System

Abstract

To provide a system capable of providing health management and an exercise plan optimum for an individual user.SOLUTION: The system includes a body weight / body fat acquisition part, a metabolism calculation part, a calorie / exercise amount calculation part, a voice advice part, a meal management part, an exercise management part, a timer part, a data storage part, and a statistical data creation part. The body weight / body fat acquisition unit acquires body weight and body fat. The metabolism calculation unit calculates basal metabolism and active metabolism. The calorie / exercise amount calculation unit calculates a required calorie intake and a required exercise amount. The audio advice unit provides audio advice. The meal management unit manages meal contents. The exercise manager may manage an exercise menu. The timer unit manages a rest time. The data storage unit stores data. The statistical data creation unit creates statistical data.SELECTED DRAWING: Figure 1

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] With conventional technology, it is difficult to provide optimal health management and exercise plans for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide optimal health management and exercise plans for individual users. [Means for solving the problem]

[0006] The system according to the embodiment includes a weight and body fat acquisition unit, a metabolism calculation unit, a calorie and exercise amount calculation unit, an audio advice unit, a diet management unit, an exercise management unit, a timer unit, a data accumulation unit, and a statistical data creation unit. The weight and body fat acquisition unit acquires weight and body fat. The metabolism calculation unit calculates basal metabolism and active metabolism based on the weight and body fat acquired by the weight and body fat acquisition unit. The calorie and exercise amount calculation unit calculates the required calorie intake and required exercise amount based on the basal metabolism and active metabolism calculated by the metabolism calculation unit. The audio advice unit provides audio advice based on the required calorie intake and required exercise amount calculated by the calorie and exercise amount calculation unit. The diet management unit manages dietary details based on the audio advice provided by the audio advice unit. The exercise management unit manages an exercise menu based on the dietary details managed by the diet management unit. The timer unit manages rest times based on the exercise menu managed by the exercise management unit. The data accumulation unit accumulates data based on the rest times managed by the timer unit. The statistical data creation unit creates statistical data based on the data stored by the data storage unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal health management and exercise plans for individual users. [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) The wearable tool according to an embodiment of the present invention is an ear-worn (ear cuff) device that supports general exercise and physical activity from three perspectives: health management, strength training, and medical care. This allows the wearer's weight, body fat, and physical condition to be communicated via voice every day, automatically calculating basal metabolic rate and activity metabolism. It also automatically calculates the required daily calorie intake and exercise volume to help the wearer achieve their ideal physique. Furthermore, the device's audio advice allows for systematic weight gain and loss, contributing to increased motivation. By inputting the wearer's dietary information via voice, the device automatically calculates calories and PFC balance based on similar menus, supporting dietary fitness. It also features a function to manage the number of repetitions, sets, intensity, and other parameters based on the exercise menu, automatically calculating the exercises to be performed that day. It also has a timer function for rest times, etc., and by accumulating user information, it can compile statistical data on physical characteristics and health status trends, enabling the aggregation of medically useful big data. By using it in medical and nursing care settings, it is expected to contribute to resource issues such as staff shortages, and it can also be deployed to other corporations.

[0029] The wearable tool according to the embodiment includes a weight and body fat acquisition unit, a metabolic calculation unit, a calorie and exercise calculation unit, an audio advice unit, a diet management unit, an exercise management unit, a timer unit, a data accumulation unit, and a statistical data creation unit. The weight and body fat acquisition unit acquires the wearer's weight and body fat. For example, the weight and body fat acquisition unit acquires the wearer's voice input, such as "Today's weight is 70 kg, and my body fat percentage is 20%." The weight and body fat acquisition unit can also automatically acquire the wearer's weight and body fat when the wearer steps on a digital scale. The weight and body fat acquisition unit can also be manually entered by the wearer via a smartphone app. The metabolic calculation unit calculates the basal metabolic rate and active metabolic rate based on the weight and body fat acquired by the weight and body fat acquisition unit. For example, the metabolic calculation unit calculates the basal metabolic rate using the Harris-Benedict equation. The metabolic calculation unit can also calculate the active metabolic rate using METs (Metabolic Equivalents). The metabolic calculation unit can also calculate the metabolic rate taking into account information such as the wearer's age, gender, and height. The calorie and exercise amount calculation unit calculates the required calorie intake and the required amount of exercise based on the basal metabolic rate and active metabolic rate calculated by the metabolic rate calculation unit. For example, when the wearer inputs his / her ideal body type (e.g., weight 65 kg, body fat percentage 15%), the calorie and exercise amount calculation unit calculates the required calorie intake and amount of exercise based on this. The calorie and exercise amount calculation unit can also adjust the required calories and amount of exercise according to the wearer's daily activity level. The calorie and exercise amount calculation unit can also calculate calories and amount of exercise taking into account the wearer's dietary content and exercise history. The audio advice unit provides audio advice based on the required calorie intake and amount of exercise calculated by the calorie and exercise amount calculation unit. For example, the audio advice unit can provide advice such as "Try walking a little more today." The audio advice unit can also provide specific advice such as "Today's calorie intake is 2000 kcal." The audio advice unit can also provide encouraging words to motivate the wearer. The diet management unit manages the diet based on the audio advice provided by the audio advice unit.For example, the diet management unit can manage a wearer's voice input such as "Bread and eggs for breakfast, salad and chicken for lunch." The diet management unit can also manually input dietary information via a smartphone app. The diet management unit can also automatically analyze the wearer's diet and calculate calories and PFC balance. The exercise management unit manages an exercise menu based on the dietary information managed by the diet management unit. For example, the exercise management unit can manage a wearer's voice input such as "I'll do 10 squats, 3 sets today." The exercise management unit can also manually input an exercise menu via a smartphone app. The exercise management unit can also adjust the exercise menu taking into account the wearer's exercise history. The timer unit manages rest times based on the exercise menu managed by the exercise management unit. For example, the timer unit can manage a wearer's voice input such as "I'll take a 5-minute break." The timer unit can also manually set rest times via a smartphone app. The timer unit can also adjust rest times taking into account the wearer's exercise intensity and fatigue level. The data accumulation unit accumulates data based on the rest period managed by the timer unit. For example, the data accumulation unit accumulates data such as the wearer's weight, body fat, exercise history, and dietary content. The data accumulation unit can also record fluctuations in the wearer's health condition and activity level. The data accumulation unit can also store the wearer's data in the cloud so that it can be accessed at any time. The statistical data creation unit creates statistical data based on the data accumulated by the data accumulation unit. For example, the statistical data creation unit analyzes fluctuation data in the wearer's weight and body fat and creates statistical data. The statistical data creation unit can also graph the progress of the wearer's health condition and activity level. The statistical data creation unit can also aggregate medically useful big data based on the wearer's data. As a result, the wearable tool according to the embodiment can support exercise and physical activity in general and achieve a healthy lifestyle. For example, the wearable tool can collect the wearer's weight and body fat every day and calculate the basal metabolic rate and active metabolic rate to help the wearer achieve their ideal body shape.Wearable tools also manage the wearer's diet and exercise routines, providing advice on how to maintain a healthy lifestyle, and collecting data on the wearer to create statistical data that can provide medically useful information.

[0030] The weight and body fat acquisition unit can acquire the wearer's weight and body fat based on voice input. For example, when the wearer vocally inputs, "Today's weight is 70 kg, and my body fat percentage is 20%," the weight and body fat acquisition unit acquires this. In addition, when the wearer vocally instructs, "Please measure my weight," the weight and body fat acquisition unit can also connect to a digital scale to acquire weight and body fat. In addition, when the wearer vocally instructs, "Please tell me my body fat percentage," the weight and body fat acquisition unit can also acquire body fat percentage from past data. This makes it easy to acquire weight and body fat through voice input.

[0031] The metabolic calculation unit can automatically calculate the wearer's basal metabolic rate and activity metabolic rate. The metabolic calculation unit calculates basal metabolic rate using, for example, the Harris-Benedict equation. For example, the basal metabolic rate is calculated by inputting the wearer's weight, height, age, and gender. The metabolic calculation unit also calculates activity metabolic rate using METs (Metabolic Equivalents). For example, the wearer's exercise intensity and exercise duration are input to calculate activity metabolic rate. The metabolic calculation unit can also monitor the wearer's heart rate and respiratory rate and calculate metabolism in real time. This allows the basal metabolic rate and activity metabolic rate to be calculated automatically.

[0032] The calorie and exercise amount calculation unit can calculate the required calorie intake and the required amount of exercise based on the wearer's ideal body type. For example, if the wearer inputs, "My ideal body type is a weight of 65 kg and a body fat percentage of 15%," the calorie and exercise amount calculation unit calculates the required calorie intake and amount of exercise based on this. The calorie and exercise amount calculation unit can also adjust the required calories and amount of exercise according to the wearer's daily activity level. The calorie and exercise amount calculation unit can also calculate the calories and amount of exercise taking into account the wearer's diet and exercise history. This makes it possible to calculate the required calories and amount of exercise based on the wearer's ideal body type.

[0033] The dietary management unit can manage the wearer's dietary information based on voice input. For example, if the wearer inputs "bread and eggs for breakfast, salad and chicken for lunch" by voice, the dietary management unit will manage this. The wearer can also manually input dietary information into the dietary management unit via a smartphone app. The dietary management unit can also automatically analyze the wearer's dietary information and calculate calories and PFC balance. This makes it easy to manage dietary information through voice input.

