system

Abstract

The system according to this embodiment aims to provide a customized meal plan based on genetic information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an upload unit. The collection unit collects genetic information. The analysis unit analyzes the genetic information collected by the collection unit. The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan created by the generation unit. The upload unit uploads health checkup results.

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 prior art, a customized diet plan based on individual genetic information has not been sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a diet plan customized based on genetic information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an upload unit. The collection unit collects genetic information. The analysis unit analyzes the genetic information collected by the collection unit. The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan created by the generation unit. The upload unit uploads health checkup results. [Effects of the Invention]

[0007] The system according to this embodiment can provide a customized meal plan based on genetic information. [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 applicable 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 customized meal plan provision system according to an embodiment of the present invention is a system that provides individually customized meal plans by combining a user's genetic information and lifestyle data. The customized meal plan provision system creates a customized meal plan based on the user's genetic information and questionnaire results, and provides meal kits on a regular basis. For example, the customized meal plan provision system allows the user to collect a sample of their own genes and request its analysis. The analysis results are uploaded to the generating AI. Next, the customized meal plan provision system allows the user to input a simple questionnaire about their current health status and lifestyle. The generating AI analyzes this information and assesses the user's risk of developing diseases. The generating AI creates a customized meal plan based on the user's genetic information and questionnaire results. Furthermore, the customized meal plan provision system allows the user to receive customized meal kits on a regular basis. This allows the user to easily consume meals that are optimal for their health condition. The customized meal plan provision system also allows the user to upload the results of their health checkups to the generating AI. This allows the generating AI to continuously monitor the user's health status and update the meal plan as needed. This service is a powerful tool for users to maintain their health by utilizing their genetic information. Users can lead healthier lives by receiving customized advice based on their genetic information. Furthermore, the use of genetic information enables early detection and prevention of diseases, contributing to a reduction in medical costs. This allows the customized meal plan system to combine users' genetic information and lifestyle data to provide individually tailored meal plans.

[0029] The customized meal plan provision system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an upload unit. The collection unit collects the user's genetic information. The collection unit can, for example, collect a user's saliva sample. The collection unit can also collect a user's blood sample. Furthermore, the collection unit can also collect a user's hair sample. The analysis unit analyzes the genetic information collected by the collection unit. The analysis unit analyzes the genetic information using, for example, bioinformatics methods. The analysis unit can also analyze the genetic information using statistical analysis. Furthermore, the analysis unit can also analyze the genetic information using machine learning algorithms. The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. The generation unit creates a meal plan using, for example, a generation AI. The generation AI can create a meal plan using a text generation AI (e.g., LLM). The generation unit can also create a meal plan using a multimodal generation AI. Furthermore, the generation unit can also create a meal plan that considers nutritional balance using a generation AI. The provision unit provides the meal plan created by the generation unit. The service provider, for example, provides users with customized meal kits. The service provider can also provide meal plans using digital distribution. Furthermore, the service provider can provide meal kits by mail. In addition, the service provider can notify users of meal plans on their smartphones. The uploading service provider uploads the user's health check results to the generating AI. The uploading service provider can, for example, upload the user's blood test results. Furthermore, the uploading service provider can upload the user's physical measurement data. Furthermore, the uploading service provider can upload the user's electrocardiogram data. As a result, the customized meal plan provision system according to this embodiment can provide individually customized meal plans by combining the user's genetic information and lifestyle data.

[0030] The collection unit collects users' genetic information. Specifically, it provides a kit for collecting users' saliva samples, allowing users to easily collect samples at home. The saliva samples are placed in a dedicated container and sent to the collection unit by mail or through a dedicated collection service. The collection unit receives these samples and stores them under appropriate storage conditions. The collection unit can also utilize affiliated medical institutions and testing centers to collect users' blood samples. Users collect blood at designated medical institutions, and the samples are sent to the collection unit. Furthermore, the collection unit can also collect users' hair samples. Hair samples are useful for genetic analysis because they contain specific genetic information. Users cut a few strands of hair, place them in a dedicated envelope, and send them to the collection unit. This allows the collection unit to collect the user's genetic information from multiple sources and prepare it for provision to the analysis unit. The collection unit centrally manages these samples and adheres to strict protocols to ensure the accuracy and reliability of the data.

[0031] The analysis unit analyzes the genetic information collected by the data collection unit. Specifically, it analyzes the genetic information using bioinformatics methods to investigate the user's gene sequence in detail. Bioinformatics methods include gene sequence alignment, gene expression analysis, and gene mutation detection. The analysis unit can also analyze the genetic information using statistical analysis. Statistical analysis evaluates the impact of specific gene mutations on health and nutrition, providing data to derive the optimal diet plan for the user. Furthermore, the analysis unit can also analyze the genetic information using machine learning algorithms. Machine learning algorithms learn from large amounts of genetic data and find patterns and relationships to provide more accurate analysis results. For example, they can predict how specific gene mutations affect the metabolism of specific nutrients. This allows the analysis unit to comprehensively analyze the collected genetic information and build a foundation for providing the optimal diet plan for the user. The analysis unit utilizes the latest technologies and methods to improve the accuracy of genetic information analysis and provide users with highly reliable information.

[0032] The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. Specifically, it creates meal plans using a generation AI. The generation AI can create meal plans using a text generation AI (e.g., LLM). LLM learns from a large amount of dietary data and nutritional information and generates an optimal meal plan based on the user's genetic information and health status. For example, for a user whose absorption of vitamin D is reduced due to a specific genetic mutation, it will create a meal plan that recommends foods rich in vitamin D. The generation unit can also create meal plans using a multimodal generation AI. The multimodal generation AI integrates and analyzes multiple data formats, such as images and audio, in addition to text information, to provide richer information. For example, by providing images of the dishes included in the meal plan and videos of cooking methods, it makes it easier for users to follow the meal plan. Furthermore, the generation unit can also create meal plans that consider nutritional balance using the generation AI. The generation AI proposes a balanced meal plan based on the user's health status and nutritional needs. This allows the generation unit to provide users with individually customized meal plans and support health maintenance and improvement. The generation unit utilizes the latest AI technology to quickly and accurately provide users with the most suitable meal plan.

[0033] The service provider delivers meal plans created by the production unit. Specifically, they provide users with customized meal kits. The meal kits include ingredients and recipes based on the meal plan, allowing users to easily prepare the meals. The service provider delivers the meal kits refrigerated or frozen to maintain the freshness of the ingredients. The service provider can also deliver meal plans digitally. Users can check the meal plan and view recipes and nutritional information through a dedicated app or website. Furthermore, the service provider can deliver meal kits by mail. Since they are delivered directly to the user's address, users can easily follow the meal plan without any hassle. In addition, the service provider can notify users of meal plan updates on their smartphones. By using smartphone notification functions to inform users of meal plan updates and cooking times, the service provider helps users remember to follow the meal plan. In this way, the service provider can provide meal plans to users in a variety of ways, increasing user convenience. The service provider adopts flexible delivery methods according to user needs and supports the implementation of meal plans.

[0034] The upload unit uploads the user's health check results to the generating AI. Specifically, users can upload their blood test results. Blood test results include important health indicators such as blood sugar levels, cholesterol levels, and vitamin levels, and the generating AI optimizes meal plans based on this data. The upload unit can also upload the user's physical measurement data. This data includes height, weight, body fat percentage, and muscle mass, and the generating AI considers this data to create a meal plan suitable for the user's body type and health condition. Furthermore, the upload unit can also upload the user's electrocardiogram (ECG) data. ECG data is an important indicator of heart health, and the generating AI can suggest a heart-friendly meal plan based on this data. The upload unit securely manages this data and implements strict security measures to protect privacy. This allows the upload unit to gain a detailed understanding of the user's health condition and provide the generating AI with data to create more accurate meal plans. The upload unit continuously updates the user's health data and supports the user's health maintenance and improvement by providing meal plans based on the latest information.

