system

Abstract

The system according to the embodiment aims to provide a new dimension of health experience that integrates reality and virtuality. [Solution] A system according to an embodiment includes an exercise unit, a learning unit, and a consultation unit. The exercise unit allows users to participate in group exercise using avatars in a virtual gym. The learning unit acquires healthy recipes based on exercise data obtained by the exercise unit. The consultation unit consults with experts based on recipe information obtained by the learning unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately integrate real and virtual worlds for health management, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a new dimension of health experience that integrates reality and virtuality. [Means for solving the problem]

[0006] The system according to the embodiment includes an exercise unit, a learning unit, and a consultation unit. The exercise unit allows users to participate in group exercise using avatars in a virtual gym. The learning unit acquires healthy recipes based on exercise data obtained by the exercise unit. The consultation unit consults with experts based on recipe information obtained by the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a new dimension of health experience by integrating reality and virtual reality. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A health experience system according to an embodiment of the present invention provides a new level of health experience by integrating reality and the Metaverse. This health experience system allows users to participate in group exercise using an avatar in a virtual gym, learn healthy recipes through virtual cooking, and consult with experts in a meeting space within the Metaverse. Furthermore, the achievements of goals and challenges are displayed on the avatar as special items and badges. Furthermore, by linking with a wearable device, a mechanism is provided in which actual exercise and health status are reflected in the avatar in real time. For example, a user may participate in group exercise using an avatar in a virtual gym, then learn healthy recipes through virtual cooking. Furthermore, consultations with experts are held in a meeting space within the Metaverse, and the achievements of goals and challenges are displayed on the avatar as special items and badges. Furthermore, by linking with a wearable device, a mechanism is provided in which actual exercise and health status are reflected in the avatar in real time. This allows users to enjoy a new health experience that integrates reality and the Metaverse. Through various functions such as the virtual gym, virtual cooking, consultations with experts, and linking with wearable devices, the system raises users' health awareness and supports healthy living. This allows the health experience system to raise the user's health awareness and support a healthy lifestyle.

[0029] A health experience system according to an embodiment includes an exercise unit, a learning unit, and a consultation unit. The exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. The exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. The learning unit acquires healthy recipes based on exercise data obtained by the exercise unit. For example, the learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. For example, the learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. The learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. The consultation unit conducts consultation with an expert based on recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. As a result, the health experience system according to the embodiment can provide a comprehensive health experience by allowing users to exercise in a virtual gym, learn healthy recipes, and consult with experts.

[0030] The exercise unit includes a reflection unit that instantly reflects the movement of the avatar. The reflection unit reflects the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. Thereby, by reflecting the movement of the avatar in real time, the exercise experience of the user is improved.

[0031] The learning unit includes a simulation unit that performs a cooking simulation. The simulation unit performs a cooking simulation. The simulation unit can, for example, perform a cooking simulation. The simulation unit can, for example, perform a cooking simulation. The simulation unit can, for example, perform a cooking simulation. Thereby, by performing a cooking simulation, the user can learn healthy recipes in a practical manner.

[0032] The consultation unit includes a display unit that displays the advice on the avatar as a special item or badge. The display unit displays the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. In this way, displaying the advice as a special item or badge improves the user's motivation.

[0033] The health experience system includes a collection unit that acquires data from the wearable device. The collection unit collects data from the wearable device. The collection unit can, for example, collect data from the wearable device. The collection unit can, for example, collect data from the wearable device. The collection unit can, for example, collect data from the wearable device. In this way, by collecting data from the wearable device, it is possible to understand the actual health condition of the user.

[0034] The health experience system includes a data reflecting unit that applies the collected data to an avatar. The data reflecting unit reflects the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. In this way, by reflecting the collected data in the avatar, the user's health condition is reflected in the avatar in real time.

[0035] The exercise unit can analyze the exercise data and suggest an optimal exercise program based on the user's physical condition or past exercise history. The exercise unit can, for example, analyze the user's heart rate data and suggest an appropriate exercise intensity. The exercise unit can also suggest a new exercise program based on the user's progress, for example, based on the user's past exercise history. The exercise unit can also suggest an exercise program tailored to the user's physical condition, for example, based on the user's physical condition data. This allows for effective exercise by suggesting an optimal exercise program based on the user's physical condition and past exercise history.

[0036] The exercise unit can add a feedback function that instantly corrects the user's posture or movements during exercise. For example, the exercise unit can detect the user's posture using a camera and provide feedback to correct the posture. The exercise unit can also detect the user's movements using a sensor and provide real-time instructions on how to improve the movements. The exercise unit can also analyze the user's exercise form and display real-time corrections to the form. This allows the user to maintain correct exercise form by correcting their posture and movements in real-time.

[0037] The sports club can add a function that allows users to share their exercise data with other users and promote competition and cooperation. For example, the sports club can share a user's exercise data with friends to promote competition. The sports club can also share a user's exercise data with a group to work together to achieve goals. For example, the sports club can share a user's exercise data with a community to create an environment where users can encourage each other. In this way, sharing exercise data promotes competition and cooperation between users.

[0038] The exercise club can add a function to provide the user with dietary and sleep advice based on the exercise data. The exercise club can, for example, propose an appropriate meal plan based on the user's exercise data. The exercise club can also, for example, propose an optimal sleep time based on the user's exercise data. The exercise club can also, for example, propose a nutritionally balanced meal based on the user's exercise data. This allows for comprehensive health management by providing dietary and sleep advice based on the exercise data.

[0039] The learning unit can analyze recipe information and suggest optimal recipes based on the user's dietary history and health condition. The learning unit can suggest nutritionally balanced recipes based on the user's dietary history, for example. The learning unit can also suggest healthy recipes based on the user's health condition, for example. The learning unit can also analyze the user's dietary history and re-suggest recipes that were popular in the past, for example. This allows for effective dietary management by suggesting optimal recipes based on the user's dietary history and health condition.

[0040] The learning unit can add a function to evaluate the nutritional balance of the user in real time based on recipe information. The learning unit, for example, evaluates the nutritional balance of a recipe selected by the user in real time. The learning unit can also evaluate the nutritional balance based on the user's diet history, for example. The learning unit can also evaluate the nutritional balance in real time based on the user's health condition, for example. This enables effective nutritional management by evaluating the nutritional balance in real time based on recipe information.

[0041] The learning unit can add a function to share recipe information with other users and enjoy cooking together. For example, the learning unit can share a recipe selected by a user with friends and enjoy cooking together. The learning unit can also share a recipe selected by a user with a group and cook together. For example, the learning unit can share a recipe selected by a user with a community and share the joy of cooking. In this way, sharing recipe information promotes collaborative cooking among users.

