system

Abstract

We provide the system. [Solution] Analytical means for analyzing biological samples, An artificial intelligence means for generating personalized guidance information based on analysis results, A communication means for providing analysis results and guidance information to the user terminal, A data management system for collecting and storing user feedback, A learning method that updates instructional information based on feedback, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 in 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] Despite the significant impact of the gut microbiota on health, the conventional methods lack means to easily grasp individual gut environments and provide specific and individualized guidance. In particular, there is a need to provide a method for ordinary people without specialized knowledge to effectively respond to various health problems such as gastrointestinal disorders, reduced immunity, and difficulty in weight management.

Means for Solving the Problems

[0005] This invention provides a system equipped with artificial intelligence means that generates guidance information based on an individual's health condition by using analytical means to analyze the intestinal microbiota. This enables the provision of analysis results to the user terminal and the collection of feedback. Furthermore, by updating the guidance information based on this feedback, more precise and personalized health management can be achieved.

[0006] A "biological sample" is a sample taken from the body of an organism and used to analyze its health status and physiological conditions.

[0007] "Analysis methods" refer to methods and techniques for extracting useful information from collected biological samples and analyzing the data.

[0008] "Artificial intelligence means" refers to technologies that use machine learning and data analysis to make judgments and predictions based on collected data and provide optimal guidance to users.

[0009] "Communication methods" refer to the infrastructure and technology used to transmit analysis results and guidance information to the user's device.

[0010] "Data management means" refers to systems and technologies used to store feedback information collected from users and to analyze and improve it.

[0011] "Learning methods" refer to techniques that use user feedback data to continuously improve system suggestions and enhance accuracy. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential 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.

Mode for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered 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.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered 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.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system that provides personalized health guidance to users based on data obtained from biological samples. This system analyzes information on the gut microbiota and uses artificial intelligence to provide users with an optimal health management plan.

[0034] The user collects a stool sample using a dedicated terminal. This stool sample is sent to a laboratory where the details of the gut microbiota are analyzed using biological methods. The laboratory terminal then transmits the analyzed data to a server.

[0035] The server uses artificial intelligence based on the received data to integrate analytical data and lifestyle information to assess the user's health status. The AI ​​generates personalized guidance information, referencing databases and the latest medical research. This information includes recommendations for diet, supplements, and lifestyle changes.

[0036] The generated guidance information is communicated from the server to the user's terminal. Upon receiving this guidance information, the user's terminal notifies the user and provides detailed guidance on how to incorporate it into their daily life.

[0037] As a concrete example, consider a case where a user uses this system for weight management. After submitting a stool sample, the server uses AI analysis to generate information recommending a reduction in carbohydrate intake and the use of supplements containing specific probiotics. This information is notified to the user's device, helping them to change their lifestyle and achieve their goals. User feedback is collected by the server, and the AI ​​continuously learns from it, improving the accuracy of the analysis and using it to inform future recommendations.

[0038] This invention is an innovative system that enables health management based on gut flora and provides continuous support tailored to the individual needs of each user.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0042] Step 2:

[0043] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology techniques. The analyzed data is then generated.

[0044] Step 3:

[0045] The lab terminal securely transmits the analysis data to the server.

[0046] Step 4:

[0047] The server stores the received gut microbiome data in a database, along with the user's basic information and lifestyle information.

[0048] Step 5:

[0049] The server uses stored data to allow AI to analyze the user's health status and generate a personalized health plan.

[0050] Step 6:

[0051] Based on the generated health plan, the AI ​​creates guidance information that includes specific dietary advice and information on recommended supplements.

[0052] Step 7:

[0053] The server sends the generated instructional information to the user's terminal.

[0054] Step 8:

[0055] The device notifies the user of instructional information and provides detailed guidance for improvement.

[0056] Step 9:

[0057] Users implement the suggested actions in their daily lives and input the results as feedback on their device.

[0058] Step 10:

[0059] The server records user feedback in a database, and the AI ​​uses this feedback as learning material to improve the accuracy of subsequent analyses.

[0060] (Example 1)

[0061] 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."

[0062] Providing detailed and personalized health plans that take into account an individual's gut microbiome and lifestyle has been difficult with conventional technologies. Furthermore, there was a lack of systems to effectively collect user feedback and incorporate it into future recommendations.

[0063] 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.

[0064] In this invention, the server includes an analysis means for analyzing biometric data, an artificial intelligence means for integrating the analysis data and lifestyle information to evaluate the health status, and a generation means for generating a personalized health plan. This makes it possible to provide a health plan adapted to the individual's health status and to improve the accuracy of improvement suggestions based on that plan.

[0065] "Biometric data" refers to information obtained from individual organisms that is used to assess an individual's health status.

[0066] "Analysis means" refers to methods and systems for processing biological data and analyzing gut microbiota and other health indicators.

[0067] "Artificial intelligence means" refers to computer programs or algorithms used to evaluate an individual's health status based on collected data and generate appropriate suggestions.

[0068] "Generation method" refers to the process of designing personalized health management plans based on analysis results and evaluations.

[0069] "Communication means" refers to the technologies and infrastructure used to transmit analysis results and plans to user devices.

[0070] "Data management means" refers to methods and systems for collecting, storing, and managing user feedback and other data.

[0071] "Learning methods" refer to the process of analyzing collected feedback and continuously improving the system to enhance the accuracy and effectiveness of the plan.

[0072] The system of this invention is designed to assess a user's health status and provide a personalized health plan. The user collects a stool sample as biometric data using a dedicated terminal. This stool sample is delivered and arrives at a laboratory equipped with specialized analytical equipment. The laboratory terminal uses biological techniques, such as DNA sequencing, to generate a detailed profile of the gut microbiota.

[0073] The analyzed data is transmitted to the server via a secure communication method. The server uses artificial intelligence to integrate this data with the user's lifestyle information and comprehensively assess their health status. Deep learning algorithms and existing medical databases are used for the assessment. Based on these results, a generative AI model inputs prompts and creates a personalized health plan, which may include recommendations for specific dietary restrictions or supplement intake.

[0074] The generated health plan is transmitted to the user's device via the communication network. The user's device receives it and notifies the user in a visually easy-to-understand format. The user then implements the guidance and sends the results and feedback back to the server via their device. This feedback is stored in the data management system and incorporated into the AI's learning process, improving the accuracy of future suggestions.

[0075] As a concrete example, consider a 40-year-old man using this system for weight management. Based on the analysis of a stool sample, the server generates a plan recommending "reducing carbohydrate intake and trying supplements containing specific probiotics." This prompt can then be used in the AI ​​model to generate a prompt like this: "40-year-old man, seeking weight management. Requesting dietary restrictions and supplement suggestions."

[0076] In this way, we provide plans optimized for each user's individual health condition and support continuous health management.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The user collects a stool sample using a dedicated terminal. The sample is placed in a sealed container and sent to the lab according to the enclosed instructions. The input is the user's stool sample, and the output is the sealed sample sent to the lab.

[0080] Step 2:

[0081] The lab terminal receives a stool sample and uses DNA sequencing technology to analyze the gut microbiota. The input is the stool sample, and the output is the analyzed microbial community data. This data shows the bacterial composition and relative abundance in the gut.

[0082] Step 3:

[0083] The lab terminal encrypts the analysis data and sends it to the server via secure communication. The input is the analysis data, and the output is encrypted data securely sent to the server.

[0084] Step 4:

[0085] The server decrypts the received encrypted data and inputs it into the AI ​​analysis engine. The input consists of decrypted analysis data and user lifestyle information, and the output is a health status assessment result. This assessment is generated by referencing a database.

[0086] Step 5:

[0087] Based on the evaluation results, the server inputs prompt messages into the generating AI model to create a personalized health plan. Specifically, this plan includes suggestions for dietary restrictions and supplements. The input is the health status evaluation result, and the output is the personalized health plan.

[0088] Step 6:

[0089] The server sends the generated health plan to the user's terminal via the communication network. The input is the personalized health plan, and the output is the guidance information received by the terminal.

[0090] Step 7:

[0091] The user's device notifies them of the received health plan and displays actionable steps. The input is guidance information from the server, and the output is detailed guidance that the user can review.

[0092] Step 8:

[0093] Users implement the instructions and send the results and feedback from their device to the server. The input is the feedback content, and the output is the feedback data sent to the server.

[0094] Step 9:

[0095] The server analyzes the collected feedback and incorporates it into the AI ​​learning model to improve the accuracy of future suggestions. The input is the feedback data, and the output is the learning outcome of the updated AI model.

[0096] (Application Example 1)

[0097] 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."

[0098] Traditional health management systems are limited to providing general information and struggle to efficiently deliver personalized health guidance tailored to individual users. Furthermore, they lack clear support on how to integrate proposed health plans into daily life, making it difficult to maintain user motivation. To address this, it is necessary to seamlessly integrate individualized guidance into users' daily lives.

[0099] 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.

[0100] In this invention, the server includes an analysis means for analyzing a biological sample, an artificial intelligence means for generating personalized guidance information based on the analysis results, a learning means for updating the guidance information based on responses from the user, and a means for notifying the user of the guidance information by voice or visual means using a consumer robot. This enables the user to naturally accept personalized health guidance in their daily life and achieve sustainable health management.

[0101] A "biological sample" is a sample taken from an individual's body that contains information about its life activities.

[0102] "Analysis means" refers to a device or process for investigating the characteristics and composition of a biological sample and outputting the results as data.

[0103] "Artificial intelligence tools" refer to systems that use machine learning and data analysis algorithms to extract meaningful information from data and generate guidance information for users.

[0104] "Communication means" refers to technologies and devices for transmitting analysis results and guidance information to information display devices, etc.

[0105] "Data management means" refers to a system or process for collecting responses from users and storing or processing that information.

[0106] A "learning tool" is a system that uses collected data to appropriately improve instructional information and generate more accurate advice.

[0107] A "consumer robot" is an automated mechanical device used in a home environment to perform simple tasks or provide information.

[0108] In the system that realizes this invention, the user first provides a biological sample through a dedicated information display device. This information display device transmits the sample to the laboratory for initial analysis. In the laboratory, the diversity and quantity of microbial communities contained in the sample are examined using analytical means. Subsequently, a server running a generative AI model generates optimal health guidance information for the user using artificial intelligence means based on the analysis data received from the laboratory.

[0109] The server sends guidance information to the information display device via communication means, presenting the information in a way that the user can intuitively understand. Furthermore, users can easily incorporate the suggested health plan into their daily lives by receiving voice and visual notifications from consumer robots. User responses are sent to the server via data management means, and the system continuously optimizes the health guidance information based on these responses through learning means.

[0110] The hardware used consists of an information display device and a consumer robot. The software includes a biometric data analysis platform as an analytical tool, machine learning algorithms for running generative AI models, and database management software for data management.

[0111] As a specific example, if a 60-year-old female user uses this system for weight management, she would provide a stool sample, which the server would analyze to generate a health plan such as: "Drink a probiotic smoothie every day at 10 a.m." This information is communicated by a consumer robot and provided in a format that makes it easy for the user to immediately implement.

[0112] An example of a prompt message is: "Please provide a health plan generated from the user's gut microbiome analysis results. Example: Low-carbohydrate diet plan."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user provides a biological sample using an information display device. The input is the biological sample collected by the user. This sample is sent to the lab for preparation for analysis. The output is the biological sample upon arrival at the lab.

[0116] Step 2:

[0117] The laboratory uses analytical tools to analyze biological samples and obtain information about the gut microbiota. The input is the biological sample that arrives at the laboratory. This sample is subjected to a specialized analytical device to collect data. The output is the microbial community composition data of the sample.

[0118] Step 3:

[0119] The server processes microbial community composition data received from the lab and generates health guidance information using a generative AI model. The input is microbial community composition data sent from the lab. Based on this data, artificial intelligence analyzes it and generates an optimal health plan. The output is personalized health guidance information.

[0120] Step 4:

[0121] The server transmits the generated health guidance information to the user's information display device via a communication method. The input is the health guidance information generated by the server. This information is sent to the information display device so that the user can confirm it. The output is the guidance information displayed on the information display device.

[0122] Step 5:

[0123] The user's consumer robot notifies the user of guidance information from the server via voice or visual means. The input is guidance information transferred from an information display device. This information is conveyed to the user via voice messages or a visual display. The output is the notification message received by the user.