[0035] The collection unit can collect user genetic samples. For example, the collection unit can collect user saliva samples. The collection unit collects the saliva samples provided by the user in a special container and sends them to a laboratory for genetic analysis. The collection unit can also collect user blood samples. The collection unit collects the blood samples provided by the user in a special kit and sends them to a laboratory for genetic analysis. Furthermore, the collection unit can also collect user hair samples. The collection unit collects the hair samples provided by the user in a special bag and sends them to a laboratory for genetic analysis. In this way, the collection unit can obtain genetic information by collecting user genetic samples.

[0036] The analysis unit can analyze questionnaire data regarding users' health status and lifestyle habits. For example, the analysis unit collects questionnaire data entered by users and analyzes it using bioinformatics methods. The analysis unit analyzes data related to users' health status (e.g., BMI, blood pressure, blood glucose levels, etc.) and assesses the user's health risks. The analysis unit can also analyze data related to users' lifestyle habits (e.g., eating patterns, exercise habits, sleep duration, etc.) and identify areas for improvement in the user's lifestyle. Furthermore, the analysis unit can use machine learning algorithms to analyze questionnaire data and provide customized advice based on the user's health status and lifestyle habits. This allows the analysis unit to obtain more accurate analysis results by analyzing questionnaire data related to users' health status and lifestyle habits.

[0037] The generation unit can assess the user's risk of developing illness and create a customized meal plan. For example, the generation unit uses a generation AI to analyze the user's genetic information and questionnaire data to assess the risk of developing illness. The generation AI uses a text generation AI (e.g., LLM) to assess the risk of developing illness based on the user's genetic information and questionnaire data. Furthermore, the generation unit can use the generation AI to create a customized meal plan based on the user's risk of developing illness. The generation AI creates a meal plan that considers nutritional balance based on the user's genetic information and questionnaire data. In addition, the generation unit can use the generation AI to create a meal plan that is optimal for the user's health condition. As a result, the generation unit can maintain the user's health by assessing the user's risk of developing illness and creating a customized meal plan.

[0038] The service provider can provide users with customized meal kits. For example, the service provider can prepare customized meal kits based on meal plans created by the generation service provider. The service provider can mail the meal kits to the user's address. The service provider can also notify users of meal plans using digital distribution. Furthermore, the service provider can notify users of meal plans on their smartphones for easy access. In this way, by providing customized meal kits, the service provider enables users to easily consume meals that are optimal for their health condition.

[0039] The upload unit can upload the user's health check results to the generating AI. For example, the upload unit can collect the results of health checks the user has received and upload them to the generating AI. The upload unit can upload the user's blood test results. The upload unit can also upload the user's physical measurement data. Furthermore, the upload unit can upload the user's electrocardiogram data. As a result, by uploading the user's health check results, the generating AI can continuously monitor the user's health status and update the meal plan as needed.

