Personalized Probiotics Using AI

Predicting host organism properties using microbiome composition data and metabolome interactions. The method involves analyzing a host's microbiome to obtain frequency values of different taxa. It preprocesses the microbiome by grouping and normalizing taxa based on classification. An FCNN embeds the preprocessed microbiome into a latent space representing microbiome-metabolome interactions. This latent space is used to predict host properties, like metabolite concentrations, by approximating the microbiome-metabolite relationship. This leverages the complex, non-linear microbiome-metabolome interactions using the latent representation.

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A method for screening personalized probiotics, prebiotics, foods, health functional foods, and drugs using fecal samples and a proprietary medium composition. The method involves treating fecal samples with candidate materials, culturing, and analyzing the resulting microbiota and metabolites to identify effective personalized microbiota-improving agents. The composition includes L-cysteine, sodium chloride, sodium carbonate, potassium chloride, and hemin, which are combined with fecal samples to create a controlled in vitro environment that mimics the human gut. The method enables rapid screening of personalized microbiota-improving agents using fecal samples and special media, and can be used to diagnose diseases caused by intestinal disorders.

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Using machine learning to design bacterial communities for specific tasks like suppressing pathogens or degrading contaminants. The method involves training a machine learning model to predict community performance based on bacterial strain composition. The training uses real experimental data on actual communities. The model is then used to identify communities with high predicted performance for a task. This allows efficient screening of large numbers of possible communities. The training strategy involves generating data with strains and task scores, and prioritizing genetically diverse communities to reduce redundancy.

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A system and method for determining a personalized probiotic therapeutic regimen based on monitoring gut microbiota composition and dietary preferences. The system employs metabolic simulations to predict the efficacy of probiotic interventions on varying gut microbiota compositions and dietary preferences, using metrics such as net-effect and sustainability to quantify the benefits of probiotic supplementation. The system receives a biological sample from the subject, extracts DNA, determines microbial abundance, and creates genome-scale metabolic models of the subject's microbiota and potential probiotic organisms. It then performs metabolic simulations to predict the growth of each organism in mono- and co-culture, and computes the net-effect and sustainability of each probiotic organism. The system selects the most efficacious probiotic organism based on these metrics and provides a personalized treatment recommendation.

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Device for providing customized probiotic recommendations based on microbial analysis information, comprising: an information acquisition unit for acquiring personal and analysis information; a health information generation unit that generates health information based on the acquired information; and a recommendation information generation unit that generates probiotic recommendation information suitable for the subject based on the generated health information.

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System and method for modifying nutrition requirements based on user attributes and real-time monitoring data, enabling personalized dietary recommendations that enhance specific health attributes. The system integrates user input, monitoring device data, and AI-driven analysis to generate tailored nutrition requirements, which are then matched to optimal food choices that fulfill those needs.

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Methods and systems for altering the microbiome composition of a subject through diet modification. The methods involve providing a subject with a personalized diet guidance program based on their microbiome analysis, and modifying their food intake accordingly. The diet guidance program is developed using machine learning algorithms that correlate food consumption with microbiome changes in a large database of individuals. By following the personalized diet guidance program, subjects can increase the presence of beneficial microbes and decrease the presence of pathogenic microbes in their gut microbiome.

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A personalized probiotic formulation that utilizes the patient's own microbiome to treat various health conditions. The formulation is produced through a targeted genetic analysis of the patient's microbiome, followed by the selection of genetically safe strains that are then grown and purified. This approach eliminates the risks associated with traditional probiotics, which often contain foreign microorganisms. The formulation can be administered orally, topically, or intravaginally, and can be customized based on the patient's specific microbiome composition.

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An AI system generates educational responses to nutrition-related inquiries based on user biological data. The system identifies a user's nutritional needs from their physiological data, selects a machine learning model trained on biological data and nutrition resources, and generates a response tailored to the user's needs.

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A system for providing personalized product recommendations using biologic data and machine learning. The system integrates multi-omic data, including genetic, environmental, and lifestyle factors, to predict user preferences and product suitability. A neural network architecture processes the biologic data to identify essential biomarkers and interpret complex biological datasets, enabling actionable insights for personalized decision-making. The system's application extends to industries such as healthcare and consumer products, where it can transform product recommendations and decision-making processes.

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A method for predicting the composition of mixed microbiota samples using a machine learning model that accounts for interactions between microorganisms. The method involves training a model on reference mix profiles to predict the composition of new mixes, and then using the trained model to optimize the selection of initial samples for a target mix profile. The model incorporates an interaction matrix that represents the positive and negative interactions between microorganisms, allowing for more accurate predictions of mix compositions compared to traditional linear models.

