system

Abstract

The system according to this embodiment aims to provide personalized meal and training menus by utilizing medical data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a delivery unit. The collection unit collects medical data. The analysis unit analyzes the medical data collected by the collection unit. The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. The provision unit provides the menus created by the creation unit. The delivery unit delivers the meal menus provided by the provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to utilize medical data to provide individualized diet and training menus, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize medical data to provide individualized diet and training menus.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a creation unit, a provision unit, and a delivery unit. The data collection unit collects medical data. The analysis unit analyzes the medical data collected by the data collection unit. The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. The provision unit provides the menus created by the creation unit. The delivery unit delivers the meal menus provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized meal and training menus by utilizing medical data. [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 controls 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 health promotion system according to an embodiment of the present invention is a system that promotes health for people who are interested in health but find it troublesome, by providing not only medical data for treatment but also meal and training menus using AI. The health promotion system acquires medical data, and based on the acquired medical data, the AI ​​creates an optimal meal and training menu for each individual user. This menu is customized according to the user's physical condition and age. Furthermore, the meal menu may be delivered via a delivery service. This mechanism allows users to easily maintain and improve their health. For example, the health promotion system collects detailed data such as the user's medical records and test results. For example, this includes data such as blood pressure, blood sugar levels, and weight. This allows for an accurate understanding of the user's health status. Next, based on the acquired medical data, the health promotion system uses AI to create an optimal meal and training menu for each individual user. The AI ​​proposes an effective menu according to the user's health status and goals. For example, a user who wants to lose weight may be proposed a meal menu that includes calorie restriction and a training menu that focuses on aerobic exercise. On the other hand, a user who wants to increase muscle strength may be proposed a meal menu that is high in protein and a menu that focuses on strength training. Furthermore, the health promotion system may also deliver the meal menu via a delivery service. This allows users to easily enjoy healthy meals at home. For example, nutritionally balanced meals are delivered based on meal menus suggested by AI. In this way, users can enjoy healthy meals without the hassle of preparing them. This enables health promotion systems to easily maintain and improve users' health. For example, it will be a very convenient service for busy business people and people who are interested in health but find it troublesome. Furthermore, it is expected that the number of gym users will increase because effective training tailored to physical condition and age can be provided at a low cost. In addition, by providing effective meal menus, it can contribute to the health promotion of users. In short, health promotion systems enable users to easily maintain and improve their health.

[0029] The health promotion system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a delivery unit. The collection unit collects medical data. The collection unit collects detailed data such as the user's medical records and test results. For example, this includes data such as blood pressure, blood glucose levels, and weight. The collection unit can also use AI to analyze the user's medical records and test results and extract necessary data. The analysis unit analyzes the medical data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to understand the user's health status. For example, the analysis unit analyzes blood pressure fluctuation patterns and provides information useful for the user's blood pressure management. The analysis unit can also analyze blood glucose fluctuations and assess the risk of diabetes. The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, according to the user's health status and goals. For example, the creation unit can also create meal menus that are high in protein and menus that focus on strength training. The provisioning unit provides the user with the menu created by the creation unit. The provisioning unit can provide the menu, for example, through an application. It can also provide the menu by email. The delivery unit delivers the meal menu provided by the provisioning unit. The delivery unit can deliver the meal menu, for example, through a delivery service. It can also deliver the meal menu using drone delivery. As a result, the health promotion system according to this embodiment can easily maintain and improve the user's health.

[0030] The data collection unit collects medical data. For example, it collects detailed data such as users' medical records and test results. Specifically, it obtains medical records from hospitals and clinics through electronic medical record systems and directly collects test results from laboratories. This includes biometric data such as blood pressure, blood glucose levels, weight, heart rate, and cholesterol levels. Furthermore, the data collection unit can also collect data from wearable devices and smartphone apps. For example, it obtains data such as heart rate, steps, and sleep patterns from smartwatches, and collects meal records and exercise history from smartphone apps. The data collection unit can also use AI to analyze users' medical records and test results and extract necessary data. The AI ​​uses natural language processing technology to analyze text data from electronic medical records and extract important medical information. It can also use image recognition technology to detect abnormalities from medical images. This allows the data collection unit to efficiently collect a wide range of medical data from diverse sources and gain a comprehensive understanding of the user's health status. Furthermore, the data collection unit implements encryption technology and access control to ensure data privacy and security, protecting users' personal information. This enables the data collection unit to achieve highly reliable data collection, thereby improving the overall reliability of the system.

[0031] The analysis unit analyzes medical data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to understand the user's health status. Specifically, it uses statistical analysis to analyze the fluctuation patterns of the user's blood pressure and blood glucose levels, identifying outliers and trends. Machine learning algorithms are used to learn from past data and predict future health risks. For example, it analyzes blood pressure fluctuation patterns to provide information useful for the user's blood pressure management. The analysis unit can also analyze blood glucose fluctuations to assess the risk of diabetes. Furthermore, the analysis unit can integrate multiple data sources to perform a comprehensive health assessment. For example, it can combine data such as heart rate, steps, and sleep patterns to evaluate the user's overall health status. The analysis unit can use AI to perform anomaly detection and pattern recognition, enabling early detection of health risks. For example, it can detect abnormal heart rate fluctuations and warn of the risk of heart disease. The analysis unit can also provide personalized health advice, taking into account the user's lifestyle and environmental factors. This allows the analysis unit to gain a detailed understanding of the user's health status and provide information for taking appropriate measures. Furthermore, the analysis unit visualizes the analysis results, making them easily understandable to the user. For example, it visually displays fluctuations in health data using graphs and charts. This allows the analysis unit to support the user's health management and maximize the effectiveness of the health promotion system.

[0032] The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, depending on the user's health condition and goals. Specifically, it calculates the optimal calorie intake considering information such as the user's weight, age, gender, and activity level, and designs a meal menu based on that. For example, it can create meal menus that are high in protein or menus that focus on strength training. The creation unit also considers nutritional balance and appropriately adjusts the intake of vitamins and minerals. Furthermore, the creation unit can provide personalized menus considering the user's preferences and allergy information. For example, it can create vegetarian or gluten-free meal menus. For training menus, it creates programs that combine aerobic exercise, strength training, flexibility training, etc., according to the user's fitness level and goals. For example, it can provide a menu that combines three sessions of aerobic exercise and two sessions of strength training per week. The creation unit can also use AI to monitor the user's progress and adjust the menu as needed. In this way, the creation unit can support the user in achieving their health goals and realize effective health promotion. Furthermore, the creation team can collect user feedback and use it to improve the menu. This allows the creation team to consistently provide the best possible menu incorporating the latest information and technology, thereby increasing user satisfaction.

