system

Abstract

The system according to this embodiment aims to provide an optimal health management plan based on individual genetic characteristics. [Solution] The system according to the embodiment comprises a testing unit, an analysis unit, a proposal unit, a collection unit, and an adjustment unit. The testing unit performs genetic testing using a saliva sample. The analysis unit analyzes the genetic testing results obtained by the testing unit. The proposal unit proposes a health management plan based on the analysis results obtained by the analysis unit. The collection unit collects data from regular blood tests and body composition measurements. The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] In the conventional technology, a health management plan based on individual genetic characteristics has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal health management plan based on individual genetic characteristics.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a testing unit, an analysis unit, a proposal unit, a collection unit, and an adjustment unit. The testing unit performs genetic testing using a saliva sample. The analysis unit analyzes the genetic testing results obtained by the testing unit. The proposal unit proposes a health management plan based on the analysis results obtained by the analysis unit. The collection unit collects data from regular blood tests and body composition measurements. The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal health management plan based on individual genetic characteristics. [Brief explanation of the drawing]

[0008] [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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving 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 receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The next-generation personal healthcare system according to an embodiment of the present invention is a system that combines genetic testing and AI technology. In this system, the user provides a saliva sample for genetic testing. Next, the AI ​​analyzes the test results to reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Based on this, it proposes an optimal diet plan and exercise regimen. Furthermore, the AI ​​analyzes the results of regular blood tests and body composition measurements to automatically adjust the health management plan. This allows the user to maintain a healthy weight suited to their body type and promote overall health improvement. For example, the user provides a saliva sample for genetic testing. Next, the AI ​​analyzes the test results to reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Based on this, it proposes an optimal diet plan and exercise regimen. Furthermore, the AI ​​analyzes the results of regular blood tests and body composition measurements to automatically adjust the health management plan. This allows the user to maintain a healthy weight suited to their body type and promote overall health improvement. Online consultation services with genetic counselors and nutrition specialists are also provided. Genetic counselors provide explanations of genetic test results and advice on health management based on genetic information, while nutrition specialists create personalized meal plans and answer nutritional questions. This allows users to achieve more precise health management and support a healthy lifestyle. As a result, next-generation personal healthcare systems can provide individualized health management plans based on users' genetic information and health data, supporting overall health improvement.

[0029] The next-generation personal healthcare system according to this embodiment comprises a testing unit, an analysis unit, a proposal unit, a collection unit, and a processing unit. The testing unit performs genetic testing using a saliva sample. For example, the testing unit tests a saliva sample provided by the user using a genetic testing kit. The testing unit can analyze the user's genetic information using the genetic testing kit. For example, the testing unit can analyze the user's genetic information with high accuracy using the genetic testing kit. The analysis unit analyzes the genetic test results obtained by the testing unit. For example, the analysis unit uses AI to analyze the genetic test results and reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The analysis unit can use AI to analyze the individual's metabolic characteristics based on the genetic test results. The analysis unit can use AI to analyze the nutrient absorption efficiency based on the genetic test results. The analysis unit can use AI to analyze responsiveness to exercise based on the genetic test results. The proposal unit proposes a health management plan based on the analysis results obtained by the analysis unit. The suggestion unit, for example, uses AI to suggest an optimal meal plan and exercise regimen based on an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The suggestion unit can suggest an optimal meal plan based on an individual's metabolic characteristics using AI. The suggestion unit can suggest an optimal meal plan based on nutrient absorption efficiency using AI. The suggestion unit can suggest an optimal exercise regimen based on responsiveness to exercise using AI. The collection unit collects data from regular blood tests and body composition measurements. The collection unit collects data from regular blood tests and body composition measurements performed by the user, for example. The collection unit can collect blood test data. The collection unit can collect body composition measurement data. The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. The adjustment unit can analyze the collected data using AI and automatically adjust the health management plan. The adjustment unit can analyze the collected blood test data using AI and adjust the health management plan. The adjustment unit can analyze the collected body composition measurement data using AI and adjust the health management plan.As a result, the next-generation personal healthcare system according to this embodiment can provide an individualized health management plan based on the user's genetic information and health data, and support overall health promotion.

[0030] The testing department performs genetic testing using saliva samples. For example, the testing department tests saliva samples provided by users using a genetic testing kit. Specifically, users collect saliva samples using a dedicated genetic testing kit and send them to the testing department. The testing department first receives the saliva samples, processes them appropriately, and then runs them through a genetic analysis device. The genetic analysis device uses next-generation sequencing technology to analyze the user's genetic information with high accuracy. This analysis process involves DNA extraction, amplification, and sequencing, and the obtained data is stored in digital format. Furthermore, the testing department registers the analysis results in a database and manages them for each user. This allows the testing department to analyze users' genetic information with high accuracy and use it for individualized health management.

[0031] The Analysis Department analyzes the genetic test results obtained by the Testing Department. For example, the Analysis Department uses AI to analyze the genetic test results, revealing an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness. Specifically, based on the genetic test results, the AI ​​references a vast dataset to analyze the user's metabolic characteristics and evaluates how specific gene mutations affect metabolism. For example, if a specific gene mutation affects fat metabolism, this information can be used to determine what kind of diet the user should follow. Regarding nutrient absorption efficiency, the AI ​​analyzes genetic information to identify gene mutations involved in vitamin and mineral absorption. This clarifies which nutrients the user should supplement. Furthermore, regarding exercise responsiveness, the AI ​​analyzes genetic information to evaluate gene mutations involved in muscle growth and recovery. This provides the foundational data for proposing an optimal exercise plan to the user. The Analysis Department comprehensively evaluates these analysis results to gain a detailed understanding of the user's health status.

[0032] The Proposal Department proposes health management plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose optimal meal plans and exercise regimens based on an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Specifically, the AI ​​creates a meal plan tailored to the user's metabolic characteristics based on the data provided by the Analysis Department. For example, it recommends a low-fat diet for users with slow fat metabolism and suggests a diet high in carbohydrates for users with fast carbohydrate metabolism. It also suggests supplements to replenish specific vitamins and minerals based on nutrient absorption efficiency. Furthermore, it proposes the optimal exercise regimen for the user based on their responsiveness to exercise. For example, it suggests a plan focusing on strength training for users with slow muscle growth and a plan centered on aerobic exercise for users with high endurance. The Proposal Department presents these suggestions to the user in an easy-to-understand and implement format. This allows users to implement a health management plan that is best suited to them.

[0033] The data collection unit collects data from regular blood tests and body composition measurements. Specifically, users regularly undergo blood tests at medical institutions and provide the results to the data collection unit. Users also use home body composition analyzers to measure data such as body fat percentage, muscle mass, and bone density, and send this data to the data collection unit. The data collection unit centrally manages this data and registers it in a database for each user. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user's health condition changes rapidly, the data collection unit can increase the frequency of data collection to collect more detailed data. This allows the data collection unit to continuously monitor the user's health condition and provide appropriate data.

[0034] The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. For example, the adjustment unit uses AI to analyze the collected data and automatically adjust the health management plan. Specifically, the AI ​​analyzes collected blood test data to detect changes in the user's health status. For example, if blood glucose or cholesterol levels exceed standard values, the AI ​​revises the meal plan and adjusts carbohydrate and fat intake. It also analyzes body composition measurement data and adjusts the exercise plan according to changes in muscle mass and body fat percentage. For example, if muscle mass is decreasing, it suggests a plan to increase the frequency and intensity of strength training. The adjustment unit makes these adjustments in real time, providing the user with the optimal health management plan. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the plan. This allows the adjustment unit to respond flexibly to the user's health condition and support overall health promotion.

[0035] The consultation service provides online consultation services with genetic counselors and nutrition specialists. For example, the consultation service provides a platform where users can consult with genetic counselors and nutrition specialists online. Genetic counselors can provide explanations of genetic test results and advice on health management based on genetic information. Nutrition specialists can create personalized meal plans and answer nutrition-related questions. For example, users can consult with a genetic counselor online and receive explanations of their genetic test results. Users can consult with a nutrition specialist online and request the creation of a personalized meal plan. This allows users to receive expert advice and manage their health more precisely. Some or all of the above processes in the consultation service may be performed using AI, or not. For example, the consultation service can input the user's consultation details into an AI, which can then generate appropriate advice.