[0034] The exercise management unit can manage the wearer's exercise menu based on voice input. For example, if the wearer inputs "Today I'll do 10 squats, 3 sets," the exercise management unit manages this. The exercise management unit can also allow the wearer to manually input an exercise menu through a smartphone app. The exercise management unit can also adjust the exercise menu taking into account the wearer's exercise history. This makes it easy to manage the exercise menu through voice input.

[0035] The timer unit can manage the wearer's rest time. For example, if the wearer voice-inputs "5 minutes rest time," the timer unit manages this. The wearer can also manually set the rest time through a smartphone app. The timer unit can also adjust the rest time taking into account the wearer's exercise intensity and fatigue level. This allows for appropriate management of rest time.

[0036] The data storage unit can store data about the wearer. For example, the data storage unit stores data about the wearer, such as weight, body fat, exercise history, and dietary details. The data storage unit can also record fluctuations in the wearer's health condition and activity level. The data storage unit can also store the wearer's data in the cloud and make it accessible at any time. This allows the data to be stored and used for later analysis.

[0037] The statistical data creation unit can create statistical data based on the wearer's data. For example, the statistical data creation unit analyzes data on fluctuations in the wearer's weight and body fat to create statistical data. The statistical data creation unit can also graph the wearer's health status and activity level over time. The statistical data creation unit can also aggregate medically useful big data based on the wearer's data. This allows the creation of statistical data and the provision of medically useful information.

[0038] The system can analyze the sleep patterns of the wearer and evaluate the relationship between sleep quality and health status. For example, the system monitors the wearer's heart rate and breathing rate while sleeping to evaluate sleep quality. For example, it analyzes the ratio of deep sleep to light sleep and quantifies sleep quality. The system also detects the wearer's movements while sleeping to evaluate sleep quality. For example, it analyzes the number of times the wearer turns over in bed and the intensity of the movements to evaluate sleep quality. The system also analyzes the sounds the wearer makes while sleeping to detect snoring and breathing disorders. For example, it analyzes the frequency and volume of snoring to evaluate sleep quality. This makes it possible to evaluate the relationship between sleep quality and health status and use it for health management.

[0039] The system can estimate the wearer's stress level from voice input and provide advice for stress management. For example, the system analyzes the wearer's voice tone and speed to estimate the stress level. For example, it analyzes the pitch and speed of the voice to calculate a stress score. The system also analyzes the content of the wearer's voice to identify the cause of stress. For example, it detects specific keywords or phrases to identify the cause of stress. The system also provides advice for stress management based on the wearer's voice input. For example, it suggests relaxation methods and exercises to relieve stress. This allows the system to estimate the stress level and provide appropriate advice.

[0040] The system can simultaneously manage the health of the wearer's pet and provide advice for maintaining the health of both the wearer and the pet. For example, the system can monitor the wearer's pet's weight and activity level to evaluate the health condition. For example, it can analyze the pet's steps and food intake to calculate a health score. The system can also integrate the health data of the wearer and the pet to provide advice for maintaining the health of both the wearer and the pet. For example, it can suggest exercises and meals to do together. The system can also monitor the wearer's pet's health in real time and issue an alert if it detects an abnormality. For example, it can analyze fluctuations in the pet's body temperature and heart rate to detect abnormalities. This allows the system to provide advice for maintaining the health of both the wearer and the pet.

[0041] The system can integrate the health data of all family members of the wearer and support health management for the entire family. For example, the system can monitor the weight and body fat percentage of all family members and evaluate their health status. For example, it can calculate a health score for each family member and manage it in an integrated manner. The system can also suggest exercise and dietary recommendations based on the health data of each family member. For example, it can provide exercise programs and meal plans that the whole family can participate in. The system can also monitor the health data of all family members in real time and issue alerts when it detects an abnormality. For example, it can notify the family if there is an abnormality in the health of any family member. This can support health management for the entire family.

[0042] The system analyzes in detail the variations in the wearer's muscle mass and fat mass, and can evaluate progress toward an ideal body shape in real time. For example, the system periodically measures the wearer's muscle mass and fat mass and analyzes the variations. For example, it displays a graph of the changes in muscle mass and fat mass from week to week. The system also evaluates progress toward an ideal body shape based on the wearer's muscle mass and fat mass data. For example, it displays the current progress toward the target body shape in numerical form. The system also monitors the variations in the wearer's muscle mass and fat mass in real time and provides advice according to progress. For example, it suggests appropriate training when muscle mass increases. This allows for a detailed analysis of variations in muscle mass and fat mass, and allows progress toward an ideal body shape to be evaluated in real time.

[0043] The system can dynamically adjust an optimal exercise plan based on the wearer's exercise history. For example, the system analyzes the wearer's past exercise history and proposes an optimal exercise plan. For example, the system adjusts an exercise menu based on past training data. The system also monitors the wearer's exercise history in real time and dynamically adjusts the exercise plan. For example, the system changes the intensity and frequency of exercise as appropriate. The system also provides an exercise plan aimed at achieving goals based on the wearer's exercise history. For example, the system proposes a specific training menu to get closer to a target body shape. This allows the system to dynamically adjust an optimal exercise plan based on the exercise history.

[0044] The system can provide body shape management advice tailored to the wearer's fashion style. For example, the system analyzes the wearer's fashion style and provides body shape management advice tailored to that style. For example, the system suggests training to achieve a body shape that suits a particular style. The system also evaluates the wearer's progress toward an ideal body shape based on the wearer's fashion style. For example, the system provides specific advice to get closer to a body shape that suits that style. The system also sets body shape management goals taking into account the wearer's fashion style. For example, the system sets a goal to achieve a body shape that suits a particular style and provides advice to achieve that goal. This makes it possible to provide body shape management advice tailored to a fashion style.

[0045] The system can suggest an exercise plan based on the wearer's hobbies and lifestyle. For example, the system analyzes the wearer's hobbies and lifestyle and suggests an exercise plan based on them. For example, a wearer who likes the outdoors can be suggested to do hiking or running. The system also provides exercise plans that match the wearer's lifestyle. For example, a busy businessman can be suggested a short but effective workout. The system also takes into account the wearer's hobbies and lifestyle and provides an exercise plan that can be enjoyed and continued. For example, a wearer who likes dancing can be suggested dance exercises. In this way, it is possible to suggest an exercise plan based on the wearer's hobbies and lifestyle.

[0046] The system can analyze the wearer's past behavioral data and provide the most effective advice. The system can, for example, analyze the wearer's past exercise history and dietary history and provide the most effective advice. For example, the system can suggest an optimal exercise menu based on past data. The system can also build a system that provides effective advice based on the wearer's past behavioral data. For example, the system can provide advice that references past success stories. The system can also analyze the wearer's past behavioral data in real time and provide effective advice. For example, the system can provide advice that is optimal for the current situation based on past data. This allows the system to analyze past behavioral data and provide the most effective advice.

[0047] The system can provide advice to improve the wearer's performance at work. For example, the system analyzes the wearer's behavioral data at work and provides advice to improve performance. For example, it suggests ways to work efficiently and how to take breaks. The system can also analyze the wearer's stress level at work and provide advice for stress management. For example, it can suggest relaxation methods and exercises to relieve stress. The system can also provide optimal advice based on the wearer's performance data at work. For example, it can provide advice based on past success stories. This makes it possible to provide advice to improve performance at work.

[0048] The system can provide advice to improve the wearer's learning efficiency. For example, the system analyzes the wearer's learning history and provides advice to improve learning efficiency. For example, it suggests effective learning methods and time management. The system also provides an optimal learning plan based on the wearer's learning data. For example, it provides advice based on past learning results. The system also monitors the wearer's learning efficiency in real time and suggests efficient learning methods. For example, it suggests taking a break when concentration is low. In this way, it can provide advice to improve learning efficiency.

[0049] The system can analyze the wearer's dietary history and make suggestions for improving nutritional balance. For example, the system can analyze the wearer's past dietary history and make suggestions for improving nutritional balance. For example, it can propose an optimal meal plan based on past data. The system can also identify areas for improvement in nutritional balance based on the wearer's dietary history. For example, it can suggest ways to supplement a specific nutrient if it is lacking. The system can also analyze the wearer's dietary history in real time and make suggestions for improving nutritional balance. For example, it can adjust nutritional balance based on the current diet. In this way, the system can analyze dietary history and make suggestions for improving nutritional balance.

[0050] The system can provide a meal plan that takes into account the wearer's allergy information. For example, the system provides a meal plan that avoids allergies based on the wearer's allergy information. For example, it may suggest a meal menu that does not contain a specific allergen. The system also analyzes the wearer's allergy information in real time and provides a meal plan that avoids allergies. For example, it may suggest recipes that use ingredients that do not contain allergens. The system also dynamically adjusts the meal plan that avoids allergies based on the wearer's allergy information. For example, it may suggest substitutions for ingredients that contain allergens. This makes it possible to provide a meal plan that takes allergy information into account.