[0040] The collection unit can select the optimal collection method by referring to the user's past health check results when collecting genetic samples. For example, if the collection unit determines that a blood sample is optimal based on past health check results, it will collect a blood sample. The collection unit collects a blood sample based on past health check results. The collection unit can also collect a saliva sample if it determines that a saliva sample is optimal based on past health check results. The collection unit collects a saliva sample based on past health check results. Furthermore, if the collection unit determines that a hair sample is optimal based on past health check results, it will collect a hair sample. The collection unit collects a hair sample based on past health check results. In this way, the collection unit can select the optimal collection method by referring to the user's past health check results. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0041] The collection unit can filter genetic samples based on the user's lifestyle and dietary habits during collection. For example, if the user is a smoker, the collection unit will filter the samples considering the effects of smoking. The collection unit will filter the genetic samples considering the effects of smoking. The collection unit can also filter samples considering the effects of dietary restrictions if the user has them. The collection unit will filter the genetic samples considering the effects of dietary restrictions. Furthermore, if the user is taking certain medications, the collection unit can filter the samples considering the effects of those medications. The collection unit will filter the genetic samples considering the effects of medications. In this way, the collection unit can collect more accurate genetic information by filtering based on the user's lifestyle and dietary habits. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0042] The collection unit can prioritize the collection of highly relevant samples by considering the user's geographical location information when collecting genetic samples. For example, if the user lives in a high-altitude area, the collection unit can collect samples considering high-altitude-specific genetic mutations. The collection unit collects samples from users living in high-altitude areas, considering high-altitude-specific genetic mutations. The collection unit can also collect samples from users living in urban areas, considering urban-specific environmental factors if the user lives in an urban area. The collection unit collects samples from users living in urban areas, considering urban-specific environmental factors. Furthermore, if the user lives in a rural area, the collection unit can collect samples considering rural-specific lifestyle habits. The collection unit collects samples from users living in rural areas, considering rural-specific lifestyle habits. In this way, the collection unit can prioritize the collection of highly relevant samples by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0043] The data collection unit can analyze a user's social media activity when collecting genetic samples and collect relevant samples. For example, if a user frequently posts about health, the data collection unit can collect samples based on that information. The data collection unit collects genetic samples based on health-related posts. The data collection unit can also collect samples based on information if a user posts about specific diets or exercises. The data collection unit collects genetic samples based on posts about specific diets or exercises. Furthermore, if a user posts about stress or emotions, the data collection unit can collect samples based on that information. The data collection unit collects genetic samples based on posts about stress or emotions. In this way, the data collection unit can collect relevant samples by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's past health checkup results when analyzing survey data. For example, the analysis unit can more accurately analyze the current health status based on past health checkup results. The analysis unit analyzes the current health status by referring to past health checkup results. The analysis unit can also identify specific risk factors based on past health checkup results. The analysis unit can identify specific risk factors by referring to past health checkup results. Furthermore, the analysis unit can improve the reliability of its analysis results based on past health checkup results. The analysis unit improves the reliability of its analysis results by referring to past health checkup results. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past health checkup results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0045] The analysis unit can apply different analysis algorithms to the user's lifestyle and diet when analyzing survey data. For example, if the user is a vegetarian, the analysis unit applies a vegetarian-specific analysis algorithm. The analysis unit applies a vegetarian-specific analysis algorithm to vegetarian users. The analysis unit can also apply an analysis algorithm that takes into account the effects of a high-calorie diet if the user consumes one. The analysis unit applies an analysis algorithm that takes into account the effects of a high-calorie diet if the user consumes one. Furthermore, the analysis unit can also apply an analysis algorithm that takes into account the effects of regular exercise if the user exercises regularly. The analysis unit applies an analysis algorithm that takes into account the effects of regular exercise if the user exercises regularly. In this way, the analysis unit can obtain more accurate analysis results by applying different analysis algorithms based on the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0046] The analysis unit can determine the priority of analysis based on when the user submitted the survey data. For example, if the user submitted the survey early in the morning, the analysis unit will prioritize the analysis. The analysis unit will prioritize the analysis of survey data submitted early in the morning. The analysis unit can also prioritize the analysis of survey data submitted late at night, on the morning of the next day. The analysis unit can also prioritize the analysis of survey data submitted over the weekend, on the morning of the following week. In this way, the analysis unit can perform more efficient analysis by determining the priority of analysis based on when the user submitted the survey. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user when analyzing survey data. For example, if the user provides information about a specific disease, the analysis unit will refer to the latest research literature on that disease. The analysis unit will refer to the latest research literature based on the information about the specific disease. The analysis unit can also refer to literature on a specific diet if the user provides information about that diet. The analysis unit will refer to relevant literature based on the information about the specific diet. Furthermore, if the user provides information about a specific exercise method, the analysis unit can also refer to literature on that exercise method. The analysis unit will refer to relevant literature based on the information about the specific exercise method. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0048] The generation unit can create an optimal meal plan by referring to the user's past eating history. For example, the generation unit can prioritize using ingredients that the user likes based on their past eating history. The generation unit can also prioritize using ingredients that the user likes based on their past eating history. Furthermore, the generation unit can create a plan that takes into account the user's nutritional balance based on their past eating history. The generation unit can create a plan that takes into account the user's nutritional balance based on their past eating history. In this way, the generation unit can create an optimal meal plan by referring to the user's past eating history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0049] The generation unit can provide different meal plans based on the user's lifestyle and health condition when creating meal plans. For example, if a user exercises regularly, the generation unit can provide a plan that takes into account the calorie intake appropriate to that amount of exercise. The generation unit can provide a plan that takes into account the calorie intake appropriate to the amount of exercise for users who exercise regularly. The generation unit can also provide a meal plan suitable for a user with a specific illness. The generation unit can provide a meal plan suitable for a user with a specific illness. Furthermore, if a user has specific dietary restrictions, the generation unit can provide a plan suitable for those restrictions. The generation unit can provide a plan suitable for a user with specific dietary restrictions. In this way, the generation unit can provide more appropriate meal plans by offering different plans based on the user's lifestyle and health condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0050] The generation unit can provide an optimal meal plan by considering the user's geographical location when creating the meal plan. For example, if the user lives in a high-altitude area, the generation unit can provide a plan using ingredients that are specific to high-altitude areas. The generation unit can provide a plan using ingredients that are specific to high-altitude areas to users living in high-altitude areas. Furthermore, if the user lives in an urban area, the generation unit can provide a plan using ingredients that are readily available in that area to users living in urban areas. In this way, the generation unit can provide an optimal meal plan by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0051] The generation unit can analyze the user's social media activity and customize the meal plan when creating it. For example, if the user frequently posts about health, the generation unit will customize the plan based on that information. The generation unit will customize the meal plan based on health-related posts. The generation unit can also customize the plan based on information if the user posts about specific foods or exercises. The generation unit will customize the meal plan based on posts about specific foods or exercises. Furthermore, if the generation unit posts about stress or emotions, the generation unit will customize the plan based on that information. The generation unit will customize the meal plan based on posts about stress or emotions. In this way, the generation unit can customize a more appropriate meal plan by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0052] The service provider can select the optimal service method by referring to the user's past eating history when providing a meal kit. For example, the service provider can provide a meal kit that prioritizes ingredients preferred by the user based on their past eating history. The service provider can also provide a meal kit that excludes ingredients that the user should avoid based on their past eating history. Furthermore, the service provider can provide a meal kit that takes into account the user's nutritional balance based on their past eating history. This allows the service provider to select the optimal service method by referring to the user's past eating history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0053] The service provider can customize the contents of the meal kits based on the user's lifestyle and health condition. For example, if a user exercises regularly, the service provider can provide a meal kit that takes into account the calorie intake appropriate for that amount of exercise. The service provider can provide a meal kit that takes into account the calorie intake appropriate for the amount of exercise for users who exercise regularly. The service provider can also provide a meal kit that is suitable for users with a specific illness. The service provider can provide a meal kit that is suitable for users with a specific illness. Furthermore, if a user has a specific dietary restriction, the service provider can provide a meal kit that is suitable for that restriction. The service provider can provide a meal kit that is suitable for users with a specific dietary restriction. In this way, the service provider can provide a more appropriate meal kit by customizing the contents based on the user's lifestyle and health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0054] The service provider can select the optimal delivery method when providing meal kits, taking into account the user's geographical location. For example, if the user lives in a high-altitude area, the service provider can provide a meal kit using ingredients specific to high-altitude areas. The service provider can provide a meal kit using ingredients specific to high-altitude areas to users living in high-altitude areas. The service provider can also provide a meal kit using ingredients readily available in urban areas to users living in urban areas. Furthermore, if the user lives in a rural area, the service provider can provide a meal kit using ingredients readily available in rural areas. The service provider can provide a meal kit using ingredients readily available in rural areas to users living in rural areas. In this way, the service provider can select the optimal delivery method by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0055] The service provider can analyze the user's social media activity when providing meal kits and customize the contents of the kits. For example, if the user frequently posts about health, the service provider can customize the meal kit based on that information. The service provider can customize the meal kit based on health-related posts. The service provider can also customize the meal kit based on information if the user posts about specific foods or exercises. The service provider can also customize the meal kit based on information if the user posts about stress or emotions. The service provider can customize the meal kit based on posts about stress or emotions. In this way, the service provider can provide more appropriate meal kits by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0056] The upload unit can select the optimal upload method by referring to the user's past health check results when uploading health check results. For example, the upload unit uploads the current health status more accurately based on past health check results. The upload unit uploads the current health status by referring to past health check results. The upload unit can also identify specific risk factors based on past health check results. The upload unit identifies specific risk factors by referring to past health check results. Furthermore, the upload unit can improve the reliability of the upload results based on past health check results. The upload unit improves the reliability of the upload results by referring to past health check results. As a result, the upload unit can select the optimal upload method by referring to the user's past health check results. Some or all of the above processing in the upload unit may be performed using AI, for example, or without using AI.

[0057] The upload unit can prioritize uploading highly relevant results by considering the user's geographical location when uploading health check results. For example, if the user lives at high altitude, the upload unit will upload the results while considering the health risks specific to high altitude. The upload unit will upload the health check results of users living at high altitude, taking into account the health risks specific to high altitude. The upload unit can also upload the results of users living in urban areas, taking into account the health risks specific to urban areas. The upload unit will upload the health check results of users living in urban areas, taking into account the health risks specific to urban areas. Furthermore, if the user lives in a rural area, the upload unit can upload the results while considering the health risks specific to rural areas. The upload unit will upload the health check results of users living in rural areas, taking into account the health risks specific to rural areas. In this way, the upload unit can prioritize uploading highly relevant results by considering the user's geographical location. Some or all of the above processing in the upload unit may be performed using AI, for example, or without using AI.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] A customized meal plan system can adjust the content of a meal plan by referring to the user's past eating history. For example, it can prioritize the use of ingredients the user likes based on their past eating history. It can also exclude ingredients the user should avoid based on their past eating history. Furthermore, it can create a plan that takes into account the user's nutritional balance based on their past eating history. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's past eating history.

[0060] The customized meal plan system can adjust the meal plan content based on the user's geographical location. For example, if a user lives in a high-altitude area, the system can offer a plan using ingredients specific to that area. If a user lives in an urban area, the system can offer a plan using ingredients readily available in urban areas. Furthermore, if a user lives in a rural area, the system can offer a plan using ingredients readily available in rural areas. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's geographical location.

[0061] A customized meal plan system can analyze a user's social media activity and adjust the content of the meal plan accordingly. For example, if a user frequently posts about health, the system can customize the plan based on that information. Similarly, if a user posts about specific foods or exercises, the system can customize the plan based on that information. Furthermore, if a user posts about stress or emotions, the system can customize the plan based on that information. This allows the customized meal plan system to provide a more appropriate meal plan based on the user's social media activity.