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A method and device for predicting microbial species using artificial intelligence. The method involves learning a specialized AI model for microbial species prediction, parsing whole genome sequences into short segments, and inputting the parsed sequences into the learned model to predict species. The AI model is trained on a vast DNA database using a novel tokenizer and pre-trained language model optimized for DNA sequence analysis.

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Personalized nutritional supplements tailored to individual health needs. The method involves ranking ingredients based on overall benefit for a subject's suboptimal health conditions. The ranked ingredients are then filled into packages until they reach capacity. This allows customization by selecting the most beneficial ingredients for the subject's specific needs.

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Method for modeling an intestinal microbiome of a person without metagenomic data, using phenotypic, medical, and lifestyle data to generate a personalized microbiome model. The method involves constructing a composite model from multiple individuals with similar characteristics, simulating diet effects on the microbiome, and identifying dietary recommendations to modify the target individual's phenotypic and medical characteristics.

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A method for identifying disease-specific microbial consortia using machine learning. The method involves preprocessing gut microbiota data, generating candidate consortia through clustering, and training a machine learning model to select the most predictive consortia. The model is then used to identify disease-relevant microbial consortia that can be used to develop effective probiotics for specific diseases.

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A system for designing personalized microbiome therapeutics and diet based on functions of an individual's microbiome. The system analyzes gut microbiome composition and function to identify dysbiotic states and perturbed metabolic pathways. It generates personalized therapeutic recommendations, including probiotics, prebiotics, and dietary interventions, based on the individual's microbiome profile and functional deficits.

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Using a digital twin to generate personalized health care plans for individuals based on their specific biomarker profiles. The digital twin is a computational model that mimics physiological processes using machine learning and equations libraries. It allows predicting health states, disease progression, and treatment outcomes by capturing multiomic interactions from genomic, transcriptomic, metabolomic, epigenetic, and microbiome data. The digital twin can also simulate the effects of exogenous factors like drugs or mutations on the body.

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Personalized recommendations for maintaining or improving gut microbiome health based on predicting microbiome status using questionnaires and biomarkers. The method involves determining microbiome status through questionnaires, biomarkers, and anthropometric measures. It then provides customized recommendations for diet, supplements, menus, and recipes to improve microbiome diversity. The recommendations consider factors like antibiotics usage, lifestyle choices, and biomarkers to tailor advice for each individual.

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A computer-implemented method for intervening on a bacterial population through metagenomics, comprising acquiring information on an analyzed bacterial population, selecting a plurality of enzymes of interest, identifying major and minor bacterial contributors, and producing an intervention plan to re-establish a correct population of the bacterial population. The method uses enzyme abundance and metabolic potential to identify contributors and develop personalized dietary interventions based on scientific literature correlations.

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Reducing the risk of injury to a pet by providing personalized pet product recommendations based on a microbiome result and pet metadata to a user. The recommendations include receiving, by one or more processors, pet metadata from a user, analyzing the pet fecal sample to determine a microbiome result, processing the pet metadata based on the microbiome result to determine one or more pet product recommendations, and displaying the pet product recommendations to the user.

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Method for providing personalized food and supplement recommendations based on multiple health conditions. The method involves determining an individual's set of conditions using phenotype and microbiome data. Food recommendations are made by predicting effects of macronutrients, specific compounds, and microbiome interactions on the conditions. If effects differ, the most restrictive is chosen. This allows combining recommendations for multiple conditions.

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A system for leveraging microbiome data to improve healthcare through personalized analysis and guidance. The system enables comprehensive data collection and analysis of microbiome samples from individuals, including humans, animals, and plants, and utilizes machine learning algorithms to generate actionable insights and recommendations for diagnosis, treatment, and wellness. The system also facilitates data sharing and collaboration between users, coaches, and subjects, enabling a community-driven approach to microbiome-based healthcare.

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A combination of two probiotic strains, Lacticaseibacillus paracasei S38 and Bacillus coagulans BC198, that effectively improve body compositions by aiding weight loss and reducing body fat. The strains, isolated from human feces and green malt, respectively, have synergistic effects when administered together at lower doses compared to individually. This combination helps inhibit lipogenesis, reduce appetite, increase butyric acid production, and promote beneficial gut bacteria like Akkermansia muciniphila and Ruminococcaceae. It can be used as a probiotic supplement, food, or medication to aid weight loss and body composition improvement.

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A system and method for calculating a personalized edible score to guide food choices. The system uses a user's performance profile and nutritional data for a selected food to generate a score through machine learning, which is then displayed to aid informed decision-making.

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A system for providing personalized nutrition services that uses artificial intelligence and big data analytics to generate dietary predictions, recommendations, and modifications based on user attributes and preferences. The system creates a food graph from a database of food items and a user graph from user input, then maps the two graphs to provide personalized nutrition guidance. The system also includes features such as food tagging, similarity metrics, and nutrient DV adjustment to enhance its nutritional analysis capabilities.