[0124] Step 6:

[0125] The user performs actions based on health guidance and inputs the results and feedback into an information display device. The input is user feedback information, which is used to improve the accuracy of future suggestions. The output is feedback data processed on the server.

[0126] Step 7:

[0127] The server uses data management tools to store and analyze user feedback using learning tools. The input is user feedback data. This data is analyzed to update the generating AI model and reflect the changes in future suggestions. The output is newly learned guidance information.

[0128] 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.

[0129] This invention is a system that combines the analysis of biological samples with an emotional engine, providing personalized guidance that takes into account both the user's health and emotional state. The user collects a stool sample using a dedicated terminal and sends it to an analysis lab. The lab's terminal receives the stool sample, analyzes the intestinal microbiota, and transmits the data to a server.

[0130] The server integrates and stores the received gut microbiome data with the user's basic information and lifestyle information. Based on this, artificial intelligence evaluates the user's health status and generates a personalized health plan. Meanwhile, the device is equipped with an emotion engine that recognizes and analyzes the user's emotional state from their daily operations and input data. The emotion engine sends this information to the server as feedback.

[0131] The server integrates emotional data from the emotion engine with health assessment data, and the AI ​​generates guidance information that reflects this information. For example, if stress is a major contributing factor, the AI ​​can suggest foods and supplements that are effective in reducing stress.

[0132] The generated guidance information is sent from the server to the user's device. The device provides the user with customized notifications and guidance to help with specific dietary changes, supplement use, and lifestyle improvements. Based on this information, the user adjusts their lifestyle and inputs feedback on their implementation status and effects into the device.

[0133] The server receives feedback information, stores it in a database, and the AI ​​uses it as learning material to improve the accuracy of future suggestions. This invention is a system that enables comprehensive health management that takes into account both the user's gut environment and emotional state.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0137] Step 2:

[0138] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology. The obtained data is then sent to the server.

[0139] Step 3:

[0140] The server stores the received gut microbiota data in a database and integrates it with the user's basic information and lifestyle information.

[0141] Step 4:

[0142] The device's emotion engine analyzes the user's emotional state from their everyday actions and inputs, and sends that emotional data to the server.

[0143] Step 5:

[0144] The server inputs gut data and emotional data into artificial intelligence, which then comprehensively evaluates the user's health based on this information.

[0145] Step 6:

[0146] Based on the evaluation results, the AI ​​creates a personalized health plan and generates dietary guidance and supplement recommendations tailored to the user's physical and emotional state.

[0147] Step 7:

[0148] The server sends instructional information to the user's terminal.

[0149] Step 8:

[0150] The device notifies the user of guidance information and presents specific improvement measures.

[0151] Step 9:

[0152] Based on the guidance information, users improve their lifestyle habits and input the results and changes in their emotions as feedback into their device.

[0153] Step 10:

[0154] The server collects feedback data and updates the database. The AI ​​learns from this feedback and improves the accuracy of its next suggestions.

[0155] (Example 2)

[0156] 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".

[0157] Conventional health management systems have limitations in providing guidance information based solely on the analysis results of biological samples, making it difficult to appropriately reflect the emotional state and lifestyle changes of users. This has resulted in insufficient creation of specific and effective health plans tailored to individual circumstances.

[0158] 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.

[0159] In this invention, the server includes analytical means for analyzing biological samples, intelligent means for generating personalized guidance information, and emotion recognition means for analyzing emotional states. This makes it possible to provide comprehensive and effective health management information that takes into account both the user's physical and emotional states.

[0160] A "biological sample" is a substance taken from a user's body and serves as a data source for evaluating their health status.

[0161] "Analysis means" refers to techniques or devices for scientifically analyzing biological samples and extracting the results as data.

[0162] "Intelligent means" refers to technologies or programs that use artificial intelligence to generate personalized guidance information and suggestions based on collected data.

[0163] "Communication means" refers to network technology or equipment for transmitting analyzed data and generated instructional information to the user's terminal.

[0164] "Emotion recognition means" refers to a technology or program that detects a user's emotional state and provides feedback on it as numerical data or other data.

[0165] "Information management means" refers to a system or program for collecting user responses and feedback, and storing and managing them in a database.

[0166] A "learning tool" is an algorithm or technology used to analyze collected feedback and improve instructional information for future sessions.

[0167] This invention relates to a system for providing personalized guidance information based on the user's health and emotional state, as outputted results. The system mainly consists of three components: a server, a terminal, and a user.

[0168] The server utilizes advanced artificial intelligence (AI) models to integrate and process analytical data obtained from biological samples with data on the user's emotional state. Specifically, data on intestinal microbiota obtained through DNA sequencing of biological samples, particularly excrement samples, collected and transmitted by the user using a dedicated terminal, is received by the server. This DNA sequencing is performed using advanced hardware such as next-generation sequencers (NGS).

[0169] The device serves as the user interface and is equipped with an emotion recognition engine. This engine analyzes text data entered by the user in their daily life using natural language processing (NLP) technology to understand the user's emotional state. It also displays personalized health guidance information and notifies the user of specific instructions.

[0170] Users use their devices to implement actions based on their health plans and provide feedback on the results and their impressions via their devices. This feedback is sent to a server and stored in a database by an information management system.

[0171] This allows the entire system to repeatedly learn and improve, resulting in more accurate guidance for the user in subsequent sessions. For example, if the balance of specific bacteria in the gut is disrupted, the AI ​​can use this information to suggest dietary guidance that includes yogurt and other fermented foods. This guidance also takes into account the user's emotional state, such as their stress level.

[0172] An example of a prompt is, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan that helps reduce stress." By inputting this prompt into the generating AI model, it becomes possible to provide advice optimized for the user.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user uses a dedicated terminal to collect a biological sample, specifically a sample of excrement. The sample is stored in a container to maintain sterility. This sample is sent to an analysis laboratory, and its receipt is confirmed via the terminal. The input is the excrement sample, and the output is the sample being sent to the laboratory.

[0176] Step 2:

[0177] The lab terminal receives the sample and registers it using a barcode scanner. An automated DNA sequencing system is used to analyze the gut microbiota of the sample and generate data. The input is a fecal sample, and the output is DNA data of the gut microbiota.

[0178] Step 3:

[0179] The lab terminal sends the analyzed DNA data to the server. The server receives this data and stores it in a database. Here, the data is processed to evaluate the state of gut health based on the DNA data. The input is DNA data of the gut microbiota, and the output is data evaluating the state of gut health.

[0180] Step 4:

[0181] The emotion recognition engine built into the device receives the user's daily input data (e.g., diary text) and analyzes it using natural language processing. It calculates the emotional state as numerical data and sends it to the server. The input is daily text data, and the output is numerical data indicating the emotional state.

[0182] Step 5:

[0183] The server integrates the received emotional data with already stored gut health assessment data and generates a personalized health guidance plan using an artificial intelligence model. In this generation process, the AI ​​utilizes a generation AI model to list suggestions regarding diet and lifestyle optimized for the user. The input is the integrated assessment data, and the output is the personalized health guidance plan.

[0184] Step 6:

[0185] The server sends the generated health guidance plan to the terminal. The terminal then uses this information to notify the user and provide specific and customized advice. For example, in the "Example Prompt," a suggestion might be, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan to help reduce stress." The input is the generated health guidance plan, and the output is the notification information for the user.

[0186] Step 7:

[0187] The user adjusts their lifestyle habits based on notifications from their device and inputs the effects and progress of these adjustments into the device. The input data is then sent back to the server as feedback. The input is the result of the lifestyle adjustments, and the output is the feedback data.

[0188] Step 8:

[0189] The server analyzes user feedback data and stores it in a database. The AI ​​model uses this information as training material to improve the accuracy of future suggestions. The input is the feedback data, and the output is the improvement in suggestion accuracy through learning.

[0190] (Application Example 2)

[0191] 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".

[0192] In modern society, personal health management is a crucial issue, but previous methods have struggled to comprehensively consider users' gut microbiome and emotional states, making it difficult to provide personalized and appropriate health guidance. Furthermore, there is a strong demand for methods that offer continuous health improvement strategies in response to real-time emotional changes.

[0193] 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.

[0194] In this invention, the server includes an emotion analysis means for analyzing emotional states and generating emotional information, a recommendation means for adjusting personalized guidance information based on the emotional information, and an analysis means for analyzing biological data. This makes it possible to provide personalized guidance based on the user's gut environment and emotional state.

[0195] "Biological data" refers to excrement samples and other substances collected to assess an individual's health status.

[0196] "Analysis means" refers to devices and methods for analyzing biological data and emotional data and generating results.

[0197] "Artificial intelligence means" refers to algorithms and technologies used to generate personalized guidance information based on analyzed data.

[0198] "Communication methods" refers to network technologies used to transmit analysis results and instructional information to user terminals.

[0199] "Information management means" refers to a database system for collecting and storing user feedback.

[0200] "Learning methods" refer to machine learning techniques used to improve and update instructional information using collected feedback data.

[0201] "Emotional analysis means" refers to technologies and devices that analyze a user's emotional state and generate emotional information.

[0202] "Recommendation methods" refer to technologies that adjust personalized guidance information based on emotional information to provide appropriate advice to users.

[0203] The system for carrying out this invention provides personalized health guidance based on the user's gut environment and emotional state. The server is equipped with multiple means for this purpose. First, the analysis means is used to analyze excrement samples and generate results. Excrement samples are collected by the user using a dedicated terminal and sent to an analysis facility. The server performs data analysis using dedicated software and machine learning algorithms (e.g., TENSORFLOW®).

[0204] Next, the emotion analysis system uses devices such as cameras and microphones to analyze the user's emotional state in real time. The analyzed emotional information is generated by the emotion analysis system and obtained from the user's daily inputs and actions. This allows the emotional state to be reflected in health guidance.

[0205] The communication method directly transfers these analysis results and generated guidance information to the user's terminal. Based on this information, the terminal provides the user with specific advice for improving their health. For example, if the user is experiencing high stress levels, the terminal will display suggestions for dietary and lifestyle improvements to reduce stress.

[0206] The information management system collects user feedback and stores it in a database. Based on this data, the server updates personalized guidance information through learning tools, enabling it to generate more accurate advice for subsequent sessions.

[0207] For example, if a user speaks to a robot saying, "I've been feeling stressed lately," the robot can analyze that emotion and provide appropriate advice on diet and exercise to reduce stress. An example of a prompt in such a situation would be, "I'm still tired from yesterday, so please tell me how to refresh myself."

[0208] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0209] Step 1:

[0210] The user collects excrement samples using a dedicated terminal. These samples are then sent to an analysis facility. The input is the excrement sample collected by the user, and the output is the physical transmission of the sample to the analysis facility.

[0211] Step 2:

[0212] The server uses analytical tools to analyze data obtained from excrement samples. The input is biological data from the excrement sample, and the output is the analysis results regarding the user's gut microbiome. In this process, machine learning algorithms are used to analyze the microbial composition in detail.

[0213] Step 3:

[0214] The device uses a camera and microphone to collect emotional data from the user's daily actions and conversations. The input is the user's actions and voice, and the output is digital data indicating their emotional state. The emotional analysis means analyzes this data to generate emotional information.

[0215] Step 4:

[0216] The server integrates analyzed microbial community data and emotional information, and generates personalized guidance information using a generative AI model. The input is microbial community data and emotional information, and the output is a personalized health guidance plan. In this step, the generative AI model is utilized to calculate optimal advice based on the latest health knowledge and the user's emotional state.

[0217] Step 5:

[0218] The device provides the user with generated health guidance information. The input is personalized guidance information sent from the server, and the output is the presentation of advice through the user's visual or auditory input. The device uses a notification function to suggest lifestyle improvements necessary for the user.

[0219] Step 6:

[0220] Users adjust their lifestyle habits based on the suggestions and input the results and feedback into the terminal. The input is the user's feedback, and the output is stored on the server and used as data to improve future guidance information.

[0221] Step 7:

[0222] The server uses machine learning based on accumulated feedback information to improve the accuracy of future instructional information. The input is feedback data, and the output is the improved instructional algorithm. In this step, a generative AI model updated based on information accumulated in the database is used.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a system that provides personalized health guidance to users based on data obtained from biological samples. This system analyzes information on the gut microbiota and uses artificial intelligence to provide users with an optimal health management plan.

