system

Abstract

We provide the system. [Solution] A means of obtaining microbial community information from biological samples, A means for analyzing acquired microbial community information and generating individual health improvement suggestions, A means of presenting generated health improvement suggestions and collecting feedback from users, A means of updating the analysis algorithm based on collected feedback and reflecting it in the next analysis, A system that includes means of providing users with daily necessities or nutritional products related to the proposed health improvements.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the increasing health problems caused by modern living habits, many people cannot grasp their own intestinal environment and have difficulty finding appropriate health improvement measures. There is also a problem that it is difficult to receive specific and personalized health proposals based on the state of individual intestinal flora. Therefore, a new system for providing effective and sustainable health improvement measures is needed.

Means for Solving the Problems

[0006] "Biological sample" refers to tissue or fluid collected from a human or other living organism, and in this invention, it specifically refers to feces.

[0007] "Bacterial community information" refers to data that includes the types and quantities of microorganisms present in a biological sample, as well as genetic information.

[0008] "Analysis" refers to the act of processing and analyzing collected data to derive specific conclusions or patterns.

[0009] A "health improvement proposal" refers to a specific action plan recommended to improve the user's gut environment and enhance their health.

[0010] "Feedback" refers to information provided by users regarding changes in their health status and their opinions on this system.

[0011] An "analysis algorithm" refers to a series of computational steps used to analyze data and derive specific conclusions.

[0012] "Life stage" refers to a specific stage or period in a person's life, and in this invention, it refers to the factors that influence proposals based on each person's health needs.

[0013] "Health goals" refer to specific health-related objectives that users aim to achieve.

[0014] "Dietary guidance" refers to guidelines for nutritional intake recommended based on specific health conditions or goals.

Brief Description of the Drawings

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] The present invention is specifically configured as a system for providing personalized health improvement services. The system acquires intestinal microbiota information from biological samples and supports the user's health by providing appropriate health improvement suggestions based on the analysis results of that information. A specific embodiment of the system is shown below.

[0037] First, the user collects a stool sample and places it in the designated kit. By scanning the QR code (registered trademark) included in the kit using a terminal, the sample information is recorded. Once the server confirms that the sample has arrived at the analysis facility, it begins the analysis process to obtain intestinal microbiota data from the sample.

[0038] The obtained bacterial community information is processed on a server and analyzed by an AI agent. During the analysis process, past data and the latest research findings are utilized to profile the user's gut microbiome. This generates recommendations for optimal probiotic foods and supplements to address identified health issues. These recommendations are then sent from the server to the user's device and notified.

[0039] For example, if a user has an unbalanced diet, the AI ​​agent might recommend taking probiotics rich in vitamins and minerals. The suggestion would include specific product names, instructions for consumption, and expected effects, allowing the user to improve their health by incorporating them into their daily life.

[0040] Furthermore, the device periodically requests feedback from the user and sends that input to the server. The server updates the analysis algorithm based on the feedback, which helps in making future suggestions and improving the accuracy of the analysis process. This ensures continuous support and improvement, enabling long-term health management.

[0041] This invention allows for flexible and effective health maintenance because the proposed solutions can be adapted to the user's life stage and individual health goals. Therefore, it provides a useful solution for a variety of users who aim to improve their health based on their gut environment.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users safely collect stool samples at home using the provided stool sample collection kit. The samples are sealed in the kit and stored hygienically.

[0045] Step 2:

[0046] The terminal scans the QR code included in the kit to register the sample. This scan associates the stool sample with a specific user and sends the data to the server.

[0047] Step 3:

[0048] The user mails the collected stool sample to the designated analysis facility.

[0049] Step 4:

[0050] The server receives notification from the analysis facility that the stool sample has arrived and confirms receipt of the sample. After confirming receipt, the sample analysis process begins.

[0051] Step 5:

[0052] The server performs the necessary processing to extract gut microbiota data from the sample. This processing includes DNA extraction, sequencing, and data transformation for analysis.

[0053] Step 6:

[0054] The AI ​​agent creates a profile of the user's gut microbiome based on the acquired gut microbiota data. The profile includes the types and quantities of bacteria present, as well as their impact on health.

[0055] Step 7:

[0056] The AI ​​agent generates suggestions for improving health based on the user's profile. These suggestions include specific probiotic foods and supplements, as well as dietary advice.

[0057] Step 8:

[0058] The server organizes the generated suggestions and sends notifications to the terminal. The notifications include details about the suggestions and how to use them, providing information to help users put them into practice.

[0059] Step 9:

[0060] Users incorporate the suggested health improvement measures into their daily lives, experience their effects, and provide feedback.

[0061] Step 10:

[0062] The device receives feedback from the user and sends it to the server. This feedback includes information about changes in health status and the effectiveness of suggestions.

[0063] Step 11:

[0064] The server shares feedback with the AI ​​agent and uses the data to improve the accuracy of the analysis algorithm. This will result in more personalized health improvement suggestions for the next time.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] In modern healthcare management, providing personalized advice in a short timeframe is crucial. However, traditional methods have struggled to efficiently and accurately deliver health improvement suggestions, particularly those based on gut health. Furthermore, there was a lack of mechanisms to effectively incorporate user feedback into the system and continuously improve the suggestions provided.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for acquiring microbiome information from a biological sample, a device for analyzing the acquired microbiome information and generating individual health improvement suggestions, and a device for presenting the generated health improvement suggestions and collecting feedback from users. This makes it possible to provide users with rapid and highly accurate health improvement suggestions tailored to their gut environment.

[0070] A "biological sample" is a substance that can reflect the physiological state of an individual, such as tissue, fluids, or excretions.

[0071] "Microbial flora information" refers to data about the types, composition, and proportions of microorganisms present in a specific environment.

[0072] A "generative model" is a type of artificial intelligence equipped with algorithms that automatically analyze data and generate information according to specified conditions.

[0073] A "prompt statement" is a statement input to a generative model that contains instructions or conditions for obtaining output appropriate to the model.

[0074] An "analysis algorithm" is a set of computational procedures and rules defined for analyzing data and extracting meaningful information.

[0075] A "platform" is a system that provides a common foundation for different devices and systems to exchange information and function in cooperation with each other.

[0076] This invention is a system aimed at improving the user's health, and it generates personalized suggestions using microbiome information. Specifically, the system is operated according to the following procedure.

[0077] The user first collects a stool sample as a biological sample and seals it in the provided kit. This kit includes a QR code, which the user scans using a device to send the sample and user information to the server. The QR code contains information to identify the sample.

[0078] When a sample arrives at the analysis facility, the server uses analytical instruments to perform DNA sequencing of the microbiome. Next-generation sequencers (e.g., Illumina MiSeq) are used for this analysis. The obtained microbiome information is stored in a database on the server.

[0079] Next, a generative AI model analyzes this microbiome information and generates personalized health improvement suggestions. Specifically, the model is input with predefined prompts and outputs suggestions tailored to the user's gut environment and individual health condition. These generated suggestions are expected to include instructions for taking probiotic foods and supplements.

[0080] The server sends the generated suggestions to the terminal, and the terminal notifies the user of the suggestions. The user can then implement these suggestions in their daily life.

[0081] For example, if you input a prompt sentence into the model such as, "I'm a woman in my 40s and I've been feeling tired lately. Please provide optimal health improvement suggestions based on my gut microbiome information," the model will output suggestions for foods containing specific vitamins and advice on improving lifestyle habits.

[0082] This ensures continuous support for individual health improvements and enables effective health management.

[0083] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0084] Step 1:

[0085] The user collects a stool sample and places it in a special kit. At this time, they scan a QR code included in the kit with their device. The information entered is the sample ID contained in the QR code, which is sent to the server to link the sample with the user information. Specifically, the device reads the digital information generated by scanning the QR code and communicates it to the server. The output is the completion of registration of the sample information in the sample database.

[0086] Step 2:

[0087] Once the server confirms the sample's arrival at the analysis facility, it begins DNA sequencing of the microbiome using high-precision analytical instruments. The input is a stool sample as a biological specimen, and the output is sequenced data. This data indicates the composition and structure of the microorganisms and is stored digitally in a database on the server. Specific analytical operations include the loading of reagents for automated sample processing and the DNA extraction process.

[0088] Step 3:

[0089] The server performs analysis using a generative AI model based on the stored microbiome information. The input data is sequence data, and the output generated by the AI ​​model is a report that profiles the user's health status. Data processing is a process of giving instructions to the model using prompt statements to generate information as health improvement suggestions. Specifically, this involves inputting data into the model and extracting results, which then creates the analysis report.

[0090] Step 4:

[0091] The server sends health improvement suggestions based on the analysis results to the terminal, and the terminal notifies the user. The input is an analysis report, and the output is specific health improvement suggestions. The information presented includes information on probiotics or supplements that should be taken. Specifically, a notification is displayed on the user's smartphone or tablet.

[0092] Step 5:

[0093] The terminal prompts the user for continuous feedback after they receive a suggestion, and sends that feedback to the server. The input data includes the user's opinions and health improvement status, which the server uses to update its analysis algorithm. The output is an updated analysis base that contributes to improving the accuracy of the system's next analysis. The specific operation involves sending periodic questionnaire forms and reflecting the results in the database.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In modern times, there is a demand for effective health improvement plans tailored to individual health conditions. However, many people have no choice but to rely on generalized health information, and appropriate improvement measures based on their individual gut environment are rarely provided. Furthermore, there is a lack of systems that can update analytical algorithms based on feedback and continuously provide personalized suggestions. In addition, there are no practical means to easily monitor health conditions at home and implement suggestions in daily life.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes means for acquiring microbial community information from a biological sample, means for analyzing the acquired microbial community information and generating individual health improvement suggestions, means for presenting the generated health improvement suggestions, collecting feedback from the user, and updating the analysis algorithm based on the collected feedback, means for providing the user with daily necessities or nutritional products related to the proposed health improvement content, and means for providing specific nutritional intake advice through a home device. This makes it possible to provide users with continuous and personalized health improvement suggestions.

[0099] A "biological sample" is a sample containing microorganisms or chemical substances collected from a specific organism.

[0100] "Microbial community information" refers to information about the types and proportions of microorganisms obtained from biological samples.

[0101] An "analysis algorithm" is a computational method used to find specific patterns in collected data and generate meaningful information.

[0102] A "health improvement suggestion" is a recommendation of specific actions or products aimed at improving health status, based on the analysis results obtained.

[0103] "Daily necessities" refers to products and goods that are commonly used in daily life.

[0104] "Nutritional products" refer to products including foods and supplements intended for nutritional supplementation.

[0105] "Household appliances" refers to electrical appliances and health management devices used within the home.

[0106] This invention is a system for providing personalized health improvement suggestions using biological samples from users. The server acquires microbial community information based on the biological sample and processes it using an analysis algorithm. Simultaneously, it receives feedback from the user via a terminal and continuously optimizes the suggestions.

[0107] The server analyzes specific health indicators based on microbial community information obtained from biological samples. The analysis uses the machine learning libraries TENSORFLOW® or PyTorch. After analysis by the AI ​​model, personalized health improvement suggestions are generated. These suggestions include probiotic foods and nutritional products tailored to the user's health condition. The suggestions are sent to the user's device and applied to their daily life.

[0108] Users receive suggestions from the server using their devices and make lifestyle improvements based on those suggestions. For example, they can purchase suggested nutritional products and incorporate them into their diet. Furthermore, providing feedback contributes to improving the accuracy of the analysis algorithm.

[0109] The device connects to home appliances and provides specific nutritional advice, making it easier to implement the suggested diet. For example, if consuming yogurt in the morning is recommended, the device will notify the user of the appropriate time and method of consumption.

[0110] Examples of prompts for a generative AI model are as follows:

[0111] "Program personalized health recommendations based on gut microbiota information. This should include a process of continuously optimizing the recommendations based on user feedback, using Python and TensorFlow."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user collects a biological sample using a dedicated kit. The collected sample information is electronically transmitted to the server by scanning the included QR code with a terminal. The input is a stool sample, and the output is the sample information contained in the QR code.

[0115] Step 2:

[0116] The server obtains microbial community information at the analysis facility based on the transmitted sample information. Analysis of biological samples is performed by identifying the types and proportions of bacteria using automated equipment. The input is QR code information, and the output is microbial community information.

[0117] Step 3:

[0118] The server uses machine learning models to analyze microbial community information. This analysis employs AI algorithms based on historical data and the latest research findings. Specifically, it uses TensorFlow to evaluate health indicators. The input is microbial community information, and the output is health improvement suggestions.