[0062] The customized meal plan system can adjust the meal plan based on the user's health check results. For example, if the health check results indicate that the user needs a specific nutrient, the system can provide a plan that includes ingredients containing that nutrient. Furthermore, if the health check results indicate that the user should avoid a specific ingredient, the system can provide a plan that excludes that ingredient. It can also provide a plan tailored to the user's health condition based on the health check results. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's health check results.

[0063] A customized meal plan system can adjust the content of meal plans based on the user's lifestyle and health condition. For example, if a user exercises regularly, the system can provide a plan that takes into account the calorie intake corresponding to that exercise level. Furthermore, if a user has a specific illness, the system can provide a meal plan tailored to that illness. Additionally, if a user has specific dietary restrictions, the system can provide a plan that accommodates those restrictions. In this way, the customized meal plan system can provide more appropriate meal plans based on the user's lifestyle and health condition.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The collection unit collects the user's genetic information. For example, the collection unit can collect the user's saliva sample, blood sample, or hair sample. Step 2: The analysis unit analyzes the genetic information collected by the collection unit. For example, the analysis unit can analyze the genetic information using bioinformatics methods, statistical analysis, and machine learning algorithms. Step 3: The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. For example, the generation unit can create a meal plan using a generation AI, and can use a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The delivery unit provides the meal plan created by the generation unit. For example, the delivery unit can provide customized meal kits to users and use digital delivery, postal mail, or smartphone notifications. Step 5: The upload unit uploads the user's health check results to the generating AI. For example, the upload unit can upload the user's blood test results, physical measurement data, and electrocardiogram data.

[0066] (Example of form 2) The customized meal plan provision system according to an embodiment of the present invention is a system that provides individually customized meal plans by combining a user's genetic information and lifestyle data. The customized meal plan provision system creates a customized meal plan based on the user's genetic information and questionnaire results, and provides meal kits on a regular basis. For example, the customized meal plan provision system allows the user to collect a sample of their own genes and request its analysis. The analysis results are uploaded to the generating AI. Next, the customized meal plan provision system allows the user to input a simple questionnaire about their current health status and lifestyle. The generating AI analyzes this information and assesses the user's risk of developing diseases. The generating AI creates a customized meal plan based on the user's genetic information and questionnaire results. Furthermore, the customized meal plan provision system allows the user to receive customized meal kits on a regular basis. This allows the user to easily consume meals that are optimal for their health condition. The customized meal plan provision system also allows the user to upload the results of their health checkups to the generating AI. This allows the generating AI to continuously monitor the user's health status and update the meal plan as needed. This service is a powerful tool for users to maintain their health by utilizing their genetic information. Users can lead healthier lives by receiving customized advice based on their genetic information. Furthermore, the use of genetic information enables early detection and prevention of diseases, contributing to a reduction in medical costs. This allows the customized meal plan system to combine users' genetic information and lifestyle data to provide individually tailored meal plans.

[0067] The customized meal plan provision system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, and an upload unit. The collection unit collects the user's genetic information. The collection unit can, for example, collect a user's saliva sample. The collection unit can also collect a user's blood sample. Furthermore, the collection unit can also collect a user's hair sample. The analysis unit analyzes the genetic information collected by the collection unit. The analysis unit analyzes the genetic information using, for example, bioinformatics methods. The analysis unit can also analyze the genetic information using statistical analysis. Furthermore, the analysis unit can also analyze the genetic information using machine learning algorithms. The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. The generation unit creates a meal plan using, for example, a generation AI. The generation AI can create a meal plan using a text generation AI (e.g., LLM). The generation unit can also create a meal plan using a multimodal generation AI. Furthermore, the generation unit can also create a meal plan that considers nutritional balance using a generation AI. The provision unit provides the meal plan created by the generation unit. The service provider, for example, provides users with customized meal kits. The service provider can also provide meal plans using digital distribution. Furthermore, the service provider can provide meal kits by mail. In addition, the service provider can notify users of meal plans on their smartphones. The uploading service provider uploads the user's health check results to the generating AI. The uploading service provider can, for example, upload the user's blood test results. Furthermore, the uploading service provider can upload the user's physical measurement data. Furthermore, the uploading service provider can upload the user's electrocardiogram data. As a result, the customized meal plan provision system according to this embodiment can provide individually customized meal plans by combining the user's genetic information and lifestyle data.

[0068] The collection unit collects users' genetic information. Specifically, it provides a kit for collecting users' saliva samples, allowing users to easily collect samples at home. The saliva samples are placed in a dedicated container and sent to the collection unit by mail or through a dedicated collection service. The collection unit receives these samples and stores them under appropriate storage conditions. The collection unit can also utilize affiliated medical institutions and testing centers to collect users' blood samples. Users collect blood at designated medical institutions, and the samples are sent to the collection unit. Furthermore, the collection unit can also collect users' hair samples. Hair samples are useful for genetic analysis because they contain specific genetic information. Users cut a few strands of hair, place them in a dedicated envelope, and send them to the collection unit. This allows the collection unit to collect the user's genetic information from multiple sources and prepare it for provision to the analysis unit. The collection unit centrally manages these samples and adheres to strict protocols to ensure the accuracy and reliability of the data.

[0069] The analysis unit analyzes the genetic information collected by the data collection unit. Specifically, it analyzes the genetic information using bioinformatics methods to investigate the user's gene sequence in detail. Bioinformatics methods include gene sequence alignment, gene expression analysis, and gene mutation detection. The analysis unit can also analyze the genetic information using statistical analysis. Statistical analysis evaluates the impact of specific gene mutations on health and nutrition, providing data to derive the optimal diet plan for the user. Furthermore, the analysis unit can also analyze the genetic information using machine learning algorithms. Machine learning algorithms learn from large amounts of genetic data and find patterns and relationships to provide more accurate analysis results. For example, they can predict how specific gene mutations affect the metabolism of specific nutrients. This allows the analysis unit to comprehensively analyze the collected genetic information and build a foundation for providing the optimal diet plan for the user. The analysis unit utilizes the latest technologies and methods to improve the accuracy of genetic information analysis and provide users with highly reliable information.

[0070] The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. Specifically, it creates meal plans using a generation AI. The generation AI can create meal plans using a text generation AI (e.g., LLM). LLM learns from a large amount of dietary data and nutritional information and generates an optimal meal plan based on the user's genetic information and health status. For example, for a user whose absorption of vitamin D is reduced due to a specific genetic mutation, it will create a meal plan that recommends foods rich in vitamin D. The generation unit can also create meal plans using a multimodal generation AI. The multimodal generation AI integrates and analyzes multiple data formats, such as images and audio, in addition to text information, to provide richer information. For example, by providing images of the dishes included in the meal plan and videos of cooking methods, it makes it easier for users to follow the meal plan. Furthermore, the generation unit can also create meal plans that consider nutritional balance using the generation AI. The generation AI proposes a balanced meal plan based on the user's health status and nutritional needs. This allows the generation unit to provide users with individually customized meal plans and support health maintenance and improvement. The generation unit utilizes the latest AI technology to quickly and accurately provide users with the most suitable meal plan.

