system

Abstract

The system according to this embodiment aims to automatically generate individually tailored meal menus based on the user's health information and step count data. [Solution] The system according to the embodiment comprises a collection unit, an acquisition unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's health information. The acquisition unit acquires step count data. The analysis unit analyzes the data collected by the collection unit and the acquisition unit. The generation unit generates a cooking menu based on the data analyzed by the analysis unit. The provision unit provides the cooking menu generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it has not been fully achieved to automatically generate a cooking menu suitable individually based on a user's health information and step count data, and there is room for improvement.

[0005] The system according to an embodiment aims to automatically generate a cooking menu suitable individually based on a user's health information and step count data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an acquisition unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects user health information. The acquisition unit acquires step count data. The analysis unit analyzes the data collected by the collection unit and the acquisition unit. The generation unit generates a cooking menu based on the data analyzed by the analysis unit. The provision unit provides the cooking menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate individually tailored meal menus based on the user's health information and step count data. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The health promotion support system according to an embodiment of the present invention is a system that collects the user's health information, acquires step count data, and automatically generates a suitable meal menu using a generative AI. This system supports the user's health promotion and reduces the time spent thinking about meal menus by collecting the user's health information, acquiring step count data, and automatically generating a suitable meal menu using a generative AI. First, the user registers information such as gender, height, weight, and allergies in HELPO. For example, by registering in advance any foods the user is allergic to or cannot eat, the generative AI can generate a menu that takes this information into consideration. Next, the health app and HELPO are linked. This allows the system to acquire step count data for the past month, week, and day (today) measured by HELPO. For example, it collects data such as how much the user walked in a day and how much exercise they did in a week. Based on this data, the generative AI automatically generates a meal menu suitable for the user. The generative AI proposes a meal menu suitable for health promotion, taking into consideration the user's gender, height, weight, allergy information, step count data, etc. For example, if the user has exercised a lot, it can propose a menu suitable for energy replenishment. This system not only supports users in improving their health but also reduces the time they spend thinking about meal menus. For example, it eliminates the hassle of planning daily meals in a busy daily life. Furthermore, the menus suggested by the generation AI are nutritionally balanced and help maintain the user's health. In this way, a meal menu generation service using generation AI contributes to improving users' health and reducing the time they spend thinking about meal menus. This allows the health promotion support system to collect users' health information, acquire step count data, and automatically generate suitable meal menus using generation AI.

[0029] The health promotion support system according to the embodiment comprises a collection unit, an acquisition unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's health information. The user's health information includes, but is not limited to, gender, height, weight, and allergy information. The collection unit collects this information, for example, by having the user register in advance any foods they are allergic to or foods they cannot eat. The collection unit can also periodically update the user's health information. For example, if a user discovers a new allergy, that information can be added to the collection unit. The acquisition unit acquires step count data for the most recent month, week, and day (today) measured by HELPO. The step count data includes, but is not limited to, information such as how many steps the user took in a day and how much exercise they did in a week. The acquisition unit acquires step count data, for example, using a pedometer application or a wearable device. The acquisition unit can also periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the acquisition unit. The analysis unit analyzes the data collected by the collection unit and the acquisition unit. The analysis unit performs analysis based on, for example, the user's gender, height, weight, allergy information, and step count data. The analysis unit can also consider the user's health information during the analysis. For example, if the user has allergies, this information is taken into account during the analysis. The generation unit generates meal menus based on the data analyzed by the analysis unit. The generation unit generates meal menus suitable for the user, for example, using a generation AI. The generation AI considers the user's gender, height, weight, allergy information, and step count data to suggest meal menus suitable for health promotion. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The delivery unit provides the meal menus generated by the generation unit to the user. The delivery unit provides the meal menus to the user, for example, through an application. The delivery unit can also provide notifications via email or in paper form. For example, the meal menus can be provided in the way the user prefers.As a result, the health promotion support system according to this embodiment can collect the user's health information, acquire step count data, automatically generate suitable meal menus using a generation AI, and provide them to the user.

[0030] The data collection unit collects user health information. This information includes, but is not limited to, gender, height, weight, and allergy information. The data collection unit collects this information, for example, by having users pre-register foods they are allergic to or foods they cannot eat. Specifically, users input their health information through a dedicated application and send it to the data collection unit. The application securely stores the information entered by the user and can update it as needed. The data collection unit can also periodically update the user's health information. For example, if a user discovers a new allergy, that information can be added to the data collection unit. The data collection unit automatically analyzes the information entered by the user and stores it in a database. This allows the data collection unit to centrally manage user health information and collaborate with other departments as needed. Furthermore, the data collection unit takes measures to anonymize user health information and protect privacy. For example, it encrypts user personal information to prevent access by third parties. This allows the data collection unit to collect user health information safely and efficiently, improving the overall system performance.

[0031] The data acquisition unit acquires step count data for the past month, week, and day (current day) measured by HELPO. This step count data includes, but is not limited to, information such as how many steps a user takes in a day or how much exercise they do in a week. The data acquisition unit acquires step count data using, for example, pedometer applications or wearable devices. Specifically, the user wears a smartphone or smartwatch, and these devices measure the number of steps. The data acquisition unit automatically collects data from these devices and transmits it to a central database. The data acquisition unit can also periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the data acquisition unit. The data acquisition unit monitors the user's step count data in real time, accurately understanding their daily exercise level. Furthermore, the data acquisition unit can analyze the user's step count data to understand exercise patterns and trends. This allows the data acquisition unit to gain a detailed understanding of the user's exercise habits and provide data for health promotion.

[0032] The analysis unit analyzes the data collected by the collection and acquisition units. The analysis unit performs analysis based on data such as the user's gender, height, weight, allergy information, and step count. Specifically, it uses AI to analyze this data and evaluate the user's health status and exercise habits. For example, it calculates appropriate calorie intake and exercise levels based on the user's gender, height, and weight. It also identifies foods and ingredients that the user should avoid, taking allergy information into consideration. Furthermore, it analyzes step count data to evaluate the user's exercise level and exercise patterns. The analysis unit integrates this data to comprehensively evaluate the user's health status. For example, it evaluates whether the user is exercising sufficiently and consuming appropriate calories, and identifies areas for improvement. This allows the analysis unit to gain a detailed understanding of the user's health status and provide specific advice for health promotion.

[0033] The generation unit generates meal menus based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate meal menus suitable for the user. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest meal menus suitable for promoting health. Specifically, the AI ​​analyzes the user's data and generates nutritionally balanced menus. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The generation AI considers the user's preferences and food allergy information to suggest menus that the user can eat with peace of mind. In addition, the generation AI can also suggest menus using seasonal ingredients, taking into account seasonal and regional characteristics. In this way, the generation unit can provide optimal meal menus tailored to the user's health condition and preferences.