[0036] The display unit presents an ideal weight range. The display unit calculates and presents the ideal weight range to the user, for example, based on the user's genetic test results. The display unit can consider the user's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise in order to calculate the ideal weight range. The display unit can calculate the ideal weight range based on the user's metabolic characteristics, for example. The display unit can calculate the ideal weight range based on nutrient absorption efficiency. The display unit can calculate the ideal weight range based on responsiveness to exercise. This allows the user to understand a healthy weight appropriate for their body type and avoid excessive thinness. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's genetic test results into AI, which can then calculate the ideal weight range.

[0037] The measurement unit analyzes the results of regular blood tests and body composition measurements. For example, the measurement unit collects and analyzes data from blood tests and body composition measurements that the user regularly performs. The measurement unit can analyze blood test data. The measurement unit can analyze body composition measurement data. For example, the measurement unit evaluates the user's health status based on blood test data. The measurement unit can evaluate the user's health status based on body composition measurement data. This allows for a comprehensive evaluation of genetic information and current health status, and adjustment of the health management plan. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input collected blood test data into AI, which can then analyze the data.

[0038] The analysis department can reveal an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. For example, the analysis department can analyze an individual's metabolic characteristics based on genetic test results. The analysis department can analyze nutrient absorption efficiency based on genetic test results. The analysis department can analyze responsiveness to exercise based on genetic test results. This allows for the provision of an optimal health management plan based on the individual's constitution. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input genetic test results into AI, which can then analyze the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise.

[0039] The suggestion unit can propose an optimal meal plan and exercise regimen. For example, the suggestion unit can propose an optimal meal plan and exercise regimen based on an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness. The suggestion unit can propose an optimal meal plan based on an individual's metabolic characteristics. The suggestion unit can propose an optimal meal plan based on nutrient absorption efficiency. The suggestion unit can propose an optimal exercise regimen based on exercise responsiveness. This allows for the provision of a health management plan tailored to the user's constitution. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness into AI, which can then propose an optimal meal plan and exercise regimen.

[0040] The testing department can analyze the user's past health data and select the optimal testing method. For example, the testing department can select the optimal method for collecting saliva samples based on the user's past health data. The testing department can analyze the user's past health data and suggest the optimal testing kit. The testing department can adjust the frequency of testing based on the user's past health data. This allows the testing department to select the optimal testing method based on the user's past health data. Some or all of the above processes in the testing department may be performed using AI, for example, or not using AI. For example, the testing department can input the user's past health data into AI, which can then select the optimal testing method.

[0041] The testing unit can filter saliva samples based on the user's lifestyle and diet. For example, the testing unit can adjust the timing of saliva sample collection based on the user's diet. The testing unit can propose the optimal method of saliva sample collection, taking into account the user's lifestyle. The testing unit can filter saliva samples to improve their quality based on the user's diet and lifestyle. This allows for the collection of optimal saliva samples based on the user's lifestyle and diet. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's lifestyle and diet into AI, which can then propose the optimal method of saliva sample collection.

[0042] The testing unit can prioritize acquiring highly relevant data by considering the user's geographical location information when collecting saliva samples. For example, the testing unit can suggest the optimal saliva sample collection location based on the user's geographical location information. The testing unit can prioritize acquiring highly relevant data by considering the user's geographical location information. The testing unit can suggest the optimal test kit based on the user's geographical location information. This allows for the acquisition of optimal data based on the user's geographical location information. Some or all of the above processing in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's geographical location information into AI, which can then suggest the optimal saliva sample collection location.

[0043] The testing unit can analyze the user's social media activity and obtain relevant data when collecting saliva samples. For example, the testing unit can suggest the optimal timing for saliva sample collection based on the user's social media activity. The testing unit can analyze the user's social media activity and obtain relevant data. The testing unit can suggest the optimal testing method based on the user's social media activity. This allows for the acquisition of optimal data based on the user's social media activity. Some or all of the above processing in the testing unit may be performed using AI, or not. For example, the testing unit can input the user's social media activity into AI, which can then suggest the optimal timing for saliva sample collection.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit can perform a detailed analysis based on important genetic data. The analysis unit can perform a simplified analysis based on less important genetic data. The analysis unit can adjust the level of detail of the analysis according to the importance of the genetic data. This allows the analysis to be performed at the optimal level of detail according to the importance of the genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the genetic data into the AI, and the AI ​​can perform the analysis at the optimal level of detail.

[0045] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit can apply a specific analysis algorithm to gene data related to metabolic characteristics. The analysis unit can apply a different analysis algorithm to gene data related to nutrient absorption efficiency. The analysis unit can apply a different analysis algorithm to gene data related to responsiveness to exercise. This allows the optimal analysis algorithm to be applied depending on the gene category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the gene category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the genetic data was acquired. For example, the analysis unit may prioritize the analysis of the most recent genetic data. The analysis unit can also analyze the latest data while referring to past genetic data. The analysis unit can determine the priority of analysis based on when the genetic data was acquired. This allows for analysis to be performed with the optimal priority based on when the genetic data was acquired. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the genetic data acquisition date into the AI, and the AI ​​can perform analysis with the optimal priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the genetic data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant genetic data. The analysis unit can postpone the analysis of less relevant genetic data. The analysis unit can adjust the order of analysis based on the relevance of the genetic data. This allows the analysis to be performed in the optimal order based on the relevance of the genetic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the genetic data into the AI, which can then perform the analysis in the optimal order.

[0048] The proposal unit can adjust the level of detail in its proposals based on the importance of the health management plans. For example, the proposal unit can provide detailed proposals based on important health management plans. For example, it can provide concise proposals based on less important health management plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the health management plans. This allows the proposals to be made with the optimal level of detail according to the importance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health management plans into the AI, which can then make proposals with the optimal level of detail.

[0049] The proposal unit can apply different proposal algorithms depending on the category of the health management plan when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to proposals related to meal plans. For example, the proposal unit can apply a different proposal algorithm to proposals related to exercise regimens. The proposal unit can apply different proposal algorithms depending on the category of the health management plan. This allows the optimal proposal algorithm to be applied according to the category of the health management plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the health management plan into the AI, and the AI ​​can apply the optimal proposal algorithm.

[0050] The proposal department can determine the priority of proposals based on the submission timing of the health management plan. For example, the proposal department may prioritize the most recent health management plan. The proposal department can also propose the latest plan while referring to past health management plans. The proposal department can determine the priority of proposals based on the submission timing of the health management plan. This allows for proposals to be made with the optimal priority based on the submission timing of the health management plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the health management plan into the AI, and the AI ​​can make proposals with the optimal priority.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the health management plans. For example, the proposal unit can prioritize proposing highly relevant health management plans. The proposal unit can postpone proposing less relevant health management plans. The proposal unit can adjust the order of proposals based on the relevance of the health management plans. This allows proposals to be made in the optimal order based on the relevance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the health management plans into the AI, and the AI ​​can make proposals in the optimal order.

[0052] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can select the optimal blood test method based on the user's past health data. The data collection unit can analyze the user's past health data and suggest the optimal body composition measurement method. The data collection unit can adjust the frequency of data collection based on the user's past health data. This allows the optimal data collection method to be selected based on the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then select the optimal data collection method.

[0053] The data collection unit can filter data collected from blood tests and body composition measurements based on the user's lifestyle and diet. For example, the data collection unit can adjust the timing of blood tests based on the user's diet. The data collection unit can consider the user's lifestyle and suggest the optimal method for measuring body composition. The data collection unit can filter data to improve the quality of data collection based on the user's diet and lifestyle. This allows for the collection of optimal data based on the user's lifestyle and diet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and diet into AI, which can then suggest the optimal data collection method.