[0033] The service provider delivers menus created by the creation provider to users. The service provider can deliver menus through applications, for example. Specifically, they can enable users to access menus anytime, anywhere via smartphone or web applications. These applications manage user schedules and progress, and provide reminders and notifications. The service provider can also deliver menus via email, for example, sending daily meal plans and training plans for easy review. Furthermore, the service provider can offer menu customization options based on user preferences, such as excluding specific ingredients or adjusting exercise intensity. The service provider can also collect user feedback to improve menus, such as ratings and comments from users, which can then be used to adjust the menus. Additionally, the service provider can provide community features for users to encourage each other and share information. This allows the service provider to boost user motivation and maximize the effectiveness of the health promotion system. Finally, the service provider implements encryption technology and access controls to protect users' personal information and ensure data privacy and security. This allows the service provider to offer highly reliable services and gain the trust of users.

[0034] The delivery department delivers the meal menus provided by the supply department. The delivery department can deliver meal menus through, for example, a delivery service. Specifically, it uses partner delivery companies to deliver meals to the user's specified time and location. The delivery department can also deliver meal menus using drone delivery. Drone delivery is particularly effective as a fast and efficient delivery method in areas with traffic congestion or difficult access. The delivery department can track the delivery progress in real time and notify the user. For example, the user can check the delivery status and estimated arrival time through an application. Furthermore, the delivery department thoroughly manages temperature and hygiene to ensure the quality and safety of the meals. For example, it uses insulated bags and containers to ensure meals are delivered at the appropriate temperature. The delivery department can also collect user feedback to improve the delivery service. For example, it can reflect requests regarding delivery times, frequency, and meal content to provide a more satisfying service. This allows the delivery department to enhance user convenience and maximize the effectiveness of the health promotion system. Furthermore, the delivery department can implement environmentally friendly delivery methods and provide sustainable services. For example, by using electric vehicles and reusable containers, they can reduce their environmental impact. This allows the delivery department to provide services that consider not only the health of users but also the environment.

[0035] The data collection unit can collect detailed data such as the user's medical records and test results. For example, the data collection unit can obtain the user's medical records from electronic medical records. It can also obtain the user's blood test results from medical institutions. Furthermore, the data collection unit can obtain the user's vital sign data from wearable devices. This allows for an accurate understanding of the user's health status through the collection of detailed medical data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input medical records obtained from electronic medical records into AI and extract the necessary data.

[0036] The analysis unit can analyze collected medical data to understand the user's health status. For example, the analysis unit can use statistical analysis to analyze the user's blood pressure fluctuation patterns. It can also use machine learning algorithms to analyze the user's blood glucose fluctuations. Furthermore, the analysis unit can analyze the user's weight fluctuations and provide information useful for weight management. In this way, the user's health status can be accurately understood through the analysis of medical data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected medical data into AI to perform health status analysis.

[0037] The creation unit can create effective meal and training menus tailored to the user's health condition and goals. For example, it can create a meal menu that includes calorie restriction based on the user's health condition. It can also create a training menu centered on aerobic exercise, tailored to the user's goals. Furthermore, it can create meal menus high in protein or menus centered on strength training. This allows for effective health promotion by creating menus tailored to the user's health condition and goals. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input the user's health condition data into AI to create an optimal menu.

[0038] The service provider can provide the created menus to users. For example, the service provider can provide menus through an application. It can also provide menus via email. Furthermore, it can provide menus through a website. This allows users to easily practice healthy eating and training through the provision of created menus. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the created menus into AI and select the optimal method of delivery.

[0039] The delivery unit can deliver the provided meal menus. The delivery unit can deliver the meal menus, for example, through a delivery service. The delivery unit can also deliver the meal menus using drone delivery. Furthermore, the delivery unit can deliver the meal menus using frozen foods. This allows users to easily consume healthy meals through meal menu delivery. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the provided meal menu into AI and select the optimal delivery method.

[0040] The data collection unit can analyze the user's past medical records and test results to select the optimal data collection method. For example, the data collection unit can prioritize the collection of specific tests from the user's past medical records if those tests are necessary. The data collection unit can also automatically select items that require regular testing based on the user's past test results. Furthermore, the data collection unit can analyze the user's past medical records and collect additional data based on specific medical history. This enables the collection of optimal medical data through the analysis of past medical records and test results. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past medical records into AI to select the optimal data collection method.

[0041] The data collection unit can filter medical data based on the user's lifestyle and dietary history. For example, the data collection unit can collect information on the intake of specific nutrients based on the user's dietary history. The data collection unit can also collect data on exercise levels and sleep patterns, taking into account the user's lifestyle. Furthermore, the data collection unit can combine the user's dietary history and lifestyle to identify factors that influence health status. This allows for the collection of more relevant medical data through filtering based on lifestyle and dietary history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and perform filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, if the user lives in a specific region, the data collection unit can collect data on health risks specific to that region. Furthermore, if the user is traveling, the data collection unit can collect data on health risks in their travel destination. In addition, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the collection of more relevant medical data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into AI and prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze users' social media activity and collect relevant data when collecting medical data. For example, the data collection unit can collect data on stress and emotional fluctuations from users' social media posts. The data collection unit can also analyze lifestyle habits and dietary trends based on users' social media activity. Furthermore, the data collection unit can collect data on specific health risks based on users' health-related posts on social media. This makes it possible to collect more relevant medical data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input users' social media data into AI and collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit will perform a detailed analysis on important medical data. It can also perform a simplified analysis on general medical data. Furthermore, the analysis unit can focus its analysis on data related to specific health risks. This allows for more effective analysis by adjusting the level of detail according to the importance of the medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and adjust the level of detail of the analysis based on its importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of medical data during analysis. For example, the analysis unit can apply a blood pressure fluctuation analysis algorithm to blood pressure data. It can also apply an algorithm to analyze blood glucose fluctuation patterns to blood glucose data. Furthermore, it can apply an algorithm to analyze weight fluctuation trends to weight data. This allows for more accurate analysis by applying analysis algorithms appropriate to the category of medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input medical data into AI and apply different analysis algorithms depending on the category.