[0034] The service provider delivers the meal menus generated by the generation unit to the user. The service provider delivers the meal menus to the user, for example, through an application. Specifically, the user can open a dedicated application and check the generated meal menus. The service provider can also provide notifications via email or in paper form. For example, the service provider can deliver the meal menus in the way the user prefers. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the menus it provides. For example, the service provider can provide feedback on the results of users actually trying the provided menus and use that information to improve the menus. The service provider can also provide individually customized menus, taking into account the user's preferences and allergy information. In this way, the service provider can provide the user with the most suitable meal menus and support their health improvement.

[0035] The data collection unit can collect the user's gender, height, weight, and allergy information. For example, the unit can collect this information when the user registers their gender, height, weight, and allergy information with HELPO. The data collection unit can also add information if the user discovers a new allergy. For example, if a user is allergic to a specific food, they can register that information in the data collection unit. Furthermore, the data collection unit can periodically update the user's health information. For example, the user can measure their weight and add that information to the data collection unit. This allows for the generation of more appropriate meal menus by collecting the user's basic health information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health information into AI, which can then analyze and collect the information.

[0036] The acquisition unit can acquire step count data for the past month, week, and day (today) measured by a pedometer application. For example, the acquisition unit can acquire step count data for the past month, week, and day (today) using a pedometer application. The acquisition unit can also acquire step count data using a wearable device. For example, it can collect data such as how many steps a user takes in a day or how much exercise they do in a week. Furthermore, the acquisition unit can periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the acquisition unit. This allows for the generation of appropriate meal menus based on the user's exercise level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input step count data acquired from a pedometer application or wearable device into an AI, which can then analyze and acquire the data.

[0037] The analysis unit can analyze collected health information and step count data. For example, the analysis unit performs analysis based on the user's gender, height, weight, allergy information, and step count data. The analysis unit can also take the user's health information into consideration during the analysis. For example, if the user has allergies, this information is taken into account during the analysis. Furthermore, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. For example, the analysis unit can calculate calorie consumption based on the user's step count data and evaluate their health status. This allows for the generation of meal menus tailored to the user by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected health information and step count data into an AI, which can then analyze the data and output the results.

[0038] The generation unit can generate a meal menu suitable for the user based on the analyzed data. For example, the generation unit can use a generation AI to generate a meal menu suitable for the user based on the analyzed data. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest a meal menu suitable for promoting health. For example, if the user has exercised a lot, it can suggest a meal menu suitable for energy replenishment. Furthermore, the generation unit can also generate a meal menu considering the user's nutritional balance. For example, the generation unit suggests a balanced meal menu based on the user's calorie consumption and nutrient intake. In this way, a meal menu suitable for the user can be generated based on the analyzed data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the analyzed data into a generation AI, and the generation AI can generate a meal menu.

[0039] The service provider can provide the generated meal menu to the user. For example, the service provider can provide the generated meal menu to the user through an application. The service provider can also provide notifications via email or in paper format. For example, the service provider can provide the meal menu in the way preferred by the user. Furthermore, the service provider can provide display methods tailored to the user's device. For example, it can provide display methods optimized for devices such as smartphones, tablets, and PCs. By providing the generated meal menu to the user, the service provider can support the user's health improvement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated meal menu into AI, and the AI ​​can select how to provide it to the user.

[0040] The analysis unit can perform analysis based on the user's allergy information. For example, the analysis unit will take the user's allergy information into consideration when performing the analysis. For example, if the user has an allergy to a specific food, the analysis will be based on that information. The analysis unit can also collect and periodically update the user's allergy information. For example, if the user discovers a new allergy, that information can be added to the analysis unit. This allows the system to generate a menu of dishes that do not contain allergens by taking the user's allergy information into consideration. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's allergy information into AI, which can then analyze the information and output the results.

[0041] The generation unit can generate menus suitable for energy replenishment based on the user's activity level. For example, the generation unit can generate meal menus suitable for energy replenishment by considering the user's activity level. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The generation unit can also periodically update the user's activity level and generate menus based on that. For example, if the user starts exercising a new way, it can generate a menu suitable for energy replenishment based on that data. This allows the generation unit to generate meal menus suitable for energy replenishment based on the user's activity level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's activity level data into a generation AI, and the generation AI can generate a menu suitable for energy replenishment.

[0042] The data collection unit can analyze the user's past health information and select an appropriate collection method. For example, the data collection unit can analyze the user's past health information and select the optimal collection method. For example, it can propose the optimal collection method based on the health information the user has provided in the past. The data collection unit can also ask detailed questions if more detailed information is needed from the user's past health information. For example, it can analyze the user's past health information and ask simplified questions. This allows the optimal collection method to be selected by analyzing the user's past health information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health information into AI, which can analyze the information and select the optimal collection method.

[0043] The data collection unit can filter health information based on the user's current lifestyle and areas of interest. For example, if the user inputs their current lifestyle, the data collection unit will filter the health information based on that information. The data collection unit can also prioritize the collection of relevant health information based on the user's areas of interest. For example, it can exclude unnecessary information based on the user's lifestyle and areas of interest. By filtering health information based on the user's lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI, which can then filter and collect the information.

[0044] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting health information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health information related to that region. The data collection unit can also collect information based on the user's geographical location, taking into account region-specific health risks. For example, it can collect health information related to the region's climate and environment based on the user's geographical location. This allows for the priority collection of region-specific health information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then analyze the information and prioritize the collection of highly relevant information.

[0045] The data collection unit can analyze users' social media activity and collect relevant information when collecting health information. For example, the data collection unit can analyze users' social media activity and collect posts and comments related to health. The data collection unit can also identify health topics of interest from users' social media activity and collect relevant information. For example, it can identify health trends based on users' social media activity and collect relevant information. This allows for the collection of highly relevant health information by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into AI, which can then analyze the information and collect relevant information.

[0046] The acquisition unit can analyze the user's past step count data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past step count data and select the optimal acquisition method. For example, it can propose the optimal acquisition method based on the step count data previously provided by the user. The acquisition unit can also select a detailed acquisition method from the user's past step count data if more detailed data is needed. For example, it can analyze the user's past step count data and select a simplified acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past step count data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past step count data into AI, and the AI ​​can analyze the information and select the optimal acquisition method.

[0047] The acquisition unit can filter step count data based on the user's current lifestyle and exercise habits when acquiring it. For example, if the user inputs their current lifestyle, the acquisition unit will filter the step count data based on that information. The acquisition unit can also prioritize acquiring relevant step count data based on the user's exercise habits. For example, it can exclude unnecessary data based on the user's lifestyle and exercise habits. By filtering step count data based on the user's lifestyle and exercise habits, it is possible to acquire highly relevant data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's lifestyle and exercise habits into an AI, which can then filter and acquire the information.