[0054] The data collection unit can prioritize the collection of highly relevant data when collecting data from blood tests and body composition measurements, taking into account the user's geographical location. For example, the data collection unit can suggest the optimal location for blood tests based on the user's geographical location. The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. The data collection unit can suggest the optimal location for body composition measurements based on the user's geographical location. This allows for the collection of optimal data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then suggest the optimal data collection method.

[0055] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data for blood tests and body composition measurements. For example, the data collection unit can suggest the optimal timing for blood tests based on the user's social media activity. The data collection unit can analyze the user's social media activity and collect relevant data. The data collection unit can suggest the optimal method for measuring body composition based on the user's social media activity. This allows for the collection of optimal data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then suggest the optimal data collection method.

[0056] The adjustment unit can analyze the user's past health data and select the optimal adjustment method when adjusting the health management plan. For example, the adjustment unit can select the optimal adjustment method for the health management plan based on the user's past health data. The adjustment unit can analyze the user's past health data and propose the optimal adjustment method. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's past health data. This allows the health management plan to be adjusted in the most optimal way based on the user's past health data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past health data into AI, and the AI ​​can select the optimal adjustment method.

[0057] The adjustment unit can customize the means of adjustment when adjusting the health management plan based on the user's current lifestyle. For example, the adjustment unit can propose the optimal method of adjusting the health management plan based on the user's current lifestyle. The adjustment unit can customize the means of adjustment, taking into account the user's current lifestyle. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's current lifestyle. This allows the health management plan to be adjusted in the most optimal way based on the user's current lifestyle. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's current lifestyle into AI, which can then propose the optimal adjustment method.

[0058] The adjustment unit can select the optimal adjustment method when adjusting the health management plan, taking into account the user's geographical location information. For example, the adjustment unit can propose the optimal health management plan adjustment method based on the user's geographical location information. The adjustment unit can select the optimal adjustment method, taking into account the user's geographical location information. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's geographical location information. This allows the health management plan to be adjusted in the most optimal way based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal adjustment method.

[0059] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting the health management plan. For example, the adjustment unit can propose the optimal method for adjusting the health management plan based on the user's social media activity. The adjustment unit can analyze the user's social media activity and propose adjustment methods. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's social media activity. This allows the health management plan to be adjusted in the most optimal way based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity into AI, and the AI ​​can propose the optimal adjustment method.

[0060] The consultation department can analyze a user's past consultation history and select the most suitable consultation method. For example, the consultation department can select the most suitable online consultation method based on the user's past consultation history. The consultation department can analyze a user's past consultation history and propose the most suitable consultation method. The consultation department can adjust the frequency of online consultations based on the user's past consultation history. This allows online consultations to be conducted in the most optimal way based on the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's past consultation history into AI, which can then select the most suitable consultation method.

[0061] The consultation department can prioritize providing highly relevant consultation content during online consultations, taking into account the user's geographical location. For example, the consultation department can propose the most suitable online consultation content based on the user's geographical location. The consultation department can prioritize providing highly relevant consultation content, taking into account the user's geographical location. The consultation department can propose the most suitable consultation method based on the user's geographical location. This allows the consultation department to provide the most suitable consultation content based on the user's geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location information into AI, which can then propose the most suitable consultation content.

[0062] The presentation unit can analyze the user's past health data to select the optimal presentation method when presenting the ideal weight range. For example, the presentation unit can select the optimal presentation method for the ideal weight range based on the user's past health data. The presentation unit can analyze the user's past health data and propose the optimal presentation method. The presentation unit can adjust the frequency of presenting the ideal weight range based on the user's past health data. This allows the ideal weight range to be presented in the most optimal way based on the user's past health data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past health data into AI, and the AI ​​can select the optimal presentation method.

[0063] The presentation unit can prioritize the presentation of highly relevant data when presenting the ideal weight range, taking into account the user's geographical location information. For example, the presentation unit can propose the optimal method for presenting the ideal weight range based on the user's geographical location information. The presentation unit can prioritize the presentation of highly relevant data, taking into account the user's geographical location information. The presentation unit can adjust the frequency of presenting the optimal ideal weight range based on the user's geographical location information. This allows the presentation unit to present the optimal data based on the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location information into AI, which can then propose the optimal data presentation method.

[0064] The measurement unit can analyze the user's past health data to select the optimal analysis method when analyzing measurement results. For example, the measurement unit can select the optimal analysis method for measurement results based on the user's past health data. The measurement unit can analyze the user's past health data and propose the optimal analysis method. The measurement unit can adjust the frequency of analysis of measurement results based on the user's past health data. This allows the measurement results to be analyzed in the most optimal way based on the user's past health data. Some or all of the above processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past health data into AI, and the AI ​​can select the optimal analysis method.

[0065] The measurement unit can prioritize the analysis of highly relevant data by considering the user's geographical location information when analyzing measurement results. For example, the measurement unit can propose the optimal method for analyzing measurement results based on the user's geographical location information. The measurement unit can prioritize the analysis of highly relevant data by considering the user's geographical location information. The measurement unit can adjust the optimal frequency of measurement result analysis based on the user's geographical location information. This allows for the analysis of optimal data based on the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal data analysis method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The testing department can analyze the user's past health data and select the optimal testing method. For example, the testing department can select the optimal method for collecting saliva samples based on the user's past health data. The testing department can analyze the user's past health data and suggest the optimal testing kit. The testing department can adjust the frequency of testing based on the user's past health data. This allows the testing department to select the optimal testing method based on the user's past health data. Some or all of the above processes in the testing department may be performed using AI, for example, or not using AI. For example, the testing department can input the user's past health data into AI, which can then select the optimal testing method.

[0068] The testing unit can filter saliva samples based on the user's lifestyle and diet. For example, the testing unit can adjust the timing of saliva sample collection based on the user's diet. The testing unit can propose the optimal method of saliva sample collection, taking into account the user's lifestyle. The testing unit can filter saliva samples to improve their quality based on the user's diet and lifestyle. This allows for the collection of optimal saliva samples based on the user's lifestyle and diet. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's lifestyle and diet into AI, which can then propose the optimal method of saliva sample collection.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit can perform a detailed analysis based on important genetic data. The analysis unit can perform a simplified analysis based on less important genetic data. The analysis unit can adjust the level of detail of the analysis according to the importance of the genetic data. This allows the analysis to be performed at the optimal level of detail according to the importance of the genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the genetic data into the AI, and the AI ​​can perform the analysis at the optimal level of detail.

[0070] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit can apply a specific analysis algorithm to gene data related to metabolic characteristics. The analysis unit can apply a different analysis algorithm to gene data related to nutrient absorption efficiency. The analysis unit can apply a different analysis algorithm to gene data related to responsiveness to exercise. This allows the optimal analysis algorithm to be applied depending on the gene category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the gene category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0071] The proposal unit can adjust the level of detail in its proposals based on the importance of the health management plans. For example, the proposal unit can provide detailed proposals based on important health management plans. For example, it can provide concise proposals based on less important health management plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the health management plans. This allows the proposals to be made with the optimal level of detail according to the importance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health management plans into the AI, which can then make proposals with the optimal level of detail.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The testing department performs genetic testing using saliva samples. For example, the saliva sample provided by the user is tested using a genetic testing kit, and the user's genetic information is analyzed with high accuracy. Step 2: The analysis department analyzes the genetic test results obtained by the testing department. For example, they use AI to analyze the genetic test results and reveal an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Step 3: The proposal department proposes a health management plan based on the analysis results obtained by the analysis department. For example, using AI, it proposes an optimal meal plan and exercise regimen based on the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Step 4: The data collection unit collects data from regular blood tests and body composition measurements. For example, it collects data from blood tests and body composition measurements that the user performs regularly. Step 5: The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. For example, it uses AI to analyze the collected blood test and body composition measurement data and automatically adjusts the health management plan.