[0119] Step 4:

[0120] The server sends the generated health improvement suggestions to the terminal. These suggestions include individually optimized recommendations for probiotic foods and nutritional products. The terminal then notifies the user of these suggestions. The input is the health improvement suggestions, and the output is the notification to the user.

[0121] Step 5:

[0122] Users review their lifestyle habits based on the suggestions and send feedback to the server via their device. The input is the user's results and impressions, and the output is feedback information.

[0123] Step 6:

[0124] The server uses user feedback to improve its analysis algorithm. The feedback is used to adapt and refine the algorithm, resulting in improved accuracy for subsequent analyses. The input is the feedback information, and the output is the improved analysis algorithm.

[0125] Step 7:

[0126] The device provides users with specific nutritional advice through integration with home appliances. For example, it might inform them of the recommended time to consume yogurt for breakfast. The input is a health improvement suggestion, and the output is a dietary instruction from the device.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention provides more effective health support by combining a system that offers personalized health improvement suggestions with an emotion engine that analyzes the user's emotional state. This system integrates information analysis from biological samples with recognition of the user's emotions.

[0129] First, the user collects a stool sample using a special kit and sends it to an analysis facility. The server obtains gut microbiota information from the biological sample, and an AI agent performs a detailed analysis based on that data. Based on the profile obtained from the analysis results, the server generates personalized health improvement suggestions. These may include probiotics or appropriate supplements to help with nutritional supplementation, or a specific meal plan.

[0130] Simultaneously, the device collects the user's emotional state through its built-in emotion engine. This information is obtained by analyzing the user's tone of voice, facial expressions, text input, and other data. Once the emotion engine recognizes the user's emotions, the server further incorporates this information into health improvement suggestions. For example, if the user is feeling stressed, the suggestions can include foods with relaxing effects or supplements that can help with mental health.

[0131] The generated suggestions are presented to the user via the device in an optimized form. The user can then make choices in their daily life based on these suggestions. After implementing the suggestions, they send feedback via the device about their feelings and health status. This feedback is collected on a server and used to update the AI ​​agent's algorithm. This update further personalizes the next set of suggestions.

[0132] For example, if a user complains of indigestion and stress, this system can suggest probiotics to improve the gut environment, as well as recommend relaxing herbal teas depending on the emotional state indicated by the emotional engine. In this way, the present invention provides comprehensive support for the user's physical and emotional health, enabling sustainable health maintenance.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The user collects a stool sample using a dedicated kit. The sample is placed in a hygienic sealing bag and sent to the analysis facility.

[0136] Step 2:

[0137] The device scans the QR code included in the sample kit, and the sample information is sent to the server. At this stage, the sample is associated with a specific user.

[0138] Step 3:

[0139] The server confirms the arrival of the stool sample from the analysis facility and initiates the necessary analysis process. Bacterial group information is extracted and digitized.

[0140] Step 4:

[0141] The AI ​​agent analyzes bacterial community information on the server and generates a profile of the user's gut environment. Based on this profile, the foundation for health improvement suggestions is formed.

[0142] Step 5:

[0143] The device uses an emotion engine to monitor the user's emotional state in real time. It collects emotional data through speech recognition, facial expression recognition, and text analysis.

[0144] Step 6:

[0145] The server integrates data from the emotion engine and optimizes improvement suggestions based on the user's emotional state. If the user is experiencing high stress levels, it prioritizes suggesting foods and supplements with relaxation effects.

[0146] Step 7:

[0147] The server organizes the generated health improvement suggestions and sends them to the terminal as detailed information. These suggestions include details about implementation methods and expected effects.

[0148] Step 8:

[0149] Users receive suggestions presented through their devices and incorporate them into their daily lives.

[0150] Step 9:

[0151] The device collects user feedback after the suggestion is implemented and sends it to the server. This feedback includes opinions on changes in health status and the effectiveness of the suggestion.

[0152] Step 10:

[0153] The server integrates feedback and updates the algorithms with AI agents. This update makes subsequent suggestions more personalized and enhances user health support.

[0154] (Example 2)

[0155] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0156] In modern society, while individualized health management is highly valued, achieving comprehensive health improvement that takes into account not only physical health but also emotional state remains challenging. Furthermore, dynamically updating health improvement suggestions to adequately adapt to the individual circumstances of each user is also a challenge.

[0157] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0158] In this invention, the server includes means for acquiring microbial information from biological data, means for analyzing the acquired microbial information and generating individual health improvement suggestions, means for collecting the user's emotional state, means for supplementing the health improvement suggestions based on the emotional state, means for presenting the generated health improvement suggestions and collecting feedback from the user, and means for updating the analysis algorithm based on the collected feedback. This enables personalized health improvement suggestions that comprehensively consider the user's physical and emotional health state.

[0159] "Biological specimens" refer to samples of tissue, fluid, secretions, etc., taken from the human body, and are particularly used to obtain microbial information.

[0160] "Microbial information" refers to data such as the type, composition, and genetic information of microorganisms obtained from biological samples.

[0161] An "analysis algorithm" is a set of calculation procedures that process data obtained from biological samples and emotional states to generate suggestions for improving health.

[0162] "Emotional state" refers to the user's psychological state and is information obtained by analyzing voice, facial expressions, text input, etc.

[0163] "Health improvement suggestions" are recommendations generated by an analytical algorithm to improve the user's physical and emotional health.

[0164] "Feedback" refers to information that users send to the server regarding the results and impressions of implementing health improvement suggestions, and this information is used to update the algorithm.

[0165] In implementing this invention, the user, server, and terminal each play their respective roles.

[0166] First, users collect biological samples using a dedicated kit and send them to a facility capable of processing them on a server. Microbial information is obtained from the biological samples using high-throughput sequencing technology. The server receives this data and uses analysis algorithms to generate personalized health improvement suggestions.

[0167] Simultaneously, the device collects the user's emotional state. Equipped with an emotion engine, the device acquires user emotional information through voice analysis, facial expression analysis, and text input analysis. This allows the server to incorporate the collected and analyzed emotional state into health improvement suggestions.

[0168] The generated health improvement suggestions, which include recommendations such as supplements for specific nutrients like probiotics and foods with relaxing effects, are presented to the user via the device. Based on these suggestions, the user can review their daily diet and lifestyle habits.

[0169] Furthermore, users provide feedback on the results of implementing the suggestions. The system includes a function to send this feedback back to the server via the device. The server then uses this feedback to update the analysis algorithm, adjusting it to make future suggestions more personalized. This iterative process ensures continuous health improvement for the user.

[0170] For example, if a user is experiencing indigestion and fatigue, the server can suggest consuming probiotics, as well as foods known to be effective in relieving fatigue. An example of a prompt to the generating AI model might be, "Generate optimal health improvement suggestions for a user complaining of indigestion and fatigue." In this way, the present invention provides a concrete and effective means to address individual health needs.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user collects biological samples using a dedicated kit. The specific action the user takes is to properly place the samples into a sealed container according to the instructions included with the product. The input is the user's collection and labeling actions, and the output is the biological sample ready for shipment to the testing facility.

[0174] Step 2:

[0175] The server receives biological samples sent to the facility and acquires microbial information using high-throughput sequencing. High-performance analytical equipment is used for DNA fragmentation, sequencing, and data collection. The input is the biological sample, and the output is the acquired microbial information data.

[0176] Step 3:

[0177] The server inputs the acquired microbial information into an analysis algorithm to generate personalized health improvement suggestions. The analysis algorithm evaluates the health status by comparing the microbial composition with existing databases. The input is microbial information, and the output is the analyzed results and the generated health improvement suggestions.

[0178] Step 4:

[0179] The device collects the user's emotional state. The user provides emotional data to the device through voice input or facial recognition, which is then analyzed by an emotion engine. The input consists of voice, facial expressions, and text obtained from the user, and the output is information on the analyzed emotional state.

[0180] Step 5:

[0181] The server incorporates information about emotional state into health improvement suggestions. Based on this data, it processes the data to include additional suggestions for stress reduction measures. The input is data on emotional state, and the output is the final health improvement suggestion that reflects this data.

[0182] Step 6:

[0183] The terminal presents the generated health improvement suggestions to the user. Here, the terminal displays the suggestions on the screen, making them easily accessible to the user. The input is the health improvement suggestions, and the output is a visual presentation to the user.

[0184] Step 7:

[0185] Users collect feedback on the results of implementing their suggestions. They input information such as the effects and impressions obtained from the suggestions via their devices. The input is user feedback information, and the output is the data transmitted to the device.

[0186] Step 8:

[0187] The server updates its analysis algorithm based on the collected feedback. It incorporates data as a reference for improving the AI ​​model and aims to increase the accuracy of the algorithm. The input is user feedback data, and the output is the improved analysis algorithm.

[0188] (Application Example 2)

[0189] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0190] In modern society, there is a need to provide specific and effective health maintenance measures tailored to the individual health and emotional states of each user. However, conventional technologies offer uniform health recommendations based on users' biometric information, lacking emotional care. A solution is needed to address this problem and realize more comprehensive health support.

[0191] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0192] In this invention, the server includes a device for acquiring microbial community information from a biological sample, a device for analyzing the acquired microbial community information and generating an individualized health improvement plan, and a device for collecting the user's emotional state using an emotion recognition engine and reflecting it in the health improvement plan. This makes it possible to provide personalized health improvement measures that take into account both the user's physical and emotional health.

[0193] A "biological sample" is a sample taken from the human body or animals, and is particularly a material that contains information about microbial communities.

[0194] "Microbial community information" refers to data on the types, composition, and activity levels of microorganisms obtained from biological samples.

[0195] An "emotion recognition engine" is a device or program that analyzes input information such as voice, facial expressions, and text to identify the user's emotional state.

[0196] A "health improvement plan" refers to a program that proposes specific behavioral guidelines and health goals based on the user's biometric information and emotional state.

[0197] An "analysis algorithm" is a computational procedure for processing acquired biological samples and emotional data to generate health recommendations tailored to the user.

[0198] "Living environment" is a general term encompassing the physical, social, and emotional factors that influence users in their daily lives.

[0199] "Nutritional guidance" is the act of advising users on appropriate diets and nutritional intake methods based on their health condition and lifestyle.

[0200] This system consists of a server, terminals, and users, with each component fulfilling its specific role. The server retrieves microbial community information from biological samples submitted by users into a database and processes this information using a specific analytical algorithm. Based on the processed data, it generates a personalized health improvement plan. This plan includes suggestions for probiotics, supplement recommendations, and specific meal plans.

[0201] Meanwhile, the device is equipped with an emotion recognition engine that collects emotional states through the user's voice, facial expressions, and daily actions. The collected emotional data is sent to a server and incorporated into the health improvement plan. The device also presents the generated health improvement plan to the user and collects feedback. This feedback allows the server to update its analysis algorithm, making subsequent health improvement plans even more personalized.

[0202] For example, if a user is suffering from constipation, the device can sense their stress level, and the server can generate suggestions recommending probiotics and lavender tea to aid digestion. The user then tries the suggestions and reports their subsequent physical and emotional changes on the device, which is then used to improve future suggestions.

[0203] For the generative AI model, a prompt such as, "Create an algorithm that analyzes the user's emotional state and suggests health improvement measures according to their stress level," is used.

[0204] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0205] Step 1:

[0206] The user collects a stool sample using a dedicated kit and sends it to the analysis facility. The input includes the stool sample. The output is sample information registered in a database for transmission to the server.

[0207] Step 2:

[0208] The server obtains microbial community information from the received stool sample and processes the acquired data using an analysis algorithm. The input includes microbial data from the stool sample. The output provides analysis results regarding the composition of the microbial community, specifically showing the types and proportions of bacteria.

[0209] Step 3:

[0210] The device uses an emotion recognition engine to analyze the user's voice and facial expressions and evaluate their emotional state. Input includes the user's voice data and visual information. Output is data indicating the user's emotional state (e.g., stress level). Operation involves analyzing changes in voice tone and facial features.

[0211] Step 4:

[0212] The server integrates the analysis results of the obtained microbial communities with emotional state data to generate a personalized health improvement plan. Input includes the output data from steps 2 and 3. Output provides a health improvement plan including probiotic suggestions and a meal plan. Specifically, the AI ​​calculates the optimal plan using the integrated data.

[0213] Step 5:

[0214] The device presents the generated health improvement plan to the user and collects feedback from the user. The input includes the health improvement plan, while the output provides user feedback on their impressions and the results of its application.

[0215] Step 6:

[0216] The server analyzes the collected feedback and updates the analysis algorithm to improve the accuracy of the next health improvement plan. Input includes user feedback. Output is the updated analysis algorithm. Specifically, the feedback is stored in a database, and the AI ​​adjusts the algorithm based on that data.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] The present invention is specifically configured as a system for providing personalized health improvement services. The system acquires intestinal microbiota information from biological samples and supports the user's health by providing appropriate health improvement suggestions based on the analysis results of that information. A specific embodiment of the system is shown below.

