system

Abstract

The system according to the embodiment aims to propose an optimal buffet menu based on each individual's health condition. [Solution] According to an embodiment, the system includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user data from a wearable device. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The suggestion unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit.

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide dietary suggestions based on individual health conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal buffet menu based on each individual's health condition. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects user data from the wearable device. The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The proposal unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the optimal buffet menu based on the individual's health condition. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health management system according to an embodiment of the present invention utilizes wearable devices and AI technology to propose optimal buffet menus based on individual health conditions. This health management system collects data such as a user's heart rate, steps, and calories burned, and AI analyzes this data to evaluate the user's health condition. For example, a high heart rate may indicate stress, so the system suggests foods with a relaxing effect. Similarly, a high calorie consumption may suggest foods that are necessary for energy replenishment. Furthermore, the AI ​​also takes into account the user's past diet history and allergy information to create individually customized buffet menus. For example, if a user has previously had an allergic reaction to a particular ingredient, the system suggests a menu that does not include that ingredient. Furthermore, if a user needs to increase their intake of a particular nutrient, the system suggests foods that are high in that nutrient. This system allows users to easily select meals that are optimal for their health condition and improve their daily eating habits in a healthy and efficient manner. For example, a user on a diet may be suggested a low-calorie, nutritionally balanced menu, while a user aiming to build muscle may be suggested a high-protein menu. This system is also intended for use in buffet-style restaurants and hotels, allowing users to easily view the menus using their smartphones or tablets. This allows users to choose meals that are optimal for their health condition and maintain healthy eating habits, allowing the health management system to suggest optimal buffet menus based on the user's health condition.

[0029] A health management system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user data from a wearable device. The collected data includes, but is not limited to, heart rate, number of steps, and calories burned. For example, the collection unit measures the user's heart rate using a heart rate sensor. The collection unit can also count the user's steps using a pedometer. The collection unit can also monitor the user's activity level to calculate calories burned. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor and collects the data. The collection unit can accurately count the user's steps using a pedometer. The collection unit can monitor the user's activity level and collect data for calculating calories burned. The analysis unit analyzes the data collected by the collection unit to evaluate the user's health condition. The analysis is performed using, for example, AI, but is not limited to, the example. The analysis unit analyzes the collected heart rate data using, for example, AI, to evaluate the user's stress level. The analysis unit can also analyze the collected step count data to evaluate the user's exercise volume. The analysis unit can also analyze the collected calorie consumption data to evaluate the user's energy consumption. For example, the analysis unit can use AI to analyze the collected heart rate data to evaluate the user's stress level. The analysis unit can also analyze the collected step count data to evaluate the user's exercise volume. The analysis unit can also analyze the collected calorie consumption data to evaluate the user's energy consumption. The suggestion unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. The suggestion can be performed, for example, using AI, but is not limited to such an example. The suggestion unit can, for example, use AI to suggest ingredients that are optimal for the user's health condition. The suggestion unit can also create a buffet menu that is optimal for the user's health condition. The suggestion unit can also provide the menu using the user's smartphone or tablet. For example, the suggestion unit can use AI to suggest ingredients that are optimal for the user's health condition.The suggestion unit can also create a buffet menu that is optimal for the user's health condition. Furthermore, the suggestion unit can provide the menu using the user's smartphone or tablet. This allows the health management system according to the embodiment to suggest an optimal buffet menu based on the user's health condition.

[0030] The collection unit can collect data on the user's heart rate, number of steps, and calories burned. The collection unit measures the user's heart rate using, for example, a heart rate sensor. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor and collects the data. The collection unit can also count the user's steps using a pedometer. For example, the collection unit can accurately count the user's steps using the pedometer. Furthermore, the collection unit can monitor the user's activity level to calculate calories burned. For example, the collection unit can monitor the user's activity level and collect data for calculating calories burned. This allows the collection unit to collect data for understanding the user's health condition in detail.

[0031] The analysis unit can evaluate the user's health condition based on the collected data. For example, the analysis unit can use AI to analyze the collected heart rate data and evaluate the user's stress level. For example, the analysis unit can use AI to analyze the collected heart rate data and evaluate the user's stress level. The analysis unit can also analyze the collected step count data and evaluate the user's exercise amount. For example, the analysis unit can analyze the collected step count data and evaluate the user's exercise amount. Furthermore, the analysis unit can analyze the collected calorie consumption data and evaluate the user's energy consumption. For example, the analysis unit can analyze the collected calorie consumption data and evaluate the user's energy consumption. This allows the analysis unit to accurately evaluate the user's health condition based on the collected data.

[0032] The analysis unit can make an evaluation based on the user's past dietary history and allergy information. The analysis unit, for example, evaluates the health state taking into account the user's past dietary history. For example, the analysis unit evaluates the health state taking into account the user's past dietary history. The analysis unit can also evaluate the health state taking into account the user's allergy information. For example, the analysis unit can evaluate the health state taking into account the user's allergy information. This enables the analysis unit to make a more appropriate evaluation of the health state by taking into account the user's past dietary history and allergy information.

[0033] The suggestion unit can suggest ingredients that suit the user's health condition. For example, the suggestion unit uses AI to suggest ingredients that are optimal for the user's health condition. For example, the suggestion unit uses AI to suggest ingredients that are optimal for the user's health condition. The suggestion unit can also take into account the user's past dietary history and allergy information in order to suggest ingredients that are optimal for the user's health condition. For example, the suggestion unit can suggest optimal ingredients by taking into account the user's past dietary history and allergy information. In this way, the suggestion unit can support healthy eating habits by suggesting ingredients that are optimal for the user's health condition.

