system

Abstract

The system according to this embodiment aims to provide an optimal customized menu based on the user's health status. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit acquires health data from a wearable device. The analysis unit analyzes the data acquired by the collection unit. The generation unit generates a customized menu based on the analysis results obtained by the analysis unit. The provision unit provides the menu generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an optimal diet menu based on the user's health condition, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal customized menu based on the user's health condition.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit acquires health data from a wearable device. The analysis unit analyzes the data acquired by the collection unit. The generation unit generates a customized menu based on the analysis results obtained by the analysis unit. The provision unit provides the menu generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal customized menu based on the user's health status. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) A health management system according to an embodiment of the present invention is a system that utilizes a wearable device and an AI agent to propose an optimal buffet menu based on the user's health condition. This health management system acquires the user's health data, and the AI ​​agent analyzes that data to generate a customized menu, which is then provided to the user, thereby supporting healthy eating habits without difficulty. For example, the health management system acquires the user's health data from the wearable device. At this time, detailed data such as heart rate, blood glucose level, and activity level are collected in real time. For example, data such as the user's heart rate after exercise and blood glucose level after a meal are acquired. This allows for an accurate understanding of the user's health condition. Next, the health management system analyzes the data acquired by the AI ​​agent. The AI ​​agent analyzes the collected data and generates an optimal buffet menu based on the user's health condition. For example, if the user's blood glucose level is high, a menu with reduced carbohydrates is proposed. Also, if the user has exercised a lot, a menu suitable for energy replenishment is proposed. This allows the user to choose meals that suit their health condition. The generated menu is provided to the user. The user can choose from the buffet by referring to the proposed menu. For example, the menu suggested by the AI ​​agent includes specific dish names and nutritional information, allowing users to choose their meals accordingly. This enables users to maintain healthy eating habits without difficulty. This system allows users to efficiently manage their health. The health management system utilizes wearable devices and an AI agent to allow users to understand their health status in real time and choose the optimal meals based on that information. It also reduces the stress of choosing meals. Users only need to follow the menu suggested by the AI ​​agent, eliminating the worry of choosing meals. Furthermore, it supports sustainable health maintenance and contributes to improving eating habits. The customized menu suggested by the AI ​​agent takes nutritional balance into consideration, allowing users to continue eating healthy meals. This can be expected to prevent lifestyle-related diseases and improve chronic health problems.Furthermore, by being offered to company employees, it contributes to improving employee productivity and performance. For example, the menu suggested by the AI ​​agent includes vegetables and fruits rich in vitamins and minerals, and meat and fish rich in protein. This allows users to eat a balanced diet and maintain their health. In addition, the menu suggested by the AI ​​agent takes into account the user's preferences and allergies, so anyone can use it with peace of mind. In this way, the health management system utilizes wearable devices and an AI agent to suggest the optimal buffet menu based on the user's health condition, providing a system that supports healthy eating habits without burden.

[0029] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit acquires health data from a wearable device. The data collection unit can acquire health data such as heart rate, blood glucose level, and activity level. The data collection unit can measure the user's heart rate in real time using a heart rate sensor. The data collection unit can also measure the user's blood glucose level using a blood glucose sensor. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. For example, the data collection unit can measure the heart rate during exercise in real time using a heart rate sensor. The data collection unit can also measure blood glucose levels after a meal using a blood glucose sensor. Furthermore, the data collection unit can measure daily activity levels using an activity tracker. The analysis unit analyzes the data acquired by the data collection unit. The analysis unit can analyze health data using AI and determine the user's health status. The analysis unit can analyze heart rate data using AI and analyze the fluctuations in the user's heart rate. Furthermore, the analysis unit can use AI to analyze blood glucose data and analyze fluctuations in the user's blood glucose levels. In addition, the analysis unit can use AI to analyze activity data and analyze fluctuations in the user's activity level. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in heart rate during exercise. The analysis unit can also use AI to analyze blood glucose data and analyze fluctuations in blood glucose levels after meals. Furthermore, the analysis unit can use AI to analyze activity data and analyze fluctuations in daily activity levels. The generation unit generates customized menus based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to generate customized menus that consider nutritional balance based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition. The generation unit can also use AI to generate menus suitable for energy replenishment based on the user's health condition. Furthermore, the generation unit can use AI to generate menus rich in vitamins and minerals based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition.Furthermore, the generation unit can use AI to generate menus suitable for energy replenishment based on the user's health condition. Additionally, the generation unit can use AI to generate menus rich in vitamins and minerals based on the user's health condition. The serving unit provides the menus generated by the generation unit to the user. The serving unit can, for example, provide the user with a customized menu generated using AI. The serving unit can, for example, notify the user of the customized menu generated using AI on their smartphone. The serving unit can also display the customized menu generated using AI on the user's wearable device. Furthermore, the serving unit can display the customized menu generated using AI on the user's personal computer. As a result, the health management system according to this embodiment can propose an optimal buffet menu based on the user's health condition and support healthy eating habits.

[0030] The data collection unit acquires health data from wearable devices. For example, the unit can acquire health data such as heart rate, blood glucose levels, and activity levels. Specifically, it measures the user's heart rate in real time using a heart rate sensor. The heart rate sensor uses optical or electrical sensors to detect the user's pulse and calculate the heart rate. This allows for detailed understanding of heart rate fluctuations during exercise and at rest. The data collection unit can also measure the user's blood glucose levels using a blood glucose sensor. The blood glucose sensor is a tiny sensor inserted under the skin to continuously monitor glucose concentration in the blood. This allows for real-time understanding of blood glucose fluctuations after meals and exercise. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. The activity tracker uses accelerometers and gyroscopes to measure the user's steps, distance traveled, calories burned, etc. This allows for detailed recording of daily life and exercise activity levels, and helps understand the user's exercise habits. For example, the data collection unit can measure heart rate in real time during exercise using a heart rate sensor. This allows for the assessment of exercise intensity and cardiopulmonary function, enabling the creation of appropriate exercise plans. Furthermore, the data collection unit can measure post-meal blood glucose levels using a blood glucose sensor. This allows for real-time monitoring of the effects of meals, aiding in dietary management. Additionally, the data collection unit can measure daily activity levels using an activity tracker. This allows for understanding the user's exercise habits and lifestyle rhythms, contributing to health management. The data collection unit centrally manages this data and transmits it to a central database in real time. This makes the collected data accessible to the analysis and generation units, enabling efficient operation of the entire system.

[0031] The analysis unit analyzes the data acquired by the data collection unit. For example, the analysis unit can use AI to analyze health data and determine the user's health status. Specifically, the AI ​​uses machine learning algorithms to analyze heart rate data, blood glucose data, and activity level data. In analyzing heart rate data, the AI ​​learns the user's heart rate fluctuation patterns and detects abnormal fluctuations and trends. For example, it can detect a sudden increase in heart rate during exercise or an abnormal decrease in resting heart rate and issue a warning to the user. In analyzing blood glucose data, the AI ​​learns the user's blood glucose fluctuation patterns and evaluates the effects of diet and exercise. For example, it can detect a sudden increase in blood glucose after a meal or a decrease in blood glucose after exercise and provide appropriate diet and exercise advice. Furthermore, in analyzing activity level data, the AI ​​learns the user's activity level fluctuation patterns and evaluates exercise habits and lifestyle rhythms. For example, it can suggest an appropriate exercise plan if daily activity levels are low or exercise habits are irregular. Based on these analysis results, the analysis department can comprehensively evaluate the user's health status and determine health risks. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. For example, based on past heart rate and blood glucose data, it can predict fluctuations in health risks during specific seasons and time periods, and formulate future countermeasures. This allows the analysis department to not only grasp real-time health status but also handle long-term health management and risk assessment, improving the reliability and safety of the entire system.

