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WO2025079046A1 - System for personalized nutrition based on individual genetics, health and clinical context, and artificial intelligence - Google Patents

System for personalized nutrition based on individual genetics, health and clinical context, and artificial intelligence Download PDF

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Publication number
WO2025079046A1
WO2025079046A1 PCT/IB2024/060007 IB2024060007W WO2025079046A1 WO 2025079046 A1 WO2025079046 A1 WO 2025079046A1 IB 2024060007 W IB2024060007 W IB 2024060007W WO 2025079046 A1 WO2025079046 A1 WO 2025079046A1
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user
genetic markers
nutritional
personalized
plan
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French (fr)
Inventor
Bernardo POLLAK WILLIAMSON
Pablo GARCÉS SÁNCHEZ
Verónica Magali MORGANTE
Juan Cristóbal JIMÉNEZ ROMAGUERA
Tomás Alberto Norambuena Arenas
Claudio Patricio PONCE ACOSTA
Cristóbal Andrés URRUTIA ILABACA
Fernanda Paz VERA VIDAL
Martín Ignacio COSTABAL CASTILLO
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Multiplex SpA
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Multiplex SpA
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/10—Office automation; Time management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F40/20—Natural language analysis
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    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06F40/30—Semantic analysis
    • G—PHYSICS
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    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06N3/0475—Generative networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
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    • G06—COMPUTING OR CALCULATING; COUNTING
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    • G06N5/04—Inference or reasoning models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/01—Customer relationship services
    • G06Q30/015—Providing customer assistance, e.g. assisting a customer within a business location or via helpdesk
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/22—Social work or social welfare, e.g. community support activities or counselling services
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    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
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    • G—PHYSICS
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
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    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
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    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • G—PHYSICS
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    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring

Definitions

  • the system further prompts the user to input a plurality of answers to a questionnaire; and constructs a user profile based on the plurality of answers.
  • the user profile is stored on the one or more databases.
  • the user profile comprises a plurality of genetic markers and a plurality of traits associated with the user.
  • the one or more genetic markers are retrieved from the plurality of genetic markers of the user profile, and the one or more traits are retrieved from the plurality of traits of the user profile. 3 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0012]
  • the user profile further comprises a plurality of biochemical test results associated with the user.
  • the method 800 further comprises obtaining an identifier associated with the user, wherein the one or more genetic markers and the one or more traits are retrieved based on the identifier.
  • the identifier associated with the user is obtained based on one or more login credentials provided by the user.
  • the one or more databases are accessible by the trained language model.
  • the one or more genetic markers comprise one or more of a gene, one or more of a single-nucleotide polymorphism, one or more insertions, one or more deletions, one or more repetitive sequences, and/or a biomarker.
  • the one or more genetic markers are obtained based on a genotyping method.
  • the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof.
  • the genotyping method comprises genotyping by sequencing.
  • the genotyping by sequencing comprises amplifying a nucleic acid around the one or more genetic markers using specific primers to obtain amplified specific nucleic acid fragments.
  • genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads.
  • the trained language model comprises one or more of an autoencoder model, a generative adversarial network, an autoregressive model, a transformer 4 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 model, a flow-based generative model, and/or a probabilistic generative model.
  • the trained language model is trained based on a training dataset comprising one or more queries paired with one or more responses.
  • a trait comprises a nutritional condition, a physical or health condition, a lifestyle, clinical parameters, and/or a combination thereof.
  • the request for additional information comprises a prompt for the user to input one or both of an additional genetic marker or an additional trait.
  • the method 800 further comprises receiving a natural- language query to retrieve user-specific information; retrieving the user-specific information; and displaying the retrieved user-specific information.
  • the method 800 further comprises receiving a natural-language query to update the personalized nutritional plan; and displaying an updated personalized nutritional plan.
  • the method further comprises retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into the trained language model to generate a 5 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 collective nutritional plan for the group; and displaying the collective nutritional plan.
  • the group comprises family members sharing meals.
  • the trained language model is trained to identify nutritional requirements for each member of the group based on the one or more genetic markers and the one or more traits; determine whether the nutritional requirements for two or more members of the group conflict; and in response to determining that the nutritional requirements for two or more members of the group conflict, include meals in the collective nutritional plan that can be modified to accommodate the conflicting nutritional requirements.
  • the trained language model is trained to generate individual nutritional plans for each member of the group in addition to the collective nutritional plan.
  • the trained language model is augmented with a numerical model, and the trained language model is trained to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts.
  • the trained language model is trained to receive feedback from the user; process the feedback using the numerical model; and adjust the recommended nutrient amounts in real-time based on the processed feedback.
  • the trained language model is trained to calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and generate the individual nutritional plan to reflect the recommended caloric intake.
  • the trained language model is trained to receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; and modify the personalized nutritional plan based on the calculated portion sizes.
  • An exemplary system for generating a personalized nutritional plan for a user comprises one or more non-transitory computer-readable storage media; and one or more processors coupled to the one or more storage media, the one or more processors configured to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures 6 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 associated with the task based on the one or more genetic markers and the one or more traits associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan.
  • the one or more processors are further configured to: retrieve, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; input the one or more data structures into the trained language model to generate a collective nutritional plan for the group; and display the collective nutritional plan.
  • the trained language model is augmented with a numerical model
  • the one or more processors are further configured to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts.
  • the one or more processors are further configured to the one or more processors are further configured to: calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and generate the individual nutritional plan to reflect the recommended caloric intake.
  • An exemplary non-transitory computer-readable medium comprises instructions that, when executed by one or more processors of a system, cause the one or more processors to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits 7 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan.
  • the instructions when executed by the one or more processors, cause the one or more processors to receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modify, using the trained language model, the personalized nutritional plan based on the calculated portion sizes; and display the modified personalized nutritional plan with the calculated portion sizes.
  • An exemplary kit for generating a personalized nutritional plan comprises: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and/or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for using the kit to perform a method for identifying the presence of one or more genetic markers in the biological sample provided by a user, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; identifying variations in the nucleic acids through a genotyping method; identifying, using the one or more processors, the presence of the one or more genetic markers in the biological sample; comparing the location of the genetic markers with a reference database containing the nucleic acid fragments comprising the reference genetic markers; using the identified genetic markers as a natural- language query for inputting a system for generating a personalized nutritional plan; and displaying the personalized nutritional plan to the user.
  • the kit comprises optionally one or more cotton swabs to extract the biological sample.
  • the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from animals.
  • the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from vertebrate organisms.
  • the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from mammals. 8 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0035]
  • the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from humans.
  • the biological sample comprises samples coming from tissues or secretions such as blood, skin, mucous membranes, saliva, semen, hair, urine, cavity secretions and/or stool.
  • the kit further comprises a binding matrix for the retention of genetic material.
  • the genotyping method looks for single nucleotide polymorphisms, insertions, deletions, and/or repetitive sequences.
  • the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof.
  • the genotyping method is genotyping by sequencing.
  • the genotyping by sequencing comprises amplifying the nucleic acid around the genetic markers using specific primers to obtain amplified specific nucleic acid fragments.
  • genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads.
  • the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences.
  • An exemplary method for generating a group nutritional plan using a trained language model includes: retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; constructing one or 9 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into a trained language model to generate a collective nutritional plan for the group; and displaying the collective nutritional plan.
  • FIG. 2 depicts an exemplary system involving the modules related to sampling kit, nucleic acid sequencing from the user sample, and the unveiling of genetic markers from the sequence of nucleic acids, in accordance with some embodiments.
  • FIG. 3 depicts an exemplary system involving the modules related to information provided by the user related to health and clinical data as well as nutrition requirements and food sensitivities in order to feed and fine-tune the AI-driven chatbot that makes a dietary recommendation to the user, in accordance with some embodiments.
  • FIG. 3 depicts an exemplary system involving the modules related to information provided by the user related to health and clinical data as well as nutrition requirements and food sensitivities in order to feed and fine-tune the AI-driven chatbot that makes a dietary recommendation to the user, in accordance with some embodiments.
  • embodiments of the present disclosure can leverage machine-learning techniques to efficiently process user-specific interaction context, nutritional preferences, and genetic profiles to provide accurate dietary recommendations.
  • the embodiments of the present disclosure can also improve the functioning of a computer by reducing processing power and increasing the processing speed of the computer to generate accurate and personalized dietary recommendations.
  • Genetic markers are typically used in genetic studies and research to analyze patterns of inheritance, genetic linkage, and association with certain traits or diseases. They allow for the identification of the presence or absence of specific genetic sequences or variations in individuals or populations.
  • a genetic marker can also be an RNA marker (e.g. transcriptome) or epigenetic marker, or any other signature or pattern on nucleic acid sequences or molecular components interacting with them or encoded in them.
  • RNA marker e.g. transcriptome
  • epigenetic marker e.g., epigenetic marker, or any other signature or pattern on nucleic acid sequences or molecular components interacting with them or encoded in them.
  • Genetic markers are also utilized in various applications such as genetic mapping, paternity testing, forensic analysis, and personalized medicine, where they can aid in predicting an individual's risk for certain diseases or response to certain treatments.
  • dietary guidelines and recommendations include: (a) food groups and nutrients, which provide guidance on the consumption of different food groups such as fruits, vegetables, grains, proteins, and dairy products.
  • portion sizes and energy balance wherein the guidelines may provide recommendations on appropriate portion sizes and calorie intake to maintain a healthy weight and prevent excessive energy intake;
  • nutrient and ingredient recommendations which may advise on limiting the consumption of certain nutrients such as added sugars, sodium, saturated fats, and trans fats, which are associated with increased risks of chronic diseases.
  • nutrient-dense foods that provide a high amount of essential nutrients relative to their calorie content, for example fruits, vegetables, whole grains, lean proteins (such as poultry, fish, legumes), and low-fat dairy products
  • personalized considerations wherein some guidelines may provide specific recommendations of nutrient intake for individuals such as infants, children, pregnant women, older adults, or individuals with certain health conditions. They may address their unique nutrient requirements or considerations for dietary restrictions or allergies;
  • healthy eating patterns wherein dietary guidelines often promote specific healthy eating patterns, such as the Mediterranean diet, DASH (Dietary Approaches to Stop Hypertension) diet, or plant-based diets, which have shown positive health outcomes in scientific studies.
  • balanced diet can refer to consuming a variety of foods from different food groups in appropriate proportions. It typically includes fruits, vegetables, whole grains, lean proteins, healthy fats, and low-fat dairy products.
  • a balanced diet ensures the intake of 15 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 essential nutrients, such as carbohydrates, proteins, fats, vitamins, minerals, and fiber, which are necessary for optimal bodily functions.
  • macronutrients can include the major nutrients required in relatively large quantities and include carbohydrates, proteins, and fats. Carbohydrates are the body's primary source of energy, while proteins are essential for tissue repair and growth. Fats provide energy, help absorb fat-soluble vitamins, and contribute to hormone production. Micronutrients, on the other hand, may be required in smaller amounts and include vitamins and minerals. They are crucial for various bodily functions, including immune support, bone health, and enzyme activities.
  • genotyping can include the process of determining an individual's genetic makeup, specifically by identifying variations in their DNA sequence (even in RNA sequences). It can involve analyzing an individual's DNA to identify specific genetic variations, such as single nucleotide polymorphisms (SNPs), insertions, deletions, or other genetic mutations. Genotyping can provide information about an individual's genetic predispositions, ancestry, and susceptibility to certain diseases or conditions. Genotyping is used in various fields, including genetics research, medical diagnostics, and personalized medicine. It can be performed using different techniques, namely: (1) Polymerase Chain Reaction (PCR): PCR is a widely used technique to amplify specific DNA regions, including genetic markers of interest.
  • PCR Polymerase Chain Reaction
  • ARMS PCR Amplification Refractory Mutation System PCR
  • ARMS PCR is a specialized PCR technique used for genotyping specific mutations or SNPs. It relies on allele-specific primers that are designed to selectively amplify the target allele. The presence or absence of PCR amplification indicates the genotype.
  • qPCR Quantitative PCR: qPCR is used not only for gene expression analysis but also for genotyping.
  • Microarrays are high-throughput platforms that can simultaneously analyze thousands to millions of genetic markers. In genotyping applications, probes specific to known genetic variants are immobilized on the microarray. The sample DNA or RNA is hybridized to the probes, and the resulting pattern of binding provides genotype information.
  • Integrated Fluid Circuits 16 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 Microfluidic devices, also known as integrated fluidic circuits, can be used for genotyping.
  • MassARRAY SNP Genotyping MassARRAY is a technology that combines mass spectrometry with PCR to analyze SNP genotypes. It involves the use of allele-specific extension primers and mass spectrometry to determine the alleles present at specific SNP positions.
  • GGS Genotyping by Sequencing
  • NGS next-generation sequencing
  • HTS high-throughput sequencing
  • the relationship between genetic markers and dietary recommendations lies in the understanding that genetic variations can influence how individuals respond to certain nutrients and dietary patterns.
  • personalized dietary recommendations can be tailored to optimize their nutritional intake and promote better health outcomes.
  • the key aspects of this relationship include: (a) nutrient metabolism, wherein the genetic markers can provide insights into an individual's metabolism of specific nutrients. For example, variations in genes related to carbohydrate metabolism can affect how efficiently an individual processes and utilizes carbohydrates. Similarly, genetic markers related to fat metabolism can influence the body's response to dietary fats.
  • dietary recommendations can be customized to accommodate individual differences in nutrient metabolism; (b) nutrient requirements, wherein the genetic markers can also indicate an individual's specific nutrient requirements. Variations in genes related to vitamin or mineral metabolism can affect the body's ability to absorb, transport, or utilize certain nutrients. For instance, genetic markers associated with vitamin D metabolism can provide insights into an individual's vitamin D needs. By considering these genetic variations, dietary recommendations can be adjusted to ensure sufficient intake of specific nutrients; (c) food sensitivities and intolerances, wherein genetic markers can reveal an individual's predisposition to food sensitivities or intolerances.
  • genes related to lactose intolerance or gluten sensitivity can inform dietary recommendations by suggesting avoidance or moderation of specific food components.
  • dietary recommendations can help manage or prevent adverse reactions to certain foods; (d) disease risk and prevention, wherein certain genetic 17 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 markers are associated with an increased risk of developing specific diseases, such as cardiovascular diseases or type 2 diabetes. By identifying these genetic markers, dietary recommendations can be tailored to mitigate the associated risks.
  • individuals with genetic markers indicating increased susceptibility to heart disease may receive recommendations to limit dietary saturated fats and increase the intake of heart-healthy foods; (e) weight management, wherein genetic markers can influence an individual's propensity to gain or lose weight in response to specific dietary factors.
  • weight management wherein genetic markers can influence an individual's propensity to gain or lose weight in response to specific dietary factors.
  • dietary recommendations can be personalized to optimize weight management strategies. This may involve modifications in macronutrient ratios, portion sizes, or specific dietary approaches based on an individual's genetic profile.
  • a user can be considered an individual utilizing the system to obtain a nutritional plan based on a genetic makeup and specific traits.
  • the biological sample from which the genetic makeup is obtained can be from the user themself or from an animal sample.
  • VAEs Variational Autoencoders
  • a natural-language query can be a question or request entered by the user (user query) into the system using everyday language, as opposed to a query formulated using specific coding or technical language.
  • a personalized nutritional plan can include a unique dietary recommendation and/or set of recommendations tailored specifically to an individual's genetic markers, and/or traits, and/or nutritional conditions to optimize their health and well-being. [0077] Therefore, integrating genetic information with dietary recommendations allows for a more precise and personalized approach to nutrition.
  • the prompt interaction 107 can be any communication device which can receive information and delivers information as well, such as (but not limited to) a computer, a mobile device, a tablet device, a voice assistant device, a watch device, a wearable device, a printer device, a display device, a touch-screen device and/or any combination of them. It allows for intuitive user 21 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 engagement, gathering necessary information to tailor the system's recommendations to the user's needs. Prompt 107 can report information to user 101 directly from repository 105, facilitating bidirectional communication between the user and the integrated system. The system also integrates the user information 108 related to health and clinical data, nutritional requirements, and food sensitivities.
  • the system comprises a data science platform 109 facilitating data analysis and pattern recognition techniques to identify correlations and associations between genetic markers and specific phenotypes (e.g. up-to- date literature regarding phenotypic associations between genes and nutrition, and/or analysis of ausers genetic data and traits to uncover new associations).
  • This information is stored in repository 110, comprising a set of one or more databases specifically designed to capture reported genetic markers and phenotype associations.
  • the repository 110 feeds the AI-driven chatbot 106, allowing the chatbot to access this valuable information and incorporate it into the user's personalized guidance.
  • optionally repository 105 can be accessed by a nutrition specialist 111, which can also access the data science platform 109.
  • This optional specialist provides expert consultation and guidance based on the user's genetic profile, health data, and nutritional requirements. This facilitates collaboration and information exchange between the chatbot and the nutrition specialist, ensuring accurate and up-to-date recommendations.
  • the user 101 gets and send the sampling kit 102 through a distribution system (FIG. 2).
  • the sampling kit comprises one or more swabs, one or more sampling tubes with appropriate solutions, and instructions for the correct use of the sampling kit.
  • the sampling kit 102 provides the necessary tools for the user to collect a biological sample (e.g.
  • Platform 103 comprises a facility 103a responsible for the automatic or manual extraction of nucleic acids from the user's collected sample, as provided in the sampling kit 102. This facility ensures the efficient extraction of nucleic acids for further analysis.
  • platform 103 comprises facility 103b dedicated to full or partial sequencing of nucleic acids.
  • Module 104 of the system comprises a bioinformatics platform 104a utilized for the search and analysis of genetic markers within the nucleic acid sequence obtained from facility 103b. This platform employs computational algorithms and tools to identify relevant genetic markers.
  • the output 104b represents the selection process of the identified genetic markers found within the nucleic acid sequence. This selection process narrows down the list of genetic markers to those that are most relevant and significant for personalized analysis.
  • repository 105 serves as a database within the system, storing the user's inventory and related data.
  • the database can contain one or more tables of genetic profile, one or more tables of traits and health information, and one or more tables of chat histories.
  • This repository ensures seamless data storage and retrieval between parts of the system, allowing for efficient information management.
  • the interaction prompt 107 comprises an extension 107a representing the part of the system responsible for reporting the results of the nucleic acid analysis to the user 101. It provides a means of communication through which the system delivers the genetic analysis findings to the user.
  • the system may also include a phenotypic association database including a description of each biomarker analyzed in the panel.
  • the system additionally comprises the provision of information 108 from the user 101 through the interaction prompt 107 (FIG. 3).
  • This interaction comprises the provision of (but not limited to): information 108a related to health and clinical user data, which in turn comprises (but not limited to) age, sex, height, weight, BMI, body composition (e.g. percentage of fat, muscle or lean body mass, visceral fat, bone mineral content, body water percentage, basal metabolic rate), lifestyle, biochemical profile clinical measures, underlying disease(s) (e.g.
  • diabetes obesity, hypertension, atopic diseases
  • other traits associated with age such as adolescence stage, pregnancy, fertility stage or elderly stage
  • information 108b related to food sensitivities which comprise (but not limited to) gluten sensitivity, lactose intolerance, egg allergy, shellfish allergy, soy sensitivity, nuts allergy, and/or any other food (or its components) sensitivities
  • information 108c related to nutrient requirements such as (but not limited to) calories requirements, proteins, carbohydrates, fats, vitamins, and/or minerals.
  • all user information 108 collected through modules 108a, 108b and/or 108c is stored in repository 105 as part of the user inventory together with the user genetic markers 104.
  • the user 101 can interact with AI- 23 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 driven chatbot 106 through the interaction prompt 107, whose chat and dialogue 107b can be stored in repository 105 as part of the chat history, and the AI-driven chatbot can make a dietary recommendation 107c based on the knowledge acquired by the AI-driven chatbot 106 and delivered to user 101 through the interaction prompt 107.
  • the dietary recommendation 107c comprises (but it is not limited to) a nutrition plan, schedule of meals, and/or suggestions for acquisition of nutrients according to the genetic markers and the information 108 provided by the user.
  • Recommendations provided by the AI-driven chatbot 106 to user 101 can be fine-tuned with the information and data provided in turn by repository 105, the information provided by optional specialist 111, and the information kept in repository 110.