[0048] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring step count data. For example, if the user is in a specific region, the data acquisition unit will prioritize the acquisition of step count data related to that region. The data acquisition unit can also acquire data by considering region-specific exercise habits based on the user's geographical location information. For example, it can acquire step count data related to the region's climate and environment based on the user's geographical location information. This allows for the priority acquisition of region-specific step count data by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into AI, which can then analyze the information and prioritize the acquisition of highly relevant data.

[0049] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring step count data. For example, the data acquisition unit can analyze the user's social media activity and collect posts and comments related to exercise. The data acquisition unit can also identify exercise topics of interest from the user's social media activity and acquire relevant data. For example, it can grasp exercise trends based on the user's social media activity and acquire relevant data. In this way, by analyzing the user's social media activity, highly relevant step count data can be acquired. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into AI, and the AI ​​can analyze the information and acquire relevant data.

[0050] The analysis unit can optimize the analysis algorithm by referring to the user's past health information and step count data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past health information and step count data. For example, it can select the optimal analysis algorithm based on the user's past health information and step count data. The analysis unit can also select a detailed algorithm from the user's past health information and step count data if a detailed analysis is required. For example, it can analyze the user's past health information and step count data and select a simplified analysis algorithm. This allows the analysis algorithm to be optimized by referring to the user's past health information and step count data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health information and step count data into AI, which can analyze the information and select the optimal algorithm.

[0051] The analysis unit can perform analysis based on the user's lifestyle and dietary history. For example, the analysis unit can analyze health risks by considering the user's lifestyle. The analysis unit can also analyze nutritional balance based on the user's dietary history. For example, it can analyze the health status by comprehensively considering the user's lifestyle and dietary history. This allows for more accurate analysis by considering the user's lifestyle and dietary history. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the user's lifestyle and dietary history into an AI, which can analyze the information and evaluate the health status.

[0052] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze region-specific health risks based on the user's geographical location information. The analysis unit can also analyze health risks related to the region's climate and environment based on the user's geographical location information. For example, it can analyze while considering the region's medical resources based on the user's geographical location information. In this way, by considering the user's geographical location information, region-specific health risks can be analyzed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and evaluate the health risks.

[0053] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can refer to the user's social media activity and reflect health-related posts and comments in the analysis. The analysis unit can also identify health topics of interest from the user's social media activity and reflect them in the analysis. For example, it can grasp health-related trends based on the user's social media activity and reflect them in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into AI, and the AI ​​can analyze the information to improve accuracy.

[0054] The generation unit can generate an optimal menu by referring to the user's past meal history and exercise level during the generation process. For example, the generation unit can generate a nutritionally balanced menu based on the user's past meal history. The generation unit can also generate a menu suitable for energy replenishment by considering the user's exercise level. For example, it can generate an optimal menu by comprehensively considering the user's past meal history and exercise level. In this way, the optimal menu can be generated by referring to the user's past meal history and exercise level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past meal history and exercise data into a generation AI, and the generation AI can generate an optimal menu.

[0055] The generation unit can customize menus during generation, taking into account the user's allergy information and dietary restrictions. For example, the generation unit can generate allergen-free menus based on the user's allergy information. The generation unit can also generate menus suitable for the user's dietary restrictions, taking those restrictions into account. For example, it can generate the optimal menu by comprehensively considering the user's allergy information and dietary restrictions. This allows for the generation of more appropriate menus by considering the user's allergy information and dietary restrictions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's allergy information and dietary restriction data into a generation AI, which can then generate the optimal menu.

[0056] The generation unit can generate an optimal menu by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate a menu using ingredients from that region. The generation unit can also suggest region-specific dishes based on the user's geographical location information. For example, it can generate a menu suitable for the region's climate and environment based on the user's geographical location information. In this way, region-specific menus can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate an optimal menu.

[0057] The generation unit can improve the accuracy of the menu by referring to the user's social media activity during generation. For example, the generation unit can refer to the user's social media activity and reflect posts and comments related to food in the menu. The generation unit can also identify food topics of interest from the user's social media activity and reflect them in the menu. For example, it can grasp food trends based on the user's social media activity and reflect them in the menu. In this way, the accuracy of the menu can be improved by referring to the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, and the generation AI can analyze the information to improve the accuracy of the menu.

[0058] The service provider can select the optimal service method by referring to the user's past menu selection history at the time of service. For example, the service provider can select the optimal service method based on the user's past menu selection history. The service provider can also select a detailed service method if more detailed information is needed from the user's past menu selection history. For example, it can analyze the user's past menu selection history and select a simplified service method. This allows the service provider to select the optimal service method by referring to the user's past menu selection history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past menu selection history into AI, which can then analyze the information and select the optimal service method.

[0059] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's device information into an AI, which can then analyze the information and select the optimal display method.

[0060] The service provider can select the optimal service method at the time of service, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can offer a menu related to that region. The service provider can also offer region-specific dishes based on the user's geographical location. For example, it can offer a menu suitable for the region's climate and environment based on the user's geographical location. In this way, by considering the user's geographical location, region-specific menus can be offered. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into AI, which can then analyze the information and select the optimal service method.

[0061] The service provider can improve the accuracy of its service by referring to the user's social media activity at the time of service delivery. For example, the service provider can refer to the user's social media activity and reflect posts and comments related to food in the service. The service provider can also identify food topics of interest from the user's social media activity and reflect them in the service. For example, it can grasp food-related trends based on the user's social media activity and reflect them in the service. In this way, the accuracy of the service can be improved by referring to the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can analyze the information to improve the accuracy of the service.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The health promotion support system can also collect and analyze the user's water intake. For example, it can record how much water a user consumes in a day and use this data to assess their health status. The analysis unit can combine water intake with other health information to provide appropriate hydration advice. For instance, if a user engages in a lot of exercise, it can recommend additional hydration. It can also assess the user's risk of dehydration based on water intake and suggest necessary countermeasures. This allows for more appropriate health support by taking the user's water intake into consideration.

[0064] The health promotion support system can also collect photos of users' meals and analyze them in its analysis unit. For example, users can take photos of their meals, and the data from these photos can be collected. The analysis unit can then combine the photo data with other health information to evaluate the user's diet. For instance, it can identify ingredients and types of dishes from the photos and evaluate the nutritional balance. It can also analyze the user's eating habits based on the photo data and suggest areas for improvement. This allows for more detailed health support by taking into account the user's meal photos.

[0065] The health promotion support system can also acquire user body temperature data and analyze it in its analysis unit. For example, it can periodically measure the user's body temperature and collect that data. The analysis unit can combine the body temperature data with other health information to evaluate the user's health status. For example, if the body temperature is high, it can suggest a menu to boost immunity. It can also detect changes in the user's physical condition early based on the body temperature data and suggest appropriate countermeasures. In this way, by considering the user's body temperature data, more comprehensive health support becomes possible.