[0046] The analysis unit can determine the priority of analysis based on when the medical data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. It can also analyze long-term trends based on regularly collected data. Furthermore, the analysis unit may prioritize the analysis of data related to specific events or symptoms. This allows for more effective analysis by prioritizing analysis based on the timing of medical data collection. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and determine the priority of analysis based on the collection timing.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the medical data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can prioritize the analysis of data related to specific health risks. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and adjust the order of analysis based on relevance.

[0048] The menu creation unit can adjust the level of detail in the menu based on the user's health condition and goals. For example, the unit can create a menu that includes detailed nutritional information according to the user's health condition. It can also create a menu that emphasizes calorie restriction or specific nutrients according to the user's goals. Furthermore, the unit can adjust the frequency and quantity of meals based on the user's health condition and goals. This allows for the provision of more effective menus by adjusting the level of detail according to the user's health condition and goals. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input the user's health data into AI and create a menu with adjusted level of detail.

[0049] The menu creation unit can apply different menu creation algorithms depending on the user's lifestyle and eating history when creating a menu. For example, the unit can create a menu that includes ingredients the user has previously enjoyed, based on the user's eating history. The unit can also adjust the timing and quantity of meals, taking into account the user's lifestyle. Furthermore, the unit can combine the user's eating history and lifestyle to create an optimal menu. This ensures that more appropriate menus are provided by applying menu creation algorithms tailored to the user's lifestyle and eating history. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the unit can input the user's lifestyle data into AI and apply different menu creation algorithms.

[0050] The creation unit can determine menu priorities based on when user health data is collected during menu creation. For example, the creation unit can create the latest menu based on recently collected health data. It can also create menus that reflect long-term trends based on regularly collected health data. Furthermore, the creation unit can prioritize menu creation based on health data related to specific events or symptoms. This allows for the provision of more effective menus by prioritizing menus based on the timing of health data collection. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input health data into AI to determine menu priorities based on the collection timing.

[0051] The menu creation unit can adjust the order of menu items based on the user's relevant data when creating a menu. For example, the unit can adjust the order of menu items based on highly relevant data. It can also postpone less relevant data. Furthermore, the unit can prioritize menu creation based on data related to specific health risks. This allows for the provision of more effective menus by adjusting the order of menu items based on relevant data. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input relevant data into AI and create a menu with the order adjusted.

[0052] The service provider can select the optimal service method when providing a menu by referring to the user's past usage history. For example, the service provider can prioritize service methods that the user has preferred in the past. Furthermore, the service provider can select a service method suitable for a specific time of day based on the user's past usage history. In addition, the service provider can analyze the user's past usage history to select the most effective service method. This allows for the provision of more effective menus by selecting service methods based on past usage history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input user usage history data into AI to select the optimal service method.

[0053] The service provider can select the optimal service method when providing a menu, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a service method that matches the screen size. If the user is using a tablet, the service provider can also select a service method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can select a concise and highly visible service method. This allows for the provision of more appropriate menus by selecting a service method based on device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into AI to select the optimal service method.

[0054] The service provider can refer to the user's calendar information when providing menus and make suggestions based on their schedule. For example, the service provider can refer to the appointments registered in the user's calendar and automatically set the menu. The service provider can also suggest menus related to specific events based on the user's calendar information. Furthermore, the service provider can suggest the most suitable menu based on the user's calendar information and their schedule. This allows for the provision of more appropriate menus through calendar-based suggestions. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's calendar information into AI and make suggestions based on their schedule.

[0055] The delivery unit can select the optimal delivery method by referring to the user's past delivery history during delivery. For example, the delivery unit may prioritize delivery methods that the user has preferred in the past. It can also select a delivery method suitable for a specific time slot based on the user's past delivery history. Furthermore, the delivery unit can analyze the user's past delivery history to select the most effective delivery method. This allows for more effective delivery by selecting a delivery method based on past delivery history. Some or all of the above processes in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's delivery history data into AI to select the optimal delivery method.

[0056] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific region, the delivery unit can select a delivery method specific to that region. Furthermore, if the user is traveling, the delivery unit can offer delivery options for their travel destination. In addition, the delivery unit can select the optimal delivery method based on the user's geographical location information. This allows for more appropriate delivery through the selection of a delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's geographical location information into AI to select the optimal delivery method.

[0057] The delivery unit can make scheduled suggestions by referring to the user's calendar information during delivery. For example, the delivery unit can refer to the appointments registered in the user's calendar and automatically set the delivery timing. The delivery unit can also suggest deliveries related to specific events based on the user's calendar information. Furthermore, the delivery unit can suggest the most suitable delivery based on the user's calendar information. This enables more appropriate deliveries through calendar-based suggestions. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's calendar information into AI and make scheduled suggestions.

[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] The health promotion system can further collect user sleep data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's sleep patterns and sleep quality from a wearable device. Based on this data, the analysis unit can evaluate the user's sleep quality and provide advice for improvement. The creation unit can also create diet and training menus to improve sleep quality based on the user's sleep data. For example, it can suggest a diet menu containing specific nutrients or a training menu with relaxing effects to improve sleep quality. This enables the user to manage their health comprehensively.

[0060] The health promotion system can further collect user exercise data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's exercise volume and type from a fitness tracker. Based on this data, the analysis unit can evaluate the user's exercise habits and provide advice for improvement. The creation unit can also create training menus to maximize the effects of exercise based on the user's exercise data. For example, it can suggest menus to strengthen specific exercises or balanced exercise menus. This allows users to manage their exercise more effectively.

[0061] The health promotion system can further collect user dietary data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's diet and calorie intake from a meal logging app. Based on this data, the analysis unit can evaluate the user's eating habits and provide advice for improvement. The creation unit can also create nutritionally balanced meal menus based on the user's dietary data. For example, it can suggest menus that fortify specific nutrients or menus that include calorie restriction. This enables users to manage their diet more effectively.

[0062] The health promotion system can further collect user hydration data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's hydration amount and timing from a smart water bottle. Based on this data, the analysis unit can evaluate the user's hydration habits and provide advice for improvement. The creation unit can also create an appropriate hydration plan based on the user's hydration data. For example, it can provide reminders to drink water at specific times and suggest appropriate hydration amounts. This enables users to manage their hydration more effectively.

[0063] The health promotion system can further monitor the user's stress level in real time and analyze it in the analysis unit. For example, the data collection unit can acquire data on the user's heart rate and skin electrical activity from a wearable device. Based on this data, the analysis unit can evaluate the user's stress level and provide advice for stress reduction. The creation unit can also create relaxing meal and training menus based on the user's stress data. For example, it can suggest meal menus containing specific nutrients or training menus aimed at relaxation to reduce stress. This allows the user to manage stress more effectively.