[0042] The learning unit can add a function to automatically generate a shopping list of ingredients for the user based on recipe information. The learning unit automatically generates a shopping list, for example, based on ingredients for a recipe selected by the user. The learning unit can also automatically generate a shopping list of necessary ingredients based on the user's diet history, for example. The learning unit can also automatically generate a shopping list of necessary ingredients based on the user's health condition, for example. In this way, automatic generation of an ingredient shopping list based on recipe information makes shopping more efficient.

[0043] The consultation unit can analyze the consultation history and provide optimal advice based on the content of the user's past consultations. The consultation unit can provide advice for similar consultation content, for example, based on the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide optimal advice. The consultation unit can also suggest areas for improvement, for example, based on the user's past consultation history. This enables effective consultation by providing optimal advice based on the content of the user's past consultations.

[0044] The consultation unit can add a function of evaluating the user's health condition in real time based on the consultation content. The consultation unit evaluates the health condition in real time, for example, based on the user's consultation content. The consultation unit can also evaluate the health condition based on the user's consultation history, for example. The consultation unit can also evaluate the health condition in real time, for example, based on the user's health condition. This enables effective health management by evaluating the health condition in real time based on the consultation content.

[0045] The consultation unit can add a function of sharing the consultation content with other experts and providing advice from multiple perspectives. For example, the consultation unit shares the user's consultation content with other experts and provides advice from multiple perspectives. The consultation unit can also share the user's consultation content with a group and provide optimal advice. For example, the consultation unit can also share the user's consultation content with a community and provide advice from various perspectives. In this way, by sharing the consultation content, advice from multiple perspectives can be provided.

[0046] The consultation unit can add a function to automatically set health goals for the user based on the content of the consultation. The consultation unit automatically sets health goals based on, for example, the content of the consultation with the user. The consultation unit can also set optimal health goals based on, for example, the consultation history of the user. The consultation unit can also automatically set health goals based on, for example, the health condition of the user. This enables effective health management by automatically setting health goals based on the content of the consultation.

[0047] The reflection unit can analyze the avatar's movements and compare them with the user's actual movements to provide optimal feedback. The reflection unit can, for example, detect the avatar's movements with a camera and compare them with the user's actual movements to provide feedback. The reflection unit can also, for example, detect the avatar's movements with a sensor and provide instructions on how to improve the movements in real time. The reflection unit can also, for example, analyze the avatar's movements and display form correction points in real time. In this way, optimal feedback for the user's actual movements can be provided by analyzing the avatar's movements.

[0048] The reflection unit can add a function of evaluating the exercise effect of the user in real time based on the movement of the avatar. The reflection unit evaluates the exercise effect in real time, for example, based on the movement of the avatar. The reflection unit can also analyze the movement of the avatar and evaluate the exercise effect, for example. The reflection unit can also display the exercise effect in real time based on the movement of the avatar. This enables effective exercise management by evaluating the exercise effect in real time based on the movement of the avatar.

[0049] The reflection unit can add a function for sharing the avatar's movements with other users and enjoying exercise together. For example, the reflection unit can share the user's avatar's movements with friends and enjoy exercise together. The reflection unit can also share the user's avatar's movements with a group and exercise together. The reflection unit can also share the user's avatar's movements with a community and share the enjoyment of exercise. In this way, sharing the avatar's movements promotes joint exercise between users.

[0050] The reflection unit can add a function to automatically generate an exercise program for the user based on the movements of the avatar. The reflection unit automatically generates an optimal exercise program based on, for example, the movements of the user's avatar. The reflection unit can also automatically generate an exercise program by analyzing, for example, the movements of the user's avatar. The reflection unit can also automatically generate an individually customized exercise program based on, for example, the movements of the user's avatar. This enables effective exercise management by automatically generating an exercise program based on the movements of the avatar.

[0051] The simulation unit can analyze the cooking simulation and provide optimal feedback based on the user's cooking skills and health condition. The simulation unit, for example, analyzes the user's cooking skills and provides feedback to improve the skills. The simulation unit can also suggest healthy cooking methods based on the user's health condition, for example. The simulation unit can also provide real-time instructions for improvement based on the user's cooking skills, for example. In this way, by analyzing the cooking simulation, optimal feedback can be provided for the user's cooking skills and health condition.

[0052] The simulation unit can add a function to evaluate the nutritional balance of the user in real time based on the cooking simulation. For example, the simulation unit evaluates the nutritional balance of the cooking simulation selected by the user in real time. The simulation unit can also evaluate the nutritional balance based on the user's diet history, for example. The simulation unit can also evaluate the nutritional balance in real time based on the user's health condition, for example. This enables effective nutritional management by evaluating the nutritional balance in real time based on the cooking simulation.

[0053] The simulation unit can add a function for sharing a cooking simulation with other users and enjoying cooking together. For example, the simulation unit allows a user to share a cooking simulation selected by the user with friends and enjoy cooking together. The simulation unit can also share a cooking simulation selected by the user with a group and cook together. For example, the simulation unit can share a cooking simulation selected by the user with a community and share the joy of cooking. This encourages collaborative cooking among users by sharing a cooking simulation.

[0054] The simulation unit can add a function to automatically generate a shopping list of ingredients for the user based on the cooking simulation. The simulation unit automatically generates a shopping list, for example, based on ingredients selected by the user in the cooking simulation. The simulation unit can also automatically generate a shopping list of necessary ingredients based on the user's diet history, for example. The simulation unit can also automatically generate a shopping list of necessary ingredients based on the user's health condition, for example. In this way, by automatically generating an ingredient shopping list based on the cooking simulation, shopping becomes more efficient.

[0055] The display unit can analyze the display of special items and badges and provide an optimal display method based on the user's motivation. The display unit, for example, analyzes the user's motivation and adjusts the display method of special items and badges. The display unit can also optimize the display method of special items and badges based on the user's motivation. The display unit can also evaluate the user's motivation in real time and adjust the display method of special items and badges. In this way, by analyzing the display of special items and badges, an optimal display based on the user's motivation is provided.

[0056] The display unit can be added with a function of evaluating the user's health condition in real time based on the display of special items and badges. The display unit evaluates the user's health condition in real time, for example, based on the display of special items and badges. The display unit can also analyze the display of special items and badges to evaluate the health condition, for example. The display unit can also display the health condition in real time, for example, based on the display of special items and badges. This enables effective health management by evaluating the health condition in real time based on the display of special items and badges.

[0057] The display unit can add a function for sharing the display of special items and badges with other users to promote competition and cooperation. For example, the display unit can share a user's special items and badges with friends to promote competition. The display unit can also share a user's special items and badges with a group to achieve goals together. The display unit can also share a user's special items and badges with a community to create an environment where users can encourage each other. In this way, sharing the display of special items and badges promotes competition and cooperation between users.