[0066] The health promotion support system can also acquire the user's blood pressure data and analyze it in the analysis unit. For example, it can periodically measure the user's blood pressure and collect that data. The analysis unit can combine the blood pressure data with other health information to evaluate the user's health status. For example, if the blood pressure is high, it can suggest a menu with reduced salt content. It can also provide advice on improving the user's lifestyle based on the blood pressure data. In this way, by taking the user's blood pressure data into consideration, more appropriate health support becomes possible.

[0067] The health promotion support system can also acquire user body fat percentage data and analyze it in the analysis unit. For example, it can periodically measure the user's body fat percentage and collect that data. The analysis unit can combine the body fat percentage data with other health information to evaluate the user's health status. For example, if the body fat percentage is high, it can suggest a menu suitable for fat burning. It can also adjust the user's exercise plan and suggest an appropriate amount of exercise based on the body fat percentage data. In this way, by taking the user's body fat percentage data into consideration, more effective health support becomes possible.

[0068] The health promotion support system can also acquire and analyze the user's bone density data. For example, it can periodically measure the user's bone density and collect the data. The analysis unit can combine the bone density data with other health information to evaluate the user's health status. For example, if bone density is low, it can suggest a menu containing calcium and vitamin D. It can also adjust the user's exercise plan based on bone density data and suggest exercises that help strengthen bones. In this way, by considering the user's bone density data, more comprehensive health support becomes possible.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The data collection unit collects the user's health information. This includes gender, height, weight, and allergy information. The data collection unit collects this information by having the user register in advance any foods they are allergic to or cannot eat. The data collection unit can also periodically update the user's health information. Step 2: The acquisition unit acquires step count data for the past month, week, and day (today) measured by HELPO. The step count data includes information such as how many steps the user took in a day and how much exercise they did in a week. The acquisition unit acquires step count data using a pedometer application or wearable device. The acquisition unit can also periodically update the step count data. Step 3: The analysis unit analyzes the data collected by the collection and acquisition units. The analysis unit performs analysis based on the user's gender, height, weight, allergy information, step count data, etc. The analysis unit can also perform analysis considering the user's health information. For example, if the user has allergies, the analysis will take that information into account. Step 4: The generation unit generates a menu based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate a menu suitable for the user. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest a menu suitable for promoting health. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. Step 5: The delivery unit provides the user with the menu generated by the generation unit. The delivery unit provides the menu to the user through the application. The delivery unit can also provide notifications via email or in paper format. For example, the menu can be provided in the way preferred by the user.

[0071] (Example of form 2) The health promotion support system according to an embodiment of the present invention is a system that collects the user's health information, acquires step count data, and automatically generates a suitable meal menu using a generative AI. This system supports the user's health promotion and reduces the time spent thinking about meal menus by collecting the user's health information, acquiring step count data, and automatically generating a suitable meal menu using a generative AI. First, the user registers information such as gender, height, weight, and allergies in HELPO. For example, by registering in advance any foods the user is allergic to or cannot eat, the generative AI can generate a menu that takes this information into consideration. Next, the health app and HELPO are linked. This allows the system to acquire step count data for the past month, week, and day (today) measured by HELPO. For example, it collects data such as how much the user walked in a day and how much exercise they did in a week. Based on this data, the generative AI automatically generates a meal menu suitable for the user. The generative AI proposes a meal menu suitable for health promotion, taking into consideration the user's gender, height, weight, allergy information, step count data, etc. For example, if the user has exercised a lot, it can propose a menu suitable for energy replenishment. This system not only supports users in improving their health but also reduces the time they spend thinking about meal menus. For example, it eliminates the hassle of planning daily meals in a busy daily life. Furthermore, the menus suggested by the generation AI are nutritionally balanced and help maintain the user's health. In this way, a meal menu generation service using generation AI contributes to improving users' health and reducing the time they spend thinking about meal menus. This allows the health promotion support system to collect users' health information, acquire step count data, and automatically generate suitable meal menus using generation AI.

[0072] The health promotion support system according to the embodiment comprises a collection unit, an acquisition unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's health information. The user's health information includes, but is not limited to, gender, height, weight, and allergy information. The collection unit collects this information, for example, by having the user register in advance any foods they are allergic to or foods they cannot eat. The collection unit can also periodically update the user's health information. For example, if a user discovers a new allergy, that information can be added to the collection unit. The acquisition unit acquires step count data for the most recent month, week, and day (today) measured by HELPO. The step count data includes, but is not limited to, information such as how many steps the user took in a day and how much exercise they did in a week. The acquisition unit acquires step count data, for example, using a pedometer application or a wearable device. The acquisition unit can also periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the acquisition unit. The analysis unit analyzes the data collected by the collection unit and the acquisition unit. The analysis unit performs analysis based on, for example, the user's gender, height, weight, allergy information, and step count data. The analysis unit can also consider the user's health information during the analysis. For example, if the user has allergies, this information is taken into account during the analysis. The generation unit generates meal menus based on the data analyzed by the analysis unit. The generation unit generates meal menus suitable for the user, for example, using a generation AI. The generation AI considers the user's gender, height, weight, allergy information, and step count data to suggest meal menus suitable for health promotion. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The delivery unit provides the meal menus generated by the generation unit to the user. The delivery unit provides the meal menus to the user, for example, through an application. The delivery unit can also provide notifications via email or in paper form. For example, the meal menus can be provided in the way the user prefers.As a result, the health promotion support system according to this embodiment can collect the user's health information, acquire step count data, automatically generate suitable meal menus using a generation AI, and provide them to the user.

[0073] The data collection unit collects user health information. This information includes, but is not limited to, gender, height, weight, and allergy information. The data collection unit collects this information, for example, by having users pre-register foods they are allergic to or foods they cannot eat. Specifically, users input their health information through a dedicated application and send it to the data collection unit. The application securely stores the information entered by the user and can update it as needed. The data collection unit can also periodically update the user's health information. For example, if a user discovers a new allergy, that information can be added to the data collection unit. The data collection unit automatically analyzes the information entered by the user and stores it in a database. This allows the data collection unit to centrally manage user health information and collaborate with other departments as needed. Furthermore, the data collection unit takes measures to anonymize user health information and protect privacy. For example, it encrypts user personal information to prevent access by third parties. This allows the data collection unit to collect user health information safely and efficiently, improving the overall system performance.

[0074] The data acquisition unit acquires step count data for the past month, week, and day (current day) measured by HELPO. This step count data includes, but is not limited to, information such as how many steps a user takes in a day or how much exercise they do in a week. The data acquisition unit acquires step count data using, for example, pedometer applications or wearable devices. Specifically, the user wears a smartphone or smartwatch, and these devices measure the number of steps. The data acquisition unit automatically collects data from these devices and transmits it to a central database. The data acquisition unit can also periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the data acquisition unit. The data acquisition unit monitors the user's step count data in real time, accurately understanding their daily exercise level. Furthermore, the data acquisition unit can analyze the user's step count data to understand exercise patterns and trends. This allows the data acquisition unit to gain a detailed understanding of the user's exercise habits and provide data for health promotion.