[0240] The user collects a stool sample using a dedicated terminal. This stool sample is sent to a laboratory where the details of the gut microbiota are analyzed using biological methods. The laboratory terminal then transmits the analyzed data to a server.

[0241] The server uses artificial intelligence based on the received data to integrate analytical data and lifestyle information to assess the user's health status. The AI ​​generates personalized guidance information, referencing databases and the latest medical research. This information includes recommendations for diet, supplements, and lifestyle changes.

[0242] The generated guidance information is communicated from the server to the user's terminal. Upon receiving this guidance information, the user's terminal notifies the user and provides detailed guidance on how to incorporate it into their daily life.

[0243] As a concrete example, consider a case where a user uses this system for weight management. After submitting a stool sample, the server uses AI analysis to generate information recommending a reduction in carbohydrate intake and the use of supplements containing specific probiotics. This information is notified to the user's device, helping them to change their lifestyle and achieve their goals. User feedback is collected by the server, and the AI ​​continuously learns from it, improving the accuracy of the analysis and using it to inform future recommendations.

[0244] This invention is an innovative system that enables health management based on gut flora and provides continuous support tailored to the individual needs of each user.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0248] Step 2:

[0249] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology techniques. The analyzed data is then generated.

[0250] Step 3:

[0251] The lab terminal securely transmits the analysis data to the server.

[0252] Step 4:

[0253] The server stores the received gut microbiome data in a database, along with the user's basic information and lifestyle information.

[0254] Step 5:

[0255] The server uses stored data to allow AI to analyze the user's health status and generate a personalized health plan.

[0256] Step 6:

[0257] Based on the generated health plan, the AI ​​creates guidance information that includes specific dietary advice and information on recommended supplements.

[0258] Step 7:

[0259] The server sends the generated instructional information to the user's terminal.

[0260] Step 8:

[0261] The device notifies the user of instructional information and provides detailed guidance for improvement.

[0262] Step 9:

[0263] Users implement the suggested actions in their daily lives and input the results as feedback on their device.

[0264] Step 10:

[0265] The server records user feedback in a database, and the AI ​​uses this feedback as learning material to improve the accuracy of subsequent analyses.

[0266] (Example 1)

[0267] 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."

[0268] Providing detailed and personalized health plans that take into account an individual's gut microbiome and lifestyle has been difficult with conventional technologies. Furthermore, there was a lack of systems to effectively collect user feedback and incorporate it into future recommendations.

[0269] 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.

[0270] In this invention, the server includes an analysis means for analyzing biometric data, an artificial intelligence means for integrating the analysis data and lifestyle information to evaluate the health status, and a generation means for generating a personalized health plan. This makes it possible to provide a health plan adapted to the individual's health status and to improve the accuracy of improvement suggestions based on that plan.

[0271] "Biometric data" refers to information obtained from individual organisms that is used to assess an individual's health status.

[0272] "Analysis means" refers to methods and systems for processing biological data and analyzing gut microbiota and other health indicators.

[0273] "Artificial intelligence means" refers to computer programs or algorithms used to evaluate an individual's health status based on collected data and generate appropriate suggestions.

[0274] "Generation method" refers to the process of designing personalized health management plans based on analysis results and evaluations.

[0275] "Communication means" refers to the technologies and infrastructure used to transmit analysis results and plans to user devices.

[0276] "Data management means" refers to methods and systems for collecting, storing, and managing user feedback and other data.

[0277] "Learning methods" refer to the process of analyzing collected feedback and continuously improving the system to enhance the accuracy and effectiveness of the plan.

[0278] The system of this invention is designed to assess a user's health status and provide a personalized health plan. The user collects a stool sample as biometric data using a dedicated terminal. This stool sample is delivered and arrives at a laboratory equipped with specialized analytical equipment. The laboratory terminal uses biological techniques, such as DNA sequencing, to generate a detailed profile of the gut microbiota.

[0279] The analyzed data is transmitted to the server via a secure communication method. The server uses artificial intelligence to integrate this data with the user's lifestyle information and comprehensively assess their health status. Deep learning algorithms and existing medical databases are used for the assessment. Based on these results, a generative AI model inputs prompts and creates a personalized health plan, which may include recommendations for specific dietary restrictions or supplement intake.

[0280] The generated health plan is transmitted to the user's device via the communication network. The user's device receives it and notifies the user in a visually easy-to-understand format. The user then implements the guidance and sends the results and feedback back to the server via their device. This feedback is stored in the data management system and incorporated into the AI's learning process, improving the accuracy of future suggestions.

[0281] As a specific example, consider the case where a 40-year-old man uses this system for weight management. Based on the analysis results of the stool sample, the server generates a plan that recommends "limiting carbohydrate intake and trying supplements containing specific probiotics". And the following prompt sentence can be used in the generation AI model: "40-year-old man, wishing to manage weight. Seeking diet restrictions and supplement plans."

[0282] In this way, a plan optimized for each user's health condition is provided to support continuous health management.

[0283] The flow of the specific process in Example 1 will be described using FIG. 11.

[0284] Step 1:

[0285] The user uses a dedicated terminal to collect a stool sample. The sample is placed in a sealed container and sent to the lab according to the enclosed instructions. The input is the user's stool sample, and the output is the sealed sample sent to the lab.

[0286] Step 2:

[0287] The lab terminal receives the stool sample and analyzes the gut microbiota using DNA sequencing technology. The input is the stool sample, and the output is the analyzed microbiota data. This data shows the bacterial composition and relative abundance in the gut.

[0288] Step 3:

[0289] The lab terminal encrypts the analysis data and sends it to the server through secure communication. The input is the analysis data, and the output is the encrypted data safely sent to the server.

[0290] Step 4:

[0291] The server decrypts the received encrypted data and inputs it into the AI ​​analysis engine. The input consists of decrypted analysis data and user lifestyle information, and the output is a health status assessment result. This assessment is generated by referencing a database.

[0292] Step 5:

[0293] Based on the evaluation results, the server inputs prompt messages into the generating AI model to create a personalized health plan. Specifically, this plan includes suggestions for dietary restrictions and supplements. The input is the health status evaluation result, and the output is the personalized health plan.

[0294] Step 6:

[0295] The server sends the generated health plan to the user's terminal via the communication network. The input is the personalized health plan, and the output is the guidance information received by the terminal.

[0296] Step 7:

[0297] The user's device notifies them of the received health plan and displays actionable steps. The input is guidance information from the server, and the output is detailed guidance that the user can review.

[0298] Step 8:

[0299] Users implement the instructions and send the results and feedback from their device to the server. The input is the feedback content, and the output is the feedback data sent to the server.

[0300] Step 9:

[0301] The server analyzes the collected feedback and incorporates it into the AI ​​learning model to improve the accuracy of future suggestions. The input is the feedback data, and the output is the learning outcome of the updated AI model.

[0302] (Application Example 1)

[0303] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0304] Conventional health management systems only provide general information, and it is difficult to efficiently provide health guidance optimized for individual users. In addition, there is a lack of clear support for how to incorporate the proposed health plan into daily life, so there is a problem that it is difficult to maintain the motivation of users. To solve this, it is necessary to naturally incorporate individualized guidance into the daily life of users.

[0305] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0306] In this invention, the server includes an analysis means for analyzing a biological sample, an artificial intelligence means for generating individualized guidance information based on the analysis result, a learning means for updating the guidance information based on a response from the user, and a means for notifying the user of the guidance information by voice or vision using a consumer robot. As a result, the user can naturally receive individualized health guidance in daily life, and it becomes possible to realize sustainable health management.

[0307] A "biological sample" is a sample collected from an individual's body and containing information related to its life activities.

[0308] The "analysis means" is a device or process for investigating the characteristics and composition of a biological sample and outputting it as data.

[0309] The "artificial intelligence means" is a system that uses algorithms of machine learning and data analysis to extract meaningful information from data and generate guidance information for users.

[0310] The "communication means" is a technology / device for transmitting the analysis result and guidance information to an information display device or the like.

[0311] "Data management means" refers to a system or process for collecting responses from users and storing or processing that information.

[0312] A "learning tool" is a system that uses collected data to appropriately improve instructional information and generate more accurate advice.

[0313] A "consumer robot" is an automated mechanical device used in a home environment to perform simple tasks or provide information.

[0314] In the system that realizes this invention, the user first provides a biological sample through a dedicated information display device. This information display device transmits the sample to the laboratory for initial analysis. In the laboratory, the diversity and quantity of microbial communities contained in the sample are examined using analytical means. Subsequently, a server running a generative AI model generates optimal health guidance information for the user using artificial intelligence means based on the analysis data received from the laboratory.

[0315] The server sends guidance information to the information display device via communication means, presenting the information in a way that the user can intuitively understand. Furthermore, users can easily incorporate the suggested health plan into their daily lives by receiving voice and visual notifications from consumer robots. User responses are sent to the server via data management means, and the system continuously optimizes the health guidance information based on these responses through learning means.

[0316] The hardware used consists of an information display device and a consumer robot. The software includes a biometric data analysis platform as an analytical tool, machine learning algorithms for running generative AI models, and database management software for data management.

[0317] As a specific example, if a 60-year-old female user uses this system for weight management, she would provide a stool sample, which the server would analyze to generate a health plan such as: "Drink a probiotic smoothie every day at 10 a.m." This information is communicated by a consumer robot and provided in a format that makes it easy for the user to immediately implement.

[0318] An example of a prompt message is: "Please provide a health plan generated from the user's gut microbiome analysis results. Example: Low-carbohydrate diet plan."

[0319] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0320] Step 1:

[0321] The user provides a biological sample using an information display device. The input is the biological sample collected by the user. This sample is sent to the lab for preparation for analysis. The output is the biological sample upon arrival at the lab.

[0322] Step 2:

[0323] The laboratory uses analytical tools to analyze biological samples and obtain information about the gut microbiota. The input is the biological sample that arrives at the laboratory. This sample is subjected to a specialized analytical device to collect data. The output is the microbial community composition data of the sample.

[0324] Step 3:

[0325] The server processes microbial community composition data received from the lab and generates health guidance information using a generative AI model. The input is microbial community composition data sent from the lab. Based on this data, artificial intelligence analyzes it and generates an optimal health plan. The output is personalized health guidance information.

[0326] Step 4:

[0327] The server transmits the generated health guidance information to the user's information display device via a communication method. The input is the health guidance information generated by the server. This information is sent to the information display device so that the user can confirm it. The output is the guidance information displayed on the information display device.

[0328] Step 5:

[0329] The user's consumer robot notifies the user of guidance information from the server via voice or visual means. The input is guidance information transferred from an information display device. This information is conveyed to the user via voice messages or a visual display. The output is the notification message received by the user.

[0330] Step 6:

[0331] The user performs actions based on health guidance and inputs the results and feedback into an information display device. The input is user feedback information, which is used to improve the accuracy of future suggestions. The output is feedback data processed on the server.

[0332] Step 7:

[0333] The server uses data management tools to store and analyze user feedback using learning tools. The input is user feedback data. This data is analyzed to update the generating AI model and reflect the changes in future suggestions. The output is newly learned guidance information.

[0334] 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.

[0335] This invention is a system that combines the analysis of biological samples with an emotional engine, providing personalized guidance that takes into account both the user's health and emotional state. The user collects a stool sample using a dedicated terminal and sends it to an analysis lab. The lab's terminal receives the stool sample, analyzes the intestinal microbiota, and transmits the data to a server.

[0336] The server integrates and stores the received gut microbiome data with the user's basic information and lifestyle information. Based on this, artificial intelligence evaluates the user's health status and generates a personalized health plan. Meanwhile, the device is equipped with an emotion engine that recognizes and analyzes the user's emotional state from their daily operations and input data. The emotion engine sends this information to the server as feedback.

[0337] The server integrates emotional data from the emotion engine with health assessment data, and the AI ​​generates guidance information that reflects this information. For example, if stress is a major contributing factor, the AI ​​can suggest foods and supplements that are effective in reducing stress.