[0034] The suggestion unit can create a buffet menu that suits the user's health condition. For example, the suggestion unit uses AI to create a buffet menu that is optimal for the user's health condition. For example, the suggestion unit uses AI to create a buffet menu that is optimal for the user's health condition. In addition, the suggestion unit can also take into account the user's past dietary history and allergy information in order to create a buffet menu that is optimal for the user's health condition. For example, the suggestion unit can create an optimal buffet menu by taking into account the user's past dietary history and allergy information. In this way, the suggestion unit can maintain healthy eating habits by creating a buffet menu that is optimal for the user's health condition.

[0035] The suggestion unit can provide a menu using a user's smartphone or tablet. The suggestion unit can provide a menu using a user's smartphone or tablet, for example. For example, the suggestion unit can provide a menu using a user's smartphone or tablet. The suggestion unit can also use a dedicated app to provide a menu using a user's smartphone or tablet. For example, the suggestion unit can display a menu on the user's smartphone or tablet using a dedicated app. This allows the suggestion unit to allow the user to easily check the menu using a smartphone or tablet.

[0036] The collection unit can analyze the user's past health data and select an appropriate data collection method. The collection unit can, for example, use AI to analyze the user's past health data. For example, the collection unit can use AI to analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past step count data and, if the user's exercise volume is low, increase the frequency of step count collection. The collection unit can also analyze the user's past calorie consumption data and, if the user's calorie consumption volume is high, increase the frequency of meal data collection. For example, the collection unit can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past step count data and, if the user's exercise volume is low, increase the frequency of step collection. The collection unit can also analyze the user's past calorie consumption data and, if the user's calorie consumption volume is high, increase the frequency of meal data collection. In this way, the collection unit can select an optimal data collection method by analyzing the user's past health data.

[0037] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. The collection unit analyzes the user's current activity status and environment using, for example, AI. For example, the collection unit can use AI to collect only exercise data when the user is exercising and filter other data. Also, the collection unit can collect only heart rate data when the user is resting and filter other data. Furthermore, the collection unit can collect only meal data when the user is eating and filter other data. For example, the collection unit can collect only exercise data when the user is exercising and filter other data. Also, the collection unit can collect only heart rate data when the user is resting and filter other data. Furthermore, the collection unit can collect only meal data when the user is eating and filter other data. In this way, the collection unit can collect only necessary data by filtering data based on the user's current activity status and environment.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit analyzes the user's geographical location information using, for example, AI. For example, the collection unit can prioritize collecting exercise data when the user is in a park using AI. Furthermore, the collection unit can prioritize collecting meal data when the user is in a restaurant. Furthermore, the collection unit can also prioritize collecting heart rate data when the user is at home. For example, the collection unit can prioritize collecting exercise data when the user is in a park. Furthermore, the collection unit can prioritize collecting meal data when the user is in a restaurant. Furthermore, the collection unit can prioritize collecting heart rate data when the user is at home. This allows the collection unit to prioritize collecting highly relevant data in consideration of the user's geographical location information.

[0039] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can, for example, use AI to analyze the user's social media activity. For example, the collection unit can use AI to collect exercise data when the user posts about exercise on social media. The collection unit can also collect diet data when the user posts about diet on social media. Furthermore, the collection unit can also collect heart rate data when the user posts about stress on social media. For example, the collection unit can collect exercise data when the user posts about exercise on social media. The collection unit can also collect diet data when the user posts about diet on social media. Furthermore, the collection unit can collect heart rate data when the user posts about stress on social media. In this way, the collection unit can collect related data by analyzing the user's social media activity.

[0040] During analysis, the analysis unit can improve the accuracy of the evaluation based on the interrelationships of the collected data. The analysis unit, for example, uses AI to analyze the interrelationships of the collected data. For example, the analysis unit can use AI to analyze the correlation between heart rate and calories burned to evaluate the health condition. The analysis unit can also analyze the correlation between the number of steps and heart rate to evaluate the amount of exercise. The analysis unit can also analyze the correlation between calories burned and dietary data to evaluate nutritional balance. For example, the analysis unit can use AI to analyze the correlation between heart rate and calories burned to evaluate the health condition. The analysis unit can also analyze the correlation between the number of steps and heart rate to evaluate the amount of exercise. The analysis unit can also analyze the correlation between calories burned and dietary data to evaluate nutritional balance. In this way, the analysis unit improves the accuracy of the evaluation by taking into account the interrelationships of the collected data.

[0041] During analysis, the analysis unit can evaluate the health state based on the user's lifestyle habits and environmental information. The analysis unit, for example, uses AI to analyze the user's lifestyle habits and environmental information. For example, the analysis unit can use AI to evaluate the health state taking into account the user's sleep patterns. The analysis unit can also evaluate the nutritional balance taking into account the user's eating habits. The analysis unit can also evaluate the amount of exercise taking into account the user's exercise habits. For example, the analysis unit can use AI to evaluate the health state taking into account the user's sleep patterns. The analysis unit can also evaluate the nutritional balance taking into account the user's eating habits. The analysis unit can also evaluate the amount of exercise taking into account the user's exercise habits. This enables the analysis unit to more accurately evaluate the health state by taking into account the user's lifestyle habits and environmental information.

[0042] During the analysis, the analysis unit can evaluate the health condition based on the geographical distribution of the user. The analysis unit, for example, uses AI to analyze the geographical distribution of the user. For example, the analysis unit can use AI to emphasize the number of steps when evaluating the amount of exercise if the user lives in an urban area. Furthermore, the analysis unit can emphasize farm work data when evaluating calories burned if the user lives in a rural area. Furthermore, the analysis unit can emphasize swimming data when evaluating the heart rate if the user lives by the sea. For example, the analysis unit can use AI to emphasize the number of steps when evaluating the amount of exercise if the user lives in an urban area. Furthermore, the analysis unit can emphasize farm work data when evaluating calories burned if the user lives in a rural area. Furthermore, the analysis unit can emphasize swimming data when evaluating the heart rate if the user lives by the sea. This enables the analysis unit to more accurately evaluate the health condition by taking the geographical distribution of the user into account.

