System

Abstract

An object of a system according to an embodiment is to provide high-quality training and meal support regardless of living standards and regions.SOLUTION: In one embodiment, the system includes a captive trainer avatar generator, a curriculum program component, a training suggestion component, and a dining support component. The exclusive trainer avatar generation unit generates an avatar of the exclusive trainer by using the generation AI. The curriculum program section programs a curriculum of a famous exclusive trainer in AR. The training suggestion unit suggests training corresponding to each stage. The meal support unit supports ideal body building by inputting daily meal contents.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem that depending on living standards and region, people may not be able to attend sports clubs or may receive low-quality training.

[0005] The system according to the embodiment aims to provide high-quality training and dietary support regardless of living standards or regions. [Means for solving the problem]

[0006] The system according to the embodiment includes a personal trainer avatar generation unit, a curriculum programming unit, a training suggestion unit, and a dietary support unit. The personal trainer avatar generation unit uses generation AI to create an avatar of a personal trainer. The curriculum programming unit programs the curriculum of a famous personal trainer using AR. The training suggestion unit suggests training appropriate for each individual stage. The dietary support unit supports the development of an ideal body by inputting daily meal content. [Effects of the Invention]

[0007] The system according to the embodiment can provide high-quality training and dietary support regardless of living standards or regions. [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 training support system according to an embodiment of the present invention is a system for maximizing the talents of children who are unable to attend sports clubs due to their living standards or location, or who are in a low-quality training environment. This system uses generative AI to create avatars of personal trainers, programs famous personal trainer curricula for each sport using AR, suggests training tailored to each individual's stage, and supports the development of an ideal physique by inputting daily dietary information. This allows the training support system to maximize the talents of children who are unable to attend sports clubs due to their living standards or location, or who are in a low-quality training environment.

[0029] A training support system according to an embodiment includes a generation AI, a personal trainer avatar generation unit, a curriculum programming unit, a training suggestion unit, and a diet support unit. The generation AI creates a personal trainer avatar based on the user's sport and training needs. For example, the generation AI uses deep learning or GAN (generative artificial network) to generate an avatar based on the knowledge and experience of a famous actual trainer. The personal trainer avatar generation unit uses the generation AI to create an avatar of the personal trainer. For example, if a user requests soccer training, the generation AI generates an avatar of a famous soccer trainer and provides the user with an appropriate training menu. The curriculum programming unit programs the curriculum of a famous personal trainer using AR technology. For example, in basketball training, the generation AI recreates the famous trainer's curriculum using AR and instructs the user on specific movements and techniques. The training suggestion unit suggests training menus appropriate for each stage based on the user's current training level and goals. For example, it provides basic training for beginners, applied training for intermediate players, and advanced training for advanced players. The dietary support section helps users build their ideal physique by inputting their daily dietary information. For example, the generative AI analyzes the user's dietary data, evaluates nutritional balance and calorie intake, and proposes appropriate meal plans and nutritional supplements. This allows the training support system to maximize the talents of children who cannot attend sports clubs due to their living standards or location, or who are in poor training environments.

[0030] The personal trainer avatar generation unit can imitate the user's movements in real time and provide feedback. For example, in the personal trainer avatar generation unit, a trainer avatar imitates the user's movements in real time and demonstrates the correct form and movements. For example, when a user performs squats, the avatar demonstrates the correct squat form in real time and corrects the user's movements. The personal trainer avatar generation unit also analyzes the user's movements in real time and provides feedback. For example, when a user runs, the avatar points out areas for improvement in the user's running form and provides specific advice. The personal trainer avatar generation unit also adds a function of imitating the user's movements in real time and providing feedback. For example, when a user practices shooting basketballs, the avatar demonstrates the correct shooting form and corrects the user's movements. In this way, the effectiveness of training can be improved by imitating the user's movements in real time and demonstrating the correct form and movements.

[0031] The personal trainer avatar generation unit can analyze the user's past training data and propose a long-term training plan. For example, the personal trainer avatar generation unit analyzes the user's past training data and proposes a long-term training plan. For example, a training plan for the next three months is generated based on the user's past training history. The personal trainer avatar generation unit also analyzes the user's past training data and visualizes the user's training progress. For example, the user's past training data is graphed so that the progress can be checked at a glance. The personal trainer avatar generation unit also analyzes the user's past training data and proposes a long-term training plan. For example, a training plan for the next six months is generated based on the user's past training data, and specific steps toward achieving the goal are indicated. By analyzing the user's past training data and proposing a long-term training plan, specific steps toward achieving the goal can be indicated.