  • the specialist can be involved in response curation (fine-tuning).
  • the system can also be used by a specialist to give nutritional advice as a proxy for their patients.
  • the system comprises additional modules regarding parts 109a-c and 111 (FIG. 4).
  • the system comprises a backend prompt 111a, which is the communication platform between the repository 105 and the specialist 111 and allows this latter to retrieve information about the user (108) and his/her genetic markers (104).
  • repository 110 can be fed by a data science platform 109, which comprises (but it is not limited to): a module 109a comprising scientific and medical public information coming from the literature review, reports, and/or studies; a module 109b comprising data from automatic search of information, which can be carried out through machine learning techniques, generative AI-models, and/or text-mining of scientific literature; and/or a module 109c comprising a database which gather information about genetic markers from other public databases including the results of GWAS and/or QTL studies, SNPs repositories, and/or the genetic information available at NCBI databases and/or other public related repositories.
  • a data science platform 109 comprises (but it is not limited to): a module 109a comprising scientific and medical public information coming from the literature review, reports, and/or studies; a module 109b comprising data from automatic search of information, which can be carried out through machine learning techniques, generative AI-models, and/or text-mining of scientific literature; and/or
  • the module of data science 109 provides information to the repository 110 regarding the genetic markers and phenotype associations as depicted in (but not limited to) Table 1.
  • This table is a collection of markers (genes) reported to be associated with nutrition. Markers are also listed together with their variants (reference or alternative alleles), their location and chromosome in the human genome (GRCh38). 24 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 Table 1. Genetic markers and phenotype associations in the human genome.
  • the system provides users with actionable insights and tailored recommendations for maintaining optimal health and well-being.
  • user 101 can retrieve information and reports from the system by performing a user query. Examples for a user query comprises (but are not limited to): [0086]
  • the system provides a login module to identify the user 101.
  • the information of the user (genetic profile, traits, and conversational history) is used to provide the AI with context.
  • the user can input the information 108 through a form requesting information of the user’s height, age, weight, lifestyle, food sensitivities, preferences, and others are submitted.
  • the data is task- specific and usually comprises of paired examples, where the input (prompt or context) is given, and the desired output or response is provided.
  • the input prompt or context
  • the desired output or response is provided.
  • the model is being fine- tuned to act as a chatbot in the context of nutrition related to genetic markers (like in our solution)
  • the data would contain dialogues or conversations where user queries are paired with the appropriate responses, and information about relationships between genes and nutrition facts.
  • the fine-tuning process involves updating the model's parameters based on the custom dataset, optimizing the model to generate relevant and coherent responses in the specific context.
  • genetic markers can be inputted into the system as: [0096] CSV or TSV format: Marker,Variant,ref,alt,genotype MARKER_1,rs111111,T,C,0/0 MARKER_2,rs222222,C,G,0/1 ...,...,...,...,...,...
  • the trained language model driving chatbot 206 may modify the personalized nutritional plan to reflect the user-selected foods, with their calculated portion sizes, while maintaining the originally recommended caloric intake and nutrient amounts reflected in the originally generated nutritional plan. For instance, if a user replaces a suggested lean protein source with a fattier option, the trained language model might reduce the portion size of that component of a meal and adjust other components of the meal to maintain the overall recommended nutrient amounts of the original nutritional plan. [0128] The trained language model driving chatbot 206 may also output the modified nutritional plan, including the calculated portion sizes of the modified foods, to the user. The trained language model may also provide an output including real-time feedback for the user on these adjustments.
  • FIG. 6 illustrates an example of a computing device in accordance with one embodiment. The operations described herein are optionally implemented by components depicted in FIG. 6.
  • Device 700 can be a host computer connected to a network.
  • Device 700 52 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 can be a client computer or a server.
  • device 700 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet.
  • the device can include, for example, one or more of processor 710, input device 720, output device 730, storage 740, and communication device 760.
  • Input device 720 and output device 730 can generally correspond to those described above and can either be connectable or integrated with the computer.
  • Input device 720 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device.
  • Output device 730 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker.
  • Storage 740 can be any suitable device that provides storage, such as an electrical, magnetic or optical memory including a RAM, cache, hard drive, or removable storage disk.
  • Communication device 760 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a physical bus or wirelessly.
  • Software 750 which can be stored in storage 740 and executed by processor 710, can include, for example, the programming that embodies the functionality of the present disclosure (e.g., as embodied in the devices as described above).
  • Software 750 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a computer-readable storage medium can be any medium, such as storage 740, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device.
  • Software 750 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions.
  • a transport medium can be any medium that can communicate, propagate or 53 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 transport programming for use by or in connection with an instruction execution system, apparatus, or device.
  • the transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium.
  • Device 700 may be connected to a network, which can be any suitable type of interconnected communication system.
  • the network can implement any suitable communications protocol and can be secured by any suitable security protocol.
  • the network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T8 or T8 lines, cable networks, DSL, or telephone lines.
  • Device 700 can implement any operating system suitable for operating on the network.
  • Software 750 can be written in any suitable programming language, such as C, C++, Java or Python.
  • application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example.
  • FIG. 7 illustrates process 800 for generating a personalized nutritional plan for a user, according to various examples.
  • Process 800 is performed, for example, using one or more electronic devices implementing a software platform.
  • process 800 is performed using a client-server system, and the blocks of process 800 are divided up in any manner between the server and a client device.
  • the blocks of process 800 are divided up between the server and multiple client devices.
  • portions of process 800 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 800 is not so limited.
  • process 800 is performed using only a client device or only multiple client devices.
  • some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted.
  • additional steps may be performed in combination with the process 800.
  • an exemplary system receives, from the user, a natural-language query.
  • the system identifies, based on the natural-language query, a task and one or more parameters.
  • the system retrieves from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user.
  • the system constructs one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user.
  • the system inputs the one or more data structures into a trained language model to generate the personalized nutritional plan.
  • the system displays the personalized nutritional plan.
  • the system further prompts the user to input a plurality of answers to a questionnaire; and constructs a user profile based on the plurality of answers.
  • the user profile is stored on the one or more databases.
  • the user profile comprises a plurality of genetic markers and a plurality of traits associated with the user.
  • the one or more genetic markers are retrieved from the plurality of genetic markers of the user profile, and the one or more traits are retrieved from the plurality of traits of the user profile.
  • the user profile further comprises a plurality of biochemical test results associated with the user.
  • the method 800 further comprises obtaining an identifier associated with the user, wherein the one or more genetic markers and the one or more traits are retrieved based on the identifier.
  • the identifier associated with the user is obtained based on one or more login credentials provided by the user.
  • the one or more databases are accessible by the trained language model.
  • the one or more genetic markers comprise one or more of a gene, one or more of a single-nucleotide polymorphism, one or more insertions, one or more deletions, one or more repetitive sequences, and/or a biomarker.
  • the one or more genetic markers are obtained based on a genotyping method.
  • the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof.
  • the 55 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 genotyping method comprises genotyping by sequencing.
  • the genotyping by sequencing comprises amplifying a nucleic acid around the one or more genetic markers using specific primers to obtain amplified specific nucleic acid fragments.
  • genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. [0144] In some embodiments, the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences.
  • the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers.
  • the one or more data structures are formatted as one or more of a string, an array, a JSON table or file, a JSONL table or file, XML table or file, CSV table or file, TSV table or file, plain text file, binary file, or any combination thereof.
  • the trained language model comprises one or more of an autoencoder model, a generative adversarial network, an autoregressive model, a transformer model, a flow-based generative model, and/or a probabilistic generative model.
  • the trained language model is trained based on a training dataset comprising one or more queries paired with one or more responses.
  • a trait comprises a nutritional condition, a physical or health condition, a lifestyle, clinical parameters, and/or a combination thereof.
  • the trained language model is trained based on nutrition dataset comprising one or more training genetic markers paired with one or more training traits or nutritional conditions.
  • the personalized nutritional plan is personalized for the user based on the one or more genetic markers and the one or more traits.
  • the trained language model may additionally or alternatively be used to generate a collective nutritional plan for a group of users based on traits, genetic markers, or nutritional needs of each group member.
  • the AI-driven chatbot was asked for information about these markers.
  • User Query (through a personal computer): "What does my genetic profile mean, specifically?”
  • the AI-driven chatbot provided the following table as a response: 67 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
  • the AI-driven chatbot was asked if these genetic markers can have a nutritional impact on dietary recommendations of the user.
  • User Query “Do these markers have a nutritional impact on my diet?”
  • AI-driven chatbot responded: “1. Variations in the FTO gene have been associated with an increased risk of obesity.
  • the SLC19A1 gene encodes a thiamine transporter protein. Variations in this gene can impact thiamine absorption and utilization. Thiamine is an essential B-vitamin involved in energy metabolism. Individuals with certain SLC19A1 variants may have an increased risk of thiamine deficiency. Adequate thiamine intake through food sources rich in thiamine, such as whole grains, legumes, and fortified foods, is important for individuals with these genetic variations. 3.
  • NAT2 gene variations affect the activity of the N-acetyltransferase 2 enzyme involved in drug metabolism and detoxification. While the impact on dietary composition is not direct, individuals with specific NAT2 variants may have altered responses to certain dietary components or dietary compounds that undergo NAT2-mediated metabolism. However, the primary impact of NAT2 variants is on drug metabolism rather than dietary composition. 4. APOA5 gene variants have been associated with altered triglyceride levels and an increased risk of cardiovascular disease.
  • individuals with APOA5 variants may benefit from dietary strategies that promote healthy lipid profiles, such as reducing the intake of saturated and trans fats, while emphasizing the consumption 68 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 of unsaturated fats, high-fiber foods, and a well-balanced diet. 5.
  • CD36 gene variations have been linked to obesity, dyslipidemia, and insulin resistance. CD36 codes for a protein involved in fatty acid uptake and metabolism. Individuals with CD36 variants may have impaired fatty acid metabolism, potentially requiring dietary adjustments.
  • a diet focusing on healthy fats (e.g., omega-3 fatty acids), moderate fat intake, and a balance of macronutrients may be beneficial for individuals with CD36 genetic variations.
  • Variations in the IGF2BP2 gene have been associated with an increased risk of type 2 diabetes. From a nutritional standpoint, individuals with IGF2BP2 variants may benefit from dietary approaches that help manage blood sugar levels and insulin sensitivity. These may include maintaining a balanced intake of carbohydrates, selecting low glycemic index foods, emphasizing fiber-rich sources, and avoiding excessive added sugars.
  • SLC2A2, or GLUT2 gene variations have been associated with an increased risk of type 2 diabetes. This gene codes for a glucose transporter protein.
  • TCF7L2 is involved in glucose metabolism and insulin secretion. From a nutritional perspective, individuals with TCF7L2 variants may benefit from dietary approaches that help manage blood sugar levels, including a balanced intake of carbohydrates, portion control, and regular physical activity.
  • FLG gene variants are associated with an increased risk of atopic dermatitis (eczema) and other skin conditions.
  • SLC23A1 gene variations can affect the absorption and utilization of vitamin C. Individuals with certain SLC23A1 variants may have altered vitamin C metabolism, potentially requiring higher dietary vitamin C intake. Consuming vitamin C-rich foods, including citrus fruits, berries, and vegetables, can help meet the increased vitamin C needs for individuals with these genetic variations.
  • AMY1 gene variants can impact the production of salivary amylase, an enzyme involved in starch digestion. Individuals with fewer copies of the AMY1 gene may have reduced amylase levels and potentially reduced starch digestion capacity.
  • MLXIPL gene variants are associated with altered glucose metabolism and an increased risk of type 2 69 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 diabetes. From a nutritional perspective, individuals with MLXIPL variants may benefit from dietary strategies that promote stable blood sugar levels, such as moderate carbohydrate intake, emphasis on whole grains, fiber-rich foods, and regular physical activity. 13. FGF21 gene variants have been associated with metabolic disorders such as obesity and insulin resistance.
  • PCSK9 gene variations affect cholesterol metabolism and have been associated with an increased risk of cardiovascular disease. From a nutritional perspective, individuals with PCSK9 variants may benefit from dietary strategies that promote heart health, such as reducing the intake of saturated and trans fats, while emphasizing the consumption of unsaturated fats, dietary fiber, and cholesterol-lowering foods”. [0175] Then the AI-driven chatbot was asked to give a dietary recommendation based on the genetic markers.
  • NAT2 While NAT2 primarily affects drug metabolism rather than dietary composition, it is important to consider any medications or dietary compounds that undergo 70 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 NAT2-mediated metabolism.
  • APOA5 and CD36 Individuals with APOA5 or CD36 variants may benefit from dietary strategies that promote healthy lipid profiles. Include foods rich in unsaturated fats (e.g., fatty fish, avocados, nuts, and seeds) while minimizing saturated and trans fats. Emphasize whole grains, fruits, vegetables, and lean protein sources to support overall heart health. 5. IGF2BP2, SLC2A2, and TCF7L2: Individuals with these genetic markers associated with an increased risk of type 2 diabetes can benefit from a balanced diet that focuses on complex carbohydrates, fiber-rich foods, moderate protein intake, and healthy fats.
  • unsaturated fats e.g., fatty fish, avocados, nuts, and seeds
  • IGF2BP2, SLC2A2, and TCF7L2 Individuals with these genetic markers associated with an increased risk of type 2 diabetes can benefit from a balanced diet that focuses on complex carbohydrates, fiber-rich foods, moderate protein intake, and healthy fats.
  • FLG While FLG primarily affects skin health, consuming a nutrient-dense diet rich in antioxidants (e.g., fruits, vegetables) and essential fatty acids (e.g., fatty fish, nuts, seeds) can support overall skin health and potentially alleviate skin conditions associated with FLG variations.
  • antioxidants e.g., fruits, vegetables
  • essential fatty acids e.g., fatty fish, nuts, seeds
  • SLC23A1 Increase your intake of vitamin C-rich foods such as citrus fruits, berries, kiwi, peppers, and leafy green vegetables to support adequate vitamin C absorption and utilization. 8.
  • AMY1 Individuals with fewer copies of the AMY1 gene may benefit from moderating their starch intake and focusing on a balanced diet that includes a variety of nutrient-dense foods, including whole grains, lean proteins, healthy fats, and a variety of fruits and vegetables.
  • MLXIPL Consider a diet that supports stable blood sugar levels, including moderate carbohydrate intake from whole grains, legumes, and vegetables. Emphasize fiber-rich foods and include regular physical activity as part of an overall healthy lifestyle.
  • FGF21 While dietary recommendations specific to FGF21 variants are limited, focusing on a balanced diet that includes a variety of whole foods, adequate protein, fiber-rich sources, and appropriate calorie management can support metabolic health. 11.
  • PCSK9 To support heart health, prioritize a diet that includes unsaturated fats (e.g., fatty fish, avocados, nuts, and seeds), dietary fiber, whole grains, and plant-based proteins. Limit the intake of saturated and trans fats, processed foods, and excessive cholesterol”.
  • unsaturated fats e.g., fatty fish, avocados, nuts, and seeds
  • dietary fiber e.g., whole grains, and plant-based proteins.
  • the AI-driven chatbot was asked to give a nutrition plan for one week based on the dietary recommendations: [0179] User Query (through the personal computer): 71 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 “Based on my genetic markers, please provide a 7-day nutrition plan” [0180] The AI-driven chatbot suggested the next schedule: [0181] Finally, the AI-driven chatbot was asked to modify the nutrition plan since the individual tested has type 2 diabetes: [0182] User Query (through the personal computer): “Recently I was diagnosed with type 2 diabetes. Please modify the nutrition plan according to this health condition” [0183] The user query can be inputted into the AI model in the plain text format.
  • Additional information such as previous nutrition plans can be retrieved from one of the databases described herein and inputted in the model as well.
  • 72 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0184]
  • the biological sample was saliva.
  • the user deposited their saliva 74 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 into a 5 mL vial tube (vial 1) through a collector funnel (as part of this kit, a second container (vial 2) which contains a solution to preserve nucleic acids, is available for when the sample delivery is too long).
  • the sample is next processed to extract the DNA to be then genotyped.
  • the DNA is submitted to genotyping by sequencing.
  • PCR reactions are performed, using multiplexed primers (Table 2) in two pools, pool A (77 pairs of primers) and pool B (23 pairs of primers), using the NEBNext® UltraTM II Q5® proofreading polymerase enzyme (New England Biolabs).
  • PCR products are purified using SpeedBeadsTM magnetic carboxylate modified particles (Cytiva), DNA ends prepared with NEBNext® UltraTM II End Repair/dA-Tailing Module (New England Biolabs), and sequencing adapters ligated with Blunt/TA Ligase Master Mix (New England Biolabs).
  • the user Before or after the genetic markers are obtained, the user fulfills a form with information regarding their life style, heath or nutrition conditions, the user profile information: 80 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0193]
  • This information is then converted into a JSON/JSONL format to be inputted into the system: ⁇ "alcoholConsumption”: “1 to 2 times per week”, “allergiesAndIntolerances”: “Nuts, Peanuts”, “birthDate”: “1996-07-03”, “currentWeight”: “76 kg”, “dailyCommute”: “Car or public transportation”, “dailyWater”: “2 to 6 glasses per day”, “dietaryPreference”: “No dietary preferences”, “engagesInSports”: “Yes”, “sportDetails”: “Gym - 2 to 3 times per week”, “gender”: “Male”, “height”: “170 cm”, “mealsPerDay”: “Bre
  • AI-driven chatbot response (through the personal computer): 81 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0197] Then, the user inquire the system for a 7-day diet. This diet is based on the genetic markers and on the profile information fulfilled previously.
  • the categories of the markers are: 83 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
  • Example 4 [0201]
  • the system demonstrates its capability to design a diet for multiple users simultaneously, creating a common meal plan that accommodates different dietary needs and preferences.
  • the system receives parameters for two users, U1 (John) and U2 (Karina), in JSON format: U1: ⁇ "allergiesOrIntolerances”: [ “egg” ], “consumesProteinShakes”: false, “dietRestrictions”: [], “foodPreferences”: [ “meat”, “wholeEgg”, “fish”, “redBeans”, “lentils”, “pasta”, “bread”, “rice”, “quinoa”, “mayonnaise”, “nuts”, “avocado”, “milk”, “whiteCheese”, “strawberry”, “banana”, “lettuce”, “cauliflower” ], “get”: 2152, "maxCarbCaloriesPercentagePerMeal”: 60, “maxDailyCarbGrams”: null, 84 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 "maxDailyCarbPercentageTDEE”:
  • the meal incorporates preferred foods from both users, such as avocado, white cheese, and fruits.
  • the nutritional composition aligns with both users' macronutrient requirements, providing a balance of carbohydrates, proteins, and fats.
  • the meal is rich in fiber, vitamins, and minerals, contributing to the daily nutritional goals of both users.” [0204] By creating this common breakfast plan, the system demonstrates its ability to cater to multiple users simultaneously, balancing individual needs and preferences while providing a nutritious and enjoyable meal for both.

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Abstract

The present invention relates to the field of nutrition and health management. An exemplary method for generating a personalized nutritional plan for a user comprises: receiving, from the user, a natural-language query; identifying, based on the natural-language query, a task and one or more parameters; retrieving from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; inputting the one or more data structures into a trained language model to generate the personalized nutritional plan; and displaying the personalized nutritional plan.