[0043] The analysis unit can improve the accuracy of the evaluation based on related medical data and literature during analysis. The analysis unit, for example, uses AI to refer to related medical data and literature. For example, the analysis unit, using AI, refers to the latest medical literature when evaluating the user's heart rate data. The analysis unit can also refer to related medical data when evaluating the user's calorie consumption data. Furthermore, the analysis unit can also refer to the latest research on exercise when evaluating the user's step count data. For example, the analysis unit, using AI, refers to the latest medical literature when evaluating the user's heart rate data. The analysis unit can also refer to related medical data when evaluating the user's calorie consumption data. Furthermore, the analysis unit can refer to the latest research on exercise when evaluating the user's step count data. In this way, the analysis unit, by referring to related medical data and literature, improves the accuracy of the evaluation.

[0044] When making a suggestion, the suggestion unit can suggest appropriate ingredients based on the user's past dietary history and allergy information. The suggestion unit, for example, uses AI to analyze the user's past dietary history and allergy information. For example, the suggestion unit can use AI to make suggestions that avoid ingredients to which the user has had an allergic reaction in the past. The suggestion unit can also prioritize suggestions of ingredients that the user has previously enjoyed. Furthermore, the suggestion unit can also suggest balanced ingredients by taking into account the nutrients the user has previously taken. For example, the suggestion unit can use AI to make suggestions that avoid ingredients to which the user has had an allergic reaction in the past. The suggestion unit can also prioritize suggestions of ingredients that the user has previously enjoyed. Furthermore, the suggestion unit can suggest balanced ingredients by taking into account the nutrients the user has previously taken. This allows the suggestion unit to suggest more appropriate ingredients by taking into account the user's past dietary history and allergy information.

[0045] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the user's health goals. The suggestion unit, for example, uses AI to analyze the user's health goals. For example, the suggestion unit can use AI to suggest low-calorie ingredients to a user on a diet. The suggestion unit can also suggest high-protein ingredients to a user aiming to build muscle. The suggestion unit can also suggest balanced ingredients to a user aiming to maintain health. For example, the suggestion unit can use AI to suggest low-calorie ingredients to a user on a diet. The suggestion unit can also suggest high-protein ingredients to a user aiming to build muscle. The suggestion unit can also suggest balanced ingredients to a user aiming to maintain health. This enables the suggestion unit to make optimal suggestions according to the user's health goals.

[0046] When making suggestions, the suggestion unit can determine the priority of suggestions based on the user's current activity status and environment. The suggestion unit, for example, uses AI to analyze the user's current activity status and environment. For example, the suggestion unit can use AI to prioritize suggesting foods that require energy replenishment when the user is exercising. Furthermore, the suggestion unit can prioritize suggesting foods that have a relaxing effect when the user is taking a break. Furthermore, the suggestion unit can prioritize suggesting foods that improve concentration when the user is working. For example, the suggestion unit can use AI to prioritize suggesting foods that require energy replenishment when the user is exercising. Furthermore, the suggestion unit can prioritize suggesting foods that have a relaxing effect when the user is taking a break. Furthermore, the suggestion unit can prioritize suggesting foods that improve concentration when the user is working. This allows the suggestion unit to determine the priority of suggestions based on the user's current activity status and environment, enabling more appropriate suggestions.

[0047] When making a suggestion, the suggestion unit can suggest appropriate ingredients and menus based on the user's geographical location information. The suggestion unit, for example, uses AI to analyze the user's geographical location information. For example, the suggestion unit, using AI, can suggest fresh seafood when the user is at the seaside. Furthermore, the suggestion unit can suggest local vegetables and fruits when the user is in a mountainous area. Furthermore, the suggestion unit can also suggest easily accessible ingredients when the user is in an urban area. For example, the suggestion unit, using AI, can suggest fresh seafood when the user is at the seaside. Furthermore, the suggestion unit can suggest local vegetables and fruits when the user is in a mountainous area. Furthermore, the suggestion unit can suggest easily accessible ingredients when the user is in an urban area. This allows the suggestion unit to suggest more appropriate ingredients and menus by taking the user's geographical location information into consideration.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The health management system can further collect the user's sleep data and analyze the data using an analysis unit. For example, the collection unit monitors the user's sleep time and sleep quality and collects the data. The analysis unit can analyze the collected sleep data and evaluate the user's sleep patterns. Furthermore, the suggestion unit can suggest ingredients and menus to improve sleep quality based on the user's sleep data. This allows the user to choose meals that will help them maintain healthy sleep habits.

[0050] The health management system can further monitor the user's fluid intake and analyze the data using the analysis unit. For example, the collection unit records the amount of fluid intake the user has per day and collects the data. The analysis unit can analyze the collected fluid intake data and evaluate the user's hydration status. Furthermore, the suggestion unit can suggest foods and beverages that promote proper hydration based on the user's fluid intake data. This allows the user to select foods and beverages that will help maintain proper hydration.

[0051] The health management system can further collect the user's body temperature data and analyze the data using the analysis unit. For example, the collection unit periodically measures the user's body temperature and collects the data. The analysis unit can analyze the collected body temperature data and evaluate fluctuations in the user's body temperature. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for regulating body temperature based on the user's body temperature data. This allows the user to select meals that support body temperature management.