[0032] The generation unit generates customized menus based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to generate customized menus that consider nutritional balance based on the user's health condition. Specifically, the AI ​​proposes an optimal meal plan for each individual user based on the user's heart rate data, blood glucose data, and activity level data. For example, it evaluates exercise intensity from heart rate data and generates a menu suitable for energy replenishment after exercise. It also evaluates the impact of meals from blood glucose data and generates a low-carbohydrate menu to prevent a rapid rise in blood glucose levels. Furthermore, it can evaluate daily calorie expenditure from activity level data and generate a menu that considers an appropriate calorie intake. The generation unit comprehensively analyzes this data and generates customized menus that are tailored to the user's health condition and lifestyle. For example, if there is a deficiency in vitamins or minerals, it will suggest a menu using ingredients rich in these nutrients. It can also generate menus that accommodate specific allergies or dietary restrictions. By providing these customized menus to users, the generation unit can support healthy eating habits and improve the user's health condition. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the menus. For example, the system can evaluate the effectiveness of a menu based on data from meals actually consumed by the user and reflect this in the generation of the next menu. This allows the generation unit to provide a customized menu that is optimal for the user's health condition, supporting their health management.

[0033] The service provider delivers menus generated by the generation unit to the user. For example, the service provider can provide users with customized menus generated using AI. Specifically, the service provider notifies the user of the generated menu on their smartphone. Through a smartphone application, the user can review the generated menu and plan their meals. The service provider can also display the generated menu on the user's wearable device. Because the wearable device is always worn by the user, the timing and content of meals can be monitored in real time. Furthermore, the service provider can display the generated menu on the user's personal computer. Using the personal computer, the user can review detailed menu and nutritional information and plan their meals. Through these devices, the service provider can quickly and reliably deliver customized menus to the user. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, it can collect data and feedback on meals actually consumed by the user and reflect this in the generation of future menus. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service department to quickly and reliably provide customized menus to users, supporting healthy eating habits.

[0034] The data collection unit can acquire health data such as heart rate, blood glucose levels, and activity levels. For example, the data collection unit can measure the user's heart rate in real time using a heart rate sensor. For example, the data collection unit can measure the heart rate during exercise in real time using a heart rate sensor. The data collection unit can also measure the user's blood glucose levels using a blood glucose sensor. For example, the data collection unit can measure blood glucose levels after meals using a blood glucose sensor. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. For example, the data collection unit can measure daily activity levels using an activity tracker. This allows the data collection unit to acquire detailed health data of the user and accurately understand their health status. Health data includes, but is not limited to, heart rate, blood glucose levels, and activity levels. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input heart rate data acquired by the heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0035] The analysis unit can analyze acquired health data and determine the user's health status. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in the user's heart rate. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in heart rate during exercise. The analysis unit can also use AI to analyze blood glucose data and analyze fluctuations in the user's blood glucose levels. For example, the analysis unit can use AI to analyze blood glucose data and analyze fluctuations in blood glucose levels after meals. Furthermore, the analysis unit can use AI to analyze activity level data and analyze fluctuations in the user's activity level. For example, the analysis unit can use AI to analyze activity level data and analyze fluctuations in daily activity levels. As a result, the analysis unit can accurately determine the user's health status and generate appropriate menus. Analysis includes, but is not limited to, data analysis methods and algorithms used. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input heart rate data into a generating AI and have the generating AI perform the analysis of the heart rate data.

[0036] The generation unit can generate customized menus that take nutritional balance into consideration based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition. The generation unit can also use AI to generate menus that are suitable for energy replenishment based on the user's health condition. Furthermore, the generation unit can use AI to generate menus that are rich in vitamins and minerals based on the user's health condition. This allows the generation unit to provide nutritionally balanced menus tailored to the user's health condition. Nutritional balance includes, but is not limited to, calories, vitamins, and minerals. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI generate customized menus.

[0037] The service provider can provide the user with the generated customized menu. The service provider can, for example, notify the user of the customized menu generated using AI on their smartphone. The service provider can, for example, notify the user of the customized menu generated using AI on their smartphone. The service provider can also display the customized menu generated using AI on the user's wearable device. The service provider can, for example, display the customized menu generated using AI on the user's wearable device. Furthermore, the service provider can display the customized menu generated using AI on the user's personal computer. The service provider can, for example, display the customized menu generated using AI on the user's personal computer. This allows the service provider to provide the user with the most suitable menu and support healthy eating habits. The service provider includes, but is not limited to, the method of notifying the user and the timing of the service provider's delivery. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the generated customized menu into a generating AI and have the generating AI execute a notification to the user.

[0038] The generation unit can generate menus that cater to the user's preferences and allergies. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. This allows the generation unit to provide menus that cater to the user's preferences and allergies, enabling them to use the service with peace of mind. Preferences and allergies include, for example, specific ingredients and allergens, but are not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can input user preference and allergy information into a generation AI and have the generation AI generate a customized menu.

[0039] The service provider can suggest the optimal buffet menu based on the user's health condition. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. This allows the service provider to suggest the optimal buffet menu based on the user's health condition and support healthy eating habits. The optimal buffet menu includes, but is not limited to, nutritional balance and the user's health condition. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI suggest the optimal buffet menu.

[0040] The data collection unit can analyze the user's past health data and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data and acquire the data at the most stable timing. The data collection unit can also analyze the user's past blood glucose level data and acquire the data at the optimal timing after a meal. Furthermore, the data collection unit can analyze the user's past activity level data and acquire the data at the optimal timing after exercise. In this way, the data collection unit can analyze the user's past health data to select the optimal acquisition method and acquire data efficiently. The optimal acquisition method includes, but is not limited to, the frequency of data acquisition and the sensors used. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past health data into a generating AI, which can then select the optimal data acquisition method.

[0041] The data collection unit can filter the health data based on the user's current activity level and lifestyle. For example, if the user is exercising, the data collection unit can prioritize acquiring data from that time. The data collection unit can also prioritize acquiring data from after a meal if the user is eating. Furthermore, if the user is sleeping, the data collection unit can prioritize acquiring data from that time. This allows the data collection unit to filter the data based on the user's activity level and lifestyle, and acquire highly relevant data. Filtering includes, but is not limited to, data relevance and importance. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's activity data into a generating AI and have the generating AI perform data filtering.

[0042] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring health data. For example, if the user is at high altitude, the data collection unit can prioritize the acquisition of health data at high altitude. The data collection unit can also prioritize the acquisition of health data at the beach if the user is at the beach. The data collection unit can also prioritize the acquisition of health data at the urban area if the user is in an urban area. This allows the data collection unit to prioritize the acquisition of highly relevant data by considering the user's geographical location and to accurately understand their health status. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to acquire highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and obtain relevant data when acquiring health data. For example, if the user is experiencing stress on social media, the data collection unit can acquire stress-related data. The data collection unit can also acquire relaxation-related data if the user is relaxing on social media. Furthermore, if the user is excited on social media, the data collection unit can acquire excitement-related data. By analyzing the user's social media activity, the data collection unit can acquire relevant health data and gain an accurate understanding of their health status. Social media activity includes, but is not limited to, posts and activity frequency. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI acquire the relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze highly important data in detail and less important data in a simplified manner. The analysis unit can also analyze highly important data in detail using multiple algorithms. Furthermore, the analysis unit can analyze highly important data in real time and less important data in batch processing. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the health data and analyze the data efficiently. Importance includes, but is not limited to, the impact and urgency of the data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis department can input health data into a generating AI and have the AI ​​adjust the level of detail in the analysis based on its importance.