[0032] The personal trainer avatar generation unit can generate multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. The personal trainer avatar generation unit generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for soccer, basketball, yoga, etc. can be generated, allowing a user to select one based on their needs. The personal trainer avatar generation unit also generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for strength training, cardio, Pilates, etc. can be generated, allowing a user to select one based on their training goals. The personal trainer avatar generation unit also generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for running, swimming, dancing, etc. can be generated, allowing a user to select one based on their interests. In this way, by generating multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs, training tailored to the user's needs can be provided.

[0033] The personal trainer avatar generation unit can suggest a training menu that is suited to the user's home environment and available equipment. In the personal trainer avatar generation unit, for example, the trainer avatar suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is no training equipment at home, the trainer avatar suggests bodyweight training or exercises using simple tools. Furthermore, the personal trainer avatar generation unit suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is not much space at home, the trainer avatar suggests exercises that can be done in a small space. Furthermore, the personal trainer avatar generation unit suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is training equipment at home, the trainer avatar suggests a training menu that makes use of that equipment. In this way, effective training can be achieved by suggesting a training menu that is suited to the user's home environment and available equipment.

[0034] The curriculum program unit can use AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. The curriculum program unit, for example, uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in soccer training, a virtual soccer field is recreated, providing a user with an experience that makes them feel as if they are actually playing. The curriculum program unit also uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in basketball training, a virtual basketball court is recreated, providing a user with an experience that makes them feel as if they are actually playing. The curriculum program unit also uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in running training, a virtual running track is recreated, providing a user with an experience that makes them feel as if they are actually running. This can increase motivation for training by providing a user with an experience that makes them feel as if they are in an actual stadium or training facility.

[0035] The curriculum program unit can analyze a user's movements in real time and provide instant feedback. The curriculum program unit adds, for example, a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user practices shooting basketball shots, the curriculum program unit analyzes the user's shooting form in real time and points out areas for improvement. The curriculum program unit also adds a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user runs, the curriculum program unit analyzes the user's running form in real time and suggests an efficient running method. The curriculum program unit also adds a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user does weight training, the curriculum program unit analyzes the user's form in real time and instructs the user on the correct movements. In this way, the effectiveness of training can be improved by analyzing a user's movements in real time and providing instant feedback.

[0036] The curriculum program unit can use AR technology to virtually compete with other users during training. The curriculum program unit, for example, uses AR technology to add a function for virtually competing with other users during training. For example, in running training, a user virtually competes with other users and the ranking and time are displayed in real time. The curriculum program unit also uses AR technology to add a function for virtually competing with other users during training. For example, in basketball shooting practice, a user virtually competes with other users and the shooting success rate and score are displayed. The curriculum program unit also uses AR technology to add a function for virtually competing with other users during training. For example, in soccer dribbling practice, a user virtually competes with other users and the dribbling speed and accuracy are displayed. This allows the user to virtually compete with other users during training, thereby increasing motivation for training.

[0037] The curriculum program unit can add music and visual effects to the training curriculum according to the user's preferences. For example, the curriculum program unit adds music to the training curriculum according to the user's preferences. For example, music from the user's favorite genre can be played during training to increase motivation. The curriculum program unit also adds visual effects to the training curriculum according to the user's preferences. For example, colorful effects and animations can be displayed during training to provide visual enjoyment. The curriculum program unit also adds music and visual effects to the training curriculum according to the user's preferences. For example, the training environment can be customized based on a theme selected by the user to provide a more enjoyable training experience. In this way, the enjoyment of training can be increased by adding music and visual effects according to the user's preferences.

[0038] The training suggestion unit can analyze the user's training data and provide a dashboard that visualizes the user's training progress. The training suggestion unit, for example, analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training frequency, intensity, and achievement level are displayed in graphs and charts. The training suggestion unit also analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training suggestion unit displays past training history in a timeline format, allowing the user to check progress at a glance. The training suggestion unit also analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training suggestion unit displays the user's training goal achievement level and areas for improvement in real time, clarifying the next challenge the user should tackle. In this way, the user's training effects can be easily confirmed by analyzing the user's training data and visualizing the user's progress.

[0039] The training suggestion unit can add a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit suggests a training menu that matches a work or school schedule. The training suggestion unit also adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit suggests appropriate training intensity and frequency based on the user's daily activity level and exercise habits. The training suggestion unit also adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training menu is adjusted based on the user's sleep pattern and eating habits. In this way, adding a customization function that takes into account the user's lifestyle and daily activity level makes it possible to make more effective training suggestions.