[0074] (Example of form 2) The next-generation personal healthcare system according to an embodiment of the present invention is a system that combines genetic testing and AI technology. In this system, the user provides a saliva sample for genetic testing. Next, the AI ​​analyzes the test results to reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Based on this, it proposes an optimal diet plan and exercise regimen. Furthermore, the AI ​​analyzes the results of regular blood tests and body composition measurements to automatically adjust the health management plan. This allows the user to maintain a healthy weight suited to their body type and promote overall health improvement. For example, the user provides a saliva sample for genetic testing. Next, the AI ​​analyzes the test results to reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Based on this, it proposes an optimal diet plan and exercise regimen. Furthermore, the AI ​​analyzes the results of regular blood tests and body composition measurements to automatically adjust the health management plan. This allows the user to maintain a healthy weight suited to their body type and promote overall health improvement. Online consultation services with genetic counselors and nutrition specialists are also provided. Genetic counselors provide explanations of genetic test results and advice on health management based on genetic information, while nutrition specialists create personalized meal plans and answer nutritional questions. This allows users to achieve more precise health management and support a healthy lifestyle. As a result, next-generation personal healthcare systems can provide individualized health management plans based on users' genetic information and health data, supporting overall health improvement.

[0075] The next-generation personal healthcare system according to this embodiment comprises a testing unit, an analysis unit, a proposal unit, a collection unit, and a processing unit. The testing unit performs genetic testing using a saliva sample. For example, the testing unit tests a saliva sample provided by the user using a genetic testing kit. The testing unit can analyze the user's genetic information using the genetic testing kit. For example, the testing unit can analyze the user's genetic information with high accuracy using the genetic testing kit. The analysis unit analyzes the genetic test results obtained by the testing unit. For example, the analysis unit uses AI to analyze the genetic test results and reveal the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The analysis unit can use AI to analyze the individual's metabolic characteristics based on the genetic test results. The analysis unit can use AI to analyze the nutrient absorption efficiency based on the genetic test results. The analysis unit can use AI to analyze responsiveness to exercise based on the genetic test results. The proposal unit proposes a health management plan based on the analysis results obtained by the analysis unit. The suggestion unit, for example, uses AI to suggest an optimal meal plan and exercise regimen based on an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The suggestion unit can suggest an optimal meal plan based on an individual's metabolic characteristics using AI. The suggestion unit can suggest an optimal meal plan based on nutrient absorption efficiency using AI. The suggestion unit can suggest an optimal exercise regimen based on responsiveness to exercise using AI. The collection unit collects data from regular blood tests and body composition measurements. The collection unit collects data from regular blood tests and body composition measurements performed by the user, for example. The collection unit can collect blood test data. The collection unit can collect body composition measurement data. The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. The adjustment unit can analyze the collected data using AI and automatically adjust the health management plan. The adjustment unit can analyze the collected blood test data using AI and adjust the health management plan. The adjustment unit can analyze the collected body composition measurement data using AI and adjust the health management plan.As a result, the next-generation personal healthcare system according to this embodiment can provide an individualized health management plan based on the user's genetic information and health data, and support overall health promotion.

[0076] The testing department performs genetic testing using saliva samples. For example, the testing department tests saliva samples provided by users using a genetic testing kit. Specifically, users collect saliva samples using a dedicated genetic testing kit and send them to the testing department. The testing department first receives the saliva samples, processes them appropriately, and then runs them through a genetic analysis device. The genetic analysis device uses next-generation sequencing technology to analyze the user's genetic information with high accuracy. This analysis process involves DNA extraction, amplification, and sequencing, and the obtained data is stored in digital format. Furthermore, the testing department registers the analysis results in a database and manages them for each user. This allows the testing department to analyze users' genetic information with high accuracy and use it for individualized health management.

[0077] The Analysis Department analyzes the genetic test results obtained by the Testing Department. For example, the Analysis Department uses AI to analyze the genetic test results, revealing an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness. Specifically, based on the genetic test results, the AI ​​references a vast dataset to analyze the user's metabolic characteristics and evaluates how specific gene mutations affect metabolism. For example, if a specific gene mutation affects fat metabolism, this information can be used to determine what kind of diet the user should follow. Regarding nutrient absorption efficiency, the AI ​​analyzes genetic information to identify gene mutations involved in vitamin and mineral absorption. This clarifies which nutrients the user should supplement. Furthermore, regarding exercise responsiveness, the AI ​​analyzes genetic information to evaluate gene mutations involved in muscle growth and recovery. This provides the foundational data for proposing an optimal exercise plan to the user. The Analysis Department comprehensively evaluates these analysis results to gain a detailed understanding of the user's health status.

[0078] The Proposal Department proposes health management plans based on the analysis results obtained by the Analysis Department. For example, the Proposal Department uses AI to propose optimal meal plans and exercise regimens based on an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Specifically, the AI ​​creates a meal plan tailored to the user's metabolic characteristics based on the data provided by the Analysis Department. For example, it recommends a low-fat diet for users with slow fat metabolism and suggests a diet high in carbohydrates for users with fast carbohydrate metabolism. It also suggests supplements to replenish specific vitamins and minerals based on nutrient absorption efficiency. Furthermore, it proposes the optimal exercise regimen for the user based on their responsiveness to exercise. For example, it suggests a plan focusing on strength training for users with slow muscle growth and a plan centered on aerobic exercise for users with high endurance. The Proposal Department presents these suggestions to the user in an easy-to-understand and implement format. This allows users to implement a health management plan that is best suited to them.

[0079] The data collection unit collects data from regular blood tests and body composition measurements. Specifically, users regularly undergo blood tests at medical institutions and provide the results to the data collection unit. Users also use home body composition analyzers to measure data such as body fat percentage, muscle mass, and bone density, and send this data to the data collection unit. The data collection unit centrally manages this data and registers it in a database for each user. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. For example, if a user's health condition changes rapidly, the data collection unit can increase the frequency of data collection to collect more detailed data. This allows the data collection unit to continuously monitor the user's health condition and provide appropriate data.

[0080] The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. For example, the adjustment unit uses AI to analyze the collected data and automatically adjust the health management plan. Specifically, the AI ​​analyzes collected blood test data to detect changes in the user's health status. For example, if blood glucose or cholesterol levels exceed standard values, the AI ​​revises the meal plan and adjusts carbohydrate and fat intake. It also analyzes body composition measurement data and adjusts the exercise plan according to changes in muscle mass and body fat percentage. For example, if muscle mass is decreasing, it suggests a plan to increase the frequency and intensity of strength training. The adjustment unit makes these adjustments in real time, providing the user with the optimal health management plan. Furthermore, the adjustment unit can collect user feedback and continuously improve the accuracy and effectiveness of the plan. This allows the adjustment unit to respond flexibly to the user's health condition and support overall health promotion.

[0081] The consultation service provides online consultation services with genetic counselors and nutrition specialists. For example, the consultation service provides a platform where users can consult with genetic counselors and nutrition specialists online. Genetic counselors can provide explanations of genetic test results and advice on health management based on genetic information. Nutrition specialists can create personalized meal plans and answer nutrition-related questions. For example, users can consult with a genetic counselor online and receive explanations of their genetic test results. Users can consult with a nutrition specialist online and request the creation of a personalized meal plan. This allows users to receive expert advice and manage their health more precisely. Some or all of the above processes in the consultation service may be performed using AI, or not. For example, the consultation service can input the user's consultation details into an AI, which can then generate appropriate advice.

[0082] The display unit presents an ideal weight range. The display unit calculates and presents the ideal weight range to the user, for example, based on the user's genetic test results. The display unit can consider the user's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise in order to calculate the ideal weight range. The display unit can calculate the ideal weight range based on the user's metabolic characteristics, for example. The display unit can calculate the ideal weight range based on nutrient absorption efficiency. The display unit can calculate the ideal weight range based on responsiveness to exercise. This allows the user to understand a healthy weight appropriate for their body type and avoid excessive thinness. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's genetic test results into AI, which can then calculate the ideal weight range.