[0045] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate-specific algorithm to heart rate data. The analysis unit can also apply a blood glucose-specific algorithm to blood glucose data. Furthermore, the analysis unit can apply an activity level-specific algorithm to activity level data. This allows the analysis unit to apply the appropriate analysis algorithm according to the category of health data and obtain accurate analysis results. Categories include, but are not limited to, data type and use. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0046] The analysis unit can determine the priority of analysis based on when the health data was acquired. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also prioritize the analysis of data acquired during specific time periods. Furthermore, the analysis unit can prioritize the analysis of data acquired during times when user activity is high. This allows the analysis unit to determine the priority of analysis based on when the health data was acquired and analyze the data efficiently. The acquisition period includes, but is not limited to, the timing and frequency of data acquisition. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data into a generating AI and have the generating AI determine the priority of analysis based on the acquisition period.

[0047] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also group highly relevant data and analyze them in batches. Furthermore, the analysis unit can analyze highly relevant data in real time and analyze less relevant data in batches. This allows the analysis unit to adjust the order of analysis based on the relevance of health data and analyze the data efficiently. Relevance includes, but is not limited to, data correlation and influence. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input health data into a generating AI and have the generating AI adjust the order of analysis based on relevance.

[0048] The generation unit can adjust the level of detail in the menu based on the user's health condition during generation. For example, if the user's health condition is good, the generation unit can generate a detailed menu. The generation unit can also generate a simplified menu if the user's health condition is poor. Furthermore, the generation unit can adjust the level of detail in the menu in stages according to the user's health condition. This allows the generation unit to adjust the level of detail in the menu according to the user's health condition and provide an appropriate menu. Level of detail includes, but is not limited to, the granularity of information and the content displayed. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user health data into a generation AI and have the generation AI perform menu generation based on level of detail.

[0049] The generation unit can apply different generation algorithms depending on the category of the user's health data during generation. For example, the generation unit can apply a generation algorithm specifically for heart rate based on heart rate data. The generation unit can also apply a generation algorithm specifically for blood glucose levels based on blood glucose level data. Furthermore, the generation unit can apply a generation algorithm specifically for activity levels based on activity level data. This allows the generation unit to apply an appropriate generation algorithm according to the category of the user's health data and generate an accurate menu. Categories include, but are not limited to, data type and usage. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI apply a generation algorithm according to the category.

[0050] The generation unit can customize menus based on user preferences and allergies during the generation process. For example, the generation unit can generate menus using the user's favorite ingredients based on their preferences. The generation unit can also generate menus that avoid allergens based on the user's allergy information. Furthermore, the generation unit can generate menus that accommodate preferences and allergies based on the user's past eating history. This allows the generation unit to customize menus based on user preferences and allergies, ensuring user confidence. Preferences and allergies include, but are not limited to, specific ingredients and allergens. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user preferences and allergy information into a generation AI and have the generation AI perform menu customization.

[0051] The generation unit can optimize menus by referring to the user's past meal history during generation. For example, the generation unit can analyze the user's past meal history and generate menus that take nutritional balance into consideration. The generation unit can also generate menus that cater to the user's preferences and allergies based on their past meal history. Furthermore, the generation unit can generate menus tailored to the user's health condition based on their past meal history. This allows the generation unit to optimize menus by referring to the user's past meal history and provide appropriate menus. Past meal history includes, but is not limited to, meal records and historical data. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past meal history into a generation AI and have the generation AI perform menu optimization.

[0052] The serving unit can select the optimal serving method by referring to the user's past menu selection history at the time of serving. The serving unit can, for example, analyze the user's past menu selection history and select a serving method that suits their preferences. The serving unit can, for example, analyze the user's past menu selection history and select a serving method that suits their preferences. The serving unit can also, for example, select a serving method that takes allergy information into account from the user's past menu selection history. The serving unit can also, for example, select a serving method that takes allergy information into account from the user's past menu selection history. Furthermore, the serving unit can also, for example, select a serving method that suits the user's health condition based on the user's past menu selection history. The serving unit can, for example, select a serving method that suits the user's health condition based on the user's past menu selection history. This allows the serving unit to select the optimal serving method by referring to the user's past menu selection history and provide an appropriate menu. Past menu selection history includes, for example, selection history data and user preferences, but is not limited to such examples. Some or all of the above processing in the serving unit may be performed using, for example, AI, or not using AI. For example, the service department can input the user's past menu selection history into a generating AI and have the AI ​​select the optimal service method.

[0053] The service provider can customize how the menu is displayed based on the user's current health status at the time of service provision. For example, if the user's health status is good, the service provider can display a detailed menu. The service provider can also display a simplified menu if the user's health status is poor. Furthermore, the service provider can customize how the menu is displayed in stages according to the user's health status. This allows the service provider to customize how the menu is displayed based on the user's current health status and provide an appropriate menu. Current health status includes, but is not limited to, the latest health data and medical records. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the customization of the display method.

[0054] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a service delivery method that matches the screen size. The service provider can also select a service delivery method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the service provider can select a concise and highly visible service delivery method. This allows the service provider to select the optimal service delivery method considering the user's device information and provide an appropriate menu. Device information includes, but is not limited to, the type of device and usage status. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

[0055] The service provider can customize the menu at the time of delivery, taking into account the user's geographical location information. For example, if the user is at high altitude, the service provider can provide a menu suitable for dining at high altitude. The service provider can also provide a menu suitable for dining by the sea if the user is by the sea. Furthermore, if the user is in an urban area, the service provider can provide a menu suitable for dining in an urban area. This allows the service provider to customize the menu and provide an appropriate menu, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the menu customization.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] A health management system can also suggest exercise based on the user's health data. For example, the data collection unit can acquire the user's heart rate and activity level, and the analysis unit can analyze this data to detect the user's lack of exercise. The generation unit can then generate an exercise menu to compensate for the lack of exercise, and the delivery unit can notify the user. This allows the user to maintain healthy exercise habits. The data collection unit can also acquire the user's sleep data, and the analysis unit can analyze this data to evaluate the quality of sleep. The generation unit can then generate advice to improve sleep quality, and the delivery unit can notify the user. Furthermore, the data collection unit can measure the user's stress level, and the analysis unit can analyze this data to generate a relaxation menu to reduce stress. This allows the user to manage their health comprehensively.

[0058] A health management system can analyze a user's dietary history and suggest improvements to their nutritional balance. For example, the data collection unit can record the user's meals, and the analysis unit can analyze this data to detect imbalances in nutrition. The generation unit can create meal menus that consider nutritional balance, and the delivery unit can notify the user. This allows the user to maintain a healthy diet. The data collection unit can also record the user's vitamin and mineral intake, and the analysis unit can analyze this data to identify any nutrient deficiencies. The generation unit can then create meal menus to supplement these deficiencies, and the delivery unit can notify the user. Furthermore, the data collection unit can record the user's calorie intake, and the analysis unit can analyze this data to detect excesses or deficiencies. This allows the user to manage their nutrition comprehensively.

[0059] A health management system can provide suggestions to improve sleep quality based on the user's health data. For example, the data collection unit can acquire the user's sleep data, and the analysis unit can analyze that data to evaluate sleep quality. The generation unit can generate advice to improve sleep quality, and the delivery unit can notify the user. This allows the user to achieve high-quality sleep. The data collection unit can also acquire the user's heart rate and blood pressure data, and the analysis unit can analyze that data to evaluate sleep quality. The generation unit can generate advice on environmental adjustments to improve sleep quality, and the delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and the analysis unit can analyze that data to evaluate sleep quality. This allows the user to manage their sleep comprehensively.

[0060] A health management system can suggest relaxation activities based on a user's health data. For example, the data collection unit can acquire the user's heart rate and blood pressure data, and the analysis unit can analyze this data to detect when relaxation is needed. The generation unit generates relaxation activities, and the delivery unit notifies the user. This allows the user to relax effectively. The data collection unit can also acquire the user's activity level data, and the analysis unit can analyze this data to detect when relaxation is needed. The generation unit generates advice on adjusting the environment for relaxation, and the delivery unit notifies the user. Furthermore, the data collection unit can acquire the user's sleep data, and the analysis unit can analyze this data to detect when relaxation is needed. This allows the user to manage their relaxation comprehensively.