[0071] The service provider delivers meal plans created by the production unit. Specifically, they provide users with customized meal kits. The meal kits include ingredients and recipes based on the meal plan, allowing users to easily prepare the meals. The service provider delivers the meal kits refrigerated or frozen to maintain the freshness of the ingredients. The service provider can also deliver meal plans digitally. Users can check the meal plan and view recipes and nutritional information through a dedicated app or website. Furthermore, the service provider can deliver meal kits by mail. Since they are delivered directly to the user's address, users can easily follow the meal plan without any hassle. In addition, the service provider can notify users of meal plan updates on their smartphones. By using smartphone notification functions to inform users of meal plan updates and cooking times, the service provider helps users remember to follow the meal plan. In this way, the service provider can provide meal plans to users in a variety of ways, increasing user convenience. The service provider adopts flexible delivery methods according to user needs and supports the implementation of meal plans.

[0072] The upload unit uploads the user's health check results to the generating AI. Specifically, users can upload their blood test results. Blood test results include important health indicators such as blood sugar levels, cholesterol levels, and vitamin levels, and the generating AI optimizes meal plans based on this data. The upload unit can also upload the user's physical measurement data. This data includes height, weight, body fat percentage, and muscle mass, and the generating AI considers this data to create a meal plan suitable for the user's body type and health condition. Furthermore, the upload unit can also upload the user's electrocardiogram (ECG) data. ECG data is an important indicator of heart health, and the generating AI can suggest a heart-friendly meal plan based on this data. The upload unit securely manages this data and implements strict security measures to protect privacy. This allows the upload unit to gain a detailed understanding of the user's health condition and provide the generating AI with data to create more accurate meal plans. The upload unit continuously updates the user's health data and supports the user's health maintenance and improvement by providing meal plans based on the latest information.

[0073] The collection unit can collect user genetic samples. For example, the collection unit can collect user saliva samples. The collection unit collects the saliva samples provided by the user in a special container and sends them to a laboratory for genetic analysis. The collection unit can also collect user blood samples. The collection unit collects the blood samples provided by the user in a special kit and sends them to a laboratory for genetic analysis. Furthermore, the collection unit can also collect user hair samples. The collection unit collects the hair samples provided by the user in a special bag and sends them to a laboratory for genetic analysis. In this way, the collection unit can obtain genetic information by collecting user genetic samples.

[0074] The analysis unit can analyze questionnaire data regarding users' health status and lifestyle habits. For example, the analysis unit collects questionnaire data entered by users and analyzes it using bioinformatics methods. The analysis unit analyzes data related to users' health status (e.g., BMI, blood pressure, blood glucose levels, etc.) and assesses the user's health risks. The analysis unit can also analyze data related to users' lifestyle habits (e.g., eating patterns, exercise habits, sleep duration, etc.) and identify areas for improvement in the user's lifestyle. Furthermore, the analysis unit can use machine learning algorithms to analyze questionnaire data and provide customized advice based on the user's health status and lifestyle habits. This allows the analysis unit to obtain more accurate analysis results by analyzing questionnaire data related to users' health status and lifestyle habits.

[0075] The generation unit can assess the user's risk of developing illness and create a customized meal plan. For example, the generation unit uses a generation AI to analyze the user's genetic information and questionnaire data to assess the risk of developing illness. The generation AI uses a text generation AI (e.g., LLM) to assess the risk of developing illness based on the user's genetic information and questionnaire data. Furthermore, the generation unit can use the generation AI to create a customized meal plan based on the user's risk of developing illness. The generation AI creates a meal plan that considers nutritional balance based on the user's genetic information and questionnaire data. In addition, the generation unit can use the generation AI to create a meal plan that is optimal for the user's health condition. As a result, the generation unit can maintain the user's health by assessing the user's risk of developing illness and creating a customized meal plan.

[0076] The service provider can provide users with customized meal kits. For example, the service provider can prepare customized meal kits based on meal plans created by the generation service provider. The service provider can mail the meal kits to the user's address. The service provider can also notify users of meal plans using digital distribution. Furthermore, the service provider can notify users of meal plans on their smartphones for easy access. In this way, by providing customized meal kits, the service provider enables users to easily consume meals that are optimal for their health condition.

[0077] The upload unit can upload the user's health check results to the generating AI. For example, the upload unit can collect the results of health checks the user has received and upload them to the generating AI. The upload unit can upload the user's blood test results. The upload unit can also upload the user's physical measurement data. Furthermore, the upload unit can upload the user's electrocardiogram data. As a result, by uploading the user's health check results, the generating AI can continuously monitor the user's health status and update the meal plan as needed.

[0078] The collection unit can estimate the user's emotions and adjust the timing of gene sample collection based on the estimated emotions. For example, if the user is relaxed, the collection unit will immediately collect the gene sample. The collection unit will collect a saliva sample when the user is relaxed. The collection unit can also delay the collection timing if the user is stressed, adjusting it to a time when the user can relax. The collection unit will postpone collection when the user is stressed and collect it during a time when the user can relax. Furthermore, if the user is busy, the collection unit can adjust the collection timing to match the user's schedule. The collection unit adjusts collection to match the user's schedule when the user is busy. In this way, the collection unit can collect samples at a more appropriate time by adjusting the timing of gene sample collection based on 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.

[0079] The collection unit can select the optimal collection method by referring to the user's past health check results when collecting genetic samples. For example, if the collection unit determines that a blood sample is optimal based on past health check results, it will collect a blood sample. The collection unit collects a blood sample based on past health check results. The collection unit can also collect a saliva sample if it determines that a saliva sample is optimal based on past health check results. The collection unit collects a saliva sample based on past health check results. Furthermore, if the collection unit determines that a hair sample is optimal based on past health check results, it will collect a hair sample. The collection unit collects a hair sample based on past health check results. In this way, the collection unit can select the optimal collection method by referring to the user's past health check results. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0080] The collection unit can filter genetic samples based on the user's lifestyle and dietary habits during collection. For example, if the user is a smoker, the collection unit will filter the samples considering the effects of smoking. The collection unit will filter the genetic samples considering the effects of smoking. The collection unit can also filter samples considering the effects of dietary restrictions if the user has them. The collection unit will filter the genetic samples considering the effects of dietary restrictions. Furthermore, if the user is taking certain medications, the collection unit can filter the samples considering the effects of those medications. The collection unit will filter the genetic samples considering the effects of medications. In this way, the collection unit can collect more accurate genetic information by filtering based on the user's lifestyle and dietary habits. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0081] The collection unit can estimate the user's emotions and determine the priority of genetic samples to collect based on the estimated emotions. For example, if the user is relaxed, the collection unit will prioritize collecting blood samples. The collection unit will prioritize collecting blood samples from relaxed users. The collection unit can also prioritize collecting saliva samples from stressed users. Furthermore, the collection unit can prioritize collecting hair samples from busy users. The collection unit will prioritize collecting hair samples from busy users. In this way, the collection unit can prioritize collecting more appropriate samples by determining the priority of genetic samples to collect based on 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.

[0082] The collection unit can prioritize the collection of highly relevant samples by considering the user's geographical location information when collecting genetic samples. For example, if the user lives in a high-altitude area, the collection unit can collect samples considering high-altitude-specific genetic mutations. The collection unit collects samples from users living in high-altitude areas, considering high-altitude-specific genetic mutations. The collection unit can also collect samples from users living in urban areas, considering urban-specific environmental factors if the user lives in an urban area. The collection unit collects samples from users living in urban areas, considering urban-specific environmental factors. Furthermore, if the user lives in a rural area, the collection unit can collect samples considering rural-specific lifestyle habits. The collection unit collects samples from users living in rural areas, considering rural-specific lifestyle habits. In this way, the collection unit can prioritize the collection of highly relevant samples by considering the user's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without using AI.

