system

Abstract

The system according to the embodiment aims to provide a sustainable meal plan based on personal health data. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan generated by the generation unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies are not yet fully capable of providing sustainable meal plans based on personal health data, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a sustainable meal plan based on personal health data. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The provision unit provides the meal plan generated by the generation unit. [Effects of the Invention]

[0007] An embodiment of the system can provide a sustainable meal plan based on personal health data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention provides sustainable meal plans based on personal health data. This system collects a user's health data (e.g., weight, height, allergy information, dietary preferences, etc.) via a wearable device or smartphone app, analyzes the collected data using AI, and generates an optimal meal plan for each individual user. This meal plan considers nutritional balance for maintaining health while using sustainable ingredients that minimize environmental impact. For example, locally grown organic vegetables and ingredients with a low carbon footprint are selected. Furthermore, the generated meal plan is provided to the user's smartphone or computer, allowing for daily dietary follow-up. This allows the user to maintain health while also leading an environmentally friendly diet. For example, the system collects a user's health data, such as weight, height, allergy information, and dietary preferences, using a wearable device or smartphone app. The collected data is then analyzed by AI. The AI ​​uses machine learning to generate an optimal meal plan for each individual user. For example, the system suggests a nutritionally balanced meal plan based on the user's health condition and dietary preferences. Furthermore, the system clarifies the reasons for choosing locally grown organic vegetables and low-carbon-footprint ingredients as criteria for selecting sustainable ingredients. For example, locally grown organic vegetables often have a lower environmental impact due to transportation and are more nutritious. Finally, the generated meal plan is provided to the user's smartphone or computer, allowing for daily dietary follow-up. For example, after eating meals according to the meal plan, the results can be recorded and reflected in the next plan. This allows users to maintain their health while also leading an environmentally conscious diet. This allows the system to provide sustainable meal plans based on personal health data.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health data of a user. The user's health data includes, but is not limited to, weight, height, allergy information, and dietary preferences. For example, the collection unit measures the user's weight and heart rate using a wearable device and collects data. The collection unit can also collect data by having the user input the user's dietary preferences and allergy information using a smartphone app. The collection unit can also store the user's health data in the cloud and provide it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using a machine learning algorithm to evaluate the user's health status. The analysis unit can also analyze the data using statistical analysis to predict the user's health risks. The analysis unit provides the data analysis results to the generation unit. The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The generation unit can propose a nutritionally balanced meal plan based on the user's health status and dietary preferences, for example. The generation unit can also generate a meal plan using sustainable ingredients. For example, the generation unit generates a meal plan using locally grown organic vegetables and ingredients with a low carbon footprint. The generation unit then provides the generated meal plan to the provision unit. The provision unit then provides the meal plan generated by the generation unit to the user's smartphone or computer. The provision unit notifies the user of the meal plan, for example, via a smartphone app. The provision unit can also provide the meal plan to the user via a website. The provision unit can also perform follow-up on the meal plan. For example, after the user eats according to the meal plan, the provision unit can record the results and reflect them in the next plan. This allows the system according to the embodiment to provide a sustainable meal plan based on individual health data.

[0030] The system includes a selection unit that selects sustainable ingredients. The selection unit selects sustainable ingredients. Examples of sustainable ingredients include, but are not limited to, organically grown vegetables, locally produced ingredients, and ingredients with a low carbon footprint. For example, the selection unit preferentially selects organically grown vegetables. The selection unit can also select locally produced ingredients. For example, the selection unit selects locally produced ingredients that require a short transportation distance and have a low environmental impact. The selection unit can also select ingredients with a low carbon footprint. For example, the selection unit selects ingredients produced using a production method that is highly energy efficient and has a low environmental impact. In this way, the selection of sustainable ingredients minimizes the burden on the environment. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input selection criteria for sustainable ingredients into AI, which then selects ingredients based on the criteria.

[0031] The system includes a follow-up unit that performs a meal plan follow-up. The follow-up unit performs the meal plan follow-up. For example, after the user eats according to the meal plan, the follow-up unit records the results and reflects them in the next meal plan. The follow-up unit can also periodically check the user's health status and adjust the meal plan as necessary. For example, the follow-up unit periodically measures the user's weight and blood pressure and collects data. The follow-up unit can also record the user's diet and evaluate nutritional balance. The follow-up unit can also provide feedback to the user. For example, the follow-up unit can provide improvements and advice based on the user's diet. This allows the user to continuously maintain their health by following up on the meal plan. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without AI. For example, the follow-up unit can input the user's health data into AI, which analyzes the data and provides feedback.

[0032] The collection unit can collect health data through a wearable device or a smartphone app. For example, the collection unit can measure the user's weight and heart rate using a wearable device and collect data. For example, the collection unit can monitor the user's heart rate in real time using a smartwatch and collect data. The collection unit can also collect data by having the user input their dietary preferences and allergy information using a smartphone app. For example, the collection unit can conduct a survey of the user through the smartphone app and collect information about their dietary preferences and allergies. The collection unit can also store the user's health data in the cloud and provide it to the analysis unit. For example, the collection unit can upload data collected through the wearable device or smartphone app to the cloud and make it accessible to the analysis unit. This allows for efficient collection of user health data by collecting health data through the wearable device or smartphone app. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data collected through the wearable device or smartphone app into AI, which can analyze the data and provide the collection results.

[0033] The analysis unit can analyze the collected data using machine learning. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health condition. For example, the analysis unit predicts changes in the user's weight using regression analysis. The analysis unit can also classify the user's eating patterns using clustering. For example, the analysis unit clusters the user's eating data and groups users with similar eating patterns. The analysis unit can also analyze the data using a neural network to predict the user's health risks. For example, the analysis unit inputs the user's health data into a neural network to predict health risks. This improves the accuracy of analyzing the collected data using machine learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and provides analysis results.

[0034] The generation unit can generate a meal plan using locally grown organic vegetables or low-carbon footprint ingredients. The generation unit, for example, generates a meal plan using locally grown organic vegetables. For example, the generation unit suggests a nutritionally balanced salad using locally grown organic vegetables. The generation unit can also generate a meal plan using low-carbon footprint ingredients. For example, the generation unit suggests an environmentally friendly menu using low-carbon footprint ingredients. The generation unit can also generate a meal plan that combines locally grown organic vegetables and low-carbon footprint ingredients. For example, the generation unit suggests a sustainable meal plan using locally grown organic vegetables and low-carbon footprint ingredients. This makes it possible to provide a meal plan that minimizes the burden on the environment by using locally grown organic vegetables and low-carbon footprint ingredients. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input information about locally grown organic vegetables and low-carbon footprint ingredients into AI, which can then generate a meal plan based on the information.

[0035] The provision unit can provide the generated meal plan to the user's smartphone or computer. The provision unit can notify the user of the meal plan, for example, through a smartphone app. For example, the provision unit can use the smartphone app to send push notifications of the daily meal plan to the user. The provision unit can also provide the meal plan to the user through a website. For example, the provision unit can display an individual meal plan to a user who logs in to the website. The provision unit can also perform follow-up on the meal plan. For example, after the user eats meals according to the meal plan, the provision unit can record the results and reflect them in the next plan. This allows the user to easily check their daily meal plan by providing the generated meal plan to the user's smartphone or computer. Some or all of the above-described processing by the provision unit can be performed using, for example, AI, or without AI. For example, the provision unit can input the generated meal plan into AI, which can then determine how to provide the plan to the user.

[0036] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit selects the collection method that obtains the most accurate data from the user's past data. For example, the collection unit analyzes the user's past health data and determines the optimal collection method. The collection unit can also optimize the collection frequency from the user's past data. For example, the collection unit sets the optimal collection frequency based on the user's past data. The collection unit can also select a collection method for a specific time period from the user's past data. For example, the collection unit analyzes the user's past data and determines the optimal collection time period. In this way, the optimal collection method can be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI, which analyzes the data and selects the optimal collection method.

[0037] When collecting health data, the collection unit can filter the data based on the user's current lifestyle and activity level. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, the collection unit collects the user's exercise data in real time and filters the exercise-related data. Furthermore, when the user is resting, the collection unit can collect only data related to rest. For example, the collection unit collects the user's rest data and filters the rest-related data. Furthermore, when the user is working, the collection unit can collect only work-related data. For example, the collection unit collects the user's work data and filters the work-related data. By filtering the data based on the user's lifestyle and activity level, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's lifestyle and activity level into AI, which then analyzes and filters the data.

[0038] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects environmental data for that area. For example, the collection unit collects environmental data such as the temperature and humidity of the area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can also collect data related to health risks at the travel destination. For example, the collection unit collects data related to health risks at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can also collect data related to the indoor environment. For example, the collection unit collects data such as indoor temperature and humidity based on the user's geographical location information. This allows highly relevant data to be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI, which then analyzes the data and prioritizes collecting highly relevant data.