[0051] The system can propose meal plans for the entire family based on the dietary content of the wearer. For example, the system proposes meal plans for the entire family based on the dietary content of the wearer. For example, it provides menus that allow the entire family to eat healthy meals. The system also provides meal plans that take into account the nutritional balance of the entire family based on the dietary data of the wearer. For example, it analyzes the nutrient intake status of each family member and proposes optimal meal menus. The system also analyzes the dietary content of the wearer in real time and dynamically adjusts the meal plans for the entire family. For example, it adjusts the nutritional balance based on the dietary content of the family. This makes it possible to propose meal plans for the entire family.

[0052] The system can suggest recipes based on the dietary content of the wearer. For example, the system analyzes the dietary content of the wearer and suggests recipes based on that. For example, it provides new recipes that match the current dietary content. The system also suggests recipes that take nutritional balance into consideration based on the dietary data of the wearer. For example, it suggests ways to supplement a specific nutrient if there is a deficiency. The system also analyzes the dietary content of the wearer in real time and suggests optimal recipes. For example, it provides recipes that adjust the nutritional balance based on the current dietary content. This makes it possible to suggest recipes based on dietary content.

[0053] The system can analyze the wearer's athletic performance in detail and provide an optimal training plan. For example, the system periodically measures the wearer's athletic performance and analyzes it in detail. For example, it analyzes the intensity and duration of exercise and provides an optimal training plan. The system also provides an optimal training plan based on the wearer's exercise data. For example, it suggests a training menu based on past exercise history. The system also monitors the wearer's athletic performance in real time and provides an optimal training plan. For example, it adjusts the training menu based on the wearer's current exercise status. This allows for a detailed analysis of athletic performance and provides an optimal training plan.

[0054] The system can monitor the wearer's heart rate and breathing rate in real time while exercising and adjust the exercise intensity. For example, the system monitors the wearer's heart rate in real time while exercising and adjusts the exercise intensity. For example, if the heart rate is too high, the system makes a suggestion to lower the exercise intensity. The system also monitors the wearer's breathing rate in real time while exercising and adjusts the exercise intensity. For example, if the breathing rate increases, the system makes a suggestion to lower the exercise intensity. The system also provides the optimal exercise intensity based on the wearer's heart rate and breathing rate. For example, the system analyzes heart rate and breathing rate data and adjusts the exercise intensity. This allows the system to monitor the wearer's heart rate and breathing rate in real time while exercising and adjust the exercise intensity.

[0055] The system can provide an exercise plan for competing with friends and family based on the wearer's exercise history. For example, the system analyzes the wearer's exercise history and provides an exercise plan for competing with friends and family. For example, the system compares exercise results and suggests a plan to enhance competitive spirit. The system also provides an exercise plan to be done together with friends and family based on the wearer's exercise data. For example, the system suggests a training menu for joint training. The system also monitors the wearer's exercise history in real time and dynamically adjusts the exercise plan for competing with friends and family. For example, the system changes the exercise menu according to the progress of the competition. This makes it possible to provide an exercise plan for competing with friends and family.

[0056] The system can suggest participation in sporting events based on the wearer's exercise history. For example, the system analyzes the wearer's exercise history and suggests participation in appropriate sporting events. For example, it suggests participation in a marathon or triathlon based on past exercise data. The system also provides advice to encourage participation in sporting events based on the wearer's exercise data. For example, it explains the benefits of participating in an event based on exercise results. The system also monitors the wearer's exercise history in real time and dynamically suggests participation in appropriate sporting events. For example, it suggests the timing of event participation based on exercise progress. This makes it possible to suggest participation in sporting events.

[0057] The system can analyze the genetic information of the wearer and provide a personalized health management plan. The system, for example, analyzes the genetic information of the wearer and provides a personalized health management plan. For example, it suggests preventive measures based on genetic risk. The system also provides an optimal health management plan based on the genetic information of the wearer. For example, it suggests diet and exercise according to genetic characteristics. The system also analyzes the genetic information of the wearer in real time and dynamically adjusts the personalized health management plan. For example, it provides health management advice according to genetic risk. In this way, it is possible to analyze genetic information and provide a personalized health management plan.

[0058] The system can analyze the lifestyle data of the wearer and predict health risks. For example, the system analyzes the lifestyle data of the wearer and predicts health risks. For example, it calculates a risk score based on diet and exercise data. The system also builds a system for predicting health risks based on the lifestyle data of the wearer. For example, it identifies high-risk behaviors based on past data. The system also analyzes the lifestyle data of the wearer in real time and predicts health risks. For example, it dynamically adjusts the risk score based on current lifestyle habits. This makes it possible to analyze lifestyle data and predict health risks.

[0059] The system can assess the health status of an entire region based on the wearer's health data. For example, the system aggregates the wearer's health data and assesses the health status of the entire region. For example, it calculates a health score for each region and provides statistical data. The system also assesses health risks for the entire region based on the wearer's health data. For example, it identifies health risks in specific regions and suggests preventive measures. The system also analyzes the wearer's health data in real time to dynamically assess the health status of the entire region. For example, it monitors fluctuations in health status by region. This allows the system to assess the health status of the entire region.

[0060] The system can propose a company's health management plan based on the wearer's health data. For example, the system aggregates the wearer's health data and proposes a company's health management plan. For example, it provides a health management plan based on the health status of employees. The system also evaluates the company's health risks based on the wearer's health data and proposes preventive measures. For example, it identifies employee health risks and provides preventive measures. The system also analyzes the wearer's health data in real time and dynamically adjusts the company's health management plan. For example, it provides a health management plan that responds to changes in employee health status. This allows the system to propose a company's health management plan.

[0061] The system can monitor a patient's vital signs in real time and issue an alert if an abnormality is detected. For example, the system can monitor a patient's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. For example, it can notify the patient if the heart rate suddenly increases. The system can also monitor a patient's respiratory rate and body temperature in real time and issue an alert if an abnormality is detected. For example, it can notify the patient if the body temperature becomes abnormally high. The system can also develop an algorithm to detect abnormalities based on the patient's vital signs. For example, it can analyze fluctuations in multiple vital signs and identify abnormalities. This makes it possible to monitor vital signs in real time and issue an alert if an abnormality is detected.

[0062] The system can individualize a patient's rehabilitation plan and monitor progress. For example, the system builds a system that individualizes a patient's rehabilitation plan and monitors progress. For example, the system adjusts the rehabilitation plan based on the patient's exercise data. The system also analyzes the patient's rehabilitation data in real time and monitors progress. For example, the system quantifies the rehabilitation results and evaluates progress. The system also suggests the optimal rehabilitation method based on the patient's rehabilitation plan. For example, the system provides a rehabilitation menu according to the patient's condition. This makes it possible to individualize a rehabilitation plan and monitor progress.

[0063] The system can support telemedicine through the use of devices at medical and nursing care sites. For example, the system builds a system that supports telemedicine through the use of devices at medical and nursing care sites. For example, a patient's vital signs are monitored remotely. The system also uses the devices to collect data for telemedicine and provide it to a doctor. For example, the system transmits patient health data to a doctor in real time. The system also develops a communication system using devices to support telemedicine. For example, a system is provided that allows patients and doctors to communicate remotely. This makes it possible to support telemedicine through the use of devices at medical and nursing care sites.

[0064] The system can share information with a patient's family through the use of devices in medical and nursing care settings. The system, for example, builds a system that shares information with a patient's family through the use of devices in medical and nursing care settings. For example, the system notifies the family of the patient's health condition in real time. The system also uses the device to provide the family with the patient's health data. For example, the system shares the patient's vital signs and rehabilitation progress with the family. The system also develops a communication system using devices to share information with the patient's family. For example, a system is provided that allows the family to check the patient's health condition. This makes it possible to share information with the patient's family through the use of devices in medical and nursing care settings.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] Wearable tools can also analyze the wearer's sleep patterns and evaluate the relationship between sleep quality and health. For example, they can monitor the wearer's heart rate and breathing rate while they sleep, and analyze the proportion of deep and light sleep. They can also detect the wearer's movements while they sleep, analyzing the number of times they turn over and the intensity of their movements. They can also analyze the wearer's sounds while they sleep and detect snoring and irregular breathing. This can help evaluate the relationship between sleep quality and health and be useful for health management.

[0067] Wearable tools can also simultaneously manage the health of the wearer's pet and provide advice on how to maintain the pet's health. For example, they can monitor the wearer's pet's weight and activity level to assess its health. They can also analyze the pet's steps and food intake to calculate a health score. They can also integrate the wearer's and pet's health data to suggest exercise and dietary recommendations for both the wearer and the pet. They can also analyze fluctuations in the pet's body temperature and heart rate and issue alerts if abnormalities are detected. This allows them to provide advice on how to maintain the pet's health.

[0068] Wearable tools can also integrate the health data of all family members wearing the device, supporting health management for the entire family. For example, they can monitor the weight and body fat percentage of each family member and evaluate their health status. They can calculate a health score for each family member and manage the health in an integrated manner. They can also suggest exercise and dietary recommendations based on the health data of each family member. They can also monitor the health data of all family members in real time and issue alerts if an abnormality is detected. This can support health management for the entire family.

[0069] The wearable tool can also perform a detailed analysis of the wearer's muscle and fat mass fluctuations and evaluate progress toward their ideal body shape in real time. For example, it can periodically measure the wearer's muscle and fat mass and display the weekly changes in a graph. It can also evaluate progress toward their ideal body shape based on muscle and fat mass data. It can also suggest appropriate training when muscle mass increases. This allows for a detailed analysis of fluctuations in muscle and fat mass and evaluation of progress toward their ideal body shape in real time.