Description

ATTORNEY DOCKET NO: 230702000340 SYSTEM FOR PERSONALIZED NUTRITION BASED ON INDIVIDUAL GENETICS, HEALTH AND CLINICAL CONTEXT, AND ARTIFICIAL INTELLIGENCE CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of U.S. Provisional Patent Application No. 63/590,273, filed October 13, 2023, the entire contents of which are incorporated by reference herein. BACKGROUND 1. Field of The Invention [0002] The present invention relates to the field of nutrition and health management. More specifically, it pertains to methods and systems for creating personalized nutritional dietary advice (also referred as precision nutrition) based on an individual's genetic information and leveraging the power of artificial intelligence (AI). 2. Description of the related art [0003] Maintaining a healthy diet is essential for overall well-being and the prevention of various health conditions. However, individual genetic variations (such as genetic predispositions to certain diseases and variations in metabolism) can determine the nutrition response to the uptake and metabolism of macro or micronutrients. Thus, nutrigenetics can be used to personalize diets by modifying them according to individual genetic variation. Conventional dietary recommendations and generic nutrition plans may not take these individual variations into account, leading to suboptimal results for individuals seeking to improve their health through diet. [0004] The emerging field of nutrigenomics describes the interaction between nutrients and genes and also links genetics to nutrition, physiology, biochemistry, metabolomics, proteomics, transcriptomics and bioinformatics. Nutrigenetics differentiates from nutrigenomics since the hereditary genetic information can provide valuable insights into an individual's response to specific nutrients and dietary patterns. By analyzing an individual's genetic profile, it is possible to identify genetic markers and variations associated with 1 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 nutrient metabolism, food sensitivities, and health risks. This information can be used to personalize dietary recommendations and optimize the nutritional intake for each individual. [0005] Existing systems and methods that incorporate genetic information for personalized nutrition are limited in their scope and efficiency. For example, WO2020210487A1 describes systems and methods for nutrigenomics and nutrigenetic analysis, including generating a nutrigenetic profile of a subject based on genetic information, identifying biological states related to various pathways, and outputting an electronic report indicating these states. US2006099310A1 describes an automatic compounding device used to create personalized nutritional supplement formulations based on input information such as personal identification, genotypic and phenotypic analysis, nutritional needs, and supplement recipes. The device processes the information into compounding instructions and compounds the unitary supplement. US11600375B2 outlines a method for providing genetically personalized food recommendations. The method involves storing a mapping of micronutrients to genetic information, medical information, and therapeutic objectives, as well as a food menu for at least one restaurant. By receiving user input, including genetic information, medical information, therapeutic objectives, and dietary preferences, the system identifies suitable menu items and outputs a personalized list of food choices. [0006] None of the above-described methods leverages AI to link the genetic information and the nutritional needs of an individual, or to provide improved outputs in an iterative manner by the interaction history with the user. Accordingly, there is a need for improved methods and systems that combine genetics, contextual information and AI to create highly tailored and effective nutritional dietary recommendations for individuals. Such an improved method should be able to enable interactive contexts generated by the AI itself taking into account proposed menus and/or physiological responses, and the history of the interaction as well. SUMMARY [0007] The present disclosure discloses devices, apparatuses, systems, methods, and non- transitory storage media for providing personalized nutritional dietary recommendations (also referred to as dietary compositions) based on an individual’s genetics and machine-learning techniques. In some embodiments, the system includes a genetic analysis module capable of analyzing an individual's genetic information to identify genetic markers, variations, and 2 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 health-related traits. This genetic information is then processed by an AI-based recommendation engine, which utilizes machine learning algorithms to generate personalized dietary advice. [0008] The AI recommendation engine takes into account the individual's genetic profile, health goals, dietary preferences, biochemical profile, and restrictions to create optimal nutritional plans. The system can consider factors such as nutrient requirements, food sensitivities, metabolism, and genetic predispositions to tailor the dietary recommendations to the individual's specific needs. The generated dietary recommendations can include recommendations for macronutrient ratios, specific food items, portion sizes, and meal plans. [0009] By leveraging the power of genetics and AI, the method and system described herein aim to provide individuals with highly precise and effective nutritional dietary recommendations (i.e., personalized nutrition). This can lead to improved health outcomes, better management of specific health conditions, and the prevention of diseases associated with dietary factors. [0010] An exemplary method for generating a personalized nutritional plan for a user comprises: receiving, from the user, a natural-language query; identifying, based on the natural-language query, a task and one or more parameters; retrieving from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; inputting the one or more data structures into a trained language model to generate the personalized nutritional plan; and displaying the personalized nutritional plan. [0011] In some embodiments, the system further prompts the user to input a plurality of answers to a questionnaire; and constructs a user profile based on the plurality of answers. In some embodiments, the user profile is stored on the one or more databases. In some embodiments, the user profile comprises a plurality of genetic markers and a plurality of traits associated with the user. In some embodiments, the one or more genetic markers are retrieved from the plurality of genetic markers of the user profile, and the one or more traits are retrieved from the plurality of traits of the user profile. 3 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0012] In some embodiments, the user profile further comprises a plurality of biochemical test results associated with the user. [0013] In some embodiments, the method 800 further comprises obtaining an identifier associated with the user, wherein the one or more genetic markers and the one or more traits are retrieved based on the identifier. In some embodiments, the identifier associated with the user is obtained based on one or more login credentials provided by the user. In some embodiments, the one or more databases are accessible by the trained language model. [0014] In some embodiments, the one or more genetic markers comprise one or more of a gene, one or more of a single-nucleotide polymorphism, one or more insertions, one or more deletions, one or more repetitive sequences, and/or a biomarker. [0015] In some embodiments, the one or more genetic markers are obtained based on a genotyping method. In some embodiments, the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof. In some embodiments, the genotyping method comprises genotyping by sequencing. In some embodiments, the genotyping by sequencing comprises amplifying a nucleic acid around the one or more genetic markers using specific primers to obtain amplified specific nucleic acid fragments. In some embodiments, genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. [0016] In some embodiments, the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences. In some embodiments, the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers. [0017] In some embodiments, the one or more data structures are formatted as one or more of a string, an array, a JSON table or file, a JSONL table or file, XML table or file, CSV table or file, TSV table or file, plain text file, binary file, or any combination thereof. [0018] In some embodiments, the trained language model comprises one or more of an autoencoder model, a generative adversarial network, an autoregressive model, a transformer 4 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 model, a flow-based generative model, and/or a probabilistic generative model. In some embodiments, the trained language model is trained based on a training dataset comprising one or more queries paired with one or more responses. [0019] In some embodiments, a trait comprises a nutritional condition, a physical or health condition, a lifestyle, clinical parameters, and/or a combination thereof. In some embodiments, the trained language model is trained based on nutrition dataset comprising one or more training genetic markers paired with one or more training traits or nutritional conditions. [0020] In some embodiments, the personalized nutritional plan is personalized for the user based on the one or more genetic markers and the one or more traits. [0021] In some embodiments, the method 800 further comprises inputting the one or more data structures into the trained language model to generate a natural-language response describing an association between the one or more genetic markers and one or more traits or nutritional conditions of the user; and displaying the natural-language response. [0022] In some embodiments, the method 800 further comprises inputting the one or more data structures into the trained language model to generate a request for additional information from the user; and displaying the request for additional information to the user. In some embodiments, the request for additional information comprises a prompt for the user to input one or both of an additional genetic marker or an additional trait. [0023] In some embodiments, the method 800 further comprises receiving a natural- language query to retrieve user-specific information; retrieving the user-specific information; and displaying the retrieved user-specific information. In some embodiments, the method 800 further comprises receiving a natural-language query to update the personalized nutritional plan; and displaying an updated personalized nutritional plan. [0024] In some embodiments, the method further comprises retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into the trained language model to generate a 5 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 collective nutritional plan for the group; and displaying the collective nutritional plan. In some embodiments, the group comprises family members sharing meals. [0025] In some embodiments, the trained language model is trained to identify nutritional requirements for each member of the group based on the one or more genetic markers and the one or more traits; determine whether the nutritional requirements for two or more members of the group conflict; and in response to determining that the nutritional requirements for two or more members of the group conflict, include meals in the collective nutritional plan that can be modified to accommodate the conflicting nutritional requirements. In some embodiments, the trained language model is trained to generate individual nutritional plans for each member of the group in addition to the collective nutritional plan. [0026] In some embodiments, the trained language model is augmented with a numerical model, and the trained language model is trained to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts. In some embodiments, the trained language model is trained to receive feedback from the user; process the feedback using the numerical model; and adjust the recommended nutrient amounts in real-time based on the processed feedback. In some embodiments, the trained language model is trained to calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and generate the individual nutritional plan to reflect the recommended caloric intake. In some embodiments, the trained language model is trained to receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; and modify the personalized nutritional plan based on the calculated portion sizes. [0027] An exemplary system for generating a personalized nutritional plan for a user comprises one or more non-transitory computer-readable storage media; and one or more processors coupled to the one or more storage media, the one or more processors configured to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures 6 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 associated with the task based on the one or more genetic markers and the one or more traits associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan. [0028] In some embodiments, the one or more processors are further configured to: retrieve, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; input the one or more data structures into the trained language model to generate a collective nutritional plan for the group; and display the collective nutritional plan. In some embodiments, the trained language model is augmented with a numerical model, and the one or more processors are further configured to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts. In some embodiments, the one or more processors are further configured to the one or more processors are further configured to: calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and generate the individual nutritional plan to reflect the recommended caloric intake. In some embodiments, receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modify, using the trained language model, the personalized nutritional plan based on the calculated portion sizes; and display the modified personalized nutritional plan with the calculated portion sizes. [0029] An exemplary non-transitory computer-readable medium comprises instructions that, when executed by one or more processors of a system, cause the one or more processors to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits 7 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan. In some embodiments, the instructions, when executed by the one or more processors, cause the one or more processors to receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modify, using the trained language model, the personalized nutritional plan based on the calculated portion sizes; and display the modified personalized nutritional plan with the calculated portion sizes. [0030] An exemplary kit for generating a personalized nutritional plan comprises: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and/or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and instructions for using the kit to perform a method for identifying the presence of one or more genetic markers in the biological sample provided by a user, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; identifying variations in the nucleic acids through a genotyping method; identifying, using the one or more processors, the presence of the one or more genetic markers in the biological sample; comparing the location of the genetic markers with a reference database containing the nucleic acid fragments comprising the reference genetic markers; using the identified genetic markers as a natural- language query for inputting a system for generating a personalized nutritional plan; and displaying the personalized nutritional plan to the user. [0031] In some embodiments, the kit comprises optionally one or more cotton swabs to extract the biological sample. [0032] In some embodiments, the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from animals. [0033] In some embodiments, the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from vertebrate organisms. [0034] In some embodiments, the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from mammals. 8 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0035] In some embodiments, the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from humans. [0036] In some embodiments, the biological sample comprises samples coming from tissues or secretions such as blood, skin, mucous membranes, saliva, semen, hair, urine, cavity secretions and/or stool. [0037] In some embodiments, the kit further comprises a binding matrix for the retention of genetic material. [0038] In some embodiments, the genotyping method looks for single nucleotide polymorphisms, insertions, deletions, and/or repetitive sequences. [0039] In some embodiments, the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof. [0040] In some embodiments, the genotyping method is genotyping by sequencing. [0041] In some embodiments, the genotyping by sequencing comprises amplifying the nucleic acid around the genetic markers using specific primers to obtain amplified specific nucleic acid fragments. [0042] In some embodiments, genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. [0043] In some embodiments, the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences. [0044] In some embodiments, the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers. [0045] An exemplary method for generating a group nutritional plan using a trained language model includes: retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; constructing one or 9 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into a trained language model to generate a collective nutritional plan for the group; and displaying the collective nutritional plan. [0046] In some embodiments, the method comprises receiving feedback on the collective nutritional plan from one or more members of the group; adjusting the collective nutritional plan based on the feedback from the more one or more members of the group; and displaying the adjusted collective nutritional plan. In some embodiments, the trained language model is augmented with a numerical model, and the trained language model is trained to: receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; and modify the personalized nutritional plan based on the calculated portion sizes. In some embodiments, calculating, using the numerical model, portion sizes of the one or more modified foods comprises: comparing nutritional compositions of the one or more modified foods with nutritional compositions of one or more foods originally in the personalized nutritional plan; and adjusting portion sizes of the one or more modified foods to match the nutritional compositions of the one or more foods originally in the personalized nutritional plan. In some embodiments, the method includes displaying real-time feedback on nutritional impacts of the one or more modified foods to the user. [0047] An exemplary method for modifying a personalized nutritional plan includes: generating an initial personalized nutritional plan based on a user's genetic markers and traits; receiving a user input modifying one or more foods in the initial personalized nutrition plan; calculating, using a trained language model augmented with a numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modifying, using the trained language model, the initial personalized nutritional plan based on the calculated portion sizes; and displaying the modified personalized nutritional plan with the calculated portion sizes. BRIEF DESCRIPTION OF FIGURES [0048] FIG. 1 depicts an exemplary system, in accordance with some embodiments. 10 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0049] FIG. 2 depicts an exemplary system involving the modules related to sampling kit, nucleic acid sequencing from the user sample, and the unveiling of genetic markers from the sequence of nucleic acids, in accordance with some embodiments. [0050] FIG. 3 depicts an exemplary system involving the modules related to information provided by the user related to health and clinical data as well as nutrition requirements and food sensitivities in order to feed and fine-tune the AI-driven chatbot that makes a dietary recommendation to the user, in accordance with some embodiments. [0051] FIG. 4 depicts an exemplary system involving the modules related to data science platform which produces information to be stored in a repository keeping genetic markers and associated phenotypes as well as the involvement of a specialist, both feeding and fine- tuning the AI-driven chatbot that makes the dietary recommendation to the user, in accordance with some embodiments. [0052] FIG. 5 depicts an exemplary system for generating a collective nutritional plan for multiple users, in accordance with some embodiments. [0053] FIG. 6 illustrates an exemplary electronic device, in accordance with some embodiments. [0054] FIG. 7 illustrates an exemplary process for generating a personalized nutritional plan for a user, in accordance with some embodiments. [0055] FIG. 8 depicts exemplary components of a kit used to analyze sample from a user, in accordance with some embodiments. [0056] FIG. 9 depicts an exemplary Genetic Markers Report which can be retrieved from the system to summarize and aggregate the results of the genetic test. Some of the markers (genes) have more than one variant, which in turn are classified as “high”, “medium” or “low” risk, according to their genotypes. Markers are also categorized according to their participation in different biological processes related to nutrition. 11 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 DESCRIPTION OF THE EMBODIMENTS 1. General description [0057] The description of the various embodiments disclosed here should not limit the present disclosure but rather instruct a skilled person in the art to carry out the different embodiments of the system explained here. [0058] The present disclosure is directed to an integrated system that combines genetic analysis, artificial intelligence (e.g., one or more machine-learning models), and nutrition expertise to offer personalized nutrition guidance based on genetic markers and health-related data. The system includes modules for user interaction, DNA analysis, genetic markers, database management, AI-driven chatbot, and/or nutrition specialist feedback. It facilitates efficient data flow, analysis, and guidance, supporting users in understanding their genetic profile and making informed dietary choices for improved health outcomes. [0059] The system comprises interconnected modules that collaborate to provide personalized genetic analysis and precision nutrition guidance. Users interact with the system to access genetic analysis results and receive tailored recommendations. Biological samples collected by users are sent for DNA sequencing and analysis. The user's genetic markers are stored in one or more databases, which can serve as an information source for an AI-driven chatbot. The chatbot leverages machine-learning techniques to deliver personalized guidance based on the user's genetic profile and health data. The system also integrates user-specific information related to health, clinical data, food sensitivities, and nutrient requirements, enhancing the accuracy of the recommendations. [0060] In addition, the system includes a data science platform for analyzing genetic markers and identifying correlations with specific phenotypes. This information is stored in a dedicated database and fed into the AI-driven chatbot. A nutrition specialist can access the database and provide expert consultation based on the user's genetic profile, health data, and nutritional requirements. The collaboration between the chatbot and the nutrition specialist ensures accurate and up-to-date recommendations for users. The system aims to optimize the flow of information, analysis, and communication to empower users in making informed dietary decisions. 12 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0061] Embodiments of the present disclosure can provide technical advantages in terms of integrating complex genetic information through deep learning methods that enable new correlations, predictions, and personalized recommendations as applied to nutrition, although the same can be said for other applications such as rare diseases, or polygenic traits. For the specific application of personalized nutrition, the user interaction context is crucial as lifestyle (e.g., exercise patterns and other social/religious constraints) and nutritional preferences are dynamic. At the same time, the genetic profiles enable an additional layer of complexity which is unlikely to be leveraged by professional nutritionists due to the sheer size of the number of genes involved in processes of metabolism, allergies, and vitamin and mineral assimilation. Personalized nutrition systems based on machine learning do not use genetic profiles currently, thus the potential synergy between the integration of these technologies. Finally, a system that enables integrating groups of people having specific nutritional needs and constraints facilitates choices of menus/diets for a whole group (such as a family where members might have particular allergies, intolerance, or diseases). Thus, embodiments of the present disclosure can leverage machine-learning techniques to efficiently process user-specific interaction context, nutritional preferences, and genetic profiles to provide accurate dietary recommendations. The embodiments of the present disclosure can also improve the functioning of a computer by reducing processing power and increasing the processing speed of the computer to generate accurate and personalized dietary recommendations. 