[0039] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user posts on social media that they are feeling stressed, the collection unit collects stress-related data. For example, the collection unit analyzes the user's social media posts and collects stress-related data. Furthermore, if the user posts on social media about health, the collection unit can also collect data related to the content of the posts. For example, the collection unit analyzes the user's social media posts and collects health-related data. Furthermore, if the user posts on social media about exercise, the collection unit can also collect exercise-related data. For example, the collection unit analyzes the user's social media posts and collects exercise-related data. In this way, related health data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which then analyzes the data and collects related health data.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit performs a detailed analysis on important health data. For example, the analysis unit may perform a detailed analysis of the user's heart rate data to check for any abnormalities. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit may perform a simplified analysis of the user's weight data to check for weight fluctuations. The analysis unit can also perform a rapid analysis on data of high urgency. For example, the analysis unit may quickly analyze the user's blood pressure data and immediately notify the user if any abnormalities are found. In this way, by adjusting the level of detail of the analysis based on the importance of the health data, detailed analysis can be performed on important data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the importance of the health data into AI, which then analyzes the data and adjusts the level of detail.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit applies a nutritional balance analysis algorithm to nutritional data. For example, the analysis unit inputs the user's dietary data into a nutritional balance analysis algorithm to evaluate nutritional balance. The analysis unit can also apply an exercise performance analysis algorithm to exercise data. For example, the analysis unit inputs the user's exercise data into an exercise performance analysis algorithm to evaluate the effectiveness of the exercise. The analysis unit can also apply a sleep quality analysis algorithm to sleep data. For example, the analysis unit inputs the user's sleep data into a sleep quality analysis algorithm to evaluate the quality of sleep. In this way, by applying different analysis algorithms depending on the category of health data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of health data into AI, which analyzes the data and applies an appropriate algorithm.

[0042] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected data. For example, the analysis unit prioritizes analysis of the user's latest health data and evaluates the user's latest health status. The analysis unit can also emphasize current data while referring to past data. For example, the analysis unit prioritizes analysis of current data while referring to the user's past health data. The analysis unit can also prioritize analysis of data collected during a specific period. For example, the analysis unit prioritizes analysis of data collected during a specific period of the user and evaluates the user's health status during that period. In this way, by determining the priority of analysis based on when the health data was collected, the most recent data can be analyzed preferentially. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time when the health data was collected into AI, which then analyzes the data and determines the priority.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data among the user's health data to extract important information. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data among the user's health data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the user's health data in real time and dynamically adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the health data, highly relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the health data into AI, which then analyzes the data and adjusts the order.

[0044] When generating a meal plan, the generation unit can adjust the level of detail of the plan based on the importance of the health data. The generation unit, for example, generates a detailed meal plan based on important health data. For example, the generation unit generates a detailed meal plan for blood pressure management based on the user's blood pressure data. The generation unit can also generate a simplified meal plan based on general health data. For example, the generation unit generates a simplified meal plan for weight management based on the user's weight data. The generation unit can also generate a meal plan that can be quickly implemented based on data with high urgency. For example, the generation unit generates a quick meal plan for blood glucose level management based on the user's blood glucose level data. In this way, by adjusting the level of detail of the plan based on the importance of the health data, a detailed meal plan based on important data can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the importance of the health data into AI, which analyzes the data and adjusts the level of detail.

[0045] When generating a meal plan, the generation unit can apply different generation algorithms depending on the category of health data. The generation unit, for example, generates a nutritionally balanced meal plan based on nutritional data. For example, the generation unit generates a nutritionally balanced meal plan based on the user's nutritional data. The generation unit can also generate a meal plan that promotes post-exercise recovery based on exercise data. For example, the generation unit generates a meal plan that promotes post-exercise recovery based on the user's exercise data. The generation unit can also generate a meal plan that improves sleep quality based on sleep data. For example, the generation unit generates a meal plan that improves sleep quality based on the user's sleep data. This allows for the application of different generation algorithms depending on the category of health data, thereby providing a more appropriate meal plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the category of health data into AI, which analyzes the data and applies an appropriate algorithm.

[0046] When generating meal plans, the generation unit can determine the priority of the plans based on the time when the health data was collected. The generation unit, for example, generates the latest meal plan based on recently collected data. For example, the generation unit generates the latest meal plan based on the user's latest health data. The generation unit can also reference past data while emphasizing current data. For example, the generation unit generates a meal plan based on the user's past health data while emphasizing current data. The generation unit can also prioritize meal plans based on data collected during a specific period. For example, the generation unit prioritizes meal plans based on the user's data collected during a specific period. This allows for the provision of meal plans based on the latest data by prioritizing plans based on the time when the health data was collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the time when the health data was collected into AI, which can analyze the data and determine the priority.

[0047] When generating meal plans, the generation unit can adjust the order of the plans based on the relevance of the health data. For example, the generation unit may generate meal plans preferentially based on highly relevant data. For example, the generation unit may generate meal plans preferentially based on highly relevant data among the user's health data. The generation unit may also postpone less relevant data. For example, the generation unit may generate meal plans by postponing less relevant data among the user's health data. The generation unit may also dynamically adjust the order of the plans based on the relevance of the data. For example, the generation unit may evaluate the relevance of the user's health data in real time and dynamically adjust the order of the plans. By adjusting the order of the plans based on the relevance of the health data, a meal plan based on highly relevant data can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may input the relevance of the health data into AI, which may analyze the data and adjust the order.

[0048] When providing food, the providing unit can select the optimal providing method by referring to the user's past meal history. For example, the providing unit prioritizes selecting a providing method that the user has used favorably in the past. For example, the providing unit analyzes the user's past meal history and prioritizes selecting a providing method that the user has used favorably in the past. The providing unit can also select the optimal providing timing from the user's past meal history. For example, the providing unit determines the optimal providing timing based on the user's past meal history. The providing unit can also analyze the user's past meal history and select the most effective providing method. For example, the providing unit selects the most effective providing method based on the user's past meal history. In this way, the optimal providing method can be selected by referring to the user's past meal history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past meal history into AI, which analyzes the data and selects the optimal providing method.

[0049] The provision unit can customize the provision method based on the user's current living situation when providing the meal plan. For example, if the user is busy, the provision unit provides the meal plan in an easily accessible manner. For example, the provision unit analyzes the user's living situation and, if the user is busy, provides the meal plan in an easily accessible manner via a smartphone app. The provision unit can also provide the meal plan in a manner including detailed information if the user is relaxing. For example, the provision unit analyzes the user's living situation and, if the user is relaxing, provides the meal plan in a detailed report. The provision unit can also provide the meal plan in a manner that takes into account ingredients available at the travel destination if the user is traveling. For example, the provision unit provides a meal plan that takes into account ingredients available at the travel destination based on the user's geographical location information. This allows the meal plan to be provided in a more appropriate manner by customizing the provision method based on the user's current living situation. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit can input the user's living situation data into AI, which can analyze the data and customize the provision method.

[0050] The provision unit can select the optimal provision method by taking into account the user's geographical location information when providing food. For example, if the user is in a specific area, the provision unit provides a plan that takes into account ingredients available in that area. For example, the provision unit provides a meal plan that takes into account ingredients available in that area based on the user's geographical location information. Furthermore, if the user is traveling, the provision unit can also provide a plan that takes into account ingredients available at the travel destination. For example, the provision unit provides a meal plan that takes into account ingredients available at the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the provision unit can also provide a plan using locally produced ingredients. For example, the provision unit provides a meal plan using locally produced ingredients based on the user's geographical location information. This allows the optimal provision method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's geographical location information into AI, which can analyze the data and select the optimal provision method.

[0051] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, if the user posts about health on social media, the providing unit can provide a plan related to that content. For example, the providing unit can analyze the user's social media post and provide a health-related plan. Also, if the user posts about a specific food ingredient on social media, the providing unit can provide a plan using that food ingredient. For example, the providing unit can analyze the user's social media post and provide a plan using that food ingredient. Also, if the user posts on social media that they are feeling stressed, the providing unit can provide a plan effective for stress reduction. For example, the providing unit can analyze the user's social media post and provide a plan effective for stress reduction. In this way, by analyzing the user's social media activity, more relevant means of provision can be suggested. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can analyze the data and suggest means of provision.

[0052] When selecting ingredients, the selection unit can adjust the level of detail of the selection based on the nutritional value of the ingredients. The selection unit, for example, prioritizes the selection of ingredients with high nutritional value. For example, the selection unit evaluates the nutritional value of ingredients and prioritizes the selection of ingredients with high nutritional value. The selection unit can also select ingredients with average nutritional value. For example, the selection unit evaluates the nutritional value of ingredients and selects ingredients with average nutritional value. The selection unit can also select ingredients rich in specific nutrients. For example, the selection unit evaluates and selects ingredients rich in specific nutrients. In this way, by adjusting the level of detail of the selection based on the nutritional value of the ingredients, it is possible to select ingredients with higher nutritional value. Some or all of the above-mentioned processes in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input nutritional value data of ingredients into AI, which analyzes the data and adjusts the level of detail.