[0070] Wearable tools can also dynamically adjust the optimal exercise plan based on the wearer's exercise history. For example, they can analyze the wearer's past exercise history and suggest the optimal exercise plan. They can also adjust the exercise menu based on past training data. They can also monitor the wearer's exercise history in real time and dynamically adjust the exercise plan. They can also suggest specific training menus to help people achieve their target body shape. This allows them to dynamically adjust the optimal exercise plan based on their exercise history.

[0071] Wearable tools can also provide body shape management advice tailored to the wearer's fashion style. For example, they can analyze the wearer's fashion style and provide body shape management advice tailored to that style. They can suggest training to achieve a body shape that suits a specific style. They can also evaluate the wearer's progress toward their ideal body shape based on their fashion style. They can also provide specific advice to get closer to a body shape that suits their style. This makes it possible to provide body shape management advice tailored to a fashion style.

[0072] Wearable tools can also suggest exercise plans based on the wearer's hobbies and lifestyle. For example, they can analyze the wearer's hobbies and lifestyle and suggest an exercise plan based on them. For a wearer who likes the outdoors, hiking or running can be suggested. It is also possible to provide exercise plans that match the wearer's lifestyle. Furthermore, they can take the wearer's hobbies and lifestyle into consideration and provide an exercise plan that can be enjoyed and continued. This makes it possible to suggest exercise plans based on hobbies and lifestyle.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The weight and body fat acquisition unit acquires the wearer's weight and body fat. For example, if the wearer verbally inputs, "Today's weight is 70 kg, and the body fat percentage is 20%," this will be acquired. Weight and body fat can also be acquired automatically when the wearer steps on a digital scale. Furthermore, the wearer can also enter the weight and body fat manually via a smartphone app. Step 2: The metabolic calculation unit calculates the basal metabolic rate and the active metabolic rate based on the weight and body fat acquired by the weight and body fat acquisition unit. For example, the basal metabolic rate can be calculated using the Harris-Benedict equation, and the active metabolic rate can be calculated using METs. Metabolism can also be calculated taking into account information such as the wearer's age, gender, and height. Step 3: The calorie and exercise calculation unit calculates the required calorie intake and the required amount of exercise based on the basal metabolic rate and active metabolic rate calculated by the metabolic calculation unit. For example, if the wearer inputs their ideal body type, the required calorie intake and amount of exercise will be calculated based on this. The calorie intake and amount of exercise can also be adjusted taking into account the wearer's daily activity level, dietary content, and exercise history. Step 4: The audio advice unit provides audio advice based on the calorie intake and exercise required calculated by the calorie and exercise amount calculation unit. For example, it provides specific advice such as "Try walking a little more today" or "Today's calorie intake is 2000 kcal." It can also provide words of encouragement to motivate the wearer. Step 5: The dietary management unit manages the dietary content based on the audio advice provided by the audio advice unit. For example, if the wearer inputs "bread and eggs for breakfast, salad and chicken for lunch" by voice, the dietary management unit will manage this. Alternatively, dietary content can be manually input via a smartphone app. Furthermore, the dietary content can be automatically analyzed to calculate calories and PFC balance. Step 6: The exercise management unit manages the exercise menu based on the dietary information managed by the diet management unit. For example, if the wearer inputs "Today I'll do 10 squats, 3 sets," the exercise management unit manages this. The wearer can also manually input the exercise menu through a smartphone app. Furthermore, the exercise menu can be adjusted taking into account the wearer's exercise history. Step 7: The timer unit manages the rest time based on the exercise menu managed by the exercise management unit. For example, if the wearer voice-inputs "Rest time is 5 minutes," the timer unit manages this. The rest time can also be set manually through a smartphone app. Furthermore, the timer unit can adjust the rest time taking into account the wearer's exercise intensity and fatigue level. Step 8: The data storage unit stores data based on the rest time managed by the timer unit. For example, it stores data such as the wearer's weight, body fat, exercise history, and dietary details. It can also record fluctuations in the wearer's health condition and activity level. Furthermore, the wearer's data can be stored in the cloud and accessed at any time. Step 9: The statistical data creation unit creates statistical data based on the data accumulated by the data accumulation unit. For example, it analyzes data on fluctuations in the wearer's weight and body fat and creates statistical data. It can also graph the wearer's health status and activity level over time. It can also aggregate medically useful big data based on the wearer's data.

[0075] (Example 2) The wearable tool according to an embodiment of the present invention is an ear-worn (ear cuff) device that supports general exercise and physical activity from three perspectives: health management, strength training, and medical care. This allows the wearer's weight, body fat, and physical condition to be communicated via voice every day, automatically calculating basal metabolic rate and activity metabolism. It also automatically calculates the required daily calorie intake and exercise volume to help the wearer achieve their ideal physique. Furthermore, the device's audio advice allows for systematic weight gain and loss, contributing to increased motivation. By inputting the wearer's dietary information via voice, the device automatically calculates calories and PFC balance based on similar menus, supporting dietary fitness. It also features a function to manage the number of repetitions, sets, intensity, and other parameters based on the exercise menu, automatically calculating the exercises to be performed that day. It also has a timer function for rest times, etc., and by accumulating user information, it can compile statistical data on physical characteristics and health status trends, enabling the aggregation of medically useful big data. By using it in medical and nursing care settings, it is expected to contribute to resource issues such as staff shortages, and it can also be deployed to other corporations.

[0076] The wearable tool according to the embodiment includes a weight and body fat acquisition unit, a metabolic calculation unit, a calorie and exercise calculation unit, an audio advice unit, a diet management unit, an exercise management unit, a timer unit, a data accumulation unit, and a statistical data creation unit. The weight and body fat acquisition unit acquires the wearer's weight and body fat. For example, the weight and body fat acquisition unit acquires the wearer's voice input, such as "Today's weight is 70 kg, and my body fat percentage is 20%." The weight and body fat acquisition unit can also automatically acquire the wearer's weight and body fat when the wearer steps on a digital scale. The weight and body fat acquisition unit can also be manually entered by the wearer via a smartphone app. The metabolic calculation unit calculates the basal metabolic rate and active metabolic rate based on the weight and body fat acquired by the weight and body fat acquisition unit. For example, the metabolic calculation unit calculates the basal metabolic rate using the Harris-Benedict equation. The metabolic calculation unit can also calculate the active metabolic rate using METs (Metabolic Equivalents). The metabolic calculation unit can also calculate the metabolic rate taking into account information such as the wearer's age, gender, and height. The calorie and exercise amount calculation unit calculates the required calorie intake and the required amount of exercise based on the basal metabolic rate and active metabolic rate calculated by the metabolic rate calculation unit. For example, when the wearer inputs his / her ideal body type (e.g., weight 65 kg, body fat percentage 15%), the calorie and exercise amount calculation unit calculates the required calorie intake and amount of exercise based on this. The calorie and exercise amount calculation unit can also adjust the required calories and amount of exercise according to the wearer's daily activity level. The calorie and exercise amount calculation unit can also calculate calories and amount of exercise taking into account the wearer's dietary content and exercise history. The audio advice unit provides audio advice based on the required calorie intake and amount of exercise calculated by the calorie and exercise amount calculation unit. For example, the audio advice unit can provide advice such as "Try walking a little more today." The audio advice unit can also provide specific advice such as "Today's calorie intake is 2000 kcal." The audio advice unit can also provide encouraging words to motivate the wearer. The diet management unit manages the diet based on the audio advice provided by the audio advice unit.For example, the diet management unit can manage a wearer's voice input such as "Bread and eggs for breakfast, salad and chicken for lunch." The diet management unit can also manually input dietary information via a smartphone app. The diet management unit can also automatically analyze the wearer's diet and calculate calories and PFC balance. The exercise management unit manages an exercise menu based on the dietary information managed by the diet management unit. For example, the exercise management unit can manage a wearer's voice input such as "I'll do 10 squats, 3 sets today." The exercise management unit can also manually input an exercise menu via a smartphone app. The exercise management unit can also adjust the exercise menu taking into account the wearer's exercise history. The timer unit manages rest times based on the exercise menu managed by the exercise management unit. For example, the timer unit can manage a wearer's voice input such as "I'll take a 5-minute break." The timer unit can also manually set rest times via a smartphone app. The timer unit can also adjust rest times taking into account the wearer's exercise intensity and fatigue level. The data accumulation unit accumulates data based on the rest period managed by the timer unit. For example, the data accumulation unit accumulates data such as the wearer's weight, body fat, exercise history, and dietary content. The data accumulation unit can also record fluctuations in the wearer's health condition and activity level. The data accumulation unit can also store the wearer's data in the cloud so that it can be accessed at any time. The statistical data creation unit creates statistical data based on the data accumulated by the data accumulation unit. For example, the statistical data creation unit analyzes fluctuation data in the wearer's weight and body fat and creates statistical data. The statistical data creation unit can also graph the progress of the wearer's health condition and activity level. The statistical data creation unit can also aggregate medically useful big data based on the wearer's data. As a result, the wearable tool according to the embodiment can support exercise and physical activity in general and achieve a healthy lifestyle. For example, the wearable tool can collect the wearer's weight and body fat every day and calculate the basal metabolic rate and active metabolic rate to help the wearer achieve their ideal body shape.Wearable tools also manage the wearer's diet and exercise routines, providing advice on how to maintain a healthy lifestyle, and collecting data on the wearer to create statistical data that can provide medically useful information.