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Automated personalized nutrition and lifestyle recommendations system based on total genotype and phenotype information. The system collects genetic, physiological, medical, environmental, and nutrition data from users. It analyzes this data to calculate personalized nutrition and lifestyle plans optimized for each user's genetics, health history, environment, and preferences. The system stores user data securely and provides a user interface to access and manage the recommendations.

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Artificial intelligence-based method for designing functional microbiomes that improves the efficacy of target probiotic strains. The method uses a database of intestinal microbial strains and their interactions to predict a group of microorganisms that can enhance the growth, activity, stability, and colonization of specific probiotic strains in the human gut.

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A system for providing personalized health information through customized search results that reflect an individual's specific health conditions. The system acquires user health data, selects relevant nutrients based on the data, and receives search requests for food, nutrients, or health products. It then provides search results that indicate whether the user's health conditions align with the search target's nutritional profile.

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Quantifying the resilience of the gut microbiome to challenges like high fat diets. The method involves determining a resilience index based on analyzing the microbiome composition, metabolites, and physiological parameters before, during, and after a challenge. A low index indicates reduced resilience. This index can be used to screen individuals for low resilience and provide personalized nutritional interventions to improve resilience.

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Cognitive menu selection service that uses AI to analyze user biometric data and make personalized menu item recommendations based on factors like physiological state, nutritional needs, dietary plans, and emotional state. The service builds a selection forecasting model from transaction histories and user state data to predict menu choices. It also filters items based on dietary restrictions. The model improves over time as it learns from user orders.

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Nutritional recommendation system using AI analysis of immune impacts. It uses machine learning to analyze biomarker levels from food tests to determine the immune system impact of specific foods. This allows generating personalized nutritional recommendations based on how foods affect the user's immune system. The system receives test results for foods and their biomarker impacts, trains an AI model using a dataset correlating biomarkers to immune function, determines immune impacts of foods using the model, and provides personalized nutrition advice based on those impacts.

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Methods for analyzing the human microbiome using a Scent Reader/Recorder that detects volatile organic compounds (VOCs) in biological samples. The device generates a pattern of sensor signals that can be analyzed using machine learning techniques to determine the microbiome profile of a subject. The methods enable rapid, inexpensive, and continuous monitoring of the microbiome, allowing for early detection of changes and personalized health and nutritional recommendations.

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A system and method for predicting gut microbiome diversity based on individual gut transit time and user data. The system generates personalized microbiome classifications by analyzing user-specific gut transit time data and user characteristics, such as age, diet, and health information. The system can also identify similar users and leverage their microbiome data to generate predictions. The method includes accessing gut transit time data, generating personalized microbiome classifications, and presenting the results to the user through a user interface.

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A method for predicting biological states using a statistical model that learns to adapt to individual users through meta-learning. The method trains a model on task data sets for multiple users, generates meta-error values from the task errors, and uses these values to learn meta-parameters that are applied to the model. The model is then trained on new user data to generate a personalized prediction model for each user.

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Estimating gut microbiome Faecalibacterium prausnitzii (Fprau) levels from dietary data to provide personalized recommendations for optimizing Fprau amounts. The method involves using machine learning models trained on nutrient intake from food frequency questionnaires to predict gut Fprau status. Based on the predicted Fprau level, recommendations are made for supplements, foods, menus, and recipes to maintain or improve Fprau.

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A personalized health formula system that combines genetic and metabolic data to provide targeted nutritional interventions. The system evaluates genetic polymorphisms and metabolic biomarkers in key biological pathways, such as the trans-sulfuration pathway, to identify areas of imbalance. Based on this analysis, the system generates a customized supplement plan that addresses specific enzyme deficiencies and metabolic imbalances, with dosages tailored to the individual's genetic profile. The system also provides lifestyle recommendations to support optimal health and prevent disease.

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Tracking diet and forming conclusions on diet quality and personalized nutrition recommendations based on intestinal microbiota analysis. The method involves obtaining biomarker values from user intestinal microbiota data, determining related trait values, generating a food product list, rating products, scoring consumed products, and forming conclusions/recommendations. The ratings consider traits like microbiota functions, fiber breakdown, gluten tolerance, lactose tolerance, disease protection, etc. Image analysis can identify foods in dishes. The method aims to improve diet quality and accuracy by personalizing recommendations based on user microbiota.

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A system and method for simulating vitality metrics using machine learning. The system receives biotic extraction data from a user, maps it to a vitality metric using a machine learning model, and simulates alternative vitality metrics by perturbing individual biotic parameters. The system provides the user with their actual vitality metric and the specific actions required to achieve the simulated metrics, enabling personalized recommendations for improving vitality.