[0053] When selecting ingredients, the selection unit can apply different selection algorithms depending on the category of the ingredients. For example, the selection unit prioritizes selecting organic vegetables for the vegetable category. For example, the selection unit evaluates ingredients in the vegetable category and prioritizes selecting organic vegetables. The selection unit can also select meat with a low carbon footprint for the meat category. For example, the selection unit evaluates ingredients in the meat category and selects meat with a low carbon footprint. The selection unit can also select fish from sustainable fisheries for the seafood category. For example, the selection unit evaluates ingredients in the seafood category and selects fish from sustainable fisheries. This allows for more appropriate ingredients to be selected by applying different selection algorithms depending on the ingredient category. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit can input ingredient category data into AI, which analyzes the data and applies an appropriate algorithm.

[0054] When selecting ingredients, the selection unit can select the optimal selection method by taking into account information about the origin of the ingredients. The selection unit, for example, prioritizes the selection of locally produced ingredients. For example, the selection unit evaluates information about the origin of the ingredients and prioritizes the selection of locally produced ingredients. The selection unit can also select ingredients that require a short transportation distance. For example, the selection unit evaluates information about the origin of the ingredients and selects ingredients that require a short transportation distance. The selection unit can also select ingredients whose production area has a low environmental impact. For example, the selection unit evaluates information about the origin of the ingredients and selects ingredients from production areas with a low environmental impact. In this way, by taking into account the information about the origin of the ingredients, ingredients with a lower environmental impact can be selected. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input information about the origin of the ingredients into AI, which analyzes the data and selects the optimal selection method.

[0055] When selecting ingredients, the selection unit can improve the accuracy of the selection by referring to literature related to the ingredients. The selection unit, for example, selects ingredients by referring to the latest nutritional research. For example, the selection unit refers to the latest nutritional research and selects ingredients based on scientific evidence. The selection unit can also select sustainable ingredients by referring to research on environmental impact. For example, the selection unit selects sustainable ingredients by referring to research on environmental impact. The selection unit can also make selections by referring to literature on the health benefits of ingredients. For example, the selection unit selects healthy ingredients by referring to literature on the health benefits of ingredients. In this way, by referring to literature related to ingredients, ingredients can be selected that are more scientifically based. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input literature data related to ingredients into AI, which analyzes the data to improve the accuracy of the selection.

[0056] The follow-up unit can select the optimal follow-up method by referring to the user's past meal history when following up. For example, the follow-up unit prioritizes selecting a follow-up method that the user has used favorably in the past. For example, the follow-up unit analyzes the user's past meal history and prioritizes selecting a follow-up method that the user has used favorably in the past. The follow-up unit can also select the optimal follow-up timing from the user's past meal history. For example, the follow-up unit determines the optimal follow-up timing based on the user's past meal history. The follow-up unit can also analyze the user's past meal history and select the most effective follow-up method. For example, the follow-up unit selects the most effective follow-up method based on the user's past meal history. In this way, the optimal follow-up method can be selected by referring to the user's past meal history. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's past meal history into AI, which analyzes the data and selects the optimal follow-up method.

[0057] The follow-up unit can customize the follow-up means based on the user's current lifestyle at the time of follow-up. For example, if the user is busy, the follow-up unit provides the follow-up in an easily accessible manner. For example, the follow-up unit analyzes the user's lifestyle and, if the user is busy, provides the follow-up in an easily accessible manner through a smartphone app. The follow-up unit can also provide the follow-up in a manner including detailed information if the user is relaxing. For example, the follow-up unit analyzes the user's lifestyle and, if the user is relaxing, provides the follow-up in a detailed report. The follow-up unit can also provide the follow-up in a manner that takes into account ingredients available at the travel destination if the user is traveling. For example, the follow-up unit provides the follow-up that takes into account ingredients available at the travel destination based on the user's geographical location information. This allows the follow-up means to be customized based on the user's current lifestyle, thereby providing more appropriate follow-up. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without AI. For example, the follow-up unit can input the user's lifestyle data into AI, which then analyzes the data and customizes the follow-up means.

[0058] The follow-up unit can select the optimal follow-up method by taking into account the user's geographical location information when performing a follow-up. For example, when the user is in a specific area, the follow-up unit provides a follow-up that takes into account ingredients available in the area. For example, the follow-up unit provides a follow-up that takes into account ingredients available in the area based on the user's geographical location information. Furthermore, when the user is traveling, the follow-up unit can also provide a follow-up that takes into account ingredients available at the travel destination. For example, the follow-up unit provides a follow-up that takes into account ingredients available at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the follow-up unit can also provide a follow-up that uses locally produced ingredients. For example, the follow-up unit provides a follow-up that uses locally produced ingredients based on the user's geographical location information. In this way, the optimal follow-up method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's geographical location information into AI, which can then analyze the data to select the optimal follow-up method.

[0059] The follow-up unit can analyze the user's social media activities and suggest follow-up measures when following up. For example, if a user posts about health on social media, the follow-up unit provides follow-up related to the content. For example, the follow-up unit analyzes the user's social media posts and provides follow-up related to health. Furthermore, if a user posts about a specific food ingredient on social media, the follow-up unit can also provide follow-up using that food ingredient. For example, the follow-up unit analyzes the user's social media posts and provides follow-up using that food ingredient. Furthermore, if a user posts about feeling stressed on social media, the follow-up unit can also provide follow-up effective for stress reduction. For example, the follow-up unit analyzes the user's social media posts and provides follow-up effective for stress reduction. In this way, by analyzing the user's social media activities, more relevant follow-up measures can be suggested. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's social media activity data into AI, which can analyze the data and suggest follow-up measures.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The analysis unit can analyze the user's past health data and select the optimal analysis method. For example, it selects the analysis method that provides the most accurate data from the user's past data. The analysis unit can also analyze the user's past health data and determine the optimal analysis method. It can also optimize the analysis frequency from the user's past data. The analysis unit sets the optimal analysis frequency based on the user's past data. It can also select an analysis method for a specific time period from the user's past data. The analysis unit analyzes the user's past data and determines the optimal analysis time period. In this way, it is possible to select the optimal analysis method by analyzing the user's past health data.

[0062] When providing food, the providing unit can select the optimal providing method by referring to the user's past meal history. For example, the providing unit preferentially selects a providing method that the user has used favorably in the past. The providing unit analyzes the user's past meal history and preferentially selects a providing method that the user has used favorably in the past. The providing unit can also select the optimal timing of providing food from the user's past meal history. The providing unit determines the optimal timing of providing food based on the user's past meal history. Furthermore, the providing unit can analyze the user's past meal history and select the most effective providing method. The providing unit selects the most effective providing method based on the user's past meal history. In this way, the optimal providing method can be selected by referring to the user's past meal history.

[0063] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, it collects environmental data for that area. The collection unit collects environmental data such as the temperature and humidity of the area based on the user's geographical location information. Furthermore, when the user is traveling, it can also collect data related to health risks at the travel destination. The collection unit collects data related to health risks at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, it can also collect data related to the indoor environment. The collection unit collects data such as indoor temperature and humidity based on the user's geographical location information. In this way, it is possible to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0064] When selecting ingredients, the selection unit can adjust the level of detail of the selection based on the nutritional value of the ingredients. For example, it prioritizes the selection of ingredients with high nutritional value. The selection unit evaluates the nutritional value of ingredients and selects ingredients with high nutritional value. It can also select ingredients with average nutritional value. The selection unit evaluates the nutritional value of ingredients and selects ingredients with average nutritional value. It can also select ingredients rich in specific nutrients. The selection unit evaluates and selects ingredients rich in specific nutrients. In this way, by adjusting the level of detail of the selection based on the nutritional value of ingredients, it is possible to select ingredients with higher nutritional value.

[0065] When following up, the follow-up unit can analyze the user's social media activity and suggest follow-up measures. For example, if a user posts about health on social media, the follow-up unit provides follow-up measures related to the content. The follow-up unit analyzes the user's social media post and provides follow-up measures related to health. Also, if a user posts about a specific food ingredient on social media, the follow-up unit can provide follow-up measures using that food ingredient. The follow-up unit analyzes the user's social media post and provides follow-up measures using that food ingredient. Furthermore, if a user posts about feeling stressed on social media, the follow-up unit can provide follow-up measures effective for reducing stress. The follow-up unit analyzes the user's social media post and provides follow-up measures effective for reducing stress. In this way, by analyzing the user's social media activity, more relevant follow-up measures can be suggested.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects the user's health data. The user's health data includes weight, height, allergy information, and dietary preferences. The collection unit uses a wearable device to measure the user's weight and heart rate and collect the data. It can also collect data by having the user input their dietary preferences and allergy information using a smartphone app. The collection unit then stores the user's health data in the cloud and provides it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health status. The analysis unit can also analyze the data using statistical analysis to predict the user's health risks. Furthermore, the analysis unit provides the data analysis results to the generation unit. Step 3: The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The generation unit proposes a nutritionally balanced meal plan based on the user's health condition and dietary preferences. The generation unit can also generate a meal plan using sustainable ingredients. For example, a meal plan using locally grown organic vegetables and ingredients with a low carbon footprint is generated. The generation unit then provides the generated meal plan to the provision unit. Step 4: The providing unit provides the meal plan generated by the generating unit to the user's smartphone or computer. The providing unit notifies the user of the meal plan through a smartphone app. The providing unit can also provide the meal plan to the user through a website. Furthermore, the providing unit can also perform follow-up on the meal plan. For example, after the user eats according to the meal plan, the results can be recorded and reflected in the next plan.