[0052] The health management system can further collect the user's blood pressure data and analyze the data using the analysis unit. For example, the collection unit periodically measures the user's blood pressure and collects the data. The analysis unit can analyze the collected blood pressure data and evaluate fluctuations in the user's blood pressure. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for blood pressure management based on the user's blood pressure data. This allows the user to select meals that support blood pressure management.

[0053] The health management system can further collect weight data of the user and analyze the data with the analysis unit. For example, the collection unit can periodically measure the user's weight and collect the data. The analysis unit can also analyze the collected weight data and evaluate fluctuations in the user's weight. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for weight management based on the user's weight data. This allows the user to select meals that support weight management.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection unit collects user data from the wearable device. The collected data includes heart rate, number of steps, calories burned, etc. For example, the collection unit measures the user's heart rate using a heart rate sensor, counts the user's steps using a pedometer, and collects data to monitor the user's activity level and calculate calories burned. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The analysis is performed using AI, for example, analyzing heart rate data to evaluate the user's stress level, analyzing step count data to evaluate the user's exercise volume, and analyzing calorie consumption data to evaluate the user's energy consumption. Step 3: The proposal unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. The proposal is made using AI, for example, by creating ingredients and buffet menus that are optimal for the user's health condition, and providing the menu via the user's smartphone or tablet.

[0056] (Example 2) A health management system according to an embodiment of the present invention utilizes wearable devices and AI technology to propose optimal buffet menus based on individual health conditions. This health management system collects data such as a user's heart rate, steps, and calories burned, and AI analyzes this data to evaluate the user's health condition. For example, a high heart rate may indicate stress, so the system suggests foods with a relaxing effect. Similarly, a high calorie consumption may suggest foods that are necessary for energy replenishment. Furthermore, the AI ​​also takes into account the user's past diet history and allergy information to create individually customized buffet menus. For example, if a user has previously had an allergic reaction to a particular ingredient, the system suggests a menu that does not include that ingredient. Furthermore, if a user needs to increase their intake of a particular nutrient, the system suggests foods that are high in that nutrient. This system allows users to easily select meals that are optimal for their health condition and improve their daily eating habits in a healthy and efficient manner. For example, a user on a diet may be suggested a low-calorie, nutritionally balanced menu, while a user aiming to build muscle may be suggested a high-protein menu. This system is also intended for use in buffet-style restaurants and hotels, allowing users to easily view the menus using their smartphones or tablets. This allows users to choose meals that are optimal for their health condition and maintain healthy eating habits, allowing the health management system to suggest optimal buffet menus based on the user's health condition.

[0057] A health management system according to an embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects user data from a wearable device. The collected data includes, but is not limited to, heart rate, number of steps, and calories burned. For example, the collection unit measures the user's heart rate using a heart rate sensor. The collection unit can also count the user's steps using a pedometer. The collection unit can also monitor the user's activity level to calculate calories burned. For example, the collection unit measures the user's heart rate in real time using a heart rate sensor and collects the data. The collection unit can accurately count the user's steps using a pedometer. The collection unit can monitor the user's activity level and collect data for calculating calories burned. The analysis unit analyzes the data collected by the collection unit to evaluate the user's health condition. The analysis is performed using, for example, AI, but is not limited to, the example. The analysis unit analyzes the collected heart rate data using, for example, AI, to evaluate the user's stress level. The analysis unit can also analyze the collected step count data to evaluate the user's exercise volume. The analysis unit can also analyze the collected calorie consumption data to evaluate the user's energy consumption. For example, the analysis unit can use AI to analyze the collected heart rate data to evaluate the user's stress level. The analysis unit can also analyze the collected step count data to evaluate the user's exercise volume. The analysis unit can also analyze the collected calorie consumption data to evaluate the user's energy consumption. The suggestion unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. The suggestion can be performed, for example, using AI, but is not limited to such an example. The suggestion unit can, for example, use AI to suggest ingredients that are optimal for the user's health condition. The suggestion unit can also create a buffet menu that is optimal for the user's health condition. The suggestion unit can also provide the menu using the user's smartphone or tablet. For example, the suggestion unit can use AI to suggest ingredients that are optimal for the user's health condition.The suggestion unit can also create a buffet menu that is optimal for the user's health condition. Furthermore, the suggestion unit can provide the menu using the user's smartphone or tablet. This allows the health management system according to the embodiment to suggest an optimal buffet menu based on the user's health condition.

[0058] The collection unit can collect data on the user's heart rate, number of steps, and calories burned. The collection unit measures the user's heart rate using, for example, a heart rate sensor. For example, the collection unit measures the user's heart rate in real time using the heart rate sensor and collects the data. The collection unit can also count the user's steps using a pedometer. For example, the collection unit can accurately count the user's steps using the pedometer. Furthermore, the collection unit can monitor the user's activity level to calculate calories burned. For example, the collection unit can monitor the user's activity level and collect data for calculating calories burned. This allows the collection unit to collect data for understanding the user's health condition in detail.

[0059] The analysis unit can evaluate the user's health condition based on the collected data. For example, the analysis unit can use AI to analyze the collected heart rate data and evaluate the user's stress level. For example, the analysis unit can use AI to analyze the collected heart rate data and evaluate the user's stress level. The analysis unit can also analyze the collected step count data and evaluate the user's exercise amount. For example, the analysis unit can analyze the collected step count data and evaluate the user's exercise amount. Furthermore, the analysis unit can analyze the collected calorie consumption data and evaluate the user's energy consumption. For example, the analysis unit can analyze the collected calorie consumption data and evaluate the user's energy consumption. This allows the analysis unit to accurately evaluate the user's health condition based on the collected data.