[0083] The measurement unit analyzes the results of regular blood tests and body composition measurements. For example, the measurement unit collects and analyzes data from blood tests and body composition measurements that the user regularly performs. The measurement unit can analyze blood test data. The measurement unit can analyze body composition measurement data. For example, the measurement unit evaluates the user's health status based on blood test data. The measurement unit can evaluate the user's health status based on body composition measurement data. This allows for a comprehensive evaluation of genetic information and current health status, and adjustment of the health management plan. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input collected blood test data into AI, which can then analyze the data.

[0084] The analysis department can reveal an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. For example, the analysis department can analyze an individual's metabolic characteristics based on genetic test results. The analysis department can analyze nutrient absorption efficiency based on genetic test results. The analysis department can analyze responsiveness to exercise based on genetic test results. This allows for the provision of an optimal health management plan based on the individual's constitution. Some or all of the above-described processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input genetic test results into AI, which can then analyze the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise.

[0085] The suggestion unit can propose an optimal meal plan and exercise regimen. For example, the suggestion unit can propose an optimal meal plan and exercise regimen based on an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness. The suggestion unit can propose an optimal meal plan based on an individual's metabolic characteristics. The suggestion unit can propose an optimal meal plan based on nutrient absorption efficiency. The suggestion unit can propose an optimal exercise regimen based on exercise responsiveness. This allows for the provision of a health management plan tailored to the user's constitution. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input an individual's metabolic characteristics, nutrient absorption efficiency, and exercise responsiveness into AI, which can then propose an optimal meal plan and exercise regimen.

[0086] The testing unit can estimate the user's emotions and adjust the timing of saliva sample collection based on the estimated emotions. For example, if the user is relaxed, the testing unit can select a suitable time to collect the saliva sample. If the user is stressed, the testing unit can provide a relaxing environment and then collect the saliva sample. If the user is in a hurry, the testing unit can quickly collect the saliva sample. This allows for the collection of a saliva sample at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI or not. For example, the testing unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of saliva sample collection.

[0087] The testing department can analyze the user's past health data and select the optimal testing method. For example, the testing department can select the optimal method for collecting saliva samples based on the user's past health data. The testing department can analyze the user's past health data and suggest the optimal testing kit. The testing department can adjust the frequency of testing based on the user's past health data. This allows the testing department to select the optimal testing method based on the user's past health data. Some or all of the above processes in the testing department may be performed using AI, for example, or not using AI. For example, the testing department can input the user's past health data into AI, which can then select the optimal testing method.

[0088] The testing unit can filter saliva samples based on the user's lifestyle and diet. For example, the testing unit can adjust the timing of saliva sample collection based on the user's diet. The testing unit can propose the optimal method of saliva sample collection, taking into account the user's lifestyle. The testing unit can filter saliva samples to improve their quality based on the user's diet and lifestyle. This allows for the collection of optimal saliva samples based on the user's lifestyle and diet. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's lifestyle and diet into AI, which can then propose the optimal method of saliva sample collection.

[0089] The testing unit can estimate the user's emotions and prioritize test results based on the estimated emotions. For example, if the user is feeling anxious, the testing unit can prioritize important test results. If the user is relaxed, the testing unit can provide detailed test results. If the user is in a hurry, the testing unit can prioritize providing concise test results. This allows the system to prioritize test results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the testing unit may be performed using AI or not using AI. For example, the testing unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of test results.

[0090] The testing unit can prioritize acquiring highly relevant data by considering the user's geographical location information when collecting saliva samples. For example, the testing unit can suggest the optimal saliva sample collection location based on the user's geographical location information. The testing unit can prioritize acquiring highly relevant data by considering the user's geographical location information. The testing unit can suggest the optimal test kit based on the user's geographical location information. This allows for the acquisition of optimal data based on the user's geographical location information. Some or all of the above processing in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's geographical location information into AI, which can then suggest the optimal saliva sample collection location.

[0091] The testing unit can analyze the user's social media activity and obtain relevant data when collecting saliva samples. For example, the testing unit can suggest the optimal timing for saliva sample collection based on the user's social media activity. The testing unit can analyze the user's social media activity and obtain relevant data. The testing unit can suggest the optimal testing method based on the user's social media activity. This allows for the acquisition of optimal data based on the user's social media activity. Some or all of the above processing in the testing unit may be performed using AI, or not. For example, the testing unit can input the user's social media activity into AI, which can then suggest the optimal timing for saliva sample collection.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is feeling anxious, the analysis unit can provide concise and easy-to-understand analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. This allows the analysis results to be presented in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the presentation of the analysis.

[0093] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit can perform a detailed analysis based on important genetic data. The analysis unit can perform a simplified analysis based on less important genetic data. The analysis unit can adjust the level of detail of the analysis according to the importance of the genetic data. This allows the analysis to be performed at the optimal level of detail according to the importance of the genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the genetic data into the AI, and the AI ​​can perform the analysis at the optimal level of detail.

[0094] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit can apply a specific analysis algorithm to gene data related to metabolic characteristics. The analysis unit can apply a different analysis algorithm to gene data related to nutrient absorption efficiency. The analysis unit can apply a different analysis algorithm to gene data related to responsiveness to exercise. This allows the optimal analysis algorithm to be applied depending on the gene category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the gene category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0095] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide a detailed analysis. If the user is in a hurry, the analysis unit can provide a concise analysis. If the user is feeling anxious, the analysis unit can provide a to-the-point analysis. This allows the analysis to be provided at an optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the analysis.

[0096] The analysis unit can determine the priority of analysis based on when the genetic data was acquired. For example, the analysis unit may prioritize the analysis of the most recent genetic data. The analysis unit can also analyze the latest data while referring to past genetic data. The analysis unit can determine the priority of analysis based on when the genetic data was acquired. This allows for analysis to be performed with the optimal priority based on when the genetic data was acquired. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the genetic data acquisition date into the AI, and the AI ​​can perform analysis with the optimal priority.

[0097] The analysis unit can adjust the order of analysis based on the relevance of the genetic data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant genetic data. The analysis unit can postpone the analysis of less relevant genetic data. The analysis unit can adjust the order of analysis based on the relevance of the genetic data. This allows the analysis to be performed in the optimal order based on the relevance of the genetic data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the genetic data into the AI, which can then perform the analysis in the optimal order.

[0098] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is feeling anxious, the suggestion unit can provide concise and easy-to-understand suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to provide suggestions in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way suggestions are presented.

[0099] The proposal unit can adjust the level of detail in its proposals based on the importance of the health management plans. For example, the proposal unit can provide detailed proposals based on important health management plans. For example, it can provide concise proposals based on less important health management plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the health management plans. This allows the proposals to be made with the optimal level of detail according to the importance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health management plans into the AI, which can then make proposals with the optimal level of detail.

[0100] The proposal unit can apply different proposal algorithms depending on the category of the health management plan when making a proposal. For example, the proposal unit can apply a specific proposal algorithm to proposals related to meal plans. For example, the proposal unit can apply a different proposal algorithm to proposals related to exercise regimens. The proposal unit can apply different proposal algorithms depending on the category of the health management plan. This allows the optimal proposal algorithm to be applied according to the category of the health management plan. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the category of the health management plan into the AI, and the AI ​​can apply the optimal proposal algorithm.

[0101] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is feeling anxious, the suggestion unit can provide concise suggestions. This allows for the provision of suggestions of the optimal length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the length of the suggestions.

[0102] The proposal department can determine the priority of proposals based on the submission timing of the health management plan. For example, the proposal department may prioritize the most recent health management plan. The proposal department can also propose the latest plan while referring to past health management plans. The proposal department can determine the priority of proposals based on the submission timing of the health management plan. This allows for proposals to be made with the optimal priority based on the submission timing of the health management plan. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the submission timing of the health management plan into the AI, and the AI ​​can make proposals with the optimal priority.

[0103] The proposal unit can adjust the order of proposals based on the relevance of the health management plans. For example, the proposal unit can prioritize proposing highly relevant health management plans. The proposal unit can postpone proposing less relevant health management plans. The proposal unit can adjust the order of proposals based on the relevance of the health management plans. This allows proposals to be made in the optimal order based on the relevance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of the health management plans into the AI, and the AI ​​can make proposals in the optimal order.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of blood test and body composition measurement data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can select the timing for blood test and body composition measurement data collection. If the user is stressed, the data collection unit can provide a relaxing environment and collect data. If the user is in a hurry, the data collection unit can collect data quickly. This allows data to be collected at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the data collection timing.