[0068] (Example 2) A system according to an embodiment of the present invention provides sustainable meal plans based on personal health data. This system collects a user's health data (e.g., weight, height, allergy information, dietary preferences, etc.) via a wearable device or smartphone app, analyzes the collected data using AI, and generates an optimal meal plan for each individual user. This meal plan considers nutritional balance for maintaining health while using sustainable ingredients that minimize environmental impact. For example, locally grown organic vegetables and ingredients with a low carbon footprint are selected. Furthermore, the generated meal plan is provided to the user's smartphone or computer, allowing for daily dietary follow-up. This allows the user to maintain health while also leading an environmentally friendly diet. For example, the system collects a user's health data, such as weight, height, allergy information, and dietary preferences, using a wearable device or smartphone app. The collected data is then analyzed by AI. The AI ​​uses machine learning to generate an optimal meal plan for each individual user. For example, the system suggests a nutritionally balanced meal plan based on the user's health condition and dietary preferences. Furthermore, the system clarifies the reasons for choosing locally grown organic vegetables and low-carbon-footprint ingredients as criteria for selecting sustainable ingredients. For example, locally grown organic vegetables often have a lower environmental impact due to transportation and are more nutritious. Finally, the generated meal plan is provided to the user's smartphone or computer, allowing for daily dietary follow-up. For example, after eating meals according to the meal plan, the results can be recorded and reflected in the next plan. This allows users to maintain their health while also leading an environmentally conscious diet. This allows the system to provide sustainable meal plans based on personal health data.

[0069] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects health data of a user. The user's health data includes, but is not limited to, weight, height, allergy information, and dietary preferences. For example, the collection unit measures the user's weight and heart rate using a wearable device and collects data. The collection unit can also collect data by having the user input the user's dietary preferences and allergy information using a smartphone app. The collection unit can also store the user's health data in the cloud and provide it to the analysis unit. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the data using a machine learning algorithm to evaluate the user's health status. The analysis unit can also analyze the data using statistical analysis to predict the user's health risks. The analysis unit provides the data analysis results to the generation unit. The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The generation unit can propose a nutritionally balanced meal plan based on the user's health status and dietary preferences, for example. The generation unit can also generate a meal plan using sustainable ingredients. For example, the generation unit generates a meal plan using locally grown organic vegetables and ingredients with a low carbon footprint. The generation unit then provides the generated meal plan to the provision unit. The provision unit then provides the meal plan generated by the generation unit to the user's smartphone or computer. The provision unit notifies the user of the meal plan, for example, via a smartphone app. The provision unit can also provide the meal plan to the user via a website. The provision unit can also perform follow-up on the meal plan. For example, after the user eats according to the meal plan, the provision unit can record the results and reflect them in the next plan. This allows the system according to the embodiment to provide a sustainable meal plan based on individual health data.

[0070] The system includes a selection unit that selects sustainable ingredients. The selection unit selects sustainable ingredients. Examples of sustainable ingredients include, but are not limited to, organically grown vegetables, locally produced ingredients, and ingredients with a low carbon footprint. For example, the selection unit preferentially selects organically grown vegetables. The selection unit can also select locally produced ingredients. For example, the selection unit selects locally produced ingredients that require a short transportation distance and have a low environmental impact. The selection unit can also select ingredients with a low carbon footprint. For example, the selection unit selects ingredients produced using a production method that is highly energy efficient and has a low environmental impact. In this way, the selection of sustainable ingredients minimizes the burden on the environment. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input selection criteria for sustainable ingredients into AI, which then selects ingredients based on the criteria.

[0071] The system includes a follow-up unit that performs a meal plan follow-up. The follow-up unit performs the meal plan follow-up. For example, after the user eats according to the meal plan, the follow-up unit records the results and reflects them in the next meal plan. The follow-up unit can also periodically check the user's health status and adjust the meal plan as necessary. For example, the follow-up unit periodically measures the user's weight and blood pressure and collects data. The follow-up unit can also record the user's diet and evaluate nutritional balance. The follow-up unit can also provide feedback to the user. For example, the follow-up unit can provide improvements and advice based on the user's diet. This allows the user to continuously maintain their health by following up on the meal plan. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without AI. For example, the follow-up unit can input the user's health data into AI, which analyzes the data and provides feedback.

[0072] The collection unit can collect health data through a wearable device or a smartphone app. For example, the collection unit can measure the user's weight and heart rate using a wearable device and collect data. For example, the collection unit can monitor the user's heart rate in real time using a smartwatch and collect data. The collection unit can also collect data by having the user input their dietary preferences and allergy information using a smartphone app. For example, the collection unit can conduct a survey of the user through the smartphone app and collect information about their dietary preferences and allergies. The collection unit can also store the user's health data in the cloud and provide it to the analysis unit. For example, the collection unit can upload data collected through the wearable device or smartphone app to the cloud and make it accessible to the analysis unit. This allows for efficient collection of user health data by collecting health data through the wearable device or smartphone app. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data collected through the wearable device or smartphone app into AI, which can analyze the data and provide the collection results.

[0073] The analysis unit can analyze the collected data using machine learning. For example, the analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health condition. For example, the analysis unit predicts changes in the user's weight using regression analysis. The analysis unit can also classify the user's eating patterns using clustering. For example, the analysis unit clusters the user's eating data and groups users with similar eating patterns. The analysis unit can also analyze the data using a neural network to predict the user's health risks. For example, the analysis unit inputs the user's health data into a neural network to predict health risks. This improves the accuracy of analyzing the collected data using machine learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the collected data into AI, which analyzes the data and provides analysis results.

[0074] The generation unit can generate a meal plan using locally grown organic vegetables or low-carbon footprint ingredients. The generation unit, for example, generates a meal plan using locally grown organic vegetables. For example, the generation unit suggests a nutritionally balanced salad using locally grown organic vegetables. The generation unit can also generate a meal plan using low-carbon footprint ingredients. For example, the generation unit suggests an environmentally friendly menu using low-carbon footprint ingredients. The generation unit can also generate a meal plan that combines locally grown organic vegetables and low-carbon footprint ingredients. For example, the generation unit suggests a sustainable meal plan using locally grown organic vegetables and low-carbon footprint ingredients. This makes it possible to provide a meal plan that minimizes the burden on the environment by using locally grown organic vegetables and low-carbon footprint ingredients. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input information about locally grown organic vegetables and low-carbon footprint ingredients into AI, which can then generate a meal plan based on the information.

[0075] The provision unit can provide the generated meal plan to the user's smartphone or computer. The provision unit can notify the user of the meal plan, for example, through a smartphone app. For example, the provision unit can use the smartphone app to send push notifications of the daily meal plan to the user. The provision unit can also provide the meal plan to the user through a website. For example, the provision unit can display an individual meal plan to a user who logs in to the website. The provision unit can also perform follow-up on the meal plan. For example, after the user eats meals according to the meal plan, the provision unit can record the results and reflect them in the next plan. This allows the user to easily check their daily meal plan by providing the generated meal plan to the user's smartphone or computer. Some or all of the above-described processing by the provision unit can be performed using, for example, AI, or without AI. For example, the provision unit can input the generated meal plan into AI, which can then determine how to provide the plan to the user.

[0076] The collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit changes the collection timing to a time when the user is relaxed. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can also follow a regular schedule. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can shorten the collection timing and collect only the minimum amount of data necessary. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows the timing of health data collection to be adjusted according to the user's emotions, thereby enabling data to be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI, which may analyze the data and adjust the collection timing.

[0077] The collection unit can analyze the user's past health data and select the optimal collection method. For example, the collection unit selects the collection method that obtains the most accurate data from the user's past data. For example, the collection unit analyzes the user's past health data and determines the optimal collection method. The collection unit can also optimize the collection frequency from the user's past data. For example, the collection unit sets the optimal collection frequency based on the user's past data. The collection unit can also select a collection method for a specific time period from the user's past data. For example, the collection unit analyzes the user's past data and determines the optimal collection time period. In this way, the optimal collection method can be selected by analyzing the user's past health data. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI, which analyzes the data and selects the optimal collection method.