[0060] The analysis unit can make an evaluation based on the user's past dietary history and allergy information. The analysis unit, for example, evaluates the health state taking into account the user's past dietary history. For example, the analysis unit evaluates the health state taking into account the user's past dietary history. The analysis unit can also evaluate the health state taking into account the user's allergy information. For example, the analysis unit can evaluate the health state taking into account the user's allergy information. This enables the analysis unit to make a more appropriate evaluation of the health state by taking into account the user's past dietary history and allergy information.

[0061] The suggestion unit can suggest ingredients that suit the user's health condition. For example, the suggestion unit uses AI to suggest ingredients that are optimal for the user's health condition. For example, the suggestion unit uses AI to suggest ingredients that are optimal for the user's health condition. The suggestion unit can also take into account the user's past dietary history and allergy information in order to suggest ingredients that are optimal for the user's health condition. For example, the suggestion unit can suggest optimal ingredients by taking into account the user's past dietary history and allergy information. In this way, the suggestion unit can support healthy eating habits by suggesting ingredients that are optimal for the user's health condition.

[0062] The suggestion unit can create a buffet menu that suits the user's health condition. For example, the suggestion unit uses AI to create a buffet menu that is optimal for the user's health condition. For example, the suggestion unit uses AI to create a buffet menu that is optimal for the user's health condition. In addition, the suggestion unit can also take into account the user's past dietary history and allergy information in order to create a buffet menu that is optimal for the user's health condition. For example, the suggestion unit can create an optimal buffet menu by taking into account the user's past dietary history and allergy information. In this way, the suggestion unit can maintain healthy eating habits by creating a buffet menu that is optimal for the user's health condition.

[0063] The suggestion unit can provide a menu using a user's smartphone or tablet. The suggestion unit can provide a menu using a user's smartphone or tablet, for example. For example, the suggestion unit can provide a menu using a user's smartphone or tablet. The suggestion unit can also use a dedicated app to provide a menu using a user's smartphone or tablet. For example, the suggestion unit can display a menu on the user's smartphone or tablet using a dedicated app. This allows the suggestion unit to allow the user to easily check the menu using a smartphone or tablet.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit can, for example, use AI to estimate the user's emotions. The collection unit can also adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can increase the collection timing and collect detailed data. Furthermore, if the user is relaxed, the collection unit can reduce the collection timing and collect the minimum amount of data necessary. Furthermore, if the user is exercising, the collection unit can also collect data during post-exercise recovery time. For example, if the user is exercising, the collection unit can collect data during post-exercise recovery time. This allows the collection unit to adjust the timing of data collection according to the user's emotions, enabling more appropriate data collection.

[0065] The collection unit can analyze the user's past health data and select an appropriate data collection method. The collection unit can, for example, use AI to analyze the user's past health data. For example, the collection unit can use AI to analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past step count data and, if the user's exercise volume is low, increase the frequency of step count collection. The collection unit can also analyze the user's past calorie consumption data and, if the user's calorie consumption volume is high, increase the frequency of meal data collection. For example, the collection unit can analyze the user's past heart rate data and, if an abnormality is found, increase the frequency of heart rate collection. The collection unit can also analyze the user's past step count data and, if the user's exercise volume is low, increase the frequency of step collection. The collection unit can also analyze the user's past calorie consumption data and, if the user's calorie consumption volume is high, increase the frequency of meal data collection. In this way, the collection unit can select an optimal data collection method by analyzing the user's past health data.

[0066] The collection unit can perform filtering based on the user's current activity status and environment when collecting data. The collection unit analyzes the user's current activity status and environment using, for example, AI. For example, the collection unit can use AI to collect only exercise data when the user is exercising and filter other data. Also, the collection unit can collect only heart rate data when the user is resting and filter other data. Furthermore, the collection unit can collect only meal data when the user is eating and filter other data. For example, the collection unit can collect only exercise data when the user is exercising and filter other data. Also, the collection unit can collect only heart rate data when the user is resting and filter other data. Furthermore, the collection unit can collect only meal data when the user is eating and filter other data. In this way, the collection unit can collect only necessary data by filtering data based on the user's current activity status and environment.

[0067] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit can estimate the user's emotions using AI, for example. For example, the collection unit can estimate the user's emotions using AI. The collection unit can also determine the priority of data to be collected based on the estimated user's emotions. For example, the collection unit can prioritize collecting heart rate data when the user is feeling stressed. The collection unit can also prioritize collecting calorie consumption data when the user is relaxed. The collection unit can also prioritize collecting step count data when the user is exercising. For example, the collection unit can prioritize collecting heart rate data when the user is feeling stressed. The collection unit can also prioritize collecting calorie consumption data when the user is relaxed. The collection unit can also prioritize collecting step count data when the user is exercising. In this way, the collection unit can prioritize collecting important data by determining the priority of data to be collected according to the user's emotions.

[0068] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. The collection unit analyzes the user's geographical location information using, for example, AI. For example, the collection unit can prioritize collecting exercise data when the user is in a park using AI. Furthermore, the collection unit can prioritize collecting meal data when the user is in a restaurant. Furthermore, the collection unit can also prioritize collecting heart rate data when the user is at home. For example, the collection unit can prioritize collecting exercise data when the user is in a park. Furthermore, the collection unit can prioritize collecting meal data when the user is in a restaurant. Furthermore, the collection unit can prioritize collecting heart rate data when the user is at home. This allows the collection unit to prioritize collecting highly relevant data in consideration of the user's geographical location information.