[0234] First, the user collects a stool sample and places it in the designated kit. By scanning the QR code included in the kit using a terminal, the sample information is recorded. Once the server confirms that the sample has arrived at the analysis facility, it begins the analysis process to obtain intestinal microbiota data from the sample.

[0235] The obtained bacterial community information is processed on a server and analyzed by an AI agent. During the analysis process, past data and the latest research findings are utilized to profile the user's gut microbiome. This generates recommendations for optimal probiotic foods and supplements to address identified health issues. These recommendations are then sent from the server to the user's device and notified.

[0236] For example, if a user has an unbalanced diet, the AI ​​agent might recommend taking probiotics rich in vitamins and minerals. The suggestion would include specific product names, instructions for consumption, and expected effects, allowing the user to improve their health by incorporating them into their daily life.

[0237] Furthermore, the device periodically requests feedback from the user and sends that input to the server. The server updates the analysis algorithm based on the feedback, which helps in making future suggestions and improving the accuracy of the analysis process. This ensures continuous support and improvement, enabling long-term health management.

[0238] This invention allows for flexible and effective health maintenance because the proposed solutions can be adapted to the user's life stage and individual health goals. Therefore, it provides a useful solution for a variety of users who aim to improve their health based on their gut environment.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] Users safely collect stool samples at home using the provided stool sample collection kit. The samples are sealed in the kit and stored hygienically.

[0242] Step 2:

[0243] The terminal scans the QR code included in the kit to register the sample. This scan associates the stool sample with a specific user and sends the data to the server.

[0244] Step 3:

[0245] The user mails the collected stool sample to the designated analysis facility.

[0246] Step 4:

[0247] The server receives notification from the analysis facility that the stool sample has arrived and confirms receipt of the sample. After confirming receipt, the analysis process of the sample begins.

[0248] Step 5:

[0249] The server performs the necessary processing to extract gut microbiota data from the sample. This processing includes DNA extraction, sequencing, and data transformation for analysis.

[0250] Step 6:

[0251] The AI ​​agent creates a profile of the user's gut microbiome based on the acquired gut microbiota data. The profile includes the types and quantities of bacteria present, as well as their impact on health.

[0252] Step 7:

[0253] The AI ​​agent generates suggestions for improving health based on the user's profile. These suggestions include specific probiotic foods and supplements, as well as dietary advice.

[0254] Step 8:

[0255] The server organizes the generated suggestions and sends notifications to the terminal. The notifications include details about the suggestions and how to use them, providing information to help users put them into practice.

[0256] Step 9:

[0257] Users incorporate the suggested health improvement measures into their daily lives, experience their effects, and provide feedback.

[0258] Step 10:

[0259] The device receives feedback from the user and sends it to the server. This feedback includes information about changes in health status and the effectiveness of suggestions.

[0260] Step 11:

[0261] The server shares feedback with the AI ​​agent and uses the data to improve the accuracy of the analysis algorithm. This makes the next health improvement suggestions even more personalized.

[0262] (Example 1)

[0263] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0264] In modern healthcare management, providing personalized advice in a short timeframe is crucial. However, traditional methods have struggled to efficiently and accurately deliver health improvement suggestions, particularly those based on gut health. Furthermore, there was a lack of mechanisms to effectively incorporate user feedback into the system and continuously improve the suggestions provided.

[0265] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0266] In this invention, the server includes means for acquiring microbiome information from a biological sample, a device for analyzing the acquired microbiome information and generating individual health improvement suggestions, and a device for presenting the generated health improvement suggestions and collecting feedback from users. This makes it possible to provide users with rapid and highly accurate health improvement suggestions tailored to their gut environment.

[0267] A "biological sample" is a substance that can reflect the physiological state of an individual, such as tissue, fluid, or excretion.

[0268] "Microbial flora information" refers to data about the types, composition, and proportions of microorganisms present in a specific environment.

[0269] A "generative model" is a type of artificial intelligence equipped with algorithms that automatically analyze data and generate information according to specified conditions.

[0270] A "prompt statement" is a statement input to a generative model that contains instructions or conditions for obtaining output appropriate to the model.

[0271] An "analysis algorithm" is a set of computational procedures and rules defined for analyzing data and extracting meaningful information.

[0272] A "platform" is a system that provides a common foundation for different devices and systems to exchange information and function in cooperation with each other.

[0273] This invention is a system aimed at improving the user's health, and it generates personalized suggestions using microbiome information. Specifically, the system is operated according to the following procedure.

[0274] The user first collects a stool sample as a biological sample and seals it in the provided kit. This kit includes a QR code, which the user scans using a device to send the sample and user information to the server. The QR code contains information to identify the sample.

[0275] When a sample arrives at the analysis facility, the server uses analytical instruments to perform DNA sequencing of the microbiome. Next-generation sequencers (e.g., Illumina MiSeq) are used for this analysis. The obtained microbiome information is stored in a database on the server.

[0276] Next, a generative AI model analyzes this microbiome information and generates personalized health improvement suggestions. Specifically, the model is input with predefined prompts and outputs suggestions tailored to the user's gut environment and individual health condition. These generated suggestions are expected to include instructions for taking probiotic foods and supplements.

[0277] The server sends the generated suggestions to the terminal, and the terminal notifies the user of the suggestions. The user can then implement these suggestions in their daily life.

[0278] For example, if you input a prompt sentence into the model such as, "I'm a woman in my 40s and I've been feeling tired lately. Please provide optimal health improvement suggestions based on my gut microbiome information," the model will output suggestions for foods containing specific vitamins and advice on improving lifestyle habits.

[0279] This ensures continuous support for individual health improvements and enables effective health management.

[0280] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0281] Step 1:

[0282] The user collects a stool sample and encloses it in a dedicated kit. At this time, the user scans the QR code attached to the kit with a terminal. The information to be input is the sample ID included in the QR code, which is sent to the server, and the sample is linked to the user information. Specifically, there is an operation where the terminal reads the digital information generated by QR code scanning and communicates it to the server. As output, the registration of sample information in the sample database is completed.

[0283] Step 2:

[0284] The server that has confirmed the arrival of the sample at the analysis facility starts DNA sequencing of the microbiota using high-precision analysis equipment. The input is the stool sample as a biological sample, and the output is sequence data. This data shows the composition and configuration of the microorganisms and is stored in a database on the server in digital form. Specific operations of the analysis include the loading of reagents for automatically processing the sample and the DNA extraction process.

[0285] Step 3:

[0286] The server performs analysis using a generated AI model based on the stored microbiota information. The data to be input is the sequence data, and the output generated by the AI model is a report profiling the user's health status. Data processing is a process of instructing the model using prompt sentences and generating information as health improvement proposals. The specific operation is the input of data to the model and the extraction of results, by which an analysis report is created.

[0287] Step 4:

[0288] The server sends health improvement proposals based on the analysis results to the terminal, and the terminal notifies the user. The input is the analysis report, and the output is specific health improvement proposals. The content to be presented includes information on probiotics or supplements to be ingested. As a specific operation, there is a process where notifications are displayed on the user's smartphone or tablet.

[0289] Step 5:

[0290] The terminal prompts the user for continuous feedback after they receive a suggestion, and sends that feedback to the server. The input data includes the user's opinions and health improvement status, which the server uses to update its analysis algorithm. The output is an updated analysis base that contributes to improving the accuracy of the system's next analysis. The specific operation involves sending periodic questionnaire forms and reflecting the results in the database.

[0291] (Application Example 1)

[0292] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0293] In modern times, there is a demand for effective health improvement plans tailored to individual health conditions. However, many people have no choice but to rely on generalized health information, and appropriate improvement measures based on their individual gut environment are rarely provided. Furthermore, there is a lack of systems that can update analytical algorithms based on feedback and continuously provide personalized suggestions. In addition, there are no practical means to easily monitor health conditions at home and implement suggestions in daily life.

[0294] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0295] In this invention, the server includes means for acquiring microbial community information from a biological sample, means for analyzing the acquired microbial community information and generating individual health improvement suggestions, means for presenting the generated health improvement suggestions, collecting feedback from the user, and updating the analysis algorithm based on the collected feedback, means for providing the user with daily necessities or nutritional products related to the proposed health improvement content, and means for providing specific nutritional intake advice through a home device. This makes it possible to provide users with continuous and personalized health improvement suggestions.

[0296] A "biological sample" is a sample containing microorganisms or chemical substances collected from a specific organism.

[0297] "Microbial community information" refers to information about the types and proportions of microorganisms obtained from biological samples.

[0298] An "analysis algorithm" is a computational method used to find specific patterns in collected data and generate meaningful information.

[0299] A "health improvement suggestion" is a recommendation of specific actions or products aimed at improving health status, based on the analysis results obtained.

[0300] "Daily necessities" refers to products and goods that are commonly used in daily life.

[0301] "Nutritional products" refer to products including foods and supplements intended for nutritional supplementation.

[0302] "Household appliances" refers to electrical appliances and health management devices used within the home.

[0303] This invention is a system for providing personalized health improvement suggestions using biological samples from users. The server acquires microbial population information based on the biological samples and processes it using an analysis algorithm. At the same time, feedback is received from the user via the terminal, and the proposed content is continuously optimized.

[0304] The server analyzes specific health indicators based on the microbial population information obtained from the biological samples. For the analysis, TensorFlow or PyTorch, machine learning libraries, are used. After analysis by the AI model, individual health improvement suggestions are generated. This suggestion includes probiotic foods and nutritional products tailored to the health condition. The proposed content is sent to the user's terminal and applied to daily life practice.

[0305] The user receives the suggestions from the server using the terminal and improves their lifestyle based on them. For example, it is possible to purchase the proposed nutritional products and incorporate them into the diet. Also, by providing feedback, it contributes to improving the accuracy of the analysis algorithm.

[0306] The terminal is connected to household devices to provide specific nutritional intake advice. This makes it easier to practice the proposed content. As an example, when the intake of morning yogurt is recommended, the terminal notifies the appropriate intake time and method.

[0307] Examples of prompt sentences for the generation AI model are as follows:

[0308] "Please program an individualized health proposal based on the intestinal flora information. Include the process of continuously optimizing the proposal according to user feedback using Python and TensorFlow."

[0309] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0310] Step 1:

[0311] The user collects a biological sample using a dedicated kit. The collected sample information is electronically transmitted to the server by scanning the included QR code with a terminal. The input is a stool sample, and the output is the sample information contained in the QR code.

[0312] Step 2:

[0313] The server obtains microbial community information at the analysis facility based on the transmitted sample information. Analysis of biological samples is performed by identifying the types and proportions of bacteria using automated equipment. The input is QR code information, and the output is microbial community information.

[0314] Step 3:

[0315] The server uses machine learning models to analyze microbial community information. This analysis employs AI algorithms based on historical data and the latest research findings. Specifically, it uses TensorFlow to evaluate health indicators. The input is microbial community information, and the output is health improvement suggestions.

[0316] Step 4:

[0317] The server sends the generated health improvement suggestions to the terminal. These suggestions include individually optimized recommendations for probiotic foods and nutritional products. The terminal then notifies the user of these suggestions. The input is the health improvement suggestions, and the output is the notification to the user.

[0318] Step 5:

[0319] Users review their lifestyle habits based on the suggestions and send feedback to the server via their device. The input is the user's results and impressions, and the output is feedback information.

[0320] Step 6:

[0321] The server uses user feedback to improve its analysis algorithm. The feedback is used to adapt and refine the algorithm, resulting in improved accuracy for subsequent analyses. The input is the feedback information, and the output is the improved analysis algorithm.

[0322] Step 7:

[0323] The device provides users with specific nutritional advice through integration with home appliances. For example, it might inform them of the recommended time to consume yogurt for breakfast. The input is a health improvement suggestion, and the output is a dietary instruction from the device.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] This invention provides more effective health support by combining a system that offers personalized health improvement suggestions with an emotion engine that analyzes the user's emotional state. This system integrates information analysis from biological samples with recognition of the user's emotions.

[0326] First, the user collects a stool sample using a special kit and sends it to an analysis facility. The server obtains gut microbiota information from the biological sample, and an AI agent performs a detailed analysis based on that data. Based on the profile obtained from the analysis results, the server generates personalized health improvement suggestions. These may include probiotics or appropriate supplements to help with nutritional supplementation, or a specific meal plan.