[0058] The display unit may add a function to automatically set a health goal for the user based on the display of special items and badges. The display unit may automatically set a health goal based on, for example, the display of special items and badges. The display unit may also analyze the display of special items and badges and set optimal health goals. The display unit may also automatically set individually customized health goals based on, for example, the display of special items and badges. This enables effective health management by automatically setting health goals based on the display of special items and badges.

[0059] The collection unit can analyze the data and provide an optimal data collection method based on the user's health condition and past data. The collection unit, for example, analyzes the user's health condition and proposes an optimal data collection method. The collection unit can also optimize the data collection method based on the user's past data, for example. The collection unit can also evaluate the user's health condition in real time and provide an optimal data collection method. In this way, by analyzing the data, an optimal data collection method based on the user's health condition and past data is provided.

[0060] The collection unit can add a function of evaluating the user's health condition in real time based on the data. The collection unit, for example, evaluates the user's health condition in real time based on the data. The collection unit can also, for example, analyze the data and evaluate the health condition. The collection unit can also, for example, display the health condition in real time based on the data. This enables effective health management by evaluating the health condition in real time based on the data.

[0061] The collection unit can add a function for sharing data with other users and performing collaborative health management. For example, the collection unit can share the user's data with friends and perform collaborative health management. The collection unit can also share the user's data with a group and perform collaborative health management. For example, the collection unit can share the user's data with a community and share the fun of health management. This promotes collaborative health management among users by sharing data.

[0062] The collection unit can add a function to automatically set health goals for the user based on the data. The collection unit, for example, automatically sets health goals based on the data. The collection unit can also, for example, analyze the data and set optimal health goals. The collection unit can also, for example, automatically set individually customized health goals based on the data. This enables effective health management by automatically setting health goals based on the data.

[0063] The data reflection unit can analyze the data and provide an optimal data reflection method based on the user's health condition and past data. The data reflection unit, for example, analyzes the user's health condition and proposes an optimal data reflection method. The data reflection unit can also optimize the data reflection method based on the user's past data. The data reflection unit can also evaluate the user's health condition in real time and provide an optimal data reflection method. In this way, by analyzing the data, an optimal data reflection method based on the user's health condition and past data is provided.

[0064] The data reflecting unit can add a function of evaluating the user's health condition in real time based on the data. The data reflecting unit, for example, evaluates the user's health condition in real time based on the data. The data reflecting unit can also, for example, analyze the data and evaluate the health condition. The data reflecting unit can also, for example, display the health condition in real time based on the data. This enables effective health management by evaluating the health condition in real time based on the data.

[0065] The data reflecting unit can add a function for sharing data with other users and performing collaborative health management. For example, the data reflecting unit can share a user's data with friends and perform collaborative health management. The data reflecting unit can also share a user's data with a group and perform collaborative health management. For example, the data reflecting unit can share a user's data with a community and share the fun of health management. In this way, sharing data promotes collaborative health management among users.

[0066] The data reflecting unit can add a function to automatically set health goals for the user based on the data. The data reflecting unit, for example, automatically sets health goals based on the data. The data reflecting unit can also, for example, analyze the data and set optimal health goals. The data reflecting unit can also, for example, automatically set individually customized health goals based on the data. This enables effective health management by automatically setting health goals based on the data.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The health experience system can include a recovery unit that suggests post-exercise recovery methods based on the user's exercise data. The recovery unit can, for example, suggest stretching or massage methods depending on the user's exercise intensity. The recovery unit can also, for example, analyze the user's post-exercise heart rate and muscle fatigue level and suggest appropriate rest periods and recovery meals. Furthermore, the recovery unit can, for example, track the user's recovery progress based on the user's exercise history and support preparation for the next exercise session. This allows the user to effectively recover after exercise and be better prepared for the next exercise session.

[0069] The sports club can suggest the timing of hydration during exercise based on the user's exercise data. For example, the sports club can monitor the user's heart rate and sweat rate in real time and notify them of the appropriate timing for hydration. The sports club can also adjust the amount and frequency of hydration taking into account the user's exercise intensity and environmental conditions (temperature and humidity). Furthermore, the sports club can analyze the user's hydration patterns based on the user's past exercise data and suggest an optimal hydration plan. This allows the user to hydrate appropriately during exercise and maintain performance.

[0070] The learning unit can suggest food preservation methods and cooking tips based on the user's meal history. For example, the learning unit can suggest ways to preserve ingredients purchased by the user and provide advice on how to keep ingredients fresh. The learning unit can also suggest cooking tips and tricks based on recipes selected by the user. Furthermore, the learning unit can analyze the user's meal history and suggest improved versions of recipes that were popular in the past. This allows the user to avoid wasting ingredients and create more delicious dishes.

[0071] The health experience system may include a nutritional supplementation unit that suggests post-exercise nutritional supplementation based on the user's exercise data. The nutritional supplementation unit may, for example, suggest meals or snacks containing appropriate nutrients based on the user's exercise intensity and calorie expenditure. The nutritional supplementation unit may also provide recipes for protein shakes or recovery drinks based on the user's post-exercise physical condition and goals. Furthermore, the nutritional supplementation unit may analyze the user's past exercise data and create an optimal nutritional supplementation plan. This allows the user to replenish their nutrition appropriately after exercise and promote recovery.

[0072] The health experience system can include a recovery unit that suggests post-exercise recovery methods based on the user's exercise data. The recovery unit can, for example, suggest stretching or massage methods depending on the user's exercise intensity. The recovery unit can also, for example, analyze the user's post-exercise heart rate and muscle fatigue level and suggest appropriate rest periods and recovery meals. Furthermore, the recovery unit can, for example, track the user's recovery progress based on the user's exercise history and support preparation for the next exercise session. This allows the user to effectively recover after exercise and be better prepared for the next exercise session.

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

[0074] Step 1: The sports club allows users to participate in group exercises using avatars in a virtual gym. This allows users to enjoy exercise with other users in a virtual space. Step 2: The learning unit obtains healthy recipes based on the exercise data obtained by the exercise unit, allowing the user to obtain a suitable meal plan based on their own exercise data. Step 3: The consultation unit consults with an expert based on the recipe information obtained by the learning unit. This allows the user to receive advice from the expert and receive specific guidance on how to live a healthier life.

[0075] (Example 2) A health experience system according to an embodiment of the present invention provides a new level of health experience by integrating reality and the Metaverse. This health experience system allows users to participate in group exercise using an avatar in a virtual gym, learn healthy recipes through virtual cooking, and consult with experts in a meeting space within the Metaverse. Furthermore, the achievements of goals and challenges are displayed on the avatar as special items and badges. Furthermore, by linking with a wearable device, a mechanism is provided in which actual exercise and health status are reflected in the avatar in real time. For example, a user may participate in group exercise using an avatar in a virtual gym, then learn healthy recipes through virtual cooking. Furthermore, consultations with experts are held in a meeting space within the Metaverse, and the achievements of goals and challenges are displayed on the avatar as special items and badges. Furthermore, by linking with a wearable device, a mechanism is provided in which actual exercise and health status are reflected in the avatar in real time. This allows users to enjoy a new health experience that integrates reality and the Metaverse. Through various functions such as the virtual gym, virtual cooking, consultations with experts, and linking with wearable devices, the system raises users' health awareness and supports healthy living. This allows the health experience system to raise the user's health awareness and support a healthy lifestyle.