[0075] The analysis unit analyzes the data collected by the collection and acquisition units. The analysis unit performs analysis based on data such as the user's gender, height, weight, allergy information, and step count. Specifically, it uses AI to analyze this data and evaluate the user's health status and exercise habits. For example, it calculates appropriate calorie intake and exercise levels based on the user's gender, height, and weight. It also identifies foods and ingredients that the user should avoid, taking allergy information into consideration. Furthermore, it analyzes step count data to evaluate the user's exercise level and exercise patterns. The analysis unit integrates this data to comprehensively evaluate the user's health status. For example, it evaluates whether the user is exercising sufficiently and consuming appropriate calories, and identifies areas for improvement. This allows the analysis unit to gain a detailed understanding of the user's health status and provide specific advice for health promotion.

[0076] The generation unit generates meal menus based on data analyzed by the analysis unit. For example, the generation unit uses a generation AI to generate meal menus suitable for the user. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest meal menus suitable for promoting health. Specifically, the AI ​​analyzes the user's data and generates nutritionally balanced menus. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The generation AI considers the user's preferences and food allergy information to suggest menus that the user can eat with peace of mind. In addition, the generation AI can also suggest menus using seasonal ingredients, taking into account seasonal and regional characteristics. In this way, the generation unit can provide optimal meal menus tailored to the user's health condition and preferences.

[0077] The service provider delivers the meal menus generated by the generation unit to the user. The service provider delivers the meal menus to the user, for example, through an application. Specifically, the user can open a dedicated application and check the generated meal menus. The service provider can also provide notifications via email or in paper form. For example, the service provider can deliver the meal menus in the way the user prefers. The service provider can collect user feedback and continuously improve the accuracy and effectiveness of the menus it provides. For example, the service provider can provide feedback on the results of users actually trying the provided menus and use that information to improve the menus. The service provider can also provide individually customized menus, taking into account the user's preferences and allergy information. In this way, the service provider can provide the user with the most suitable meal menus and support their health improvement.

[0078] The data collection unit can collect the user's gender, height, weight, and allergy information. For example, the unit can collect this information when the user registers their gender, height, weight, and allergy information with HELPO. The data collection unit can also add information if the user discovers a new allergy. For example, if a user is allergic to a specific food, they can register that information in the data collection unit. Furthermore, the data collection unit can periodically update the user's health information. For example, the user can measure their weight and add that information to the data collection unit. This allows for the generation of more appropriate meal menus by collecting the user's basic health information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's health information into AI, which can then analyze and collect the information.

[0079] The acquisition unit can acquire step count data for the past month, week, and day (today) measured by a pedometer application. For example, the acquisition unit can acquire step count data for the past month, week, and day (today) using a pedometer application. The acquisition unit can also acquire step count data using a wearable device. For example, it can collect data such as how many steps a user takes in a day or how much exercise they do in a week. Furthermore, the acquisition unit can periodically update the step count data. For example, if a user starts a new exercise routine, that data can be added to the acquisition unit. This allows for the generation of appropriate meal menus based on the user's exercise level. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input step count data acquired from a pedometer application or wearable device into an AI, which can then analyze and acquire the data.

[0080] The analysis unit can analyze collected health information and step count data. For example, the analysis unit performs analysis based on the user's gender, height, weight, allergy information, and step count data. The analysis unit can also take the user's health information into consideration during the analysis. For example, if the user has allergies, this information is taken into account during the analysis. Furthermore, the analysis unit can analyze the collected data using statistical analysis and machine learning algorithms. For example, the analysis unit can calculate calorie consumption based on the user's step count data and evaluate their health status. This allows for the generation of meal menus tailored to the user by analyzing the collected data. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the collected health information and step count data into an AI, which can then analyze the data and output the results.

[0081] The generation unit can generate a meal menu suitable for the user based on the analyzed data. For example, the generation unit can use a generation AI to generate a meal menu suitable for the user based on the analyzed data. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest a meal menu suitable for promoting health. For example, if the user has exercised a lot, it can suggest a meal menu suitable for energy replenishment. Furthermore, the generation unit can also generate a meal menu considering the user's nutritional balance. For example, the generation unit suggests a balanced meal menu based on the user's calorie consumption and nutrient intake. In this way, a meal menu suitable for the user can be generated based on the analyzed data. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input the analyzed data into a generation AI, and the generation AI can generate a meal menu.

[0082] The service provider can provide the generated meal menu to the user. For example, the service provider can provide the generated meal menu to the user through an application. The service provider can also provide notifications via email or in paper format. For example, the service provider can provide the meal menu in the way preferred by the user. Furthermore, the service provider can provide display methods tailored to the user's device. For example, it can provide display methods optimized for devices such as smartphones, tablets, and PCs. By providing the generated meal menu to the user, the service provider can support the user's health improvement. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the generated meal menu into AI, and the AI ​​can select how to provide it to the user.

[0083] The analysis unit can perform analysis based on the user's allergy information. For example, the analysis unit will take the user's allergy information into consideration when performing the analysis. For example, if the user has an allergy to a specific food, the analysis will be based on that information. The analysis unit can also collect and periodically update the user's allergy information. For example, if the user discovers a new allergy, that information can be added to the analysis unit. This allows the system to generate a menu of dishes that do not contain allergens by taking the user's allergy information into consideration. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's allergy information into AI, which can then analyze the information and output the results.

[0084] The generation unit can generate menus suitable for energy replenishment based on the user's activity level. For example, the generation unit can generate meal menus suitable for energy replenishment by considering the user's activity level. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. The generation unit can also periodically update the user's activity level and generate menus based on that. For example, if the user starts exercising a new way, it can generate a menu suitable for energy replenishment based on that data. This allows the generation unit to generate meal menus suitable for energy replenishment based on the user's activity level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's activity level data into a generation AI, and the generation AI can generate a menu suitable for energy replenishment.

[0085] The data collection unit can estimate the user's emotions and adjust the timing of health information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can collect health information during times when the user is relaxed. The data collection unit can also collect detailed health information if the user is relaxed. For example, if the user is in a hurry, simplified health information can be collected. This allows for information to be collected at a more appropriate time by adjusting the timing of health information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the collection timing.

[0086] The data collection unit can analyze the user's past health information and select an appropriate collection method. For example, the data collection unit can analyze the user's past health information and select the optimal collection method. For example, it can propose the optimal collection method based on the health information the user has provided in the past. The data collection unit can also ask detailed questions if more detailed information is needed from the user's past health information. For example, it can analyze the user's past health information and ask simplified questions. This allows the optimal collection method to be selected by analyzing the user's past health information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past health information into AI, which can analyze the information and select the optimal collection method.