2. Definitions [0062] In order to provide greater clarity to the disclosure in this disclosure, the following are definitions of some relevant terms and concepts that will aid in understanding the content presented here. [0063] As disclosed herein, it is appropriate to differentiate the terms nutrigenetics and nutrigenomics. Nutrigenetics generally focuses on how genetic variations or variations in individual genes affect the body's response to nutrients and dietary factors. It examines how genetic differences influence an individual's susceptibility to certain diseases or conditions and how they respond to specific nutrients. Nutrigenetic research seeks to identify genetic variations that impact an individual's nutritional requirements and help tailor personalized dietary recommendations based on their genetic profile. On the other hand, Nutrigenomics looks at the broader effects of nutrients and dietary components on gene expression and how 13 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 they influence the body's metabolic pathways. It examines how dietary factors can affect gene expression, protein synthesis, and metabolism. Nutrigenomics investigates how specific nutrients can interact with genes and either promote or suppress certain biological processes. It aims to understand how diet-gene interactions impact an individual's health and disease risk. [0064] As described herein, a genetic marker, also known as a nucleic acid marker or a molecular marker, can refer to a specific nucleic sequence or variation in the nucleic acid sequence that can be used to identify and locate genes or genetic regions on a chromosome. Genetic markers serve as recognizable features within the genome that can be inherited and tracked through generations. These markers can be single nucleotide polymorphisms (SNPs), which are variations in a single DNA base pair, or they can be larger-scale structural variations such as insertions, deletions, or duplications. Genetic markers can also include variations in the number of repetitive DNA sequences, known as short tandem repeats (STRs) or microsatellites. Genetic markers are typically used in genetic studies and research to analyze patterns of inheritance, genetic linkage, and association with certain traits or diseases. They allow for the identification of the presence or absence of specific genetic sequences or variations in individuals or populations. A genetic marker can also be an RNA marker (e.g. transcriptome) or epigenetic marker, or any other signature or pattern on nucleic acid sequences or molecular components interacting with them or encoded in them. By examining the distribution of genetic markers within a population, insights can be gained into genetic diversity, evolutionary relationships, and the inheritance patterns of specific traits or diseases. Genetic markers are also utilized in various applications such as genetic mapping, paternity testing, forensic analysis, and personalized medicine, where they can aid in predicting an individual's risk for certain diseases or response to certain treatments. [0065] As described herein, dietary guidelines and recommendations can refer to evidence- based guidelines (e.g., developed by governmental, authoritative and/or scientific bodies) to provide guidance on healthy eating patterns and lifestyle choices. These guidelines aim to promote optimal nutrition, prevent chronic diseases, and support overall health and well- being. The primary purpose of dietary guidelines and recommendations is to offer scientifically backed advice on food choices and dietary patterns that can help individuals make informed decisions about their nutrition. They are typically developed based on a comprehensive review of scientific research, including studies on nutrition, metabolism, 14 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 chronic disease prevention, and public health. These guidelines often address various aspects of a healthy diet, including the types and amounts of foods to consume, as well as other lifestyle factors such as physical activity, hydration, and weight management. They are typically tailored to specific populations or age groups, taking into consideration the unique dietary needs and cultural preferences of different individuals or communities. Some common components covered in dietary guidelines and recommendations include: (a) food groups and nutrients, which provide guidance on the consumption of different food groups such as fruits, vegetables, grains, proteins, and dairy products. They may also highlight the importance of specific nutrients like fiber, vitamins, minerals, and essential fatty acids; (b) portion sizes and energy balance, wherein the guidelines may provide recommendations on appropriate portion sizes and calorie intake to maintain a healthy weight and prevent excessive energy intake; (c) nutrient and ingredient recommendations, which may advise on limiting the consumption of certain nutrients such as added sugars, sodium, saturated fats, and trans fats, which are associated with increased risks of chronic diseases. They may also recommend increasing the intake of nutrient-dense foods (those that provide a high amount of essential nutrients relative to their calorie content, for example fruits, vegetables, whole grains, lean proteins (such as poultry, fish, legumes), and low-fat dairy products); (d) personalized considerations, wherein some guidelines may provide specific recommendations of nutrient intake for individuals such as infants, children, pregnant women, older adults, or individuals with certain health conditions. They may address their unique nutrient requirements or considerations for dietary restrictions or allergies; (e) healthy eating patterns, wherein dietary guidelines often promote specific healthy eating patterns, such as the Mediterranean diet, DASH (Dietary Approaches to Stop Hypertension) diet, or plant-based diets, which have shown positive health outcomes in scientific studies. It is important to note that dietary guidelines and recommendations are periodically reviewed and updated to reflect the latest scientific research and emerging evidence. They are designed to inform the general public, healthcare professionals, policymakers, and other stakeholders about best practices for healthy eating and nutrition. By following dietary guidelines and recommendations, individuals can make informed choices about their diet, leading to improved health outcomes, reduced risk of chronic diseases, and overall well-being. [0066] The term balanced diet can refer to consuming a variety of foods from different food groups in appropriate proportions. It typically includes fruits, vegetables, whole grains, lean proteins, healthy fats, and low-fat dairy products. A balanced diet ensures the intake of 15 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 essential nutrients, such as carbohydrates, proteins, fats, vitamins, minerals, and fiber, which are necessary for optimal bodily functions. [0067] As described herein, macronutrients can include the major nutrients required in relatively large quantities and include carbohydrates, proteins, and fats. Carbohydrates are the body's primary source of energy, while proteins are essential for tissue repair and growth. Fats provide energy, help absorb fat-soluble vitamins, and contribute to hormone production. Micronutrients, on the other hand, may be required in smaller amounts and include vitamins and minerals. They are crucial for various bodily functions, including immune support, bone health, and enzyme activities. [0068] As described herein, genotyping can include the process of determining an individual's genetic makeup, specifically by identifying variations in their DNA sequence (even in RNA sequences). It can involve analyzing an individual's DNA to identify specific genetic variations, such as single nucleotide polymorphisms (SNPs), insertions, deletions, or other genetic mutations. Genotyping can provide information about an individual's genetic predispositions, ancestry, and susceptibility to certain diseases or conditions. Genotyping is used in various fields, including genetics research, medical diagnostics, and personalized medicine. It can be performed using different techniques, namely: (1) Polymerase Chain Reaction (PCR): PCR is a widely used technique to amplify specific DNA regions, including genetic markers of interest. To genotype a particular SNP, specific primers are designed to target the SNP site. By analyzing the resulting PCR product, researchers can determine the genotype based on the presence or absence of a particular DNA sequence variant. (2) ARMS PCR (Amplification Refractory Mutation System PCR): ARMS PCR is a specialized PCR technique used for genotyping specific mutations or SNPs. It relies on allele-specific primers that are designed to selectively amplify the target allele. The presence or absence of PCR amplification indicates the genotype. (3) qPCR (Quantitative PCR): qPCR is used not only for gene expression analysis but also for genotyping. It can be used in allelic discrimination assays where different fluorescent probes or dyes are used to detect specific alleles, allowing for quantification of different genotypes. (4) DNA or RNA Microarrays: Microarrays are high-throughput platforms that can simultaneously analyze thousands to millions of genetic markers. In genotyping applications, probes specific to known genetic variants are immobilized on the microarray. The sample DNA or RNA is hybridized to the probes, and the resulting pattern of binding provides genotype information. (5) Integrated Fluid Circuits: 16 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 Microfluidic devices, also known as integrated fluidic circuits, can be used for genotyping. These devices allow for precise control of small volumes of liquid, making them suitable for high-throughput genotyping assays. (6) MassARRAY SNP Genotyping: MassARRAY is a technology that combines mass spectrometry with PCR to analyze SNP genotypes. It involves the use of allele-specific extension primers and mass spectrometry to determine the alleles present at specific SNP positions. (7) Genotyping by Sequencing (GBS): This method involves next-generation sequencing (NGS) or high-throughput sequencing (HTS) technologies to simultaneously genotype a large number of SNPs across the genome, and sometimes a process of variant calling to unveil the identity, location and quality of the variants present in the genome. It's a cost-effective approach for genotyping in research and agricultural or clinical applications. [0069] The relationship between genetic markers and dietary recommendations lies in the understanding that genetic variations can influence how individuals respond to certain nutrients and dietary patterns. By considering an individual's genetic markers, personalized dietary recommendations can be tailored to optimize their nutritional intake and promote better health outcomes. The key aspects of this relationship include: (a) nutrient metabolism, wherein the genetic markers can provide insights into an individual's metabolism of specific nutrients. For example, variations in genes related to carbohydrate metabolism can affect how efficiently an individual processes and utilizes carbohydrates. Similarly, genetic markers related to fat metabolism can influence the body's response to dietary fats. By identifying these genetic variations, dietary recommendations can be customized to accommodate individual differences in nutrient metabolism; (b) nutrient requirements, wherein the genetic markers can also indicate an individual's specific nutrient requirements. Variations in genes related to vitamin or mineral metabolism can affect the body's ability to absorb, transport, or utilize certain nutrients. For instance, genetic markers associated with vitamin D metabolism can provide insights into an individual's vitamin D needs. By considering these genetic variations, dietary recommendations can be adjusted to ensure sufficient intake of specific nutrients; (c) food sensitivities and intolerances, wherein genetic markers can reveal an individual's predisposition to food sensitivities or intolerances. For example, variations in genes related to lactose intolerance or gluten sensitivity can inform dietary recommendations by suggesting avoidance or moderation of specific food components. By understanding an individual's genetic predispositions, dietary recommendations can help manage or prevent adverse reactions to certain foods; (d) disease risk and prevention, wherein certain genetic 17 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 markers are associated with an increased risk of developing specific diseases, such as cardiovascular diseases or type 2 diabetes. By identifying these genetic markers, dietary recommendations can be tailored to mitigate the associated risks. For instance, individuals with genetic markers indicating increased susceptibility to heart disease may receive recommendations to limit dietary saturated fats and increase the intake of heart-healthy foods; (e) weight management, wherein genetic markers can influence an individual's propensity to gain or lose weight in response to specific dietary factors. By analyzing genetic markers associated with obesity or weight regulation, dietary recommendations can be personalized to optimize weight management strategies. This may involve modifications in macronutrient ratios, portion sizes, or specific dietary approaches based on an individual's genetic profile. [0070] As described herein, a user can be considered an individual utilizing the system to obtain a nutritional plan based on a genetic makeup and specific traits. The biological sample from which the genetic makeup is obtained can be from the user themself or from an animal sample. In either case, the biological sample can be a sample from tissues or secretions such as blood, skin, mucous membranes, saliva, semen, hair, urine, cavity secretions and/or stool. [0071] As described herein, a trait is an inherited or acquired characteristics or qualities of an individual, especially when distinguishing them from others. In the context of this system, traits can include physical or biological attributes influenced by one or more genes. In this context, a trait comprises a nutritional condition, which is the health status of an individual as it pertains to their diet, including any deficiencies, intolerances, or specific dietary needs they may have. Traits may also include inherited or acquired health and fitness traits, such as an individual’s basal metabolic rate, genetic predisposition to weight gain or loss, and/or an individual’s daily activity level. [0072] As described herein, a trained language model can include a computational model that has been trained on training datasets to understand, interpret, and generate human-like text (i.e., natural-language text) based on the input it receives. These models include the Large Language Models (LLM) with examples such as GPT-3 or BERT. Developed by OpenAI, GPT-3 (Generative Pre-trained Transformer 3) is one of the most well-known language models. It has 175 billion parameters and can perform a wide range of natural language understanding and generation tasks. Meanwhile, BERT (Bidirectional Encoder Representations from Transformers), developed by Google, is designed for understanding the 18 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 context of words in a sentence. It has been influential in the field of natural language processing (NLP). [0073] As described herein, different AI models can be used, in particular Generative models, which can include: (1) Variational Autoencoders (VAEs), which are a type of autoencoder neural network that learns to encode data into a lower-dimensional representation (encoder) and decode it back to the original space (decoder). They can generate new samples by sampling from the learned latent space. (2) Generative Adversarial Networks (GANs), which comprise two neural networks, a generator, and a discriminator, that play a two-player minimax game. The generator tries to create realistic samples to deceive the discriminator, which, in turn, tries to distinguish between real and generated samples. GANs have been very successful in generating high-quality images, videos, and other data types. (3) Autoregressive Models, which are generative models that model the joint probability distribution of the data by factorizing it as a product of conditional probabilities. They generate samples by sequentially predicting one element of the data at a time, conditioning on the previous elements. (4) Transformers, which have shown remarkable success in natural language processing tasks, but they can also be used for image generation. Variants like the GPT-3 model can generate text and even images given appropriate conditioning. Transformers are a type of neural network architecture with the ability to handle long-range dependencies in input sequences (of words), making it particularly effective for NLP tasks such as machine translation, text summarization, text generation, sentiment analysis, among others. (5) Flow-Based Models, which aim to transform a simple probability distribution into a more complex one to model the data distribution. They provide tractable likelihoods, making sampling and evaluation straightforward. (6) Boltzmann Machines, which are a type of probabilistic generative model that use energy-based learning. They have been largely replaced by more modern architectures like VAEs and GANs but are worth mentioning as a historical model. [0074] As described herein, embeddings are a numeric representation of the sentence that captures its semantic meaning. The specific numbers in the embeddings are determined by the pre-trained AI-model and are not directly interpretable by humans. They are a way to represent the sentence's meaning in a high-dimensional vector space for further analysis or processing. An embedding is obtained through the use of deep learning models, such as Transformer-based models, which have been trained on large text corpora. Here's how the 19 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 process generally works: (1) Tokenization: The input sentence is first tokenized into individual words or subword units. Each token is typically associated with an integer ID, and this tokenized sequence becomes the input to the model. (2) Word Embeddings: Each token's integer ID is converted into a dense vector representation, known as a word embedding. Word embeddings capture the semantic meaning of individual words or subword units based on their context in the training data. (3) Model Architecture: The sentence, represented as a sequence of word embeddings, is passed through a deep neural network, often a transformer- based architecture. This network has multiple layers, each with its own set of learnable parameters. (4) Encoding the Sentence: The model processes the tokenized input sentence through its layers, and during this process, it learns to encode the sentence's meaning in the form of a high-dimensional vector representation. The internal states of the model at various layers capture different aspects of the sentence's semantics. (5) Output Layer: The final layer of the model typically produces the embeddings you're interested in. These embeddings represent the overall meaning of the sentence in the context of the training data and the specific model's architecture. (6) Numerical Values: The embeddings are essentially the numerical values of the vector produced by the final layer of the model. Each dimension of the vector corresponds to a different feature or characteristic that helps encode the meaning of the sentence. The specific numbers in the vector are determined by the training process and are not directly interpretable, but they encode the semantic information of the sentence. [0075] Regarding the system disclosed herein, a task can include a specific activity or set of operations the system is instructed to perform based on the user's natural-language query. The system besides integrates parameters, which are specific criteria or data points that the system uses to refine and execute a task, which might be derived from the user's query or from the system's internal databases. In that sense, a natural-language query can be a question or request entered by the user (user query) into the system using everyday language, as opposed to a query formulated using specific coding or technical language. [0076] Finally, a personalized nutritional plan can include a unique dietary recommendation and/or set of recommendations tailored specifically to an individual's genetic markers, and/or traits, and/or nutritional conditions to optimize their health and well-being. [0077] Therefore, integrating genetic information with dietary recommendations allows for a more precise and personalized approach to nutrition. By considering an individual's genetic markers, dietary plans can be tailored to their unique genetic makeup, enhancing the potential 20 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 for improved health outcomes, better management of certain conditions, and optimized nutrient utilization. However, it is important to note that genetic information may be just one aspect of an individual's overall health profile, and dietary recommendations should be considered in conjunction with other factors such as lifestyle, medical history, and individual preferences. 3. Detailed description [0078] The present disclosure relates to an integrated system that combines genetic analysis, artificial intelligence, and nutrition expertise to provide personalized nutrition guidance based on an individual's genetic markers and health-related data. The system includes modules for user interaction, sampling, DNA analysis, genetic markers, database management, AI-driven chatbot, and nutrition specialist consultation. The system facilitates efficient data flow, analysis, and guidance, enhancing the user's understanding of their genetic profile and supporting their dietary decisions for improved health outcomes. [0079] FIG. 1 depicts an exemplary integrated system. The integrated system comprises several interconnected modules that collaborate to provide personalized genetic analysis and nutritional guidance to users. Each module performs specific functions and contributes to the overall functionality of the system. A user 101 of the system interacts with the integrated system to access genetic analysis results and receive nutritional recommendations. The user 101 acquires and then sends a sampling kit 102 with collected biological samples from themselves. The sample collected by sampling kit 102 is then directed to facilities 103, which perform DNA sequencing and analysis to generate the user's genetic markers. The user's genetic markers 104 are then stored in repository 105, a set of one or more databases responsible for storing the user's inventory and chat history. The repository 105 also facilitates seamless data retrieval and storage between modules and parts of the system. Repository 105 can serve as an information source for an AI-driven chatbot 106 designed to interact with the user, leveraging artificial intelligence techniques to provide personalized guidance based on the user's genetic profile and health-related data. The interaction prompt 107 is the part of the system through which the chatbot 106 communicates with the user. The prompt interaction 107 can be any communication device which can receive information and delivers information as well, such as (but not limited to) a computer, a mobile device, a tablet device, a voice assistant device, a watch device, a wearable device, a printer device, a display device, a touch-screen device and/or any combination of them. It allows for intuitive user 21 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 engagement, gathering necessary information to tailor the system's recommendations to the user's needs. Prompt 107 can report information to user 101 directly from repository 105, facilitating bidirectional communication between the user and the integrated system. The system also integrates the user information 108 related to health and clinical data, nutritional requirements, and food sensitivities. It enables the integration of relevant user-specific information into the system, enhancing the accuracy of the recommendations provided by the chatbot and the subsequent nutritional guidance. The system comprises a data science platform 109 facilitating data analysis and pattern recognition techniques to identify correlations and associations between genetic markers and specific phenotypes (e.g. up-to- date literature regarding phenotypic associations between genes and nutrition, and/or analysis of ausers genetic data and traits to uncover new associations). This information is stored in repository 110, comprising a set of one or more databases specifically designed to capture reported genetic markers and phenotype associations. The repository 110 feeds the AI-driven chatbot 106, allowing the chatbot to access this valuable information and incorporate it into the user's personalized guidance. Furthermore, optionally repository 105 can be accessed by a nutrition specialist 111, which can also access the data science platform 109. This optional specialist provides expert consultation and guidance based on the user's genetic profile, health data, and nutritional requirements. This facilitates collaboration and information exchange between the chatbot and the nutrition specialist, ensuring accurate and up-to-date recommendations. [0080] In one embodiment, as previously described the user 101 gets and send the sampling kit 102 through a distribution system (FIG. 2). The sampling kit comprises one or more swabs, one or more sampling tubes with appropriate solutions, and instructions for the correct use of the sampling kit. Thus, the sampling kit 102 provides the necessary tools for the user to collect a biological sample (e.g. tissues or secretions such as blood, skin, mucous membranes, saliva, semen, hair, urine, cavity secretions, stool, or any biological sample that contains the user’s DNA). Platform 103 comprises a facility 103a responsible for the automatic or manual extraction of nucleic acids from the user's collected sample, as provided in the sampling kit 102. This facility ensures the efficient extraction of nucleic acids for further analysis. In addition, platform 103 comprises facility 103b dedicated to full or partial sequencing of nucleic acids. It utilizes advanced sequencing techniques to analyze the genetic information contained within the nucleic acid sample, such as Illumina, Oxford Nanopore, Pacific Biosciences, and/or other sequencing technology proper to unveil the sequence of 22 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 nucleic acids. Module 104 of the system comprises a bioinformatics platform 104a utilized for the search and analysis of genetic markers within the nucleic acid sequence obtained from facility 103b. This platform employs computational algorithms and tools to identify relevant genetic markers. The output 104b represents the selection process of the identified genetic markers found within the nucleic acid sequence. This selection process narrows down the list of genetic markers to those that are most relevant and significant for personalized analysis. As described above, repository 105 serves as a database within the system, storing the user's inventory and related data. For example, for a specific user, the database can contain one or more tables of genetic profile, one or more tables of traits and health information, and one or more tables of chat histories. This repository ensures seamless data storage and retrieval between parts of the system, allowing for efficient information management. The interaction prompt 107 comprises an extension 107a representing the part of the system responsible for reporting the results of the nucleic acid analysis to the user 101. It provides a means of communication through which the system delivers the genetic analysis findings to the user. The system may also include a phenotypic association database including a description of each biomarker analyzed in the panel. This phenotypic association database can be updated according to new scientific evidence and our in-house analyses. This information is also used during fine-tuning. [0081] In some embodiments, the system additionally comprises the provision of information 108 from the user 101 through the interaction prompt 107 (FIG. 3). This interaction comprises the provision of (but not limited to): information 108a related to health and clinical user data, which in turn comprises (but not limited to) age, sex, height, weight, BMI, body composition (e.g. percentage of fat, muscle or lean body mass, visceral fat, bone mineral content, body water percentage, basal metabolic rate), lifestyle, biochemical profile clinical measures, underlying disease(s) (e.g. diabetes, obesity, hypertension, atopic diseases) and/or other traits associated with age such as adolescence stage, pregnancy, fertility stage or elderly stage; information 108b related to food sensitivities, which comprise (but not limited to) gluten sensitivity, lactose intolerance, egg allergy, shellfish allergy, soy sensitivity, nuts allergy, and/or any other food (or its components) sensitivities; and/or information 108c related to nutrient requirements, such as (but not limited to) calories requirements, proteins, carbohydrates, fats, vitamins, and/or minerals. In addition, all user information 108 collected through modules 108a, 108b and/or 108c is stored in repository 105 as part of the user inventory together with the user genetic markers 104. The user 101 can interact with AI- 23 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 driven chatbot 106 through the interaction prompt 107, whose chat and dialogue 107b can be stored in repository 105 as part of the chat history, and the AI-driven chatbot can make a dietary recommendation 107c based on the knowledge acquired by the AI-driven chatbot 106 and delivered to user 101 through the interaction prompt 107. The dietary recommendation 107c comprises (but it is not limited to) a nutrition plan, schedule of meals, and/or suggestions for acquisition of nutrients according to the genetic markers and the information 108 provided by the user. Recommendations provided by the AI-driven chatbot 106 to user 101 can be fine-tuned with the information and data provided in turn by repository 105, the information provided by optional specialist 111, and the information kept in repository 110. In some embodiments, the specialist can be involved in response curation (fine-tuning). The system can also be used by a specialist to give nutritional advice as a proxy for their patients. [0082] In some embodiments, the system comprises additional modules regarding parts 109a-c and 111 (FIG. 4). As part of the optional specialist module 111, the system comprises a backend prompt 111a, which is the communication platform between the repository 105 and the specialist 111 and allows this latter to retrieve information about the user (108) and his/her genetic markers (104). For his/her part, the specialist can issue an expert report 111b with recommendations, suggestions, and/or key aspects, which in turn can feed and fine-tune the AI-driven chatbot 106. On the other hand, repository 110 can be fed by a data science platform 109, which comprises (but it is not limited to): a module 109a comprising scientific and medical public information coming from the literature review, reports, and/or studies; a module 109b comprising data from automatic search of information, which can be carried out through machine learning techniques, generative AI-models, and/or text-mining of scientific literature; and/or a module 109c comprising a database which gather information about genetic markers from other public databases including the results of GWAS and/or QTL studies, SNPs repositories, and/or the genetic information available at NCBI databases and/or other public related repositories. [0083] In a specific embodiment, the module of data science 109 provides information to the repository 110 regarding the genetic markers and phenotype associations as depicted in (but not limited to) Table 1. This table is a collection of markers (genes) reported to be associated with nutrition. Markers are also listed together with their variants (reference or alternative alleles), their location and chromosome in the human genome (GRCh38). 24 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 Table 1. Genetic markers and phenotype associations in the human genome.