[0069] The collection unit can analyze the user's social media activity and collect related data when collecting data. The collection unit can, for example, use AI to analyze the user's social media activity. For example, the collection unit can use AI to collect exercise data when the user posts about exercise on social media. The collection unit can also collect diet data when the user posts about diet on social media. Furthermore, the collection unit can also collect heart rate data when the user posts about stress on social media. For example, the collection unit can collect exercise data when the user posts about exercise on social media. The collection unit can also collect diet data when the user posts about diet on social media. Furthermore, the collection unit can collect heart rate data when the user posts about stress on social media. In this way, the collection unit can collect related data by analyzing the user's social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the evaluation criteria for the health state based on the estimated user's emotions. The analysis unit can, for example, use AI to estimate the user's emotions. The analysis unit can also adjust the evaluation criteria for the health state based on the estimated user's emotions. For example, the analysis unit can tighten the evaluation criteria for heart rate when the user is feeling stressed. The analysis unit can also loosen the evaluation criteria for calories burned when the user is relaxed. The analysis unit can also tighten the evaluation criteria for the number of steps when the user is exercising. For example, the analysis unit can tighten the evaluation criteria for heart rate when the user is feeling stressed. The analysis unit can also loosen the evaluation criteria for calories burned when the user is relaxed. The analysis unit can also tighten the evaluation criteria for the number of steps when the user is exercising. In this way, the analysis unit can adjust the evaluation criteria for the health state according to the user's emotions, thereby enabling a more accurate evaluation.

[0071] During analysis, the analysis unit can improve the accuracy of the evaluation based on the interrelationships of the collected data. The analysis unit, for example, uses AI to analyze the interrelationships of the collected data. For example, the analysis unit can use AI to analyze the correlation between heart rate and calories burned to evaluate the health condition. The analysis unit can also analyze the correlation between the number of steps and heart rate to evaluate the amount of exercise. The analysis unit can also analyze the correlation between calories burned and dietary data to evaluate nutritional balance. For example, the analysis unit can use AI to analyze the correlation between heart rate and calories burned to evaluate the health condition. The analysis unit can also analyze the correlation between the number of steps and heart rate to evaluate the amount of exercise. The analysis unit can also analyze the correlation between calories burned and dietary data to evaluate nutritional balance. In this way, the analysis unit improves the accuracy of the evaluation by taking into account the interrelationships of the collected data.

[0072] During analysis, the analysis unit can evaluate the health state based on the user's lifestyle habits and environmental information. The analysis unit, for example, uses AI to analyze the user's lifestyle habits and environmental information. For example, the analysis unit can use AI to evaluate the health state taking into account the user's sleep patterns. The analysis unit can also evaluate the nutritional balance taking into account the user's eating habits. The analysis unit can also evaluate the amount of exercise taking into account the user's exercise habits. For example, the analysis unit can use AI to evaluate the health state taking into account the user's sleep patterns. The analysis unit can also evaluate the nutritional balance taking into account the user's eating habits. The analysis unit can also evaluate the amount of exercise taking into account the user's exercise habits. This enables the analysis unit to more accurately evaluate the health state by taking into account the user's lifestyle habits and environmental information.

[0073] The analysis unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user's emotions. The analysis unit can estimate the user's emotions using, for example, AI. For example, the analysis unit can estimate the user's emotions using AI. The analysis unit can also adjust the display method of the evaluation results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method including detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the evaluation results according to the user's emotions, thereby enabling more appropriate display.

[0074] During the analysis, the analysis unit can evaluate the health condition based on the geographical distribution of the user. The analysis unit, for example, uses AI to analyze the geographical distribution of the user. For example, the analysis unit can use AI to emphasize the number of steps when evaluating the amount of exercise if the user lives in an urban area. Furthermore, the analysis unit can emphasize farm work data when evaluating calories burned if the user lives in a rural area. Furthermore, the analysis unit can emphasize swimming data when evaluating the heart rate if the user lives by the sea. For example, the analysis unit can use AI to emphasize the number of steps when evaluating the amount of exercise if the user lives in an urban area. Furthermore, the analysis unit can emphasize farm work data when evaluating calories burned if the user lives in a rural area. Furthermore, the analysis unit can emphasize swimming data when evaluating the heart rate if the user lives by the sea. This enables the analysis unit to more accurately evaluate the health condition by taking the geographical distribution of the user into account.

[0075] The analysis unit can improve the accuracy of the evaluation based on related medical data and literature during analysis. The analysis unit, for example, uses AI to refer to related medical data and literature. For example, the analysis unit, using AI, refers to the latest medical literature when evaluating the user's heart rate data. The analysis unit can also refer to related medical data when evaluating the user's calorie consumption data. Furthermore, the analysis unit can also refer to the latest research on exercise when evaluating the user's step count data. For example, the analysis unit, using AI, refers to the latest medical literature when evaluating the user's heart rate data. The analysis unit can also refer to related medical data when evaluating the user's calorie consumption data. Furthermore, the analysis unit can refer to the latest research on exercise when evaluating the user's step count data. In this way, the analysis unit, by referring to related medical data and literature, improves the accuracy of the evaluation.

[0076] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. The suggestion unit can estimate the user's emotions using, for example, AI. For example, the suggestion unit can estimate the user's emotions using AI. The suggestion unit can also adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can make a simple and highly visible suggestion. Furthermore, when the user is relaxed, the suggestion unit can make a suggestion including detailed information. Furthermore, when the user is in a hurry, the suggestion unit can also make a suggestion that focuses on the main points. For example, when the user is feeling stressed, the suggestion unit can make a simple and highly visible suggestion. Furthermore, when the user is relaxed, the suggestion unit can make a suggestion that includes detailed information. Furthermore, when the user is in a hurry, the suggestion unit can make a suggestion that focuses on the main points. In this way, the suggestion unit can adjust the way in which suggestions are expressed based on the user's emotions, thereby enabling more appropriate suggestions.