[0077] The weight and body fat acquisition unit can acquire the wearer's weight and body fat based on voice input. For example, when the wearer vocally inputs, "Today's weight is 70 kg, and my body fat percentage is 20%," the weight and body fat acquisition unit acquires this. In addition, when the wearer vocally instructs, "Please measure my weight," the weight and body fat acquisition unit can also connect to a digital scale to acquire weight and body fat. In addition, when the wearer vocally instructs, "Please tell me my body fat percentage," the weight and body fat acquisition unit can also acquire body fat percentage from past data. This makes it easy to acquire weight and body fat through voice input.

[0078] The metabolic calculation unit can automatically calculate the wearer's basal metabolic rate and activity metabolic rate. The metabolic calculation unit calculates basal metabolic rate using, for example, the Harris-Benedict equation. For example, the basal metabolic rate is calculated by inputting the wearer's weight, height, age, and gender. The metabolic calculation unit also calculates activity metabolic rate using METs (Metabolic Equivalents). For example, the wearer's exercise intensity and exercise duration are input to calculate activity metabolic rate. The metabolic calculation unit can also monitor the wearer's heart rate and respiratory rate and calculate metabolism in real time. This allows the basal metabolic rate and activity metabolic rate to be calculated automatically.

[0079] The calorie and exercise amount calculation unit can calculate the required calorie intake and the required amount of exercise based on the wearer's ideal body type. For example, if the wearer inputs, "My ideal body type is a weight of 65 kg and a body fat percentage of 15%," the calorie and exercise amount calculation unit calculates the required calorie intake and amount of exercise based on this. The calorie and exercise amount calculation unit can also adjust the required calories and amount of exercise according to the wearer's daily activity level. The calorie and exercise amount calculation unit can also calculate the calories and amount of exercise taking into account the wearer's diet and exercise history. This makes it possible to calculate the required calories and amount of exercise based on the wearer's ideal body type.

[0080] The audio advice unit can provide audio advice based on the emotional state of the wearer. The audio advice unit, for example, analyzes the tone and speed of the wearer's voice to estimate the emotional state. For example, it analyzes the pitch and speed of the voice to calculate an emotional score. The audio advice unit also provides optimal advice based on the emotional state of the wearer. For example, it suggests relaxation methods when stress levels rise. The audio advice unit also monitors the tone and speed of the wearer's voice in real time to provide advice according to the emotional state. For example, it suggests positive actions when emotions are calm. This makes it possible to provide appropriate advice based on the emotional state.

[0081] The dietary management unit can manage the wearer's dietary information based on voice input. For example, if the wearer inputs "bread and eggs for breakfast, salad and chicken for lunch" by voice, the dietary management unit will manage this. The wearer can also manually input dietary information into the dietary management unit via a smartphone app. The dietary management unit can also automatically analyze the wearer's dietary information and calculate calories and PFC balance. This makes it easy to manage dietary information through voice input.

[0082] The exercise management unit can manage the wearer's exercise menu based on voice input. For example, if the wearer inputs "Today I'll do 10 squats, 3 sets," the exercise management unit manages this. The exercise management unit can also allow the wearer to manually input an exercise menu through a smartphone app. The exercise management unit can also adjust the exercise menu taking into account the wearer's exercise history. This makes it easy to manage the exercise menu through voice input.

[0083] The timer unit can manage the wearer's rest time. For example, if the wearer voice-inputs "5 minutes rest time," the timer unit manages this. The wearer can also manually set the rest time through a smartphone app. The timer unit can also adjust the rest time taking into account the wearer's exercise intensity and fatigue level. This allows for appropriate management of rest time.

[0084] The data storage unit can store data about the wearer. For example, the data storage unit stores data about the wearer, such as weight, body fat, exercise history, and dietary details. The data storage unit can also record fluctuations in the wearer's health condition and activity level. The data storage unit can also store the wearer's data in the cloud and make it accessible at any time. This allows the data to be stored and used for later analysis.

[0085] The statistical data creation unit can create statistical data based on the wearer's data. For example, the statistical data creation unit analyzes data on fluctuations in the wearer's weight and body fat to create statistical data. The statistical data creation unit can also graph the wearer's health status and activity level over time. The statistical data creation unit can also aggregate medically useful big data based on the wearer's data. This allows the creation of statistical data and the provision of medically useful information.

[0086] The system can analyze the sleep patterns of the wearer and evaluate the relationship between sleep quality and health status. For example, the system monitors the wearer's heart rate and breathing rate while sleeping to evaluate sleep quality. For example, it analyzes the ratio of deep sleep to light sleep and quantifies sleep quality. The system also detects the wearer's movements while sleeping to evaluate sleep quality. For example, it analyzes the number of times the wearer turns over in bed and the intensity of the movements to evaluate sleep quality. The system also analyzes the sounds the wearer makes while sleeping to detect snoring and breathing disorders. For example, it analyzes the frequency and volume of snoring to evaluate sleep quality. This makes it possible to evaluate the relationship between sleep quality and health status and use it for health management.

[0087] The system can estimate the wearer's stress level from voice input and provide advice for stress management. For example, the system analyzes the wearer's voice tone and speed to estimate the stress level. For example, it analyzes the pitch and speed of the voice to calculate a stress score. The system also analyzes the content of the wearer's voice to identify the cause of stress. For example, it detects specific keywords or phrases to identify the cause of stress. The system also provides advice for stress management based on the wearer's voice input. For example, it suggests relaxation methods and exercises to relieve stress. This allows the system to estimate the stress level and provide appropriate advice.

[0088] The system can use the emotion estimation function to analyze the emotional state of the wearer and provide health care advice based on the emotion. For example, the system analyzes the wearer's vocal tone and facial expression to estimate the emotional state. For example, it analyzes changes in the pitch of the voice and facial expression to calculate an emotion score. The system also provides health care advice based on the wearer's emotional state. For example, it suggests exercise and diet to maintain positive emotions. The system also monitors the wearer's emotional state in real time and provides advice according to changes in emotion. For example, it suggests relaxation methods when stress increases. This makes it possible to provide appropriate health care advice based on the emotional state.

[0089] The system can simultaneously manage the health of the wearer's pet and provide advice for maintaining the health of both the wearer and the pet. For example, the system can monitor the wearer's pet's weight and activity level to evaluate the health condition. For example, it can analyze the pet's steps and food intake to calculate a health score. The system can also integrate the health data of the wearer and the pet to provide advice for maintaining the health of both the wearer and the pet. For example, it can suggest exercises and meals to do together. The system can also monitor the wearer's pet's health in real time and issue an alert if it detects an abnormality. For example, it can analyze fluctuations in the pet's body temperature and heart rate to detect abnormalities. This allows the system to provide advice for maintaining the health of both the wearer and the pet.

[0090] The system can integrate the health data of all family members of the wearer and support health management for the entire family. For example, the system can monitor the weight and body fat percentage of all family members and evaluate their health status. For example, it can calculate a health score for each family member and manage it in an integrated manner. The system can also suggest exercise and dietary recommendations based on the health data of each family member. For example, it can provide exercise programs and meal plans that the whole family can participate in. The system can also monitor the health data of all family members in real time and issue alerts when it detects an abnormality. For example, it can notify the family if there is an abnormality in the health of any family member. This can support health management for the entire family.

[0091] The system can use the emotion estimation function to automatically play relaxation music according to the wearer's emotions. For example, the system analyzes the wearer's emotional state in real time and automatically plays relaxation music. For example, it plays relaxing music when stress levels rise. The system also selects optimal relaxation music based on the wearer's emotion score. For example, it suggests music to maintain positive emotions. The system also monitors the wearer's emotional state and adjusts the relaxation music according to changes in emotions. For example, it lowers the volume of the music when emotions calm down. This allows the system to automatically play relaxation music according to emotions.

[0092] The system analyzes in detail the variations in the wearer's muscle mass and fat mass, and can evaluate progress toward an ideal body shape in real time. For example, the system periodically measures the wearer's muscle mass and fat mass and analyzes the variations. For example, it displays a graph of the changes in muscle mass and fat mass from week to week. The system also evaluates progress toward an ideal body shape based on the wearer's muscle mass and fat mass data. For example, it displays the current progress toward the target body shape in numerical form. The system also monitors the variations in the wearer's muscle mass and fat mass in real time and provides advice according to progress. For example, it suggests appropriate training when muscle mass increases. This allows for a detailed analysis of variations in muscle mass and fat mass, and allows progress toward an ideal body shape to be evaluated in real time.

[0093] The system can dynamically adjust an optimal exercise plan based on the wearer's exercise history. For example, the system analyzes the wearer's past exercise history and proposes an optimal exercise plan. For example, the system adjusts an exercise menu based on past training data. The system also monitors the wearer's exercise history in real time and dynamically adjusts the exercise plan. For example, the system changes the intensity and frequency of exercise as appropriate. The system also provides an exercise plan aimed at achieving goals based on the wearer's exercise history. For example, the system proposes a specific training menu to get closer to a target body shape. This allows the system to dynamically adjust an optimal exercise plan based on the exercise history.