[0040] The training suggestion unit can add a group training menu to be done with the user's friends and family to the training suggestion. For example, the training suggestion unit adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a fitness challenge or group exercise that the whole family can participate in. The training suggestion unit also adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a running challenge in which the user competes with friends or a training menu for team sports. The training suggestion unit also adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a virtual fitness session that the user can participate in online with friends and family. In this way, adding a group training menu to be done with the user's friends and family can increase the enjoyment and motivation of training.

[0041] The training suggestion unit can incorporate activities related to the user's hobbies and interests into the training suggestion. For example, the training suggestion unit incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes dancing, it proposes a dance exercise menu or dance fitness menu. The training suggestion unit also incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes the outdoors, it proposes a hiking menu or trail running menu. The training suggestion unit also incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes music, it proposes an exercise menu or rhythmic training menu set to music. In this way, by incorporating activities related to the user's hobbies and interests, it is possible to improve the enjoyment and continuity of training.

[0042] The dietary support unit can analyze the user's dietary data and automatically generate recipes for optimizing nutritional balance. The dietary support unit, for example, analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes for supplementing necessary nutrients based on the user's dietary history. The dietary support unit also analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes suitable for building muscle or reducing body fat according to the user's goals. The dietary support unit also analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes that accommodate the user's allergies and dietary restrictions. In this way, by analyzing the user's dietary data and automatically generating recipes for optimizing nutritional balance, it is possible to support the creation of an ideal body.

[0043] The meal support unit can add an ingredient barcode scanning function to simplify the input of meal details. The meal support unit, for example, adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which meal details are automatically input by the user simply scanning the barcodes of ingredients. The meal support unit also adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which the user scans the barcodes of food items purchased and automatically records nutritional information and calories. The meal support unit also adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which meal details are automatically input by the user simply scanning the ingredients of a recipe. By adding an ingredient barcode scanning function to simplify the input of meal details, the burden on the user can be reduced.

[0044] The meal support unit can add a community function for sharing recipes with other users based on the user's meal data. The meal support unit, for example, adds a community function for sharing recipes with other users based on the user's meal data. For example, a user publishes their own meal data and shares recipes and meal plans with other users. The meal support unit also adds a community function for sharing recipes with other users based on the user's meal data. For example, a user posts recipes created based on their own meal data and receives feedback from other users. The meal support unit also adds a community function for sharing recipes with other users based on the user's meal data. For example, a user shares recipes created based on their own meal data and exchanges meal plans with other users. In this way, adding a community function for sharing recipes with other users based on the user's meal data can increase the enjoyment and variety of meals.

[0045] The meal support unit can add a customization function that takes into account the user's culture and regional eating habits to the input of meal details. The meal support unit, for example, adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, ingredients and dishes specific to the user's region are registered in a database to simplify the input of meal details. The meal support unit also adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, input options are provided that accommodate dietary restrictions and preferences based on the user's culture or religion. The meal support unit also adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, recipes are suggested based on ingredients and dishes specific to the user's region to simplify the input of meal details. In this way, adding a customization function that takes into account the user's culture and regional eating habits to the input of meal details simplifies the input of meal details.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The training support system can also acquire the user's sleep data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user is not getting enough sleep, it can suggest a lighter training menu, and conversely, if the user is getting enough sleep, it can suggest a more challenging training menu. The system can also suggest the user's optimal training time based on the sleep data. This makes it possible to maximize the effectiveness of training by providing a training menu that suits the user's sleep state.

[0048] The training support system also acquires the user's heart rate data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user's heart rate is high, it will suggest a recovery menu or light exercises, and if the heart rate is stable, it will suggest a more intense training menu. It can also monitor the user's training progress in real time based on the heart rate data and provide appropriate feedback. This allows the system to maximize the effectiveness of training by providing a training menu based on the user's heart rate data.