[0338] The generated guidance information is sent from the server to the user's device. The device provides the user with customized notifications and guidance to help with specific dietary changes, supplement use, and lifestyle improvements. Based on this information, the user adjusts their lifestyle and inputs feedback on their implementation status and effects into the device.

[0339] The server receives feedback information, stores it in a database, and the AI ​​uses it as learning material to improve the accuracy of future suggestions. This invention is a system that enables comprehensive health management that takes into account both the user's gut environment and emotional state.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0343] Step 2:

[0344] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology. The obtained data is then sent to the server.

[0345] Step 3:

[0346] The server stores the received gut microbiota data in a database and integrates it with the user's basic information and lifestyle information.

[0347] Step 4:

[0348] The device's emotion engine analyzes the user's emotional state from their everyday actions and inputs, and sends that emotional data to the server.

[0349] Step 5:

[0350] The server inputs gut data and emotional data into artificial intelligence, which then comprehensively evaluates the user's health based on this information.

[0351] Step 6:

[0352] Based on the evaluation results, the AI ​​creates a personalized health plan and generates dietary guidance and supplement recommendations tailored to the user's physical and emotional state.

[0353] Step 7:

[0354] The server sends instructional information to the user's terminal.

[0355] Step 8:

[0356] The device notifies the user of guidance information and presents specific improvement measures.

[0357] Step 9:

[0358] Based on the guidance information, users improve their lifestyle habits and input the results and changes in their emotions as feedback into their device.

[0359] Step 10:

[0360] The server collects feedback data and updates the database. The AI ​​learns from this feedback and improves the accuracy of its next suggestions.

[0361] (Example 2)

[0362] 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".

[0363] Conventional health management systems have limitations in providing guidance information based solely on the analysis results of biological samples, making it difficult to appropriately reflect the emotional state and lifestyle changes of users. This has resulted in insufficient creation of specific and effective health plans tailored to individual circumstances.

[0364] 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.

[0365] In this invention, the server includes analytical means for analyzing biological samples, intelligent means for generating personalized guidance information, and emotion recognition means for analyzing emotional states. This makes it possible to provide comprehensive and effective health management information that takes into account both the user's physical and emotional states.

[0366] A "biological sample" is a substance taken from a user's body and serves as a data source for evaluating their health status.

[0367] "Analysis means" refers to techniques or devices for scientifically analyzing biological samples and extracting the results as data.

[0368] "Intelligent means" refers to technologies or programs that use artificial intelligence to generate personalized guidance information and suggestions based on collected data.

[0369] "Communication means" refers to network technology or equipment for transmitting analyzed data and generated instructional information to the user's terminal.

[0370] "Emotion recognition means" refers to a technology or program that detects a user's emotional state and provides feedback on it as numerical data or other data.

[0371] "Information management means" refers to a system or program for collecting user responses and feedback, and storing and managing them in a database.

[0372] A "learning tool" is an algorithm or technology used to analyze collected feedback and improve instructional information for future sessions.

[0373] This invention relates to a system for providing personalized guidance information based on the user's health and emotional state, as outputted results. The system mainly consists of three components: a server, a terminal, and a user.

[0374] The server utilizes advanced artificial intelligence (AI) models to integrate and process analytical data obtained from biological samples with data on the user's emotional state. Specifically, data on intestinal microbiota obtained through DNA sequencing of biological samples, particularly excrement samples, collected and transmitted by the user using a dedicated terminal, is received by the server. This DNA sequencing is performed using advanced hardware such as next-generation sequencers (NGS).

[0375] The device serves as the user interface and is equipped with an emotion recognition engine. This engine analyzes text data entered by the user in their daily life using natural language processing (NLP) technology to understand the user's emotional state. It also displays personalized health guidance information and notifies the user of specific instructions.

[0376] Users use their devices to implement actions based on their health plans and provide feedback on the results and their impressions via their devices. This feedback is sent to a server and stored in a database by an information management system.

[0377] This allows the entire system to repeatedly learn and improve, resulting in more accurate guidance for the user in subsequent sessions. For example, if the balance of specific bacteria in the gut is disrupted, the AI ​​can use this information to suggest dietary guidance that includes yogurt and other fermented foods. This guidance also takes into account the user's emotional state, such as their stress level.

[0378] An example of a prompt is, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan that helps reduce stress." By inputting this prompt into the generating AI model, it becomes possible to provide advice optimized for the user.

[0379] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0380] Step 1:

[0381] The user uses a dedicated terminal to collect a biological sample, specifically a sample of excrement. The sample is stored in a container to maintain sterility. This sample is sent to an analysis laboratory, and its receipt is confirmed via the terminal. The input is the excrement sample, and the output is the sample being sent to the laboratory.

[0382] Step 2:

[0383] The lab terminal receives the sample and registers it using a barcode scanner. An automated DNA sequencing system is used to analyze the gut microbiota of the sample and generate data. The input is a fecal sample, and the output is DNA data of the gut microbiota.

[0384] Step 3:

[0385] The lab terminal sends the analyzed DNA data to the server. The server receives this data and stores it in a database. Here, the data is processed to evaluate the state of gut health based on the DNA data. The input is DNA data of the gut microbiota, and the output is data evaluating the state of gut health.

[0386] Step 4:

[0387] The emotion recognition engine built into the device receives the user's daily input data (e.g., diary text) and analyzes it using natural language processing. It calculates the emotional state as numerical data and sends it to the server. The input is daily text data, and the output is numerical data indicating the emotional state.

[0388] Step 5:

[0389] The server integrates the received emotional data with already stored gut health assessment data and generates a personalized health guidance plan using an artificial intelligence model. In this generation process, the AI ​​utilizes a generation AI model to list suggestions regarding diet and lifestyle optimized for the user. The input is the integrated assessment data, and the output is the personalized health guidance plan.

[0390] Step 6:

[0391] The server sends the generated health guidance plan to the terminal. The terminal then uses this information to notify the user and provide specific and customized advice. For example, in the "Example Prompt," a suggestion might be, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan to help reduce stress." The input is the generated health guidance plan, and the output is the notification information for the user.

[0392] Step 7:

[0393] The user adjusts their lifestyle habits based on notifications from their device and inputs the effects and progress of these adjustments into the device. The input data is then sent back to the server as feedback. The input is the result of the lifestyle adjustments, and the output is the feedback data.

[0394] Step 8:

[0395] The server analyzes user feedback data and stores it in a database. The AI ​​model uses this information as training material to improve the accuracy of future suggestions. The input is the feedback data, and the output is the improvement in suggestion accuracy through learning.

[0396] (Application Example 2)

[0397] 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 as the "terminal".

[0398] In modern society, personal health management is a crucial issue, but previous methods have struggled to comprehensively consider users' gut microbiome and emotional states, making it difficult to provide personalized and appropriate health guidance. Furthermore, there is a strong demand for methods that offer continuous health improvement strategies in response to real-time emotional changes.

[0399] 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.

[0400] In this invention, the server includes an emotion analysis means for analyzing emotional states and generating emotional information, a recommendation means for adjusting personalized guidance information based on the emotional information, and an analysis means for analyzing biological data. This makes it possible to provide personalized guidance based on the user's gut environment and emotional state.

[0401] "Biological data" refers to excrement samples and other substances collected to assess an individual's health status.

[0402] "Analysis means" refers to devices and methods for analyzing biological data and emotional data and generating results.

[0403] "Artificial intelligence means" refers to algorithms and technologies used to generate personalized guidance information based on analyzed data.

[0404] "Communication methods" refers to network technologies used to transmit analysis results and instructional information to user terminals.

[0405] "Information management means" refers to a database system for collecting and storing user feedback.

[0406] "Learning methods" refer to machine learning techniques used to improve and update instructional information using collected feedback data.

[0407] "Emotional analysis means" refers to technologies and devices that analyze a user's emotional state and generate emotional information.

[0408] "Recommendation methods" refer to technologies that adjust personalized guidance information based on emotional information to provide appropriate advice to users.

[0409] The system for carrying out this invention provides personalized health guidance based on the user's gut environment and emotional state. The server is equipped with multiple means for this purpose. First, the analysis means is used to analyze excrement samples and generate results. Excrement samples are collected by the user using a dedicated terminal and sent to an analysis facility. The server performs data analysis using dedicated software and machine learning algorithms (e.g., TensorFlow).

[0410] Next, the emotion analysis system uses devices such as cameras and microphones to analyze the user's emotional state in real time. The analyzed emotional information is generated by the emotion analysis system and obtained from the user's daily inputs and actions. This allows the emotional state to be reflected in health guidance.

[0411] The communication method directly transfers these analysis results and generated guidance information to the user's terminal. Based on this information, the terminal provides the user with specific advice for improving their health. For example, if the user is experiencing high stress levels, the terminal will display suggestions for dietary and lifestyle improvements to reduce stress.

[0412] The information management system collects user feedback and stores it in a database. Based on this data, the server updates personalized guidance information through learning tools, enabling it to generate more accurate advice for subsequent sessions.

[0413] For example, if a user speaks to a robot saying, "I've been feeling stressed lately," the robot can analyze that emotion and provide appropriate advice on diet and exercise to reduce stress. An example of a prompt in such a situation would be, "I'm still tired from yesterday, so please tell me how to refresh myself."

[0414] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0415] Step 1:

[0416] The user collects excrement samples using a dedicated terminal. These samples are then sent to an analysis facility. The input is the excrement sample collected by the user, and the output is the physical transmission of the sample to the analysis facility.

[0417] Step 2:

[0418] The server uses analytical tools to analyze data obtained from excrement samples. The input is biological data from the excrement sample, and the output is the analysis results regarding the user's gut microbiome. In this process, machine learning algorithms are used to analyze the microbial composition in detail.

[0419] Step 3:

[0420] The device uses a camera and microphone to collect emotional data from the user's daily actions and conversations. The input is the user's actions and voice, and the output is digital data indicating their emotional state. The emotional analysis means analyzes this data to generate emotional information.

[0421] Step 4:

[0422] The server integrates analyzed microbial community data and emotional information, and generates personalized guidance information using a generative AI model. The input is microbial community data and emotional information, and the output is a personalized health guidance plan. In this step, the generative AI model is utilized to calculate optimal advice based on the latest health knowledge and the user's emotional state.

[0423] Step 5:

[0424] The device provides the user with generated health guidance information. The input is personalized guidance information sent from the server, and the output is the presentation of advice through the user's visual or auditory input. The device uses a notification function to suggest lifestyle improvements necessary for the user.

[0425] Step 6:

[0426] Users adjust their lifestyle habits based on the suggestions and input the results and feedback into the terminal. The input is the user's feedback, and the output is stored on the server and used as data to improve future guidance information.

[0427] Step 7:

[0428] The server uses machine learning based on accumulated feedback information to improve the accuracy of future instructional information. The input is feedback data, and the output is the improved instructional algorithm. In this step, a generative AI model updated based on information accumulated in the database is used.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Third Embodiment]

[0433] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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".

[0445] This invention is a system that provides personalized health guidance to users based on data obtained from biological samples. This system analyzes information on the gut microbiota and uses artificial intelligence to provide users with an optimal health management plan.

[0446] The user collects a stool sample using a dedicated terminal. This stool sample is sent to a laboratory where the details of the gut microbiota are analyzed using biological methods. The laboratory terminal then transmits the analyzed data to a server.

[0447] The server uses artificial intelligence based on the received data to integrate analytical data and lifestyle information to assess the user's health status. The AI ​​generates personalized guidance information, referencing databases and the latest medical research. This information includes recommendations for diet, supplements, and lifestyle changes.

[0448] The generated guidance information is communicated from the server to the user's terminal. Upon receiving this guidance information, the user's terminal notifies the user and provides detailed guidance on how to incorporate it into their daily life.

[0449] As a concrete example, consider a case where a user uses this system for weight management. After submitting a stool sample, the server uses AI analysis to generate information recommending a reduction in carbohydrate intake and the use of supplements containing specific probiotics. This information is notified to the user's device, helping them to change their lifestyle and achieve their goals. User feedback is collected by the server, and the AI ​​continuously learns from it, improving the accuracy of the analysis and using it to inform future recommendations.

[0450] This invention is an innovative system that enables health management based on gut flora and provides continuous support tailored to the individual needs of each user.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0454] Step 2:

[0455] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology techniques. The analyzed data is then generated.