[0083] The data collection unit can analyze a user's social media activity when collecting genetic samples and collect relevant samples. For example, if a user frequently posts about health, the data collection unit can collect samples based on that information. The data collection unit collects genetic samples based on health-related posts. The data collection unit can also collect samples based on information if a user posts about specific diets or exercises. The data collection unit collects genetic samples based on posts about specific diets or exercises. Furthermore, if a user posts about stress or emotions, the data collection unit can collect samples based on that information. The data collection unit collects genetic samples based on posts about stress or emotions. In this way, the data collection unit can collect relevant samples by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis method of the survey data based on the estimated user emotions. For example, if the user is relaxed, the analysis unit performs a detailed analysis. The analysis unit performs a detailed analysis of the survey data of a relaxed user. The analysis unit can also perform a simplified analysis if the user is stressed. The analysis unit performs a simplified analysis of the survey data of a stressed user. Furthermore, the analysis unit can perform a rapid analysis if the user is in a hurry. The analysis unit performs a rapid analysis of the survey data of a hurryed user. In this way, the analysis unit can perform a more appropriate analysis by adjusting the analysis method of the survey data based on 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.

[0085] The analysis unit can improve the accuracy of its analysis by referring to the user's past health checkup results when analyzing survey data. For example, the analysis unit can more accurately analyze the current health status based on past health checkup results. The analysis unit analyzes the current health status by referring to past health checkup results. The analysis unit can also identify specific risk factors based on past health checkup results. The analysis unit can identify specific risk factors by referring to past health checkup results. Furthermore, the analysis unit can improve the reliability of its analysis results based on past health checkup results. The analysis unit improves the reliability of its analysis results by referring to past health checkup results. In this way, the analysis unit can improve the accuracy of its analysis by referring to the user's past health checkup results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0086] The analysis unit can apply different analysis algorithms to the user's lifestyle and diet when analyzing survey data. For example, if the user is a vegetarian, the analysis unit applies a vegetarian-specific analysis algorithm. The analysis unit applies a vegetarian-specific analysis algorithm to vegetarian users. The analysis unit can also apply an analysis algorithm that takes into account the effects of a high-calorie diet if the user consumes one. The analysis unit applies an analysis algorithm that takes into account the effects of a high-calorie diet if the user consumes one. Furthermore, the analysis unit can also apply an analysis algorithm that takes into account the effects of regular exercise if the user exercises regularly. The analysis unit applies an analysis algorithm that takes into account the effects of regular exercise if the user exercises regularly. In this way, the analysis unit can obtain more accurate analysis results by applying different analysis algorithms based on the user's lifestyle and diet. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0087] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. The analysis unit can display detailed analysis results for relaxed users. The analysis unit can also display simplified analysis results for stressed users. Furthermore, if the user is in a hurry, the analysis unit can display concise analysis results for users in a hurry. In this way, the analysis unit can provide more appropriate displays by adjusting how the analysis results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The analysis unit can determine the priority of analysis based on when the user submitted the survey data. For example, if the user submitted the survey early in the morning, the analysis unit will prioritize the analysis. The analysis unit will prioritize the analysis of survey data submitted early in the morning. The analysis unit can also prioritize the analysis of survey data submitted late at night, on the morning of the next day. The analysis unit can also prioritize the analysis of survey data submitted over the weekend, on the morning of the following week. In this way, the analysis unit can perform more efficient analysis by determining the priority of analysis based on when the user submitted the survey. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0089] The analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user when analyzing survey data. For example, if the user provides information about a specific disease, the analysis unit will refer to the latest research literature on that disease. The analysis unit will refer to the latest research literature based on the information about the specific disease. The analysis unit can also refer to literature on a specific diet if the user provides information about that diet. The analysis unit will refer to relevant literature based on the information about the specific diet. Furthermore, if the user provides information about a specific exercise method, the analysis unit can also refer to literature on that exercise method. The analysis unit will refer to relevant literature based on the information about the specific exercise method. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature related to the user. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0090] The generation unit can estimate the user's emotions and adjust how it creates meal plans based on those emotions. For example, if the user is relaxed, the generation unit can create a detailed meal plan. The generation unit creates a detailed meal plan for relaxed users. The generation unit can also create a simplified meal plan for stressed users. Furthermore, if the user is in a hurry, the generation unit can provide a meal plan that can be created quickly. The generation unit provides a meal plan that can be created quickly for users in a hurry. This allows the generation unit to create more appropriate plans by adjusting how it creates meal plans based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The generation unit can create an optimal meal plan by referring to the user's past eating history. For example, the generation unit can prioritize using ingredients that the user likes based on their past eating history. The generation unit can also prioritize using ingredients that the user likes based on their past eating history. Furthermore, the generation unit can create a plan that takes into account the user's nutritional balance based on their past eating history. The generation unit can create a plan that takes into account the user's nutritional balance based on their past eating history. In this way, the generation unit can create an optimal meal plan by referring to the user's past eating history. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0092] The generation unit can provide different meal plans based on the user's lifestyle and health condition when creating meal plans. For example, if a user exercises regularly, the generation unit can provide a plan that takes into account the calorie intake appropriate to that amount of exercise. The generation unit can provide a plan that takes into account the calorie intake appropriate to the amount of exercise for users who exercise regularly. The generation unit can also provide a meal plan suitable for a user with a specific illness. The generation unit can provide a meal plan suitable for a user with a specific illness. Furthermore, if a user has specific dietary restrictions, the generation unit can provide a plan suitable for those restrictions. The generation unit can provide a plan suitable for a user with specific dietary restrictions. In this way, the generation unit can provide more appropriate meal plans by offering different plans based on the user's lifestyle and health condition. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0093] The generation unit can estimate the user's emotions and prioritize meal plans based on those emotions. For example, if the user is relaxed, the generation unit will prioritize providing a detailed meal plan. The generation unit will prioritize providing a detailed meal plan to a relaxed user. The generation unit can also prioritize providing a simplified meal plan to a stressed user. Furthermore, if the user is in a hurry, the generation unit can prioritize providing a meal plan that can be quickly prepared. The generation unit will prioritize providing a meal plan that can be quickly prepared to a hurryed user. In this way, the generation unit can prioritize providing a more appropriate plan by prioritizing meal plans based on 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.

[0094] The generation unit can provide an optimal meal plan by considering the user's geographical location when creating the meal plan. For example, if the user lives in a high-altitude area, the generation unit can provide a plan using ingredients that are specific to high-altitude areas. The generation unit can provide a plan using ingredients that are specific to high-altitude areas to users living in high-altitude areas. Furthermore, if the user lives in an urban area, the generation unit can provide a plan using ingredients that are readily available in that area to users living in urban areas. In this way, the generation unit can provide an optimal meal plan by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0095] The generation unit can analyze the user's social media activity and customize the meal plan when creating it. For example, if the user frequently posts about health, the generation unit will customize the plan based on that information. The generation unit will customize the meal plan based on health-related posts. The generation unit can also customize the plan based on information if the user posts about specific foods or exercises. The generation unit will customize the meal plan based on posts about specific foods or exercises. Furthermore, if the generation unit posts about stress or emotions, the generation unit will customize the plan based on that information. The generation unit will customize the meal plan based on posts about stress or emotions. In this way, the generation unit can customize a more appropriate meal plan by analyzing the user's social media activity. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without using a generation AI.