[0105] The data collection unit can analyze the user's past health data and select the optimal data collection method. For example, the data collection unit can select the optimal blood test method based on the user's past health data. The data collection unit can analyze the user's past health data and suggest the optimal body composition measurement method. The data collection unit can adjust the frequency of data collection based on the user's past health data. This allows the optimal data collection method to be selected based on the user's past health data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health data into AI, which can then select the optimal data collection method.

[0106] The data collection unit can filter data collected from blood tests and body composition measurements based on the user's lifestyle and diet. For example, the data collection unit can adjust the timing of blood tests based on the user's diet. The data collection unit can consider the user's lifestyle and suggest the optimal method for measuring body composition. The data collection unit can filter data to improve the quality of data collection based on the user's diet and lifestyle. This allows for the collection of optimal data based on the user's lifestyle and diet. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's lifestyle and diet into AI, which can then suggest the optimal data collection method.

[0107] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can collect detailed data. If the user is in a hurry, the data collection unit can prioritize collecting concise data. This allows data to be collected with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of data to collect.

[0108] The data collection unit can prioritize the collection of highly relevant data when collecting data from blood tests and body composition measurements, taking into account the user's geographical location. For example, the data collection unit can suggest the optimal location for blood tests based on the user's geographical location. The data collection unit can prioritize the collection of highly relevant data, taking into account the user's geographical location. The data collection unit can suggest the optimal location for body composition measurements based on the user's geographical location. This allows for the collection of optimal data based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then suggest the optimal data collection method.

[0109] The data collection unit can analyze the user's social media activity and collect relevant data when collecting data for blood tests and body composition measurements. For example, the data collection unit can suggest the optimal timing for blood tests based on the user's social media activity. The data collection unit can analyze the user's social media activity and collect relevant data. The data collection unit can suggest the optimal method for measuring body composition based on the user's social media activity. This allows for the collection of optimal data based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then suggest the optimal data collection method.

[0110] The adjustment unit can estimate the user's emotions and adjust the method of adjusting the health management plan based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed health management plan. If the user is feeling anxious, the adjustment unit can provide a concise and easy-to-understand health management plan. If the user is in a hurry, the adjustment unit can provide a concise health management plan. This allows the health management plan to be adjusted in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the method of adjusting the health management plan can be adjusted.

[0111] The adjustment unit can analyze the user's past health data and select the optimal adjustment method when adjusting the health management plan. For example, the adjustment unit can select the optimal adjustment method for the health management plan based on the user's past health data. The adjustment unit can analyze the user's past health data and propose the optimal adjustment method. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's past health data. This allows the health management plan to be adjusted in the most optimal way based on the user's past health data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's past health data into AI, and the AI ​​can select the optimal adjustment method.

[0112] The adjustment unit can customize the means of adjustment when adjusting the health management plan based on the user's current lifestyle. For example, the adjustment unit can propose the optimal method of adjusting the health management plan based on the user's current lifestyle. The adjustment unit can customize the means of adjustment, taking into account the user's current lifestyle. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's current lifestyle. This allows the health management plan to be adjusted in the most optimal way based on the user's current lifestyle. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's current lifestyle into AI, which can then propose the optimal adjustment method.

[0113] The adjustment unit can estimate the user's emotions and determine the priority of adjustments to the health management plan based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit will prioritize important adjustments. If the user is relaxed, the adjustment unit can perform detailed adjustments. If the user is in a hurry, the adjustment unit can prioritize concise adjustments. This allows the health management plan to be adjusted with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of adjustments to the health management plan.

[0114] The adjustment unit can select the optimal adjustment method when adjusting the health management plan, taking into account the user's geographical location information. For example, the adjustment unit can propose the optimal health management plan adjustment method based on the user's geographical location information. The adjustment unit can select the optimal adjustment method, taking into account the user's geographical location information. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's geographical location information. This allows the health management plan to be adjusted in the most optimal way based on the user's geographical location information. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal adjustment method.

[0115] The adjustment unit can analyze the user's social media activity and propose adjustment methods when adjusting the health management plan. For example, the adjustment unit can propose the optimal method for adjusting the health management plan based on the user's social media activity. The adjustment unit can analyze the user's social media activity and propose adjustment methods. The adjustment unit can adjust the frequency of health management plan adjustments based on the user's social media activity. This allows the health management plan to be adjusted in the most optimal way based on the user's social media activity. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's social media activity into AI, and the AI ​​can propose the optimal adjustment method.

[0116] The consultation unit can estimate the user's emotions and adjust the timing of the online consultation based on the estimated emotions. For example, if the user is relaxed, the consultation unit can select a suitable time for the online consultation. If the user is stressed, the consultation unit can provide a relaxing environment and conduct the online consultation. If the user is in a hurry, the consultation unit can conduct the online consultation quickly. This allows for online consultations to be conducted at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of the online consultation.

[0117] The consultation department can analyze a user's past consultation history and select the most suitable consultation method. For example, the consultation department can select the most suitable online consultation method based on the user's past consultation history. The consultation department can analyze a user's past consultation history and propose the most suitable consultation method. The consultation department can adjust the frequency of online consultations based on the user's past consultation history. This allows online consultations to be conducted in the most optimal way based on the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's past consultation history into AI, which can then select the most suitable consultation method.

[0118] The consultation unit can estimate the user's emotions and prioritize consultation topics based on the estimated emotions. For example, if the user is feeling anxious, the consultation unit will prioritize providing important consultation topics. If the user is relaxed, the consultation unit can provide detailed consultation topics. If the user is in a hurry, the consultation unit can prioritize providing concise consultation topics. This allows for the provision of consultation topics with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI, or not using AI. For example, the consultation unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of consultation topics.

[0119] The consultation department can prioritize providing highly relevant consultation content during online consultations, taking into account the user's geographical location. For example, the consultation department can propose the most suitable online consultation content based on the user's geographical location. The consultation department can prioritize providing highly relevant consultation content, taking into account the user's geographical location. The consultation department can propose the most suitable consultation method based on the user's geographical location. This allows the consultation department to provide the most suitable consultation content based on the user's geographical location. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the user's geographical location information into AI, which can then propose the most suitable consultation content.

[0120] The presentation unit can estimate the user's emotions and adjust the presentation method of the ideal weight range based on the estimated emotions. For example, if the user is relaxed, the presentation unit can provide a detailed ideal weight range. If the user is feeling anxious, the presentation unit can provide a concise and easy-to-understand ideal weight range. If the user is in a hurry, the presentation unit can provide a concise ideal weight range. This allows the ideal weight range to be presented in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the presentation method of the ideal weight range.

[0121] The presentation unit can analyze the user's past health data to select the optimal presentation method when presenting the ideal weight range. For example, the presentation unit can select the optimal presentation method for the ideal weight range based on the user's past health data. The presentation unit can analyze the user's past health data and propose the optimal presentation method. The presentation unit can adjust the frequency of presenting the ideal weight range based on the user's past health data. This allows the ideal weight range to be presented in the most optimal way based on the user's past health data. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past health data into AI, and the AI ​​can select the optimal presentation method.

[0122] The presentation unit can estimate the user's emotions and determine the priority of ideal weight ranges based on the estimated emotions. For example, if the user is feeling anxious, the presentation unit can prioritize providing important ideal weight ranges. If the user is relaxed, the presentation unit can provide detailed ideal weight ranges. If the user is in a hurry, the presentation unit can prioritize providing concise ideal weight ranges. This allows the ideal weight ranges to be presented with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of ideal weight ranges.