[0087] The data collection unit can filter health information based on the user's current lifestyle and areas of interest. For example, if the user inputs their current lifestyle, the data collection unit will filter the health information based on that information. The data collection unit can also prioritize the collection of relevant health information based on the user's areas of interest. For example, it can exclude unnecessary information based on the user's lifestyle and areas of interest. By filtering health information based on the user's lifestyle and areas of interest, it is possible to collect highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI, which can then filter and collect the information.

[0088] The data collection unit can estimate the user's emotions and determine the priority of health information to collect based on the estimated emotions. For example, if the user is stressed, stress-related health information will be prioritized. The data collection unit can also prioritize collecting detailed health information if the user is relaxed. For example, if the user is in a hurry, simplified health information will be prioritized. This allows for the collection of more important information by prioritizing health information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of health information to collect.

[0089] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting health information. For example, if the user is in a specific region, the data collection unit will prioritize the collection of health information related to that region. The data collection unit can also collect information based on the user's geographical location, taking into account region-specific health risks. For example, it can collect health information related to the region's climate and environment based on the user's geographical location. This allows for the priority collection of region-specific health information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location into AI, which can then analyze the information and prioritize the collection of highly relevant information.

[0090] The data collection unit can analyze users' social media activity and collect relevant information when collecting health information. For example, the data collection unit can analyze users' social media activity and collect posts and comments related to health. The data collection unit can also identify health topics of interest from users' social media activity and collect relevant information. For example, it can identify health trends based on users' social media activity and collect relevant information. This allows for the collection of highly relevant health information by analyzing users' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user social media activity data into AI, which can then analyze the information and collect relevant information.

[0091] The acquisition unit can estimate the user's emotions and adjust the timing of step count data acquisition based on the estimated user emotions. For example, if the user is stressed, step count data is acquired during times when the user is relaxed. The acquisition unit can also acquire detailed step count data when the user is relaxed. For example, if the user is in a hurry, simplified step count data is acquired. By adjusting the timing of step count data acquisition according to the user's emotions, data can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the acquisition timing.

[0092] The acquisition unit can analyze the user's past step count data and select the optimal acquisition method. For example, the acquisition unit can analyze the user's past step count data and select the optimal acquisition method. For example, it can propose the optimal acquisition method based on the step count data previously provided by the user. The acquisition unit can also select a detailed acquisition method from the user's past step count data if more detailed data is needed. For example, it can analyze the user's past step count data and select a simplified acquisition method. In this way, the optimal acquisition method can be selected by analyzing the user's past step count data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's past step count data into AI, and the AI ​​can analyze the information and select the optimal acquisition method.

[0093] The acquisition unit can filter step count data based on the user's current lifestyle and exercise habits when acquiring it. For example, if the user inputs their current lifestyle, the acquisition unit will filter the step count data based on that information. The acquisition unit can also prioritize acquiring relevant step count data based on the user's exercise habits. For example, it can exclude unnecessary data based on the user's lifestyle and exercise habits. By filtering step count data based on the user's lifestyle and exercise habits, it is possible to acquire highly relevant data. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the user's lifestyle and exercise habits into an AI, which can then filter and acquire the information.

[0094] The acquisition unit can estimate the user's emotions and determine the priority of step count data to acquire based on the estimated user emotions. For example, if the user is stressed, stress-related step count data is prioritized. The acquisition unit can also prioritize acquiring detailed step count data if the user is relaxed. For example, if the user is in a hurry, simplified step count data is prioritized. This allows for the prioritization of more important data by determining the priority of step count data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can input user emotion data into a generative AI, which can then estimate the emotions and determine the priority of step count data to acquire.

[0095] The data acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring step count data. For example, if the user is in a specific region, the data acquisition unit will prioritize the acquisition of step count data related to that region. The data acquisition unit can also acquire data by considering region-specific exercise habits based on the user's geographical location information. For example, it can acquire step count data related to the region's climate and environment based on the user's geographical location information. This allows for the priority acquisition of region-specific step count data by considering the user's geographical location information. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's geographical location information into AI, which can then analyze the information and prioritize the acquisition of highly relevant data.

[0096] The data acquisition unit can analyze the user's social media activity and acquire relevant data when acquiring step count data. For example, the data acquisition unit can analyze the user's social media activity and collect posts and comments related to exercise. The data acquisition unit can also identify exercise topics of interest from the user's social media activity and acquire relevant data. For example, it can grasp exercise trends based on the user's social media activity and acquire relevant data. In this way, by analyzing the user's social media activity, highly relevant step count data can be acquired. Some or all of the above processing in the data acquisition unit may be performed using AI, for example, or without AI. For example, the data acquisition unit can input the user's social media activity data into AI, and the AI ​​can analyze the information and acquire relevant data.

[0097] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can focus on stress reduction. The analysis unit can also perform a detailed analysis if the user is relaxed. For example, if the user is in a hurry, a simplified analysis can be performed. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the analysis method.

[0098] The analysis unit can optimize the analysis algorithm by referring to the user's past health information and step count data during analysis. For example, the analysis unit can optimize the analysis algorithm by referring to the user's past health information and step count data. For example, it can select the optimal analysis algorithm based on the user's past health information and step count data. The analysis unit can also select a detailed algorithm from the user's past health information and step count data if a detailed analysis is required. For example, it can analyze the user's past health information and step count data and select a simplified analysis algorithm. This allows the analysis algorithm to be optimized by referring to the user's past health information and step count data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past health information and step count data into AI, which can analyze the information and select the optimal algorithm.

[0099] The analysis unit can perform analysis based on the user's lifestyle and dietary history. For example, the analysis unit can analyze health risks by considering the user's lifestyle. The analysis unit can also analyze nutritional balance based on the user's dietary history. For example, it can analyze the health status by comprehensively considering the user's lifestyle and dietary history. This allows for more accurate analysis by considering the user's lifestyle and dietary history. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input data on the user's lifestyle and dietary history into an AI, which can analyze the information and evaluate the health status.

[0100] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the display method of the analysis results.

[0101] The analysis unit can perform analysis while considering the user's geographical location information. For example, the analysis unit can analyze region-specific health risks based on the user's geographical location information. The analysis unit can also analyze health risks related to the region's climate and environment based on the user's geographical location information. For example, it can analyze while considering the region's medical resources based on the user's geographical location information. In this way, by considering the user's geographical location information, region-specific health risks can be analyzed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the user's geographical location information into AI, and the AI ​​can analyze the information and evaluate the health risks.

[0102] The analysis unit can improve the accuracy of its analysis by referring to the user's social media activity during the analysis process. For example, the analysis unit can refer to the user's social media activity and reflect health-related posts and comments in the analysis. The analysis unit can also identify health topics of interest from the user's social media activity and reflect them in the analysis. For example, it can grasp health-related trends based on the user's social media activity and reflect them in the analysis. In this way, the accuracy of the analysis can be improved by referring to the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into AI, and the AI ​​can analyze the information to improve accuracy.