[0456] Step 3:

[0457] The lab terminal securely transmits the analysis data to the server.

[0458] Step 4:

[0459] The server stores the received gut microbiome data in a database, along with the user's basic information and lifestyle information.

[0460] Step 5:

[0461] The server uses stored data to allow AI to analyze the user's health status and generate a personalized health plan.

[0462] Step 6:

[0463] Based on the generated health plan, the AI ​​creates guidance information that includes specific dietary advice and information on recommended supplements.

[0464] Step 7:

[0465] The server sends the generated instructional information to the user's terminal.

[0466] Step 8:

[0467] The device notifies the user of instructional information and provides detailed guidance for improvement.

[0468] Step 9:

[0469] Users implement the suggested actions in their daily lives and input the results as feedback on their device.

[0470] Step 10:

[0471] The server records user feedback in a database, and the AI ​​uses this feedback as learning material to improve the accuracy of subsequent analyses.

[0472] (Example 1)

[0473] 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."

[0474] Providing detailed and personalized health plans that take into account an individual's gut microbiome and lifestyle has been difficult with conventional technologies. Furthermore, there was a lack of systems to effectively collect user feedback and incorporate it into future recommendations.

[0475] 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.

[0476] In this invention, the server includes an analysis means for analyzing biometric data, an artificial intelligence means for integrating the analysis data and lifestyle information to evaluate the health status, and a generation means for generating a personalized health plan. This makes it possible to provide a health plan adapted to the individual's health status and to improve the accuracy of improvement suggestions based on that plan.

[0477] "Biometric data" refers to information obtained from individual organisms that is used to assess an individual's health status.

[0478] "Analysis means" refers to methods and systems for processing biological data and analyzing gut microbiota and other health indicators.

[0479] "Artificial intelligence means" refers to computer programs or algorithms used to evaluate an individual's health status based on collected data and generate appropriate suggestions.

[0480] "Generation method" refers to the process of designing personalized health management plans based on analysis results and evaluations.

[0481] "Communication means" refers to the technologies and infrastructure used to transmit analysis results and plans to user devices.

[0482] "Data management means" refers to methods and systems for collecting, storing, and managing user feedback and other data.

[0483] "Learning methods" refer to the process of analyzing collected feedback and continuously improving the system to enhance the accuracy and effectiveness of the plan.

[0484] The system of this invention is designed to assess a user's health status and provide a personalized health plan. The user collects a stool sample as biometric data using a dedicated terminal. This stool sample is delivered and arrives at a laboratory equipped with specialized analytical equipment. The laboratory terminal uses biological techniques, such as DNA sequencing, to generate a detailed profile of the gut microbiota.

[0485] The analyzed data is transmitted to the server via a secure communication method. The server uses artificial intelligence to integrate this data with the user's lifestyle information and comprehensively assess their health status. Deep learning algorithms and existing medical databases are used for the assessment. Based on these results, a generative AI model inputs prompts and creates a personalized health plan, which may include recommendations for specific dietary restrictions or supplement intake.

[0486] The generated health plan is transmitted to the user's device via the communication network. The user's device receives it and notifies the user in a visually easy-to-understand format. The user then implements the guidance and sends the results and feedback back to the server via their device. This feedback is stored in the data management system and incorporated into the AI's learning process, improving the accuracy of future suggestions.

[0487] As a concrete example, consider a 40-year-old man using this system for weight management. Based on the analysis of a stool sample, the server generates a plan recommending "reducing carbohydrate intake and trying supplements containing specific probiotics." This prompt can then be used in the AI ​​model to generate a prompt like this: "40-year-old man, seeking weight management. Requesting dietary restrictions and supplement suggestions."

[0488] In this way, we provide plans optimized for each user's individual health condition and support continuous health management.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The user collects a stool sample using a dedicated terminal. The sample is placed in a sealed container and sent to the lab according to the enclosed instructions. The input is the user's stool sample, and the output is the sealed sample sent to the lab.

[0492] Step 2:

[0493] The lab terminal receives a stool sample and uses DNA sequencing technology to analyze the gut microbiota. The input is the stool sample, and the output is the analyzed microbial community data. This data shows the bacterial composition and relative abundance in the gut.

[0494] Step 3:

[0495] The lab terminal encrypts the analysis data and sends it to the server via secure communication. The input is the analysis data, and the output is encrypted data securely sent to the server.

[0496] Step 4:

[0497] The server decrypts the received encrypted data and inputs it into the AI ​​analysis engine. The input consists of decrypted analysis data and user lifestyle information, and the output is a health status assessment result. This assessment is generated by referencing a database.

[0498] Step 5:

[0499] Based on the evaluation results, the server inputs prompt messages into the generating AI model to create a personalized health plan. Specifically, this plan includes suggestions for dietary restrictions and supplements. The input is the health status evaluation result, and the output is the personalized health plan.

[0500] Step 6:

[0501] The server sends the generated health plan to the user's terminal via the communication network. The input is the personalized health plan, and the output is the guidance information received by the terminal.

[0502] Step 7:

[0503] The user's device notifies them of the received health plan and displays actionable steps. The input is guidance information from the server, and the output is detailed guidance that the user can review.

[0504] Step 8:

[0505] Users implement the instructions and send the results and feedback from their device to the server. The input is the feedback content, and the output is the feedback data sent to the server.

[0506] Step 9:

[0507] The server analyzes the collected feedback and incorporates it into the AI ​​learning model to improve the accuracy of future suggestions. The input is the feedback data, and the output is the learning outcome of the updated AI model.

[0508] (Application Example 1)

[0509] 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."

[0510] Traditional health management systems are limited to providing general information and struggle to efficiently deliver personalized health guidance tailored to individual users. Furthermore, they lack clear support on how to integrate proposed health plans into daily life, making it difficult to maintain user motivation. To address this, it is necessary to seamlessly integrate individualized guidance into users' daily lives.

[0511] 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.

[0512] In this invention, the server includes an analysis means for analyzing a biological sample, an artificial intelligence means for generating personalized guidance information based on the analysis results, a learning means for updating the guidance information based on responses from the user, and a means for notifying the user of the guidance information by voice or visual means using a consumer robot. This enables the user to naturally accept personalized health guidance in their daily life and achieve sustainable health management.

[0513] A "biological sample" is a sample taken from an individual's body that contains information about its life activities.

[0514] "Analysis means" refers to a device or process for investigating the characteristics and composition of a biological sample and outputting the results as data.

[0515] "Artificial intelligence tools" refer to systems that use machine learning and data analysis algorithms to extract meaningful information from data and generate guidance information for users.

[0516] "Communication means" refers to technologies and devices for transmitting analysis results and guidance information to information display devices, etc.

[0517] "Data management means" refers to a system or process for collecting responses from users and storing or processing that information.

[0518] A "learning tool" is a system that uses collected data to appropriately improve instructional information and generate more accurate advice.

[0519] A "consumer robot" is an automated mechanical device used in a home environment to perform simple tasks or provide information.

[0520] In the system that realizes this invention, the user first provides a biological sample through a dedicated information display device. This information display device transmits the sample to the laboratory for initial analysis. In the laboratory, the diversity and quantity of microbial communities contained in the sample are examined using analytical means. Subsequently, a server running a generative AI model generates optimal health guidance information for the user using artificial intelligence means based on the analysis data received from the laboratory.

[0521] The server sends guidance information to the information display device via communication means, presenting the information in a way that the user can intuitively understand. Furthermore, users can easily incorporate the suggested health plan into their daily lives by receiving voice and visual notifications from consumer robots. User responses are sent to the server via data management means, and the system continuously optimizes the health guidance information based on these responses through learning means.

[0522] The hardware used consists of an information display device and a consumer robot. The software includes a biometric data analysis platform as an analytical tool, machine learning algorithms for running generative AI models, and database management software for data management.

[0523] As a specific example, if a 60-year-old female user uses this system for weight management, she would provide a stool sample, which the server would analyze to generate a health plan such as: "Drink a probiotic smoothie every day at 10 a.m." This information is communicated by a consumer robot and provided in a format that makes it easy for the user to immediately implement.

[0524] An example of a prompt message is: "Please provide a health plan generated from the user's gut microbiome analysis results. Example: Low-carbohydrate diet plan."

[0525] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0526] Step 1:

[0527] The user provides a biological sample using an information display device. The input is the biological sample collected by the user. This sample is sent to the lab for preparation for analysis. The output is the biological sample upon arrival at the lab.

[0528] Step 2:

[0529] The laboratory uses analytical tools to analyze biological samples and obtain information about the gut microbiota. The input is the biological sample that arrives at the laboratory. This sample is subjected to a specialized analytical device to collect data. The output is the microbial community composition data of the sample.

[0530] Step 3:

[0531] The server processes microbial community composition data received from the lab and generates health guidance information using a generative AI model. The input is microbial community composition data sent from the lab. Based on this data, artificial intelligence analyzes it and generates an optimal health plan. The output is personalized health guidance information.

[0532] Step 4:

[0533] The server transmits the generated health guidance information to the user's information display device via a communication method. The input is the health guidance information generated by the server. This information is sent to the information display device so that the user can confirm it. The output is the guidance information displayed on the information display device.

[0534] Step 5:

[0535] The user's consumer robot notifies the user of guidance information from the server via voice or visual means. The input is guidance information transferred from an information display device. This information is conveyed to the user via voice messages or a visual display. The output is the notification message received by the user.

[0536] Step 6:

[0537] The user performs actions based on health guidance and inputs the results and feedback into an information display device. The input is user feedback information, which is used to improve the accuracy of future suggestions. The output is feedback data processed on the server.

[0538] Step 7:

[0539] The server uses data management tools to store and analyze user feedback using learning tools. The input is user feedback data. This data is analyzed to update the generating AI model and reflect the changes in future suggestions. The output is newly learned guidance information.

[0540] 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.

[0541] This invention is a system that combines the analysis of biological samples with an emotional engine, providing personalized guidance that takes into account both the user's health and emotional state. The user collects a stool sample using a dedicated terminal and sends it to an analysis lab. The lab's terminal receives the stool sample, analyzes the intestinal microbiota, and transmits the data to a server.

[0542] The server integrates and stores the received gut microbiome data with the user's basic information and lifestyle information. Based on this, artificial intelligence evaluates the user's health status and generates a personalized health plan. Meanwhile, the device is equipped with an emotion engine that recognizes and analyzes the user's emotional state from their daily operations and input data. The emotion engine sends this information to the server as feedback.

[0543] The server integrates emotional data from the emotion engine with health assessment data, and the AI ​​generates guidance information that reflects this information. For example, if stress is a major contributing factor, the AI ​​can suggest foods and supplements that are effective in reducing stress.

[0544] The generated guidance information is sent from the server to the user's device. The device provides the user with customized notifications and guidance to help with specific dietary changes, supplement use, and lifestyle improvements. Based on this information, the user adjusts their lifestyle and inputs feedback on their implementation status and effects into the device.

[0545] The server receives feedback information, stores it in a database, and the AI ​​uses it as learning material to improve the accuracy of future suggestions. This invention is a system that enables comprehensive health management that takes into account both the user's gut environment and emotional state.

[0546] The following describes the processing flow.

[0547] Step 1:

[0548] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0549] Step 2:

[0550] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology. The obtained data is then sent to the server.

[0551] Step 3:

[0552] The server stores the received gut microbiota data in a database and integrates it with the user's basic information and lifestyle information.

[0553] Step 4:

[0554] The device's emotion engine analyzes the user's emotional state from their everyday actions and inputs, and sends that emotional data to the server.

[0555] Step 5:

[0556] The server inputs gut data and emotional data into artificial intelligence, which then comprehensively evaluates the user's health based on this information.

[0557] Step 6:

[0558] Based on the evaluation results, the AI ​​creates a personalized health plan and generates dietary guidance and supplement recommendations tailored to the user's physical and emotional state.

[0559] Step 7:

[0560] The server sends instructional information to the user's terminal.

[0561] Step 8:

[0562] The device notifies the user of guidance information and presents specific improvement measures.

[0563] Step 9:

[0564] Based on the guidance information, users improve their lifestyle habits and input the results and changes in their emotions as feedback into their device.