[0327] Simultaneously, the device collects the user's emotional state through its built-in emotion engine. This information is obtained by analyzing the user's tone of voice, facial expressions, text input, and other data. Once the emotion engine recognizes the user's emotions, the server further incorporates this information into health improvement suggestions. For example, if the user is feeling stressed, the suggestions can include foods with relaxing effects or supplements that can help with mental health.

[0328] The generated suggestions are presented to the user via the device in an optimized form. The user can then make choices in their daily life based on these suggestions. After implementing the suggestions, they send feedback via the device about their feelings and health status. This feedback is collected on a server and used to update the AI ​​agent's algorithm. This update further personalizes the next set of suggestions.

[0329] For example, if a user complains of indigestion and stress, this system can suggest probiotics to improve the gut environment, as well as recommend relaxing herbal teas depending on the emotional state indicated by the emotional engine. In this way, the present invention provides comprehensive support for the user's physical and emotional health, enabling sustainable health maintenance.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user collects a stool sample using a dedicated kit. The sample is placed in a hygienic sealing bag and sent to the analysis facility.

[0333] Step 2:

[0334] The device scans the QR code included in the sample kit, and the sample information is sent to the server. At this stage, the sample is associated with a specific user.

[0335] Step 3:

[0336] The server confirms the arrival of the stool sample from the analysis facility and initiates the necessary analysis process. Bacterial group information is extracted and digitized.

[0337] Step 4:

[0338] The AI ​​agent analyzes bacterial community information on the server and generates a profile of the user's gut environment. Based on this profile, the foundation for health improvement suggestions is formed.

[0339] Step 5:

[0340] The device uses an emotion engine to monitor the user's emotional state in real time. It collects emotional data through speech recognition, facial expression recognition, and text analysis.

[0341] Step 6:

[0342] The server integrates data from the emotion engine and optimizes improvement suggestions based on the user's emotional state. If the user is experiencing high stress levels, it prioritizes suggesting foods and supplements with relaxation effects.

[0343] Step 7:

[0344] The server organizes the generated health improvement suggestions and sends them to the terminal as detailed information. These suggestions include details about implementation methods and expected effects.

[0345] Step 8:

[0346] Users receive suggestions presented through their devices and incorporate them into their daily lives.

[0347] Step 9:

[0348] The device collects user feedback after the suggestion is implemented and sends it to the server. This feedback includes opinions on changes in health status and the effectiveness of the suggestion.

[0349] Step 10:

[0350] The server integrates feedback and updates the algorithms with AI agents. This update makes subsequent suggestions more personalized and enhances user health support.

[0351] (Example 2)

[0352] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0353] In modern society, while individualized health management is highly valued, achieving comprehensive health improvement that takes into account not only physical health but also emotional state remains challenging. Furthermore, dynamically updating health improvement suggestions to adequately adapt to the individual circumstances of each user is also a challenge.

[0354] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0355] In this invention, the server includes means for acquiring microbial information from biological data, means for analyzing the acquired microbial information and generating individual health improvement suggestions, means for collecting the user's emotional state, means for supplementing the health improvement suggestions based on the emotional state, means for presenting the generated health improvement suggestions and collecting feedback from the user, and means for updating the analysis algorithm based on the collected feedback. This enables personalized health improvement suggestions that comprehensively consider the user's physical and emotional health state.

[0356] "Biological specimens" refer to samples of tissue, fluid, secretions, etc., taken from the human body, and are particularly used to obtain microbial information.

[0357] "Microbial information" refers to data such as the type, composition, and genetic information of microorganisms obtained from biological samples.

[0358] An "analysis algorithm" is a set of calculation procedures that process data obtained from biological samples and emotional states to generate suggestions for improving health.

[0359] "Emotional state" refers to the user's psychological state and is information obtained by analyzing voice, facial expressions, text input, etc.

[0360] "Health improvement suggestions" are recommendations generated by an analytical algorithm to improve the user's physical and emotional health.

[0361] "Feedback" refers to information that users send to the server regarding the results and impressions of implementing health improvement suggestions, and this information is used to update the algorithm.

[0362] In implementing this invention, the user, server, and terminal each play their respective roles.

[0363] First, users collect biological samples using a dedicated kit and send them to a facility capable of processing them on a server. Microbial information is obtained from the biological samples using high-throughput sequencing technology. The server receives this data and uses analysis algorithms to generate personalized health improvement suggestions.

[0364] Simultaneously, the device collects the user's emotional state. Equipped with an emotion engine, the device acquires user emotional information through voice analysis, facial expression analysis, and text input analysis. This allows the server to incorporate the collected and analyzed emotional state into health improvement suggestions.

[0365] The generated health improvement suggestions, which include recommendations such as supplements for specific nutrients like probiotics and foods with relaxing effects, are presented to the user via the device. Based on these suggestions, the user can review their daily diet and lifestyle habits.

[0366] Furthermore, users provide feedback on the results of implementing the suggestions. The system includes a function to send this feedback back to the server via the device. The server then uses this feedback to update the analysis algorithm, adjusting it to make future suggestions more personalized. This iterative process ensures continuous health improvement for the user.

[0367] For example, if a user is experiencing indigestion and fatigue, the server can suggest consuming probiotics, as well as foods known to be effective in relieving fatigue. An example of a prompt to the generating AI model might be, "Generate optimal health improvement suggestions for a user complaining of indigestion and fatigue." In this way, the present invention provides a concrete and effective means to address individual health needs.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user collects biological samples using a dedicated kit. The specific action the user takes is to properly place the samples into a sealed container according to the instructions included with the product. The input is the user's collection and labeling actions, and the output is the biological sample ready for shipment to the testing facility.

[0371] Step 2:

[0372] The server receives biological samples sent to the facility and acquires microbial information using high-throughput sequencing. High-performance analytical equipment is used for DNA fragmentation, sequencing, and data collection. The input is the biological sample, and the output is the acquired microbial information data.

[0373] Step 3:

[0374] The server inputs the acquired microbial information into an analysis algorithm to generate personalized health improvement suggestions. The analysis algorithm evaluates the health status by comparing the microbial composition with existing databases. The input is microbial information, and the output is the analyzed results and the generated health improvement suggestions.

[0375] Step 4:

[0376] The device collects the user's emotional state. The user provides emotional data to the device through voice input or facial recognition, which is then analyzed by an emotion engine. The input consists of voice, facial expressions, and text obtained from the user, and the output is information on the analyzed emotional state.

[0377] Step 5:

[0378] The server incorporates information about emotional state into health improvement suggestions. Based on this data, it processes the data to include additional suggestions for stress reduction measures. The input is data on emotional state, and the output is the final health improvement suggestion that reflects this data.

[0379] Step 6:

[0380] The terminal presents the generated health improvement suggestions to the user. Here, the terminal displays the suggestions on the screen, making them easily accessible to the user. The input is the health improvement suggestions, and the output is a visual presentation to the user.

[0381] Step 7:

[0382] Users collect feedback on the results of implementing their suggestions. They input information such as the effects and impressions obtained from the suggestions via their devices. The input is user feedback information, and the output is the data transmitted to the device.

[0383] Step 8:

[0384] The server updates its analysis algorithm based on the collected feedback. It incorporates data as a reference for improving the AI ​​model and aims to increase the accuracy of the algorithm. The input is user feedback data, and the output is the improved analysis algorithm.

[0385] (Application Example 2)

[0386] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0387] In modern society, there is a need to provide specific and effective health maintenance measures tailored to the individual health and emotional states of each user. However, conventional technologies offer uniform health recommendations based on users' biometric information, lacking emotional care. A solution is needed to address this problem and realize more comprehensive health support.

[0388] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0389] In this invention, the server includes a device for acquiring microbial community information from a biological sample, a device for analyzing the acquired microbial community information and generating an individualized health improvement plan, and a device for collecting the user's emotional state using an emotion recognition engine and reflecting it in the health improvement plan. This makes it possible to provide personalized health improvement measures that take into account both the user's physical and emotional health.

[0390] A "biological sample" is a sample taken from the human body or animals, and is particularly a material that contains information about microbial communities.

[0391] "Microbial community information" refers to data on the types, composition, and activity levels of microorganisms obtained from biological samples.

[0392] An "emotion recognition engine" is a device or program that analyzes input information such as voice, facial expressions, and text to identify the user's emotional state.

[0393] A "health improvement plan" refers to a program that proposes specific behavioral guidelines and health goals based on the user's biometric information and emotional state.

[0394] An "analysis algorithm" is a computational procedure for processing acquired biological samples and emotional data to generate health recommendations tailored to the user.

[0395] "Living environment" is a general term encompassing the physical, social, and emotional factors that influence users in their daily lives.

[0396] "Nutritional guidance" is the act of advising users on appropriate diets and nutritional intake methods based on their health condition and lifestyle.

[0397] This system consists of a server, terminals, and users, with each component fulfilling its specific role. The server retrieves microbial community information from biological samples submitted by users into a database and processes this information using a specific analytical algorithm. Based on the processed data, it generates a personalized health improvement plan. This plan includes suggestions for probiotics, supplement recommendations, and specific meal plans.

[0398] Meanwhile, the device is equipped with an emotion recognition engine that collects emotional states through the user's voice, facial expressions, and daily actions. The collected emotional data is sent to a server and incorporated into the health improvement plan. The device also presents the generated health improvement plan to the user and collects feedback. This feedback allows the server to update its analysis algorithm, making subsequent health improvement plans even more personalized.

[0399] For example, if a user is suffering from constipation, the device can sense their stress level, and the server can generate suggestions recommending probiotics and lavender tea to aid digestion. The user then tries the suggestions and reports their subsequent physical and emotional changes on the device, which is then used to improve future suggestions.

[0400] For the generative AI model, a prompt such as, "Create an algorithm that analyzes the user's emotional state and suggests health improvement measures according to their stress level," is used.

[0401] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0402] Step 1:

[0403] The user collects a stool sample using a dedicated kit and sends it to the analysis facility. The input includes the stool sample. The output is sample information registered in a database for transmission to the server.

[0404] Step 2:

[0405] The server obtains microbial community information from the received stool sample and processes the acquired data using an analysis algorithm. The input includes microbial data from the stool sample. The output provides analysis results regarding the composition of the microbial community, specifically showing the types and proportions of bacteria.

[0406] Step 3:

[0407] The device uses an emotion recognition engine to analyze the user's voice and facial expressions and evaluate their emotional state. Input includes the user's voice data and visual information. Output is data indicating the user's emotional state (e.g., stress level). Operation involves analyzing changes in voice tone and facial features.

[0408] Step 4:

[0409] The server integrates the analysis results of the obtained microbial communities with emotional state data to generate a personalized health improvement plan. Input includes the output data from steps 2 and 3. Output provides a health improvement plan including probiotic suggestions and a meal plan. Specifically, the AI ​​calculates the optimal plan using the integrated data.

[0410] Step 5:

[0411] The device presents the generated health improvement plan to the user and collects feedback from the user. The input includes the health improvement plan, while the output provides user feedback on their impressions and the results of its application.

[0412] Step 6:

[0413] The server analyzes the collected feedback and updates the analysis algorithm to improve the accuracy of the next health improvement plan. Input includes user feedback. Output is the updated analysis algorithm. Specifically, the feedback is stored in a database, and the AI ​​adjusts the algorithm based on that data.

[0414] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0415] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0420] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0422] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0424] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0425] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0426] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0427] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0429] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0430] The present invention is specifically configured as a system for providing personalized health improvement services. The system acquires intestinal microbiota information from biological samples and supports the user's health by providing appropriate health improvement suggestions based on the analysis results of that information. A specific embodiment of the system is shown below.

[0431] First, the user collects a stool sample and places it in the designated kit. By scanning the QR code included in the kit using a terminal, the sample information is recorded. Once the server confirms that the sample has arrived at the analysis facility, it begins the analysis process to obtain intestinal microbiota data from the sample.

[0432] The obtained bacterial community information is processed on a server and analyzed by an AI agent. During the analysis process, past data and the latest research findings are utilized to profile the user's gut microbiome. This generates recommendations for optimal probiotic foods and supplements to address identified health issues. These recommendations are then sent from the server to the user's device and notified.

[0433] For example, if a user has an unbalanced diet, the AI ​​agent might recommend taking probiotics rich in vitamins and minerals. The suggestion would include specific product names, instructions for consumption, and expected effects, allowing the user to improve their health by incorporating them into their daily life.

[0434] Furthermore, the device periodically requests feedback from the user and sends that input to the server. The server updates the analysis algorithm based on the feedback, which helps in making future suggestions and improving the accuracy of the analysis process. This ensures continuous support and improvement, enabling long-term health management.

[0435] This invention allows for flexible and effective health maintenance because the proposed solutions can be adapted to the user's life stage and individual health goals. Therefore, it provides a useful solution for a variety of users who aim to improve their health based on their gut environment.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] Users safely collect stool samples at home using the provided stool sample collection kit. The samples are sealed in the kit and stored hygienically.