[0078] When collecting health data, the collection unit can filter the data based on the user's current lifestyle and activity level. For example, when the user is exercising, the collection unit collects only data related to exercise. For example, the collection unit collects the user's exercise data in real time and filters the exercise-related data. Furthermore, when the user is resting, the collection unit can collect only data related to rest. For example, the collection unit collects the user's rest data and filters the rest-related data. Furthermore, when the user is working, the collection unit can collect only work-related data. For example, the collection unit collects the user's work data and filters the work-related data. By filtering the data based on the user's lifestyle and activity level, more relevant data can be collected. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input data on the user's lifestyle and activity level into AI, which then analyzes and filters the data.

[0079] The collection unit can estimate the user's emotions and prioritize the health data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting stress-related data. For example, the collection unit may capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting stress-related data. Alternatively, if the user is relaxed, the collection unit can prioritize collecting general health data. For example, the collection unit may record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize collecting general health data. Alternatively, if the user is in a hurry, the collection unit can prioritize collecting only important data. For example, the collection unit may collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize collecting only important data. This allows important data to be prioritized by prioritizing health data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into AI, which may analyze the data and determine priorities.

[0080] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects environmental data for that area. For example, the collection unit collects environmental data such as the temperature and humidity of the area based on the user's geographical location information. Furthermore, when the user is traveling, the collection unit can also collect data related to health risks at the travel destination. For example, the collection unit collects data related to health risks at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the collection unit can also collect data related to the indoor environment. For example, the collection unit collects data such as indoor temperature and humidity based on the user's geographical location information. This allows highly relevant data to be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information into AI, which then analyzes the data and prioritizes collecting highly relevant data.

[0081] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, if the user posts on social media that they are feeling stressed, the collection unit collects stress-related data. For example, the collection unit analyzes the user's social media posts and collects stress-related data. Furthermore, if the user posts on social media about health, the collection unit can also collect data related to the content of the posts. For example, the collection unit analyzes the user's social media posts and collects health-related data. Furthermore, if the user posts on social media about exercise, the collection unit can also collect exercise-related data. For example, the collection unit analyzes the user's social media posts and collects exercise-related data. In this way, related health data can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI, which then analyzes the data and collects related health data.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. For example, the analysis unit can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and display the analysis results in a simple graph or chart. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can record the user's voice, estimate their emotions using voice analysis technology, and provide a detailed report. The analysis unit can also provide analysis results that are concise to the main points if the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate their emotions using an emotion estimation algorithm, and provide a short, concise report. This allows the analysis results to be more easily understood by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may analyze the data and adjust the expression method.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. For example, the analysis unit performs a detailed analysis on important health data. For example, the analysis unit may perform a detailed analysis of the user's heart rate data to check for any abnormalities. The analysis unit can also perform a simplified analysis on general health data. For example, the analysis unit may perform a simplified analysis of the user's weight data to check for weight fluctuations. The analysis unit can also perform a rapid analysis on data of high urgency. For example, the analysis unit may quickly analyze the user's blood pressure data and immediately notify the user if any abnormalities are found. In this way, by adjusting the level of detail of the analysis based on the importance of the health data, detailed analysis can be performed on important data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the importance of the health data into AI, which then analyzes the data and adjusts the level of detail.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit applies a nutritional balance analysis algorithm to nutritional data. For example, the analysis unit inputs the user's dietary data into a nutritional balance analysis algorithm to evaluate nutritional balance. The analysis unit can also apply an exercise performance analysis algorithm to exercise data. For example, the analysis unit inputs the user's exercise data into an exercise performance analysis algorithm to evaluate the effectiveness of the exercise. The analysis unit can also apply a sleep quality analysis algorithm to sleep data. For example, the analysis unit inputs the user's sleep data into a sleep quality analysis algorithm to evaluate the quality of sleep. In this way, by applying different analysis algorithms depending on the category of health data, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of health data into AI, which analyzes the data and applies an appropriate algorithm.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide a short report. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a detailed report. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide visually appealing graphs and charts. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into AI, which may analyze the data and adjust the length.

[0086] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. For example, the analysis unit prioritizes analysis of recently collected data. For example, the analysis unit prioritizes analysis of the user's latest health data and evaluates the user's latest health status. The analysis unit can also emphasize current data while referring to past data. For example, the analysis unit prioritizes analysis of current data while referring to the user's past health data. The analysis unit can also prioritize analysis of data collected during a specific period. For example, the analysis unit prioritizes analysis of data collected during a specific period of the user and evaluates the user's health status during that period. In this way, by determining the priority of analysis based on when the health data was collected, the most recent data can be analyzed preferentially. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the time when the health data was collected into AI, which then analyzes the data and determines the priority.

[0087] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. For example, the analysis unit prioritizes analysis of highly relevant data. For example, the analysis unit prioritizes analysis of highly relevant data among the user's health data to extract important information. The analysis unit can also postpone analysis of less relevant data. For example, the analysis unit postpones analysis of less relevant data among the user's health data. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the user's health data in real time and dynamically adjusts the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the health data, highly relevant data can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the health data into AI, which then analyzes the data and adjusts the order.

[0088] The generation unit can estimate the user's emotions and adjust the meal plan generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a meal plan using ingredients effective for stress reduction. For example, the generation unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and generates a meal plan using ingredients effective for stress reduction. The generation unit can also generate a regular meal plan if the user is relaxed. For example, the generation unit can record the user's voice, estimate the user's emotions using voice analysis technology, and generate a regular meal plan. The generation unit can also generate a meal plan that is easy to prepare if the user is in a hurry. For example, the generation unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, estimate the user's emotions using an emotion estimation algorithm, and generate a meal plan that is easy to prepare. This allows the system to adjust the meal plan generation method according to the user's emotions, thereby providing a more appropriate meal plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the AI, which may analyze the data and adjust the generation method.

[0089] When generating a meal plan, the generation unit can adjust the level of detail of the plan based on the importance of the health data. The generation unit, for example, generates a detailed meal plan based on important health data. For example, the generation unit generates a detailed meal plan for blood pressure management based on the user's blood pressure data. The generation unit can also generate a simplified meal plan based on general health data. For example, the generation unit generates a simplified meal plan for weight management based on the user's weight data. The generation unit can also generate a meal plan that can be quickly implemented based on data with high urgency. For example, the generation unit generates a quick meal plan for blood glucose level management based on the user's blood glucose level data. In this way, by adjusting the level of detail of the plan based on the importance of the health data, a detailed meal plan based on important data can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the importance of the health data into AI, which analyzes the data and adjusts the level of detail.

[0090] When generating a meal plan, the generation unit can apply different generation algorithms depending on the category of health data. The generation unit, for example, generates a nutritionally balanced meal plan based on nutritional data. For example, the generation unit generates a nutritionally balanced meal plan based on the user's nutritional data. The generation unit can also generate a meal plan that promotes post-exercise recovery based on exercise data. For example, the generation unit generates a meal plan that promotes post-exercise recovery based on the user's exercise data. The generation unit can also generate a meal plan that improves sleep quality based on sleep data. For example, the generation unit generates a meal plan that improves sleep quality based on the user's sleep data. This allows for the application of different generation algorithms depending on the category of health data, thereby providing a more appropriate meal plan. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the category of health data into AI, which analyzes the data and applies an appropriate algorithm.

[0091] The generation unit can estimate the user's emotions and adjust the length of the meal plan based on the estimated user emotions. For example, if the user is in a hurry, the generation unit generates a meal plan that provides short-term results. For example, the generation unit captures the user's facial expressions with a camera, estimates the user's emotions using an emotion estimation algorithm, and generates a meal plan that provides short-term results. The generation unit can also generate a long-term meal plan if the user is relaxed. For example, the generation unit records the user's voice, estimates the user's emotions using voice analysis technology, and generates a long-term meal plan. The generation unit can also generate a visually appealing meal plan if the user is excited. For example, the generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the user's emotions using an emotion estimation algorithm, and generates a visually appealing meal plan. This allows the length of the meal plan to be adjusted according to the user's emotions, thereby providing a meal plan with a more appropriate duration. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit may input user emotion data into the AI, which may analyze the data and adjust the length.

[0092] When generating meal plans, the generation unit can determine the priority of the plans based on the time when the health data was collected. The generation unit, for example, generates the latest meal plan based on recently collected data. For example, the generation unit generates the latest meal plan based on the user's latest health data. The generation unit can also reference past data while emphasizing current data. For example, the generation unit generates a meal plan based on the user's past health data while emphasizing current data. The generation unit can also prioritize meal plans based on data collected during a specific period. For example, the generation unit prioritizes meal plans based on the user's data collected during a specific period. This allows for the provision of meal plans based on the latest data by prioritizing plans based on the time when the health data was collected. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the time when the health data was collected into AI, which can analyze the data and determine the priority.