[0565] Step 10:

[0566] The server collects feedback data and updates the database. The AI ​​learns from this feedback and improves the accuracy of its next suggestions.

[0567] (Example 2)

[0568] 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."

[0569] Conventional health management systems have limitations in providing guidance information based solely on the analysis results of biological samples, making it difficult to appropriately reflect the emotional state and lifestyle changes of users. This has resulted in insufficient creation of specific and effective health plans tailored to individual circumstances.

[0570] 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.

[0571] In this invention, the server includes analytical means for analyzing biological samples, intelligent means for generating personalized guidance information, and emotion recognition means for analyzing emotional states. This makes it possible to provide comprehensive and effective health management information that takes into account both the user's physical and emotional states.

[0572] A "biological sample" is a substance taken from a user's body and serves as a data source for evaluating their health status.

[0573] "Analysis means" refers to techniques or devices for scientifically analyzing biological samples and extracting the results as data.

[0574] "Intelligent means" refers to technologies or programs that use artificial intelligence to generate personalized guidance information and suggestions based on collected data.

[0575] "Communication means" refers to network technology or equipment for transmitting analyzed data and generated instructional information to the user's terminal.

[0576] "Emotion recognition means" refers to a technology or program that detects a user's emotional state and provides feedback on it as numerical data or other data.

[0577] "Information management means" refers to a system or program for collecting user responses and feedback, and storing and managing them in a database.

[0578] A "learning tool" is an algorithm or technology used to analyze collected feedback and improve instructional information for future sessions.

[0579] This invention relates to a system for providing personalized guidance information based on the user's health and emotional state, as outputted results. The system mainly consists of three components: a server, a terminal, and a user.

[0580] The server utilizes advanced artificial intelligence (AI) models to integrate and process analytical data obtained from biological samples with data on the user's emotional state. Specifically, data on intestinal microbiota obtained through DNA sequencing of biological samples, particularly excrement samples, collected and transmitted by the user using a dedicated terminal, is received by the server. This DNA sequencing is performed using advanced hardware such as next-generation sequencers (NGS).

[0581] The device serves as the user interface and is equipped with an emotion recognition engine. This engine analyzes text data entered by the user in their daily life using natural language processing (NLP) technology to understand the user's emotional state. It also displays personalized health guidance information and notifies the user of specific instructions.

[0582] Users use their devices to implement actions based on their health plans and provide feedback on the results and their impressions via their devices. This feedback is sent to a server and stored in a database by an information management system.

[0583] This allows the entire system to repeatedly learn and improve, resulting in more accurate guidance for the user in subsequent sessions. For example, if the balance of specific bacteria in the gut is disrupted, the AI ​​can use this information to suggest dietary guidance that includes yogurt and other fermented foods. This guidance also takes into account the user's emotional state, such as their stress level.

[0584] An example of a prompt is, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan that helps reduce stress." By inputting this prompt into the generating AI model, it becomes possible to provide advice optimized for the user.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The user uses a dedicated terminal to collect a biological sample, specifically a sample of excrement. The sample is stored in a container to maintain sterility. This sample is sent to an analysis laboratory, and its receipt is confirmed via the terminal. The input is the excrement sample, and the output is the sample being sent to the laboratory.

[0588] Step 2:

[0589] The lab terminal receives the sample and registers it using a barcode scanner. An automated DNA sequencing system is used to analyze the gut microbiota of the sample and generate data. The input is a fecal sample, and the output is DNA data of the gut microbiota.

[0590] Step 3:

[0591] The lab terminal sends the analyzed DNA data to the server. The server receives this data and stores it in a database. Here, the data is processed to evaluate the state of gut health based on the DNA data. The input is DNA data of the gut microbiota, and the output is data evaluating the state of gut health.

[0592] Step 4:

[0593] The emotion recognition engine built into the device receives the user's daily input data (e.g., diary text) and analyzes it using natural language processing. It calculates the emotional state as numerical data and sends it to the server. The input is daily text data, and the output is numerical data indicating the emotional state.

[0594] Step 5:

[0595] The server integrates the received emotional data with already stored gut health assessment data and generates a personalized health guidance plan using an artificial intelligence model. In this generation process, the AI ​​utilizes a generation AI model to list suggestions regarding diet and lifestyle optimized for the user. The input is the integrated assessment data, and the output is the personalized health guidance plan.

[0596] Step 6:

[0597] The server sends the generated health guidance plan to the terminal. The terminal then uses this information to notify the user and provide specific and customized advice. For example, in the "Example Prompt," a suggestion might be, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan to help reduce stress." The input is the generated health guidance plan, and the output is the notification information for the user.

[0598] Step 7:

[0599] The user adjusts their lifestyle habits based on notifications from their device and inputs the effects and progress of these adjustments into the device. The input data is then sent back to the server as feedback. The input is the result of the lifestyle adjustments, and the output is the feedback data.

[0600] Step 8:

[0601] The server analyzes user feedback data and stores it in a database. The AI ​​model uses this information as training material to improve the accuracy of future suggestions. The input is the feedback data, and the output is the improvement in suggestion accuracy through learning.

[0602] (Application Example 2)

[0603] 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."

[0604] In modern society, personal health management is a crucial issue, but previous methods have struggled to comprehensively consider users' gut microbiome and emotional states, making it difficult to provide personalized and appropriate health guidance. Furthermore, there is a strong demand for methods that offer continuous health improvement strategies in response to real-time emotional changes.

[0605] 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.

[0606] In this invention, the server includes an emotion analysis means for analyzing emotional states and generating emotional information, a recommendation means for adjusting personalized guidance information based on the emotional information, and an analysis means for analyzing biological data. This makes it possible to provide personalized guidance based on the user's gut environment and emotional state.

[0607] "Biological data" refers to excrement samples and other substances collected to assess an individual's health status.

[0608] "Analysis means" refers to devices and methods for analyzing biological data and emotional data and generating results.

[0609] "Artificial intelligence means" refers to algorithms and technologies used to generate personalized guidance information based on analyzed data.

[0610] "Communication methods" refers to network technologies used to transmit analysis results and instructional information to user terminals.

[0611] "Information management means" refers to a database system for collecting and storing user feedback.

[0612] "Learning methods" refer to machine learning techniques used to improve and update instructional information using collected feedback data.

[0613] "Emotional analysis means" refers to technologies and devices that analyze a user's emotional state and generate emotional information.

[0614] "Recommendation methods" refer to technologies that adjust personalized guidance information based on emotional information to provide appropriate advice to users.

[0615] The system for carrying out this invention provides personalized health guidance based on the user's gut environment and emotional state. The server is equipped with multiple means for this purpose. First, the analysis means is used to analyze excrement samples and generate results. Excrement samples are collected by the user using a dedicated terminal and sent to an analysis facility. The server performs data analysis using dedicated software and machine learning algorithms (e.g., TensorFlow).

[0616] Next, the emotion analysis system uses devices such as cameras and microphones to analyze the user's emotional state in real time. The analyzed emotional information is generated by the emotion analysis system and obtained from the user's daily inputs and actions. This allows the emotional state to be reflected in health guidance.

[0617] The communication method directly transfers these analysis results and generated guidance information to the user's terminal. Based on this information, the terminal provides the user with specific advice for improving their health. For example, if the user is experiencing high stress levels, the terminal will display suggestions for dietary and lifestyle improvements to reduce stress.

[0618] The information management system collects user feedback and stores it in a database. Based on this data, the server updates personalized guidance information through learning tools, enabling it to generate more accurate advice for subsequent sessions.

[0619] For example, if a user speaks to a robot saying, "I've been feeling stressed lately," the robot can analyze that emotion and provide appropriate advice on diet and exercise to reduce stress. An example of a prompt in such a situation would be, "I'm still tired from yesterday, so please tell me how to refresh myself."

[0620] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0621] Step 1:

[0622] The user collects excrement samples using a dedicated terminal. These samples are then sent to an analysis facility. The input is the excrement sample collected by the user, and the output is the physical transmission of the sample to the analysis facility.

[0623] Step 2:

[0624] The server uses analytical tools to analyze data obtained from excrement samples. The input is biological data from the excrement sample, and the output is the analysis results regarding the user's gut microbiome. In this process, machine learning algorithms are used to analyze the microbial composition in detail.

[0625] Step 3:

[0626] The device uses a camera and microphone to collect emotional data from the user's daily actions and conversations. The input is the user's actions and voice, and the output is digital data indicating their emotional state. The emotional analysis means analyzes this data to generate emotional information.

[0627] Step 4:

[0628] The server integrates analyzed microbial community data and emotional information, and generates personalized guidance information using a generative AI model. The input is microbial community data and emotional information, and the output is a personalized health guidance plan. In this step, the generative AI model is utilized to calculate optimal advice based on the latest health knowledge and the user's emotional state.

[0629] Step 5:

[0630] The device provides the user with generated health guidance information. The input is personalized guidance information sent from the server, and the output is the presentation of advice through the user's visual or auditory input. The device uses a notification function to suggest lifestyle improvements necessary for the user.

[0631] Step 6:

[0632] Users adjust their lifestyle habits based on the suggestions and input the results and feedback into the terminal. The input is the user's feedback, and the output is stored on the server and used as data to improve future guidance information.

[0633] Step 7:

[0634] The server uses machine learning based on accumulated feedback information to improve the accuracy of future instructional information. The input is feedback data, and the output is the improved instructional algorithm. In this step, a generative AI model updated based on information accumulated in the database is used.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] [Fourth Embodiment]

[0639] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0640] 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.

[0641] 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).

[0642] 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.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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".

[0652] This invention is a system that provides personalized health guidance to users based on data obtained from biological samples. This system analyzes information on the gut microbiota and uses artificial intelligence to provide users with an optimal health management plan.

[0653] The user collects a stool sample using a dedicated terminal. This stool sample is sent to a laboratory where the details of the gut microbiota are analyzed using biological methods. The laboratory terminal then transmits the analyzed data to a server.

[0654] The server uses artificial intelligence based on the received data to integrate analytical data and lifestyle information to assess the user's health status. The AI ​​generates personalized guidance information, referencing databases and the latest medical research. This information includes recommendations for diet, supplements, and lifestyle changes.

[0655] The generated guidance information is communicated from the server to the user's terminal. Upon receiving this guidance information, the user's terminal notifies the user and provides detailed guidance on how to incorporate it into their daily life.

[0656] As a concrete example, consider a case where a user uses this system for weight management. After submitting a stool sample, the server uses AI analysis to generate information recommending a reduction in carbohydrate intake and the use of supplements containing specific probiotics. This information is notified to the user's device, helping them to change their lifestyle and achieve their goals. User feedback is collected by the server, and the AI ​​continuously learns from it, improving the accuracy of the analysis and using it to inform future recommendations.

[0657] This invention is an innovative system that enables health management based on gut flora and provides continuous support tailored to the individual needs of each user.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0661] Step 2:

[0662] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology techniques. The analyzed data is then generated.

[0663] Step 3:

[0664] The lab terminal securely transmits the analysis data to the server.

[0665] Step 4:

[0666] The server stores the received gut microbiome data in a database, along with the user's basic information and lifestyle information.

[0667] Step 5:

[0668] The server uses stored data to allow AI to analyze the user's health status and generate a personalized health plan.

[0669] Step 6:

[0670] Based on the generated health plan, the AI ​​creates guidance information that includes specific dietary advice and information on recommended supplements.

[0671] Step 7:

[0672] The server sends the generated instructional information to the user's terminal.

[0673] Step 8:

[0674] The device notifies the user of instructional information and provides detailed guidance for improvement.

[0675] Step 9:

[0676] Users implement the suggested actions in their daily lives and input the results as feedback on their device.

[0677] Step 10:

[0678] The server records user feedback in a database, and the AI ​​uses this feedback as learning material to improve the accuracy of subsequent analyses.

[0679] (Example 1)

[0680] 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".

[0681] Providing detailed and personalized health plans that take into account an individual's gut microbiome and lifestyle has been difficult with conventional technologies. Furthermore, there was a lack of systems to effectively collect user feedback and incorporate it into future recommendations.

[0682] 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.

[0683] In this invention, the server includes an analysis means for analyzing biometric data, an artificial intelligence means for integrating the analysis data and lifestyle information to evaluate the health status, and a generation means for generating a personalized health plan. This makes it possible to provide a health plan adapted to the individual's health status and to improve the accuracy of improvement suggestions based on that plan.