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

[0065] Step 1: The data collection unit collects medical data. The data collection unit collects detailed data such as the user's medical records and test results. Specifically, this includes data such as blood pressure, blood sugar levels, and weight. The data collection unit can also use AI to analyze the user's medical records and test results and extract the necessary data. Step 2: The analysis unit analyzes the medical data collected by the data collection unit. The analysis unit uses statistical analysis and machine learning algorithms, for example, to understand the user's health status. Specifically, it analyzes blood pressure fluctuation patterns to provide information useful for the user's blood pressure management. It can also analyze blood glucose fluctuations to assess the risk of diabetes. Step 3: The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, depending on the user's health condition and goals. Specifically, it can also create meal menus that are high in protein and menus that focus on strength training. Step 4: The delivery unit provides the menu created by the creation unit to the user. The delivery unit can provide the menu, for example, through an application. Alternatively, the menu can be provided via email. Step 5: The delivery department delivers the meal menus provided by the serving department. The delivery department can deliver the meal menus, for example, through a delivery service. Alternatively, they can deliver the meal menus using drone delivery.

[0066] (Example of form 2) The health promotion system according to an embodiment of the present invention is a system that promotes health for people who are interested in health but find it troublesome, by providing not only medical data for treatment but also meal and training menus using AI. The health promotion system acquires medical data, and based on the acquired medical data, the AI ​​creates an optimal meal and training menu for each individual user. This menu is customized according to the user's physical condition and age. Furthermore, the meal menu may be delivered via a delivery service. This mechanism allows users to easily maintain and improve their health. For example, the health promotion system collects detailed data such as the user's medical records and test results. For example, this includes data such as blood pressure, blood sugar levels, and weight. This allows for an accurate understanding of the user's health status. Next, based on the acquired medical data, the health promotion system uses AI to create an optimal meal and training menu for each individual user. The AI ​​proposes an effective menu according to the user's health status and goals. For example, a user who wants to lose weight may be proposed a meal menu that includes calorie restriction and a training menu that focuses on aerobic exercise. On the other hand, a user who wants to increase muscle strength may be proposed a meal menu that is high in protein and a menu that focuses on strength training. Furthermore, the health promotion system may also deliver the meal menu via a delivery service. This allows users to easily enjoy healthy meals at home. For example, nutritionally balanced meals are delivered based on meal menus suggested by AI. In this way, users can enjoy healthy meals without the hassle of preparing them. This enables health promotion systems to easily maintain and improve users' health. For example, it will be a very convenient service for busy business people and people who are interested in health but find it troublesome. Furthermore, it is expected that the number of gym users will increase because effective training tailored to physical condition and age can be provided at a low cost. In addition, by providing effective meal menus, it can contribute to the health promotion of users. In short, health promotion systems enable users to easily maintain and improve their health.

[0067] The health promotion system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a delivery unit. The collection unit collects medical data. The collection unit collects detailed data such as the user's medical records and test results. For example, this includes data such as blood pressure, blood glucose levels, and weight. The collection unit can also use AI to analyze the user's medical records and test results and extract necessary data. The analysis unit analyzes the medical data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to understand the user's health status. For example, the analysis unit analyzes blood pressure fluctuation patterns and provides information useful for the user's blood pressure management. The analysis unit can also analyze blood glucose fluctuations and assess the risk of diabetes. The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, according to the user's health status and goals. For example, the creation unit can also create meal menus that are high in protein and menus that focus on strength training. The provisioning unit provides the user with the menu created by the creation unit. The provisioning unit can provide the menu, for example, through an application. It can also provide the menu by email. The delivery unit delivers the meal menu provided by the provisioning unit. The delivery unit can deliver the meal menu, for example, through a delivery service. It can also deliver the meal menu using drone delivery. As a result, the health promotion system according to this embodiment can easily maintain and improve the user's health.

[0068] The data collection unit collects medical data. For example, it collects detailed data such as users' medical records and test results. Specifically, it obtains medical records from hospitals and clinics through electronic medical record systems and directly collects test results from laboratories. This includes biometric data such as blood pressure, blood glucose levels, weight, heart rate, and cholesterol levels. Furthermore, the data collection unit can also collect data from wearable devices and smartphone apps. For example, it obtains data such as heart rate, steps, and sleep patterns from smartwatches, and collects meal records and exercise history from smartphone apps. The data collection unit can also use AI to analyze users' medical records and test results and extract necessary data. The AI ​​uses natural language processing technology to analyze text data from electronic medical records and extract important medical information. It can also use image recognition technology to detect abnormalities from medical images. This allows the data collection unit to efficiently collect a wide range of medical data from diverse sources and gain a comprehensive understanding of the user's health status. Furthermore, the data collection unit implements encryption technology and access control to ensure data privacy and security, protecting users' personal information. This enables the data collection unit to achieve highly reliable data collection, thereby improving the overall reliability of the system.

[0069] The analysis unit analyzes medical data collected by the data collection unit. For example, the analysis unit uses statistical analysis and machine learning algorithms to understand the user's health status. Specifically, it uses statistical analysis to analyze the fluctuation patterns of the user's blood pressure and blood glucose levels, identifying outliers and trends. Machine learning algorithms are used to learn from past data and predict future health risks. For example, it analyzes blood pressure fluctuation patterns to provide information useful for the user's blood pressure management. The analysis unit can also analyze blood glucose fluctuations to assess the risk of diabetes. Furthermore, the analysis unit can integrate multiple data sources to perform a comprehensive health assessment. For example, it can combine data such as heart rate, steps, and sleep patterns to evaluate the user's overall health status. The analysis unit can use AI to perform anomaly detection and pattern recognition, enabling early detection of health risks. For example, it can detect abnormal heart rate fluctuations and warn of the risk of heart disease. The analysis unit can also provide personalized health advice, taking into account the user's lifestyle and environmental factors. This allows the analysis unit to gain a detailed understanding of the user's health status and provide information for taking appropriate measures. Furthermore, the analysis unit visualizes the analysis results, making them easily understandable to the user. For example, it visually displays fluctuations in health data using graphs and charts. This allows the analysis unit to support the user's health management and maximize the effectiveness of the health promotion system.