[0096] The service provider can estimate the user's emotions and adjust the way the meal kit is delivered based on those emotions. For example, if the user is relaxed, the service provider can provide a meal kit with detailed cooking instructions. The service provider can provide a meal kit with detailed cooking instructions to a relaxed user. The service provider can also provide a meal kit with simplified cooking instructions to a stressed user. The service provider can also provide a meal kit with simplified cooking instructions to a stressed user. Furthermore, if the user is in a hurry, the service provider can provide a meal kit that can be prepared quickly. The service provider can provide a meal kit that can be prepared quickly to a user in a hurry. This allows the service provider to select a more appropriate delivery method by adjusting the way the meal kit is delivered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The service provider can select the optimal service method by referring to the user's past eating history when providing a meal kit. For example, the service provider can provide a meal kit that prioritizes ingredients preferred by the user based on their past eating history. The service provider can also provide a meal kit that excludes ingredients that the user should avoid based on their past eating history. Furthermore, the service provider can provide a meal kit that takes into account the user's nutritional balance based on their past eating history. This allows the service provider to select the optimal service method by referring to the user's past eating history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI.

[0098] The service provider can customize the contents of the meal kits based on the user's lifestyle and health condition. For example, if a user exercises regularly, the service provider can provide a meal kit that takes into account the calorie intake appropriate for that amount of exercise. The service provider can provide a meal kit that takes into account the calorie intake appropriate for the amount of exercise for users who exercise regularly. The service provider can also provide a meal kit that is suitable for users with a specific illness. The service provider can provide a meal kit that is suitable for users with a specific illness. Furthermore, if a user has a specific dietary restriction, the service provider can provide a meal kit that is suitable for that restriction. The service provider can provide a meal kit that is suitable for users with a specific dietary restriction. In this way, the service provider can provide a more appropriate meal kit by customizing the contents based on the user's lifestyle and health condition. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0099] The service provider can estimate the user's emotions and adjust the frequency of meal kit delivery based on the estimated emotions. For example, if the user is relaxed, the service provider can deliver a meal kit once a week. The service provider can deliver a meal kit once a week to users who are relaxed. The service provider can also deliver a meal kit twice a week to users who are stressed. The service provider can deliver a meal kit twice a week to users who are stressed. Furthermore, if the user is in a hurry, the service provider can deliver a meal kit daily. The service provider can deliver a meal kit daily to users who are in a hurry. In this way, the service provider can deliver meal kits at a more appropriate frequency by adjusting the delivery frequency based on 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.

[0100] The service provider can select the optimal delivery method when providing meal kits, taking into account the user's geographical location. For example, if the user lives in a high-altitude area, the service provider can provide a meal kit using ingredients specific to high-altitude areas. The service provider can provide a meal kit using ingredients specific to high-altitude areas to users living in high-altitude areas. The service provider can also provide a meal kit using ingredients readily available in urban areas to users living in urban areas. Furthermore, if the user lives in a rural area, the service provider can provide a meal kit using ingredients readily available in rural areas. The service provider can provide a meal kit using ingredients readily available in rural areas to users living in rural areas. In this way, the service provider can select the optimal delivery method by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI.

[0101] The service provider can analyze the user's social media activity when providing meal kits and customize the contents of the kits. For example, if the user frequently posts about health, the service provider can customize the meal kit based on that information. The service provider can customize the meal kit based on health-related posts. The service provider can also customize the meal kit based on information if the user posts about specific foods or exercises. The service provider can also customize the meal kit based on information if the user posts about stress or emotions. The service provider can customize the meal kit based on posts about stress or emotions. In this way, the service provider can provide more appropriate meal kits by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI.

[0102] The upload unit can estimate the user's emotions and adjust the timing of uploading health check results based on the estimated emotions. For example, if the user is relaxed, the upload unit will upload the health check results immediately. The upload unit will immediately upload the health check results of a relaxed user. The upload unit can also delay the upload timing if the user is stressed, adjusting it to a time when they can relax. The upload unit will delay the upload of health check results for stressed users, adjusting it to a time when they can relax. Furthermore, if the user is busy, the upload unit can adjust the upload timing to match the user's schedule. The upload unit will adjust the upload timing of health check results for busy users to match their schedule. In this way, the upload unit can upload health check results at a more appropriate time by adjusting the upload timing based on 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.

[0103] The upload unit can select the optimal upload method by referring to the user's past health check results when uploading health check results. For example, the upload unit uploads the current health status more accurately based on past health check results. The upload unit uploads the current health status by referring to past health check results. The upload unit can also identify specific risk factors based on past health check results. The upload unit identifies specific risk factors by referring to past health check results. Furthermore, the upload unit can improve the reliability of the upload results based on past health check results. The upload unit improves the reliability of the upload results by referring to past health check results. As a result, the upload unit can select the optimal upload method by referring to the user's past health check results. Some or all of the above processing in the upload unit may be performed using AI, for example, or without using AI.

[0104] The upload unit can estimate the user's emotions and determine the priority of health check results to upload based on the estimated emotions. For example, if the user is relaxed, the upload unit will prioritize uploading detailed health check results. The upload unit will prioritize uploading detailed health check results for relaxed users. The upload unit can also prioritize uploading simplified health check results for stressed users. Furthermore, if the user is in a hurry, the upload unit can prioritize uploading health check results that can be uploaded quickly. The upload unit will prioritize uploading health check results that can be uploaded quickly for users in a hurry. In this way, the upload unit can prioritize uploading more appropriate results by determining the priority of health check results based on 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.

[0105] The upload unit can prioritize uploading highly relevant results by considering the user's geographical location when uploading health check results. For example, if the user lives at high altitude, the upload unit will upload the results while considering the health risks specific to high altitude. The upload unit will upload the health check results of users living at high altitude, taking into account the health risks specific to high altitude. The upload unit can also upload the results of users living in urban areas, taking into account the health risks specific to urban areas. The upload unit will upload the health check results of users living in urban areas, taking into account the health risks specific to urban areas. Furthermore, if the user lives in a rural area, the upload unit can upload the results while considering the health risks specific to rural areas. The upload unit will upload the health check results of users living in rural areas, taking into account the health risks specific to rural areas. In this way, the upload unit can prioritize uploading highly relevant results by considering the user's geographical location. Some or all of the above processing in the upload unit may be performed using AI, for example, or without using AI.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] A customized meal plan system can estimate a user's emotions and adjust the meal plan based on those emotions. For example, if a user is stressed, the system can provide a plan that includes ingredients effective in reducing stress. If a user is relaxed, it can provide a plan that includes ingredients that enhance relaxation. Furthermore, if a user is fatigued, it can provide a plan that includes ingredients suitable for energy replenishment. In this way, the customized meal plan system can provide the optimal meal plan tailored to the user's emotions.

[0108] A customized meal plan system can adjust the content of a meal plan by referring to the user's past eating history. For example, it can prioritize the use of ingredients the user likes based on their past eating history. It can also exclude ingredients the user should avoid based on their past eating history. Furthermore, it can create a plan that takes into account the user's nutritional balance based on their past eating history. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's past eating history.

[0109] The customized meal plan system can adjust the meal plan content based on the user's geographical location. For example, if a user lives in a high-altitude area, the system can offer a plan using ingredients specific to that area. If a user lives in an urban area, the system can offer a plan using ingredients readily available in urban areas. Furthermore, if a user lives in a rural area, the system can offer a plan using ingredients readily available in rural areas. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's geographical location.

[0110] A customized meal plan system can analyze a user's social media activity and adjust the content of the meal plan accordingly. For example, if a user frequently posts about health, the system can customize the plan based on that information. Similarly, if a user posts about specific foods or exercises, the system can customize the plan based on that information. Furthermore, if a user posts about stress or emotions, the system can customize the plan based on that information. This allows the customized meal plan system to provide a more appropriate meal plan based on the user's social media activity.