[0093] When generating meal plans, the generation unit can adjust the order of the plans based on the relevance of the health data. For example, the generation unit may generate meal plans preferentially based on highly relevant data. For example, the generation unit may generate meal plans preferentially based on highly relevant data among the user's health data. The generation unit may also postpone less relevant data. For example, the generation unit may generate meal plans by postponing less relevant data among the user's health data. The generation unit may also dynamically adjust the order of the plans based on the relevance of the data. For example, the generation unit may evaluate the relevance of the user's health data in real time and dynamically adjust the order of the plans. By adjusting the order of the plans based on the relevance of the health data, a meal plan based on highly relevant data can be provided. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit may input the relevance of the health data into AI, which may analyze the data and adjust the order.

[0094] The provision unit can estimate the user's emotions and adjust the method of providing the meal plan based on the estimated user emotions. For example, if the user is feeling stressed, the provision unit provides the meal plan in a simple, visually easy-to-understand manner. For example, the provision unit captures the user's facial expression with a camera, estimates the emotion using an emotion estimation algorithm, and provides the meal plan in the form of a simple graph or chart. Alternatively, if the user is relaxed, the provision unit can provide the meal plan in a manner that includes detailed information. For example, the provision unit records the user's voice, estimates the emotion using voice analysis technology, and provides a detailed report with the meal plan. Alternatively, if the user is in a hurry, the provision unit can provide the meal plan in a manner that focuses on the main points. For example, the provision unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the emotion using an emotion estimation algorithm, and provides the meal plan in a short report with the main points. This allows the provision of a meal plan in a more appropriate manner by adjusting the method of providing the meal plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit may input user emotion data into the AI, which may analyze the data and adjust the presentation method.

[0095] When providing food, the providing unit can select the optimal providing method by referring to the user's past meal history. For example, the providing unit prioritizes selecting a providing method that the user has used favorably in the past. For example, the providing unit analyzes the user's past meal history and prioritizes selecting a providing method that the user has used favorably in the past. The providing unit can also select the optimal providing timing from the user's past meal history. For example, the providing unit determines the optimal providing timing based on the user's past meal history. The providing unit can also analyze the user's past meal history and select the most effective providing method. For example, the providing unit selects the most effective providing method based on the user's past meal history. In this way, the optimal providing method can be selected by referring to the user's past meal history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past meal history into AI, which analyzes the data and selects the optimal providing method.

[0096] The provision unit can customize the provision method based on the user's current living situation when providing the meal plan. For example, if the user is busy, the provision unit provides the meal plan in an easily accessible manner. For example, the provision unit analyzes the user's living situation and, if the user is busy, provides the meal plan in an easily accessible manner via a smartphone app. The provision unit can also provide the meal plan in a manner including detailed information if the user is relaxing. For example, the provision unit analyzes the user's living situation and, if the user is relaxing, provides the meal plan in a detailed report. The provision unit can also provide the meal plan in a manner that takes into account ingredients available at the travel destination if the user is traveling. For example, the provision unit provides a meal plan that takes into account ingredients available at the travel destination based on the user's geographical location information. This allows the meal plan to be provided in a more appropriate manner by customizing the provision method based on the user's current living situation. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without AI. For example, the provision unit can input the user's living situation data into AI, which can analyze the data and customize the provision method.

[0097] The providing unit can estimate the user's emotions and determine the priority of meal plan provision based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can prioritize plans that are effective for stress reduction. For example, the providing unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize plans that are effective for stress reduction. The providing unit can also provide a regular plan if the user is relaxed. For example, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide a regular plan. The providing unit can also prioritize plans that can be prepared quickly if the user is in a hurry. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize plans that can be prepared quickly. This allows the system to prioritize the provision of meal plans based on the user's emotions, thereby providing more appropriate plans. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, or without, an AI. For example, the providing unit may input user emotion data into the AI, which may analyze the data and determine the provision priority.

[0098] The provision unit can select the optimal provision method by taking into account the user's geographical location information when providing food. For example, if the user is in a specific area, the provision unit provides a plan that takes into account ingredients available in that area. For example, the provision unit provides a meal plan that takes into account ingredients available in that area based on the user's geographical location information. Furthermore, if the user is traveling, the provision unit can also provide a plan that takes into account ingredients available at the travel destination. For example, the provision unit provides a meal plan that takes into account ingredients available at the travel destination based on the user's geographical location information. Furthermore, if the user is at home, the provision unit can also provide a plan using locally produced ingredients. For example, the provision unit provides a meal plan using locally produced ingredients based on the user's geographical location information. This allows the optimal provision method to be selected by taking into account the user's geographical location information. Some or all of the above-described processing by the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the user's geographical location information into AI, which can analyze the data and select the optimal provision method.

[0099] The providing unit can analyze the user's social media activity and suggest a means of provision at the time of provision. For example, if the user posts about health on social media, the providing unit can provide a plan related to that content. For example, the providing unit can analyze the user's social media post and provide a health-related plan. Also, if the user posts about a specific food ingredient on social media, the providing unit can provide a plan using that food ingredient. For example, the providing unit can analyze the user's social media post and provide a plan using that food ingredient. Also, if the user posts on social media that they are feeling stressed, the providing unit can provide a plan effective for stress reduction. For example, the providing unit can analyze the user's social media post and provide a plan effective for stress reduction. In this way, by analyzing the user's social media activity, more relevant means of provision can be suggested. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's social media activity data into AI, which can analyze the data and suggest means of provision.

[0100] The selection unit can estimate the user's emotions and adjust the selection method for sustainable ingredients based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit prioritizes selecting ingredients that are effective for stress reduction. For example, the selection unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and prioritizes selecting ingredients that are effective for stress reduction. The selection unit can also select regular sustainable ingredients if the user is relaxed. For example, the selection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and select regular sustainable ingredients. The selection unit can also select sustainable ingredients that can be quickly prepared if the user is in a hurry. For example, the selection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and select sustainable ingredients that can be quickly prepared. This allows the selection of more appropriate ingredients to be selected by adjusting the selection method for sustainable ingredients according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, or without, an AI. For example, the selection unit may input user emotion data into the AI, which may analyze the data and adjust the selection method.

[0101] When selecting ingredients, the selection unit can adjust the level of detail of the selection based on the nutritional value of the ingredients. The selection unit, for example, prioritizes the selection of ingredients with high nutritional value. For example, the selection unit evaluates the nutritional value of ingredients and prioritizes the selection of ingredients with high nutritional value. The selection unit can also select ingredients with average nutritional value. For example, the selection unit evaluates the nutritional value of ingredients and selects ingredients with average nutritional value. The selection unit can also select ingredients rich in specific nutrients. For example, the selection unit evaluates and selects ingredients rich in specific nutrients. In this way, by adjusting the level of detail of the selection based on the nutritional value of the ingredients, it is possible to select ingredients with higher nutritional value. Some or all of the above-mentioned processes in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input nutritional value data of ingredients into AI, which analyzes the data and adjusts the level of detail.

[0102] When selecting ingredients, the selection unit can apply different selection algorithms depending on the category of the ingredients. For example, the selection unit prioritizes selecting organic vegetables for the vegetable category. For example, the selection unit evaluates ingredients in the vegetable category and prioritizes selecting organic vegetables. The selection unit can also select meat with a low carbon footprint for the meat category. For example, the selection unit evaluates ingredients in the meat category and selects meat with a low carbon footprint. The selection unit can also select fish from sustainable fisheries for the seafood category. For example, the selection unit evaluates ingredients in the seafood category and selects fish from sustainable fisheries. This allows for more appropriate ingredients to be selected by applying different selection algorithms depending on the ingredient category. Some or all of the above-mentioned processing by the selection unit may be performed using, for example, AI, or without AI. For example, the selection unit can input ingredient category data into AI, which analyzes the data and applies an appropriate algorithm.

[0103] The selection unit can estimate the user's emotions and determine a selection priority for sustainable ingredients based on the estimated user emotions. For example, if the user is feeling stressed, the selection unit prioritizes the selection of ingredients that are effective in reducing stress. For example, the selection unit captures the user's facial expression with a camera, estimates the user's emotions using an emotion estimation algorithm, and prioritizes the selection of ingredients that are effective in reducing stress. The selection unit can also select standard sustainable ingredients if the user is relaxed. For example, the selection unit can record the user's voice, estimate the user's emotions using voice analysis technology, and select standard sustainable ingredients. The selection unit can also select sustainable ingredients that can be quickly prepared if the user is in a hurry. For example, the selection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and select sustainable ingredients that can be quickly prepared. This allows the selection priority for sustainable ingredients to be determined according to the user's emotions, thereby prioritizing the selection of more appropriate ingredients. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the selection unit may be performed using, or without, an AI. For example, the selection unit may input user emotion data into the AI, which may analyze the data and determine selection priorities.