[0077] When making a suggestion, the suggestion unit can suggest appropriate ingredients based on the user's past dietary history and allergy information. The suggestion unit, for example, uses AI to analyze the user's past dietary history and allergy information. For example, the suggestion unit can use AI to make suggestions that avoid ingredients to which the user has had an allergic reaction in the past. The suggestion unit can also prioritize suggestions of ingredients that the user has previously enjoyed. Furthermore, the suggestion unit can also suggest balanced ingredients by taking into account the nutrients the user has previously taken. For example, the suggestion unit can use AI to make suggestions that avoid ingredients to which the user has had an allergic reaction in the past. The suggestion unit can also prioritize suggestions of ingredients that the user has previously enjoyed. Furthermore, the suggestion unit can suggest balanced ingredients by taking into account the nutrients the user has previously taken. This allows the suggestion unit to suggest more appropriate ingredients by taking into account the user's past dietary history and allergy information.

[0078] When making suggestions, the suggestion unit can apply different suggestion algorithms depending on the user's health goals. The suggestion unit, for example, uses AI to analyze the user's health goals. For example, the suggestion unit can use AI to suggest low-calorie ingredients to a user on a diet. The suggestion unit can also suggest high-protein ingredients to a user aiming to build muscle. The suggestion unit can also suggest balanced ingredients to a user aiming to maintain health. For example, the suggestion unit can use AI to suggest low-calorie ingredients to a user on a diet. The suggestion unit can also suggest high-protein ingredients to a user aiming to build muscle. The suggestion unit can also suggest balanced ingredients to a user aiming to maintain health. This enables the suggestion unit to make optimal suggestions according to the user's health goals.

[0079] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, AI. For example, the suggestion unit can estimate the user's emotion using AI. The suggestion unit can also adjust the length of the suggestion based on the estimated user's emotion. For example, if the user is feeling stressed, the suggestion unit can make a short and to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can make a quick and concise suggestion. For example, if the user is feeling stressed, the suggestion unit can make a short and to-the-point suggestion. Furthermore, if the user is relaxed, the suggestion unit can make a longer suggestion including detailed explanations. Furthermore, if the user is in a hurry, the suggestion unit can make a quick and concise suggestion. In this way, the suggestion unit can adjust the length of the suggestion according to the user's emotion, thereby enabling more appropriate suggestions.

[0080] When making suggestions, the suggestion unit can determine the priority of suggestions based on the user's current activity status and environment. The suggestion unit, for example, uses AI to analyze the user's current activity status and environment. For example, the suggestion unit can use AI to prioritize suggesting foods that require energy replenishment when the user is exercising. Furthermore, the suggestion unit can prioritize suggesting foods that have a relaxing effect when the user is taking a break. Furthermore, the suggestion unit can prioritize suggesting foods that improve concentration when the user is working. For example, the suggestion unit can use AI to prioritize suggesting foods that require energy replenishment when the user is exercising. Furthermore, the suggestion unit can prioritize suggesting foods that have a relaxing effect when the user is taking a break. Furthermore, the suggestion unit can prioritize suggesting foods that improve concentration when the user is working. This allows the suggestion unit to determine the priority of suggestions based on the user's current activity status and environment, enabling more appropriate suggestions.

[0081] When making a suggestion, the suggestion unit can suggest appropriate ingredients and menus based on the user's geographical location information. The suggestion unit, for example, uses AI to analyze the user's geographical location information. For example, the suggestion unit, using AI, can suggest fresh seafood when the user is at the seaside. Furthermore, the suggestion unit can suggest local vegetables and fruits when the user is in a mountainous area. Furthermore, the suggestion unit can also suggest easily accessible ingredients when the user is in an urban area. For example, the suggestion unit, using AI, can suggest fresh seafood when the user is at the seaside. Furthermore, the suggestion unit can suggest local vegetables and fruits when the user is in a mountainous area. Furthermore, the suggestion unit can suggest easily accessible ingredients when the user is in an urban area. This allows the suggestion unit to suggest more appropriate ingredients and menus by taking the user's geographical location information into consideration. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user data using a heart rate sensor or pedometer of the smart device 14 and transmits the collected data to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and evaluates the user's health condition. The suggestion unit, realized, for example, by the specific processing unit 290 of the data processing device 12, suggests an optimal buffet menu based on the analysis results. The suggestion unit, also realized, for example, by the control unit 46A of the smart device 14, can provide the menu via the user's smartphone or tablet. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user data using a heart rate sensor or pedometer in the smart glasses 214 and transmits the data to the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 in the data processing device 12, analyzes the collected data and evaluates the user's health condition. The suggestion unit, realized, for example, by the specific processing unit 290 in the data processing device 12, suggests an optimal buffet menu based on the analysis results. The suggestion unit, also realized, for example, by the control unit 46A of the smart glasses 214, can provide the menu via the user's smartphone or tablet. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects user data using a heart rate sensor or pedometer in the headset-type terminal 314 and transmits the data to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized, for example, by the specific processing unit 290 in the data processing device 12 and suggests an optimal buffet menu based on the analysis results. The suggestion unit is also realized, for example, by the control unit 46A of the headset-type terminal 314 and can provide the menu via the user's smartphone or tablet. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user data using a heart rate sensor or pedometer of the robot 414 and transmits the data to the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to evaluate the user's health condition. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and suggests an optimal buffet menu based on the analysis results. The suggestion unit is also realized, for example, by the control unit 46A of the robot 414 and can provide the menu via the user's smartphone or tablet.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The health management system can further collect the user's sleep data and analyze the data using an analysis unit. For example, the collection unit monitors the user's sleep time and sleep quality and collects the data. The analysis unit can analyze the collected sleep data and evaluate the user's sleep patterns. Furthermore, the suggestion unit can suggest ingredients and menus to improve sleep quality based on the user's sleep data. This allows the user to choose meals that will help them maintain healthy sleep habits.