Figure imgf000027_0001
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Figure imgf000035_0001
Chr: Chromosome in the human genome; Pos: initial coordinate of the marker; rsID: variant ID; Function/Association or Description: Description/Function of the marker; Closest Gene (location): marker; dbSNP: variant as retrieved from database dbSNP; REF: the reference variant (allele); ALT: the alternative variant (allele) [0084] The integrated system described herein offers a novel approach to personalized genetic analysis and nutritional guidance, utilizing the synergies between genetic markers, artificial intelligence, and nutrition expertise. By combining these elements, the system provides users with actionable insights and tailored recommendations for maintaining optimal health and well-being. 33 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0085] Through interaction prompt 107, user 101 can retrieve information and reports from the system by performing a user query. Examples for a user query comprises (but are not limited to):
Figure imgf000036_0001
[0086] In one embodiment, the system provides a login module to identify the user 101. The information of the user (genetic profile, traits, and conversational history) is used to provide the AI with context. When the user creates their account in the system, the user can input the information 108 through a form requesting information of the user’s height, age, weight, lifestyle, food sensitivities, preferences, and others are submitted. This information can be edited from the profile afterwards, and also during prompt interaction, new information can be submitted in the form of chatbot conversational history. [0087] For processing the user query, this can be a text that is firstly tokenized and processed into numerical representations (e.g. converted to embeddings), which are then passed through multiple layers of neural networks. These networks analyze the patterns and relationships within the text to perform statistical correlations between the context and meaning of the questions. Once the processing is complete, the model generates a response based on the learned patterns from the training data and provides an answer back to the user. [0088] The system can require additional inputs when needed by the AI-model. The system recognizes the lack of information when the user inquires answers from the AI. The model parses the questions of users semantically and extracts parameters so as to compare what is asked to the content of the database including the user's own profile. In case of lacking or insufficient information to elaborate an answer, the model should inquire for such information, which can be health or clinical information, lifestyle, underlying diseases, and so on, wherein the user themself should provide it in a proper input format (e.g. by fulfilling an online form or by submitting a file with the proper information). In some embodiments, the missing information can be obtained from one or more of the databases described herein. 34 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0089] Additional sources of information are module 109. For instance, module 109b, is an alternative way of gathering information to record into the main database. In this case, the information can come from text-mining analyses (e.g. text-mining of biomedical abstracts or full manuscripts, searching for associations between health or nutritional conditions and genetic markers), machine learning algorithms and/or more sophisticated generative AI models. Thus, repository 110 should collect all of this information as a relational database to be queried when required by the AI model. [0090] On the other hand, the output of the model is an AI-generated text, describing associations between genes and nutritional conditions and/or recommendations on nutritional facts regarding the genetic markers and/or the traits (health condition, nutritional condition, life style) of the user. Such a text is parsed in order to be presented back in a friendly manner to the user (e.g. as plots, charts, tables, and/or a dialogue) through interaction prompt 107. [0091] In some embodiments, the AI-driven chatbot 106 can be fine-tunned. The fine-tuning process is performed in a context where the AI-driven chatbot 106 is a pre-trained model. In this case, the pre-trained model contains all the information necessary to understand linguistically the questions from the user 101 and recognize the important elements of the sentences. The process comprises adapting the model to perform specific tasks or exhibit certain behaviors more effectively. The fine-tuning process is used to customize the AI's behavior for particular applications, making it more useful and relevant to our specific goals. For example, by feeding the model with gene-condition associations and their relationship with nutrition, the system can make the AI model to adapt to this “knowledge” and elaborate responses only in this context. Thus, the fine-tuning process is mainly associated to the server side and the content of the database which feeds the AI model. The role of the user here is through an indirect way, which refers to the additional information the user provides as input to the system so it could modify or elaborate a more specific answer. For example, if the AI- model has already provided a recommendation on a dietary regimen based on the genetic markers which are also stored in the database, in order to fine-tune such a recommendation additional information is inputted into the system. If the user informs the system that they were recently diagnosed with diabetes, a bowel disease or another chronic disease, the AI- model will modify the initial recommendation based on this new information. Thus, fine- tuning can also be part of an update of the genetic marker panel (i.e. additional genetic markers analyzed) when the user decides to incorporate the results of the new markers to 35 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 their profile (stored in the database), or alternatively, including a response curation by an expert nutritionist (e.g. specialist 111) for the purpose of selecting adequate responses and instructing the system on the type of responses expected by the user. Finally, in addition to the fine-tuning process, the model behind the AI-driven chatbot 106 can also be partially or fully retrained so the responses to be even more specific. [0092] The system can include different AI-models. Generative models aim to learn the underlying probability distribution of a dataset to generate new samples that resemble the training data. Examples of AI models that can be used to program generative models include: Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Autoregressive Models, Transformers, Flow-Based Models, and/or Boltzmann Machines. [0093] In some embodiments, the AI chatbot 106 can be driven by a Transformer model. AI models based on Transformers, can be trained in two stages: pre-training and fine-tuning. In the first case, in order for the model to learn the patterns, grammar, and contextual information present in the language, it is trained on a large corpus of text data (e.g. from the internet), which contains a wide range of texts and covers diverse topics and domains. In particular, the training data comprises of raw text from sources such as websites, books, articles, social media, and other publicly available texts. The data is typically tokenized, breaking the text into smaller units like words or subwords to handle the input efficiently. These units can further be converted into embeddings (list of vectors of numbers) for inputting the model. The model learns to predict the likelihood of a word or subword given the surrounding context. On the other hand, in the second stage (fine-tuning) the data is task- specific and usually comprises of paired examples, where the input (prompt or context) is given, and the desired output or response is provided. For example, if the model is being fine- tuned to act as a chatbot in the context of nutrition related to genetic markers (like in our solution), the data would contain dialogues or conversations where user queries are paired with the appropriate responses, and information about relationships between genes and nutrition facts. The fine-tuning process involves updating the model's parameters based on the custom dataset, optimizing the model to generate relevant and coherent responses in the specific context. Fine-tuning is performed using supervised learning, where the model learns from the labeled examples and adjusts its weights accordingly to minimize the difference between the predicted outputs and the ground truth responses. 36 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0094] Regarding the inputs of the system for retrain and/or fin-tune the AI-model, a variety of data structures can be used such as a string, an array, a JSON table or file, a JSONL table or file, an XML table or file, a HTML table or file, CSV table or file, TSV table or file, plain text file, and/or a binary file. All data structures can represent tables, in which columns designate the appropriate fields. [0095] For example, genetic markers can be inputted into the system as: [0096] CSV or TSV format: Marker,Variant,ref,alt,genotype MARKER_1,rs111111,T,C,0/0 MARKER_2,rs222222,C,G,0/1 ...,...,...,...,... MARKER_N,rsNNNNNN,A,T,1/1 [0097] JSON or JSONL format: [ { "Marker": "MARKER_1", "Variant": "rs111111", "ref": "T", "alt": "C", "genotype": "0/0" }, { "Marker": "MARKER_2", "Variant": "rs222222", "ref": "C", "alt": "G", "genotype": "0/1" }, { "Marker": "...", "Variant": "...", "ref": "...", "alt": "...", "genotype": "..." }, { "Marker": "MARKER_N", "Variant": "rsNNNNNN", "ref": "A", "alt": "T", "genotype": "1/1" } ] [0098] XML, HTML or any Markup language format:
Figure imgf000039_0001
37 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 <Marker>MARKER_2</Marker> <Variant>rs222222</Variant> <ref>C</ref> <alt>G</alt> <genotype>0/1</genotype>
Figure imgf000040_0001
<Marker>...</Marker> <Variant>...</Variant> <ref>...</ref> <alt>...</alt> <genotype>...</genotype> </2> <3> <Marker>MARKER_N</Marker> <Variant>rsNNNNNN</Variant> <ref>A</ref> <alt>T</alt> <genotype>1/1</genotype>
Figure imgf000040_0002
[0100] Additional information regarding the traits of the user can be inputted into the system. For example, information regarding the user’s life style can be gathered through a questionnaire: ● Age ● Gender ● Weight ● Height ● Type of diet (carnivore, pescatarian, vegetarian, vegan, other). ● Are you on a diet? ● Exercise habits (Ex: sedentary, once a week, 3 times a week, every day, etc). Intensity level, provide a short description of the exercise activity you usually do. ● Do you have any known medical conditions that may affect your nutrition, such as diabetes, high blood pressure, heart disease, food allergies or intolerances? ● Do you take medication regularly? 38 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
Figure imgf000041_0001
[0101] Such a questionnaire can be categorized, and the information can be gathered through an on-line form, which in turn can be converted into a format proper to be inputted into the system. For example, the questionnaire information in plain text table format can be:
Figure imgf000041_0002
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Figure imgf000042_0001
[0102] In some embodiments, information of the user regarding their clinical parameters as part of their traits, can also be inputted into the system. This information can be formatted as plain text table:
Figure imgf000042_0002
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Figure imgf000044_0001
[0103] or JSON / JSONL format: [ { "Measurement": "Albumin", "Identification": "ALB", "Units": "g/l", "Quantity": "N" }, { "Measurement": "Glycated hemoglobin", "Identification": "HbA1c, A1c, glycosylated hemoglobin", "Units": "%", "Quantity": "N" }, { "Measurement": "Total protein", "Identification": "TP, TProt, Serum", "Units": "g/l", "Quantity": "N" }, { "Measurement": "C-reactive protein", "Identification": "CRP, CRP", "Units": "g/l", "Quantity": "N" }, { "Measurement": "Serum iron-binding capacity", "Identification": "TIBC, IBC, TIBC", "Units": "Μmol/l", "Quantity": "N" }, { "Measurement": "Myoglobin", "Identification": "Myoglobin", "Units": "mcg/l", "Quantity": "N" }, { "Measurement": "Transferrin", "Identification": "Tf", "Units": "g/l", "Quantity": "N" }, { "Measurement": "Ferritin", "Identification": "Ferritin", "Units": "mcg/l", "Quantity": "N" }, { "Measurement": "Triglycerides", "Identification": "TRIG", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Total cholesterol", "Identification": "CHOL", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "HDL cholesterol", "Identification": "HDL", "Units": "mmol/l", "Quantity": "N" }, 42 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 { "Measurement": "LDL cholesterol", "Identification": "LDL", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Glucose", "Identification": "GLUC", "Units": "Mmol/l", "Quantity": "N" }, { "Measurement": "Fructosamine", "Identification": "FRA", "Units": "µmol/l", "Quantity": "N" }, { "Measurement": "Vitamin B12", "Identification": "VitB12", "Units": "pg/ml", "Quantity": "N" }, { "Measurement": "Iron", "Identification": "Fe, IRON", "Units": "μmol/l", "Quantity": "N" }, { "Measurement": "Potassium", "Identification": "K", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Calcium", "Identification": "Ca", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Magnesium", "Identification": "Mg", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Sodium", "Identification": "Na", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Phosphorus", "Identification": "P", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Chlorine", "Identification": "Cl", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Creatinine", "Identification": "CREA", "Units": "μmol/", "Quantity": "N" }, { "Measurement": "Uric acid", "Identification": "UA", "Units": "μmol/l", "Quantity": "N" 43 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 }, { "Measurement": "Urea", "Identification": "UREA", "Units": "mmol/l", "Quantity": "N" }, { "Measurement": "Total bilirubin", "Identification": "BILT", "Units": "µmol/l", "Quantity": "N" }, { "Measurement": "Direct bilirubin", "Identification": "BILD, D-BIL", "Units": "µmol/l", "Quantity": "N" }, { "Measurement": "Indirect bilirubin", "Identification": "ID-BIL", "Units": "µmol/l", "Quantity": "N" }, { "Measurement": "Alanine aminotransferas", "Identification": "ALT", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Amylase", "Identification": "AMY", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Pancreatic amylase", "Identification": "AMY-P", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Aspartate aminotransferase", "Identification": "AST", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Gamma-glutamyltransferase", "Identification": "GGT", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Creatine kinase", "Identification": "CK", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Lactate dehydrogenase", "Identification": "LDH", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Lipase", "Identification": "LIP", "Units": "units/l", "Quantity": "N" }, { "Measurement": "Alkaline phosphatase", "Identification": "ALP", "Units": "units/l", 44 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 "Quantity": "N" }, { "Measurement": "Cholinesterase", "Identification": "CHE", "Units": "units/l", "Quantity": "N" } ]. [0104] In one embodiment, for the fine-tuning process, data is collected from reputable sources, such as research articles, Single Nucleotide Polymorphism databases, genetic databases and medical websites. This data is then tabulated and each column covered tokens as gene name, functional description, chromosome position, allele risk mutation, odd ratios, GWAS studies and scientific publication. Additionally, phenotypic associations involve a complete questionnaire (see above) about each individual such as aging, gender, weight, heigh, body mass index, etc. Traits and additional information (e.g. biochemical profile (see above)) are also collected and tabulated as context covering genetics, nutrition, health, wellness, physical training, and any related domains. Finally, the fine-tuning comprises dialogues or interactions representative of user interactions with the AI-driven chatbot. Each dialogue includes user inputs (e.g. questions, queries) and appropriate model responses (e.g. nutrition recommendations, genetic insights). Example of chatbot questions: ● Do you want to update your goals? What are your goals? Have your goals changed? ● Is there something you've eaten from the diet and you didn't like it? Is there something you have eaten outside of the diet that makes you feel uncomfortable? ● Do you have any special activity/goal that you want to prepare in a special way? ● Do you have any disagreement with your current diet? Have you had any negative effects? ● Do you have a question about the ingredients of a meal or how it is cooked? ● Do you have any questions about any recommended food/ingredient in your diet? ● Do you have any questions about any food that you want to consume regularly or sporadically? [0105] Regarding the genetic markers, several factors and scientific criteria are considered in selecting genes and SNPs for the genetic panel, namely: ● Repeatability of the studies. ● Sample population sizes. ● Ethnicity, SNPs with robust associations in multiple ethnicities were considered. ● Assessment of outcomes in control individuals vs. variant-carrying individuals. 45 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 ● Genome-wide association studies (GWAS) which provides insight into understanding. genotype-phenotype associations of complex disease. ● Significant SNP-trait associations reported. ● Putative disease risk described to be related to nutrition, food intolerance or food allergies. ● Statistical values (odd risk, p-value, beta value, confidence interval of allelic odds ratios) for association between disease and alleles at each SNP. ● Quality of genotyping in the study methods. ● Association with catastrophic disease or actionable disease. SNPs associated with catastrophic disease were removed from the panel, only SNPs associated with actionable conditions were considered. [0106] For the sake of an exemplary realization of this disclosure, genes are chosen based on the criteria described above, after a rigorous assessment of the quality of the scientific data that supports the association of the SNPs with nutrition. [0107] As will be described with respect to Example 3, the systems described herein may be configured to generate, via a trained language model, a personalized nutritional plan for the user based on the genetic markers and traits described above. As will be described with respect to Example 4, in some embodiments, the system is configured to generate collective nutritional plans for multiple individuals simultaneously, such as for a family unit or any group of individuals sharing meals. This feature allows for the creation of comprehensive meal plans that address the nutritional needs of all group members while considering their individual genetic markers and traits. Genetic markers and traits may be received for multiple users associated with a group, which may include family members, roommates, or other cohabiting individuals. The trained language model may then analyze the collective nutritional requirements and restrictions of the group, taking into account any dietary preferences, allergies, or health conditions of each member. Based on this analysis, the trained language model may generate a collective nutritional plan that optimizes nutrient intake for all members of the group. [0108] The group’s collective nutritional plan can include meal suggestions and recipes that cater to the combined needs of the group, ensuring that each meal provides appropriate nutrients for all individuals while minimizing the need for separate meal preparations. This approach not only simplifies meal planning and preparation but also promotes better nutrition at a household or group level. The trained language model may also provide options for customizing portions or adding side dishes to meet specific individual needs 46 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 within the context of shared meals. In some examples, the trained language model may be trained to generate individual nutritional plans for each member of the group, in addition to the collective nutritional plan. [0109] Furthermore, the systems provided herein may include features for managing conflicting nutritional needs within the group. For instance, the trained language model may identify nutritional requirements for each individual member of the group based on the genetic markers and traits for each group member. The trained language model may be trained to determine whether the nutritional requirements for two or more members of the group conflict, and if so, the trained language model may include meals in the collective nutritional plan that can be modified to accommodate the conflicting nutritional requirements. For example, if one member requires a high-protein diet while another needs to limit protein intake, the system will suggest meals that can be easily modified to accommodate both needs, such as a dish where protein sources can be added or removed as needed. The collective nutritional plan can also take into account the frequency of shared meals, allowing for individual plans for meals eaten separately while optimizing for nutrition in communal meals. [0110] In some embodiments, the trained language model is augmented with a numerical model to more precisely delimit the nutritional needs of a user. This hybrid approach combines the natural language processing capabilities of the trained language model with quantitative analysis to enhance the accuracy of nutritional recommendations. The numerical model may be designed to calculate specific nutrient requirements based on a comprehensive set of user data, including but not limited to genetic markers, age, weight, height, body composition, activity level, and existing health conditions. [0111] FIG. 5 depicts an exemplary integrated system for generating a nutritional plan for multiple users. The integrated system comprises several interconnected modules that collaborate to provide personalized genetic analysis and nutritional guidance to multiple users. Each module performs specific functions and contributes to the overall functionality of the system. Multiple users (1 to N Users) 201 of the system interact with the integrated system to access genetic analysis results and receive nutritional recommendations. The users 201 acquire and then send sampling kits 202 with collected biological samples from themselves. The samples collected by sampling kits 202 are then directed to facilities 203, which perform DNA sequencing and analysis to generate the users' genetic markers. The 47 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 users' genetic markers 204 are then stored in repository 205, a set of one or more databases responsible for storing the users' inventories and chat histories. The repository 205 also facilitates seamless data retrieval and storage between modules and parts of the system. Repository 205 can serve as an information source for an AI-driven chatbot 206 designed to interact with the users, leveraging a trained language model to provide personalized guidance based on the users' genetic profiles and health-related data. [0112] The interaction prompt 207 is the part of the system through which the chatbot 206 communicates with the users. The prompt interaction 207 can be any communication device which can receive information and delivers information as well. It allows for intuitive user engagement, gathering necessary information to tailor the system's recommendations to the users' needs. Prompt 207 can report information to users 201 directly from repository 205, facilitating bidirectional communication between the users and the integrated system. The system also integrates the user information 208 related to health and clinical data, nutritional requirements, and food sensitivities for all users. It enables the integration of relevant user-specific information into the system, enhancing the accuracy of the recommendations provided by the chatbot and the subsequent nutritional guidance. The system comprises a data science platform 209 facilitating data analysis and pattern recognition techniques to identify correlations and associations between genetic markers and specific phenotypes. This information is stored in repository 210, comprising a set of one or more databases specifically designed to capture reported genetic markers and phenotype associations. [0113] The repository 210 feeds the AI-driven chatbot 206, allowing the chatbot to access this valuable information and incorporate it into the users' personalized guidance. Furthermore, optionally repository 205 can be accessed by a nutrition specialist 211, which can also access the data science platform 209. This optional specialist provides expert consultation and guidance based on the users' genetic profiles, health data, and nutritional requirements. This facilitates collaboration and information exchange between the chatbot and the nutrition specialist, ensuring accurate and up-to-date recommendations. Additionally, the system includes a numerical model 212 that processes the aggregated data 48 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 from one or multiple users to enhance the overall accuracy and personalization of the nutritional recommendations. [0114] The numerical model 212 employs advanced algorithms to optimize macronutrient and micronutrient ratios tailored to the user's genetic profile. These algorithms may take into account the specific genetic markers identified in the user's profile and correlate them with established research on nutrigenomics and nutrigenetics. In some embodiments, a numerical model may be employed for recommending diets. The numerical model may be based on a sophisticated mathematical approach that frames the problem of food recommendations as a constraint satisfaction problem. This approach aims to find an assignment of variables that satisfies all given constraints within the problem domain. [0115] The numerical model 212 may conceptualize a diet as a subset of a carefully curated recipe ensemble. This ensemble may include a dataset of popular, hand-picked local foods, ensuring that recommendations are culturally relevant and appealing to users. The diet may be further defined over a subset of weekdays and a subset of daily meals, which may include breakfast, lunch, afternoon snack, dinner, or other meal categories as appropriate for the user's lifestyle and cultural context. [0116] Constraints within the numerical model may be designed with two primary objectives: meeting user-specific nutritional needs and ensuring that meal compositions align with normal human eating patterns. The first category of constraints is derived from the user's genetic markers, personal traits, and health goals, as determined by the genetic analysis and user profile components of the system. These constraints might include specific nutrient amounts (e.g. macronutrients and/or micronutrient amounts), micronutrient requirements, recommended portion sizes, caloric intake limits, or restrictions based on food sensitivities or health conditions. [0117] The second category of constraints focuses on maintaining the cultural and logical integrity of meal compositions. For example, the numerical model may include restrictions that prevent the recommendation of dinner-specific recipes, such as lasagna, for breakfast. Similarly, it limits the quantity of desserts that can be included in lunch recommendations. These constraints ensure that while the diet is optimized for nutrition, it remains palatable and socially acceptable for the user. 49 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0118] Importantly, this numerical model is designed with the capability to evaluate constraints for one or more users simultaneously. This feature enables the system to generate optimized meal plans for families or groups, balancing the nutritional needs and preferences of multiple individuals within a single, cohesive diet plan. The model can adjust portion sizes, ingredient quantities, and meal compositions to satisfy the collective constraints of all users while maintaining individual nutritional requirements. [0119] The numerical model 212 expresses all relevant variables and constraints in precise mathematical language. This includes variables representing food items, their nutritional content, portion sizes, and meal timings. Constraints are expressed as