[0094] The system can use the emotion estimation function to provide personalized messages to maintain the wearer's motivation. For example, the system analyzes the wearer's emotional state and provides messages to maintain motivation. For example, it sends encouraging words that elicit positive emotions. The system also generates personalized messages based on the wearer's emotion score. For example, it sends encouraging messages when the wearer is feeling down. The system also monitors the wearer's emotional state in real time and provides advice to maintain motivation. For example, it suggests specific actions to achieve goals when emotions are high. This makes it possible to provide personalized messages to maintain motivation.

[0095] The system can provide body shape management advice tailored to the wearer's fashion style. For example, the system analyzes the wearer's fashion style and provides body shape management advice tailored to that style. For example, the system suggests training to achieve a body shape that suits a particular style. The system also evaluates the wearer's progress toward an ideal body shape based on the wearer's fashion style. For example, the system provides specific advice to get closer to a body shape that suits that style. The system also sets body shape management goals taking into account the wearer's fashion style. For example, the system sets a goal to achieve a body shape that suits a particular style and provides advice to achieve that goal. This makes it possible to provide body shape management advice tailored to a fashion style.

[0096] The system can suggest an exercise plan based on the wearer's hobbies and lifestyle. For example, the system analyzes the wearer's hobbies and lifestyle and suggests an exercise plan based on them. For example, a wearer who likes the outdoors can be suggested to do hiking or running. The system also provides exercise plans that match the wearer's lifestyle. For example, a busy businessman can be suggested a short but effective workout. The system also takes into account the wearer's hobbies and lifestyle and provides an exercise plan that can be enjoyed and continued. For example, a wearer who likes dancing can be suggested dance exercises. In this way, it is possible to suggest an exercise plan based on the wearer's hobbies and lifestyle.

[0097] The system can use the emotion estimation function to automatically play exercise music that corresponds to the wearer's emotions. For example, the system analyzes the wearer's emotional state and automatically plays the most appropriate music during exercise. For example, it plays up-tempo music when emotions are high. The system also selects exercise music based on the wearer's emotion score. For example, it plays relaxing music when emotions are calm. The system also monitors the wearer's emotional state in real time and dynamically adjusts the exercise music. For example, it changes the music genre or tempo whenever emotions change. This allows the system to automatically play exercise music that corresponds to emotions.

[0098] The system can analyze the wearer's past behavioral data and provide the most effective advice. The system can, for example, analyze the wearer's past exercise history and dietary history and provide the most effective advice. For example, the system can suggest an optimal exercise menu based on past data. The system can also build a system that provides effective advice based on the wearer's past behavioral data. For example, the system can provide advice that references past success stories. The system can also analyze the wearer's past behavioral data in real time and provide effective advice. For example, the system can provide advice that is optimal for the current situation based on past data. This allows the system to analyze past behavioral data and provide the most effective advice.

[0099] The system can analyze the tone and speed of the wearer's voice and provide advice according to their emotional state. For example, the system can analyze the tone and speed of the wearer's voice to estimate their emotional state. For example, it can analyze the pitch and speed of the voice and calculate an emotional score. The system also provides optimal advice based on the wearer's emotional state. For example, it can suggest relaxation methods when stress levels rise. The system also monitors the tone and speed of the wearer's voice in real time and provides advice according to their emotional state. For example, it can suggest positive actions when emotions are calm. This makes it possible to analyze the tone and speed of the voice and provide advice according to their emotional state.

[0100] The system can use the emotion estimation function to provide motivational advice based on the wearer's emotions. For example, the system analyzes the wearer's emotional state and provides advice to improve motivation. For example, it sends encouraging words that elicit positive emotions. The system also generates personalized motivational advice based on the wearer's emotion score. For example, it sends encouraging messages when the wearer is feeling down. The system also monitors the wearer's emotional state in real time and provides advice to improve motivation. For example, it suggests specific actions to achieve goals when emotions are elevated. This makes it possible to provide motivational advice based on emotions.

[0101] The system can provide advice to improve the wearer's performance at work. For example, the system analyzes the wearer's behavioral data at work and provides advice to improve performance. For example, it suggests ways to work efficiently and how to take breaks. The system can also analyze the wearer's stress level at work and provide advice for stress management. For example, it can suggest relaxation methods and exercises to relieve stress. The system can also provide optimal advice based on the wearer's performance data at work. For example, it can provide advice based on past success stories. This makes it possible to provide advice to improve performance at work.

[0102] The system can provide advice to improve the wearer's learning efficiency. For example, the system analyzes the wearer's learning history and provides advice to improve learning efficiency. For example, it suggests effective learning methods and time management. The system also provides an optimal learning plan based on the wearer's learning data. For example, it provides advice based on past learning results. The system also monitors the wearer's learning efficiency in real time and suggests efficient learning methods. For example, it suggests taking a break when concentration is low. In this way, it can provide advice to improve learning efficiency.

[0103] The system can use the emotion estimation function to provide relaxation advice according to the wearer's emotions. For example, the system analyzes the wearer's emotional state and provides relaxation advice. For example, it suggests ways to relax when stress levels rise. The system also suggests optimal relaxation methods based on the wearer's emotion score. For example, it suggests relaxing music when emotions are calm. The system also monitors the wearer's emotional state in real time and provides relaxation advice according to changes in emotions. For example, it suggests relaxation methods when emotions rise. This makes it possible to provide relaxation advice according to emotions.

[0104] The system can analyze the wearer's dietary history and make suggestions for improving nutritional balance. For example, the system can analyze the wearer's past dietary history and make suggestions for improving nutritional balance. For example, it can propose an optimal meal plan based on past data. The system can also identify areas for improvement in nutritional balance based on the wearer's dietary history. For example, it can suggest ways to supplement a specific nutrient if it is lacking. The system can also analyze the wearer's dietary history in real time and make suggestions for improving nutritional balance. For example, it can adjust nutritional balance based on the current diet. In this way, the system can analyze dietary history and make suggestions for improving nutritional balance.

[0105] The system can provide a meal plan that takes into account the wearer's allergy information. For example, the system provides a meal plan that avoids allergies based on the wearer's allergy information. For example, it may suggest a meal menu that does not contain a specific allergen. The system also analyzes the wearer's allergy information in real time and provides a meal plan that avoids allergies. For example, it may suggest recipes that use ingredients that do not contain allergens. The system also dynamically adjusts the meal plan that avoids allergies based on the wearer's allergy information. For example, it may suggest substitutions for ingredients that contain allergens. This makes it possible to provide a meal plan that takes allergy information into account.

[0106] The system can use the emotion estimation function to make meal suggestions based on the wearer's emotions. For example, the system analyzes the wearer's emotional state and makes meal suggestions based on the emotions. For example, it suggests meals that will help you relax when you are stressed. The system also provides an optimal meal plan based on the wearer's emotion score. For example, it suggests meals that will help you maintain positive emotions. The system also monitors the wearer's emotional state in real time and makes meal suggestions based on changes in emotions. For example, it suggests meals that will help you relax when you are emotionally calm. This makes it possible to make meal suggestions based on emotions.

[0107] The system can propose meal plans for the entire family based on the dietary content of the wearer. For example, the system proposes meal plans for the entire family based on the dietary content of the wearer. For example, it provides menus that allow the entire family to eat healthy meals. The system also provides meal plans that take into account the nutritional balance of the entire family based on the dietary data of the wearer. For example, it analyzes the nutrient intake status of each family member and proposes optimal meal menus. The system also analyzes the dietary content of the wearer in real time and dynamically adjusts the meal plans for the entire family. For example, it adjusts the nutritional balance based on the dietary content of the family. This makes it possible to propose meal plans for the entire family.

[0108] The system can suggest recipes based on the dietary content of the wearer. For example, the system analyzes the dietary content of the wearer and suggests recipes based on that. For example, it provides new recipes that match the current dietary content. The system also suggests recipes that take nutritional balance into consideration based on the dietary data of the wearer. For example, it suggests ways to supplement a specific nutrient if there is a deficiency. The system also analyzes the dietary content of the wearer in real time and suggests optimal recipes. For example, it provides recipes that adjust the nutritional balance based on the current dietary content. This makes it possible to suggest recipes based on dietary content.

[0109] The system can use the emotion estimation function to make meal suggestions based on the wearer's emotions. For example, the system analyzes the wearer's emotional state and makes meal suggestions based on the emotions. For example, it can suggest meals that will help you relax when you are stressed. The system also provides an optimal meal plan based on the wearer's emotional score. For example, it can suggest meals that will help you maintain positive emotions. The system also monitors the wearer's emotional state in real time and makes meal suggestions based on changes in emotions. For example, it can suggest meals that will help you relax when you are feeling calm. This makes it possible to make meal suggestions based on emotions.

[0110] The system can analyze the wearer's athletic performance in detail and provide an optimal training plan. For example, the system periodically measures the wearer's athletic performance and analyzes it in detail. For example, it analyzes the intensity and duration of exercise and provides an optimal training plan. The system also provides an optimal training plan based on the wearer's exercise data. For example, it suggests a training menu based on past exercise history. The system also monitors the wearer's athletic performance in real time and provides an optimal training plan. For example, it adjusts the training menu based on the wearer's current exercise status. This allows for a detailed analysis of athletic performance and provides an optimal training plan.

