4862results about "Nutrition control" patented technology

A computing system may include non-transitory computer-readable media storing instructions that when executed by the one or more computer processors, causes the computing system to perform operations comprising: causing presentation of a graphical user interface, the graphical user interface being associated with patient health data, wherein the graphical user interface is configured to: display one or more user interface elements, receive information identifying a logging action and at least one classification associated with the logging action, generate, by the processor, one or more models based on the received information, based on the generated one or more models, automatically associate the one or more user interface elements with one or more logging actions and update the graphical user interface with at least a portion of the one or more user interface elements.

A system can process an indication of user data, wherein the user data comprises a food item including at least one recipe having one or more ingredients, and a query, wherein the query is associated with at least one ingredient, and generate, based at least in part on the query, nutrition data associated with the at least one ingredient, wherein the nutrition data comprises preparation data, sourcing data, delivery data, and a list of nutrients for the at least one ingredient. The system can further estimate, based at least in part on the nutrition data associated with the least one ingredient, a nutritional score, then compare the estimated nutritional score to a threshold, and based on the compared nutritional score, generate one or more instructions to cause an indication of the nutritional score on a user device.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention relates to the technical field of health state analysis, in particular to an intelligent health state analysis system and method based on artificial intelligence, and the method comprises the following steps: S1, collecting multi-modal health data; s2, data preprocessing and anomaly detection: preprocessing the collected multi-modal data; s3, multi-modal data fusion and feature selection: carrying out feature fusion on data of different modals through a deep learning model, and carrying out feature extraction on time sequence data by adopting a convolutional neural network (CNN); s4, health state prediction and trend analysis: performing prediction analysis on the health state of the user based on a long and short term memory network (LSTM), and generating a health state trend chart of the user in combination with historical health data and real-time data; and S5, personalized health intervention suggestions are generated: personalized health management suggestions are automatically generated, a more comprehensive health assessment result is provided, the user can better know the own health condition, and personalized management is carried out.

A spatiotemporal modeling system for Personal Health Database (PHDB) platforms integrates diverse health data types into a comprehensive 4D model of an individual's health status. By combining genomic, imaging, clinical, and real-time health data, the system creates a dynamic, time-based representation of the user's anatomy and physiology. This model enables real-time analysis, pattern recognition, and predictive forecasting of health outcomes. The system preprocesses and aligns data from various sources, constructs a detailed spatial framework, and continuously updates the model with new inputs. Through interactive visualizations, it provides users and healthcare providers with intuitive, personalized insights for improved health management and decision-making.

The invention discloses a sheep flock accurate nutrition supply method based on an intelligent feeding algorithm, and the method comprises the following steps: collecting the body type, physiology, movement and environment data of sheep through a multi-modal sensing device, and constructing individual three-dimensional representation based on a neural radiation field model, historical growth data and a real-time health state are analyzed by utilizing an individual nutritional requirement calculation model, an optimal feed distribution strategy is determined by combining an intelligent sheep flock optimization algorithm, a feeding scheme is dynamically adjusted by adopting deep reinforcement learning, and through a cloud-side collaborative calculation architecture, the calculation efficiency is improved and a feeding decision is optimized. A non-intrusive health monitoring technology is combined, a group health evaluation model is constructed, the feed ratio is adjusted in real time, accurate matching of individual and group nutritional requirements is achieved, and through prediction of the individual and group nutritional requirements, self-adaptive adjustment is conducted on feeding parameters, and a dynamic nutrition supply strategy is optimized. The system effectively improves the feeding precision, reduces the feed waste, and optimizes the health management of the sheep flock.

The invention relates to the field of nutrition and health management, and discloses a nutrition evaluation and prediction method and system based on big data, and the method comprises the following steps: firstly collecting the physiological data, diet record and activity data of a user, and carrying out the preprocessing of the data, and guaranteeing the data quality; dimensionality reduction is carried out through feature extraction and principal component analysis, and key features are extracted; the method comprises the following steps: evaluating daily nutritional requirements of a user by using a multivariable linear regression model, a Bayesian network and causal inference, predicting a nutritional requirement change trend of the user in a future period of time based on a long-short-term memory network, and optimizing daily diet selection of the user by using a Markov decision process; and finally, generating a personalized nutrition scheme by adopting linear programming and a genetic algorithm, and ensuring that the nutritional requirements and health targets of the user are met. According to the method, personalized and scientific diet suggestions can be provided, and the quality and effectiveness of individual nutrition management are improved.