[0123] The presentation unit can prioritize the presentation of highly relevant data when presenting the ideal weight range, taking into account the user's geographical location information. For example, the presentation unit can propose the optimal method for presenting the ideal weight range based on the user's geographical location information. The presentation unit can prioritize the presentation of highly relevant data, taking into account the user's geographical location information. The presentation unit can adjust the frequency of presenting the optimal ideal weight range based on the user's geographical location information. This allows the presentation unit to present the optimal data based on the user's geographical location information. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's geographical location information into AI, which can then propose the optimal data presentation method.

[0124] The measurement unit can estimate the user's emotions and adjust the analysis method of the measurement results based on the estimated user emotions. For example, if the user is relaxed, the measurement unit can provide detailed measurement results. If the user is feeling anxious, the measurement unit can provide concise and easy-to-understand measurement results. If the user is in a hurry, the measurement unit can provide concise measurement results. This allows the measurement results to be analyzed in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or not using AI. For example, the measurement unit can input user emotion data into a generative AI, which can estimate emotions and adjust the analysis method of the measurement results.

[0125] The measurement unit can analyze the user's past health data to select the optimal analysis method when analyzing measurement results. For example, the measurement unit can select the optimal analysis method for measurement results based on the user's past health data. The measurement unit can analyze the user's past health data and propose the optimal analysis method. The measurement unit can adjust the frequency of analysis of measurement results based on the user's past health data. This allows the measurement results to be analyzed in the most optimal way based on the user's past health data. Some or all of the above processes in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's past health data into AI, and the AI ​​can select the optimal analysis method.

[0126] The measurement unit can estimate the user's emotions and determine the priority of measurement results based on the estimated user emotions. For example, if the user is feeling anxious, the measurement unit can prioritize providing important measurement results. If the user is relaxed, the measurement unit can provide detailed measurement results. If the user is in a hurry, the measurement unit can prioritize providing concise measurement results. This allows the measurement results to be provided with the optimal priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the measurement unit may be performed using AI, for example, or not using AI. For example, the measurement unit can input user emotion data into a generative AI, which can estimate emotions and determine the priority of measurement results.

[0127] The measurement unit can prioritize the analysis of highly relevant data by considering the user's geographical location information when analyzing measurement results. For example, the measurement unit can propose the optimal method for analyzing measurement results based on the user's geographical location information. The measurement unit can prioritize the analysis of highly relevant data by considering the user's geographical location information. The measurement unit can adjust the optimal frequency of measurement result analysis based on the user's geographical location information. This allows for the analysis of optimal data based on the user's geographical location information. Some or all of the above processing in the measurement unit may be performed using AI, for example, or without AI. For example, the measurement unit can input the user's geographical location information into AI, and the AI ​​can propose the optimal data analysis method.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is feeling anxious, the suggestion unit can provide concise and easy-to-understand suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. This allows the suggestion unit to provide suggestions in the most appropriate way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the way suggestions are presented.

[0130] The data collection unit can estimate the user's emotions and adjust the timing of blood test and body composition measurement data collection based on the estimated emotions. For example, if the user is relaxed, the data collection unit can select the timing for blood test and body composition measurement data collection. If the user is stressed, the data collection unit can provide a relaxing environment and collect data. If the user is in a hurry, the data collection unit can collect data quickly. This allows data to be collected at the optimal timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input the user's emotion data into a generative AI, which can estimate the emotions and adjust the data collection timing.

[0131] The adjustment unit can estimate the user's emotions and adjust the method of adjusting the health management plan based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit can provide a detailed health management plan. If the user is feeling anxious, the adjustment unit can provide a concise and easy-to-understand health management plan. If the user is in a hurry, the adjustment unit can provide a concise health management plan. This allows the health management plan to be adjusted in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI, the generative AI can estimate the emotions, and the method of adjusting the health management plan can be adjusted.

[0132] The consultation unit can estimate the user's emotions and adjust the timing of the online consultation based on the estimated emotions. For example, if the user is relaxed, the consultation unit can select a suitable time for the online consultation. If the user is stressed, the consultation unit can provide a relaxing environment and conduct the online consultation. If the user is in a hurry, the consultation unit can conduct the online consultation quickly. This allows for online consultations to be conducted at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not. For example, the consultation unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of the online consultation.

[0133] The presentation unit can estimate the user's emotions and adjust the presentation method of the ideal weight range based on the estimated emotions. For example, if the user is relaxed, the presentation unit can provide a detailed ideal weight range. If the user is feeling anxious, the presentation unit can provide a concise and easy-to-understand ideal weight range. If the user is in a hurry, the presentation unit can provide a concise ideal weight range. This allows the ideal weight range to be presented in the most optimal way according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the presentation method of the ideal weight range.

[0134] The testing department can analyze the user's past health data and select the optimal testing method. For example, the testing department can select the optimal method for collecting saliva samples based on the user's past health data. The testing department can analyze the user's past health data and suggest the optimal testing kit. The testing department can adjust the frequency of testing based on the user's past health data. This allows the testing department to select the optimal testing method based on the user's past health data. Some or all of the above processes in the testing department may be performed using AI, for example, or not using AI. For example, the testing department can input the user's past health data into AI, which can then select the optimal testing method.

[0135] The testing unit can filter saliva samples based on the user's lifestyle and diet. For example, the testing unit can adjust the timing of saliva sample collection based on the user's diet. The testing unit can propose the optimal method of saliva sample collection, taking into account the user's lifestyle. The testing unit can filter saliva samples to improve their quality based on the user's diet and lifestyle. This allows for the collection of optimal saliva samples based on the user's lifestyle and diet. Some or all of the above processes in the testing unit may be performed using AI, for example, or without AI. For example, the testing unit can input the user's lifestyle and diet into AI, which can then propose the optimal method of saliva sample collection.

[0136] The analysis unit can adjust the level of detail of the analysis based on the importance of the genetic data during the analysis. For example, the analysis unit can perform a detailed analysis based on important genetic data. The analysis unit can perform a simplified analysis based on less important genetic data. The analysis unit can adjust the level of detail of the analysis according to the importance of the genetic data. This allows the analysis to be performed at the optimal level of detail according to the importance of the genetic data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the genetic data into the AI, and the AI ​​can perform the analysis at the optimal level of detail.

[0137] The analysis unit can apply different analysis algorithms depending on the gene category during analysis. For example, the analysis unit can apply a specific analysis algorithm to gene data related to metabolic characteristics. The analysis unit can apply a different analysis algorithm to gene data related to nutrient absorption efficiency. The analysis unit can apply a different analysis algorithm to gene data related to responsiveness to exercise. This allows the optimal analysis algorithm to be applied depending on the gene category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the gene category into the AI, and the AI ​​can apply the optimal analysis algorithm.

[0138] The proposal unit can adjust the level of detail in its proposals based on the importance of the health management plans. For example, the proposal unit can provide detailed proposals based on important health management plans. For example, it can provide concise proposals based on less important health management plans. The proposal unit can adjust the level of detail in its proposals according to the importance of the health management plans. This allows the proposals to be made with the optimal level of detail according to the importance of the health management plans. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the health management plans into the AI, which can then make proposals with the optimal level of detail.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The testing department performs genetic testing using saliva samples. For example, the saliva sample provided by the user is tested using a genetic testing kit, and the user's genetic information is analyzed with high accuracy. Step 2: The analysis department analyzes the genetic test results obtained by the testing department. For example, they use AI to analyze the genetic test results and reveal an individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Step 3: The proposal department proposes a health management plan based on the analysis results obtained by the analysis department. For example, using AI, it proposes an optimal meal plan and exercise regimen based on the individual's metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. Step 4: The data collection unit collects data from regular blood tests and body composition measurements. For example, it collects data from blood tests and body composition measurements that the user performs regularly. Step 5: The adjustment unit automatically adjusts the health management plan based on the data collected by the collection unit. For example, it uses AI to analyze the collected blood test and body composition measurement data and automatically adjusts the health management plan.

[0141] 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.