[0084] The health management system can further monitor the user's fluid intake and analyze the data using the analysis unit. For example, the collection unit records the amount of fluid intake the user has per day and collects the data. The analysis unit can analyze the collected fluid intake data and evaluate the user's hydration status. Furthermore, the suggestion unit can suggest foods and beverages that promote proper hydration based on the user's fluid intake data. This allows the user to select foods and beverages that will help maintain proper hydration.

[0085] The health management system can further collect the user's body temperature data and analyze the data using the analysis unit. For example, the collection unit periodically measures the user's body temperature and collects the data. The analysis unit can analyze the collected body temperature data and evaluate fluctuations in the user's body temperature. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for regulating body temperature based on the user's body temperature data. This allows the user to select meals that support body temperature management.

[0086] The health management system can further collect the user's blood pressure data and analyze the data using the analysis unit. For example, the collection unit periodically measures the user's blood pressure and collects the data. The analysis unit can analyze the collected blood pressure data and evaluate fluctuations in the user's blood pressure. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for blood pressure management based on the user's blood pressure data. This allows the user to select meals that support blood pressure management.

[0087] The health management system can further collect weight data of the user and analyze the data with the analysis unit. For example, the collection unit can periodically measure the user's weight and collect the data. The analysis unit can also analyze the collected weight data and evaluate fluctuations in the user's weight. Furthermore, the suggestion unit can suggest ingredients and menus that are useful for weight management based on the user's weight data. This allows the user to select meals that support weight management.

[0088] The analysis unit can estimate the user's emotions and suggest ingredients for stress reduction based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can suggest ingredients with a relaxing effect. Also, if the user is relaxed, the analysis unit can suggest nutritionally balanced ingredients. Furthermore, if the user is feeling tired, the analysis unit can suggest ingredients that need energy replenishment. This allows the user to select appropriate ingredients according to their emotions.

[0089] The analysis unit can estimate the user's emotions and suggest meal timings based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can suggest meals during times when the user can relax. Also, if the user is relaxed, the analysis unit can suggest meals during times when nutritional supplementation is needed. Furthermore, if the user is feeling tired, the analysis unit can also suggest meals during times when energy supplementation is needed. This allows the user to choose appropriate meal timings according to their emotions.

[0090] The suggestion unit can estimate the user's emotions and adjust the amount of food to be eaten based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest a small but satisfying meal. If the user is relaxed, the suggestion unit can also suggest a normal amount of food. Furthermore, if the user is feeling tired, the suggestion unit can also suggest an amount of food that is sufficient to replenish energy. This allows the user to select an appropriate amount of food according to their emotions.

[0091] The suggestion unit can estimate the user's emotions and suggest a type of meal based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest relaxing herbal tea or light meals. If the user is relaxed, the suggestion unit can suggest a nutritionally balanced main dish. Furthermore, if the user is feeling tired, the suggestion unit can suggest a smoothie or protein bar that will provide an energy replenishment. This allows the user to select an appropriate type of meal according to their emotions.

[0092] The suggestion unit can estimate the user's emotions and adjust the frequency of meals based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can suggest small and frequent meals. Also, if the user is relaxed, the suggestion unit can suggest a normal meal frequency. Furthermore, if the user is feeling tired, the suggestion unit can suggest meals at a frequency that requires energy replenishment. This allows the user to select an appropriate meal frequency according to their emotions.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The collection unit collects user data from the wearable device. The collected data includes heart rate, number of steps, calories burned, etc. For example, the collection unit measures the user's heart rate using a heart rate sensor, counts the user's steps using a pedometer, and collects data to monitor the user's activity level and calculate calories burned. Step 2: The analysis unit analyzes the data collected by the collection unit and evaluates the user's health condition. The analysis is performed using AI, for example, analyzing heart rate data to evaluate the user's stress level, analyzing step count data to evaluate the user's exercise volume, and analyzing calorie consumption data to evaluate the user's energy consumption. Step 3: The proposal unit proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. The proposal is made using AI, for example, by creating ingredients and buffet menus that are optimal for the user's health condition, and providing the menu via the user's smartphone or tablet.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0097] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0098] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0099] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0100] 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.

[0101] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0102] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0103] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0104] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0105] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0106] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0107] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0108] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0109] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0110] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0113] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0114] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0139] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0142] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0143] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0144] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0145] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0146] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0147] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0148] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0149] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0150] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0151] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0152] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0153] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0154] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0155] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0156] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0157] 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.

[0158] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0159] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0160] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0161] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0162] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0163] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0164] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0165] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0166] [Explanation of symbols]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects user data from the wearable device; an analysis unit that analyzes the data collected by the collection unit and evaluates the health condition of the user; a proposal unit that proposes an individually customized buffet menu based on the evaluation results obtained by the analysis unit. A system characterized by:

2. The collecting unit Collecting data on the user's heart rate, steps taken, and calories burned 2. The system of claim 1.

3. The analysis unit Evaluate the user's health status based on collected data 2. The system of claim 1.

4. The analysis unit Evaluate based on the user's past dietary history and allergy information 2. The system of claim 1.

5. The proposal unit Suggesting ingredients that suit the user's health condition 2. The system of claim 1.

6. The proposal unit Create a buffet menu tailored to the user's health condition 2. The system of claim 1.

7. The proposal unit Provide menus using the user's smartphone or tablet 2. The system of claim 1.

8. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions 2. The system of claim 1.

9. The collecting unit Analyze users' past health data and select the appropriate data collection method 2. The system of claim 1.

10. The collecting unit Filtering data collection based on the user's current activity and environment 2. The system of claim 1.

Images