mathematical equations or inequalities that define the relationships between these variables and the desired outcomes. [0120] To solve this complex system of variables and constraints, the numerical model may utilize PuLP, a free, open-source mathematical optimization software designed for use with the Python programming language. PuLP employs advanced algorithms to efficiently navigate the solution space defined by the constraints, identifying optimal or near-optimal diet plans that satisfy all specified requirements for single users or groups. [0121] This numerical model may augment the trained language model driving chatbot 206, providing a robust, quantitative foundation for the personalized nutritional recommendations. The trained language model may interpret the output of the numerical model, translating the mathematical solutions into user-friendly meal plans and dietary advice. This synergy between quantitative optimization and natural language processing ensures that users receive scientifically sound, personalized nutrition guidance presented in an accessible and actionable format, whether for individuals or groups. [0122] The trained language model driving chatbot 206, augmented with the numerical model 212, may be used to calculate recommended nutrient amounts (e.g. macronutrient and/or micronutrient amounts) using the numerical model based on the user’s genetic markers and traits. For example, if a user's genetic profile indicates a predisposition to inefficient vitamin D metabolism, the trained language model may generate a nutritional plan to reflect the recommended nutrient amounts. The trained language model may adjust the recommended intake of vitamin D and suggest appropriate food sources or supplements in the nutritional plan. In some examples, the trained language model may provide 50 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 recommended nutrient amounts in an individual nutritional plan or a collective nutritional plan. [0123] Additionally, the trained language model driving chatbot 206, augmented with the numerical model 212, can provide precise caloric recommendations for weight management goals. The trained language model may leverage the numerical model to calculate a recommended caloric intake based on the user’s genetic markers. The numerical model may consider various health and fitness traits of the user, such as the user's basal metabolic rate, daily activity level, and specific weight goals (maintenance, loss, or gain) to calculate an appropriate daily caloric intake. In some examples, these traits may be inputted and/or adjusted by the user, for example, through the user interface. The numerical model may also factor in the thermic effect of food and the user's genetic predisposition to weight gain or loss when making these calculations. The trained language model may generate an individual or collective nutritional plan that reflects the determined recommended caloric intake using the numerical model. [0124] The trained language model driving chatbot 206 may be trained to adjust, in real time, nutrient intake recommendations based on ongoing user feedback and progress. Users can input data such as weight changes, energy levels, and any symptoms or health changes they experience. The trained language model, augmented with the numerical model, may process this feedback and make real-time adjustments to the nutritional plan, ensuring that the recommendations remain relevant and effective over time. [0125] By incorporating this numerical model 212, the systems described herein may provide more granular and scientifically rigorous nutritional guidance. The numerical model’s outputs may be translated into user-friendly recommendations by the trained language model, which may communicate these detailed nutritional plans in an easily understandable format. This combination of quantitative precision and conversational interface allows users to receive highly personalized and accurate nutritional guidance while maintaining an engaging and accessible user experience. [0126] In some embodiments, the system allows users to customize their individual or collective nutrition plans by modifying the types of foods and products to be consumed for each meal, while leveraging the trained language model and numerical model to calculate optimal portions and quantities. This feature provides users with greater flexibility and 51 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 control over their meal plans while ensuring that their nutritional needs are met. The user interface allows individuals to select preferred foods or substitute suggested items with alternatives of their choice. Once the user has made these selections, the trained language model, in conjunction with the numerical model, processes this user feedback by recalculating the appropriate portion sizes of each food item to maintain the recommended caloric intake and nutrient amounts as determined by the user's genetic profile and personal traits. Accordingly, the trained language model may adjust the personalized nutritional plan by comparing the nutritional compositions of the user-modified foods with the nutritional requirements of foods originally in the personalized nutritional plan. [0127] The trained language model driving chatbot 206 may modify the personalized nutritional plan to reflect the user-selected foods, with their calculated portion sizes, while maintaining the originally recommended caloric intake and nutrient amounts reflected in the originally generated nutritional plan. For instance, if a user replaces a suggested lean protein source with a fattier option, the trained language model might reduce the portion size of that component of a meal and adjust other components of the meal to maintain the overall recommended nutrient amounts of the original nutritional plan. [0128] The trained language model driving chatbot 206 may also output the modified nutritional plan, including the calculated portion sizes of the modified foods, to the user. The trained language model may also provide an output including real-time feedback for the user on these adjustments. This output may be displayed in the user interface, for example, as a natural language text output informing the user of any potential nutritional impacts and offering suggestions for additional modifications if necessary. Further, if the user attempts to unilaterally modify a collective nutritional plan, the trained language model may take into account any adjustments that would need to be made to the shared meals based on any determined conflicting nutritional requirements. The trained language model may optionally provide an output that warns the user if their proposed food modification affects the nutritional requirements or implicates dietary restrictions of others in the group. This approach empowers users to make informed decisions about their diet while adhering to their personalized and collective nutritional guidelines. [0129] FIG. 6 illustrates an example of a computing device in accordance with one embodiment. The operations described herein are optionally implemented by components depicted in FIG. 6. Device 700 can be a host computer connected to a network. Device 700 52 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 can be a client computer or a server. As shown in FIG. 6, device 700 can be any suitable type of microprocessor-based device, such as a personal computer, workstation, server or handheld computing device (portable electronic device) such as a phone or tablet. The device can include, for example, one or more of processor 710, input device 720, output device 730, storage 740, and communication device 760. Input device 720 and output device 730 can generally correspond to those described above and can either be connectable or integrated with the computer. [0130] Input device 720 can be any suitable device that provides input, such as a touch screen, keyboard or keypad, mouse, or voice-recognition device. Output device 730 can be any suitable device that provides output, such as a touch screen, haptics device, or speaker. [0131] Storage 740 can be any suitable device that provides storage, such as an electrical, magnetic or optical memory including a RAM, cache, hard drive, or removable storage disk. Communication device 760 can include any suitable device capable of transmitting and receiving signals over a network, such as a network interface chip or device. The components of the computer can be connected in any suitable manner, such as via a physical bus or wirelessly. [0132] Software 750, which can be stored in storage 740 and executed by processor 710, can include, for example, the programming that embodies the functionality of the present disclosure (e.g., as embodied in the devices as described above). [0133] Software 750 can also be stored and/or transported within any non-transitory computer-readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a computer-readable storage medium can be any medium, such as storage 740, that can contain or store programming for use by or in connection with an instruction execution system, apparatus, or device. [0134] Software 750 can also be propagated within any transport medium for use by or in connection with an instruction execution system, apparatus, or device, such as those described above, that can fetch instructions associated with the software from the instruction execution system, apparatus, or device and execute the instructions. In the context of this disclosure, a transport medium can be any medium that can communicate, propagate or 53 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 transport programming for use by or in connection with an instruction execution system, apparatus, or device. The transport readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic or infrared wired or wireless propagation medium. [0135] Device 700 may be connected to a network, which can be any suitable type of interconnected communication system. The network can implement any suitable communications protocol and can be secured by any suitable security protocol. The network can comprise network links of any suitable arrangement that can implement the transmission and reception of network signals, such as wireless network connections, T8 or T8 lines, cable networks, DSL, or telephone lines. [0136] Device 700 can implement any operating system suitable for operating on the network. Software 750 can be written in any suitable programming language, such as C, C++, Java or Python. In various embodiments, application software embodying the functionality of the present disclosure can be deployed in different configurations, such as in a client/server arrangement or through a Web browser as a Web-based application or Web service, for example. [0137] FIG. 7 illustrates process 800 for generating a personalized nutritional plan for a user, according to various examples. Process 800 is performed, for example, using one or more electronic devices implementing a software platform. In some examples, process 800 is performed using a client-server system, and the blocks of process 800 are divided up in any manner between the server and a client device. In other examples, the blocks of process 800 are divided up between the server and multiple client devices. Thus, while portions of process 800 are described herein as being performed by particular devices of a client-server system, it will be appreciated that process 800 is not so limited. In other examples, process 800 is performed using only a client device or only multiple client devices. In process 800, some blocks are, optionally, combined, the order of some blocks is, optionally, changed, and some blocks are, optionally, omitted. In some examples, additional steps may be performed in combination with the process 800. Accordingly, the operations as illustrated (and described in greater detail below) are exemplary by nature and, as such, should not be viewed as limiting. 54 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0138] At block 802, an exemplary system (e.g., one or more electronic devices) receives, from the user, a natural-language query. At block 804, the system identifies, based on the natural-language query, a task and one or more parameters. At block 806, the system retrieves from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user. At block 808, the system constructs one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user. At block 810, the system inputs the one or more data structures into a trained language model to generate the personalized nutritional plan. At block 812, the system displays the personalized nutritional plan. [0139] In some embodiments, the system further prompts the user to input a plurality of answers to a questionnaire; and constructs a user profile based on the plurality of answers. In some embodiments, the user profile is stored on the one or more databases. In some embodiments, the user profile comprises a plurality of genetic markers and a plurality of traits associated with the user. In some embodiments, the one or more genetic markers are retrieved from the plurality of genetic markers of the user profile, and the one or more traits are retrieved from the plurality of traits of the user profile. [0140] In some embodiments, the user profile further comprises a plurality of biochemical test results associated with the user. [0141] In some embodiments, the method 800 further comprises obtaining an identifier associated with the user, wherein the one or more genetic markers and the one or more traits are retrieved based on the identifier. In some embodiments, the identifier associated with the user is obtained based on one or more login credentials provided by the user. In some embodiments, the one or more databases are accessible by the trained language model. [0142] In some embodiments, the one or more genetic markers comprise one or more of a gene, one or more of a single-nucleotide polymorphism, one or more insertions, one or more deletions, one or more repetitive sequences, and/or a biomarker. [0143] In some embodiments, the one or more genetic markers are obtained based on a genotyping method. In some embodiments, the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof. In some embodiments, the 55 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 genotyping method comprises genotyping by sequencing. In some embodiments, the genotyping by sequencing comprises amplifying a nucleic acid around the one or more genetic markers using specific primers to obtain amplified specific nucleic acid fragments. In some embodiments, genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. [0144] In some embodiments, the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences. In some embodiments, the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers. [0145] In some embodiments, the one or more data structures are formatted as one or more of a string, an array, a JSON table or file, a JSONL table or file, XML table or file, CSV table or file, TSV table or file, plain text file, binary file, or any combination thereof. [0146] In some embodiments, the trained language model comprises one or more of an autoencoder model, a generative adversarial network, an autoregressive model, a transformer model, a flow-based generative model, and/or a probabilistic generative model. In some embodiments, the trained language model is trained based on a training dataset comprising one or more queries paired with one or more responses. [0147] In some embodiments, a trait comprises a nutritional condition, a physical or health condition, a lifestyle, clinical parameters, and/or a combination thereof. In some embodiments, the trained language model is trained based on nutrition dataset comprising one or more training genetic markers paired with one or more training traits or nutritional conditions. [0148] In some embodiments, the personalized nutritional plan is personalized for the user based on the one or more genetic markers and the one or more traits. In some embodiments, the trained language model may additionally or alternatively be used to generate a collective nutritional plan for a group of users based on traits, genetic markers, or nutritional needs of each group member. 56 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0149] In some embodiments, the method 800 further comprises inputting the one or more data structures into the trained language model to generate a natural-language response describing an association between the one or more genetic markers and one or more traits or nutritional conditions of the user; and displaying the natural-language response. [0150] In some embodiments, the method 800 further comprises inputting the one or more data structures into the trained language model to generate a request for additional information from the user; and displaying the request for additional information to the user. In some embodiments, the request for additional information comprises a prompt for the user to input one or both of an additional genetic marker or an additional trait. [0151] In some embodiments, the method 800 further comprises receiving a natural- language query to retrieve user-specific information; retrieving the user-specific information; and displaying the retrieved user-specific information. In some embodiments, the method 800 further comprises receiving a natural-language query to update the personalized nutritional plan; and displaying an updated personalized nutritional plan. [0152] The foregoing description, for the purpose of explanation, has been described with reference to specific examples or aspects. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. For the purpose of clarity and a concise description, features are described herein as part of the same or separate variations; however, it will be appreciated that the scope of the disclosure includes variations having combinations of all or some of the features described. Many modifications and variations are possible in view of the above teachings. The variations were chosen and described in order to best explain the principles of the techniques and their practical applications. Others skilled in the art are thereby enabled to best utilize the techniques and various variations with various modifications as are suited to the particular use contemplated. [0153] Although the disclosure and examples have been fully described with reference to the accompanying figures, it is to be noted that various changes and modifications will become apparent to those skilled in the art. Such changes and modifications are to be understood as being included within the scope of the disclosure and examples as defined by the claims. Finally, the entire disclosure of the patents and publications referred to in this application are hereby incorporated herein by reference. 57 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0154] For the purpose of clarity and a concise description, features are described herein as part of the same or separate examples; however, it will be appreciated that the scope of the disclosure includes examples having combinations of all or some of the features described. [0155] Finally, the embodiments of the system here disclosed can be implemented, in whole or in part, through a machine that is configured to receive a computer-readable medium containing the necessary instructions. These instructions can be executed by computer-executable components integrated within various platforms such as applications, applets, hosts, servers, networks, websites, communication services, communication interfaces, and the hardware, firmware, or software elements of personal computers or mobile devices. Additionally, other embodiments encompass systems and methods that can be embodied, at least partially, within a machine that is configured to receive a computer- readable medium storing the required instructions. These instructions can be executed by computer-executable components integrated with apparatuses and networks similar to those described earlier. The computer-readable medium can be stored on a range of suitable storage devices, including RAMs, ROMs, flash memory, EEPROMs, optical devices (such as CD, DVD, Blu-Ray), hard drives, floppy drives, cloud storage, or any other appropriate device. While the computer-executable component is typically a processor, it is also possible for the instructions to be executed by any dedicated hardware device that is suitable for the task. 4. Examples Example 1 [0156] Given the following set of genetic markers found in the DNA sequence of a user: ADH1B, ADIPOQ, AGT, APOA2, CD28, CHDH, CLDN1, CLOCK, FLG LOF, GC- globulin, GPX1, IL10, IL23R, IL5, ILR6, LCT, LEPR, MCM6, MTHFR, PDCD1, PPARγ, SEPP1, SLC22A4, SLC23A1, SLC2A2, TGFB2, and UCP1. The AI-driven chatbot was asked for information about these markers: [0157] User Query (through a mobile device): “What are my genetic markers? and what do they mean?” [0158] The AI-driven chatbot provided the following table: 58 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
Figure imgf000061_0001
59 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0159] Then, the AI-driven chatbot was asked if these genetic markers can have a nutritional impact on dietary composition of the user: [0160] User Query (through the mobile device): "What do my biomarkers mean for my nutrition?" [0161] AI-driven chatbot responded: “1. LCT and MCM6: Variations in the LCT and MCM6 genes can affect lactase production, which is responsible for digesting lactose, the sugar found in dairy products. Individuals with lactose intolerance may need to limit or avoid dairy consumption or choose lactose-free alternatives. 2. PPARγ: Genetic variations in the PPARγ gene have been associated with differences in lipid and glucose metabolism. Individuals with certain PPARγ variants may have a higher risk of developing metabolic disorders, such as insulin resistance and obesity. They may benefit from a dietary approach that focuses on managing carbohydrate and fat intake to support optimal metabolic health. 3. ADIPOQ: Variations in the ADIPOQ gene have been associated with obesity and metabolic disorders. Individuals with specific ADIPOQ variants may have altered adiponectin levels, a hormone involved in regulating glucose levels and fatty acid breakdown. Such individuals may benefit from a diet that promotes weight management and metabolic health, such as a balanced diet with controlled calorie intake and a focus on nutrient-dense foods. 4. AGT: Genetic variants in the AGT gene have been linked to hypertension and cardiovascular disease. Individuals with certain AGT variants may need to be mindful of their sodium intake, as excessive sodium consumption can contribute to high blood pressure. A heart-healthy diet, low in sodium and rich in fruits, vegetables, whole grains, and lean proteins, is generally recommended. 5. GPX1 and SEPP1: Genetic variations in GPX1 and SEPP1 genes can influence antioxidant capacity and selenium metabolism, respectively. These factors may affect an individual's response to oxidative stress and the availability of selenium, an essential micronutrient. Consuming a well- balanced diet that includes antioxidant-rich foods (e.g., fruits, vegetables) and selenium- containing foods (e.g., seafood, nuts, whole grains) may be beneficial. 6. IL10, IL23R, IL5, IL6R: Variations in genes encoding certain interleukins (IL10, IL23R, IL5, IL6R) can impact immune function and inflammatory responses. These variations may influence an individual's susceptibility to immune-mediated conditions, such as autoimmune diseases or allergies. In such cases, dietary choices that support a healthy immune system, such as a 60 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 balanced and anti-inflammatory diet rich in fruits, vegetables, whole grains, and omega-3 fatty acids, may be beneficial”. [0162] Then the AI-driven chatbot was asked to give a dietary recommendation based on the genetic markers: [0163] User Query (through the mobile device): “Please, provide dietary recommendations based on my genetic markers” [0164] AI-driven chatbot responded: “While genetic markers can provide valuable insights, it's important to note that they are just one piece of the puzzle when it comes to designing a personalized dietary recommendation. A comprehensive approach that considers an individual's overall health status, lifestyle, preferences, and specific nutritional needs is crucial. It's always recommended to consult with a registered dietitian or healthcare professional who can take into account your genetic markers alongside other relevant factors. With that said, here are some general dietary recommendations that could be considered based on your genetic markers: 1. Lactose Intolerance (LCT and MCM6): If you have genetic variants associated with lactose intolerance, you may need to limit or avoid dairy products. However, it's essential to ensure you obtain adequate amounts of calcium and other nutrients found in dairy. Consider incorporating alternative sources of calcium, such as fortified plant-based milk, leafy greens, almonds, and calcium-fortified foods. 2. Obesity and Metabolic Disorders (ADIPOQ and PPARγ): If you have genetic variants associated with obesity and metabolic disorders, focus on a balanced diet that includes nutrient-dense foods. Emphasize whole foods, including fruits, vegetables, lean proteins, whole grains, and healthy fats. Monitor portion sizes and aim for a calorie intake that aligns with your individual needs and goals. Consider consulting with a registered dietitian for personalized recommendations. 3. Hypertension and Cardiovascular Health (AGT): If you have genetic variants associated with hypertension, it may be beneficial to adopt a heart-healthy diet. Emphasize fruits, vegetables, whole grains, lean proteins (such as fish and poultry), and healthy fats (such as olive oil and nuts). Limit your sodium intake by reducing processed and high-sodium foods. Moderating alcohol consumption and maintaining a healthy weight are also important for cardiovascular health. 4. Antioxidant Capacity and Selenium Metabolism (GPX1 and SEPP1): To support antioxidant capacity and ensure adequate selenium intake, focus on a varied diet rich in fruits, vegetables, whole grains, nuts, seeds, and seafood. These foods provide antioxidants, such as vitamins C and 61 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 E, as well as selenium. Incorporating a wide range of colorful plant-based foods will help provide a diverse array of antioxidants. 5. Immune Function and Inflammatory Responses (IL10, IL23R, IL5, IL6R): While dietary recommendations for immune function and inflammation are not solely based on genetic markers, incorporating an overall balanced and anti-inflammatory diet may be beneficial. Include a variety of fruits, vegetables, whole grains, lean proteins, and healthy fats. Omega-3 fatty acids found in fatty fish, flaxseeds, and walnuts may have anti-inflammatory properties”. [0165] Finally, the AI-driven chatbot was asked to give a nutrition plan for one week based on the dietary recommendations: [0166] User Query (through the mobile device): “Based on my altered genetic markers, please provide a 7-day nutrition plan” [0167] Based on the user query, the system may determine a task for providing a nutrition plan and a parameter of “7-day.” The system may further determine that the user’s altered genetic markers are needed and thus retrieve such information from one or more of the databases described herein or from the chat histories. The system can input the query and the user’s altered genetic markers in the format of embeddings into one or more machine- learning models described herein to provide the nutritional plan. For example, the AI-driven chatbot can provide the next schedule:
Figure imgf000064_0001
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Figure imgf000065_0001
Example 2 [0168] In another example, primers were synthesized to amplify genomic sections of 100 genetic markers (shown in Table 2), from the genomic DNA of an individual. Two PCR reactions were performed, using multiplexed primers in two pools, pool A (77 pairs of primers) and pool B (23 pairs of primers), using the NEBNext® Ultra™ II Q5® proofreading polymerase enzyme (New England Biolabs). PCR products were purified using SpeedBeads™ magnetic carboxylate modified particles (Cytiva), DNA ends prepared with NEBNext® Ultra™ II End Repair/dA-Tailing Module (New England Biolabs), and sequencing adapters ligated with Blunt/TA Ligase Master Mix (New England Biolabs). After another round of purification, barcodes and final adapters were added by PCR using NEBNext® Multiplex Oligos for Illumina®, samples consolidated, purified and loaded in an Illumina iSeq100 sequencer. Table 2. Primers and amplicon sizes to perform identification of variants in genetic markers.