[0076] A health experience system according to an embodiment includes an exercise unit, a learning unit, and a consultation unit. The exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. For example, the exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. The exercise unit allows a user to participate in group exercise using an avatar at a virtual gym. The learning unit acquires healthy recipes based on exercise data obtained by the exercise unit. For example, the learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. For example, the learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. The learning unit can acquire healthy recipes based on the exercise data obtained by the exercise unit. The consultation unit conducts consultation with an expert based on recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. For example, the consultation unit can conduct consultation with an expert based on the recipe information obtained by the learning unit. As a result, the health experience system according to the embodiment can provide a comprehensive health experience by allowing users to exercise in a virtual gym, learn healthy recipes, and consult with experts.

[0077] The exercise unit includes a reflection unit that instantly reflects the movement of the avatar. The reflection unit reflects the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. The reflection unit can, for example, reflect the movement of the avatar in real time. Thereby, by reflecting the movement of the avatar in real time, the exercise experience of the user is improved.

[0078] The learning unit includes a simulation unit that performs a cooking simulation. The simulation unit performs a cooking simulation. The simulation unit can, for example, perform a cooking simulation. The simulation unit can, for example, perform a cooking simulation. The simulation unit can, for example, perform a cooking simulation. Thereby, by performing a cooking simulation, the user can learn healthy recipes in a practical manner.

[0079] The consultation unit includes a display unit that displays the advice on the avatar as a special item or badge. The display unit displays the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. The display unit can, for example, display the advice on the avatar as a special item or badge. In this way, displaying the advice as a special item or badge improves the user's motivation.

[0080] The health experience system includes a collection unit that acquires data from the wearable device. The collection unit collects data from the wearable device. The collection unit can, for example, collect data from the wearable device. The collection unit can, for example, collect data from the wearable device. The collection unit can, for example, collect data from the wearable device. In this way, by collecting data from the wearable device, it is possible to understand the actual health condition of the user.

[0081] The health experience system includes a data reflecting unit that applies the collected data to an avatar. The data reflecting unit reflects the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. The data reflecting unit can, for example, reflect the collected data in the avatar. In this way, by reflecting the collected data in the avatar, the user's health condition is reflected in the avatar in real time.

[0082] The exercise unit can estimate the user's emotions and adjust the intensity or type of exercise based on the estimated user's emotions. For example, if the user is feeling stressed, the exercise unit can suggest yoga or stretching, which has a relaxing effect. Furthermore, for example, if the user is energetic, the exercise unit can suggest high-intensity training. Furthermore, for example, if the user is tired, the exercise unit can suggest light walking or recovery exercise. This allows the user to have a more appropriate exercise experience by adjusting the intensity and type of exercise according to their emotions.

[0083] The exercise unit can analyze the exercise data and suggest an optimal exercise program based on the user's physical condition or past exercise history. The exercise unit can, for example, analyze the user's heart rate data and suggest an appropriate exercise intensity. The exercise unit can also suggest a new exercise program based on the user's progress, for example, based on the user's past exercise history. The exercise unit can also suggest an exercise program tailored to the user's physical condition, for example, based on the user's physical condition data. This allows for effective exercise by suggesting an optimal exercise program based on the user's physical condition and past exercise history.

[0084] The exercise unit can add a feedback function that instantly corrects the user's posture or movements during exercise. For example, the exercise unit can detect the user's posture using a camera and provide feedback to correct the posture. The exercise unit can also detect the user's movements using a sensor and provide real-time instructions on how to improve the movements. The exercise unit can also analyze the user's exercise form and display real-time corrections to the form. This allows the user to maintain correct exercise form by correcting their posture and movements in real-time.

[0085] The exercise unit can estimate the user's emotions and adjust the timing of starting exercise based on the estimated user's emotions. For example, if the user is relaxed, the exercise unit can delay the start of exercise and suggest warm-up exercises. Furthermore, for example, if the user is in a hurry, the exercise unit can enable the user to start exercising immediately. Furthermore, for example, if the user is feeling anxious, the exercise unit can suggest breathing techniques to relax before starting the exercise. In this way, by adjusting the start timing of exercise according to the user's emotions, a more appropriate exercise experience can be provided.

[0086] The sports club can add a function that allows users to share their exercise data with other users and promote competition and cooperation. For example, the sports club can share a user's exercise data with friends to promote competition. The sports club can also share a user's exercise data with a group to work together to achieve goals. For example, the sports club can share a user's exercise data with a community to create an environment where users can encourage each other. In this way, sharing exercise data promotes competition and cooperation between users.

[0087] The exercise club can add a function to provide the user with dietary and sleep advice based on the exercise data. The exercise club can, for example, propose an appropriate meal plan based on the user's exercise data. The exercise club can also, for example, propose an optimal sleep time based on the user's exercise data. The exercise club can also, for example, propose a nutritionally balanced meal based on the user's exercise data. This allows for comprehensive health management by providing dietary and sleep advice based on the exercise data.

[0088] The learning unit can estimate the user's emotions and adjust the difficulty and content of the recipe based on the estimated user's emotions. For example, if the user is relaxed, the learning unit can suggest a more difficult recipe. Furthermore, for example, if the user is feeling stressed, the learning unit can suggest a simple and easy recipe. Furthermore, for example, if the user is having fun, the learning unit can suggest a recipe that allows the user to demonstrate their creativity. In this way, by adjusting the difficulty and content of the recipe according to the user's emotions, more appropriate recipes can be provided.

[0089] The learning unit can analyze recipe information and suggest optimal recipes based on the user's dietary history and health condition. The learning unit can suggest nutritionally balanced recipes based on the user's dietary history, for example. The learning unit can also suggest healthy recipes based on the user's health condition, for example. The learning unit can also analyze the user's dietary history and re-suggest recipes that were popular in the past, for example. This allows for effective dietary management by suggesting optimal recipes based on the user's dietary history and health condition.

[0090] The learning unit can add a function to evaluate the nutritional balance of the user in real time based on recipe information. The learning unit, for example, evaluates the nutritional balance of a recipe selected by the user in real time. The learning unit can also evaluate the nutritional balance based on the user's diet history, for example. The learning unit can also evaluate the nutritional balance in real time based on the user's health condition, for example. This enables effective nutritional management by evaluating the nutritional balance in real time based on recipe information.