The invention relates to the technical field of cardiovascular disease treatment, and discloses a cardiovascular disease medical decision support method based on a large model, and the method comprises the steps: obtaining multi-modal cardiovascular disease medical data, constructing a multi-source heterogeneous data set, carrying out the preprocessing and feature fusion, and constructing a combined representation model based on the pre-training large model and the fusion of traditional and western medicine knowledge. And designing a hierarchical space-time diagram convolutional network to generate a dynamic physiological state code of the patient, and constructing a multi-target optimization decision tree to generate a treatment scheme containing western medicines, traditional Chinese medicine prescriptions and non-drug intervention. A federal learning framework is adopted to cooperatively train a local model, data security is guaranteed, a global model is optimized, and a curative effect attribution module is further arranged to correct decision deviation. According to the method, multi-modal data and traditional Chinese and western medicine knowledge are integrated, personalized treatment scheme generation is achieved, data safety is guaranteed, and the cardiovascular disease diagnosis and treatment level is improved.

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 traditional Chinese medicine diagnosis and treatment, in particular to an Al-based traditional Chinese medicine diagnosis and treatment suggestion system, which comprises a traditional Chinese medicine constitution detection module used for collecting multi-dimensional traditional Chinese medicine physical sign data of a user; the AI physique analysis engine is used for outputting quantitative scores of yang-deficiency physique and yin-deficiency physique and a mixed physique analysis result; the personalized conditioning scheme generation module is used for forming dynamically adjustable personalized health suggestions; through a multi-modal data fusion technology, face diagnosis, tongue diagnosis, pulse diagnosis and inquiry data are subjected to collaborative analysis, and a deep learning model is combined with a traditional Chinese medicine knowledge graph for comprehensive differentiation, so that error interference of a single data source is effectively reduced, the accuracy of physique classification is improved, and a diagnosis result better fits the real physique condition of a user.

A method of dynamically monitoring a health goal using a wearable device. The method may include receiving, by a processor, user input associated with the wearable device worn by an individual. The method may include obtaining, by the processor, health parameters associated with the individual from the wearable device and user feedback from the individual based on a physical condition of the individual associated with the health plan. Additionally, the method may include evaluating whether the health goal will be successfully reached by the individual following the health plan. Responsive to determining that the health goal will not be successfully reached, the health plan may be dynamically adjusted and provided to the individual to reach the health goal.

The invention belongs to the technical field of federated learning, and particularly relates to a self-adaptive bilateral distillation personalized federated learning method based on a diffusion model. According to the method, a guidance mechanism is designed, so that mutual distillation is carried out between a global model and a local model, efficient transmission of knowledge is realized, and the adaptability of a personalized model to specific data distribution of a client is enhanced. Meanwhile, a conditional diffusion model is introduced to generate high-quality pseudo data, and the aggregated global model is finely adjusted by using the pseudo data. The process not only effectively makes up global information possibly lost in the local-global mutual distillation process, but also further optimizes the performance of the global model. By combining the mutual distillation and conditional diffusion fine tuning technology, the data privacy is protected, meanwhile, the balance between the personalized performance and the global generalization ability is achieved, and the method is suitable for a multi-client cooperation scene in a non-independent identically-distributed non-IID data environment.

The invention discloses a chronic disease patient personalized diet and exercise guidance system and method, and relates to the technical field of medical health information, and the system comprises a data sensing module which continuously collects the dynamic physiological data, behavior data and environment variable data of a patient through an intelligent sensing device, the dynamic physiological data comprises a heart rate time sequence, a step number time sequence and a blood glucose concentration time sequence monitored by the wearable device, and the behavior data comprises a medication operation record with a timestamp and a patient's daily symptom self-grading number. According to the personalized diet and exercise guidance system and method for the chronic disease patient, the time synchronization precision of multi-source data is effectively improved, the accuracy of medication compliance monitoring and physiological index correlation analysis is ensured, and by establishing the dynamic correlation model of the environment temperature and the human body metabolic rate, the accuracy of medication compliance monitoring and physiological index correlation analysis is improved. The timeliness and safety of clinical intervention are improved, and powerful support is provided for health management of chronic disease patients.

The invention relates to the technical field of health management, in particular to a gastrointestinal health assessment and management method based on big data, and the method comprises the following steps: S1, health data collection: collecting multi-dimensional health data of a user; s2, data preprocessing: preprocessing the collected multi-dimensional health data; s3, regulating and analyzing intestinal microecology, namely classifying and analyzing intestinal flora based on the intestinal flora gene sequencing data of the user in combination with living habits and diet data, identifying the condition of flora imbalance, and dynamically regulating flora balance through personalized regulation suggestions; s4, gastrointestinal health state evaluation: comprehensively evaluating the gastrointestinal health state of the user; and S5, generating a personalized health management scheme: generating the personalized health management scheme. According to the invention, by monitoring the health feedback of the user in real time, the scheme can be dynamically adjusted, so that the intervention is more flexible and targeted.