[0142] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0143] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the testing unit, analysis unit, proposal unit, collection unit, adjustment unit, consultation unit, presentation unit, and measurement unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the testing unit acquires a saliva sample using the camera 42 and microphone 38B of the smart device 14 and performs genetic testing using the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the genetic test results using AI. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes an optimal diet plan and exercise regimen using AI. The collection unit collects data from regular blood tests and body composition measurements using, for example, the camera 42 and microphone 38B of the smart device 14. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data and automatically adjusts the health management plan. The consultation unit provides online consultation services using, for example, the control unit 46A of the smart device 14. The presentation unit, for example, uses the display 40A of the smart device 14 to display the ideal weight range. The measurement unit, for example, uses the camera 42 and microphone 38B of the smart device 14 to analyze data from blood tests and body composition measurements. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0150] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0153] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the testing unit, analysis unit, proposal unit, collection unit, adjustment unit, consultation unit, presentation unit, and measurement unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the testing unit acquires a saliva sample using the camera 42 and microphone 238 of the smart glasses 214 and performs genetic testing using the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the genetic test results using AI. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and proposes an optimal diet plan and exercise regimen using AI. The collection unit collects data from regular blood tests and body composition measurements using, for example, the camera 42 and microphone 238 of the smart glasses 214. The adjustment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to automatically adjust the health management plan. The consultation unit provides online consultation services using, for example, the control unit 46A of the smart glasses 214. The display unit, for example, uses the display of the smart glasses 214 to show the ideal weight range. The measurement unit, for example, uses the camera 42 and microphone 238 of the smart glasses 214 to analyze data from blood tests and body composition measurements. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0166] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] 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.

[0168] 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.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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.

[0174] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the testing unit, analysis unit, proposal unit, collection unit, adjustment unit, consultation unit, presentation unit, and measurement unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the testing unit acquires a saliva sample using the camera 42 and microphone 238 of the headset terminal 314 and performs genetic testing using the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the genetic test results using AI. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes an optimal diet plan and exercise regimen using AI. The collection unit collects data from regular blood tests and body composition measurements using, for example, the camera 42 and microphone 238 of the headset terminal 314. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to automatically adjust the health management plan. The consultation unit provides online consultation services using, for example, the control unit 46A of the headset terminal 314. The presentation unit, for example, uses the display 343 of the headset terminal 314 to display the ideal weight range. The measurement unit, for example, uses the camera 42 and microphone 238 of the headset terminal 314 to analyze data from blood tests and body composition measurements. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0180] 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.

[0181] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0182] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0183] 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.

[0184] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0185] 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.

[0186] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0187] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0188] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0189] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0190] 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.

[0191] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0192] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0193] Each of the multiple elements described above, including the testing unit, analysis unit, proposal unit, collection unit, adjustment unit, consultation unit, presentation unit, and measurement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the testing unit acquires a saliva sample using the camera 42 and microphone 238 of the robot 414 and performs genetic testing using the control unit 46A. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the genetic test results using AI. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and proposes an optimal diet plan and exercise regimen using AI. The collection unit collects data from regular blood tests and body composition measurements using, for example, the camera 42 and microphone 238 of the robot 414. The adjustment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to automatically adjust the health management plan. The consultation unit provides online consultation services using, for example, the control unit 46A of the robot 414. The presentation unit, for example, uses the display of the robot 414 to present the ideal weight range. The measurement unit, for example, uses the camera 42 and microphone 238 of the robot 414 to analyze data from blood tests and body composition measurements. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0194] 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.

[0195] Figure 9 shows the 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.

[0196] 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.

[0197] 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.

[0198] 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, and motorcycles, 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 based, for example, 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.

[0199] 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."

[0200] 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.

[0201] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0210] 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 other things 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.

[0211] 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.

[0212] (Note 1) The laboratory performs genetic testing using saliva samples, An analysis unit that analyzes the genetic test results obtained by the aforementioned testing unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a health management plan. A data collection unit that collects data from regular blood tests and body composition measurements, The system includes an adjustment unit that automatically adjusts the health management plan based on the data collected by the aforementioned collection unit. A system characterized by the following features. (Note 2) The facility includes a consultation department that provides online consultation services with genetic counselors and nutrition specialists. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a display unit that shows the ideal weight range. The system described in Appendix 1, characterized by the features described herein. (Note 4) It is equipped with a measurement unit that analyzes the results of regular blood tests and body composition measurements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is To clarify individual metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We propose the optimal meal plan and exercise regimen. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned inspection unit is The system estimates the user's emotions and adjusts the timing of saliva sample collection based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned inspection unit is Analyze the user's past health data to select the most suitable testing method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned inspection unit is When collecting saliva samples, filtering is performed based on the user's lifestyle and diet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned inspection unit is It estimates the user's emotions and prioritizes the test results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned inspection unit is When collecting saliva samples, the system prioritizes obtaining highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned inspection unit is When collecting saliva samples, we analyze the user's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the genetic data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the gene category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, the priority of the analysis is determined based on when the genetic data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the genetic data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the health management plan will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the health management plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of data collection for blood tests and body composition measurements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned collection unit is Analyze the user's past health data and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned collection unit is When collecting data from blood tests and body composition measurements, filtering is performed based on the user's lifestyle and diet. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned collection unit is When collecting data from blood tests and body composition measurements, the system prioritizes the collection of highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned collection unit is When collecting data from blood tests and body composition measurements, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, The system estimates the user's emotions and adjusts the health management plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, When adjusting a health management plan, the system analyzes the user's past health data to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, When adjusting a health management plan, customize the adjustment methods based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, The system estimates the user's emotions and determines the priority of adjustments to the health management plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When adjusting the health management plan, the optimal adjustment method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, When adjusting health management plans, we analyze users' social media activity and suggest ways to make adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned consultation department, The system estimates the user's emotions and adjusts the timing of online consultations based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned consultation department, Analyze the user's past consultation history and select the most suitable consultation method. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned consultation department, The system estimates the user's emotions and prioritizes the consultation topics based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned consultation department, During online consultations, the system prioritizes providing highly relevant consultation topics by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned display unit is, The system estimates the user's emotions and adjusts how the ideal weight range is presented based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned display unit is, When presenting an ideal weight range, the system analyzes the user's past health data to select the most appropriate presentation method. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned display unit is, The system estimates the user's emotions and determines the priority of ideal weight ranges based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned display unit is, When presenting an ideal weight range, the system prioritizes displaying highly relevant data by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned measuring unit is We estimate the user's emotions and adjust the analysis method of the measurement results based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned measuring unit is When analyzing measurement results, the system analyzes the user's past health data to select the most suitable analysis method. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned measuring unit is It estimates the user's emotions and prioritizes the measurement results based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned measuring unit is When analyzing measurement results, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0213] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The laboratory performs genetic testing using saliva samples, An analysis unit that analyzes the genetic test results obtained by the aforementioned testing unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a health management plan. A data collection unit that collects data from regular blood tests and body composition measurements, The system includes an adjustment unit that automatically adjusts the health management plan based on the data collected by the aforementioned collection unit. A system characterized by the following features.

2. The facility includes a consultation department that provides online consultation services with genetic counselors and nutrition specialists. The system according to feature 1.

3. It includes a display unit that shows the ideal weight range. The system according to feature 1.

4. It is equipped with a measurement unit that analyzes the results of regular blood tests and body composition measurements. The system according to feature 1.

5. The aforementioned analysis unit is To clarify individual metabolic characteristics, nutrient absorption efficiency, and responsiveness to exercise. The system according to feature 1.

6. The aforementioned proposal section is, We propose the optimal meal plan and exercise regimen. The system according to feature 1.

7. The aforementioned inspection unit is The system estimates the user's emotions and adjusts the timing of saliva sample collection based on those emotions. The system according to feature 1.

8. The aforementioned inspection unit is Analyze the user's past health data to select the most suitable testing method. The system according to feature 1.

9. The aforementioned inspection unit is When collecting saliva samples, filtering is performed based on the user's lifestyle and diet. The system according to feature 1.

10. The aforementioned inspection unit is It estimates the user's emotions and prioritizes the test results based on the estimated emotions. The system according to feature 1.

Images