[0111] The system can monitor the wearer's heart rate and breathing rate in real time while exercising and adjust the exercise intensity. For example, the system monitors the wearer's heart rate in real time while exercising and adjusts the exercise intensity. For example, if the heart rate is too high, the system makes a suggestion to lower the exercise intensity. The system also monitors the wearer's breathing rate in real time while exercising and adjusts the exercise intensity. For example, if the breathing rate increases, the system makes a suggestion to lower the exercise intensity. The system also provides the optimal exercise intensity based on the wearer's heart rate and breathing rate. For example, the system analyzes heart rate and breathing rate data and adjusts the exercise intensity. This allows the system to monitor the wearer's heart rate and breathing rate in real time while exercising and adjust the exercise intensity.

[0112] The system can use the emotion estimation function to provide exercise advice based on the wearer's emotions. For example, the system analyzes the wearer's emotional state and provides exercise advice based on the emotions. For example, it suggests exercises that will help you relax when stress levels rise. The system also provides an optimal exercise plan based on the wearer's emotion score. For example, it suggests exercises to maintain positive emotions. The system also monitors the wearer's emotional state in real time and provides exercise advice according to changes in emotions. For example, it suggests exercises that will help you relax when your emotions are calm. This makes it possible to provide exercise advice based on emotions.

[0113] The system can provide an exercise plan for competing with friends and family based on the wearer's exercise history. For example, the system analyzes the wearer's exercise history and provides an exercise plan for competing with friends and family. For example, the system compares exercise results and suggests a plan to enhance competitive spirit. The system also provides an exercise plan to be done together with friends and family based on the wearer's exercise data. For example, the system suggests a training menu for joint training. The system also monitors the wearer's exercise history in real time and dynamically adjusts the exercise plan for competing with friends and family. For example, the system changes the exercise menu according to the progress of the competition. This makes it possible to provide an exercise plan for competing with friends and family.

[0114] The system can suggest participation in sporting events based on the wearer's exercise history. For example, the system analyzes the wearer's exercise history and suggests participation in appropriate sporting events. For example, it suggests participation in a marathon or triathlon based on past exercise data. The system also provides advice to encourage participation in sporting events based on the wearer's exercise data. For example, it explains the benefits of participating in an event based on exercise results. The system also monitors the wearer's exercise history in real time and dynamically suggests participation in appropriate sporting events. For example, it suggests the timing of event participation based on exercise progress. This makes it possible to suggest participation in sporting events.

[0115] The system can use the emotion estimation function to provide exercise advice according to the wearer's emotions. For example, the system analyzes the wearer's emotional state and provides exercise advice based on the emotion. For example, it suggests exercises that will help you relax when stress levels rise. The system also provides an optimal exercise plan based on the wearer's emotion score. For example, it suggests exercises to maintain positive emotions. The system also monitors the wearer's emotional state in real time and provides exercise advice according to changes in emotions. For example, it suggests exercises that will help you relax when your emotions are calm. This makes it possible to provide exercise advice according to emotions.

[0116] The system can analyze the genetic information of the wearer and provide a personalized health management plan. The system, for example, analyzes the genetic information of the wearer and provides a personalized health management plan. For example, it suggests preventive measures based on genetic risk. The system also provides an optimal health management plan based on the genetic information of the wearer. For example, it suggests diet and exercise according to genetic characteristics. The system also analyzes the genetic information of the wearer in real time and dynamically adjusts the personalized health management plan. For example, it provides health management advice according to genetic risk. In this way, it is possible to analyze genetic information and provide a personalized health management plan.

[0117] The system can analyze the lifestyle data of the wearer and predict health risks. For example, the system analyzes the lifestyle data of the wearer and predicts health risks. For example, it calculates a risk score based on diet and exercise data. The system also builds a system for predicting health risks based on the lifestyle data of the wearer. For example, it identifies high-risk behaviors based on past data. The system also analyzes the lifestyle data of the wearer in real time and predicts health risks. For example, it dynamically adjusts the risk score based on current lifestyle habits. This makes it possible to analyze lifestyle data and predict health risks.

[0118] The system can use the emotion estimation function to provide a health management plan that takes into account the emotional state of the wearer. For example, the system analyzes the emotional state of the wearer and provides a health management plan based on the emotion. For example, it suggests ways to relax when stress levels rise. The system also provides an optimal health management plan based on the wearer's emotion score. For example, it provides health management advice to maintain positive emotions. The system also monitors the emotional state of the wearer in real time and provides a health management plan that responds to changes in emotion. For example, it suggests ways to relax when emotions are calm. This makes it possible to provide a health management plan that takes into account the emotional state.

[0119] The system can assess the health status of an entire region based on the wearer's health data. For example, the system aggregates the wearer's health data and assesses the health status of the entire region. For example, it calculates a health score for each region and provides statistical data. The system also assesses health risks for the entire region based on the wearer's health data. For example, it identifies health risks in specific regions and suggests preventive measures. The system also analyzes the wearer's health data in real time to dynamically assess the health status of the entire region. For example, it monitors fluctuations in health status by region. This allows the system to assess the health status of the entire region.

[0120] The system can propose a company's health management plan based on the wearer's health data. For example, the system aggregates the wearer's health data and proposes a company's health management plan. For example, it provides a health management plan based on the health status of employees. The system also evaluates the company's health risks based on the wearer's health data and proposes preventive measures. For example, it identifies employee health risks and provides preventive measures. The system also analyzes the wearer's health data in real time and dynamically adjusts the company's health management plan. For example, it provides a health management plan that responds to changes in employee health status. This allows the system to propose a company's health management plan.

[0121] The system can use the emotion estimation function to provide a health management plan based on the wearer's emotions. For example, the system analyzes the wearer's emotional state and provides a health management plan based on the emotions. For example, it suggests ways to relax when stress levels rise. The system also provides an optimal health management plan based on the wearer's emotion score. For example, it provides health management advice to maintain positive emotions. The system also monitors the wearer's emotional state in real time and provides a health management plan according to changes in emotions. For example, it suggests ways to relax when emotions are calm. In this way, a health management plan based on emotions can be provided.

[0122] The system can monitor a patient's vital signs in real time and issue an alert if an abnormality is detected. For example, the system can monitor a patient's heart rate and blood pressure in real time and issue an alert if an abnormality is detected. For example, it can notify the patient if the heart rate suddenly increases. The system can also monitor a patient's respiratory rate and body temperature in real time and issue an alert if an abnormality is detected. For example, it can notify the patient if the body temperature becomes abnormally high. The system can also develop an algorithm to detect abnormalities based on the patient's vital signs. For example, it can analyze fluctuations in multiple vital signs and identify abnormalities. This makes it possible to monitor vital signs in real time and issue an alert if an abnormality is detected.

[0123] The system can individualize a patient's rehabilitation plan and monitor progress. For example, the system builds a system that individualizes a patient's rehabilitation plan and monitors progress. For example, the system adjusts the rehabilitation plan based on the patient's exercise data. The system also analyzes the patient's rehabilitation data in real time and monitors progress. For example, the system quantifies the rehabilitation results and evaluates progress. The system also suggests the optimal rehabilitation method based on the patient's rehabilitation plan. For example, the system provides a rehabilitation menu according to the patient's condition. This makes it possible to individualize a rehabilitation plan and monitor progress.

[0124] The system can use the emotion estimation function to provide a care plan that takes into account the patient's emotional state. For example, the system analyzes the patient's emotional state and provides a care plan based on the emotion. For example, it suggests ways to help the patient relax when stress levels rise. The system also provides an optimal care plan based on the patient's emotion score. For example, it provides care advice to maintain positive emotions. The system also monitors the patient's emotional state in real time and provides a care plan according to changes in emotion. For example, it suggests ways to help the patient relax when emotions are calm. This makes it possible to provide a care plan that takes into account the patient's emotional state.

[0125] The system can support telemedicine through the use of devices at medical and nursing care sites. For example, the system builds a system that supports telemedicine through the use of devices at medical and nursing care sites. For example, a patient's vital signs are monitored remotely. The system also uses the devices to collect data for telemedicine and provide it to a doctor. For example, the system transmits patient health data to a doctor in real time. The system also develops a communication system using devices to support telemedicine. For example, a system is provided that allows patients and doctors to communicate remotely. This makes it possible to support telemedicine through the use of devices at medical and nursing care sites.

[0126] The system can share information with a patient's family through the use of devices in medical and nursing care settings. The system, for example, builds a system that shares information with a patient's family through the use of devices in medical and nursing care settings. For example, the system notifies the family of the patient's health condition in real time. The system also uses the device to provide the family with the patient's health data. For example, the system shares the patient's vital signs and rehabilitation progress with the family. The system also develops a communication system using devices to share information with the patient's family. For example, a system is provided that allows the family to check the patient's health condition. This makes it possible to share information with the patient's family through the use of devices in medical and nursing care settings.