[0061] A health management system can suggest meal timings based on a user's health data. For example, a data collection unit can acquire the user's blood glucose levels and activity data, and an analysis unit can analyze this data to detect the optimal meal timing. A generation unit generates advice regarding meal timing, and a delivery unit notifies the user. This allows the user to maintain healthy meal timings. The data collection unit can also acquire the user's heart rate and blood pressure data, and an analysis unit can analyze this data to adjust meal timing. The generation unit generates advice regarding meal timing, and a delivery unit notifies the user. Furthermore, the data collection unit can acquire the user's sleep data, and an analysis unit can analyze this data to adjust meal timing. This allows the user to manage their diet comprehensively.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit acquires health data from the wearable device. For example, it can acquire health data such as heart rate, blood glucose level, and activity level. The data collection unit can measure the user's heart rate in real time using a heart rate sensor, measure the user's blood glucose level using a blood glucose sensor, and measure the user's activity level using an activity tracker. Step 2: The analysis unit analyzes the data acquired by the collection unit. For example, it uses AI to analyze health data and determine the user's health status. It can analyze heart rate data, blood glucose data, and activity level data, and analyze the fluctuations in each. Step 3: The generation unit generates a customized menu based on the analysis results obtained by the analysis unit. For example, it can use AI to generate a customized menu that takes nutritional balance into account based on the user's health condition. It can generate menus that are low in carbohydrates, menus suitable for energy replenishment, menus rich in vitamins and minerals, etc. Step 4: The providing unit provides the user with the menu generated by the generating unit. For example, a customized menu generated using AI can be notified to or displayed on the user's smartphone, wearable device, or personal computer.

[0064] (Example of form 2) A health management system according to an embodiment of the present invention is a system that utilizes a wearable device and an AI agent to propose an optimal buffet menu based on the user's health condition. This health management system acquires the user's health data, and the AI ​​agent analyzes that data to generate a customized menu, which is then provided to the user, thereby supporting healthy eating habits without difficulty. For example, the health management system acquires the user's health data from the wearable device. At this time, detailed data such as heart rate, blood glucose level, and activity level are collected in real time. For example, data such as the user's heart rate after exercise and blood glucose level after a meal are acquired. This allows for an accurate understanding of the user's health condition. Next, the health management system analyzes the data acquired by the AI ​​agent. The AI ​​agent analyzes the collected data and generates an optimal buffet menu based on the user's health condition. For example, if the user's blood glucose level is high, a menu with reduced carbohydrates is proposed. Also, if the user has exercised a lot, a menu suitable for energy replenishment is proposed. This allows the user to choose meals that suit their health condition. The generated menu is provided to the user. The user can choose from the buffet by referring to the proposed menu. For example, the menu suggested by the AI ​​agent includes specific dish names and nutritional information, allowing users to choose their meals accordingly. This enables users to maintain healthy eating habits without difficulty. This system allows users to efficiently manage their health. The health management system utilizes wearable devices and an AI agent to allow users to understand their health status in real time and choose the optimal meals based on that information. It also reduces the stress of choosing meals. Users only need to follow the menu suggested by the AI ​​agent, eliminating the worry of choosing meals. Furthermore, it supports sustainable health maintenance and contributes to improving eating habits. The customized menu suggested by the AI ​​agent takes nutritional balance into consideration, allowing users to continue eating healthy meals. This can be expected to prevent lifestyle-related diseases and improve chronic health problems.Furthermore, by being offered to company employees, it contributes to improving employee productivity and performance. For example, the menu suggested by the AI ​​agent includes vegetables and fruits rich in vitamins and minerals, and meat and fish rich in protein. This allows users to eat a balanced diet and maintain their health. In addition, the menu suggested by the AI ​​agent takes into account the user's preferences and allergies, so anyone can use it with peace of mind. In this way, the health management system utilizes wearable devices and an AI agent to suggest the optimal buffet menu based on the user's health condition, providing a system that supports healthy eating habits without burden.

[0065] The health management system according to this embodiment comprises a data collection unit, an analysis unit, a data generation unit, and a data provision unit. The data collection unit acquires health data from a wearable device. The data collection unit can acquire health data such as heart rate, blood glucose level, and activity level. The data collection unit can measure the user's heart rate in real time using a heart rate sensor. The data collection unit can also measure the user's blood glucose level using a blood glucose sensor. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. For example, the data collection unit can measure the heart rate during exercise in real time using a heart rate sensor. The data collection unit can also measure blood glucose levels after a meal using a blood glucose sensor. Furthermore, the data collection unit can measure daily activity levels using an activity tracker. The analysis unit analyzes the data acquired by the data collection unit. The analysis unit can analyze health data using AI and determine the user's health status. The analysis unit can analyze heart rate data using AI and analyze the fluctuations in the user's heart rate. Furthermore, the analysis unit can use AI to analyze blood glucose data and analyze fluctuations in the user's blood glucose levels. In addition, the analysis unit can use AI to analyze activity data and analyze fluctuations in the user's activity level. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in heart rate during exercise. The analysis unit can also use AI to analyze blood glucose data and analyze fluctuations in blood glucose levels after meals. Furthermore, the analysis unit can use AI to analyze activity data and analyze fluctuations in daily activity levels. The generation unit generates customized menus based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to generate customized menus that consider nutritional balance based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition. The generation unit can also use AI to generate menus suitable for energy replenishment based on the user's health condition. Furthermore, the generation unit can use AI to generate menus rich in vitamins and minerals based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition.Furthermore, the generation unit can use AI to generate menus suitable for energy replenishment based on the user's health condition. Additionally, the generation unit can use AI to generate menus rich in vitamins and minerals based on the user's health condition. The serving unit provides the menus generated by the generation unit to the user. The serving unit can, for example, provide the user with a customized menu generated using AI. The serving unit can, for example, notify the user of the customized menu generated using AI on their smartphone. The serving unit can also display the customized menu generated using AI on the user's wearable device. Furthermore, the serving unit can display the customized menu generated using AI on the user's personal computer. As a result, the health management system according to this embodiment can propose an optimal buffet menu based on the user's health condition and support healthy eating habits.

[0066] The data collection unit acquires health data from wearable devices. For example, the unit can acquire health data such as heart rate, blood glucose levels, and activity levels. Specifically, it measures the user's heart rate in real time using a heart rate sensor. The heart rate sensor uses optical or electrical sensors to detect the user's pulse and calculate the heart rate. This allows for detailed understanding of heart rate fluctuations during exercise and at rest. The data collection unit can also measure the user's blood glucose levels using a blood glucose sensor. The blood glucose sensor is a tiny sensor inserted under the skin to continuously monitor glucose concentration in the blood. This allows for real-time understanding of blood glucose fluctuations after meals and exercise. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. The activity tracker uses accelerometers and gyroscopes to measure the user's steps, distance traveled, calories burned, etc. This allows for detailed recording of daily life and exercise activity levels, and helps understand the user's exercise habits. For example, the data collection unit can measure heart rate in real time during exercise using a heart rate sensor. This allows for the assessment of exercise intensity and cardiopulmonary function, enabling the creation of appropriate exercise plans. Furthermore, the data collection unit can measure post-meal blood glucose levels using a blood glucose sensor. This allows for real-time monitoring of the effects of meals, aiding in dietary management. Additionally, the data collection unit can measure daily activity levels using an activity tracker. This allows for understanding the user's exercise habits and lifestyle rhythms, contributing to health management. The data collection unit centrally manages this data and transmits it to a central database in real time. This makes the collected data accessible to the analysis and generation units, enabling efficient operation of the entire system.