[0439] Step 2:

[0440] The terminal scans the QR code included in the kit to register the sample. This scan associates the stool sample with a specific user and sends the data to the server.

[0441] Step 3:

[0442] The user mails the collected stool sample to the designated analysis facility.

[0443] Step 4:

[0444] The server receives notification from the analysis facility that the stool sample has arrived and confirms receipt of the sample. After confirming receipt, the analysis process of the sample begins.

[0445] Step 5:

[0446] The server performs the necessary processing to extract gut microbiota data from the sample. This processing includes DNA extraction, sequencing, and data transformation for analysis.

[0447] Step 6:

[0448] The AI ​​agent creates a profile of the user's gut microbiome based on the acquired gut microbiota data. The profile includes the types and quantities of bacteria present, as well as their impact on health.

[0449] Step 7:

[0450] The AI ​​agent generates suggestions for improving health based on the user's profile. These suggestions include specific probiotic foods and supplements, as well as dietary advice.

[0451] Step 8:

[0452] The server organizes the generated suggestions and sends notifications to the terminal. The notifications include details about the suggestions and how to use them, providing information to help users put them into practice.

[0453] Step 9:

[0454] Users incorporate the suggested health improvement measures into their daily lives, experience their effects, and provide feedback.

[0455] Step 10:

[0456] The device receives feedback from the user and sends it to the server. This feedback includes information about changes in health status and the effectiveness of suggestions.

[0457] Step 11:

[0458] The server shares feedback with the AI ​​agent and uses the data to improve the accuracy of the analysis algorithm. This makes the next health improvement suggestions even more personalized.

[0459] (Example 1)

[0460] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0461] In modern healthcare management, providing personalized advice in a short timeframe is crucial. However, traditional methods have struggled to efficiently and accurately deliver health improvement suggestions, particularly those based on gut health. Furthermore, there was a lack of mechanisms to effectively incorporate user feedback into the system and continuously improve the suggestions provided.

[0462] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0463] In this invention, the server includes means for acquiring microbiome information from a biological sample, a device for analyzing the acquired microbiome information and generating individual health improvement suggestions, and a device for presenting the generated health improvement suggestions and collecting feedback from users. This makes it possible to provide users with rapid and highly accurate health improvement suggestions tailored to their gut environment.

[0464] A "biological sample" is a substance that can reflect the physiological state of an individual, such as tissue, fluid, or excretion.

[0465] "Microbial flora information" refers to data about the types, composition, and proportions of microorganisms present in a specific environment.

[0466] A "generative model" is a type of artificial intelligence equipped with algorithms that automatically analyze data and generate information according to specified conditions.

[0467] A "prompt statement" is a statement input to a generative model that contains instructions or conditions for obtaining output appropriate to the model.

[0468] An "analysis algorithm" is a set of computational procedures and rules defined for analyzing data and extracting meaningful information.

[0469] A "platform" is a system that provides a common foundation for different devices and systems to exchange information and function in cooperation with each other.

[0470] This invention is a system aimed at improving the user's health, and it generates personalized suggestions using microbiome information. Specifically, the system is operated according to the following procedure.

[0471] The user first collects a stool sample as a biological sample and seals it in the provided kit. This kit includes a QR code, which the user scans using a device to send the sample and user information to the server. The QR code contains information to identify the sample.

[0472] When a sample arrives at the analysis facility, the server uses analytical instruments to perform DNA sequencing of the microbiome. Next-generation sequencers (e.g., Illumina MiSeq) are used for this analysis. The obtained microbiome information is stored in a database on the server.

[0473] Next, a generative AI model analyzes this microbiome information and generates personalized health improvement suggestions. Specifically, the model is input with predefined prompts and outputs suggestions tailored to the user's gut environment and individual health condition. These generated suggestions are expected to include instructions for taking probiotic foods and supplements.

[0474] The server sends the generated suggestions to the terminal, and the terminal notifies the user of the suggestions. The user can then implement these suggestions in their daily life.

[0475] For example, if you input a prompt sentence into the model such as, "I'm a woman in my 40s and I've been feeling tired lately. Please provide optimal health improvement suggestions based on my gut microbiome information," the model will output suggestions for foods containing specific vitamins and advice on improving lifestyle habits.

[0476] This ensures continuous support for individual health improvements and enables effective health management.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user collects a stool sample and places it in a special kit. At this time, they scan a QR code included in the kit with their device. The information entered is the sample ID contained in the QR code, which is sent to the server to link the sample with the user information. Specifically, the device reads the digital information generated by scanning the QR code and communicates it to the server. The output is the completion of registration of the sample information in the sample database.

[0480] Step 2:

[0481] Once the server confirms the sample's arrival at the analysis facility, it begins DNA sequencing of the microbiome using high-precision analytical instruments. The input is a stool sample as a biological specimen, and the output is sequenced data. This data indicates the composition and structure of the microorganisms and is stored digitally in a database on the server. Specific analytical operations include the loading of reagents for automated sample processing and the DNA extraction process.

[0482] Step 3:

[0483] The server performs analysis using a generative AI model based on the stored microbiome information. The input data is sequence data, and the output generated by the AI ​​model is a report that profiles the user's health status. Data processing is a process of giving instructions to the model using prompt statements to generate information as health improvement suggestions. Specifically, this involves inputting data into the model and extracting results, which then creates the analysis report.

[0484] Step 4:

[0485] The server sends health improvement suggestions based on the analysis results to the terminal, and the terminal notifies the user. The input is an analysis report, and the output is specific health improvement suggestions. The information presented includes information on probiotics or supplements that should be taken. Specifically, a notification is displayed on the user's smartphone or tablet.

[0486] Step 5:

[0487] The terminal prompts the user for continuous feedback after they receive a suggestion, and sends that feedback to the server. The input data includes the user's opinions and health improvement status, which the server uses to update its analysis algorithm. The output is an updated analysis base that contributes to improving the accuracy of the system's next analysis. The specific operation involves sending periodic questionnaire forms and reflecting the results in the database.

[0488] (Application Example 1)

[0489] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0490] In modern times, there is a demand for effective health improvement plans tailored to individual health conditions. However, many people have no choice but to rely on generalized health information, and appropriate improvement measures based on their individual gut environment are rarely provided. Furthermore, there is a lack of systems that can update analytical algorithms based on feedback and continuously provide personalized suggestions. In addition, there are no practical means to easily monitor health conditions at home and implement suggestions in daily life.

[0491] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0492] In this invention, the server includes means for acquiring microbial community information from a biological sample, means for analyzing the acquired microbial community information and generating individual health improvement suggestions, means for presenting the generated health improvement suggestions, collecting feedback from the user, and updating the analysis algorithm based on the collected feedback, means for providing the user with daily necessities or nutritional products related to the proposed health improvement content, and means for providing specific nutritional intake advice through a home device. This makes it possible to provide users with continuous and personalized health improvement suggestions.

[0493] A "biological sample" is a sample containing microorganisms or chemical substances collected from a specific organism.

[0494] "Microbial community information" refers to information about the types and proportions of microorganisms obtained from biological samples.

[0495] An "analysis algorithm" is a computational method used to find specific patterns in collected data and generate meaningful information.

[0496] A "health improvement suggestion" is a recommendation of specific actions or products aimed at improving health status, based on the analysis results obtained.

[0497] "Daily necessities" refers to products and goods that are commonly used in daily life.

[0498] "Nutritional products" refer to products including foods and supplements intended for nutritional supplementation.

[0499] "Household appliances" refers to electrical appliances and health management devices used within the home.

[0500] This invention is a system for providing personalized health improvement suggestions using biological samples from users. The server acquires microbial community information based on the biological sample and processes it using an analysis algorithm. Simultaneously, it receives feedback from the user via a terminal and continuously optimizes the suggestions.

[0501] The server analyzes specific health indicators based on microbial community information obtained from biological samples. The analysis uses machine learning libraries such as TensorFlow or PyTorch. After analysis by the AI ​​model, personalized health improvement suggestions are generated. These suggestions include probiotic foods and nutritional products tailored to the user's health condition. The suggestions are sent to the user's device and applied to their daily life.

[0502] Users receive suggestions from the server using their devices and make lifestyle improvements based on those suggestions. For example, they can purchase suggested nutritional products and incorporate them into their diet. Furthermore, providing feedback contributes to improving the accuracy of the analysis algorithm.

[0503] The device connects to home appliances and provides specific nutritional advice, making it easier to implement the suggested diet. For example, if consuming yogurt in the morning is recommended, the device will notify the user of the appropriate time and method of consumption.

[0504] Examples of prompts for a generative AI model are as follows:

[0505] "Program personalized health recommendations based on gut microbiota information. This should include a process of continuously optimizing the recommendations based on user feedback, using Python and TensorFlow."

[0506] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0507] Step 1:

[0508] The user collects a biological sample using a dedicated kit. The collected sample information is electronically transmitted to the server by scanning the included QR code with a terminal. The input is a stool sample, and the output is the sample information contained in the QR code.

[0509] Step 2:

[0510] The server obtains microbial community information at the analysis facility based on the transmitted sample information. Analysis of biological samples is performed by identifying the types and proportions of bacteria using automated equipment. The input is QR code information, and the output is microbial community information.

[0511] Step 3:

[0512] The server uses machine learning models to analyze microbial community information. This analysis employs AI algorithms based on historical data and the latest research findings. Specifically, it uses TensorFlow to evaluate health indicators. The input is microbial community information, and the output is health improvement suggestions.

[0513] Step 4:

[0514] The server sends the generated health improvement suggestions to the terminal. These suggestions include individually optimized recommendations for probiotic foods and nutritional products. The terminal then notifies the user of these suggestions. The input is the health improvement suggestions, and the output is the notification to the user.

[0515] Step 5:

[0516] Users review their lifestyle habits based on the suggestions and send feedback to the server via their device. The input is the user's results and impressions, and the output is feedback information.

[0517] Step 6:

[0518] The server uses user feedback to improve its analysis algorithm. The feedback is used to adapt and refine the algorithm, resulting in improved accuracy for subsequent analyses. The input is the feedback information, and the output is the improved analysis algorithm.

[0519] Step 7:

[0520] The device provides users with specific nutritional advice through integration with home appliances. For example, it might notify them of the recommended time to consume yogurt for breakfast. The input is a health improvement suggestion, and the output is a dietary instruction from the device.

[0521] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0522] This invention provides more effective health support by combining a system that offers personalized health improvement suggestions with an emotion engine that analyzes the user's emotional state. This system integrates information analysis from biological samples with recognition of the user's emotions.

[0523] First, the user collects a stool sample using a special kit and sends it to an analysis facility. The server obtains gut microbiota information from the biological sample, and an AI agent performs a detailed analysis based on that data. Based on the profile obtained from the analysis results, the server generates personalized health improvement suggestions. These may include probiotics or appropriate supplements to help with nutritional supplementation, or a specific meal plan.

[0524] Simultaneously, the device collects the user's emotional state through its built-in emotion engine. This information is obtained by analyzing the user's tone of voice, facial expressions, text input, and other data. Once the emotion engine recognizes the user's emotions, the server further incorporates this information into health improvement suggestions. For example, if the user is feeling stressed, the suggestions can include foods with relaxing effects or supplements that can help with mental health.

[0525] The generated suggestions are presented to the user via the device in an optimized form. The user can then make choices in their daily life based on these suggestions. After implementing the suggestions, they send feedback via the device about their feelings and health status. This feedback is collected on a server and used to update the AI ​​agent's algorithm. This update further personalizes the next set of suggestions.

[0526] For example, if a user complains of indigestion and stress, this system can suggest probiotics to improve the gut environment, as well as recommend relaxing herbal teas depending on the emotional state indicated by the emotional engine. In this way, the present invention provides comprehensive support for the user's physical and emotional health, enabling sustainable health maintenance.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The user collects a stool sample using a dedicated kit. The sample is placed in a hygienic sealing bag and sent to the analysis facility.

[0530] Step 2:

[0531] The device scans the QR code included in the sample kit, and the sample information is sent to the server. At this stage, the sample is associated with a specific user.

[0532] Step 3:

[0533] The server confirms the arrival of the stool sample from the analysis facility and initiates the necessary analysis process. Bacterial group information is extracted and digitized.

[0534] Step 4:

[0535] The AI ​​agent analyzes bacterial community information on the server and generates a profile of the user's gut environment. Based on this profile, the foundation for health improvement suggestions is formed.

[0536] Step 5:

[0537] The device uses an emotion engine to monitor the user's emotional state in real time. It collects emotional data through speech recognition, facial expression recognition, and text analysis.