Figure imgf000065_0002
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[0169] From the 100 genetic markers listed in Table 2, the individual tested gave rise to a bioinformatics report accounting for alternative single nucleotide polymorphisms in the following 14 markers: FTO, SLC19A1, NAT2, APOA5, CD36, IGF2BP2, SLC2A2, TCF7L2, FLG, SLC23A1, AMY1, MLXIPL, FGF21, and PCSK9. The AI-driven chatbot was asked for information about these markers. [0170] User Query (through a personal computer): "What does my genetic profile mean, specifically?” [0171] The AI-driven chatbot provided the following table as a response:
Figure imgf000069_0002
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Figure imgf000070_0001
[0172] Then, the AI-driven chatbot was asked if these genetic markers can have a nutritional impact on dietary recommendations of the user. [0173] User Query: “Do these markers have a nutritional impact on my diet?” [0174] AI-driven chatbot responded: “1. Variations in the FTO gene have been associated with an increased risk of obesity. While the exact mechanisms are not fully understood, it is believed that certain FTO variants may affect appetite regulation and energy expenditure. From a nutritional perspective, individuals with FTO variants linked to obesity may benefit from personalized dietary approaches that focus on portion control, macronutrient balance, and healthy food choices to support weight management. 2. The SLC19A1 gene encodes a thiamine transporter protein. Variations in this gene can impact thiamine absorption and utilization. Thiamine is an essential B-vitamin involved in energy metabolism. Individuals with certain SLC19A1 variants may have an increased risk of thiamine deficiency. Adequate thiamine intake through food sources rich in thiamine, such as whole grains, legumes, and fortified foods, is important for individuals with these genetic variations. 3. NAT2 gene variations affect the activity of the N-acetyltransferase 2 enzyme involved in drug metabolism and detoxification. While the impact on dietary composition is not direct, individuals with specific NAT2 variants may have altered responses to certain dietary components or dietary compounds that undergo NAT2-mediated metabolism. However, the primary impact of NAT2 variants is on drug metabolism rather than dietary composition. 4. APOA5 gene variants have been associated with altered triglyceride levels and an increased risk of cardiovascular disease. From a nutritional perspective, individuals with APOA5 variants may benefit from dietary strategies that promote healthy lipid profiles, such as reducing the intake of saturated and trans fats, while emphasizing the consumption 68 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 of unsaturated fats, high-fiber foods, and a well-balanced diet. 5. CD36 gene variations have been linked to obesity, dyslipidemia, and insulin resistance. CD36 codes for a protein involved in fatty acid uptake and metabolism. Individuals with CD36 variants may have impaired fatty acid metabolism, potentially requiring dietary adjustments. A diet focusing on healthy fats (e.g., omega-3 fatty acids), moderate fat intake, and a balance of macronutrients may be beneficial for individuals with CD36 genetic variations. 6. Variations in the IGF2BP2 gene have been associated with an increased risk of type 2 diabetes. From a nutritional standpoint, individuals with IGF2BP2 variants may benefit from dietary approaches that help manage blood sugar levels and insulin sensitivity. These may include maintaining a balanced intake of carbohydrates, selecting low glycemic index foods, emphasizing fiber-rich sources, and avoiding excessive added sugars. 7. SLC2A2, or GLUT2 gene variations, have been associated with an increased risk of type 2 diabetes. This gene codes for a glucose transporter protein. Individuals with SLC2A2 variants may have altered glucose metabolism and may benefit from dietary strategies that promote stable blood sugar levels, such as carbohydrate moderation, portion control, and regular meal timing. 8. Variations in the TCF7L2 gene have a strong association with an increased risk of type 2 diabetes. TCF7L2 is involved in glucose metabolism and insulin secretion. From a nutritional perspective, individuals with TCF7L2 variants may benefit from dietary approaches that help manage blood sugar levels, including a balanced intake of carbohydrates, portion control, and regular physical activity. 9. FLG gene variants are associated with an increased risk of atopic dermatitis (eczema) and other skin conditions. While the impact on dietary composition is not direct, individuals with FLG variants may benefit from dietary strategies that support skin health, such as consuming a nutrient-dense diet rich in antioxidants, essential fatty acids, and hydration. 10. SLC23A1 gene variations can affect the absorption and utilization of vitamin C. Individuals with certain SLC23A1 variants may have altered vitamin C metabolism, potentially requiring higher dietary vitamin C intake. Consuming vitamin C-rich foods, including citrus fruits, berries, and vegetables, can help meet the increased vitamin C needs for individuals with these genetic variations. 11. AMY1 gene variants can impact the production of salivary amylase, an enzyme involved in starch digestion. Individuals with fewer copies of the AMY1 gene may have reduced amylase levels and potentially reduced starch digestion capacity. This may influence the individual's tolerance for high-starch diets, and they may benefit from consuming a balanced diet with a moderate intake of starchy foods. 12. MLXIPL gene variants are associated with altered glucose metabolism and an increased risk of type 2 69 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 diabetes. From a nutritional perspective, individuals with MLXIPL variants may benefit from dietary strategies that promote stable blood sugar levels, such as moderate carbohydrate intake, emphasis on whole grains, fiber-rich foods, and regular physical activity. 13. FGF21 gene variants have been associated with metabolic disorders such as obesity and insulin resistance. While the impact on dietary composition is not direct, individuals with FGF21 variants may benefit from dietary approaches that support metabolic health, including a well-balanced diet, adequate protein intake, and appropriate calorie management. 14. PCSK9 gene variations affect cholesterol metabolism and have been associated with an increased risk of cardiovascular disease. From a nutritional perspective, individuals with PCSK9 variants may benefit from dietary strategies that promote heart health, such as reducing the intake of saturated and trans fats, while emphasizing the consumption of unsaturated fats, dietary fiber, and cholesterol-lowering foods”. [0175] Then the AI-driven chatbot was asked to give a dietary recommendation based on the genetic markers. [0176] User Query: “Please, provide dietary recommendations based on my genetic markers” [0177] The response of the AI-driven chatbot: “While I can provide some general dietary recommendations based on the genetic markers you mentioned, it's important to note that individualized dietary recommendations should take into account various factors such as overall health, lifestyle, personal preferences, and specific genetic variations. It is always best to consult with a healthcare professional or a registered dietitian who specializes in nutrigenomics for personalized advice. Here are my suggestions: 1. FTO: Individuals with FTO variants associated with obesity may benefit from a well-balanced diet that includes portion control, a variety of nutrient-dense foods, and an emphasis on whole foods. Focus on consuming adequate protein, fiber-rich foods, fruits, vegetables, and healthy fats while moderating calorie intake. Avoid or limit processed foods, sugary beverages, and high- calorie snacks. 2. SLC19A1: To support thiamine utilization, ensure a diet that includes thiamine-rich foods such as whole grains, legumes, nuts, seeds, lean meats, and fortified foods. These foods can help prevent thiamine deficiency and support optimal energy metabolism. 3. NAT2: While NAT2 primarily affects drug metabolism rather than dietary composition, it is important to consider any medications or dietary compounds that undergo 70 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 NAT2-mediated metabolism. If you are taking medications affected by NAT2 variations, consult with your healthcare provider for any dietary considerations or potential interactions. 4. APOA5 and CD36: Individuals with APOA5 or CD36 variants may benefit from dietary strategies that promote healthy lipid profiles. Include foods rich in unsaturated fats (e.g., fatty fish, avocados, nuts, and seeds) while minimizing saturated and trans fats. Emphasize whole grains, fruits, vegetables, and lean protein sources to support overall heart health. 5. IGF2BP2, SLC2A2, and TCF7L2: Individuals with these genetic markers associated with an increased risk of type 2 diabetes can benefit from a balanced diet that focuses on complex carbohydrates, fiber-rich foods, moderate protein intake, and healthy fats. Incorporate whole grains, legumes, vegetables, and low-glycemic index foods. Be mindful of portion sizes and consider regular physical activity. 6. FLG: While FLG primarily affects skin health, consuming a nutrient-dense diet rich in antioxidants (e.g., fruits, vegetables) and essential fatty acids (e.g., fatty fish, nuts, seeds) can support overall skin health and potentially alleviate skin conditions associated with FLG variations. 7. SLC23A1: Increase your intake of vitamin C-rich foods such as citrus fruits, berries, kiwi, peppers, and leafy green vegetables to support adequate vitamin C absorption and utilization. 8. AMY1: Individuals with fewer copies of the AMY1 gene may benefit from moderating their starch intake and focusing on a balanced diet that includes a variety of nutrient-dense foods, including whole grains, lean proteins, healthy fats, and a variety of fruits and vegetables. 9. MLXIPL: Consider a diet that supports stable blood sugar levels, including moderate carbohydrate intake from whole grains, legumes, and vegetables. Emphasize fiber-rich foods and include regular physical activity as part of an overall healthy lifestyle. 10. FGF21: While dietary recommendations specific to FGF21 variants are limited, focusing on a balanced diet that includes a variety of whole foods, adequate protein, fiber-rich sources, and appropriate calorie management can support metabolic health. 11. PCSK9: To support heart health, prioritize a diet that includes unsaturated fats (e.g., fatty fish, avocados, nuts, and seeds), dietary fiber, whole grains, and plant-based proteins. Limit the intake of saturated and trans fats, processed foods, and excessive cholesterol”. [0178] The AI-driven chatbot was asked to give a nutrition plan for one week based on the dietary recommendations: [0179] User Query (through the personal computer): 71 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 “Based on my genetic markers, please provide a 7-day nutrition plan” [0180] The AI-driven chatbot suggested the next schedule:
Figure imgf000074_0001
[0181] Finally, the AI-driven chatbot was asked to modify the nutrition plan since the individual tested has type 2 diabetes: [0182] User Query (through the personal computer): “Recently I was diagnosed with type 2 diabetes. Please modify the nutrition plan according to this health condition” [0183] The user query can be inputted into the AI model in the plain text format. Additional information such as previous nutrition plans can be retrieved from one of the databases described herein and inputted in the model as well. 72 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0184] In this case, the sentence “Recently I was diagnosed with type 2 diabetes. Please modify the nutrition plan according to this health condition” is processed by the AI-model (using OpenAI libray in Python): import openai prompt = "You are an expert personalized nutrition assistant. You use genetic profiles and user traits to provide advice regarding nutrition" sentence = "Recently I was diagnosed with type 2 diabetes. Please modify the nutrition plan according to this health condition" information = get_database_data(user) messages = [ {"role": "system", "content": prompt} {"role": "assistant", "content": information} {"role": "user", "content": sentence} ] response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, temperature=0, ) [0185] Internally, the AI-model produces an embedding for the sentence, which is a series of numerical values, used for semantic comparisons: [-0.00941, -0.02098, 0.01403,..., 0.00526, -0.02170, -0.00784] [0186] Finally, the AI-driven chatbot suggested the next modified plan:
Figure imgf000075_0001
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Example 3 [0187] A user was given a kit to collect their biological sample (FIG. 8). In this case, the biological sample was saliva. For this example of realization, the user deposited their saliva 74 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 into a 5 mL vial tube (vial 1) through a collector funnel (as part of this kit, a second container (vial 2) which contains a solution to preserve nucleic acids, is available for when the sample delivery is too long). The sample is next processed to extract the DNA to be then genotyped. In this example, the DNA is submitted to genotyping by sequencing. As in Example 2, two PCR reactions are performed, using multiplexed primers (Table 2) in two pools, pool A (77 pairs of primers) and pool B (23 pairs of primers), using the NEBNext® Ultra™ II Q5® proofreading polymerase enzyme (New England Biolabs). PCR products are purified using SpeedBeads™ magnetic carboxylate modified particles (Cytiva), DNA ends prepared with NEBNext® Ultra™ II End Repair/dA-Tailing Module (New England Biolabs), and sequencing adapters ligated with Blunt/TA Ligase Master Mix (New England Biolabs). After another round of purification, barcodes and final adapters are added by PCR using NEBNext® Multiplex Oligos for Illumina®, samples consolidated, purified and loaded in an Illumina iSeq100 sequencer. [0188] From the sequencing runs, FASTQ files are obtained containing all the sequence reads representing the DNA fragments previously amplified. The sequence reads are then inputted into a variant calling pipeline which maps the reads onto the reference fragments to identify the location of the sequence variants (genotypes). For the user of this example, the following results are obtained:
Figure imgf000077_0001
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[0189] Before inputting any piece of information into the system, the AI-model behind the chatbot is prompted to provide responses related to nutrition only (using OpenAI libray in Python): import openai prompt = "You are an expert personalized nutrition assistant. You use genetic profiles and user traits to provide advice regarding nutrition" messages = [{"role": "user", "content": prompt}] response = openai.ChatCompletion.create( model="gpt-3.5-turbo", messages=messages, temperature=0, ) [0190] These results are then converted into JSON/JSONL format so as to input the AI- driven chatbot of the system: [{"marker":"TAS2R38","variant":"rs10246939","vc_ref":"T","vc_alt":"C","genotype":"1/1"}, {"marker":"NAT2","variant":"rs1041983","vc_ref":"C","vc_alt":"T","genotype":"0/0"}, {"marker":"SLC6A4","variant":"rs1042173","vc_ref":"A","vc_alt":"C","genotype":"0/1"}, {"marker":"CD36","variant":"rs1049654","vc_ref":"A","vc_alt":"C","genotype":"0/1"}, {"marker":"CD36","variant":"rs10499859","vc_ref":"A","vc_alt":"G","genotype":"-/-"}, {"marker":"SLC22A4","variant":"rs1050152","vc_ref":"C","vc_alt":"T","genotype":"0/1"}, {"marker":"GPX1","variant":"rs1050450","vc_ref":"G","vc_alt":"A","genotype":"0/0"}, {"marker":"SLC19A1","variant":"rs1051266","vc_ref":"T","vc_alt":"C","genotype":"0/0"}, {"marker":"CD36","variant":"rs1054516","vc_ref":"T","vc_alt":"C","genotype":"0/1"}, {"marker":"STAT6","variant":"rs1059513","vc_ref":"T","vc_alt":"C","genotype":"0/1"}, {"marker":"CYP2R1","variant":"rs10741657","vc_ref":"A","vc_alt":"G","genotype":"1/1"}, {"marker":"CDKN2A/2B","variant":"rs10811661","vc_ref":"T","vc_alt":"C","genotype":"0/0"}, 78 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
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79 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 {"marker":"TRHR","variant":"rs7832552","vc_ref":"C","vc_alt":"T","genotype":"0/1"}, {"marker":"TCF7L2","variant":"rs7903146","vc_ref":"C","vc_alt":"T","genotype":"0/1"}, {"marker":"PEMT","variant":"rs7946","vc_ref":"C","vc_alt":"T","genotype":"-/-"}, {"marker":"FTO","variant":"rs8050136","vc_ref":"C","vc_alt":"A","genotype":"0/1"}, {"marker":"TGFB1","variant":"rs8109627","vc_ref":"T","vc_alt":"C","genotype":"0/0"}, {"marker":"FGF21","variant":"rs838133","vc_ref":"A","vc_alt":"G","genotype":"0/0"}, {"marker":"FGF21","variant":"rs838145","vc_ref":"G","vc_alt":"A","genotype":"0/1"}, {"marker":"TGFB2","variant":"rs900","vc_ref":"A","vc_alt":"T","genotype":"0/1"}, {"marker":"FTO","variant":"rs9939609","vc_ref":"T","vc_alt":"A","genotype":"0/1"}] [0191] Regarding the genetic markers’ information, the AI-model is prompted to consider those markers with at least one altered allele, that is whose genotypes are 0/1 or 1/1. So markers comprising variants with genotypes “0/0” or “-/-” (unknown) are not considered by the AI-model. In this case, the AI-model takes into account the following 38 markers: ACE, AGT, APOA2, APOA5, APOB, APOC3, BCMO1, CD36, CYP1A2, CYP2R1, FGF21, FTO, FUT2, GATA3, GC-globulin, GPBAR1, HLA, IRS1, LCT, LDLR, LEPR, MTHFR, NAT2, PCSK9, PNPLA3, SCNN1A, SEPP1, SLC22A4, SLC23A1, SLC6A4, STAT6, TAS2R38, TCF7L2, TGFB2, TGFB3, TNF-alpha, TRHR, and UCP1. [0192] Before or after the genetic markers are obtained, the user fulfills a form with information regarding their life style, heath or nutrition conditions, the user profile information:
Figure imgf000082_0001
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[0193] This information is then converted into a JSON/JSONL format to be inputted into the system: { "alcoholConsumption": "1 to 2 times per week", "allergiesAndIntolerances": "Nuts, Peanuts", "birthDate": "1996-07-03", "currentWeight": "76 kg", "dailyCommute": "Car or public transportation", "dailyWater": "2 to 6 glasses per day", "dietaryPreference": "No dietary preferences", "engagesInSports": "Yes", "sportDetails": "Gym - 2 to 3 times per week", "gender": "Male", "height": "170 cm", "mealsPerDay": "Breakfast, Lunch, Dinner", "medicalCondition": "No medical conditions", "preferredFoods": "Chicken, Beef, Fish, Tuna, Ham, Whole Egg, Protein Powder, Seafood, Pork, Rice, Pasta, Bread, Quinoa, Potato, Avocado, Sunflower Oil, Vegetable Oil, Olive Oil, Mayonnaise, Milk, Butter, Yellow Cheese, White Cheese, Banana, Strawberry, Apple, Orange, Pineapple, Cherries, Lettuce, Tomato, Onion, Asparagus, Mushrooms", "primaryGoal": "Gain muscle mass", "secondaryGoals": "Toning", "sedentaryLevel": "Office job", "sleepHours": "7-8 hours", "targetWeight": "80 kg" } [0194] The user is now able to query the AI-driven chatbot by requesting their altered markers and the implications in nutrition: [0195] User Query (using a personal computer): “What type of genetic markers do I have? and How these markers are involved in my nutrition? Please, provide a summary” [0196] AI-driven chatbot response (through the personal computer): 81 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
Figure imgf000084_0001
[0197] Then, the user inquire the system for a 7-day diet. This diet is based on the genetic markers and on the profile information fulfilled previously. 82 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0198] User Query (using a personal computer): “Please, give me a menu for this week” [0199] AI-driven chatbot response (through the personal computer):
Figure imgf000085_0001
[0200] Alternatively, information about the user’s genetic markers and the implications in nutrition can be retrieved as a report (FIG. 9) by the user, in this case, through the personal computer. This report (Genetic Markers Report) is a summary of the genetic markers of the user showing the risk of altered markers as a result of the variant calling process, wherein markers having variants with genotype “1/1” (homozygous for the alternative allele) are considered of “high” risk, with genotype “0/1” (heterozygous) are considered of “medium” risk, and with genotype “0/0” (homozygous for the reference allele) are considered of “low” risk. Additionally, markers are classified according to nutrients involved in the biological process to which the altered markers are associated. In this example, the categories of the markers are:
Figure imgf000085_0002
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Figure imgf000086_0001