[0067] The analysis unit analyzes the data acquired by the data collection unit. For example, the analysis unit can use AI to analyze health data and determine the user's health status. Specifically, the AI ​​uses machine learning algorithms to analyze heart rate data, blood glucose data, and activity level data. In analyzing heart rate data, the AI ​​learns the user's heart rate fluctuation patterns and detects abnormal fluctuations and trends. For example, it can detect a sudden increase in heart rate during exercise or an abnormal decrease in resting heart rate and issue a warning to the user. In analyzing blood glucose data, the AI ​​learns the user's blood glucose fluctuation patterns and evaluates the effects of diet and exercise. For example, it can detect a sudden increase in blood glucose after a meal or a decrease in blood glucose after exercise and provide appropriate diet and exercise advice. Furthermore, in analyzing activity level data, the AI ​​learns the user's activity level fluctuation patterns and evaluates exercise habits and lifestyle rhythms. For example, it can suggest an appropriate exercise plan if daily activity levels are low or exercise habits are irregular. Based on these analysis results, the analysis department can comprehensively evaluate the user's health status and determine health risks. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term health risk assessments and trend analyses. For example, based on past heart rate and blood glucose data, it can predict fluctuations in health risks during specific seasons and time periods, and formulate future countermeasures. This allows the analysis department to not only grasp real-time health status but also handle long-term health management and risk assessment, improving the reliability and safety of the entire system.

[0068] The generation unit generates customized menus based on the analysis results obtained by the analysis unit. For example, the generation unit can use AI to generate customized menus that consider nutritional balance based on the user's health condition. Specifically, the AI ​​proposes an optimal meal plan for each individual user based on the user's heart rate data, blood glucose data, and activity level data. For example, it evaluates exercise intensity from heart rate data and generates a menu suitable for energy replenishment after exercise. It also evaluates the impact of meals from blood glucose data and generates a low-carbohydrate menu to prevent a rapid rise in blood glucose levels. Furthermore, it can evaluate daily calorie expenditure from activity level data and generate a menu that considers an appropriate calorie intake. The generation unit comprehensively analyzes this data and generates customized menus that are tailored to the user's health condition and lifestyle. For example, if there is a deficiency in vitamins or minerals, it will suggest a menu using ingredients rich in these nutrients. It can also generate menus that accommodate specific allergies or dietary restrictions. By providing these customized menus to users, the generation unit can support healthy eating habits and improve the user's health condition. Furthermore, the generation unit can collect user feedback and continuously improve the accuracy and effectiveness of the menus. For example, the system can evaluate the effectiveness of a menu based on data from meals actually consumed by the user and reflect this in the generation of the next menu. This allows the generation unit to provide a customized menu that is optimal for the user's health condition, supporting their health management.

[0069] The service provider delivers menus generated by the generation unit to the user. For example, the service provider can provide users with customized menus generated using AI. Specifically, the service provider notifies the user of the generated menu on their smartphone. Through a smartphone application, the user can review the generated menu and plan their meals. The service provider can also display the generated menu on the user's wearable device. Because the wearable device is always worn by the user, the timing and content of meals can be monitored in real time. Furthermore, the service provider can display the generated menu on the user's personal computer. Using the personal computer, the user can review detailed menu and nutritional information and plan their meals. Through these devices, the service provider can quickly and reliably deliver customized menus to the user. In addition, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of its offerings. For example, it can collect data and feedback on meals actually consumed by the user and reflect this in the generation of future menus. Furthermore, the service provider can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information using not only smartphone notifications but also voice calls, SMS, and email. This allows the service department to quickly and reliably provide customized menus to users, supporting healthy eating habits.

[0070] The data collection unit can acquire health data such as heart rate, blood glucose levels, and activity levels. For example, the data collection unit can measure the user's heart rate in real time using a heart rate sensor. For example, the data collection unit can measure the heart rate during exercise in real time using a heart rate sensor. The data collection unit can also measure the user's blood glucose levels using a blood glucose sensor. For example, the data collection unit can measure blood glucose levels after meals using a blood glucose sensor. Furthermore, the data collection unit can measure the user's activity level using an activity tracker. For example, the data collection unit can measure daily activity levels using an activity tracker. This allows the data collection unit to acquire detailed health data of the user and accurately understand their health status. Health data includes, but is not limited to, heart rate, blood glucose levels, and activity levels. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input heart rate data acquired by the heart rate sensor into a generating AI and have the generating AI perform analysis of the heart rate data.

[0071] The analysis unit can analyze acquired health data and determine the user's health status. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in the user's heart rate. For example, the analysis unit can use AI to analyze heart rate data and analyze fluctuations in heart rate during exercise. The analysis unit can also use AI to analyze blood glucose data and analyze fluctuations in the user's blood glucose levels. For example, the analysis unit can use AI to analyze blood glucose data and analyze fluctuations in blood glucose levels after meals. Furthermore, the analysis unit can use AI to analyze activity level data and analyze fluctuations in the user's activity level. For example, the analysis unit can use AI to analyze activity level data and analyze fluctuations in daily activity levels. As a result, the analysis unit can accurately determine the user's health status and generate appropriate menus. Analysis includes, but is not limited to, data analysis methods and algorithms used. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input heart rate data into a generating AI and have the generating AI perform the analysis of the heart rate data.

[0072] The generation unit can generate customized menus that take nutritional balance into consideration based on the user's health condition. For example, the generation unit can use AI to generate menus that are low in carbohydrates based on the user's health condition. The generation unit can also use AI to generate menus that are suitable for energy replenishment based on the user's health condition. Furthermore, the generation unit can use AI to generate menus that are rich in vitamins and minerals based on the user's health condition. This allows the generation unit to provide nutritionally balanced menus tailored to the user's health condition. Nutritional balance includes, but is not limited to, calories, vitamins, and minerals. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI generate customized menus.

[0073] The service provider can provide the user with the generated customized menu. The service provider can, for example, notify the user of the customized menu generated using AI on their smartphone. The service provider can, for example, notify the user of the customized menu generated using AI on their smartphone. The service provider can also display the customized menu generated using AI on the user's wearable device. The service provider can, for example, display the customized menu generated using AI on the user's wearable device. Furthermore, the service provider can display the customized menu generated using AI on the user's personal computer. The service provider can, for example, display the customized menu generated using AI on the user's personal computer. This allows the service provider to provide the user with the most suitable menu and support healthy eating habits. The service provider includes, but is not limited to, the method of notifying the user and the timing of the service provider's delivery. Some or all of the above-described processes in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the generated customized menu into a generating AI and have the generating AI execute a notification to the user.

[0074] The generation unit can generate menus that cater to the user's preferences and allergies. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. The generation unit can generate menus that cater to the user's preferences and allergies, for example, by using AI. This allows the generation unit to provide menus that cater to the user's preferences and allergies, enabling them to use the service with peace of mind. Preferences and allergies include, for example, specific ingredients and allergens, but are not limited to such examples. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without using AI. For example, the generation unit can input user preference and allergy information into a generation AI and have the generation AI generate a customized menu.

[0075] The service provider can suggest the optimal buffet menu based on the user's health condition. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. The service provider can suggest the optimal buffet menu based on the user's health condition, for example, by using AI. This allows the service provider to suggest the optimal buffet menu based on the user's health condition and support healthy eating habits. The optimal buffet menu includes, but is not limited to, nutritional balance and the user's health condition. Some or all of the processing described above in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI suggest the optimal buffet menu.