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System and method for generating personalized nutrition plans tailored to an individual's addiction status and symptoms. The system uses machine learning models to analyze addiction elements, produce an addiction signature, identify physiological impacts, determine edible recommendations, and generate a customized nourishment program.

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System for dietary communications using intelligent systems regarding endocrinal measurements, comprising a computing device that obtains endocrinal measurements, compares them to endocrinal system effects, generates body dysfunction labels, identifies dietary communications using machine learning, and presents personalized nutrition recommendations based on the labels and measurements.

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Probiotic bacteria are microorganisms with beneficial effects on human health and are currently used in numerous food supplements. However, no selection process is able to effectively distinguish probiotics from non-probiotic organisms on the basis of their genomic characteristics. In the current study, four Machine Learning algorithms were employed to accurately identify probiotic bacteria based on their DNA characteristics. Although the prediction accuracies of all algorithms were excellent, the Neural Network returned the highest scores in all the evaluation metrics, managing to discriminate probiotics from non-probiotics with an accuracy greater than 90%. Interestingly, our analysis also highlighted the information content of the tRNA sequences as the most important feature in distinguishing the two groups of organisms probably because tRNAs have regulatory functions and might have allowed probiotics to evolve faster in the human gut environment. Through the methodology presented here, it was also possible to identify seven promising new probiotics that have a higher information ... Read More

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A cloud-based system for personalized food item design that uses machine learning to create customized recipes based on user biomarker data, dietary needs, ingredient availability, and culinary skills. The system integrates with restaurants and patrons through APIs and mobile apps, enabling real-time optimization of menu offerings and dining experiences. By analyzing a wide range of factors, including user preferences, restaurant capabilities, and nutritional information, the system generates unique recipe recommendations that cater to individual needs and goals.

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System and method for generating a personalized lifestyle plan to prevent infectious diseases based on user biomarkers, using machine learning to correlate lifestyle elements with disease prevention outcomes. The system receives user biomarker data, generates a user profile, and trains a machine learning model on lifestyle-disease prevention data to produce a tailored prevention plan.

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A method for identifying and isolating microorganisms that provide specific functions, particularly in microbial consortia. The method involves testing a set of environmental samples against a target species, selecting samples that show a desired result, and isolating the microorganisms responsible. The process is repeated iteratively to identify the causal organisms. The method also includes generating a machine learning model based on the identification information to predict microorganisms that can achieve specific functions in different environments.

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A system and method for customized nutritional recommendations and manufacturing of personalized edible combinations based on biochemical analysis. The system involves extracting biochemical profiles from an individual, generating a new profile from another source, analyzing the differences between the profiles to identify nutritional deficiencies, and using that analysis to create a personalized nutritional program. This program is then used to manufacture an edible combination tailored to the individual's specific nutritional needs.

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System and method for generating personalized immune protocols to identify and reverse autoimmune disease through machine learning-based analysis of immune biomarkers, immune profiles, and dietary elements. The system assigns users to an autoimmune category based on their immune profile, determines an elimination plan to remove contributing dietary elements, and a reintroduction phase to reintroduce beneficial elements, while optimizing nutrient amounts through machine learning-based objective functions.

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System and method for predicting food ordering based on biological extraction, using machine learning to analyze user's nutritional needs and suggest personalized food options. The system receives user's biological extraction data and order history, identifies optimal food matches using predictive models, and presents alternative food options ranked by nutritional alignment.

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Modulating the metabolome of a subject by administering a composition comprising one or more microbial strains, wherein the composition modulates the levels of one or more metabolites associated with a disease, disorder, or condition. The composition can comprise a combination of microbial strains selected from Gluconacetobacter hansenii, Terrisporobacter glycolicus, Coprococcus sp., Lactobacillus plantarum, Clostridium butyricum, Paenibacillus sp., Veillonella sp., Bifidobacterium sp., Bacillus subtilis, and Acidaminococcus sp. The method can further comprise determining the levels of one or more metabolites in a sample from the subject before and after administration of the composition, and comparing the levels to a reference value to assess the effect of the composition on the subject's metabolome.

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System and method for determining nutritional needs and generating a personalized supplement plan using artificial intelligence. The system receives biological extraction data and user-reported nutritional intake, applies machine learning algorithms to determine nutritional needs and validate user input, detects deficiencies, and calculates supplement doses to address those deficiencies.

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A patient health management platform for managing metabolic diseases using machine learning and continuous biosignal analysis. The platform generates personalized treatment recommendations by analyzing a unique combination of biosignals from wearable sensors, lab tests, nutrition data, medication data, and patient symptoms. Machine-learned models analyze the biosignals to establish a patient's metabolic profile and predict the impact of specific foods on their blood glucose levels. The platform provides real-time, data-driven insights to guide patients towards optimal metabolic health.

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