[0091] The learning unit can estimate the user's emotions and adjust the recipe display method based on the estimated user emotions. For example, if the user is relaxed, the learning unit can display detailed recipe descriptions. Also, for example, if the user is in a hurry, the learning unit can display concise recipe descriptions. Also, for example, if the user is enjoying themselves, the learning unit can display a visually pleasing recipe. This allows for more appropriate recipe display by adjusting the recipe display method according to the user's emotions.

[0092] The learning unit can add a function to share recipe information with other users and enjoy cooking together. For example, the learning unit can share a recipe selected by a user with friends and enjoy cooking together. The learning unit can also share a recipe selected by a user with a group and cook together. For example, the learning unit can share a recipe selected by a user with a community and share the joy of cooking. In this way, sharing recipe information promotes collaborative cooking among users.

[0093] The learning unit can add a function to automatically generate a shopping list of ingredients for the user based on recipe information. The learning unit automatically generates a shopping list, for example, based on ingredients for a recipe selected by the user. The learning unit can also automatically generate a shopping list of necessary ingredients based on the user's diet history, for example. The learning unit can also automatically generate a shopping list of necessary ingredients based on the user's health condition, for example. In this way, automatic generation of an ingredient shopping list based on recipe information makes shopping more efficient.

[0094] The consultation unit can estimate the user's emotions and adjust the content of the consultation and the way in which advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the consultation unit can provide advice in gentle words. For example, if the user is relaxed, the consultation unit can also provide detailed advice. For example, if the user is in a hurry, the consultation unit can also provide concise and quick advice. In this way, more appropriate advice can be provided by adjusting the content of the consultation and the way in which advice is expressed based on the user's emotions.

[0095] The consultation unit can analyze the consultation history and provide optimal advice based on the content of the user's past consultations. The consultation unit can provide advice for similar consultation content, for example, based on the user's past consultation history. The consultation unit can also analyze the user's past consultation history and provide optimal advice. The consultation unit can also suggest areas for improvement, for example, based on the user's past consultation history. This enables effective consultation by providing optimal advice based on the content of the user's past consultations.

[0096] The consultation unit can add a function of evaluating the user's health condition in real time based on the consultation content. The consultation unit evaluates the health condition in real time, for example, based on the user's consultation content. The consultation unit can also evaluate the health condition based on the user's consultation history, for example. The consultation unit can also evaluate the health condition in real time, for example, based on the user's health condition. This enables effective health management by evaluating the health condition in real time based on the consultation content.

[0097] The consultation unit can estimate the user's emotions and adjust the timing of the consultation based on the estimated user's emotions. For example, if the user is relaxed, the consultation unit delays the timing of the consultation. Furthermore, for example, if the user is in a hurry, the consultation unit can start the consultation quickly. Furthermore, for example, if the user is feeling anxious, the consultation unit can allow time for the user to relax before starting the consultation. In this way, by adjusting the timing of the consultation according to the user's emotions, more appropriate consultation is possible.

[0098] The consultation unit can add a function of sharing the consultation content with other experts and providing advice from multiple perspectives. For example, the consultation unit shares the user's consultation content with other experts and provides advice from multiple perspectives. The consultation unit can also share the user's consultation content with a group and provide optimal advice. For example, the consultation unit can also share the user's consultation content with a community and provide advice from various perspectives. In this way, by sharing the consultation content, advice from multiple perspectives can be provided.

[0099] The consultation unit can add a function to automatically set health goals for the user based on the content of the consultation. The consultation unit automatically sets health goals based on, for example, the content of the consultation with the user. The consultation unit can also set optimal health goals based on, for example, the consultation history of the user. The consultation unit can also automatically set health goals based on, for example, the health condition of the user. This enables effective health management by automatically setting health goals based on the content of the consultation.

[0100] The reflection unit can estimate the user's emotions and adjust the avatar's movements based on the estimated user's emotions. For example, if the user is relaxed, the reflection unit can adjust the avatar's movements to be more relaxed. Also, for example, if the user is energetic, the reflection unit can adjust the avatar's movements to be more active. Also, for example, if the user is tired, the reflection unit can adjust the avatar's movements to be more gentle. In this way, by adjusting the avatar's movements according to the user's emotions, a more appropriate exercise experience can be provided.

[0101] The reflection unit can analyze the avatar's movements and compare them with the user's actual movements to provide optimal feedback. The reflection unit can, for example, detect the avatar's movements with a camera and compare them with the user's actual movements to provide feedback. The reflection unit can also, for example, detect the avatar's movements with a sensor and provide instructions on how to improve the movements in real time. The reflection unit can also, for example, analyze the avatar's movements and display form correction points in real time. In this way, optimal feedback for the user's actual movements can be provided by analyzing the avatar's movements.

[0102] The reflection unit can add a function of evaluating the exercise effect of the user in real time based on the movement of the avatar. The reflection unit evaluates the exercise effect in real time, for example, based on the movement of the avatar. The reflection unit can also analyze the movement of the avatar and evaluate the exercise effect, for example. The reflection unit can also display the exercise effect in real time based on the movement of the avatar. This enables effective exercise management by evaluating the exercise effect in real time based on the movement of the avatar.

[0103] The reflection unit can estimate the user's emotions and adjust the avatar's facial expressions and movements based on the estimated user emotions. For example, if the user is relaxed, the reflection unit can calm the avatar's facial expressions and adjust its movements to be relaxed. Furthermore, for example, if the user is energetic, the reflection unit can also adjust the avatar's facial expressions to be lively and its movements to be powerful. Furthermore, for example, if the user is tired, the reflection unit can calm the avatar's facial expressions and adjust its movements to be gentle. In this way, by adjusting the avatar's facial expressions and movements according to the user's emotions, more appropriate avatar expression is possible.

[0104] The reflection unit can add a function for sharing the avatar's movements with other users and enjoying exercise together. For example, the reflection unit can share the user's avatar's movements with friends and enjoy exercise together. The reflection unit can also share the user's avatar's movements with a group and exercise together. The reflection unit can also share the user's avatar's movements with a community and share the enjoyment of exercise. In this way, sharing the avatar's movements promotes joint exercise between users.

[0105] The reflection unit can add a function to automatically generate an exercise program for the user based on the movements of the avatar. The reflection unit automatically generates an optimal exercise program based on, for example, the movements of the user's avatar. The reflection unit can also automatically generate an exercise program by analyzing, for example, the movements of the user's avatar. The reflection unit can also automatically generate an individually customized exercise program based on, for example, the movements of the user's avatar. This enables effective exercise management by automatically generating an exercise program based on the movements of the avatar.