[0076] The data collection unit can estimate the user's emotions and adjust the timing of health data acquisition based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit can adjust the acquisition timing to acquire health data when the user is relaxed. The data collection unit can also acquire heart rate and blood glucose levels when the user is relaxed after exercise. Furthermore, if the user is relaxed during sleep, the data collection unit can acquire health data at night to capture data during sleep. In this way, the data collection unit can adjust the timing of health data acquisition according to the user's emotions, thereby acquiring more accurate data. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the processing described above in the data collection unit may be performed using, for example, an emotion engine or a generative AI, or it may be performed without using an emotion engine or a generative AI. For example, the data collection unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can analyze the user's past health data and select the optimal acquisition method. For example, the data collection unit can analyze the user's past heart rate data and acquire the data at the most stable timing. The data collection unit can also analyze the user's past blood glucose level data and acquire the data at the optimal timing after a meal. Furthermore, the data collection unit can analyze the user's past activity level data and acquire the data at the optimal timing after exercise. In this way, the data collection unit can analyze the user's past health data to select the optimal acquisition method and acquire data efficiently. The optimal acquisition method includes, but is not limited to, the frequency of data acquisition and the sensors used. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past health data into a generating AI, which can then select the optimal data acquisition method.

[0078] The data collection unit can filter the health data based on the user's current activity level and lifestyle. For example, if the user is exercising, the data collection unit can prioritize acquiring data from that time. The data collection unit can also prioritize acquiring data from after a meal if the user is eating. Furthermore, if the user is sleeping, the data collection unit can prioritize acquiring data from that time. This allows the data collection unit to filter the data based on the user's activity level and lifestyle, and acquire highly relevant data. Filtering includes, but is not limited to, data relevance and importance. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's activity data into a generating AI and have the generating AI perform data filtering.

[0079] The data collection unit can estimate the user's emotions and determine the priority of health data to acquire based on the estimated user emotions. For example, if the user is feeling stressed, the data collection unit can prioritize the acquisition of stress-related data. The data collection unit can also prioritize the acquisition of relaxation-related data if the user is relaxed. Furthermore, if the user is excited, the data collection unit can prioritize the acquisition of excitement-related data. This allows the data collection unit to prioritize health data according to the user's emotions and acquire important data preferentially. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the data collection unit may be performed using, for example, an emotion engine or a generative AI, or without using an emotion engine or a generative AI. For example, the data collection unit can input the user's facial data into a generative AI and have the generative AI perform emotion estimation.

[0080] The data collection unit can prioritize the acquisition of highly relevant data by considering the user's geographical location when acquiring health data. For example, if the user is at high altitude, the data collection unit can prioritize the acquisition of health data at high altitude. The data collection unit can also prioritize the acquisition of health data at the beach if the user is at the beach. The data collection unit can also prioritize the acquisition of health data at the urban area if the user is in an urban area. This allows the data collection unit to prioritize the acquisition of highly relevant data by considering the user's geographical location and to accurately understand their health status. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input the user's geographical location information into the generating AI, allowing the AI ​​to acquire highly relevant data.

[0081] The data collection unit can analyze the user's social media activity and obtain relevant data when acquiring health data. For example, if the user is experiencing stress on social media, the data collection unit can acquire stress-related data. The data collection unit can also acquire relaxation-related data if the user is relaxing on social media. Furthermore, if the user is excited on social media, the data collection unit can acquire excitement-related data. By analyzing the user's social media activity, the data collection unit can acquire relevant health data and gain an accurate understanding of their health status. Social media activity includes, but is not limited to, posts and activity frequency. Some or all of the processing described above in the data collection unit may be performed using, for example, AI, or not. For example, the data collection unit can input the user's social media data into a generating AI and have the generating AI acquire the relevant data.

[0082] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing stress-related data. The analysis unit can also prioritize analyzing relaxation-related data if the user is relaxed. Furthermore, if the user is excited, the analysis unit can prioritize analyzing excitement-related data. This allows the analysis unit to adjust the data analysis method according to the user's emotions and obtain more accurate analysis results. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze highly important data in detail and less important data in a simplified manner. The analysis unit can also analyze highly important data in detail using multiple algorithms. Furthermore, the analysis unit can analyze highly important data in real time and less important data in batch processing. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the health data and analyze the data efficiently. Importance includes, but is not limited to, the impact and urgency of the data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis department can input health data into a generating AI and have the AI ​​adjust the level of detail in the analysis based on its importance.

[0084] The analysis unit can apply different analysis algorithms depending on the category of health data during analysis. For example, the analysis unit can apply a heart rate-specific algorithm to heart rate data. The analysis unit can also apply a blood glucose-specific algorithm to blood glucose data. Furthermore, the analysis unit can apply an activity level-specific algorithm to activity level data. This allows the analysis unit to apply the appropriate analysis algorithm according to the category of health data and obtain accurate analysis results. Categories include, but are not limited to, data type and use. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data into a generating AI and have the generating AI execute the application of analysis algorithms according to the category.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions and provide a highly visible display. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the analysis unit may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the analysis unit can input user facial expression data into a generating AI and have the generating AI perform emotion estimation.

[0086] The analysis unit can determine the priority of analysis based on when the health data was acquired. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also prioritize the analysis of data acquired during specific time periods. Furthermore, the analysis unit can prioritize the analysis of data acquired during times when user activity is high. This allows the analysis unit to determine the priority of analysis based on when the health data was acquired and analyze the data efficiently. The acquisition period includes, but is not limited to, the timing and frequency of data acquisition. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input health data into a generating AI and have the generating AI determine the priority of analysis based on the acquisition period.

[0087] The analysis unit can adjust the order of analysis based on the relevance of health data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also group highly relevant data and analyze them in batches. Furthermore, the analysis unit can analyze highly relevant data in real time and analyze less relevant data in batches. This allows the analysis unit to adjust the order of analysis based on the relevance of health data and analyze the data efficiently. Relevance includes, but is not limited to, data correlation and influence. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input health data into a generating AI and have the generating AI adjust the order of analysis based on relevance.

[0088] The generation unit can estimate the user's emotions and adjust the method of generating customized menus based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can generate menus that help reduce stress. The generation unit can also generate menus that have a relaxing effect if the user is relaxed. Furthermore, if the user is excited, the generation unit can generate menus that calm the excitement. In this way, the generation unit can adjust the method of generating customized menus according to the user's emotions and provide more appropriate menus. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the generation unit may be performed using, for example, an emotion engine or a generation AI, or without using an emotion engine or a generation AI. For example, the generation unit can input the user's facial data into a generation AI and have the generation AI perform emotion estimation.

[0089] The generation unit can adjust the level of detail in the menu based on the user's health condition during generation. For example, if the user's health condition is good, the generation unit can generate a detailed menu. The generation unit can also generate a simplified menu if the user's health condition is poor. Furthermore, the generation unit can adjust the level of detail in the menu in stages according to the user's health condition. This allows the generation unit to adjust the level of detail in the menu according to the user's health condition and provide an appropriate menu. Level of detail includes, but is not limited to, the granularity of information and the content displayed. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user health data into a generation AI and have the generation AI perform menu generation based on level of detail.

[0090] The generation unit can apply different generation algorithms depending on the category of the user's health data during generation. For example, the generation unit can apply a generation algorithm specifically for heart rate based on heart rate data. The generation unit can also apply a generation algorithm specifically for blood glucose levels based on blood glucose level data. Furthermore, the generation unit can apply a generation algorithm specifically for activity levels based on activity level data. This allows the generation unit to apply an appropriate generation algorithm according to the category of the user's health data and generate an accurate menu. Categories include, but are not limited to, data type and usage. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the user's health data into a generation AI and have the generation AI apply a generation algorithm according to the category.