[0684] "Biometric data" refers to information obtained from individual organisms that is used to assess an individual's health status.

[0685] "Analysis means" refers to methods and systems for processing biological data and analyzing gut microbiota and other health indicators.

[0686] "Artificial intelligence means" refers to computer programs or algorithms used to evaluate an individual's health status based on collected data and generate appropriate suggestions.

[0687] "Generation method" refers to the process of designing personalized health management plans based on analysis results and evaluations.

[0688] "Communication means" refers to the technologies and infrastructure used to transmit analysis results and plans to user devices.

[0689] "Data management means" refers to methods and systems for collecting, storing, and managing user feedback and other data.

[0690] "Learning methods" refer to the process of analyzing collected feedback and continuously improving the system to enhance the accuracy and effectiveness of the plan.

[0691] The system of this invention is designed to assess a user's health status and provide a personalized health plan. The user collects a stool sample as biometric data using a dedicated terminal. This stool sample is delivered and arrives at a laboratory equipped with specialized analytical equipment. The laboratory terminal uses biological techniques, such as DNA sequencing, to generate a detailed profile of the gut microbiota.

[0692] The analyzed data is transmitted to the server via a secure communication method. The server uses artificial intelligence to integrate this data with the user's lifestyle information and comprehensively assess their health status. Deep learning algorithms and existing medical databases are used for the assessment. Based on these results, a generative AI model inputs prompts and creates a personalized health plan, which may include recommendations for specific dietary restrictions or supplement intake.

[0693] The generated health plan is transmitted to the user's device via the communication network. The user's device receives it and notifies the user in a visually easy-to-understand format. The user then implements the guidance and sends the results and feedback back to the server via their device. This feedback is stored in the data management system and incorporated into the AI's learning process, improving the accuracy of future suggestions.

[0694] As a concrete example, consider a 40-year-old man using this system for weight management. Based on the analysis of a stool sample, the server generates a plan recommending "reducing carbohydrate intake and trying supplements containing specific probiotics." This prompt can then be used in the AI ​​model to generate a prompt like this: "40-year-old man, seeking weight management. Requesting dietary restrictions and supplement suggestions."

[0695] In this way, we provide plans optimized for each user's individual health condition and support continuous health management.

[0696] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0697] Step 1:

[0698] The user collects a stool sample using a dedicated terminal. The sample is placed in a sealed container and sent to the lab according to the enclosed instructions. The input is the user's stool sample, and the output is the sealed sample sent to the lab.

[0699] Step 2:

[0700] The lab terminal receives a stool sample and uses DNA sequencing technology to analyze the gut microbiota. The input is the stool sample, and the output is the analyzed microbial community data. This data shows the bacterial composition and relative abundance in the gut.

[0701] Step 3:

[0702] The lab terminal encrypts the analysis data and sends it to the server via secure communication. The input is the analysis data, and the output is encrypted data securely sent to the server.

[0703] Step 4:

[0704] The server decrypts the received encrypted data and inputs it into the AI ​​analysis engine. The input consists of decrypted analysis data and user lifestyle information, and the output is a health status assessment result. This assessment is generated by referencing a database.

[0705] Step 5:

[0706] Based on the evaluation results, the server inputs prompt messages into the generating AI model to create a personalized health plan. Specifically, this plan includes suggestions for dietary restrictions and supplements. The input is the health status evaluation result, and the output is the personalized health plan.

[0707] Step 6:

[0708] The server sends the generated health plan to the user's terminal via the communication network. The input is the personalized health plan, and the output is the guidance information received by the terminal.

[0709] Step 7:

[0710] The user's device notifies them of the received health plan and displays actionable steps. The input is guidance information from the server, and the output is detailed guidance that the user can review.

[0711] Step 8:

[0712] Users implement the instructions and send the results and feedback from their device to the server. The input is the feedback content, and the output is the feedback data sent to the server.

[0713] Step 9:

[0714] The server analyzes the collected feedback and incorporates it into the AI ​​learning model to improve the accuracy of future suggestions. The input is the feedback data, and the output is the learning outcome of the updated AI model.

[0715] (Application Example 1)

[0716] 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".

[0717] Traditional health management systems are limited to providing general information and struggle to efficiently deliver personalized health guidance tailored to individual users. Furthermore, they lack clear support on how to integrate proposed health plans into daily life, making it difficult to maintain user motivation. To address this, it is necessary to seamlessly integrate individualized guidance into users' daily lives.

[0718] 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.

[0719] In this invention, the server includes an analysis means for analyzing a biological sample, an artificial intelligence means for generating personalized guidance information based on the analysis results, a learning means for updating the guidance information based on responses from the user, and a means for notifying the user of the guidance information by voice or visual means using a consumer robot. This enables the user to naturally accept personalized health guidance in their daily life and achieve sustainable health management.

[0720] A "biological sample" is a sample taken from an individual's body that contains information about its life activities.

[0721] "Analysis means" refers to a device or process for investigating the characteristics and composition of a biological sample and outputting the results as data.

[0722] "Artificial intelligence tools" refer to systems that use machine learning and data analysis algorithms to extract meaningful information from data and generate guidance information for users.

[0723] "Communication means" refers to technologies and devices for transmitting analysis results and guidance information to information display devices, etc.

[0724] "Data management means" refers to a system or process for collecting responses from users and storing or processing that information.

[0725] A "learning tool" is a system that uses collected data to appropriately improve instructional information and generate more accurate advice.

[0726] A "consumer robot" is an automated mechanical device used in a home environment to perform simple tasks or provide information.

[0727] In the system that realizes this invention, the user first provides a biological sample through a dedicated information display device. This information display device transmits the sample to the laboratory for initial analysis. In the laboratory, the diversity and quantity of microbial communities contained in the sample are examined using analytical means. Subsequently, a server running a generative AI model generates optimal health guidance information for the user using artificial intelligence means based on the analysis data received from the laboratory.

[0728] The server sends guidance information to the information display device via communication means, presenting the information in a way that the user can intuitively understand. Furthermore, users can easily incorporate the suggested health plan into their daily lives by receiving voice and visual notifications from consumer robots. User responses are sent to the server via data management means, and the system continuously optimizes the health guidance information based on these responses through learning means.

[0729] The hardware used consists of an information display device and a consumer robot. The software includes a biometric data analysis platform as an analytical tool, machine learning algorithms for running generative AI models, and database management software for data management.

[0730] As a specific example, if a 60-year-old female user uses this system for weight management, she would provide a stool sample, which the server would analyze to generate a health plan such as: "Drink a probiotic smoothie every day at 10 a.m." This information is communicated by a consumer robot and provided in a format that makes it easy for the user to immediately implement.

[0731] An example of a prompt message is: "Please provide a health plan generated from the user's gut microbiome analysis results. Example: Low-carbohydrate diet plan."

[0732] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0733] Step 1:

[0734] The user provides a biological sample using an information display device. The input is the biological sample collected by the user. This sample is sent to the lab for preparation for analysis. The output is the biological sample upon arrival at the lab.

[0735] Step 2:

[0736] The laboratory uses analytical tools to analyze biological samples and obtain information about the gut microbiota. The input is the biological sample that arrives at the laboratory. This sample is subjected to a specialized analytical device to collect data. The output is the microbial community composition data of the sample.

[0737] Step 3:

[0738] The server processes microbial community composition data received from the lab and generates health guidance information using a generative AI model. The input is microbial community composition data sent from the lab. Based on this data, artificial intelligence analyzes it and generates an optimal health plan. The output is personalized health guidance information.

[0739] Step 4:

[0740] The server transmits the generated health guidance information to the user's information display device via a communication method. The input is the health guidance information generated by the server. This information is sent to the information display device so that the user can confirm it. The output is the guidance information displayed on the information display device.

[0741] Step 5:

[0742] The user's consumer robot notifies the user of guidance information from the server via voice or visual means. The input is guidance information transferred from an information display device. This information is conveyed to the user via voice messages or a visual display. The output is the notification message received by the user.

[0743] Step 6:

[0744] The user performs actions based on health guidance and inputs the results and feedback into an information display device. The input is user feedback information, which is used to improve the accuracy of future suggestions. The output is feedback data processed on the server.

[0745] Step 7:

[0746] The server uses data management tools to store and analyze user feedback using learning tools. The input is user feedback data. This data is analyzed to update the generating AI model and reflect the changes in future suggestions. The output is newly learned guidance information.

[0747] 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.

[0748] This invention is a system that combines the analysis of biological samples with an emotional engine, providing personalized guidance that takes into account both the user's health and emotional state. The user collects a stool sample using a dedicated terminal and sends it to an analysis lab. The lab's terminal receives the stool sample, analyzes the intestinal microbiota, and transmits the data to a server.

[0749] The server integrates and stores the received gut microbiome data with the user's basic information and lifestyle information. Based on this, artificial intelligence evaluates the user's health status and generates a personalized health plan. Meanwhile, the device is equipped with an emotion engine that recognizes and analyzes the user's emotional state from their daily operations and input data. The emotion engine sends this information to the server as feedback.

[0750] The server integrates emotional data from the emotion engine with health assessment data, and the AI ​​generates guidance information that reflects this information. For example, if stress is a major contributing factor, the AI ​​can suggest foods and supplements that are effective in reducing stress.

[0751] The generated guidance information is sent from the server to the user's device. The device provides the user with customized notifications and guidance to help with specific dietary changes, supplement use, and lifestyle improvements. Based on this information, the user adjusts their lifestyle and inputs feedback on their implementation status and effects into the device.

[0752] The server receives feedback information, stores it in a database, and the AI ​​uses it as learning material to improve the accuracy of future suggestions. This invention is a system that enables comprehensive health management that takes into account both the user's gut environment and emotional state.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] Users collect stool samples using a dedicated terminal kit and send them to an analysis lab.

[0756] Step 2:

[0757] The lab terminal receives the stool sample and analyzes the gut microbiota using biotechnology. The obtained data is then sent to the server.

[0758] Step 3:

[0759] The server stores the received gut microbiota data in a database and integrates it with the user's basic information and lifestyle information.

[0760] Step 4:

[0761] The device's emotion engine analyzes the user's emotional state from their everyday actions and inputs, and sends that emotional data to the server.

[0762] Step 5:

[0763] The server inputs gut data and emotional data into artificial intelligence, which then comprehensively evaluates the user's health based on this information.

[0764] Step 6:

[0765] Based on the evaluation results, the AI ​​creates a personalized health plan and generates dietary guidance and supplement recommendations tailored to the user's physical and emotional state.

[0766] Step 7:

[0767] The server sends instructional information to the user's terminal.

[0768] Step 8:

[0769] The device notifies the user of guidance information and presents specific improvement measures.

[0770] Step 9:

[0771] Based on the guidance information, users improve their lifestyle habits and input the results and changes in their emotions as feedback into their device.

[0772] Step 10:

[0773] The server collects feedback data and updates the database. The AI ​​learns from this feedback and improves the accuracy of its next suggestions.

[0774] (Example 2)

[0775] 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".

[0776] Conventional health management systems have limitations in providing guidance information based solely on the analysis results of biological samples, making it difficult to appropriately reflect the emotional state and lifestyle changes of users. This has resulted in insufficient creation of specific and effective health plans tailored to individual circumstances.

[0777] 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.

[0778] In this invention, the server includes analytical means for analyzing biological samples, intelligent means for generating personalized guidance information, and emotion recognition means for analyzing emotional states. This makes it possible to provide comprehensive and effective health management information that takes into account both the user's physical and emotional states.

[0779] A "biological sample" is a substance taken from a user's body and serves as a data source for evaluating their health status.

[0780] "Analysis means" refers to techniques or devices for scientifically analyzing biological samples and extracting the results as data.

[0781] "Intelligent means" refers to technologies or programs that use artificial intelligence to generate personalized guidance information and suggestions based on collected data.

[0782] "Communication means" refers to network technology or equipment for transmitting analyzed data and generated instructional information to the user's terminal.

[0783] "Emotion recognition means" refers to a technology or program that detects a user's emotional state and provides feedback on it as numerical data or other data.