[0538] Step 6:

[0539] The server integrates data from the emotion engine and optimizes improvement suggestions based on the user's emotional state. If the user is experiencing high stress levels, it prioritizes suggesting foods and supplements with relaxation effects.

[0540] Step 7:

[0541] The server organizes the generated health improvement suggestions and sends them to the terminal as detailed information. These suggestions include details about implementation methods and expected effects.

[0542] Step 8:

[0543] Users receive suggestions presented through their devices and incorporate them into their daily lives.

[0544] Step 9:

[0545] The device collects user feedback after the suggestion is implemented and sends it to the server. This feedback includes opinions on changes in health status and the effectiveness of the suggestion.

[0546] Step 10:

[0547] The server integrates feedback and updates the algorithms with AI agents. This update makes subsequent suggestions more personalized and enhances user health support.

[0548] (Example 2)

[0549] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] In modern society, while individualized health management is highly valued, achieving comprehensive health improvement that takes into account not only physical health but also emotional state remains challenging. Furthermore, dynamically updating health improvement suggestions to adequately adapt to the individual circumstances of each user is also a challenge.

[0551] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0552] In this invention, the server includes means for acquiring microbial information from biological data, means for analyzing the acquired microbial information and generating individual health improvement suggestions, means for collecting the user's emotional state, means for supplementing the health improvement suggestions based on the emotional state, means for presenting the generated health improvement suggestions and collecting feedback from the user, and means for updating the analysis algorithm based on the collected feedback. This enables personalized health improvement suggestions that comprehensively consider the user's physical and emotional health state.

[0553] "Biological specimens" refer to samples of tissue, fluid, secretions, etc., taken from the human body, and are particularly used to obtain microbial information.

[0554] "Microbial information" refers to data such as the type, composition, and genetic information of microorganisms obtained from biological samples.

[0555] An "analysis algorithm" is a set of calculation procedures that process data obtained from biological samples and emotional states to generate suggestions for improving health.

[0556] "Emotional state" refers to the user's psychological state and is information obtained by analyzing voice, facial expressions, text input, etc.

[0557] "Health improvement suggestions" are recommendations generated by an analytical algorithm to improve the user's physical and emotional health.

[0558] "Feedback" refers to information that users send to the server regarding the results and impressions of implementing health improvement suggestions, and this information is used to update the algorithm.

[0559] In implementing this invention, the user, server, and terminal each play their respective roles.

[0560] First, users collect biological samples using a dedicated kit and send them to a facility capable of processing them on a server. Microbial information is obtained from the biological samples using high-throughput sequencing technology. The server receives this data and uses analysis algorithms to generate personalized health improvement suggestions.

[0561] Simultaneously, the device collects the user's emotional state. Equipped with an emotion engine, the device acquires user emotional information through voice analysis, facial expression analysis, and text input analysis. This allows the server to incorporate the collected and analyzed emotional state into health improvement suggestions.

[0562] The generated health improvement suggestions, which include recommendations such as supplements for specific nutrients like probiotics and foods with relaxing effects, are presented to the user via the device. Based on these suggestions, the user can review their daily diet and lifestyle habits.

[0563] Furthermore, users provide feedback on the results of implementing the suggestions. The system includes a function to send this feedback back to the server via the device. The server then uses this feedback to update the analysis algorithm, adjusting it to make future suggestions more personalized. This iterative process ensures continuous health improvement for the user.

[0564] For example, if a user is experiencing indigestion and fatigue, the server can suggest consuming probiotics, as well as foods known to be effective in relieving fatigue. An example of a prompt to the generating AI model might be, "Generate optimal health improvement suggestions for a user complaining of indigestion and fatigue." In this way, the present invention provides a concrete and effective means to address individual health needs.

[0565] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0566] Step 1:

[0567] The user collects biological samples using a dedicated kit. The specific action the user takes is to properly place the samples into a sealed container according to the instructions included with the product. The input is the user's collection and labeling actions, and the output is the biological sample ready for shipment to the testing facility.

[0568] Step 2:

[0569] The server receives biological samples sent to the facility and acquires microbial information using high-throughput sequencing. High-performance analytical equipment is used for DNA fragmentation, sequencing, and data collection. The input is the biological sample, and the output is the acquired microbial information data.

[0570] Step 3:

[0571] The server inputs the acquired microbial information into an analysis algorithm to generate personalized health improvement suggestions. The analysis algorithm evaluates the health status by comparing the microbial composition with existing databases. The input is microbial information, and the output is the analyzed results and the generated health improvement suggestions.

[0572] Step 4:

[0573] The device collects the user's emotional state. The user provides emotional data to the device through voice input or facial recognition, which is then analyzed by an emotion engine. The input consists of voice, facial expressions, and text obtained from the user, and the output is information about the analyzed emotional state.

[0574] Step 5:

[0575] The server incorporates information about emotional state into health improvement suggestions. Based on this data, it processes the data to include additional suggestions for stress reduction measures. The input is data on emotional state, and the output is the final health improvement suggestion that reflects this data.

[0576] Step 6:

[0577] The terminal presents the generated health improvement suggestions to the user. Here, the terminal displays the suggestions on the screen, making them easily accessible to the user. The input is the health improvement suggestions, and the output is a visual presentation to the user.

[0578] Step 7:

[0579] Users collect feedback on the results of implementing their suggestions. They input information such as the effects and impressions obtained from the suggestions via their devices. The input is user feedback information, and the output is the data transmitted to the device.

[0580] Step 8:

[0581] The server updates its analysis algorithm based on the collected feedback. It incorporates data as a reference for improving the AI ​​model and aims to increase the accuracy of the algorithm. The input is user feedback data, and the output is the improved analysis algorithm.

[0582] (Application Example 2)

[0583] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0584] In modern society, there is a need to provide specific and effective health maintenance measures tailored to the individual health and emotional states of each user. However, conventional technologies offer uniform health recommendations based on users' biometric information, lacking emotional care. A solution is needed to address this problem and realize more comprehensive health support.

[0585] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0586] In this invention, the server includes a device for acquiring microbial community information from a biological sample, a device for analyzing the acquired microbial community information and generating an individualized health improvement plan, and a device for collecting the user's emotional state using an emotion recognition engine and reflecting it in the health improvement plan. This makes it possible to provide personalized health improvement measures that take into account both the user's physical and emotional health.

[0587] A "biological sample" is a sample taken from the human body or animals, and is particularly a material that contains information about microbial communities.

[0588] "Microbial community information" refers to data on the types, composition, and activity levels of microorganisms obtained from biological samples.

[0589] An "emotion recognition engine" is a device or program that analyzes input information such as voice, facial expressions, and text to identify the user's emotional state.

[0590] A "health improvement plan" refers to a program that proposes specific behavioral guidelines and health goals based on the user's biometric information and emotional state.

[0591] An "analysis algorithm" is a computational procedure for processing acquired biological samples and emotional data to generate health recommendations tailored to the user.

[0592] "Living environment" is a general term encompassing the physical, social, and emotional factors that influence users in their daily lives.

[0593] "Nutritional guidance" is the act of advising users on appropriate diets and nutritional intake methods based on their health condition and lifestyle.

[0594] This system consists of a server, terminals, and users, with each component fulfilling its specific role. The server retrieves microbial community information from biological samples submitted by users into a database and processes this information using a specific analytical algorithm. Based on the processed data, it generates a personalized health improvement plan. This plan includes suggestions for probiotics, supplement recommendations, and specific meal plans.

[0595] Meanwhile, the device is equipped with an emotion recognition engine that collects emotional states through the user's voice, facial expressions, and daily actions. The collected emotional data is sent to a server and incorporated into the health improvement plan. The device also presents the generated health improvement plan to the user and collects feedback. This feedback allows the server to update its analysis algorithm, making subsequent health improvement plans even more personalized.

[0596] For example, if a user is suffering from constipation, the device can sense their stress level, and the server can generate suggestions recommending probiotics and lavender tea to aid digestion. The user then tries the suggestions and reports their subsequent physical and emotional changes on the device, which is then used to improve future suggestions.

[0597] For the generative AI model, a prompt such as, "Create an algorithm that analyzes the user's emotional state and suggests health improvement measures according to their stress level," is used.

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user collects a stool sample using a dedicated kit and sends it to the analysis facility. The input includes the stool sample. The output is sample information registered in a database for transmission to the server.

[0601] Step 2:

[0602] The server obtains microbial community information from the received stool sample and processes the acquired data using an analysis algorithm. The input includes microbial data from the stool sample. The output provides analysis results regarding the composition of the microbial community, specifically showing the types and proportions of bacteria.

[0603] Step 3:

[0604] The device uses an emotion recognition engine to analyze the user's voice and facial expressions and evaluate their emotional state. Input includes the user's voice data and visual information. Output is data indicating the user's emotional state (e.g., stress level). Operation involves analyzing changes in voice tone and facial features.

[0605] Step 4:

[0606] The server integrates the analysis results of the obtained microbial communities with emotional state data to generate a personalized health improvement plan. Input includes the output data from steps 2 and 3. Output provides a health improvement plan including probiotic suggestions and a meal plan. Specifically, the AI ​​calculates the optimal plan using the integrated data.

[0607] Step 5:

[0608] The device presents the generated health improvement plan to the user and collects feedback from the user. The input includes the health improvement plan, while the output provides user feedback on their impressions and the results of its application.

[0609] Step 6:

[0610] The server analyzes the collected feedback and updates the analysis algorithm to improve the accuracy of the next health improvement plan. Input includes user feedback. Output is the updated analysis algorithm. Specifically, the feedback is stored in a database, and the AI ​​adjusts the algorithm based on that data.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] The present invention is specifically configured as a system for providing personalized health improvement services. The system acquires intestinal microbiota information from biological samples and supports the user's health by providing appropriate health improvement suggestions based on the analysis results of that information. A specific embodiment of the system is shown below.

[0629] First, the user collects a stool sample and places it in the designated kit. By scanning the QR code included in the kit using a terminal, the sample information is recorded. Once the server confirms that the sample has arrived at the analysis facility, it begins the analysis process to obtain intestinal microbiota data from the sample.

[0630] The obtained bacterial community information is processed on a server and analyzed by an AI agent. During the analysis process, past data and the latest research findings are utilized to profile the user's gut microbiome. This generates recommendations for optimal probiotic foods and supplements to address identified health issues. These recommendations are then sent from the server to the user's device and notified.

[0631] For example, if a user has an unbalanced diet, the AI ​​agent might recommend taking probiotics rich in vitamins and minerals. The suggestion would include specific product names, instructions for consumption, and expected effects, allowing the user to improve their health by incorporating them into their daily life.

[0632] Furthermore, the device periodically requests feedback from the user and sends that input to the server. The server updates the analysis algorithm based on the feedback, which helps in making future suggestions and improving the accuracy of the analysis process. This ensures continuous support and improvement, enabling long-term health management.

[0633] This invention allows for flexible and effective health maintenance because the proposed solutions can be adapted to the user's life stage and individual health goals. Therefore, it provides a useful solution for a variety of users who aim to improve their health based on their gut environment.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] Users safely collect stool samples at home using the provided stool sample collection kit. The samples are sealed in the kit and stored hygienically.

[0637] Step 2:

[0638] The terminal scans the QR code included in the kit to register the sample. This scan associates the stool sample with a specific user and sends the data to the server.

[0639] Step 3:

[0640] The user mails the collected stool sample to the designated analysis facility.

[0641] Step 4:

[0642] The server receives notification from the analysis facility that the stool sample has arrived and confirms receipt of the sample. After confirming receipt, the analysis process of the sample begins.

[0643] Step 5:

[0644] The server performs the necessary processing to extract gut microbiota data from the sample. This processing includes DNA extraction, sequencing, and data transformation for analysis.

[0645] Step 6:

[0646] The AI ​​agent creates a profile of the user's gut microbiome based on the acquired gut microbiota data. The profile includes the types and quantities of bacteria present, as well as their impact on health.

[0647] Step 7:

[0648] The AI ​​agent generates suggestions for improving health based on the user's profile. These suggestions include specific probiotic foods and supplements, as well as dietary advice.

[0649] Step 8:

[0650] The server organizes the generated suggestions and sends notifications to the terminal. The notifications include details about the suggestions and how to use them, providing information to help users put them into practice.

[0651] Step 9:

[0652] Users incorporate the suggested health improvement measures into their daily lives, experience their effects, and provide feedback.

[0653] Step 10:

[0654] The device receives feedback from the user and sends it to the server. This feedback includes information about changes in health status and the effectiveness of suggestions.