[0091] The generation unit can estimate the user's emotions and determine the priority of the menus to be generated based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit can prioritize generating menus that help reduce stress. The generation unit can also prioritize generating menus that have a relaxing effect if the user is relaxed. Furthermore, if the user is excited, the generation unit can prioritize generating menus that calm the excitement. In this way, the generation unit can determine the priority of the menus to be generated according to the user's emotions and provide appropriate menus. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the generation unit may be performed using, for example, an emotion engine or a generation AI, or without using an emotion engine or a generation AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The generation unit can customize menus based on user preferences and allergies during the generation process. For example, the generation unit can generate menus using the user's favorite ingredients based on their preferences. The generation unit can also generate menus that avoid allergens based on the user's allergy information. Furthermore, the generation unit can generate menus that accommodate preferences and allergies based on the user's past eating history. This allows the generation unit to customize menus based on user preferences and allergies, ensuring user confidence. Preferences and allergies include, but are not limited to, specific ingredients and allergens. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input user preferences and allergy information into a generation AI and have the generation AI perform menu customization.

[0093] The generation unit can optimize menus by referring to the user's past meal history during generation. For example, the generation unit can analyze the user's past meal history and generate menus that take nutritional balance into consideration. The generation unit can also generate menus that cater to the user's preferences and allergies based on their past meal history. Furthermore, the generation unit can generate menus tailored to the user's health condition based on their past meal history. This allows the generation unit to optimize menus by referring to the user's past meal history and provide appropriate menus. Past meal history includes, but is not limited to, meal records and historical data. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the user's past meal history into a generation AI and have the generation AI perform menu optimization.

[0094] The service provider can estimate the user's emotions and adjust the menu delivery method based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visual delivery method. The service provider can also provide a delivery method that includes detailed information if the user is relaxed. Furthermore, if the user is excited, the service provider can provide a visually stimulating delivery method. This allows the service provider to adjust the menu delivery method according to the user's emotions and provide a highly visual delivery. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the service provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the service provider can input the user's facial data into a generative AI and have the generative AI perform emotion estimation.

[0095] The serving unit can select the optimal serving method by referring to the user's past menu selection history at the time of serving. The serving unit can, for example, analyze the user's past menu selection history and select a serving method that suits their preferences. The serving unit can, for example, analyze the user's past menu selection history and select a serving method that suits their preferences. The serving unit can also, for example, select a serving method that takes allergy information into account from the user's past menu selection history. The serving unit can also, for example, select a serving method that takes allergy information into account from the user's past menu selection history. Furthermore, the serving unit can also, for example, select a serving method that suits the user's health condition based on the user's past menu selection history. The serving unit can, for example, select a serving method that suits the user's health condition based on the user's past menu selection history. This allows the serving unit to select the optimal serving method by referring to the user's past menu selection history and provide an appropriate menu. Past menu selection history includes, for example, selection history data and user preferences, but is not limited to such examples. Some or all of the above processing in the serving unit may be performed using, for example, AI, or not using AI. For example, the service department can input the user's past menu selection history into a generating AI and have the AI ​​select the optimal service method.

[0096] The service provider can customize how the menu is displayed based on the user's current health status at the time of service provision. For example, if the user's health status is good, the service provider can display a detailed menu. The service provider can also display a simplified menu if the user's health status is poor. Furthermore, the service provider can customize how the menu is displayed in stages according to the user's health status. This allows the service provider to customize how the menu is displayed based on the user's current health status and provide an appropriate menu. Current health status includes, but is not limited to, the latest health data and medical records. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's health data into a generating AI and have the generating AI perform the customization of the display method.

[0097] The service provider can estimate the user's emotions and adjust the timing of menu delivery based on the estimated emotions. For example, if the user is feeling stressed, the service provider can deliver the menu at a time when the user is relaxed. The service provider can also deliver the menu when the user is relaxed. The service provider can also deliver the menu when the user is relaxed. Furthermore, if the user is excited, the service provider can deliver the menu when the excitement has subsided. In this way, the service provider can adjust the timing of menu delivery according to the user's emotions and deliver the menu at the appropriate time. Emotions include, but are not limited to, facial recognition and voice analysis. Some or all of the above processing in the service provider may be performed using, for example, an emotion engine or generative AI, or without using an emotion engine or generative AI. For example, the service provider can input the user's facial data into a generative AI and have the generative AI perform emotion estimation.

[0098] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can select a service delivery method that matches the screen size. The service provider can also select a service delivery method optimized for a larger screen if the user is using a tablet. Furthermore, if the user is using a smartwatch, the service provider can select a concise and highly visible service delivery method. This allows the service provider to select the optimal service delivery method considering the user's device information and provide an appropriate menu. Device information includes, but is not limited to, the type of device and usage status. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's device information into a generating AI and have the generating AI select the optimal service delivery method.

[0099] The service provider can customize the menu at the time of delivery, taking into account the user's geographical location information. For example, if the user is at high altitude, the service provider can provide a menu suitable for dining at high altitude. The service provider can also provide a menu suitable for dining by the sea if the user is by the sea. Furthermore, if the user is in an urban area, the service provider can provide a menu suitable for dining in an urban area. This allows the service provider to customize the menu and provide an appropriate menu, taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI perform the menu customization.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] A health management system can also suggest exercise based on the user's health data. For example, the data collection unit can acquire the user's heart rate and activity level, and the analysis unit can analyze this data to detect the user's lack of exercise. The generation unit can then generate an exercise menu to compensate for the lack of exercise, and the delivery unit can notify the user. This allows the user to maintain healthy exercise habits. The data collection unit can also acquire the user's sleep data, and the analysis unit can analyze this data to evaluate the quality of sleep. The generation unit can then generate advice to improve sleep quality, and the delivery unit can notify the user. Furthermore, the data collection unit can measure the user's stress level, and the analysis unit can analyze this data to generate a relaxation menu to reduce stress. This allows the user to manage their health comprehensively.

[0102] A health management system can estimate a user's emotions and, based on those estimates, provide suggestions for stress management. For example, the data collection unit can acquire the user's facial expressions and voice data, and the analysis unit can analyze this data to estimate the user's stress level. The generation unit can generate relaxation menus and activities to reduce stress, and the delivery unit can notify the user. This allows the user to effectively manage their stress. The data collection unit can also acquire the user's heart rate and blood pressure data, and the analysis unit can analyze this data to detect signs of stress. The generation unit can generate advice on breathing techniques and meditation to reduce stress, and the delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and the analysis unit can analyze this data to detect stress caused by lack of exercise. This allows the user to manage their stress comprehensively.

[0103] A health management system can analyze a user's dietary history and suggest improvements to their nutritional balance. For example, the data collection unit can record the user's meals, and the analysis unit can analyze this data to detect imbalances in nutrition. The generation unit can create meal menus that consider nutritional balance, and the delivery unit can notify the user. This allows the user to maintain a healthy diet. The data collection unit can also record the user's vitamin and mineral intake, and the analysis unit can analyze this data to identify any nutrient deficiencies. The generation unit can then create meal menus to supplement these deficiencies, and the delivery unit can notify the user. Furthermore, the data collection unit can record the user's calorie intake, and the analysis unit can analyze this data to detect excesses or deficiencies. This allows the user to manage their nutrition comprehensively.

[0104] A health management system can estimate a user's emotions and suggest meals based on those emotions. For example, a data collection unit can acquire the user's facial expressions and voice data, and an analysis unit can analyze this data to estimate the user's emotional state. A generation unit can create a meal menu corresponding to the emotional state, and a delivery unit can notify the user. This allows the user to choose meals that match their emotions. The data collection unit can also acquire the user's heart rate and blood pressure data, and an analysis unit can analyze this data to detect emotional fluctuations. A generation unit can then create a meal menu corresponding to these emotional fluctuations, and a delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and an analysis unit can analyze this data to detect emotional fluctuations. This allows the user to manage their emotions comprehensively.