[0111] The customized meal plan delivery system can estimate the user's emotions and adjust the frequency of meal plan delivery based on those emotions. For example, if the user is relaxed, meal kits can be delivered once a week. If the user is stressed, meal kits can be delivered twice a week. Furthermore, if the user is in a hurry, meal kits can be delivered daily. In this way, the customized meal plan delivery system can deliver meal kits at a more appropriate frequency based on the user's emotions.

[0112] The customized meal plan system can adjust the meal plan based on the user's health check results. For example, if the health check results indicate that the user needs a specific nutrient, the system can provide a plan that includes ingredients containing that nutrient. Furthermore, if the health check results indicate that the user should avoid a specific ingredient, the system can provide a plan that excludes that ingredient. It can also provide a plan tailored to the user's health condition based on the health check results. In this way, the customized meal plan system can provide a more appropriate meal plan based on the user's health check results.

[0113] A customized meal plan delivery system can estimate the user's emotions and prioritize meal plans based on those emotions. For example, if the user is relaxed, a detailed meal plan can be prioritized. If the user is stressed, a simplified meal plan can be prioritized. Furthermore, if the user is in a hurry, a meal plan that can be prepared quickly can be prioritized. In this way, the customized meal plan delivery system can prioritize and deliver a more appropriate plan based on the user's emotions.

[0114] A customized meal plan system can adjust the content of meal plans based on the user's lifestyle and health condition. For example, if a user exercises regularly, the system can provide a plan that takes into account the calorie intake corresponding to that exercise level. Furthermore, if a user has a specific illness, the system can provide a meal plan tailored to that illness. Additionally, if a user has specific dietary restrictions, the system can provide a plan that accommodates those restrictions. In this way, the customized meal plan system can provide more appropriate meal plans based on the user's lifestyle and health condition.

[0115] A customized meal plan system can estimate the user's emotions and adjust how the meal plan is created based on those emotions. For example, if the user is relaxed, a detailed meal plan can be created. If the user is stressed, a simplified meal plan can be created. Furthermore, if the user is in a hurry, a meal plan that can be quickly created can be provided. In this way, the customized meal plan system can create a more appropriate plan based on the user's emotions.

[0116] A customized meal plan system can estimate a user's emotions and adjust the meal plan based on those emotions. For example, if a user is stressed, the system can provide a plan that includes ingredients effective in reducing stress. If a user is relaxed, it can provide a plan that includes ingredients that enhance relaxation. Furthermore, if a user is fatigued, it can provide a plan that includes ingredients suitable for energy replenishment. In this way, the customized meal plan system can provide the optimal meal plan tailored to the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The collection unit collects the user's genetic information. For example, the collection unit can collect the user's saliva sample, blood sample, or hair sample. Step 2: The analysis unit analyzes the genetic information collected by the collection unit. For example, the analysis unit can analyze the genetic information using bioinformatics methods, statistical analysis, and machine learning algorithms. Step 3: The generation unit creates a customized meal plan based on the analysis results obtained by the analysis unit. For example, the generation unit can create a meal plan using a generation AI, and can use a text generation AI (e.g., LLM) or a multimodal generation AI. Step 4: The delivery unit provides the meal plan created by the generation unit. For example, the delivery unit can provide customized meal kits to users and use digital delivery, postal mail, or smartphone notifications. Step 5: The upload unit uploads the user's health check results to the generating AI. For example, the upload unit can upload the user's blood test results, physical measurement data, and electrocardiogram data.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and upload unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's genetic information using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the genetic information using bioinformatics methods and machine learning algorithms. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and creates a customized meal plan using a generation AI. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the user with a customized meal kit. The upload unit uploads the user's health check results to the generation AI using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and upload unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's genetic information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the genetic information using bioinformatics methods and machine learning algorithms. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and creates a customized meal plan using a generation AI. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the user with a customized meal kit. The upload unit uploads the user's health check results to the generation AI using the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and upload unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's genetic information using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the genetic information using bioinformatics methods and machine learning algorithms. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and creates a customized meal plan using a generation AI. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the user with a customized meal kit. The upload unit uploads the user's health check results to the generation AI using the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, and upload unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's genetic information using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the genetic information using bioinformatics methods and machine learning algorithms. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and creates a customized meal plan using a generation AI. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with a customized meal kit. The upload unit uploads the user's health check results to the generation AI using, for example, the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) A collection unit that collects genetic information, An analysis unit analyzes the genetic information collected by the aforementioned collection unit, A generation unit that creates a customized meal plan based on the analysis results obtained by the analysis unit, A provisioning unit that provides the meal plan created by the generation unit, It includes an upload unit for uploading health checkup results. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user genetic samples. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze survey data regarding users' health status and lifestyle habits. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We assess the user's risk of developing illness and create a customized meal plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide users with customized meal kits. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned upload unit, Upload the user's health check results to the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of gene sample collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting genetic samples, the system selects the optimal collection method by referring to the user's past health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting genetic samples, filtering is performed based on the user's living environment and diet. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of gene samples to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting genetic samples, the system prioritizes the collection of highly relevant samples by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting genetic samples, we analyze users' social media activity and collect relevant samples. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method of the survey data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing survey data, we improve the accuracy of the analysis by referring to the user's past health checkup results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing survey data, different analysis algorithms are applied based on the user's lifestyle and eating habits. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing survey data, the priority of analysis is determined based on when the user submitted the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing survey data, we improve the accuracy of the analysis by referring to relevant literature from the users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how meal plans are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When creating a meal plan, the system references the user's past meal history to create the most suitable plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When creating a meal plan, we provide different plans based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of meal plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When creating meal plans, we provide the optimal plan by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When creating meal plans, analyze the user's social media activity to customize the plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates user emotions and adjusts the meal kit delivery method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing meal kits, the system selects the optimal delivery method by referring to the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing meal kits, the contents are customized based on the user's lifestyle and health condition. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates user sentiment and adjusts the frequency of meal kit delivery based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing meal kits, the optimal delivery method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing meal kits, we analyze users' social media activity to customize the content of the offerings. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned upload unit, The system estimates the user's emotions and adjusts the timing of uploading health check results based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned upload unit, When uploading health check results, the system will refer to the user's past health check results to select the most suitable upload method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned upload unit, It estimates the user's emotions and determines the priority of uploaded health check results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned upload unit, When uploading health checkup results, the system prioritizes uploading highly relevant results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 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. A collection unit that collects genetic information, An analysis unit analyzes the genetic information collected by the aforementioned collection unit, A generation unit that creates a customized meal plan based on the analysis results obtained by the analysis unit, A provisioning unit that provides the meal plan created by the generation unit, It includes an upload unit for uploading health checkup results. A system characterized by the following features.

2. The aforementioned collection unit is Collect user genetic samples. The system according to feature 1.

3. The aforementioned analysis unit, Analyze survey data regarding users' health status and lifestyle habits. The system according to feature 1.

4. The generating unit is We assess the user's risk of developing illness and create a customized meal plan. The system according to feature 1.

5. The aforementioned supply unit is, Provide users with customized meal kits. The system according to feature 1.

6. The aforementioned upload unit, Upload the user's health check results to the generating AI. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting genetic samples, the system selects the optimal collection method by referring to the user's past health checkup results. The system according to feature 1.

9. The aforementioned collection unit is When collecting genetic samples, filtering is performed based on the user's living environment and diet. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and determines the priority of gene samples to collect based on the estimated user emotions. The system according to feature 1.

Images