[0103] The generation unit can estimate the user's emotions and adjust the method of generating the menu based on the estimated emotions. For example, if the user is stressed, it can generate a relaxing menu. The generation unit can also generate a detailed menu if the user is relaxed. For example, if the user is in a hurry, it can generate a simplified menu. By adjusting the menu generation method according to the user's emotions, a more appropriate menu can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and adjust the menu generation method.

[0104] The generation unit can generate an optimal menu by referring to the user's past meal history and exercise level during the generation process. For example, the generation unit can generate a nutritionally balanced menu based on the user's past meal history. The generation unit can also generate a menu suitable for energy replenishment by considering the user's exercise level. For example, it can generate an optimal menu by comprehensively considering the user's past meal history and exercise level. In this way, the optimal menu can be generated by referring to the user's past meal history and exercise level. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past meal history and exercise data into a generation AI, and the generation AI can generate an optimal menu.

[0105] The generation unit can customize menus during generation, taking into account the user's allergy information and dietary restrictions. For example, the generation unit can generate allergen-free menus based on the user's allergy information. The generation unit can also generate menus suitable for the user's dietary restrictions, taking those restrictions into account. For example, it can generate the optimal menu by comprehensively considering the user's allergy information and dietary restrictions. This allows for the generation of more appropriate menus by considering the user's allergy information and dietary restrictions. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input the user's allergy information and dietary restriction data into a generation AI, which can then generate the optimal menu.

[0106] The generation unit can estimate the user's emotions and determine the priority of the menus to be generated based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating menus suitable for stress reduction. The generation unit can also prioritize generating detailed menus if the user is relaxed. For example, if the user is in a hurry, the generation unit will prioritize generating simplified menus. In this way, by determining the priority of menus according to the user's emotions, more important menus can be generated preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, which can estimate the emotions and determine the menu priorities.

[0107] The generation unit can generate an optimal menu by considering the user's geographical location information during the generation process. For example, if the user is in a specific region, the generation unit can generate a menu using ingredients from that region. The generation unit can also suggest region-specific dishes based on the user's geographical location information. For example, it can generate a menu suitable for the region's climate and environment based on the user's geographical location information. In this way, region-specific menus can be generated by considering the user's geographical location information. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's geographical location information into the generation AI, which can then generate an optimal menu.

[0108] The generation unit can improve the accuracy of the menu by referring to the user's social media activity during generation. For example, the generation unit can refer to the user's social media activity and reflect posts and comments related to food in the menu. The generation unit can also identify food topics of interest from the user's social media activity and reflect them in the menu. For example, it can grasp food trends based on the user's social media activity and reflect them in the menu. In this way, the accuracy of the menu can be improved by referring to the user's social media activity. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's social media activity data into a generation AI, and the generation AI can analyze the information to improve the accuracy of the menu.

[0109] The service provider can estimate the user's emotions and adjust the menu presentation based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and visually clear presentation. If the user is relaxed, the service provider can also provide a presentation that includes detailed information. If the user is in a hurry, the service provider can provide a concise presentation. By adjusting the menu presentation according to the user's emotions, a more appropriate presentation can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation method.

[0110] The service provider can select the optimal service method by referring to the user's past menu selection history at the time of service. For example, the service provider can select the optimal service method based on the user's past menu selection history. The service provider can also select a detailed service method if more detailed information is needed from the user's past menu selection history. For example, it can analyze the user's past menu selection history and select a simplified service method. This allows the service provider to select the optimal service method by referring to the user's past menu selection history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past menu selection history into AI, which can then analyze the information and select the optimal service method.

[0111] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider can input the user's device information into an AI, which can then analyze the information and select the optimal display method.

[0112] The service provider can estimate the user's emotions and adjust the timing of menu delivery based on the estimated emotions. For example, if the user is stressed, the service provider can offer menus during a time when the user can relax. The service provider can also offer a detailed menu if the user is relaxed. For example, if the user is in a hurry, a simplified menu can be offered. By adjusting the timing of menu delivery according to the user's emotions, the service can be provided at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of delivery.

[0113] The service provider can select the optimal service method at the time of service, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can offer a menu related to that region. The service provider can also offer region-specific dishes based on the user's geographical location. For example, it can offer a menu suitable for the region's climate and environment based on the user's geographical location. In this way, by considering the user's geographical location, region-specific menus can be offered. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into AI, which can then analyze the information and select the optimal service method.

[0114] The service provider can improve the accuracy of its service by referring to the user's social media activity at the time of service delivery. For example, the service provider can refer to the user's social media activity and reflect posts and comments related to food in the service. The service provider can also identify food topics of interest from the user's social media activity and reflect them in the service. For example, it can grasp food-related trends based on the user's social media activity and reflect them in the service. In this way, the accuracy of the service can be improved by referring to the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can analyze the information to improve the accuracy of the service.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The health promotion support system can also acquire user sleep data and analyze it in its analysis unit. For example, it can collect the user's sleep duration and sleep quality, and use this data to evaluate their health status. The analysis unit can combine sleep data with other health information to perform more accurate analyses. For example, if a user is not getting enough sleep, it can suggest a menu suitable for nutritional supplementation. It can also estimate the user's stress level based on sleep data and generate menus that help reduce stress. In this way, by considering the user's sleep data, more comprehensive health support becomes possible.

[0117] The health promotion support system can also collect and analyze the user's water intake. For example, it can record how much water a user consumes in a day and use this data to assess their health status. The analysis unit can combine water intake with other health information to provide appropriate hydration advice. For instance, if a user engages in a lot of exercise, it can recommend additional hydration. It can also assess the user's risk of dehydration based on water intake and suggest necessary countermeasures. This allows for more appropriate health support by taking the user's water intake into consideration.

[0118] The health promotion support system can also acquire user heart rate data and analyze it in its analysis unit. For example, it can periodically monitor the user's heart rate and evaluate their health status based on this data. The analysis unit can combine heart rate data with other health information to evaluate the user's exercise intensity and stress level. For example, if the user's heart rate is high, it can suggest a relaxing exercise routine. It can also adjust the user's exercise plan based on heart rate data and suggest an appropriate amount of exercise. In this way, by considering the user's heart rate data, more accurate health support becomes possible.

[0119] The health promotion support system can also collect and analyze user satisfaction with meals. For example, users can rate their satisfaction after a meal, and this data can be collected. The analysis unit can combine this satisfaction data with other health information to suggest the most suitable menu for the user. For instance, if a user shows high satisfaction with a particular menu item, that menu item can be suggested again. Furthermore, based on the satisfaction data, the system can analyze user preferences and generate menus that better suit their tastes. This allows for more personalized health support by taking into account the user's satisfaction with meals.