[0784] "Information management means" refers to a system or program for collecting user responses and feedback, and storing and managing them in a database.

[0785] A "learning tool" is an algorithm or technology used to analyze collected feedback and improve instructional information for future sessions.

[0786] This invention relates to a system for providing personalized guidance information based on the user's health and emotional state, as outputted results. The system mainly consists of three components: a server, a terminal, and a user.

[0787] The server utilizes advanced artificial intelligence (AI) models to integrate and process analytical data obtained from biological samples with data on the user's emotional state. Specifically, data on intestinal microbiota obtained through DNA sequencing of biological samples, particularly excrement samples, collected and transmitted by the user using a dedicated terminal, is received by the server. This DNA sequencing is performed using advanced hardware such as next-generation sequencers (NGS).

[0788] The device serves as the user interface and is equipped with an emotion recognition engine. This engine analyzes text data entered by the user in their daily life using natural language processing (NLP) technology to understand the user's emotional state. It also displays personalized health guidance information and notifies the user of specific instructions.

[0789] Users use their devices to implement actions based on their health plans and provide feedback on the results and their impressions via their devices. This feedback is sent to a server and stored in a database by an information management system.

[0790] This allows the entire system to repeatedly learn and improve, resulting in more accurate guidance for the user in subsequent sessions. For example, if the balance of specific bacteria in the gut is disrupted, the AI ​​can use this information to suggest dietary guidance that includes yogurt and other fermented foods. This guidance also takes into account the user's emotional state, such as their stress level.

[0791] An example of a prompt is, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan that helps reduce stress." By inputting this prompt into the generating AI model, it becomes possible to provide advice optimized for the user.

[0792] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0793] Step 1:

[0794] The user uses a dedicated terminal to collect a biological sample, specifically a sample of excrement. The sample is stored in a container to maintain sterility. This sample is sent to an analysis laboratory, and its receipt is confirmed via the terminal. The input is the excrement sample, and the output is the sample being sent to the laboratory.

[0795] Step 2:

[0796] The lab terminal receives the sample and registers it using a barcode scanner. An automated DNA sequencing system is used to analyze the gut microbiota of the sample and generate data. The input is a fecal sample, and the output is DNA data of the gut microbiota.

[0797] Step 3:

[0798] The lab terminal sends the analyzed DNA data to the server. The server receives this data and stores it in a database. Here, the data is processed to evaluate the state of gut health based on the DNA data. The input is DNA data of the gut microbiota, and the output is data evaluating the state of gut health.

[0799] Step 4:

[0800] The emotion recognition engine built into the device receives the user's daily input data (e.g., diary text) and analyzes it using natural language processing. It calculates the emotional state as numerical data and sends it to the server. The input is daily text data, and the output is numerical data indicating the emotional state.

[0801] Step 5:

[0802] The server integrates the received emotional data with already stored gut health assessment data and generates a personalized health guidance plan using an artificial intelligence model. In this generation process, the AI ​​utilizes a generation AI model to list suggestions regarding diet and lifestyle optimized for the user. The input is the integrated assessment data, and the output is the personalized health guidance plan.

[0803] Step 6:

[0804] The server sends the generated health guidance plan to the terminal. The terminal then uses this information to notify the user and provide specific and customized advice. For example, in the "Example Prompt," a suggestion might be, "Based on the user's recent gut microbiome analysis data and emotional state data, please suggest a personalized meal plan to help reduce stress." The input is the generated health guidance plan, and the output is the notification information for the user.

[0805] Step 7:

[0806] The user adjusts their lifestyle habits based on notifications from their device and inputs the effects and progress of these adjustments into the device. The input data is then sent back to the server as feedback. The input is the result of the lifestyle adjustments, and the output is the feedback data.

[0807] Step 8:

[0808] The server analyzes user feedback data and stores it in a database. The AI ​​model uses this information as training material to improve the accuracy of future suggestions. The input is the feedback data, and the output is the improvement in suggestion accuracy through learning.

[0809] (Application Example 2)

[0810] 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".

[0811] In modern society, personal health management is a crucial issue, but previous methods have struggled to comprehensively consider users' gut microbiome and emotional states, making it difficult to provide personalized and appropriate health guidance. Furthermore, there is a strong demand for methods that offer continuous health improvement strategies in response to real-time emotional changes.

[0812] 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.

[0813] In this invention, the server includes an emotion analysis means for analyzing emotional states and generating emotional information, a recommendation means for adjusting personalized guidance information based on the emotional information, and an analysis means for analyzing biological data. This makes it possible to provide personalized guidance based on the user's gut environment and emotional state.

[0814] "Biological data" refers to excrement samples and other substances collected to assess an individual's health status.

[0815] "Analysis means" refers to devices and methods for analyzing biological data and emotional data and generating results.

[0816] "Artificial intelligence means" refers to algorithms and technologies used to generate personalized guidance information based on analyzed data.

[0817] "Communication methods" refers to network technologies used to transmit analysis results and instructional information to user terminals.

[0818] "Information management means" refers to a database system for collecting and storing user feedback.

[0819] "Learning methods" refer to machine learning techniques used to improve and update instructional information using collected feedback data.

[0820] "Emotional analysis means" refers to technologies and devices that analyze a user's emotional state and generate emotional information.

[0821] "Recommendation methods" refer to technologies that adjust personalized guidance information based on emotional information to provide appropriate advice to users.

[0822] The system for carrying out this invention provides personalized health guidance based on the user's gut environment and emotional state. The server is equipped with multiple means for this purpose. First, the analysis means is used to analyze excrement samples and generate results. Excrement samples are collected by the user using a dedicated terminal and sent to an analysis facility. The server performs data analysis using dedicated software and machine learning algorithms (e.g., TensorFlow).

[0823] Next, the emotion analysis system uses devices such as cameras and microphones to analyze the user's emotional state in real time. The analyzed emotional information is generated by the emotion analysis system and obtained from the user's daily inputs and actions. This allows the emotional state to be reflected in health guidance.

[0824] The communication method directly transfers these analysis results and generated guidance information to the user's terminal. Based on this information, the terminal provides the user with specific advice for improving their health. For example, if the user is experiencing high stress levels, the terminal will display suggestions for dietary and lifestyle improvements to reduce stress.

[0825] The information management system collects user feedback and stores it in a database. Based on this data, the server updates personalized guidance information through learning tools, enabling it to generate more accurate advice for subsequent sessions.

[0826] For example, if a user speaks to a robot saying, "I've been feeling stressed lately," the robot can analyze that emotion and provide appropriate advice on diet and exercise to reduce stress. An example of a prompt in such a situation would be, "I'm still tired from yesterday, so please tell me how to refresh myself."

[0827] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0828] Step 1:

[0829] The user collects excrement samples using a dedicated terminal. These samples are then sent to an analysis facility. The input is the excrement sample collected by the user, and the output is the physical transmission of the sample to the analysis facility.

[0830] Step 2:

[0831] The server uses analytical tools to analyze data obtained from excrement samples. The input is biological data from the excrement sample, and the output is the analysis results regarding the user's gut microbiome. In this process, machine learning algorithms are used to analyze the microbial composition in detail.

[0832] Step 3:

[0833] The device uses a camera and microphone to collect emotional data from the user's daily actions and conversations. The input is the user's actions and voice, and the output is digital data indicating their emotional state. The emotional analysis means analyzes this data to generate emotional information.

[0834] Step 4:

[0835] The server integrates analyzed microbial community data and emotional information, and generates personalized guidance information using a generative AI model. The input is microbial community data and emotional information, and the output is a personalized health guidance plan. In this step, the generative AI model is utilized to calculate optimal advice based on the latest health knowledge and the user's emotional state.

[0836] Step 5:

[0837] The device provides the user with generated health guidance information. The input is personalized guidance information sent from the server, and the output is the presentation of advice through the user's visual or auditory input. The device uses a notification function to suggest lifestyle improvements necessary for the user.

[0838] Step 6:

[0839] Users adjust their lifestyle habits based on the suggestions and input the results and feedback into the terminal. The input is the user's feedback, and the output is stored on the server and used as data to improve future guidance information.

[0840] Step 7:

[0841] The server uses machine learning based on accumulated feedback information to improve the accuracy of future instructional information. The input is feedback data, and the output is the improved instructional algorithm. In this step, a generative AI model updated based on information accumulated in the database is used.

[0842] 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.

[0843] 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.

[0844] In the above embodiment, an example was given in which the 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.

[0845] 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.

[0846] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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."

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] The following is further disclosed regarding the embodiments described above.

[0864] (Claim 1)

[0865] Analytical means for analyzing biological samples,

[0866] An artificial intelligence means for generating personalized guidance information based on analysis results,

[0867] A communication means for providing analysis results and guidance information to the user terminal,

[0868] A data management system for collecting and storing user feedback,

[0869] A learning method that updates instructional information based on feedback,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising an analytical means for analyzing the intestinal microbiota of a fecal sample used as a biological sample.

[0873] (Claim 3)

[0874] The system according to claim 1, comprising artificial intelligence means for evaluating the user's health status and generating suggestions based on accumulated data and the latest medical research.

[0875] "Example 1"

[0876] (Claim 1)

[0877] Analytical means for analyzing biological data,

[0878] An artificial intelligence method that integrates analytical data and lifestyle information to evaluate health status,

[0879] A means for generating an individualized health plan,

[0880] A communication means for providing analysis data and health plans to user devices,

[0881] A data management system for collecting and storing user feedback,

[0882] A learning method for updating health plans based on feedback,

[0883] A system that includes this.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising an analytical means for using stool data as biological data and analyzing the intestinal microbiota of said data.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising artificial intelligence means for evaluating the user's health status and generating suggestions based on accumulated data and the latest research.

[0888] "Application Example 1"

[0889] (Claim 1)

[0890] Analytical means for analyzing biological samples,

[0891] An artificial intelligence means for generating personalized guidance information based on analysis results,

[0892] A communication means for providing analysis results and guidance information to an information display device,

[0893] A data management means for collecting and storing responses from users,

[0894] A learning method that updates instructional information based on responses,

[0895] A means of notifying users of instructional information via voice or visual means using a consumer robot,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, comprising an analytical means for analyzing the microbial community in a sample of excrement, which is used as a biological sample.

[0899] (Claim 3)

[0900] The system according to claim 1, comprising artificial intelligence means for evaluating the user's health status and generating suggestions based on accumulated data and the latest medical research.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] Analytical means for analyzing biological samples,

[0904] An intelligent means for generating personalized guidance information based on analysis results,

[0905] A communication means for providing analysis results and guidance information to the user's terminal,

[0906] An emotion recognition means that analyzes emotional states and transmits that data as feedback,

[0907] Information management means for collecting and storing user feedback,

[0908] A learning method that updates guidance information based on responses,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, comprising an analytical means for analyzing the intestinal microbiota of a sample of excrement used as a biological sample.

[0912] (Claim 3)

[0913] The system according to claim 1, comprising intelligent means for evaluating the user's health status and generating suggestions based on accumulated information and the latest medical research.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] Analytical means for analyzing biological data,

[0917] An artificial intelligence means for generating personalized guidance information based on analysis results,

[0918] A communication means for providing analysis results and guidance information to the user's terminal,

[0919] Information management means for collecting and storing user feedback,

[0920] A learning method that updates instructional information based on feedback,

[0921] An emotion analysis means for analyzing emotional states and generating emotional information,

[0922] A recommendation system that adjusts individualized guidance information based on emotional information,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, comprising an analytical means for analyzing the microbial community in a sample of excrement used as a biological material.

[0926] (Claim 3)

[0927] The system according to claim 1, comprising artificial intelligence means for evaluating the user's health status and generating suggestions based on accumulated information and the latest medical research. [Explanation of symbols]

[0928] 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. Analytical means for analyzing biological samples, An artificial intelligence means for generating personalized guidance information based on analysis results, A communication means for providing analysis results and guidance information to the user terminal, A data management system for collecting and storing user feedback, A learning method that updates instructional information based on feedback, A system that includes this.

2. The system according to claim 1, comprising an analytical means for analyzing the intestinal microbiota of a fecal sample used as a biological sample.

3. The system according to claim 1, comprising artificial intelligence means for evaluating the user's health status and generating suggestions based on accumulated data and the latest medical research.

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