[0106] The simulation unit can estimate the user's emotions and adjust the difficulty and content of the cooking simulation based on the estimated user's emotions. For example, if the user is relaxed, the simulation unit can suggest a cooking simulation with a high level of difficulty. Furthermore, for example, if the user is feeling stressed, the simulation unit can suggest a cooking simulation that is easy and simple to make. Furthermore, for example, if the user is enjoying themselves, the simulation unit can suggest a cooking simulation that allows the user to demonstrate their creativity. In this way, by adjusting the difficulty and content of the cooking simulation according to the user's emotions, a more appropriate cooking experience can be provided.

[0107] The simulation unit can analyze the cooking simulation and provide optimal feedback based on the user's cooking skills and health condition. The simulation unit, for example, analyzes the user's cooking skills and provides feedback to improve the skills. The simulation unit can also suggest healthy cooking methods based on the user's health condition, for example. The simulation unit can also provide real-time instructions for improvement based on the user's cooking skills, for example. In this way, by analyzing the cooking simulation, optimal feedback can be provided for the user's cooking skills and health condition.

[0108] The simulation unit can add a function to evaluate the nutritional balance of the user in real time based on the cooking simulation. For example, the simulation unit evaluates the nutritional balance of the cooking simulation selected by the user in real time. The simulation unit can also evaluate the nutritional balance based on the user's diet history, for example. The simulation unit can also evaluate the nutritional balance in real time based on the user's health condition, for example. This enables effective nutritional management by evaluating the nutritional balance in real time based on the cooking simulation.

[0109] The simulation unit can estimate the user's emotions and adjust the display method of the cooking simulation based on the estimated user's emotions. For example, the simulation unit can display detailed simulation instructions when the user is relaxed. For example, the simulation unit can display concise simulation instructions when the user is in a hurry. For example, the simulation unit can display a visually pleasing simulation when the user is having fun. In this way, by adjusting the display method of the cooking simulation according to the user's emotions, a more appropriate cooking experience can be provided.

[0110] The simulation unit can add a function for sharing a cooking simulation with other users and enjoying cooking together. For example, the simulation unit allows a user to share a cooking simulation selected by the user with friends and enjoy cooking together. The simulation unit can also share a cooking simulation selected by the user with a group and cook together. For example, the simulation unit can share a cooking simulation selected by the user with a community and share the joy of cooking. This encourages collaborative cooking among users by sharing a cooking simulation.

[0111] The simulation unit can add a function to automatically generate a shopping list of ingredients for the user based on the cooking simulation. The simulation unit automatically generates a shopping list, for example, based on ingredients selected by the user in the cooking simulation. The simulation unit can also automatically generate a shopping list of necessary ingredients based on the user's diet history, for example. The simulation unit can also automatically generate a shopping list of necessary ingredients based on the user's health condition, for example. In this way, by automatically generating an ingredient shopping list based on the cooking simulation, shopping becomes more efficient.

[0112] The display unit can estimate the user's emotions and adjust the display method of the special items and badges based on the estimated user's emotions. For example, if the user is relaxed, the display unit can display the special items and badges calmly. Also, for example, if the user is energetic, the display unit can display the special items and badges actively. Also, for example, if the user is tired, the display unit can display the special items and badges calmly. In this way, by adjusting the display method of the special items and badges according to the user's emotions, more appropriate display is possible.

[0113] The display unit can analyze the display of special items and badges and provide an optimal display method based on the user's motivation. The display unit, for example, analyzes the user's motivation and adjusts the display method of special items and badges. The display unit can also optimize the display method of special items and badges based on the user's motivation. The display unit can also evaluate the user's motivation in real time and adjust the display method of special items and badges. In this way, by analyzing the display of special items and badges, an optimal display based on the user's motivation is provided.

[0114] The display unit can be added with a function of evaluating the user's health condition in real time based on the display of special items and badges. The display unit evaluates the user's health condition in real time, for example, based on the display of special items and badges. The display unit can also analyze the display of special items and badges to evaluate the health condition, for example. The display unit can also display the health condition in real time, for example, based on the display of special items and badges. This enables effective health management by evaluating the health condition in real time based on the display of special items and badges.

[0115] The display unit can estimate the user's emotions and adjust the conditions for obtaining special items and badges based on the estimated user's emotions. For example, the display unit can relax the conditions for obtaining special items and badges when the user is relaxed. The display unit can also tighten the conditions for obtaining special items and badges when the user is energetic. The display unit can also relax the conditions for obtaining special items and badges when the user is tired. In this way, by adjusting the conditions for obtaining special items and badges according to the user's emotions, more appropriate acquisition conditions can be provided.

[0116] The display unit can add a function for sharing the display of special items and badges with other users to promote competition and cooperation. For example, the display unit can share a user's special items and badges with friends to promote competition. The display unit can also share a user's special items and badges with a group to achieve goals together. The display unit can also share a user's special items and badges with a community to create an environment where users can encourage each other. In this way, sharing the display of special items and badges promotes competition and cooperation between users.

[0117] The display unit may add a function to automatically set a health goal for the user based on the display of special items and badges. The display unit may automatically set a health goal based on, for example, the display of special items and badges. The display unit may also analyze the display of special items and badges and set optimal health goals. The display unit may also automatically set individually customized health goals based on, for example, the display of special items and badges. This enables effective health management by automatically setting health goals based on the display of special items and badges.

[0118] The collection unit can estimate the user's emotions and adjust the timing of data collection from the wearable device based on the estimated user emotions. For example, the collection unit can reduce the frequency of data collection when the user is relaxed. Also, for example, the collection unit can increase the frequency of data collection when the user is energetic. Also, for example, the collection unit can reduce the frequency of data collection when the user is tired. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions.

[0119] The collection unit can analyze the data and provide an optimal data collection method based on the user's health condition and past data. The collection unit, for example, analyzes the user's health condition and proposes an optimal data collection method. The collection unit can also optimize the data collection method based on the user's past data, for example. The collection unit can also evaluate the user's health condition in real time and provide an optimal data collection method. In this way, by analyzing the data, an optimal data collection method based on the user's health condition and past data is provided.

[0120] The collection unit can add a function of evaluating the user's health condition in real time based on the data. The collection unit, for example, evaluates the user's health condition in real time based on the data. The collection unit can also, for example, analyze the data and evaluate the health condition. The collection unit can also, for example, display the health condition in real time based on the data. This enables effective health management by evaluating the health condition in real time based on the data.

[0121] The collection unit can estimate the user's emotions and adjust the types of data to be collected based on the estimated user's emotions. For example, the collection unit can reduce the types of data to be collected when the user is relaxed. Also, for example, the collection unit can increase the types of data to be collected when the user is energetic. Also, for example, the collection unit can reduce the types of data to be collected when the user is tired. In this way, by adjusting the types of data to be collected according to the user's emotions, more appropriate data collection is possible.