[0070] The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, depending on the user's health condition and goals. Specifically, it calculates the optimal calorie intake considering information such as the user's weight, age, gender, and activity level, and designs a meal menu based on that. For example, it can create meal menus that are high in protein or menus that focus on strength training. The creation unit also considers nutritional balance and appropriately adjusts the intake of vitamins and minerals. Furthermore, the creation unit can provide personalized menus considering the user's preferences and allergy information. For example, it can create vegetarian or gluten-free meal menus. For training menus, it creates programs that combine aerobic exercise, strength training, flexibility training, etc., according to the user's fitness level and goals. For example, it can provide a menu that combines three sessions of aerobic exercise and two sessions of strength training per week. The creation unit can also use AI to monitor the user's progress and adjust the menu as needed. In this way, the creation unit can support the user in achieving their health goals and realize effective health promotion. Furthermore, the creation team can collect user feedback and use it to improve the menu. This allows the creation team to consistently provide the best possible menu incorporating the latest information and technology, thereby increasing user satisfaction.

[0071] The service provider delivers menus created by the creation provider to users. The service provider can deliver menus through applications, for example. Specifically, they can enable users to access menus anytime, anywhere via smartphone or web applications. These applications manage user schedules and progress, and provide reminders and notifications. The service provider can also deliver menus via email, for example, sending daily meal plans and training plans for easy review. Furthermore, the service provider can offer menu customization options based on user preferences, such as excluding specific ingredients or adjusting exercise intensity. The service provider can also collect user feedback to improve menus, such as ratings and comments from users, which can then be used to adjust the menus. Additionally, the service provider can provide community features for users to encourage each other and share information. This allows the service provider to boost user motivation and maximize the effectiveness of the health promotion system. Finally, the service provider implements encryption technology and access controls to protect users' personal information and ensure data privacy and security. This allows the service provider to offer highly reliable services and gain the trust of users.

[0072] The delivery department delivers the meal menus provided by the supply department. The delivery department can deliver meal menus through, for example, a delivery service. Specifically, it uses partner delivery companies to deliver meals to the user's specified time and location. The delivery department can also deliver meal menus using drone delivery. Drone delivery is particularly effective as a fast and efficient delivery method in areas with traffic congestion or difficult access. The delivery department can track the delivery progress in real time and notify the user. For example, the user can check the delivery status and estimated arrival time through an application. Furthermore, the delivery department thoroughly manages temperature and hygiene to ensure the quality and safety of the meals. For example, it uses insulated bags and containers to ensure meals are delivered at the appropriate temperature. The delivery department can also collect user feedback to improve the delivery service. For example, it can reflect requests regarding delivery times, frequency, and meal content to provide a more satisfying service. This allows the delivery department to enhance user convenience and maximize the effectiveness of the health promotion system. Furthermore, the delivery department can implement environmentally friendly delivery methods and provide sustainable services. For example, by using electric vehicles and reusable containers, they can reduce their environmental impact. This allows the delivery department to provide services that consider not only the health of users but also the environment.

[0073] The data collection unit can collect detailed data such as the user's medical records and test results. For example, the data collection unit can obtain the user's medical records from electronic medical records. It can also obtain the user's blood test results from medical institutions. Furthermore, the data collection unit can obtain the user's vital sign data from wearable devices. This allows for an accurate understanding of the user's health status through the collection of detailed medical data. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input medical records obtained from electronic medical records into AI and extract the necessary data.

[0074] The analysis unit can analyze collected medical data to understand the user's health status. For example, the analysis unit can use statistical analysis to analyze the user's blood pressure fluctuation patterns. It can also use machine learning algorithms to analyze the user's blood glucose fluctuations. Furthermore, the analysis unit can analyze the user's weight fluctuations and provide information useful for weight management. In this way, the user's health status can be accurately understood through the analysis of medical data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input collected medical data into AI to perform health status analysis.

[0075] The creation unit can create effective meal and training menus tailored to the user's health condition and goals. For example, it can create a meal menu that includes calorie restriction based on the user's health condition. It can also create a training menu centered on aerobic exercise, tailored to the user's goals. Furthermore, it can create meal menus high in protein or menus centered on strength training. This allows for effective health promotion by creating menus tailored to the user's health condition and goals. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input the user's health condition data into AI to create an optimal menu.

[0076] The service provider can provide the created menus to users. For example, the service provider can provide menus through an application. It can also provide menus via email. Furthermore, it can provide menus through a website. This allows users to easily practice healthy eating and training through the provision of created menus. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input the created menus into AI and select the optimal method of delivery.

[0077] The delivery unit can deliver the provided meal menus. The delivery unit can deliver the meal menus, for example, through a delivery service. The delivery unit can also deliver the meal menus using drone delivery. Furthermore, the delivery unit can deliver the meal menus using frozen foods. This allows users to easily consume healthy meals through meal menu delivery. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the provided meal menu into AI and select the optimal delivery method.

[0078] The data collection unit can estimate the user's emotions and adjust the timing of medical data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect medical data during relaxed periods. If the user is relaxed, the data collection unit can also schedule longer sessions to collect more detailed data. Furthermore, if the user is in a hurry, the data collection unit can collect the necessary data in a shorter time. This allows for the collection of more appropriate medical data by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into AI to adjust the collection timing.

[0079] The data collection unit can analyze the user's past medical records and test results to select the optimal data collection method. For example, the data collection unit can prioritize the collection of specific tests from the user's past medical records if those tests are necessary. The data collection unit can also automatically select items that require regular testing based on the user's past test results. Furthermore, the data collection unit can analyze the user's past medical records and collect additional data based on specific medical history. This enables the collection of optimal medical data through the analysis of past medical records and test results. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past medical records into AI to select the optimal data collection method.

[0080] The data collection unit can filter medical data based on the user's lifestyle and dietary history. For example, the data collection unit can collect information on the intake of specific nutrients based on the user's dietary history. The data collection unit can also collect data on exercise levels and sleep patterns, taking into account the user's lifestyle. Furthermore, the data collection unit can combine the user's dietary history and lifestyle to identify factors that influence health status. This allows for the collection of more relevant medical data through filtering based on lifestyle and dietary history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's lifestyle data into AI and perform filtering.

[0081] The data collection unit can estimate the user's emotions and determine the priority of medical data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related data. It can also collect general health data if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting the most important data. This allows for the collection of more important medical data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI to determine priorities.