[0655] Step 11:

[0656] The server shares feedback with the AI ​​agent and uses the data to improve the accuracy of the analysis algorithm. This makes the next health improvement suggestions even more personalized.

[0657] (Example 1)

[0658] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0659] In modern healthcare management, providing personalized advice in a short timeframe is crucial. However, traditional methods have struggled to efficiently and accurately deliver health improvement suggestions, particularly those based on gut health. Furthermore, there was a lack of mechanisms to effectively incorporate user feedback into the system and continuously improve the suggestions provided.

[0660] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0661] In this invention, the server includes means for acquiring microbiome information from a biological sample, a device for analyzing the acquired microbiome information and generating individual health improvement suggestions, and a device for presenting the generated health improvement suggestions and collecting feedback from users. This makes it possible to provide users with rapid and highly accurate health improvement suggestions tailored to their gut environment.

[0662] A "biological sample" is a substance that can reflect the physiological state of an individual, such as tissue, fluid, or excretion.

[0663] "Microbial flora information" refers to data about the types, composition, and proportions of microorganisms present in a specific environment.

[0664] A "generative model" is a type of artificial intelligence equipped with algorithms that automatically analyze data and generate information according to specified conditions.

[0665] A "prompt statement" is a statement input to a generative model that contains instructions or conditions for obtaining output appropriate to the model.

[0666] An "analysis algorithm" is a set of computational procedures and rules defined for analyzing data and extracting meaningful information.

[0667] A "platform" is a system that provides a common foundation for different devices and systems to exchange information and function in cooperation with each other.

[0668] This invention is a system aimed at improving the user's health, and it generates personalized suggestions using microbiome information. Specifically, the system is operated according to the following procedure.

[0669] The user first collects a stool sample as a biological sample and seals it in the provided kit. This kit includes a QR code, which the user scans using a device to send the sample and user information to the server. The QR code contains information to identify the sample.

[0670] When a sample arrives at the analysis facility, the server uses analytical instruments to perform DNA sequencing of the microbiome. Next-generation sequencers (e.g., Illumina MiSeq) are used for this analysis. The obtained microbiome information is stored in a database on the server.

[0671] Next, a generative AI model analyzes this microbiome information and generates personalized health improvement suggestions. Specifically, the model is input with predefined prompts and outputs suggestions tailored to the user's gut environment and individual health condition. These generated suggestions are expected to include instructions for taking probiotic foods and supplements.

[0672] The server sends the generated suggestions to the terminal, and the terminal notifies the user of the suggestions. The user can then implement these suggestions in their daily life.

[0673] For example, if you input a prompt sentence into the model such as, "I'm a woman in my 40s and I've been feeling tired lately. Please provide optimal health improvement suggestions based on my gut microbiome information," the model will output suggestions for foods containing specific vitamins and advice on improving lifestyle habits.

[0674] This ensures continuous support for individual health improvements and enables effective health management.

[0675] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0676] Step 1:

[0677] The user collects a stool sample and places it in a special kit. At this time, they scan a QR code included in the kit with their device. The information entered is the sample ID contained in the QR code, which is sent to the server to link the sample with the user information. Specifically, the device reads the digital information generated by scanning the QR code and communicates it to the server. The output is the completion of registration of the sample information in the sample database.

[0678] Step 2:

[0679] Once the server confirms the sample's arrival at the analysis facility, it begins DNA sequencing of the microbiome using high-precision analytical instruments. The input is a stool sample as a biological specimen, and the output is sequenced data. This data indicates the composition and structure of the microorganisms and is stored digitally in a database on the server. Specific analytical operations include the loading of reagents for automated sample processing and the DNA extraction process.

[0680] Step 3:

[0681] The server performs analysis using a generative AI model based on the stored microbiome information. The input data is sequence data, and the output generated by the AI ​​model is a report that profiles the user's health status. Data processing is a process of giving instructions to the model using prompt statements to generate information as health improvement suggestions. Specifically, this involves inputting data into the model and extracting results, which then creates the analysis report.

[0682] Step 4:

[0683] The server sends health improvement suggestions based on the analysis results to the terminal, and the terminal notifies the user. The input is an analysis report, and the output is specific health improvement suggestions. The information presented includes information on probiotics or supplements that should be taken. Specifically, a notification is displayed on the user's smartphone or tablet.

[0684] Step 5:

[0685] The terminal prompts the user for continuous feedback after they receive a suggestion, and sends that feedback to the server. The input data includes the user's opinions and health improvement status, which the server uses to update its analysis algorithm. The output is an updated analysis base that contributes to improving the accuracy of the system's next analysis. The specific operation involves sending periodic questionnaire forms and reflecting the results in the database.

[0686] (Application Example 1)

[0687] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0688] In modern times, there is a demand for effective health improvement plans tailored to individual health conditions. However, many people have no choice but to rely on generalized health information, and appropriate improvement measures based on their individual gut environment are rarely provided. Furthermore, there is a lack of systems that can update analytical algorithms based on feedback and continuously provide personalized suggestions. In addition, there are no practical means to easily monitor health conditions at home and implement suggestions in daily life.

[0689] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0690] In this invention, the server includes means for acquiring microbial community information from a biological sample, means for analyzing the acquired microbial community information and generating individual health improvement suggestions, means for presenting the generated health improvement suggestions, collecting feedback from the user, and updating the analysis algorithm based on the collected feedback, means for providing the user with daily necessities or nutritional products related to the proposed health improvement content, and means for providing specific nutritional intake advice through a home device. This makes it possible to provide users with continuous and personalized health improvement suggestions.

[0691] A "biological sample" is a sample containing microorganisms or chemical substances collected from a specific organism.

[0692] "Microbial community information" refers to information about the types and proportions of microorganisms obtained from biological samples.

[0693] An "analysis algorithm" is a computational method used to find specific patterns in collected data and generate meaningful information.

[0694] A "health improvement suggestion" is a recommendation of specific actions or products aimed at improving health status, based on the analysis results obtained.

[0695] "Daily necessities" refers to products and goods that are commonly used in daily life.

[0696] "Nutritional products" refer to products including foods and supplements intended for nutritional supplementation.

[0697] "Household appliances" refers to electrical appliances and health management devices used within the home.

[0698] This invention is a system for providing personalized health improvement suggestions using biological samples from users. The server acquires microbial community information based on the biological sample and processes it using an analysis algorithm. Simultaneously, it receives feedback from the user via a terminal and continuously optimizes the suggestions.

[0699] The server analyzes specific health indicators based on microbial community information obtained from biological samples. The analysis uses machine learning libraries such as TensorFlow or PyTorch. After analysis by the AI ​​model, personalized health improvement suggestions are generated. These suggestions include probiotic foods and nutritional products tailored to the user's health condition. The suggestions are sent to the user's device and applied to their daily life.

[0700] Users receive suggestions from the server using their devices and make lifestyle improvements based on those suggestions. For example, they can purchase suggested nutritional products and incorporate them into their diet. Furthermore, providing feedback contributes to improving the accuracy of the analysis algorithm.

[0701] The device connects to home appliances and provides specific nutritional advice, making it easier to implement the suggested diet. For example, if consuming yogurt in the morning is recommended, the device will notify the user of the appropriate time and method of consumption.

[0702] Examples of prompts for a generative AI model are as follows:

[0703] "Program personalized health recommendations based on gut microbiota information. This should include a process of continuously optimizing the recommendations based on user feedback, using Python and TensorFlow."

[0704] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0705] Step 1:

[0706] The user collects a biological sample using a dedicated kit. The collected sample information is electronically transmitted to the server by scanning the included QR code with a terminal. The input is a stool sample, and the output is the sample information contained in the QR code.

[0707] Step 2:

[0708] The server obtains microbial community information at the analysis facility based on the transmitted sample information. Analysis of biological samples is performed by identifying the types and proportions of bacteria using automated equipment. The input is QR code information, and the output is microbial community information.

[0709] Step 3:

[0710] The server uses machine learning models to analyze microbial community information. This analysis employs AI algorithms based on historical data and the latest research findings. Specifically, it uses TensorFlow to evaluate health indicators. The input is microbial community information, and the output is health improvement suggestions.

[0711] Step 4:

[0712] The server sends the generated health improvement suggestions to the terminal. These suggestions include individually optimized recommendations for probiotic foods and nutritional products. The terminal then notifies the user of these suggestions. The input is the health improvement suggestions, and the output is the notification to the user.

[0713] Step 5:

[0714] Users review their lifestyle habits based on the suggestions and send feedback to the server via their device. The input is the user's results and impressions, and the output is feedback information.

[0715] Step 6:

[0716] The server uses user feedback to improve its analysis algorithm. The feedback is used to adapt and refine the algorithm, resulting in improved accuracy for subsequent analyses. The input is the feedback information, and the output is the improved analysis algorithm.

[0717] Step 7:

[0718] The device provides users with specific nutritional advice through integration with home appliances. For example, it might notify them of the recommended time to consume yogurt for breakfast. The input is a health improvement suggestion, and the output is a dietary instruction from the device.

[0719] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0720] This invention provides more effective health support by combining a system that offers personalized health improvement suggestions with an emotion engine that analyzes the user's emotional state. This system integrates information analysis from biological samples with recognition of the user's emotions.

[0721] First, the user collects a stool sample using a special kit and sends it to an analysis facility. The server obtains gut microbiota information from the biological sample, and an AI agent performs a detailed analysis based on that data. Based on the profile obtained from the analysis results, the server generates personalized health improvement suggestions. These may include probiotics or appropriate supplements to help with nutritional supplementation, or a specific meal plan.

[0722] Simultaneously, the device collects the user's emotional state through its built-in emotion engine. This information is obtained by analyzing the user's tone of voice, facial expressions, text input, and other data. Once the emotion engine recognizes the user's emotions, the server further incorporates this information into health improvement suggestions. For example, if the user is feeling stressed, the suggestions can include foods with relaxing effects or supplements that can help with mental health.

[0723] The generated suggestions are presented to the user via the device in an optimized form. The user can then make choices in their daily life based on these suggestions. After implementing the suggestions, they send feedback via the device about their feelings and health status. This feedback is collected on a server and used to update the AI ​​agent's algorithm. This update further personalizes the next set of suggestions.

[0724] For example, if a user complains of indigestion and stress, this system can suggest probiotics to improve the gut environment, as well as recommend relaxing herbal teas depending on the emotional state indicated by the emotional engine. In this way, the present invention provides comprehensive support for the user's physical and emotional health, enabling sustainable health maintenance.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The user collects a stool sample using a dedicated kit. The sample is placed in a hygienic sealing bag and sent to the analysis facility.

[0728] Step 2:

[0729] The device scans the QR code included in the sample kit, and the sample information is sent to the server. At this stage, the sample is associated with a specific user.

[0730] Step 3:

[0731] The server confirms the arrival of the stool sample from the analysis facility and initiates the necessary analysis process. Bacterial group information is extracted and digitized.

[0732] Step 4:

[0733] The AI ​​agent analyzes bacterial community information on the server and generates a profile of the user's gut environment. Based on this profile, the foundation for health improvement suggestions is formed.

[0734] Step 5:

[0735] The device uses an emotion engine to monitor the user's emotional state in real time. It collects emotional data through speech recognition, facial expression recognition, and text analysis.

[0736] Step 6:

[0737] The server integrates data from the emotion engine and optimizes improvement suggestions based on the user's emotional state. If the user is experiencing high stress levels, it prioritizes suggesting foods and supplements with relaxation effects.

[0738] Step 7:

[0739] The server organizes the generated health improvement suggestions and sends them to the terminal as detailed information. These suggestions include details about implementation methods and expected effects.

[0740] Step 8:

[0741] Users receive suggestions presented through their devices and incorporate them into their daily lives.

[0742] Step 9:

[0743] The device collects user feedback after the suggestion is implemented and sends it to the server. This feedback includes opinions on changes in health status and the effectiveness of the suggestion.

[0744] Step 10:

[0745] The server integrates feedback and updates the algorithms with AI agents. This update makes subsequent suggestions more personalized and enhances user health support.

[0746] (Example 2)

[0747] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0748] In modern society, while individualized health management is highly valued, achieving comprehensive health improvement that takes into account not only physical health but also emotional state remains challenging. Furthermore, dynamically updating health improvement suggestions to adequately adapt to the individual circumstances of each user is also a challenge.

[0749] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0750] In this invention, the server includes means for acquiring microbial information from biological data, means for analyzing the acquired microbial information and generating individual health improvement suggestions, means for collecting the user's emotional state, means for supplementing the health improvement suggestions based on the emotional state, means for presenting the generated health improvement suggestions and collecting feedback from the user, and means for updating the analysis algorithm based on the collected feedback. This enables personalized health improvement suggestions that comprehensively consider the user's physical and emotional health state.