[0122] The collection unit can add a function for sharing data with other users and performing collaborative health management. For example, the collection unit can share the user's data with friends and perform collaborative health management. The collection unit can also share the user's data with a group and perform collaborative health management. For example, the collection unit can share the user's data with a community and share the fun of health management. This promotes collaborative health management among users by sharing data.

[0123] The collection unit can add a function to automatically set health goals for the user based on the data. The collection unit, for example, automatically sets health goals based on the data. The collection unit can also, for example, analyze the data and set optimal health goals. The collection unit can also, for example, automatically set individually customized health goals based on the data. This enables effective health management by automatically setting health goals based on the data.

[0124] The data reflection unit can estimate the user's emotions and adjust the method of reflecting data to the avatar based on the estimated user's emotions. For example, if the user is relaxed, the data reflection unit can gently reflect data to the avatar. Also, for example, if the user is energetic, the data reflection unit can actively reflect data to the avatar. Also, for example, if the user is tired, the data reflection unit can gently reflect data to the avatar. This allows for more appropriate data reflection by adjusting the data reflection method according to the user's emotions.

[0125] The data reflection unit can analyze the data and provide an optimal data reflection method based on the user's health condition and past data. The data reflection unit, for example, analyzes the user's health condition and proposes an optimal data reflection method. The data reflection unit can also optimize the data reflection method based on the user's past data. The data reflection unit can also evaluate the user's health condition in real time and provide an optimal data reflection method. In this way, by analyzing the data, an optimal data reflection method based on the user's health condition and past data is provided.

[0126] The data reflecting unit can add a function of evaluating the user's health condition in real time based on the data. The data reflecting unit, for example, evaluates the user's health condition in real time based on the data. The data reflecting unit can also, for example, analyze the data and evaluate the health condition. The data reflecting unit can also, for example, display the health condition in real time based on the data. This enables effective health management by evaluating the health condition in real time based on the data.

[0127] The data reflection unit can estimate the user's emotions and adjust the timing of reflecting data to the avatar based on the estimated user's emotions. For example, the data reflection unit can delay the timing of reflecting data when the user is relaxed. Furthermore, for example, the data reflection unit can advance the timing of reflecting data when the user is energetic. Furthermore, for example, the data reflection unit can delay the timing of reflecting data when the user is tired. This allows for more appropriate data reflection by adjusting the timing of reflecting data according to the user's emotions.

[0128] The data reflecting unit can add a function for sharing data with other users and performing collaborative health management. For example, the data reflecting unit can share a user's data with friends and perform collaborative health management. The data reflecting unit can also share a user's data with a group and perform collaborative health management. For example, the data reflecting unit can share a user's data with a community and share the fun of health management. In this way, sharing data promotes collaborative health management among users.

[0129] The data reflecting unit can add a function to automatically set health goals for the user based on the data. The data reflecting unit, for example, automatically sets health goals based on the data. The data reflecting unit can also, for example, analyze the data and set optimal health goals. The data reflecting unit can also, for example, automatically set individually customized health goals based on the data. This enables effective health management by automatically setting health goals based on the data. === Hard Collateral 1-1 === Each of the multiple elements, including the exercise unit, learning unit, consultation unit, reflection unit, simulation unit, display unit, collection unit, and data reflection unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the exercise unit is realized by the control unit 46A of the smart device 14, allowing a user to participate in group exercise using an avatar in a virtual gym. The learning unit is realized by the specific processing unit 290 of the data processing device 12, obtaining healthy recipes based on exercise data. The consultation unit is realized by the specific processing unit 290 of the data processing device 12, conducting consultations with experts. The reflection unit is realized by the control unit 46A of the smart device 14, reflecting the avatar's movements in real time. The simulation unit is realized by the specific processing unit 290 of the data processing device 12, performing cooking simulations. The display unit is realized by the control unit 46A of the smart device 14, displaying advice on the avatar as special items or badges. The collection unit is realized by the control unit 46A of the smart device 14, collecting data from the wearable device. The data reflecting unit is realized by the specific processing unit 290 of the data processing device 12, and reflects the collected data in the avatar. === Hard Collateral 1-2 === Each of the multiple elements, including the exercise unit, learning unit, consultation unit, reflection unit, simulation unit, display unit, collection unit, and data reflection unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the exercise unit is realized by the control unit 46A of the smart glasses 214, allowing a user to participate in group exercise using an avatar in a virtual gym. The learning unit is realized by the specific processing unit 290 of the data processing device 12, obtaining healthy recipes based on exercise data. The consultation unit is realized by the specific processing unit 290 of the data processing device 12, conducting consultations with experts. The reflection unit is realized by the control unit 46A of the smart glasses 214, reflecting the avatar's movements in real time. The simulation unit is realized by the specific processing unit 290 of the data processing device 12, performing cooking simulations. The display unit is realized by the control unit 46A of the smart glasses 214, displaying advice on the avatar as special items or badges. The collection unit is realized by the control unit 46A of the smart glasses 214, collecting data from the wearable device. The data reflecting unit is realized by the specific processing unit 290 of the data processing device 12, and reflects the collected data in the avatar. === Hard Collateral 1-3 === Each of the multiple elements, including the exercise unit, learning unit, consultation unit, reflection unit, simulation unit, display unit, collection unit, and data reflection unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the exercise unit is realized by the control unit 46A of the headset-type terminal 314, allowing a user to participate in group exercise using an avatar in a virtual gym. The learning unit is realized by the specific processing unit 290 of the data processing device 12, obtaining healthy recipes based on exercise data. The consultation unit is realized by the specific processing unit 290 of the data processing device 12, conducting consultations with experts. The reflection unit is realized by the control unit 46A of the headset-type terminal 314, reflecting the avatar's movements in real time. The simulation unit is realized by the specific processing unit 290 of the data processing device 12, performing cooking simulations. The display unit is realized by the control unit 46A of the headset-type terminal 314, displaying advice on the avatar as special items or badges. The collection unit is realized by the control unit 46A of the headset terminal 314, and collects data from the wearable device. The data reflection unit is realized by the specific processing unit 290 of the data processing device 12, and reflects the collected data in the avatar. === Hard Collateral 1-4 === Each of the multiple elements, including the exercise unit, learning unit, consultation unit, reflection unit, simulation unit, display unit, collection unit, and data reflection unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the exercise unit is realized by the control unit 46A of the robot 414, allowing a user to participate in group exercise using an avatar in a virtual gym. The learning unit is realized by the specific processing unit 290 of the data processing device 12, obtaining healthy recipes based on exercise data. The consultation unit is realized by the specific processing unit 290 of the data processing device 12, conducting consultations with experts. The reflection unit is realized by the control unit 46A of the robot 414, reflecting the avatar's movements in real time. The simulation unit is realized by the specific processing unit 290 of the data processing device 12, performing cooking simulations. The display unit is realized by the control unit 46A of the robot 414, displaying advice on the avatar as special items or badges. The collection unit is realized by the control unit 46A of the robot 414, collecting data from wearable devices. The data reflecting unit is realized by the specific processing unit 290 of the data processing device 12, and reflects the collected data in the avatar.