[0104] The selection unit can select the optimal selection method when selecting ingredients, taking into account information about the ingredients' production locations. For example, the selection unit can prioritize locally produced ingredients. For example, the selection unit can evaluate the ingredients' production locations and prioritize locally produced ingredients. The selection unit can also select ingredients with short transport distances. For example, the selection unit can evaluate the ingredients' production locations and select ingredients with short transport distances. The selection unit can also select ingredients with low environmental impact from their production locations. For example, the selection unit can evaluate the ingredients' production locations and select ingredients from production locations with low environmental impact. In this way, by considering the ingredients' production locations, it is possible to select ingredients with a lower environmental impact. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the ingredients' production location information into AI, and the AI ​​can analyze the data and select the optimal selection method.

[0105] The selection unit can improve the accuracy of its selection by referring to relevant literature on ingredients. For example, the selection unit can select ingredients by referring to the latest nutritional research. For example, the selection unit can select ingredients based on scientific evidence by referring to the latest nutritional research. The selection unit can also select sustainable ingredients by referring to research on environmental impact. For example, the selection unit can select sustainable ingredients by referring to research on environmental impact. The selection unit can also select ingredients by referring to literature on the health effects of ingredients. For example, the selection unit can select healthy ingredients by referring to literature on the health effects of ingredients. In this way, by referring to relevant literature on ingredients, it is possible to select ingredients that are more based on scientific evidence. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input relevant literature data on ingredients into AI, and the AI ​​can analyze the data to improve the accuracy of the selection.

[0106] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated user emotions. For example, if the user is feeling stressed, the follow-up unit provides simple, visually easy-to-understand follow-up. For example, the follow-up unit may capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and provide follow-up in the form of a simple graph or chart. The follow-up unit can also provide detailed follow-up if the user is relaxed. For example, the follow-up unit may record the user's voice, estimate the user's emotions using voice analysis technology, and provide follow-up in the form of a detailed report. The follow-up unit can also provide follow-up that focuses on the main points if the user is in a hurry. For example, the follow-up unit may collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and provide follow-up in the form of a short, concise report. This allows the follow-up method to be adjusted according to the user's emotions, thereby providing more appropriate follow-up. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the follow-up unit may be performed using AI, or may be performed without using AI. For example, the follow-up unit may input the user's emotional data into AI, which may analyze the data and adjust the follow-up method.

[0107] The follow-up unit can select the optimal follow-up method by referring to the user's past meal history when following up. For example, the follow-up unit prioritizes selecting a follow-up method that the user has used favorably in the past. For example, the follow-up unit analyzes the user's past meal history and prioritizes selecting a follow-up method that the user has used favorably in the past. The follow-up unit can also select the optimal follow-up timing from the user's past meal history. For example, the follow-up unit determines the optimal follow-up timing based on the user's past meal history. The follow-up unit can also analyze the user's past meal history and select the most effective follow-up method. For example, the follow-up unit selects the most effective follow-up method based on the user's past meal history. In this way, the optimal follow-up method can be selected by referring to the user's past meal history. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's past meal history into AI, which analyzes the data and selects the optimal follow-up method.

[0108] The follow-up unit can customize the follow-up means based on the user's current lifestyle at the time of follow-up. For example, if the user is busy, the follow-up unit provides the follow-up in an easily accessible manner. For example, the follow-up unit analyzes the user's lifestyle and, if the user is busy, provides the follow-up in an easily accessible manner through a smartphone app. The follow-up unit can also provide the follow-up in a manner including detailed information if the user is relaxing. For example, the follow-up unit analyzes the user's lifestyle and, if the user is relaxing, provides the follow-up in a detailed report. The follow-up unit can also provide the follow-up in a manner that takes into account ingredients available at the travel destination if the user is traveling. For example, the follow-up unit provides the follow-up that takes into account ingredients available at the travel destination based on the user's geographical location information. This allows the follow-up means to be customized based on the user's current lifestyle, thereby providing more appropriate follow-up. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without AI. For example, the follow-up unit can input the user's lifestyle data into AI, which then analyzes the data and customizes the follow-up means.

[0109] The follow-up unit can estimate the user's emotions and prioritize follow-ups based on the estimated user emotions. For example, if the user is feeling stressed, the follow-up unit can prioritize providing follow-ups that are effective in reducing stress. For example, the follow-up unit can capture the user's facial expression with a camera, estimate the user's emotions using an emotion estimation algorithm, and prioritize providing follow-ups that are effective in reducing stress. The follow-up unit can also provide regular follow-ups if the user is relaxed. For example, the follow-up unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide regular follow-ups. The follow-up unit can also prioritize providing follow-ups that can be handled quickly if the user is in a hurry. For example, the follow-up unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor, estimate the user's emotions using an emotion estimation algorithm, and prioritize providing follow-ups that can be handled quickly. This allows the priority of follow-ups to be determined according to the user's emotions, thereby enabling more appropriate follow-ups to be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the follow-up unit may be performed using AI, or may be performed without using AI. For example, the follow-up unit may input user emotion data into AI, which may analyze the data and determine priorities.

[0110] The follow-up unit can select the optimal follow-up method by taking into account the user's geographical location information when performing a follow-up. For example, when the user is in a specific area, the follow-up unit provides a follow-up that takes into account ingredients available in the area. For example, the follow-up unit provides a follow-up that takes into account ingredients available in the area based on the user's geographical location information. Furthermore, when the user is traveling, the follow-up unit can also provide a follow-up that takes into account ingredients available at the travel destination. For example, the follow-up unit provides a follow-up that takes into account ingredients available at the travel destination based on the user's geographical location information. Furthermore, when the user is at home, the follow-up unit can also provide a follow-up that uses locally produced ingredients. For example, the follow-up unit provides a follow-up that uses locally produced ingredients based on the user's geographical location information. In this way, the optimal follow-up method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's geographical location information into AI, which can then analyze the data to select the optimal follow-up method.

[0111] The follow-up unit can analyze the user's social media activities and suggest follow-up measures when following up. For example, if a user posts about health on social media, the follow-up unit provides follow-up related to the content. For example, the follow-up unit analyzes the user's social media posts and provides follow-up related to health. Furthermore, if a user posts about a specific food ingredient on social media, the follow-up unit can also provide follow-up using that food ingredient. For example, the follow-up unit analyzes the user's social media posts and provides follow-up using that food ingredient. Furthermore, if a user posts on social media that they are feeling stressed, the follow-up unit can also provide follow-up effective for stress reduction. For example, the follow-up unit analyzes the user's social media posts and provides follow-up effective for stress reduction. In this way, by analyzing the user's social media activities, more relevant follow-up measures can be suggested. Some or all of the above-described processing in the follow-up unit may be performed using, for example, AI, or may be performed without using AI. For example, the follow-up unit can input the user's social media activity data into AI, which can analyze the data and suggest follow-up measures. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, selection unit, follow-up unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's health data and emotion data using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a meal plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the generated meal plan to the user's smartphone or PC. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects sustainable ingredients. The follow-up unit is implemented, for example, by the control unit 46A of the smart device 14 and follows up on the user's meal plan. The emotion estimation function estimates the user's emotions using, for example, the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of data collection. === Hard Collateral 1-2 === Each of the above-mentioned components, including the data collection unit, analysis unit, generation unit, provision unit, selection unit, follow-up unit, and emotion estimation function, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's health data and emotion data using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A analyzes the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to evaluate the user's health status. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates a meal plan based on the analysis results. The provision unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and provides the generated meal plan to the user's smartphone or PC. The selection unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and selects sustainable ingredients. The follow-up unit is implemented in the specific processing unit 46A of the smart glasses 214, for example, and follows up on the user's meal plan. The emotion estimation function estimates the user's emotion using, for example, the camera 42 and microphone 238 of the smart glasses 214 and adjusts the collection timing. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, selection unit, follow-up unit, and emotion estimation function, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the user's health data and emotion data using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes the data using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates a meal plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated meal plan to the user's smartphone or PC. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and selects sustainable ingredients. The follow-up unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and follows up on the user's meal plan. The emotion estimation function estimates the user's emotion using, for example, the camera 42 and microphone 238 of the headset terminal 314 and adjusts the collection timing. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, provision unit, selection unit, follow-up unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's health data and emotion data using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a meal plan based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated meal plan to the user's smartphone or PC. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects sustainable ingredients. The follow-up unit is realized, for example, by the control unit 46A of the robot 414 and follows up on the user's meal plan. The emotion estimation function estimates the user's emotion using, for example, the camera 42 and microphone 238 of the robot 414 and adjusts the timing of collection.