[0049] The training support system also acquires the user's body temperature data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user's body temperature is high, it can suggest a cool-down menu or light exercises, and if the user's body temperature is normal, it can suggest a regular training menu. The system can also monitor the user's training progress in real time based on the body temperature data and provide appropriate feedback. This makes it possible to maximize the effectiveness of training by providing a training menu based on the user's body temperature data.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The personal trainer avatar generation unit uses generation AI to create an avatar of a personal trainer. The generation AI creates a personal trainer avatar based on the user's sport and training needs. For example, using deep learning or GAN (generative artificial network), it generates an avatar based on the knowledge and experience of an actual famous trainer. If a user requests soccer training, the generation AI creates an avatar of a famous soccer trainer and provides the user with an appropriate training menu. Step 2: The Curriculum Programming Department uses AR technology to program the curriculum of a famous personal trainer. For example, in basketball training, the generative AI recreates the famous trainer's curriculum in AR and instructs the user on specific movements and techniques. Step 3: The training suggestion unit proposes training menus tailored to each individual level based on the user's current training level and goals. For example, it offers basic training for beginners, applied training for intermediate users, and advanced training for advanced users. Step 4: The dietary support section helps users build their ideal body by inputting their daily dietary information. The generation AI analyzes the user's dietary data, evaluates nutritional balance and calorie intake, and proposes appropriate meal plans and nutritional supplements.

[0052] (Example 2) A training support system according to an embodiment of the present invention is a system for maximizing the talents of children who are unable to attend sports clubs due to their living standards or location, or who are in a low-quality training environment. This system uses generative AI to create avatars of personal trainers, programs famous personal trainer curricula for each sport using AR, suggests training tailored to each individual's stage, and supports the development of an ideal physique by inputting daily dietary information. This allows the training support system to maximize the talents of children who are unable to attend sports clubs due to their living standards or location, or who are in a low-quality training environment.

[0053] A training support system according to an embodiment includes a generation AI, a personal trainer avatar generation unit, a curriculum programming unit, a training suggestion unit, and a diet support unit. The generation AI creates a personal trainer avatar based on the user's sport and training needs. For example, the generation AI uses deep learning or GAN (generative artificial network) to generate an avatar based on the knowledge and experience of a famous actual trainer. The personal trainer avatar generation unit uses the generation AI to create an avatar of the personal trainer. For example, if a user requests soccer training, the generation AI generates an avatar of a famous soccer trainer and provides the user with an appropriate training menu. The curriculum programming unit programs the curriculum of a famous personal trainer using AR technology. For example, in basketball training, the generation AI recreates the famous trainer's curriculum using AR and instructs the user on specific movements and techniques. The training suggestion unit suggests training menus appropriate for each stage based on the user's current training level and goals. For example, it provides basic training for beginners, applied training for intermediate players, and advanced training for advanced players. The dietary support section helps users build their ideal physique by inputting their daily dietary information. For example, the generative AI analyzes the user's dietary data, evaluates nutritional balance and calorie intake, and proposes appropriate meal plans and nutritional supplements. This allows the training support system to maximize the talents of children who cannot attend sports clubs due to their living standards or location, or who are in poor training environments.

[0054] The personal trainer avatar generation unit can imitate the user's movements in real time and provide feedback. For example, in the personal trainer avatar generation unit, a trainer avatar imitates the user's movements in real time and demonstrates the correct form and movements. For example, when a user performs squats, the avatar demonstrates the correct squat form in real time and corrects the user's movements. The personal trainer avatar generation unit also analyzes the user's movements in real time and provides feedback. For example, when a user runs, the avatar points out areas for improvement in the user's running form and provides specific advice. The personal trainer avatar generation unit also adds a function of imitating the user's movements in real time and providing feedback. For example, when a user practices shooting basketballs, the avatar demonstrates the correct shooting form and corrects the user's movements. In this way, the effectiveness of training can be improved by imitating the user's movements in real time and demonstrating the correct form and movements.

[0055] The personal trainer avatar generation unit can analyze the user's past training data and propose a long-term training plan. For example, the personal trainer avatar generation unit analyzes the user's past training data and proposes a long-term training plan. For example, a training plan for the next three months is generated based on the user's past training history. The personal trainer avatar generation unit also analyzes the user's past training data and visualizes the user's training progress. For example, the user's past training data is graphed so that the progress can be checked at a glance. The personal trainer avatar generation unit also analyzes the user's past training data and proposes a long-term training plan. For example, a training plan for the next six months is generated based on the user's past training data, and specific steps toward achieving the goal are indicated. By analyzing the user's past training data and proposing a long-term training plan, specific steps toward achieving the goal can be indicated.