[0130] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0131] The health experience system can include a recovery unit that suggests post-exercise recovery methods based on the user's exercise data. The recovery unit can, for example, suggest stretching or massage methods depending on the user's exercise intensity. The recovery unit can also, for example, analyze the user's post-exercise heart rate and muscle fatigue level and suggest appropriate rest periods and recovery meals. Furthermore, the recovery unit can, for example, track the user's recovery progress based on the user's exercise history and support preparation for the next exercise session. This allows the user to effectively recover after exercise and be better prepared for the next exercise session.

[0132] The sports club can suggest the timing of hydration during exercise based on the user's exercise data. For example, the sports club can monitor the user's heart rate and sweat rate in real time and notify them of the appropriate timing for hydration. The sports club can also adjust the amount and frequency of hydration taking into account the user's exercise intensity and environmental conditions (temperature and humidity). Furthermore, the sports club can analyze the user's hydration patterns based on the user's past exercise data and suggest an optimal hydration plan. This allows the user to hydrate appropriately during exercise and maintain performance.

[0133] The learning unit can suggest food preservation methods and cooking tips based on the user's meal history. For example, the learning unit can suggest ways to preserve ingredients purchased by the user and provide advice on how to keep ingredients fresh. The learning unit can also suggest cooking tips and tricks based on recipes selected by the user. Furthermore, the learning unit can analyze the user's meal history and suggest improved versions of recipes that were popular in the past. This allows the user to avoid wasting ingredients and create more delicious dishes.

[0134] The counseling unit can provide mental health support based on the user's health status. For example, the counseling unit can analyze the user's stress level and sleep data and provide relaxation techniques and stress management advice. The counseling unit can also monitor the user's emotional state and suggest consulting with a specialist as needed. Furthermore, the counseling unit can provide resources and tools to help improve mental health based on the user's past consultation history. This allows the user to effectively manage their mental health and improve their overall well-being.

[0135] The health experience system may include a nutritional supplementation unit that suggests post-exercise nutritional supplementation based on the user's exercise data. The nutritional supplementation unit may, for example, suggest meals or snacks containing appropriate nutrients based on the user's exercise intensity and calorie expenditure. The nutritional supplementation unit may also provide recipes for protein shakes or recovery drinks based on the user's post-exercise physical condition and goals. Furthermore, the nutritional supplementation unit may analyze the user's past exercise data and create an optimal nutritional supplementation plan. This allows the user to replenish their nutrition appropriately after exercise and promote recovery.

[0136] The exercise unit can estimate the user's emotions and select exercise music based on the estimated user's emotions. For example, the exercise unit can suggest calm music if the user is relaxed. Alternatively, the exercise unit can suggest up-tempo music if the user is energetic. Furthermore, the exercise unit can suggest music with a relaxing effect if the user is feeling stressed. This makes the exercise experience more comfortable by providing music that matches the user's emotions.

[0137] The learning unit can estimate the user's emotions and adjust the recipe presentation method based on the estimated user emotions. For example, the learning unit can display detailed recipe descriptions when the user is relaxed. Alternatively, the learning unit can display concise recipe descriptions when the user is in a hurry. Furthermore, the learning unit can display a visually appealing recipe when the user is enjoying themselves. This allows for more appropriate recipe display by adjusting the recipe presentation method according to the user's emotions.

[0138] The consultation unit can estimate the user's emotions and adjust the timing of the consultation based on the estimated user's emotions. For example, if the user is relaxed, the consultation unit can delay the timing of the consultation. Also, if the user is in a hurry, the consultation unit can start the consultation quickly. Furthermore, if the user is feeling anxious, the consultation unit can allow time for the user to relax before starting the consultation. In this way, by adjusting the timing of the consultation according to the user's emotions, more appropriate consultation is possible.

[0139] The exercise unit can estimate the user's emotions and adjust the exercise intervals based on the estimated user emotions. For example, the exercise unit can lengthen the intervals if the user is tired. Alternatively, the exercise unit can shorten the intervals if the user is energetic. Furthermore, the exercise unit can suggest interval exercises that have a relaxing effect if the user is feeling stressed. In this way, by adjusting the exercise intervals according to the user's emotions, a more appropriate exercise experience can be provided.

[0140] The health experience system can include a recovery unit that suggests post-exercise recovery methods based on the user's exercise data. The recovery unit can, for example, suggest stretching or massage methods depending on the user's exercise intensity. The recovery unit can also, for example, analyze the user's post-exercise heart rate and muscle fatigue level and suggest appropriate rest periods and recovery meals. Furthermore, the recovery unit can, for example, track the user's recovery progress based on the user's exercise history and support preparation for the next exercise session. This allows the user to effectively recover after exercise and be better prepared for the next exercise session.

[0141] The processing flow of the second embodiment will be briefly explained below.

[0142] Step 1: The sports club allows users to participate in group exercises using avatars in a virtual gym. This allows users to enjoy exercise with other users in a virtual space. Step 2: The learning unit obtains healthy recipes based on the exercise data obtained by the exercise unit, allowing the user to obtain a suitable meal plan based on their own exercise data. Step 3: The consultation unit consults with an expert based on the recipe information obtained by the learning unit. This allows the user to receive advice from the expert and receive specific guidance on how to live a healthier life.

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0174] 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.

[0175] 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0180] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0191] 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.

[0192] 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.

[0193] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0194] 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.

[0195] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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).

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] [Explanation of symbols]

[0215] 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 sports club where users participate in group exercises using avatars in a virtual gym; a learning unit that acquires healthy recipes based on the exercise data obtained by the exercise unit; a consultation unit that consults with an expert based on the recipe information obtained by the learning unit; Equipped with A system characterized by:

2. The exercise section includes: Equipped with a reflection section that instantly reflects the avatar's movements The system of claim 1 .

3. The learning unit Equipped with a simulation section that performs cooking simulations The system of claim 1 .

4. The consultation department: A display unit is provided that displays advice on the avatar as a special item or badge. The system of claim 1 .

5. A collection unit for acquiring data from the wearable device is provided. The system of claim 1 .

6. Equipped with a data reflection unit that applies collected data to the avatar The system of claim 1 .

7. The exercise section includes: Estimating a user's emotion and adjusting the intensity or type of exercise based on the estimated user's emotion The system of claim 1 .

8. The exercise section includes: Analyzes exercise data and suggests optimal exercise programs based on the user's physical condition or past exercise history The system of claim 1 .

9. The exercise section includes: Adding feedback to instantly correct the user's posture or movement during exercise The system of claim 1 .

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