[0112] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it will prioritize the analysis of stress-related data. The analysis unit captures the user's facial expressions with a camera, estimates their emotions using an emotion estimation algorithm, and prioritizes the analysis of stress-related data. If the user is relaxed, it can also prioritize the analysis of general health data. The analysis unit records the user's voice, estimates their emotions using voice analysis technology, and prioritizes the analysis of general health data. Furthermore, if the user is in a hurry, it can prioritize the analysis of only the important data. The analysis unit collects the user's biometric data (heart rate and skin electrical activity) with sensors, estimates their emotions using an emotion estimation algorithm, and prioritizes the analysis of only the important data. In this way, by determining the priority of analysis according to the user's emotions, it is possible to prioritize the analysis of important data.

[0114] The service provider can estimate the user's emotions and adjust the way the meal plan is delivered based on those emotions. For example, if the user is stressed, the meal plan can be delivered in a simple and visually easy-to-understand way. The service provider can capture the user's facial expressions with a camera, estimate their emotions using an emotion estimation algorithm, and deliver the meal plan in a simple graph or chart. If the user is relaxed, the meal plan can also be delivered in a way that includes detailed information. The service provider can record the user's voice, estimate their emotions using voice analysis technology, and deliver the meal plan in a detailed report. Furthermore, if the user is in a hurry, the meal plan can be delivered in a concise manner. The service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors, estimate their emotions using an emotion estimation algorithm, and deliver the meal plan in a concise report. This allows the service provider to deliver the meal plan in a more appropriate way by adjusting the delivery method according to the user's emotions.

[0115] The collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be changed to a time when the user is relaxed. The collection unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Alternatively, if the user is relaxed, the collection timing can follow a regular schedule. The collection unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection timing can be shortened to collect only the minimum amount of data necessary. The collection unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. This allows the timing of health data collection to be adjusted according to the user's emotions, allowing data to be collected at a more appropriate time.

[0116] The generation unit can estimate the user's emotions and adjust the meal plan generation method based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit generates a meal plan using ingredients effective for stress reduction. The generation unit captures the user's facial expression with a camera, estimates the emotion using an emotion estimation algorithm, and generates a meal plan using ingredients effective for stress reduction. Alternatively, if the user is relaxed, the generation unit can generate a regular meal plan. The generation unit records the user's voice, estimates the emotion using voice analysis technology, and generates a regular meal plan. Furthermore, if the user is in a hurry, the generation unit can generate a meal plan that is easy to prepare. The generation unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor, estimates the emotion using an emotion estimation algorithm, and generates a meal plan that is easy to prepare. This allows the generation unit to adjust the meal plan generation method according to the user's emotions, thereby providing a more appropriate meal plan.

[0117] The follow-up unit can estimate the user's emotions and adjust the follow-up method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and visually easy-to-understand follow-up. The follow-up unit captures the user's facial expressions with a camera, estimates their emotions using an emotion estimation algorithm, and provides follow-up in simple graphs and charts. If the user is relaxed, it can also provide a detailed follow-up. The follow-up unit records the user's voice, estimates their emotions using voice analysis technology, and provides follow-up in a detailed report. Furthermore, if the user is in a hurry, it can provide concise follow-up. The follow-up unit collects the user's biometric data (heart rate and skin electrical activity) with sensors, estimates their emotions using an emotion estimation algorithm, and provides follow-up in a concise report. This allows for more appropriate follow-up by adjusting the follow-up method according to the user's emotions.

[0118] The analysis unit can analyze the user's past health data and select the optimal analysis method. For example, it can select the analysis method that yields the most accurate data from the user's past data. The analysis unit analyzes the user's past health data and determines the optimal analysis method. It can also optimize the analysis frequency based on the user's past data. The analysis unit sets the optimal analysis frequency based on the user's past data. Furthermore, it can select an analysis method for a specific time period based on the user's past data. The analysis unit analyzes the user's past data and determines the optimal analysis time period. In this way, the optimal analysis method can be selected by analyzing the user's past health data.

[0119] The service department can select the optimal service method by referring to the user's past meal history at the time of service. For example, it can prioritize selecting service methods that the user has preferred in the past. The service department analyzes the user's past meal history and prioritizes selecting service methods that the user has preferred in the past. It can also select the optimal service timing based on the user's past meal history. The service department determines the optimal service timing based on the user's past meal history. Furthermore, it can analyze the user's past meal history and select the most effective service method. The service department selects the most effective service method based on the user's past meal history. In this way, the optimal service method can be selected by referring to the user's past meal history.

[0120] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is in a specific region, it can collect environmental data for that region. Based on the user's geographical location, the data collection unit collects environmental data such as temperature and humidity for that region. Furthermore, if the user is traveling, it can collect data related to health risks at their travel destination. Based on the user's geographical location, the data collection unit collects data related to health risks at their travel destination. Additionally, if the user is at home, it can collect data related to the indoor environment. Based on the user's geographical location, the data collection unit collects data such as indoor temperature and humidity. This allows for the priority collection of highly relevant data by considering the user's geographical location.

[0121] The selection unit can adjust the level of detail in its selection process based on the nutritional value of the ingredients. For example, it can prioritize the selection of ingredients with high nutritional value. The selection unit evaluates the nutritional value of the ingredients and prioritizes the selection of ingredients with high nutritional value. It can also select ingredients with general nutritional value. The selection unit evaluates the nutritional value of the ingredients and selects ingredients with general nutritional value. Furthermore, it can select ingredients that are rich in specific nutrients. The selection unit evaluates and selects ingredients that are rich in specific nutrients. In this way, by adjusting the level of detail in the selection process based on the nutritional value of the ingredients, it is possible to select ingredients with higher nutritional value.

[0122] When following up, the follow-up unit can analyze the user's social media activity and suggest follow-up measures. For example, if a user posts about health on social media, the follow-up unit provides follow-up measures related to the content. The follow-up unit analyzes the user's social media post and provides follow-up measures related to health. Also, if a user posts about a specific food ingredient on social media, the follow-up unit can provide follow-up measures using that food ingredient. The follow-up unit analyzes the user's social media post and provides follow-up measures using that food ingredient. Furthermore, if a user posts about feeling stressed on social media, the follow-up unit can provide follow-up measures effective for reducing stress. The follow-up unit analyzes the user's social media post and provides follow-up measures effective for reducing stress. In this way, by analyzing the user's social media activity, more relevant follow-up measures can be suggested.

[0123] The processing flow of the second embodiment will be briefly explained below.

[0124] Step 1: The collection unit collects the user's health data. The user's health data includes weight, height, allergy information, and dietary preferences. The collection unit uses a wearable device to measure the user's weight and heart rate and collect the data. It can also collect data by having the user input their dietary preferences and allergy information using a smartphone app. The collection unit then stores the user's health data in the cloud and provides it to the analysis unit. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using a machine learning algorithm to evaluate the user's health status. The analysis unit can also analyze the data using statistical analysis to predict the user's health risks. Furthermore, the analysis unit provides the data analysis results to the generation unit. Step 3: The generation unit generates a meal plan based on the analysis results obtained by the analysis unit. The generation unit proposes a nutritionally balanced meal plan based on the user's health condition and dietary preferences. The generation unit can also generate a meal plan using sustainable ingredients. For example, a meal plan using locally grown organic vegetables and ingredients with a low carbon footprint is generated. The generation unit then provides the generated meal plan to the provision unit. Step 4: The providing unit provides the meal plan generated by the generating unit to the user's smartphone or computer. The providing unit notifies the user of the meal plan through a smartphone app. The providing unit can also provide the meal plan to the user through a website. Furthermore, the providing unit can also perform follow-up on the meal plan. For example, after the user eats according to the meal plan, the results can be recorded and reflected in the next plan.

[0125] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0126] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0127] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0130] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0146] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0147] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0158] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0159] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0162] 7, a 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.

[0163] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0164] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0165] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0167] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0168] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0169] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0170] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0171] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0172] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0173] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0174] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0175] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0176] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0177] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0178] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0179] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0180] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0181] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0182] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0183] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0184] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0185] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0186] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0187] 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.

[0188] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0189] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0190] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0191] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0192] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0193] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0194] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0195] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0196] [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects health data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates a meal plan based on the analysis results obtained by the analysis unit; a providing unit that provides the meal plan generated by the generating unit; Equipped with A system characterized by:

2. Establish a selection department to select sustainable ingredients The system of claim 1 .

3. Have a follow-up department to follow up on meal plans The system of claim 1 .

4. The collecting unit Collecting health data through wearable devices or smartphone apps The system of claim 1 .

5. The analysis unit Analyzing collected data using machine learning The system of claim 1 .

6. The generation unit Generate meal plans using locally grown, organic or low-carbon footprint ingredients The system of claim 1 .

7. The providing unit The generated meal plan is provided to the user's smartphone or computer. The system of claim 1 .

8. The collecting unit The system estimates the user's emotions and adjusts the timing of health data collection based on the estimated user emotions. The system of claim 1 .

9. The collecting unit Analyze the user's past health data and select the optimal collection method The system of claim 1 .

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