[0056] The personal trainer avatar generation unit can use the emotion estimation function to suggest a training menu that corresponds to the user's emotional state. The personal trainer avatar generation unit, for example, uses the emotion estimation function to suggest a training menu that corresponds to the user's emotional state. For example, if the user is feeling stressed, it suggests a relaxation or stretching menu. The personal trainer avatar generation unit also uses the emotion estimation function to suggest a training menu that corresponds to the user's emotional state. For example, if the user is low motivated, it suggests a menu that includes fun exercises or game elements. The personal trainer avatar generation unit also uses the emotion estimation function to suggest a training menu that corresponds to the user's emotional state. For example, if the user is tired, it suggests light exercises or a recovery menu. In this way, by suggesting a training menu that corresponds to the user's emotional state, it is possible to maximize the effectiveness of training.

[0057] The personal trainer avatar generation unit can generate multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. The personal trainer avatar generation unit generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for soccer, basketball, yoga, etc. can be generated, allowing a user to select one based on their needs. The personal trainer avatar generation unit also generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for strength training, cardio, Pilates, etc. can be generated, allowing a user to select one based on their training goals. The personal trainer avatar generation unit also generates multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs. For example, trainer avatars for running, swimming, dancing, etc. can be generated, allowing a user to select one based on their interests. In this way, by generating multiple trainer avatars so that the trainer avatars can be adapted to different sports and fitness programs, training tailored to the user's needs can be provided.

[0058] The personal trainer avatar generation unit can suggest a training menu that is suited to the user's home environment and available equipment. In the personal trainer avatar generation unit, for example, the trainer avatar suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is no training equipment at home, the trainer avatar suggests bodyweight training or exercises using simple tools. Furthermore, the personal trainer avatar generation unit suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is not much space at home, the trainer avatar suggests exercises that can be done in a small space. Furthermore, the personal trainer avatar generation unit suggests a training menu that is suited to the user's home environment and available equipment. For example, if there is training equipment at home, the trainer avatar suggests a training menu that makes use of that equipment. In this way, effective training can be achieved by suggesting a training menu that is suited to the user's home environment and available equipment.

[0059] The personal trainer avatar generation unit can use the emotion estimation function to suggest a relaxation menu for reducing stress and fatigue felt by the user during training. The personal trainer avatar generation unit, for example, uses the emotion estimation function to suggest a relaxation menu for reducing stress and fatigue felt by the user during training. For example, it may suggest a yoga or meditation menu to encourage the user to relax. The personal trainer avatar generation unit also uses the emotion estimation function to suggest a relaxation menu for reducing stress and fatigue felt by the user during training. For example, it may suggest a stretching or breathing exercise menu to encourage the user to feel refreshed. The personal trainer avatar generation unit also uses the emotion estimation function to suggest a relaxation menu for reducing stress and fatigue felt by the user during training. For example, it may suggest a relaxation music or aromatherapy menu to encourage the user to relax. In this way, suggesting a relaxation menu for reducing stress and fatigue felt by the user during training can increase the continuity of training.

[0060] The curriculum program unit can use AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. The curriculum program unit, for example, uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in soccer training, a virtual soccer field is recreated, providing a user with an experience that makes them feel as if they are actually playing. The curriculum program unit also uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in basketball training, a virtual basketball court is recreated, providing a user with an experience that makes them feel as if they are actually playing. The curriculum program unit also uses AR technology to virtually recreate a training environment, providing a user with an experience that makes them feel as if they are in an actual stadium or training facility. For example, in running training, a virtual running track is recreated, providing a user with an experience that makes them feel as if they are actually running. This can increase motivation for training by providing a user with an experience that makes them feel as if they are in an actual stadium or training facility.

[0061] The curriculum program unit can analyze a user's movements in real time and provide instant feedback. The curriculum program unit adds, for example, a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user practices shooting basketball shots, the curriculum program unit analyzes the user's shooting form in real time and points out areas for improvement. The curriculum program unit also adds a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user runs, the curriculum program unit analyzes the user's running form in real time and suggests an efficient running method. The curriculum program unit also adds a function to analyze a user's movements during training in real time and provide instant feedback. For example, when a user does weight training, the curriculum program unit analyzes the user's form in real time and instructs the user on the correct movements. In this way, the effectiveness of training can be improved by analyzing a user's movements in real time and providing instant feedback.