[0082] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting medical data. For example, if the user lives in a specific region, the data collection unit can collect data on health risks specific to that region. Furthermore, if the user is traveling, the data collection unit can collect data on health risks in their travel destination. In addition, the data collection unit can collect data related to environmental factors based on the user's geographical location. This allows for the collection of more relevant medical data by considering geographical location. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input the user's geographical location into AI and prioritize the collection of highly relevant data.

[0083] The data collection unit can analyze users' social media activity and collect relevant data when collecting medical data. For example, the data collection unit can collect data on stress and emotional fluctuations from users' social media posts. The data collection unit can also analyze lifestyle habits and dietary trends based on users' social media activity. Furthermore, the data collection unit can collect data on specific health risks based on users' health-related posts on social media. This makes it possible to collect more relevant medical data through the analysis of social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input users' social media data into AI and collect relevant data.

[0084] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result that gets straight to the point. This allows for more easily understandable analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and adjust the presentation of the analysis.

[0085] The analysis unit can adjust the level of detail of the analysis based on the importance of the medical data during the analysis. For example, the analysis unit will perform a detailed analysis on important medical data. It can also perform a simplified analysis on general medical data. Furthermore, the analysis unit can focus its analysis on data related to specific health risks. This allows for more effective analysis by adjusting the level of detail according to the importance of the medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and adjust the level of detail of the analysis based on its importance.

[0086] The analysis unit can apply different analysis algorithms depending on the category of medical data during analysis. For example, the analysis unit can apply a blood pressure fluctuation analysis algorithm to blood pressure data. It can also apply an algorithm to analyze blood glucose fluctuation patterns to blood glucose data. Furthermore, it can apply an algorithm to analyze weight fluctuation trends to weight data. This allows for more accurate analysis by applying analysis algorithms appropriate to the category of medical data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input medical data into AI and apply different analysis algorithms depending on the category.

[0087] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with visually stimulating effects. This allows for more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI ​​and adjust the length of the analysis.

[0088] The analysis unit can determine the priority of analysis based on when the medical data was collected. For example, the analysis unit may prioritize the analysis of recently collected data. It can also analyze long-term trends based on regularly collected data. Furthermore, the analysis unit may prioritize the analysis of data related to specific events or symptoms. This allows for more effective analysis by prioritizing analysis based on the timing of medical data collection. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and determine the priority of analysis based on the collection timing.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the medical data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. It can also postpone the analysis of less relevant data. Furthermore, the analysis unit can prioritize the analysis of data related to specific health risks. This allows for more effective analysis by adjusting the order of analysis based on the relevance of the medical data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input medical data into AI and adjust the order of analysis based on relevance.

[0090] The creation unit can estimate the user's emotions and adjust the menu creation method based on the estimated emotions. For example, if the user is stressed, the creation unit can create a meal menu with a relaxing effect. It can also create a balanced meal menu if the user is relaxed. Furthermore, if the user is in a hurry, the creation unit can create a meal menu that is easy to prepare. This allows for the provision of more appropriate menus by adjusting the menu creation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into AI and adjust the menu creation method.

[0091] The menu creation unit can adjust the level of detail in the menu based on the user's health condition and goals. For example, the unit can create a menu that includes detailed nutritional information according to the user's health condition. It can also create a menu that emphasizes calorie restriction or specific nutrients according to the user's goals. Furthermore, the unit can adjust the frequency and quantity of meals based on the user's health condition and goals. This allows for the provision of more effective menus by adjusting the level of detail according to the user's health condition and goals. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input the user's health data into AI and create a menu with adjusted level of detail.

[0092] The menu creation unit can apply different menu creation algorithms depending on the user's lifestyle and eating history when creating a menu. For example, the unit can create a menu that includes ingredients the user has previously enjoyed, based on the user's eating history. The unit can also adjust the timing and quantity of meals, taking into account the user's lifestyle. Furthermore, the unit can combine the user's eating history and lifestyle to create an optimal menu. This ensures that more appropriate menus are provided by applying menu creation algorithms tailored to the user's lifestyle and eating history. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the unit can input the user's lifestyle data into AI and apply different menu creation algorithms.

[0093] The creation unit can estimate the user's emotions and adjust the length of the menu based on the estimated emotions. For example, if the user is in a hurry, the creation unit can create a short, concise menu. If the user is relaxed, the creation unit can create a longer menu with detailed explanations. Furthermore, if the user is excited, the creation unit can create a menu with visually stimulating effects. This allows for the provision of more appropriate menus by adjusting the length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can input user emotion data into AI and adjust the length of the menu.

[0094] The creation unit can determine menu priorities based on when user health data is collected during menu creation. For example, the creation unit can create the latest menu based on recently collected health data. It can also create menus that reflect long-term trends based on regularly collected health data. Furthermore, the creation unit can prioritize menu creation based on health data related to specific events or symptoms. This allows for the provision of more effective menus by prioritizing menus based on the timing of health data collection. Some or all of the above processes in the creation unit may be performed using AI or not. For example, the creation unit can input health data into AI to determine menu priorities based on the collection timing.

[0095] The menu creation unit can adjust the order of menu items based on the user's relevant data when creating a menu. For example, the unit can adjust the order of menu items based on highly relevant data. It can also postpone less relevant data. Furthermore, the unit can prioritize menu creation based on data related to specific health risks. This allows for the provision of more effective menus by adjusting the order of menu items based on relevant data. Some or all of the above processes in the menu creation unit may be performed using AI or not. For example, the menu creation unit can input relevant data into AI and create a menu with the order adjusted.

[0096] The service provider can estimate the user's emotions and adjust the menu presentation based on those emotions. For example, if the user is stressed, the service provider can present the menu in a simple and visually easy-to-understand manner. If the user is relaxed, the service provider can present the menu in a way that includes detailed explanations. Furthermore, if the user is in a hurry, the service provider can present the menu in a concise and to-the-point manner. This allows for the provision of a more appropriate menu by adjusting the presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and adjust the presentation method.

[0097] The service provider can select the optimal service method when providing a menu by referring to the user's past usage history. For example, the service provider can prioritize service methods that the user has preferred in the past. Furthermore, the service provider can select a service method suitable for a specific time of day based on the user's past usage history. In addition, the service provider can analyze the user's past usage history to select the most effective service method. This allows for the provision of more effective menus by selecting service methods based on past usage history. Some or all of the above processes in the service provider may be performed using AI, or not. For example, the service provider can input user usage history data into AI to select the optimal service method.

[0098] The service provider can select the optimal service method when providing a menu, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a service method that matches the screen size. If the user is using a tablet, the service provider can also select a service method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can select a concise and highly visible service method. This allows for the provision of more appropriate menus by selecting a service method based on device information. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input the user's device information into AI to select the optimal service method.

