like), and stability additives. The preparation comprises raw material pretreatment, small material premixing, large material mixing, microencapsulation of fat-soluble components, total mixing, sterilization and packaging, and the key lies in that the fat-soluble components are wrapped by wall materials and spray dried into microcapsules. The meal replacement powder can balance nutrition, strengthen satiety, promote fat metabolism, avoid muscle loss and electrolyte imbalance, has a shelf life of 12 months at room temperature, has good palatability, has good weight loss effect (average weight loss of 5.8 kg in 2 months), has no adverse reactions, and is suitable for long-term weight loss diet management of obese patients.

The invention belongs to the technical field of health management, and particularly relates to an artificial intelligence health management method and system based on big data, and the method comprises the following specific steps: S1, data collection: collecting physiological data, environment and behavior data and medical historical data; s2, data preprocessing: integrating multi-source data according to the credibility of data sources by adopting a weighted fusion algorithm, and generating a unified user health data matrix; s3, AI intelligent analysis: constructing a dynamic evolutionary Transform time sequence prediction model combined with a medical knowledge graph, and realizing health risk prediction and user portrait generation; and S4, performing health intervention: forming a closed-loop intervention mechanism of scheme generation-scheme tracking-scheme adjustment. By integrating physiological, environment, behavior and medical data, the problem of analysis one-sidedness caused by single data can be solved, for example, when the blood glucose of a diabetic user is increased, the system can accurately position reasons in combination with diet, environment and medication data.

The invention discloses a composite combined preparation for synergistically treating and conditioning diabetes, relates to the technical field of foods, health foods or dietary supplements for adjuvant therapy and health care of diabetes, and in particular relates to a composite combined preparation for synergistically treating and conditioning diabetes. The preparation comprises albumin peptide, saussurea involucrata culture, vitamin B1, vitamin B2, wheat oligopeptide, corn oligopeptide, collagen peptide, mulberry leaf extract, rhizoma polygonati extract, sea-buckthorn extract, isomaltitol, maltodextrin, ginseng extract and bitter gourd extract. Through the synergistic effect of all the components, comprehensive auxiliary conditioning on diabetes is realized from multiple targets of maintaining colloid osmotic pressure and nutrition, regulating glycolipid metabolism in multiple ways, protecting pancreatic beta cell functions, enhancing insulin sensitivity, preventing nerve and blood vessel complications, meeting special diet requirements and the like. The composition can be prepared into food, health-care food or dietary supplement and other dosage forms.

The invention discloses a large-scale personalized diet scheme solving method and system, and belongs to the technical field of artificial intelligence and health nutrition, and the large-scale personalized diet scheme solving method comprises the three steps of double preference quantification, initial solution generation and personalized diet scheme solving. Personal and regional preferences of the user are converted into multi-dimensional features through double-preference quantification, the diet preference of the user is anchored, and the personalized degree of diet recommendation is improved; a collaborative architecture of a large language model and a mixed integer linear programming model is adopted, in a large-scale solving scene, the large language model can quickly screen key information, an initial solution is provided for the mixed integer linear programming model, and the problem that a traditional method is low in solving efficiency is effectively solved; by solving the mixed integer linear programming model added with the complex diet constraint rule, the diet constraint rule requirement is met, the preference is optimal, and it is ensured that the solution result of the personalized diet scheme is credible.

The present application relates to a terminal device for blood sugar management during pregnancy and its cloud server, the terminal device collects physiological data and blood sugar data of a target user including the gestation period, the cloud server obtains a plurality of blood sugar data of reference users from a plurality of terminal devices and forms an overall reference data set in a manner associated with gestational age, the cloud server calculates basic statistical indicators for each group in the overall reference data set, combines the specific percentile position of the user's blood sugar data in its own group distribution, judges the current blood sugar state of the user by comparing the user's blood sugar data with the preset healthy blood sugar range, and generates a graded diet suggestion accordingly, the diet suggestion optimizes the diet structure of the target user by selecting food categories with different GI values; the terminal device receives and visually displays personalized diet suggestions matching the blood sugar status of the target user to the target user.

The application discloses an intelligent diet management method and system based on multi-source health data fusion, and relates to the field of health diet management. In order to solve the defects of the existing diet management system, such as data fragmentation, manual input dependence, incapability of generating personalized diet plans and incapability of forming a closed-loop feedback, basic information, medical information and body composition data of a user are acquired; initial health parameters are generated according to the basic information, the medical information and the body composition data of the user, wherein the initial health parameters include basal metabolic rate, total energy consumption and total energy requirement; and the initial health parameters are updated according to dynamic input real-time data, so as to realize intervention and management on diet behaviors, wherein the dynamic input real-time data includes diet data and exercise data. The application is mainly used for intervention and management on diet behaviors according to the results of multi-source health data fusion.

A personalized supplement formulation system integrates biological data, structured qualitative feedback, and machine learning algorithms to generate individualized nutritional protocols. The system receives biological inputs such as genetic single nucleotide polymorphism (SNP) data, blood biomarkers (e.g., ferritin, vitamin D, B12), lipid profiles, and urinary metabolites. A digital user profile is created by organizing these inputs and calculating derived indices relevant to supplementation. Structured qualitative feedback, including dietary restrictions, perceived wellness, and training goals, is normalized and processed alongside the biological data. A trained machine learning engine analyzes combined inputs to output a tailored supplement formulation specifying ingredient selection, dosage, delivery format, and timing. Instructions are transmitted to a manufacturing system capable of producing the custom formulation. The system supports periodic re-evaluation and iteration based on new biological samples or user-reported feedback, enabling dynamic personalization over time and improving efficacy, compliance, and outcome tracking in athletic and wellness domains.

Containers for facilitating adherence to dietary plans are provided, along with related systems and methods. A locking mechanism secures an access panel moveable between an opened position and a closed position in the closed position. A controller receives user input regarding desired caloric intake amount for a time period and regarding a type or types of foodstuff stored at the container, and weight readings from at least one weight sensor. The controller determines a caloric value of removed foodstuffs from the containers from the weight readings and the type or types of foodstuff indicated in the user input, and operates the locking mechanism to secure the access panel in the closed position where the caloric value of the removed foodstuffs for the time period exceeds the desired caloric intake amount for the time period.