Example 4 [0201] In another example, the system demonstrates its capability to design a diet for multiple users simultaneously, creating a common meal plan that accommodates different dietary needs and preferences. In this case, the system receives parameters for two users, U1 (John) and U2 (Karina), in JSON format: U1: { "allergiesOrIntolerances": [ "egg" ], "consumesProteinShakes": false, "dietRestrictions": [], "foodPreferences": [ "meat", "wholeEgg", "fish", "redBeans", "lentils", "pasta", "bread", "rice", "quinoa", "mayonnaise", "nuts", "avocado", "milk", "whiteCheese", "strawberry", "banana", "lettuce", "cauliflower" ], "get": 2152, "maxCarbCaloriesPercentagePerMeal": 60, "maxDailyCarbGrams": null, 84 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 "maxDailyCarbPercentageTDEE": 50, "maxDailyCopperMug": 10000, "maxDailyFolateMug": 1000, "maxDailyGlycemicLoad": null, "maxDailyLipidPercentageTDEE": 30, "maxDailyProteinGrams": 107.3, "maxDailySaturatedFatPercentage": 7.0, "maxDailySeleniumMug": 400, "maxDailySodiumMg": 4000, "maxDailyVitaminB6Mg": 12, "maxDailyVitaminCMg": 2000, "maxDailyZincMg": 34, "maxPolyInsaturatedToTotalFatPercentage": null, "maxSaturatedToTotalFatPercentage": null, "maxWeeklySugaryRecipes": 2, "mealsPerDay": [ "breakfast", "midMorning", "lunch", "dinner" ], "minDailyCalciumMg": 750, "minDailyCarbGrams": null, "minDailyCarbPercentageTDEE": 40, "minDailyCopperMug": 675, "minDailyFiberGrams": 26.0, "minDailyFolateMug": 300, "minDailyIronMg": 7.2, "minDailyLipidPercentageTDEE": 27, "minDailyPotassiumMg": 3000, "minDailyProteinGrams": 95.4, "minDailySeleniumMug": 41.25, "minDailyVitaminAIU": 3000, "minDailyVitaminB12Mug": 1.8, "minDailyVitaminB6Mg": 1.3, "minDailyVitaminCMg": 90.0, "minDailyZincMg": 8.25, "minPolyInsaturatedToTotalFatPercentage": null, "testName": "test_basic_0", "userName": "John" } U2: { "allergiesOrIntolerances": [ "gluten", "shrimp" ], "consumesProteinShakes": false, "dietRestrictions": [ "gluten_free" ], "foodPreferences": [ "chicken", "ham", "wholeEgg", "redBeans", "peas", "rice", "potato", 85 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 "pasta", "peanuts", "chocolate", "oliveOil", "almondMilk", "whiteCheese", "yellowCheese", "plum", "pear", "tomato", "cucumber" ], "get": 1593, "maxCarbCaloriesPercentagePerMeal": 60, "maxDailyCarbGrams": null, "maxDailyCarbPercentageTDEE": 55, "maxDailyCopperMug": 10000, "maxDailyFolateMug": 1000, "maxDailyGlycemicLoad": null, "maxDailyLipidPercentageTDEE": 30, "maxDailyProteinGrams": 87.8, "maxDailySaturatedFatPercentage": 10.0, "maxDailySeleniumMug": 400, "maxDailySodiumMg": 3500, "maxDailyVitaminB6Mg": 12, "maxDailyVitaminCMg": 2000, "maxDailyZincMg": 34, "maxPolyInsaturatedToTotalFatPercentage": null, "maxSaturatedToTotalFatPercentage": null, "maxWeeklySugaryRecipes": 4, "mealsPerDay": [ "breakfast", "lunch", "midAfternoon", "dinner" ], "minDailyCalciumMg": 750, "minDailyCarbGrams": null, "minDailyCarbPercentageTDEE": 45, "minDailyCopperMug": 900, "minDailyFiberGrams": 20.0, "minDailyFolateMug": 300, "minDailyIronMg": 16.2, "minDailyLipidPercentageTDEE": 27, "minDailyPotassiumMg": 3000, "minDailyProteinGrams": 78.0, "minDailySeleniumMug": 68.75, "minDailyVitaminAIU": 2917, "minDailyVitaminB12Mug": 1.8, "minDailyVitaminB6Mg": 0.9, "minDailyVitaminCMg": 75.0, "minDailyZincMg": 6.0, "minPolyInsaturatedToTotalFatPercentage": null, "testName": "test_variant_celiac_0", "userName": "Karina" } 86 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 [0202] Based on this input, the system's numerical model first generates a common breakfast program for the week: Day: Monday ● Green tea: 1.0 cup(s) ● Peanuts: 1.0 handful(s) ● Mixed nuts: 1.0 handful(s) Day: Tuesday ● Chicken paste with paprika and mayonnaise: 1.0 tablespoon(s) ● Sugar-free chocolate milk: 1.0 cup(s) ● Rice crackers: 1.0 unit(s) ● Poultry paste: 1.0 tablespoon(s) ● Blueberries: 1.0 cup(s) Day: Wednesday ● Chicken paste with paprika and mayonnaise: 1.0 tablespoon(s) ● Apple: 1.0 unit(s) ● Sugar-free chocolate milk: 1.0 cup(s) ● Rice crackers: 1.0 unit(s) ● Poultry paste: 1.0 tablespoon(s) Day: Thursday ● Green tea: 1.0 cup(s) ● Peanuts: 2.0 handful(s) ● Pear: 1.0 unit(s) ● Oatmeal with unsweetened yogurt: 1.0 unit(s) [0203] The AI-driven chatbot then processes this information and elaborates a more detailed and personalized breakfast plan that accommodates both users' needs and preferences. Here's the chatbot's response: “Based on the dietary requirements and preferences of both John and Karina, I have created a common breakfast plan that caters to their nutritional needs while respecting their allergies and intolerances. Here is a detailed breakdown of the breakfast: 87 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340
Figure imgf000090_0001
Breakfast Description: ● Gluten-free toast with avocado and white cheese: Uses gluten-free bread to accommodate Karina's restriction. Spread a generous layer of mashed avocado and top with white cheese, providing a nutritious and delicious combination. ● Mixed nuts: A handful of almonds, walnuts, and peanuts offers a source of healthy fats and proteins that benefit both users. ● Fresh fruits: Sliced pear is ideal for Karina, while strawberries are an excellent option for John, ensuring both enjoy their preferred fruits. ● Green tea: A traditional Chilean beverage that perfectly complements the breakfast, providing antioxidants without adding significant calories. Nutritional Benefits: ● Allergen-free: This breakfast avoids eggs and gluten, complying with both users' intolerances. ● Balanced: Combines complex carbohydrates, proteins, healthy fats, and fiber, aligning with both users' nutritional recommendations. ● Customizable: Ingredients can be adjusted in portions according to caloric needs and personal preferences. This breakfast plan takes into account the specific requirements of both John and Karina: ● It's gluten-free to accommodate Karina's celiac disease. ● It avoids eggs, respecting John's allergy. ● The meal incorporates preferred foods from both users, such as avocado, white cheese, and fruits. ● The nutritional composition aligns with both users' macronutrient requirements, providing a balance of carbohydrates, proteins, and fats. 88 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 ● The meal is rich in fiber, vitamins, and minerals, contributing to the daily nutritional goals of both users.” [0204] By creating this common breakfast plan, the system demonstrates its ability to cater to multiple users simultaneously, balancing individual needs and preferences while providing a nutritious and enjoyable meal for both. 89 of 102 sf-6139563

Claims

ATTORNEY DOCKET NO: 230702000340 CLAIMS 1. A method for generating a personalized nutritional plan for a user, comprising: receiving, from the user, a natural-language query; identifying, based on the natural-language query, a task and one or more parameters; retrieving from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; inputting the one or more data structures into a trained language model to generate the personalized nutritional plan; and displaying the personalized nutritional plan. 2. The method of claim 1, further comprising: prompting the user to input a plurality of answers to a questionnaire; and constructing a user profile based on the plurality of answers. 3. The method of claim 2, wherein the user profile is stored on the one or more databases. 4. The method of claim 2 or claim 3, wherein the user profile comprises a plurality of genetic markers and a plurality of traits associated with the user. 5. The method of claim 4, wherein the one or more genetic markers are retrieved from the plurality of genetic markers of the user profile, and the one or more traits are retrieved from the plurality of traits of the user profile. 6. The method of claim 4, wherein the user profile further comprises a plurality of biochemical test results associated with the user. 90 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 7. The method of any one of the previous claims, further comprising: obtaining an identifier associated with the user, wherein the one or more genetic markers and the one or more traits are retrieved based on the identifier. 8. The method of claim 7, wherein the identifier associated with the user is obtained based on one or more login credentials provided by the user. 9. The method of any one of the previous claims, wherein the one or more databases are accessible by the trained language model. 10. The method of any one of the previous claims, wherein the one or more genetic markers comprise one or more of a gene, one or more of a single-nucleotide polymorphism, one or more insertions, one or more deletions, one or more repetitive sequences, and/or a biomarker. 11. The method of any one of the previous claims, wherein the one or more genetic markers are obtained based on a genotyping method. 12. The method of claim 11, wherein the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof. 13. The method of claims 11-12, wherein the genotyping method comprises genotyping by sequencing. 14. The method of claim 13, wherein the genotyping by sequencing comprises amplifying a nucleic acid around the one or more genetic markers using specific primers to obtain amplified specific nucleic acid fragments. 15. The method of claim 14, wherein genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. 16. The method of claim 15, wherein the plurality of sequence reads are obtained from high-throughput sequencing using Illumina sequencing, Oxford Nanopore 91 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences. 17. The method of any one of claims 15-16, wherein the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers. 18. The method of any one of the previous claims, wherein the one or more data structures are formatted as one or more of a string, an array, a JSON table or file, a JSONL table or file, XML table or file, CSV table or file, TSV table or file, plain text file, binary file, or any combination thereof. 19. The method of any one of the previous claims, wherein the trained language model comprises one or more of an autoencoder model, a generative adversarial network, an autoregressive model, a transformer model, a flow-based generative model, and/or a probabilistic generative model. 20. The method of any one of the previous claims, wherein the trained language model is trained based on a training dataset comprising one or more queries paired with one or more responses. 21. The method of any one of the previous claims, wherein a trait comprises a nutritional condition, a physical or health condition, a lifestyle, clinical parameters, and/or a combination thereof. 22. The method of any one of the previous claims, wherein the trained language model is trained based on nutrition dataset comprising one or more training genetic markers paired with one or more training traits or nutritional conditions. 23. The method of any one of the previous claims, wherein the personalized nutritional plan is personalized for the user based on the one or more genetic markers and the one or more traits. 24. The method of any one of the previous claims, further comprising: 92 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 inputting the one or more data structures into the trained language model to generate a natural-language response describing an association between the one or more genetic markers and one or more traits or nutritional conditions of the user; and displaying the natural-language response. 25. The method of any one of the previous claims, further comprising: inputting the one or more data structures into the trained language model to generate a request for additional information from the user; and displaying the request for additional information to the user. 26. The method of claim 25, wherein the request for additional information comprises a prompt for the user to input one or both of an additional genetic marker or an additional trait. 27. The method of any one of the previous claims, further comprising: receiving a natural-language query to retrieve user-specific information; retrieving the user-specific information; and displaying the retrieved user-specific information. 28. The method of any one of the previous claims, further comprising: receiving a natural-language query to update the personalized nutritional plan; and displaying an updated personalized nutritional plan. 29. The method of claim 1, further comprising: retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into the trained language model to generate a collective nutritional plan for the group; and displaying the collective nutritional plan. 93 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 30. The method of claim 29, wherein the group comprises family members sharing meals. 31. The method of claim 29, wherein the trained language model is trained to: identify nutritional requirements for each member of the group based on the one or more genetic markers and the one or more traits; determine whether the nutritional requirements for two or more members of the group conflict; and in response to determining that the nutritional requirements for two or more members of the group conflict, include meals in the collective nutritional plan that can be modified to accommodate the conflicting nutritional requirements. 32. The method of claim 29, wherein the trained language model is trained to generate individual nutritional plans for each member of the group in addition to the collective nutritional plan. 33. The method of claim 1, wherein the trained language model is augmented with a numerical model, and wherein the trained language model is trained to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts. 34. The method of claim 33, wherein the trained language model is trained to: receive feedback from the user; process the feedback using the numerical model; and adjust the recommended nutrient amounts in real-time based on the processed feedback. 35. The method of claim 1, wherein the trained language model is augmented with a numerical model, and wherein the trained language model is trained to: calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and 94 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 generate the individual nutritional plan to reflect the recommended caloric intake. 36. The method of claim 1, wherein the trained language model is augmented with a numerical model, and wherein the trained language model is trained to: receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; and modify the personalized nutritional plan based on the calculated portion sizes. 37. A system for generating a personalized nutritional plan for a user, the system comprising: one or more non-transitory computer-readable storage media; and one or more processors coupled to the one or more storage media, the one or more processors configured to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan. 38. The system of claim 37, wherein the one or more processors are further configured to: retrieve, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; 95 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; input the one or more data structures into the trained language model to generate a collective nutritional plan for the group; and display the collective nutritional plan. 39. The system of claim 37, wherein the trained language model is augmented with a numerical model, and wherein the one or more processors are further configured to: calculate recommended nutrient amounts using the numerical model based on the one or more genetic markers and the one or more traits; and generate the individual nutritional plan to reflect the recommended nutrient amounts. 40. The system of claim 37, wherein the trained language model is augmented with a numerical model, and wherein the one or more processors are further configured to: calculate a recommended caloric intake using the numerical model based on the one or more genetic markers and the one or more traits associated with the user; and generate the individual nutritional plan to reflect the recommended caloric intake. 41. The system of claim 37, wherein the one or more processors are further configured to: receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modify, using the trained language model, the personalized nutritional plan based on the calculated portion sizes; and display the modified personalized nutritional plan with the calculated portion sizes. 96 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 42. A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors of a system, cause the one or more processors to: receive, from the user, a natural-language query; identify, based on the natural-language query, a task and one or more parameters; retrieve from one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with the user, and one or more traits associated with the user; construct one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with the user; input the one or more data structures into a trained language model to generate the personalized nutritional plan; and display the personalized nutritional plan. 43. The non-transitory computer-readable medium of claim 41, further comprising instructions that, when executed by the one or more processors, cause the one or more processors to: receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modify, using the trained language model, the personalized nutritional plan based on the calculated portion sizes; and display the modified personalized nutritional plan with the calculated portion sizes. 44. A kit for generating a personalized nutritional plan, the kit comprising: one or more containers for holding one or more biological samples comprising nucleic acids; one or more solutions for the isolation and/or preservation of the nucleic acids within the one or more containers, wherein said one or more solutions comprise at least one of a lysis buffer solution and an elution buffer solution; and 97 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 instructions for using the kit to perform a method for identifying the presence of one or more genetic markers in the biological sample provided by a user, the method comprising: extracting nucleic acids from the biological sample using the one or more containers and the one or more solutions; identifying variations in the nucleic acids through a genotyping method; identifying, using the one or more processors, the presence of the one or more genetic markers in the biological sample; comparing the location of the genetic markers with a reference database containing the nucleic acid fragments comprising the reference genetic markers; using the identified genetic markers as a natural-language query for inputting a system for generating a personalized nutritional plan; and displaying the personalized nutritional plan to the user. 45. The kit of claim 44, wherein the kit comprises optionally one or more cotton swabs to extract the biological sample. 46. The kit of claim 44, wherein the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from animals. 47. The kit of claim 46, wherein the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from vertebrate organisms. 48. The kit of claim 47, wherein the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from mammals. 49. The kit of claim 48, wherein the biological sample comprises nucleic acids derived from the biological samples and nucleic acids derived from humans. 50. The kit of claims 46-49, wherein the biological sample comprises samples coming from tissues or secretions such as blood, skin, mucous membranes, saliva, semen, hair, urine, cavity secretions and/or stool. 98 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 51. The kit of claim 44, wherein the kit further comprises a binding matrix for the retention of genetic material. 52. The kit of claim 44, wherein the genotyping method looks for single nucleotide polymorphisms, insertions, deletions, and/or repetitive sequences. 53. The kit of claims 44 or 52, wherein the genotyping method is selected from PCR, ARMS PCR, qPCR, DNA or RNA microarrays, integrated fluid circuits, MassARRAY SNP genotyping, genotyping by sequencing, or a combination thereof. 54. The kit of claim 53, wherein the genotyping method is genotyping by sequencing. 55. The kit of claim 54, wherein the genotyping by sequencing comprises amplifying the nucleic acid around the genetic markers using specific primers to obtain amplified specific nucleic acid fragments. 56. The kit of claim 55, wherein genotyping by sequencing comprises sequencing the nucleic acids fragments to obtain a plurality of sequence reads. 57. The kit of claim 56, wherein the plurality of sequence reads are obtained from high- throughput sequencing using Illumina sequencing, Oxford Nanopore sequencing, Pacific Biosciences sequencing, Sanger sequencing, Avidity sequencing, Open fluidics sequencing, and/or any method to obtain the identity of nucleic acid sequences. 58. The kit of claims 56-57, wherein the plurality of sequence reads are used to perform a variant calling process to identify the genetic markers. 59. A method for generating a group nutritional plan using a trained language model, comprising: retrieving, from the one or more databases, based on the task and the one or more parameters, one or more genetic markers associated with each member of a group, and one or more traits associated with each member of the group; 99 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 constructing one or more data structures associated with the task based on the one or more genetic markers and the one or more traits associated with each member of the group; inputting the one or more data structures into a trained language model to generate a collective nutritional plan for the group; and displaying the collective nutritional plan. 60. The method of claim 59, further comprising: receiving feedback on the collective nutritional plan from one or more members of the group; adjusting the collective nutritional plan based on the feedback from the more one or more members of the group; and displaying the adjusted collective nutritional plan. 61. The method of claim 59, wherein the trained language model is augmented with a numerical model, and wherein the trained language model is trained to: receive a user input modifying one or more foods in the personalized nutrition plan; calculate, using the numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; and modify the personalized nutritional plan based on the calculated portion sizes. 62. The method of claim 61, wherein calculating, using the numerical model, portion sizes of the one or more modified foods comprises: comparing nutritional compositions of the one or more modified foods with nutritional compositions of one or more foods originally in the personalized nutritional plan; and adjusting portion sizes of the one or more modified foods to match the nutritional compositions of the one or more foods originally in the personalized nutritional plan. 63. The method of claim 61, further comprising displaying real-time feedback on nutritional impacts of the one or more modified foods to the user. 100 of 102 sf-6139563 ATTORNEY DOCKET NO: 230702000340 64. A method for modifying a personalized nutritional plan, comprising: generating an initial personalized nutritional plan based on a user's genetic markers and traits; receiving a user input modifying one or more foods in the initial personalized nutrition plan; calculating, using a trained language model augmented with a numerical model, portion sizes of the one or more modified foods based on a recommended caloric intake and recommended nutrient amounts; modifying, using the trained language model, the initial personalized nutritional plan based on the calculated portion sizes; and displaying the modified personalized nutritional plan with the calculated portion sizes. 101 of 102 sf-6139563
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