[0099] The service provider can estimate the user's emotions and adjust the order in which menu items are served based on those emotions. For example, if the user is stressed, the service provider may prioritize offering menu items with a relaxing effect. If the user is relaxed, the service provider may also offer a balanced menu. Furthermore, if the user is in a hurry, the service provider may prioritize offering menu items that are easy to prepare. This allows for the provision of more appropriate menu items by adjusting the order of service according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI to adjust the order of service.

[0100] The service provider can refer to the user's calendar information when providing menus and make suggestions based on their schedule. For example, the service provider can refer to the appointments registered in the user's calendar and automatically set the menu. The service provider can also suggest menus related to specific events based on the user's calendar information. Furthermore, the service provider can suggest the most suitable menu based on the user's calendar information and their schedule. This allows for the provision of more appropriate menus through calendar-based suggestions. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the user's calendar information into AI and make suggestions based on their schedule.

[0101] The delivery unit can estimate the user's emotions and adjust the delivery timing based on the estimated emotions. For example, if the user is feeling stressed, the delivery unit will deliver during a time when the user is relaxed. The delivery unit can also provide detailed delivery options if the user is relaxed. Furthermore, if the user is in a hurry, the delivery unit can deliver quickly. This allows for more appropriate delivery timing by adjusting the delivery timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into AI to adjust the delivery timing.

[0102] The delivery unit can select the optimal delivery method by referring to the user's past delivery history during delivery. For example, the delivery unit may prioritize delivery methods that the user has preferred in the past. It can also select a delivery method suitable for a specific time slot based on the user's past delivery history. Furthermore, the delivery unit can analyze the user's past delivery history to select the most effective delivery method. This allows for more effective delivery by selecting a delivery method based on past delivery history. Some or all of the above processes in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's delivery history data into AI to select the optimal delivery method.

[0103] The delivery unit can select the optimal delivery method at the time of delivery, taking into account the user's geographical location information. For example, if the user lives in a specific region, the delivery unit can select a delivery method specific to that region. Furthermore, if the user is traveling, the delivery unit can offer delivery options for their travel destination. In addition, the delivery unit can select the optimal delivery method based on the user's geographical location information. This allows for more appropriate delivery through the selection of a delivery method based on geographical location information. Some or all of the above processing in the delivery unit may be performed using AI, or not. For example, the delivery unit can input the user's geographical location information into AI to select the optimal delivery method.

[0104] The delivery unit can estimate the user's emotions and determine delivery priorities based on those emotions. For example, if the user is stressed, the delivery unit will prioritize deliveries that have a relaxing effect. If the user is relaxed, the delivery unit can also provide balanced deliveries. Furthermore, if the user is in a hurry, the delivery unit can provide quick deliveries. This allows for more appropriate deliveries by prioritizing deliveries according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI or not. For example, the delivery unit can input user emotion data into AI to determine delivery priorities.

[0105] The delivery unit can make scheduled suggestions by referring to the user's calendar information during delivery. For example, the delivery unit can refer to the appointments registered in the user's calendar and automatically set the delivery timing. The delivery unit can also suggest deliveries related to specific events based on the user's calendar information. Furthermore, the delivery unit can suggest the most suitable delivery based on the user's calendar information. This enables more appropriate deliveries through calendar-based suggestions. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the user's calendar information into AI and make scheduled suggestions.

[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] The health promotion system can further collect user sleep data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's sleep patterns and sleep quality from a wearable device. Based on this data, the analysis unit can evaluate the user's sleep quality and provide advice for improvement. The creation unit can also create diet and training menus to improve sleep quality based on the user's sleep data. For example, it can suggest a diet menu containing specific nutrients or a training menu with relaxing effects to improve sleep quality. This enables the user to manage their health comprehensively.

[0108] The health promotion system can further estimate the user's emotions and adjust the content and timing of feedback based on those estimates. For example, the analysis unit can provide relaxing feedback if the user is stressed. The feedback unit can also provide detailed feedback if the user is relaxed. Furthermore, the feedback unit can provide concise and to-the-point feedback if the user is in a hurry. This allows for more effective health management by adjusting feedback according to the user's emotions.

[0109] The health promotion system can further collect user exercise data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's exercise volume and type from a fitness tracker. Based on this data, the analysis unit can evaluate the user's exercise habits and provide advice for improvement. The creation unit can also create training menus to maximize the effects of exercise based on the user's exercise data. For example, it can suggest menus to strengthen specific exercises or balanced exercise menus. This allows users to manage their exercise more effectively.

[0110] The health promotion system can further estimate the user's emotions and, based on those emotions, provide messages to boost their motivation. For example, if the user is feeling stressed, the system can provide encouraging messages. If the user is relaxed, it can provide messages that evoke a sense of accomplishment. Furthermore, if the user is in a hurry, it can provide concise and effective messages. This allows for more effective health management by providing motivational messages tailored to the user's emotions.

[0111] The health promotion system can further collect user dietary data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's diet and calorie intake from a meal logging app. Based on this data, the analysis unit can evaluate the user's eating habits and provide advice for improvement. The creation unit can also create nutritionally balanced meal menus based on the user's dietary data. For example, it can suggest menus that fortify specific nutrients or menus that include calorie restriction. This enables users to manage their diet more effectively.

[0112] The health promotion system can further estimate the user's emotions and adjust the content and timing of reminders based on those estimates. For example, if the user is feeling stressed, the system can provide a relaxing reminder. If the user is relaxed, the system can provide a more detailed reminder. Furthermore, if the user is in a hurry, the system can provide a concise and to-the-point reminder. This allows for more effective health management by adjusting reminders according to the user's emotions.

[0113] The health promotion system can further collect user hydration data and analyze it in the analysis unit. For example, the collection unit can obtain data on the user's hydration amount and timing from a smart water bottle. Based on this data, the analysis unit can evaluate the user's hydration habits and provide advice for improvement. The creation unit can also create an appropriate hydration plan based on the user's hydration data. For example, it can provide reminders to drink water at specific times and suggest appropriate hydration amounts. This enables users to manage their hydration more effectively.

[0114] The health promotion system can further estimate the user's emotions and adjust the reward system based on those emotions. For example, if the user is stressed, the system can offer a relaxing reward. If the user is relaxed, the system can offer a reward that gives a sense of accomplishment. Furthermore, if the user is in a hurry, the system can offer a concise and effective reward. This allows for more effective health management by adjusting the reward system according to the user's emotions.