[0751] "Biological specimens" refer to samples of tissue, fluid, secretions, etc., taken from the human body, and are particularly used to obtain microbial information.

[0752] "Microbial information" refers to data such as the type, composition, and genetic information of microorganisms obtained from biological samples.

[0753] An "analysis algorithm" is a set of calculation procedures that process data obtained from biological samples and emotional states to generate suggestions for improving health.

[0754] "Emotional state" refers to the user's psychological state and is information obtained by analyzing voice, facial expressions, text input, etc.

[0755] "Health improvement suggestions" are recommendations generated by an analytical algorithm to improve the user's physical and emotional health.

[0756] "Feedback" refers to information that users send to the server regarding the results and impressions of implementing health improvement suggestions, and this information is used to update the algorithm.

[0757] In implementing this invention, the user, server, and terminal each play their respective roles.

[0758] First, users collect biological samples using a dedicated kit and send them to a facility capable of processing them on a server. Microbial information is obtained from the biological samples using high-throughput sequencing technology. The server receives this data and uses analysis algorithms to generate personalized health improvement suggestions.

[0759] Simultaneously, the device collects the user's emotional state. Equipped with an emotion engine, the device acquires user emotional information through voice analysis, facial expression analysis, and text input analysis. This allows the server to incorporate the collected and analyzed emotional state into health improvement suggestions.

[0760] The generated health improvement suggestions, which include recommendations such as supplements for specific nutrients like probiotics and foods with relaxing effects, are presented to the user via the device. Based on these suggestions, the user can review their daily diet and lifestyle habits.

[0761] Furthermore, users provide feedback on the results of implementing the suggestions. The system includes a function to send this feedback back to the server via the device. The server then uses this feedback to update the analysis algorithm, adjusting it to make future suggestions more personalized. This iterative process ensures continuous health improvement for the user.

[0762] For example, if a user is experiencing indigestion and fatigue, the server can suggest consuming probiotics, as well as foods known to be effective in relieving fatigue. An example of a prompt to the generating AI model might be, "Generate optimal health improvement suggestions for a user complaining of indigestion and fatigue." In this way, the present invention provides a concrete and effective means to address individual health needs.

[0763] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0764] Step 1:

[0765] The user collects biological samples using a dedicated kit. The specific action the user takes is to properly place the samples into a sealed container according to the instructions included with the product. The input is the user's collection and labeling actions, and the output is the biological sample ready for shipment to the testing facility.

[0766] Step 2:

[0767] The server receives biological samples sent to the facility and acquires microbial information using high-throughput sequencing. High-performance analytical equipment is used for DNA fragmentation, sequencing, and data collection. The input is the biological sample, and the output is the acquired microbial information data.

[0768] Step 3:

[0769] The server inputs the acquired microbial information into an analysis algorithm to generate personalized health improvement suggestions. The analysis algorithm evaluates the health status by comparing the microbial composition with existing databases. The input is microbial information, and the output is the analyzed results and the generated health improvement suggestions.

[0770] Step 4:

[0771] The device collects the user's emotional state. The user provides emotional data to the device through voice input or facial recognition, which is then analyzed by an emotion engine. The input consists of voice, facial expressions, and text obtained from the user, and the output is information about the analyzed emotional state.

[0772] Step 5:

[0773] The server incorporates information about emotional state into health improvement suggestions. Based on this data, it processes the data to include additional suggestions for stress reduction measures. The input is data on emotional state, and the output is the final health improvement suggestion that reflects this data.

[0774] Step 6:

[0775] The terminal presents the generated health improvement suggestions to the user. Here, the terminal displays the suggestions on the screen, making them easily accessible to the user. The input is the health improvement suggestions, and the output is a visual presentation to the user.

[0776] Step 7:

[0777] Users collect feedback on the results of implementing their suggestions. They input information such as the effects and impressions obtained from the suggestions via their devices. The input is user feedback information, and the output is the data transmitted to the device.

[0778] Step 8:

[0779] The server updates its analysis algorithm based on the collected feedback. It incorporates data as a reference for improving the AI ​​model and aims to increase the accuracy of the algorithm. The input is user feedback data, and the output is the improved analysis algorithm.

[0780] (Application Example 2)

[0781] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0782] In modern society, there is a need to provide specific and effective health maintenance measures tailored to the individual health and emotional states of each user. However, conventional technologies offer uniform health recommendations based on users' biometric information, lacking emotional care. A solution is needed to address this problem and realize more comprehensive health support.

[0783] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0784] In this invention, the server includes a device for acquiring microbial community information from a biological sample, a device for analyzing the acquired microbial community information and generating an individualized health improvement plan, and a device for collecting the user's emotional state using an emotion recognition engine and reflecting it in the health improvement plan. This makes it possible to provide personalized health improvement measures that take into account both the user's physical and emotional health.

[0785] A "biological sample" is a sample taken from the human body or animals, and is particularly a material that contains information about microbial communities.

[0786] "Microbial community information" refers to data on the types, composition, and activity levels of microorganisms obtained from biological samples.

[0787] An "emotion recognition engine" is a device or program that analyzes input information such as voice, facial expressions, and text to identify the user's emotional state.

[0788] A "health improvement plan" refers to a program that proposes specific behavioral guidelines and health goals based on the user's biometric information and emotional state.

[0789] An "analysis algorithm" is a computational procedure for processing acquired biological samples and emotional data to generate health recommendations tailored to the user.

[0790] "Living environment" is a general term encompassing the physical, social, and emotional factors that influence users in their daily lives.

[0791] "Nutritional guidance" is the act of advising users on appropriate diets and nutritional intake methods based on their health condition and lifestyle.

[0792] This system consists of a server, terminals, and users, with each component fulfilling its specific role. The server retrieves microbial community information from biological samples submitted by users into a database and processes this information using a specific analytical algorithm. Based on the processed data, it generates a personalized health improvement plan. This plan includes suggestions for probiotics, supplement recommendations, and specific meal plans.

[0793] Meanwhile, the device is equipped with an emotion recognition engine that collects emotional states through the user's voice, facial expressions, and daily actions. The collected emotional data is sent to a server and incorporated into the health improvement plan. The device also presents the generated health improvement plan to the user and collects feedback. This feedback allows the server to update its analysis algorithm, making subsequent health improvement plans even more personalized.

[0794] For example, if a user is suffering from constipation, the device can sense their stress level, and the server can generate suggestions recommending probiotics and lavender tea to aid digestion. The user then tries the suggestions and reports their subsequent physical and emotional changes on the device, which is then used to improve future suggestions.

[0795] For the generative AI model, a prompt such as, "Create an algorithm that analyzes the user's emotional state and suggests health improvement measures according to their stress level," is used.

[0796] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0797] Step 1:

[0798] The user collects a stool sample using a dedicated kit and sends it to the analysis facility. The input includes the stool sample. The output is sample information registered in a database for transmission to the server.

[0799] Step 2:

[0800] The server obtains microbial community information from the received stool sample and processes the acquired data using an analysis algorithm. The input includes microbial data from the stool sample. The output provides analysis results regarding the composition of the microbial community, specifically showing the types and proportions of bacteria.

[0801] Step 3:

[0802] The device uses an emotion recognition engine to analyze the user's voice and facial expressions and evaluate their emotional state. Input includes the user's voice data and visual information. Output is data indicating the user's emotional state (e.g., stress level). Operation involves analyzing changes in voice tone and facial features.

[0803] Step 4:

[0804] The server integrates the analysis results of the obtained microbial communities with emotional state data to generate a personalized health improvement plan. Input includes the output data from steps 2 and 3. Output provides a health improvement plan including probiotic suggestions and a meal plan. Specifically, the AI ​​calculates the optimal plan using the integrated data.

[0805] Step 5:

[0806] The device presents the generated health improvement plan to the user and collects feedback from the user. The input includes the health improvement plan, while the output provides user feedback on their impressions and the results of its application.

[0807] Step 6:

[0808] The server analyzes the collected feedback and updates the analysis algorithm to improve the accuracy of the next health improvement plan. Input includes user feedback. Output is the updated analysis algorithm. Specifically, the feedback is stored in a database, and the AI ​​adjusts the algorithm based on that data.

[0809] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0810] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0811] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0812] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0813] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0814] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0815] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0816] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0817] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0818] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0819] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0820] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0821] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0822] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0823] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0824] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0825] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0826] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0827] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0828] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0829] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means of obtaining bacterial community information from biological samples,

[0833] A means for analyzing acquired bacterial community information and generating personalized health improvement suggestions,

[0834] A means of presenting generated health improvement suggestions and collecting feedback from users,

[0835] A system that includes means for updating the analysis algorithm based on collected feedback.

[0836] (Claim 2)

[0837] The system according to claim 1, characterized in that it sets health goals based on analysis results according to the user's life stage and provides a continuous improvement plan.

[0838] (Claim 3)

[0839] The system according to claim 1, characterized in that it provides specific dietary guidance to users based on the analysis results and proposed content.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of obtaining microbiome information from biological samples,

[0843] A device that analyzes acquired microbiome information and generates personalized health improvement suggestions,

[0844] A device that presents generated health improvement suggestions and collects feedback from users,

[0845] A device that updates the analysis algorithm based on collected opinions,

[0846] A means for performing the analysis using a generative model in an analysis device and refining the proposal by inputting prompt sentences into the generative model,

[0847] A system that includes a platform for information communication between devices.

[0848] (Claim 2)

[0849] The system according to claim 1, characterized in that it sets health goals based on analysis results according to the user's age group and provides a continuous improvement plan.

[0850] (Claim 3)

[0851] The system according to claim 1, characterized in that it provides specific dietary guidance to users based on the analysis results and proposed content.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A means of obtaining microbial community information from biological samples,

[0855] A means for analyzing acquired microbial community information and generating individual health improvement suggestions,

[0856] A means of presenting generated health improvement suggestions and collecting feedback from users,

[0857] A means of updating the analysis algorithm based on collected feedback and reflecting it in the next analysis,

[0858] A system that includes means of providing users with daily necessities or nutritional products related to the proposed health improvements.

[0859] (Claim 2)

[0860] The system according to claim 1, characterized in that it sets health goals based on analysis results and provides a continuous improvement plan according to the user's life stage and daily life circumstances.

[0861] (Claim 3)

[0862] The system according to claim 1, characterized in that it provides users with specific nutritional intake advice through a home-use device based on the analysis results and proposed content.

[0863] "Example 2 of combining an emotion engine"

[0864] (Claim 1)

[0865] Methods for obtaining microbial information from biological samples,

[0866] A means for analyzing acquired microbial information and generating personalized health improvement suggestions,

[0867] A means of collecting the emotional state of users,

[0868] A means to complement health improvement suggestions based on emotional state,

[0869] A means of presenting generated health improvement suggestions and collecting feedback from users,

[0870] A system that includes means for updating the analysis algorithm based on collected feedback.

[0871] (Claim 2)

[0872] The system according to claim 1, characterized in that it sets health goals based on analysis results and emotional state according to the user's life stage and provides a continuous improvement plan.

[0873] (Claim 3)

[0874] The system according to claim 1, characterized in that it provides specific dietary guidance to the user based on the analysis results, suggested content, and emotional state.

[0875] "Application example 2 when combining with an emotional engine"

[0876] (Claim 1)

[0877] A device for acquiring microbial community information from biological samples,

[0878] A device that analyzes acquired microbial community information and generates individual health improvement plans,

[0879] A device that uses an emotion recognition engine to collect the user's emotional state and reflect it in a health improvement plan,

[0880] A device that presents a generated health improvement plan and collects feedback from users,

[0881] A system that includes a device for updating analysis algorithms based on collected feedback.

[0882] (Claim 2)

[0883] The system according to claim 1, characterized in that it sets health goals based on analysis results according to the user's living environment and provides a continuous improvement strategy.

[0884] (Claim 3)

[0885] The system according to claim 1, characterized in that it provides specific nutritional guidance to users based on the analysis results and plan contents. [Explanation of symbols]

[0886] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining microbial community information from biological samples, A means for analyzing acquired microbial community information and generating individual health improvement suggestions, A means of presenting generated health improvement suggestions and collecting feedback from users, A means of updating the analysis algorithm based on collected feedback and reflecting it in the next analysis, A system that includes means of providing users with daily necessities or nutritional products related to the proposed health improvements.

2. The system according to claim 1, characterized in that it sets health goals based on analysis results and provides a continuous improvement plan according to the user's life stage and daily life circumstances.

3. The system according to claim 1, characterized in that it provides specific nutritional intake advice to users through a home-use device based on the analysis results and proposed content.

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