[0127] The system can use the emotion estimation function to provide a care plan based on the patient's emotions. For example, the system analyzes the patient's emotional state and provides a care plan based on the emotions. For example, it suggests ways to relax when stress levels rise. The system also provides an optimal care plan based on the patient's emotion score. For example, it provides care advice to maintain positive emotions. The system also monitors the patient's emotional state in real time and provides a care plan based on changes in emotions. For example, it suggests ways to relax when emotions are calm. This makes it possible to provide a care plan based on emotions.

[0128] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0129] Wearable tools can also analyze the wearer's sleep patterns and evaluate the relationship between sleep quality and health. For example, they can monitor the wearer's heart rate and breathing rate while they sleep, and analyze the proportion of deep and light sleep. They can also detect the wearer's movements while they sleep, analyzing the number of times they turn over and the intensity of their movements. They can also analyze the wearer's sounds while they sleep and detect snoring and irregular breathing. This can help evaluate the relationship between sleep quality and health and be useful for health management.

[0130] Wearable tools can also estimate the wearer's stress level from voice input and provide advice for stress management. For example, stress levels can be estimated by analyzing the wearer's voice tone and speed. Stress scores can be calculated by analyzing the pitch and speed of the voice. The content of the wearer's voice can also be analyzed to identify the cause of stress. Furthermore, relaxation methods and exercises for stress relief can be suggested. This allows for stress levels to be estimated and appropriate advice to be provided.

[0131] Wearable tools can also simultaneously manage the health of the wearer's pet and provide advice on how to maintain the pet's health. For example, they can monitor the wearer's pet's weight and activity level to assess its health. They can also analyze the pet's steps and food intake to calculate a health score. They can also integrate the wearer's and pet's health data to suggest exercise and dietary recommendations for both the wearer and the pet. They can also analyze fluctuations in the pet's body temperature and heart rate and issue alerts if abnormalities are detected. This allows them to provide advice on how to maintain the pet's health.

[0132] Wearable tools can also integrate the health data of all family members wearing the device, supporting health management for the entire family. For example, they can monitor the weight and body fat percentage of each family member and evaluate their health status. They can calculate a health score for each family member and manage the health in an integrated manner. They can also suggest exercise and dietary recommendations based on the health data of each family member. They can also monitor the health data of all family members in real time and issue alerts if an abnormality is detected. This can support health management for the entire family.

[0133] Wearable tools can also automatically play relaxation music based on the wearer's emotional state. For example, they can analyze the wearer's emotional state in real time and play relaxing music when stress levels rise. They can also select optimal relaxation music based on the wearer's emotional score. Furthermore, they can monitor the wearer's emotional state and adjust the relaxation music according to changes in emotion. This allows them to automatically play relaxation music according to emotions.

[0134] The wearable tool can also perform a detailed analysis of the wearer's muscle and fat mass fluctuations and evaluate progress toward their ideal body shape in real time. For example, it can periodically measure the wearer's muscle and fat mass and display the weekly changes in a graph. It can also evaluate progress toward their ideal body shape based on muscle and fat mass data. It can also suggest appropriate training when muscle mass increases. This allows for a detailed analysis of fluctuations in muscle and fat mass and evaluation of progress toward their ideal body shape in real time.

[0135] Wearable tools can also dynamically adjust the optimal exercise plan based on the wearer's exercise history. For example, they can analyze the wearer's past exercise history and suggest the optimal exercise plan. They can also adjust the exercise menu based on past training data. They can also monitor the wearer's exercise history in real time and dynamically adjust the exercise plan. They can also suggest specific training menus to help people achieve their target body shape. This allows them to dynamically adjust the optimal exercise plan based on their exercise history.

[0136] Wearable tools can also provide personalized messages to keep the wearer motivated. For example, they can analyze the wearer's emotional state and send encouraging words that elicit positive emotions. They can also generate personalized messages based on the wearer's emotional score. Furthermore, they can monitor the wearer's emotional state in real time and provide advice to keep the wearer motivated. This allows them to provide personalized messages to keep the wearer motivated.

[0137] Wearable tools can also provide body shape management advice tailored to the wearer's fashion style. For example, they can analyze the wearer's fashion style and provide body shape management advice tailored to that style. They can suggest training to achieve a body shape that suits a specific style. They can also evaluate the wearer's progress toward their ideal body shape based on their fashion style. They can also provide specific advice to get closer to a body shape that suits their style. This makes it possible to provide body shape management advice tailored to a fashion style.

[0138] Wearable tools can also suggest exercise plans based on the wearer's hobbies and lifestyle. For example, they can analyze the wearer's hobbies and lifestyle and suggest an exercise plan based on them. For a wearer who likes the outdoors, hiking or running can be suggested. It is also possible to provide exercise plans that match the wearer's lifestyle. Furthermore, they can take the wearer's hobbies and lifestyle into consideration and provide an exercise plan that can be enjoyed and continued. This makes it possible to suggest exercise plans based on hobbies and lifestyle.

[0139] The processing flow of the second embodiment will be briefly explained below.

[0140] Step 1: The weight and body fat acquisition unit acquires the wearer's weight and body fat. For example, if the wearer verbally inputs, "Today's weight is 70 kg, and the body fat percentage is 20%," this will be acquired. Weight and body fat can also be acquired automatically when the wearer steps on a digital scale. Furthermore, the wearer can also enter the weight and body fat manually via a smartphone app. Step 2: The metabolic calculation unit calculates the basal metabolic rate and the active metabolic rate based on the weight and body fat acquired by the weight and body fat acquisition unit. For example, the basal metabolic rate can be calculated using the Harris-Benedict equation, and the active metabolic rate can be calculated using METs. Metabolism can also be calculated taking into account information such as the wearer's age, gender, and height. Step 3: The calorie and exercise calculation unit calculates the required calorie intake and the required amount of exercise based on the basal metabolic rate and active metabolic rate calculated by the metabolic calculation unit. For example, if the wearer inputs their ideal body type, the required calorie intake and amount of exercise will be calculated based on this. The calorie intake and amount of exercise can also be adjusted taking into account the wearer's daily activity level, dietary content, and exercise history. Step 4: The audio advice unit provides audio advice based on the calorie intake and exercise required calculated by the calorie and exercise amount calculation unit. For example, it provides specific advice such as "Try walking a little more today" or "Today's calorie intake is 2000 kcal." It can also provide words of encouragement to motivate the wearer. Step 5: The dietary management unit manages the dietary content based on the audio advice provided by the audio advice unit. For example, if the wearer inputs "bread and eggs for breakfast, salad and chicken for lunch" by voice, the dietary management unit will manage this. Alternatively, dietary content can be manually input via a smartphone app. Furthermore, the dietary content can be automatically analyzed to calculate calories and PFC balance. Step 6: The exercise management unit manages the exercise menu based on the dietary information managed by the diet management unit. For example, if the wearer inputs "Today I'll do 10 squats, 3 sets," the exercise management unit manages this. The wearer can also manually input the exercise menu through a smartphone app. Furthermore, the exercise menu can be adjusted taking into account the wearer's exercise history. Step 7: The timer unit manages the rest time based on the exercise menu managed by the exercise management unit. For example, if the wearer voice-inputs "Rest time is 5 minutes," the timer unit manages this. The rest time can also be set manually through a smartphone app. Furthermore, the timer unit can adjust the rest time taking into account the wearer's exercise intensity and fatigue level. Step 8: The data storage unit stores data based on the rest time managed by the timer unit. For example, it stores data such as the wearer's weight, body fat, exercise history, and dietary details. It can also record fluctuations in the wearer's health condition and activity level. Furthermore, the wearer's data can be stored in the cloud and accessed at any time. Step 9: The statistical data creation unit creates statistical data based on the data accumulated by the data accumulation unit. For example, it analyzes data on fluctuations in the wearer's weight and body fat and creates statistical data. It can also graph the wearer's health status and activity level over time. It can also aggregate medically useful big data based on the wearer's data.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0158] 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.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0170] 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.

[0171] 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.

[0172] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0175] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0186] 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.

[0187] 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.

[0188] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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. [Explanation of symbols]

[0208] 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 weight and body fat acquisition unit that acquires weight and body fat; a metabolic rate calculation unit that calculates a basal metabolic rate and an active metabolic rate based on the body weight and the body fat acquired by the body weight / body fat acquisition unit; a calorie / exercise amount calculation unit that calculates a required calorie intake and a required amount of exercise based on the basal metabolism and the activity metabolism calculated by the metabolism calculation unit; a voice advice unit that provides voice advice based on the required calorie intake and the required amount of exercise calculated by the calorie / exercise amount calculation unit; a meal management unit that manages meal contents based on the audio advice provided by the audio advice unit; an exercise management unit that manages an exercise menu based on the meal contents managed by the diet management unit; a timer unit that manages rest periods based on the exercise menu managed by the exercise management unit; a data storage unit that stores data based on the rest time managed by the timer unit; a statistical data creation unit that creates statistical data based on the data stored by the data storage unit. A system characterized by:

2. The weight and body fat acquisition unit Obtaining the wearer's weight and body fat based on voice input 2. The system of claim 1.

3. The metabolic calculation unit Automatically calculates the wearer's basal metabolic rate and activity metabolic rate 2. The system of claim 1.

4. The calorie and exercise amount calculation unit Calculating the required calorie intake and the required amount of exercise based on the wearer's ideal body type 2. The system of claim 1.

5. The audio advice unit Providing said audio advice based on the emotional state of the wearer 2. The system of claim 1.

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