[0115] The health promotion system can further monitor the user's stress level in real time and analyze it in the analysis unit. For example, the data collection unit can acquire data on the user's heart rate and skin electrical activity from a wearable device. Based on this data, the analysis unit can evaluate the user's stress level and provide advice for stress reduction. The creation unit can also create relaxing meal and training menus based on the user's stress data. For example, it can suggest meal menus containing specific nutrients or training menus aimed at relaxation to reduce stress. This allows the user to manage stress more effectively.

[0116] The health promotion system can further estimate the user's emotions and provide customized health information based on those emotions. For example, if the user is feeling stressed, the system can provide information on stress reduction. If the user is relaxed, it can provide information on maintaining good health. Furthermore, if the user is in a hurry, it can provide concise and to-the-point health information. This allows for more effective health management by providing customized health information tailored to the user's emotions.

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

[0118] Step 1: The data collection unit collects medical data. The data collection unit collects detailed data such as the user's medical records and test results. Specifically, this includes data such as blood pressure, blood sugar levels, and weight. The data collection unit can also use AI to analyze the user's medical records and test results and extract the necessary data. Step 2: The analysis unit analyzes the medical data collected by the data collection unit. The analysis unit uses statistical analysis and machine learning algorithms, for example, to understand the user's health status. Specifically, it analyzes blood pressure fluctuation patterns to provide information useful for the user's blood pressure management. It can also analyze blood glucose fluctuations to assess the risk of diabetes. Step 3: The creation unit creates meal and training menus based on the analysis results obtained by the analysis unit. For example, the creation unit creates meal menus that include calorie restriction and training menus that focus on aerobic exercise, depending on the user's health condition and goals. Specifically, it can also create meal menus that are high in protein and menus that focus on strength training. Step 4: The delivery unit provides the menu created by the creation unit to the user. The delivery unit can provide the menu, for example, through an application. Alternatively, the menu can be provided via email. Step 5: The delivery department delivers the meal menus provided by the serving department. The delivery department can deliver the meal menus, for example, through a delivery service. Alternatively, they can deliver the meal menus using drone delivery.

[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, creation unit, provision unit, and delivery unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's medical records and test results using the camera 42 and microphone 38B of the smart device 14, and acquires medical data with the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the user's health status using statistical analysis and machine learning algorithms. The creation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and creates meal and training menus based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14, and provides the menus through applications or email. The delivery unit is implemented, for example, by the control unit 46A of the smart device 14, and delivers the meal menus through courier services or drone delivery. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 commands and other instructions from the user by receiving voice signals. 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, creation unit, provision unit, and delivery unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's medical records and test results using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A acquires the medical data. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the user's health status using statistical analysis and machine learning algorithms. The creation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and creates meal and training menus based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the smart glasses 214, and provides the menus through applications or email. The delivery unit is implemented, for example, in the control unit 46A of the smart glasses 214, and delivers the meal menus through courier services or drone delivery. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[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 commands and other instructions from the user by receiving voice signals. 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, creation unit, provision unit, and delivery unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's medical records and test results using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A acquires the medical data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the user's health status using statistical analysis and machine learning algorithms. The creation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and creates meal and training menus based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset terminal 314, and provides the menus through applications or email. The delivery unit is implemented, for example, by the control unit 46A of the headset terminal 314, and delivers the meal menus through courier services or drone delivery. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[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 commands and other instructions from the user by receiving voice signals. 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, creation unit, provision unit, and delivery unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's medical records and test results using the camera 42 and microphone 238 of the robot 414, and the control unit 46A acquires the medical data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the user's health status using statistical analysis and machine learning algorithms. The creation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and creates meal and training menus based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the menus through applications or email. The delivery unit is implemented, for example, by the control unit 46A of the robot 414, and delivers the meal menus through courier services or drone delivery. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[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) The data collection department collects medical data, An analysis unit analyzes the medical data collected by the aforementioned collection unit, A creation unit that creates meal and training menus based on the analysis results obtained by the analysis unit, A supply unit that provides the menu created by the creation unit, The system includes a delivery unit that delivers the meal menus provided by the aforementioned supply unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed data such as the user's medical records and test results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected medical data is analyzed to understand the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned creation unit, We create effective meal and training plans tailored to the user's health condition and goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the created menu to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned delivery unit is Deliver the provided meal menu. 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 medical data 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 Analyze the user's past medical records and test results to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting medical data, filtering is performed based on the user's lifestyle and dietary history. 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 prioritizes the medical data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting medical data, the system prioritizes the collection of highly relevant data, taking into account 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 medical data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the medical data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of medical data. 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 the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the medical data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the medical data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, It estimates the user's emotions and adjusts how menus are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating a menu, adjust the level of detail based on the user's health status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating a menu, different menu creation algorithms are applied depending on the user's lifestyle and eating history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, It estimates the user's emotions and adjusts the length of the menu based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When creating menus, prioritize menu items based on when user health data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When creating a menu, adjust the order of the menu items based on the user's relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the menu presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing menu items, the system selects the optimal delivery method by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing menus, the optimal delivery method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the order in which menu items are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing menus, the system references the user's calendar information to make suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned delivery unit is It estimates the user's emotions and adjusts the delivery timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned delivery unit is During delivery, the system selects the optimal delivery method by referring to the user's past delivery history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned delivery unit is During delivery, 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 33) The aforementioned delivery unit is It estimates user sentiment and determines delivery priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned delivery unit is During delivery, we refer to the user's calendar information to make suggestions based on their schedule. 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. The data collection department collects medical data, An analysis unit analyzes the medical data collected by the aforementioned collection unit, A creation unit that creates meal and training menus based on the analysis results obtained by the analysis unit, A supply unit that provides the menu created by the creation unit, The system includes a delivery unit that delivers the meal menus provided by the aforementioned supply unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed data such as the user's medical records and test results. The system according to feature 1.

3. The aforementioned analysis unit, The collected medical data is analyzed to understand the user's health status. The system according to feature 1.

4. The aforementioned creation unit, We create effective meal and training plans tailored to the user's health condition and goals. The system according to feature 1.

5. The aforementioned supply unit is, Provide the created menu to the user. The system according to feature 1.

6. The aforementioned delivery unit is Deliver the provided meal menu. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past medical records and test results to select the optimal data collection method. The system according to feature 1.

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