The invention relates to the technical field of health management, in particular to a personalized diet and exercise health management system fusing large model analysis ability, which comprises a multi-source data acquisition module, an integrated intelligent wearable equipment interface and the like, and is used for acquiring multi-dimensional health data; the health portrait modeling module fuses multi-modal data based on an improved Transform architecture, and outputs a dynamic portrait containing risk early warning and trend prediction; the personalized scheme generation module integrates the medical knowledge graph and the user gene features to generate a personalized health scheme; the scenarized recommendation engine matches diet and exercise resources in combination with a real-time scene; the closed-loop supervision optimization module implements scheme execution evaluation and adaptive adjustment through federated learning; and the risk early warning subsystem constructs an acute and chronic disease prediction model to realize risk early warning. According to the invention, precise health management is realized, the scheme effectiveness and the user experience are improved, the data security is guaranteed, and the intelligent development of health management is promoted.

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 engine that exceeds human capability, designed for the ultimate purpose of supporting sustainable health, resilience, and human thriving.

The invention discloses an oral and maxillofacial head and neck tumor rehabilitation nursing system based on artificial intelligence, which belongs to the technical field of medical auxiliary systems and comprises a special data acquisition and monitoring module, a deep reinforcement learning analysis module, a biological feedback analysis module, a personalized rehabilitation plan generation module and the like. The system can monitor physiological parameters and biological feedback signals of a patient in real time, generate a personalized rehabilitation plan and provide real-time monitoring and early warning functions, in addition, the system further comprises a plurality of auxiliary modules such as a local database, a patient interaction module and an intelligent early warning and emergency intervention module, the reliability and practicability of the system are further improved, and the system is suitable for popularization and application. According to the system, the defects of a traditional rehabilitation nursing method can be overcome, the rehabilitation precision and effectiveness are improved, the participation degree and confidence of a patient are enhanced, and more comprehensive and efficient rehabilitation nursing service is provided for the oral and maxillofacial head and neck tumor patient.

Embodiments described herein relate to systems and methods for an individualized intervention to generate and provide wellness pattern aware interventions. The system generates digital instructions and output for an online web application, device hosted application, smart device, or similar system. In one embodiment, the system provides the individualized intervention that matches device capacity, user context, and the best intervention type for a multi feel state longitudinal journey over a multi-day duration.

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 invention discloses a cardiovascular disease risk prediction method and system based on dietary multi-modal data and ensemble learning, and the method comprises the steps: constructing a multi-modal set through integrating multi-source heterogeneous data such as demographic statistics, dietary nutrition, clinical physiological and biochemical indexes and lifestyles; data cleaning is completed based on a box plot method and missing value processing, and key features are screened through Pearson's correlation coefficients, variance expansion factors and feature importance evaluation; a plurality of heterogeneous base models are fused by adopting a Stacking integration framework, a meta-feature matrix is generated through five-fold layered cross validation, and multi-level decision fusion is realized through a logistic regression meta-model; and quantifying the contribution weight of the dietary characteristics to the risk in combination with an SHAP method, and generating a visual interpretation chart and personalized intervention suggestions. According to the method, the accuracy and the stability of a prediction result are remarkably improved, the contribution degree and the action mechanism of dietary factors and other characteristics to the prediction result can be deeply analyzed, powerful support is provided for accurate prevention and personalized treatment of cardiovascular diseases, and the method has good practical value.

A wearable device consists of a smart band and a display unit. The smart band comprises a microbial biosensor, a particulate matter sensor, an enviro sensor, a physiological sensor, a biofluid sensor, a biokinetics sensor, a lifestyle sensor, and a single board computer. The microbial biosensor detects, measures, and monitors microorganisms, and a sterilizer kills pathogens. The particulate matter sensor detects, measures, and monitors a set of suspended particles in the surrounding air. The enviro sensor monitors environmental conditions surrounding the user. The biofluid sensor detects, measures, and monitors biological fluid parameters of the user. The physiological sensor detects, measures, and monitors physiological parameters of the user. The biokinetics sensor detects, measures, and monitors physical activities of the user. The lifestyle sensor detects, measures, and monitors healthy lifestyle activities of the user. The wearable device allows for continuous monitoring of user health for accurate clinical outcomes and wellness programs.