[0120] The health promotion support system can also collect and analyze the user's post-exercise fatigue levels. For example, the user can evaluate their fatigue level after exercise, and this data can be collected. The analysis unit can combine the fatigue level data with other health information to suggest a recovery menu suitable for the user. For instance, if the user shows high fatigue levels, the system can suggest a menu containing nutrients that aid in recovery. It can also adjust the user's exercise plan and suggest appropriate rest based on the fatigue level data. This allows for more effective health support by taking into account the user's post-exercise fatigue levels.

[0121] The health promotion support system can also collect photos of users' meals and analyze them in its analysis unit. For example, users can take photos of their meals, and the data from these photos can be collected. The analysis unit can then combine the photo data with other health information to evaluate the user's diet. For instance, it can identify ingredients and types of dishes from the photos and evaluate the nutritional balance. It can also analyze the user's eating habits based on the photo data and suggest areas for improvement. This allows for more detailed health support by taking into account the user's meal photos.

[0122] The health promotion support system can also acquire user body temperature data and analyze it in its analysis unit. For example, it can periodically measure the user's body temperature and collect that data. The analysis unit can combine the body temperature data with other health information to evaluate the user's health status. For example, if the body temperature is high, it can suggest a menu to boost immunity. It can also detect changes in the user's physical condition early based on the body temperature data and suggest appropriate countermeasures. In this way, by considering the user's body temperature data, more comprehensive health support becomes possible.

[0123] The health promotion support system can also acquire the user's blood pressure data and analyze it in the analysis unit. For example, it can periodically measure the user's blood pressure and collect that data. The analysis unit can combine the blood pressure data with other health information to evaluate the user's health status. For example, if the blood pressure is high, it can suggest a menu with reduced salt content. It can also provide advice on improving the user's lifestyle based on the blood pressure data. In this way, by taking the user's blood pressure data into consideration, more appropriate health support becomes possible.

[0124] The health promotion support system can also acquire user body fat percentage data and analyze it in the analysis unit. For example, it can periodically measure the user's body fat percentage and collect that data. The analysis unit can combine the body fat percentage data with other health information to evaluate the user's health status. For example, if the body fat percentage is high, it can suggest a menu suitable for fat burning. It can also adjust the user's exercise plan and suggest an appropriate amount of exercise based on the body fat percentage data. In this way, by taking the user's body fat percentage data into consideration, more effective health support becomes possible.

[0125] The health promotion support system can also acquire and analyze the user's bone density data. For example, it can periodically measure the user's bone density and collect the data. The analysis unit can combine the bone density data with other health information to evaluate the user's health status. For example, if bone density is low, it can suggest a menu containing calcium and vitamin D. It can also adjust the user's exercise plan based on bone density data and suggest exercises that help strengthen bones. In this way, by considering the user's bone density data, more comprehensive health support becomes possible.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The data collection unit collects the user's health information. This includes gender, height, weight, and allergy information. The data collection unit collects this information by having the user register in advance any foods they are allergic to or cannot eat. The data collection unit can also periodically update the user's health information. Step 2: The acquisition unit acquires step count data for the past month, week, and day (today) measured by HELPO. The step count data includes information such as how many steps the user took in a day and how much exercise they did in a week. The acquisition unit acquires step count data using a pedometer application or wearable device. The acquisition unit can also periodically update the step count data. Step 3: The analysis unit analyzes the data collected by the collection and acquisition units. The analysis unit performs analysis based on the user's gender, height, weight, allergy information, step count data, etc. The analysis unit can also perform analysis considering the user's health information. For example, if the user has allergies, the analysis will take that information into account. Step 4: The generation unit generates a menu based on the data analyzed by the analysis unit. The generation unit uses a generation AI to generate a menu suitable for the user. The generation AI considers the user's gender, height, weight, allergy information, step count data, etc., to suggest a menu suitable for promoting health. For example, if the user has exercised a lot, it can suggest a menu suitable for energy replenishment. Step 5: The delivery unit provides the user with the menu generated by the generation unit. The delivery unit provides the menu to the user through the application. The delivery unit can also provide notifications via email or in paper format. For example, the menu can be provided in the way preferred by the user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the robot 414 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the speaker 240 of the robot 414 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A collection unit that collects user health information, A unit for acquiring step count data, An analysis unit that analyzes the data collected by the collection unit and the acquisition unit, A generation unit generates a menu based on the data analyzed by the analysis unit, The system includes a serving unit that provides the food menu generated by the generating unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user gender, height, weight, and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Retrieve step count data for the past month, week, and day (today) measured by a pedometer application. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the collected health information and step count data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Based on the analyzed data, a menu tailored to the user is generated. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the generated menu to the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The analysis is performed based on the user's allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It generates a menu suitable for energy replenishment based on the user's activity level. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health information collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past health information and select the appropriate data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting health information, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of health information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting health information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting health information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of step count data acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The acquisition unit is, Analyze the user's past step count data and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 17) The acquisition unit is, When acquiring step count data, filtering is performed based on the user's current lifestyle and exercise habits. The system described in Appendix 1, characterized by the features described herein. (Note 18) The acquisition unit is, The system estimates the user's emotions and determines the priority of step count data to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The acquisition unit is, When acquiring step count data, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The acquisition unit is, When acquiring step count data, the system analyzes the user's social media activity and retrieves relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referencing the user's past health information and step count data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the analysis, the analysis is performed based on the user's lifestyle and dietary history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the user's geographical location information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, During analysis, we improve the accuracy of the analysis by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is The system estimates the user's emotions and adjusts the menu generation method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is During generation, the system references the user's past meal history and exercise levels to generate the optimal menu. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is During generation, the menu is customized to take into account the user's allergy information and dietary restrictions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is It estimates the user's emotions and determines the priority of the menu generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is During generation, the system considers the user's geographical location to generate the optimal menu. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is During generation, we refer to the user's social media activity to improve the accuracy of the menu. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the menu presentation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the service, the system will refer to the user's past menu selection history to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the timing of menu item delivery based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned supply unit is, We improve the accuracy of deliveries by referencing users' social media activity during the delivery process. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user health information, A unit for acquiring step count data, An analysis unit that analyzes the data collected by the collection unit and the acquisition unit, A generation unit generates a menu based on the data analyzed by the analysis unit, The system includes a serving unit that provides the food menu generated by the generating unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect user gender, height, weight, and allergy information. The system according to feature 1.

3. The acquisition unit is, Retrieve step count data for the past month, week, and day (today) measured by a pedometer application. The system according to feature 1.

4. The aforementioned analysis unit, Analyze the collected health information and step count data. The system according to feature 1.

5. The generating unit is Based on the analyzed data, a menu tailored to the user is generated. The system according to feature 1.

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

7. The aforementioned analysis unit, The analysis is performed based on the user's allergy information. The system according to feature 1.

8. The generating unit is It generates a menu suitable for energy replenishment based on the user's activity level. The system according to feature 1.

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