[0105] A health management system can provide suggestions to improve sleep quality based on the user's health data. For example, the data collection unit can acquire the user's sleep data, and the analysis unit can analyze that data to evaluate sleep quality. The generation unit can generate advice to improve sleep quality, and the delivery unit can notify the user. This allows the user to achieve high-quality sleep. The data collection unit can also acquire the user's heart rate and blood pressure data, and the analysis unit can analyze that data to evaluate sleep quality. The generation unit can generate advice on environmental adjustments to improve sleep quality, and the delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and the analysis unit can analyze that data to evaluate sleep quality. This allows the user to manage their sleep comprehensively.

[0106] A health management system can estimate a user's emotions and suggest exercise based on those emotions. For example, a data collection unit can acquire the user's facial expressions and voice data, and an analysis unit can analyze this data to estimate the user's emotional state. A generation unit can create an exercise menu corresponding to the emotional state, and a delivery unit can notify the user. This allows the user to choose exercise that matches their emotions. The data collection unit can also acquire the user's heart rate and blood pressure data, and an analysis unit can analyze this data to detect emotional fluctuations. A generation unit can then create an exercise menu corresponding to these emotional fluctuations, and a delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and an analysis unit can analyze this data to detect emotional fluctuations. This allows the user to perform comprehensive emotional management.

[0107] A health management system can suggest relaxation activities based on a user's health data. For example, the data collection unit can acquire the user's heart rate and blood pressure data, and the analysis unit can analyze this data to detect when relaxation is needed. The generation unit generates relaxation activities, and the delivery unit notifies the user. This allows the user to relax effectively. The data collection unit can also acquire the user's activity level data, and the analysis unit can analyze this data to detect when relaxation is needed. The generation unit generates advice on adjusting the environment for relaxation, and the delivery unit notifies the user. Furthermore, the data collection unit can acquire the user's sleep data, and the analysis unit can analyze this data to detect when relaxation is needed. This allows the user to manage their relaxation comprehensively.

[0108] A health management system can estimate a user's emotions and, based on those estimates, make suggestions to improve sleep quality. For example, the data collection unit can acquire the user's facial expressions and voice data, and the analysis unit can analyze this data to estimate the user's emotional state. The generation unit generates advice on adjusting the sleep environment according to the emotional state, and the delivery unit notifies the user. This allows the user to adjust their sleep environment to suit their emotions. The data collection unit can also acquire the user's heart rate and blood pressure data, and the analysis unit can analyze this data to detect emotional fluctuations. The generation unit can generate advice on improving sleep quality according to these emotional fluctuations, and the delivery unit notifies the user. Furthermore, the data collection unit can acquire the user's activity level data, and the analysis unit can analyze this data to detect emotional fluctuations. This allows the user to manage their emotions comprehensively.

[0109] A health management system can suggest meal timings based on a user's health data. For example, a data collection unit can acquire the user's blood glucose levels and activity data, and an analysis unit can analyze this data to detect the optimal meal timing. A generation unit generates advice regarding meal timing, and a delivery unit notifies the user. This allows the user to maintain healthy meal timings. The data collection unit can also acquire the user's heart rate and blood pressure data, and an analysis unit can analyze this data to adjust meal timing. The generation unit generates advice regarding meal timing, and a delivery unit notifies the user. Furthermore, the data collection unit can acquire the user's sleep data, and an analysis unit can analyze this data to adjust meal timing. This allows the user to manage their diet comprehensively.

[0110] A health management system can estimate a user's emotions and suggest meal timings based on those estimates. For example, a data collection unit can acquire the user's facial expressions and voice data, and an analysis unit can analyze this data to estimate the user's emotional state. A generation unit can then suggest meal timings that match the emotional state, and a delivery unit can notify the user. This allows the user to choose meal timings that align with their emotions. The data collection unit can also acquire the user's heart rate and blood pressure data, and an analysis unit can analyze this data to detect emotional fluctuations. The generation unit can then suggest meal timings that match these emotional fluctuations, and the delivery unit can notify the user. Furthermore, the data collection unit can acquire the user's activity level data, and an analysis unit can analyze this data to detect emotional fluctuations. This enables the user to manage their emotions comprehensively.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The data collection unit acquires health data from the wearable device. For example, it can acquire health data such as heart rate, blood glucose level, and activity level. The data collection unit can measure the user's heart rate in real time using a heart rate sensor, measure the user's blood glucose level using a blood glucose sensor, and measure the user's activity level using an activity tracker. Step 2: The analysis unit analyzes the data acquired by the collection unit. For example, it uses AI to analyze health data and determine the user's health status. It can analyze heart rate data, blood glucose data, and activity level data, and analyze the fluctuations in each. Step 3: The generation unit generates a customized menu based on the analysis results obtained by the analysis unit. For example, it can use AI to generate a customized menu that takes nutritional balance into account based on the user's health condition. It can generate menus that are low in carbohydrates, menus suitable for energy replenishment, menus rich in vitamins and minerals, etc. Step 4: The providing unit provides the user with the menu generated by the generating unit. For example, a customized menu generated using AI can be notified to or displayed on the user's smartphone, wearable device, or personal computer.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit acquires health data using the heart rate sensor and blood glucose sensor of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized menu based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit acquires health data using the heart rate sensor and blood glucose sensor of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized menu based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit acquires health data using the heart rate sensor and blood glucose sensor of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates a customized menu based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, and provision unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit acquires health data using the heart rate sensor and blood glucose sensor of the robot 414. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the acquired data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and generates a customized menu based on the analysis results. The provision unit is implemented, for example, in the control unit 46A of the robot 414, and provides the generated menu to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A data collection unit that acquires health data from wearable devices, An analysis unit analyzes the data acquired by the aforementioned collection unit, A generation unit that generates a customized menu based on the analysis results obtained by the analysis unit, The system includes a provisioning unit that provides the menu generated by the generation unit to the user. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects health data such as heart rate, blood sugar levels, and activity levels. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The acquired health data is analyzed to determine the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generates a customized menu that takes nutritional balance into account based on the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the generated customized menu to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate menus that cater to user preferences and allergies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, We suggest the optimal buffet menu based on the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past health data and select the optimal method of data acquisition. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When acquiring health data, filtering is performed based on the user's current activity level and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the priority of health data to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When acquiring health data, the system prioritizes the acquisition 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 13) The aforementioned collection unit is When acquiring health data, we analyze the user's social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the category of health data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the health data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of health data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts how customized menus are generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the level of detail in the menu is adjusted based on the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, different generation algorithms are applied depending on the category of the user's health data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and determines the priority of the menu generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the menu is customized based on the user's preferences and allergies. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is During generation, the menu is optimized by referencing the user's past meal history. The system described in Appendix 1, characterized by the features described herein. (Note 26) 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 27) The aforementioned supply unit is, When providing the service, the system will refer to the user's past menu selection history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, customize how the menu is displayed based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of menu item delivery based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the menu will be customized to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that acquires health data from wearable devices, An analysis unit analyzes the data acquired by the aforementioned collection unit, A generation unit that generates a customized menu based on the analysis results obtained by the analysis unit, The system includes a provisioning unit that provides the menu generated by the generation unit to the user. A system characterized by the following features.

2. The aforementioned collection unit is It collects health data such as heart rate, blood sugar levels, and activity levels. The system according to feature 1.

3. The aforementioned analysis unit is The acquired health data is analyzed to determine the user's health status. The system according to feature 1.

4. The generating unit is Generates a customized menu that takes nutritional balance into account based on the user's health condition. The system according to feature 1.

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

6. The generating unit is Generate menus that cater to user preferences and allergies. The system according to feature 1.

7. The aforementioned supply unit is, We suggest the optimal buffet menu based on the user's health condition. The system according to feature 1.

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

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