The invention discloses a cooking robot control method and system based on a knowledge graph, and relates to the technical field of cooking robot control, and the method comprises the steps: collecting multi-source cooking data, constructing the knowledge graph, receiving user demands, recommending dishes based on the graph, determining cooking parameters according to the recommended dishes, and controlling the cooking robot according to the cooking parameters. Comprise time, temperature, seasoning dosage and the like. The parameters are sent to a cooking robot controller, data are collected by a sensor during cooking, the data are compared with standard data, and the parameters are adjusted by a feedback algorithm to ensure that cooking meets the expectation. According to the method, personalized dish recommendation and cooking parameter adaptive adjustment can be realized, dish quality and taste are ensured, user health is considered, nutrition balance is promoted, the intelligent degree of the cooking robot is improved, the market competitiveness of the cooking robot is enhanced, and the requirement of people for high-quality cooking experience is met.

Techniques disclosed herein relate to nutritional content determination based on gesture detection data. In some embodiments, the techniques involve obtaining gesture detection data corresponding to consumption of a food item or a drink; determining, based on the gesture detection data, nutritional content of the food item or the drink; and causing delivery of insulin in accordance with the nutritional content of the food item or the drink.

Disclosed are methods of improving treatment adherence and compliance after discontinuation of incretin-based therapies, such as GLP-1 and / or GIP agonists, using pharmacotherapies comprising the disclosed therapeutic compounds and combinations. In some aspects, methods include treatment of psychological factors affecting other medical conditions (PFAOMC) following the administration of incretin-based therapies to treat obesity or hyperglycemia, and related disorders. In some aspects, disclosed pharmacotherapies for use in the methods include agents that modulate monoaminergic neurotransmission, administered alone or together, such as in disclosed combinations having advantageous synergistic effects.

To provide a system which can confirm a health state of a user by using a numerical value and a time detected by a wearable activity measurement device, and can provide the user of a proper advice by using a method which can further effectively individualize the health state of the user.SOLUTION: A system can confirm a health state of a user by using a numerical value and a time which are detected by a wearable activity measurement device, and can provide the user of a proper advice.SELECTED DRAWING: Figure 1

The invention discloses a whole course rehabilitation management system and management method based on an AI technology, and relates to the technical field of medical health management. The system mainly comprises a medical record file module, a rehabilitation management module and a doctor-patient communication module. Personal medical records of a patient are stored and managed through the structured information storage technology, free interaction between the system and a doctor-patient terminal is achieved through the doctor-patient communication module, a channel for timely consultation and feedback is provided for the patient, and doctor-patient communication is promoted. Rehabilitation schemes of different disease course stages are provided for each patient through the rehabilitation management module, a rehabilitation file of the next stage is adjusted according to execution data of the patient in each stage, and the whole disease course of the patient is throughout until complete rehabilitation is achieved. Personalized whole course rehabilitation management for different patients is realized, and the overall efficiency and the service quality of medical services are improved.

Systems and techniques are described for predicting one or more behaviors and generating one or more interventions based on the behavior(s). For instance, a system (e.g., an extended reality (XR) device) can obtain behavioral information associated with a user of the XR device and can determine, based on the behavioral information, a likelihood of the user engaging in a behavior. The system can determine, based on the determined likelihood exceeding a likelihood threshold, an intervention. The system can generate (e.g., display or otherwise output) the intervention. The system can determine, subsequent to generating the intervention, whether the user engaged in the behavior and can determine an effectiveness of the intervention based on whether the user engaged in the behavior. The system can send, to a server, an indication of the effectiveness of the intervention for use in determining interventions for one or more additional users.

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 invention discloses a personalized rehabilitation plan generation method and system based on multi-modal data, and relates to the technical field of medical rehabilitation, and the method comprises the steps: obtaining physiological data, motion data and emotion and cognition data of a patient; preprocessing the physiological data, the motion data and the emotion and cognition data to obtain multi-modal fusion data; analyzing the multi-modal fusion data by adopting a pre-trained machine learning model, and determining the rehabilitation potential and demand of the patient to obtain an evaluation result; a personalized rehabilitation plan of a rehabilitation training type is automatically generated according to an evaluation result, and training parameters, psychological counseling and nutrition suggestions are generated according to the conditions of the patient. According to the personalized rehabilitation plan generation method and system based on the multi-modal data, accurate evaluation can be carried out according to the conditions of the patient, the personalized rehabilitation plan is generated, the rehabilitation efficiency of the patient can be improved, and it is ensured that the rehabilitation process is more scientific and efficient.

A system uses a large language model (LLM) to implement a controlled artificial intelligence chat environment. The system may control interaction with the LLM using prompt templates that may be selected, customized, and / or modified based on information known about the user with whom the LLM will be interacting. Further, the system may evaluate output of the LLM to make changes to the LLM, the prompt templates, and so on. In some implementations, the system may use evaluation training data to adapt and fine-tune the LLM and / or another language model to evaluate output of the LLM in order to evaluate the efficacy of the chronic condition and / or disease management coaching path(s), and make improvements to the online or offline implementation of the language model in the future.