[0062] The curriculum program unit can use the emotion estimation function to display encouragement and advice to maintain the user's motivation in AR. For example, the curriculum program unit uses the emotion estimation function to display encouragement and advice to maintain the user's motivation in AR. For example, when the user is tired, it displays an encouraging message or advice to increase motivation. The curriculum program unit also uses the emotion estimation function to display encouragement and advice to maintain the user's motivation in AR. For example, when the user is feeling stressed, it displays advice to help the user relax or a relaxation menu. The curriculum program unit also uses the emotion estimation function to display encouragement and advice to maintain the user's motivation in AR. For example, when the user is working hard to achieve a goal, it displays an encouraging message or a success story. In this way, by displaying encouragement and advice to maintain the user's motivation in AR, it is possible to increase the continuity of training.

[0063] The curriculum program unit can use AR technology to virtually compete with other users during training. The curriculum program unit, for example, uses AR technology to add a function for virtually competing with other users during training. For example, in running training, a user virtually competes with other users and the ranking and time are displayed in real time. The curriculum program unit also uses AR technology to add a function for virtually competing with other users during training. For example, in basketball shooting practice, a user virtually competes with other users and the shooting success rate and score are displayed. The curriculum program unit also uses AR technology to add a function for virtually competing with other users during training. For example, in soccer dribbling practice, a user virtually competes with other users and the dribbling speed and accuracy are displayed. This allows the user to virtually compete with other users during training, thereby increasing motivation for training.

[0064] The curriculum program unit can add music and visual effects to the training curriculum according to the user's preferences. For example, the curriculum program unit adds music to the training curriculum according to the user's preferences. For example, music from the user's favorite genre can be played during training to increase motivation. The curriculum program unit also adds visual effects to the training curriculum according to the user's preferences. For example, colorful effects and animations can be displayed during training to provide visual enjoyment. The curriculum program unit also adds music and visual effects to the training curriculum according to the user's preferences. For example, the training environment can be customized based on a theme selected by the user to provide a more enjoyable training experience. In this way, the enjoyment of training can be increased by adding music and visual effects according to the user's preferences.

[0065] The curriculum program unit can use the emotion estimation function to introduce a reward system for enhancing the sense of accomplishment felt by the user during training. The curriculum program unit, for example, uses the emotion estimation function to introduce a reward system for enhancing the sense of accomplishment felt by the user during training. For example, a system is provided in which a user can earn badges or points when a training goal is achieved. The curriculum program unit also uses the emotion estimation function to introduce a reward system for enhancing the sense of accomplishment felt by the user during training. For example, a system is provided in which a user can earn virtual trophies or medals according to the progress of training. The curriculum program unit also uses the emotion estimation function to introduce a reward system for enhancing the sense of accomplishment felt by the user during training. For example, a system is provided in which special content or bonuses are provided according to the results of training. In this way, by introducing a reward system for enhancing the sense of accomplishment felt by the user during training, it is possible to increase motivation for training.

[0066] The training suggestion unit can analyze the user's training data and provide a dashboard that visualizes the user's training progress. The training suggestion unit, for example, analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training frequency, intensity, and achievement level are displayed in graphs and charts. The training suggestion unit also analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training suggestion unit displays past training history in a timeline format, allowing the user to check progress at a glance. The training suggestion unit also analyzes the user's training data and provides a dashboard that visualizes the user's training progress. For example, the training suggestion unit displays the user's training goal achievement level and areas for improvement in real time, clarifying the next challenge the user should tackle. In this way, the user's training effects can be easily confirmed by analyzing the user's training data and visualizing the user's progress.

[0067] The training suggestion unit can add a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit suggests a training menu that matches a work or school schedule. The training suggestion unit also adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training suggestion unit suggests appropriate training intensity and frequency based on the user's daily activity level and exercise habits. The training suggestion unit also adds a customization function that takes into account the user's lifestyle and daily activity level to the training suggestion. For example, the training menu is adjusted based on the user's sleep pattern and eating habits. In this way, adding a customization function that takes into account the user's lifestyle and daily activity level makes it possible to make more effective training suggestions.

[0068] The training suggestion unit can use the emotion estimation function to adjust the training intensity according to the user's emotional state. The training suggestion unit, for example, uses the emotion estimation function to adjust the training intensity according to the user's emotional state. For example, if the user is feeling stressed, it suggests a lighter training menu. The training suggestion unit also uses the emotion estimation function to adjust the training intensity according to the user's emotional state. For example, if the user is highly motivated, it suggests a more challenging training menu. The training suggestion unit also uses the emotion estimation function to adjust the training intensity according to the user's emotional state. For example, if the user is tired, it suggests a recovery menu or relaxation exercises. In this way, by adjusting the training intensity according to the user's emotional state, it is possible to maximize the effectiveness of the training.

[0069] The training suggestion unit can add a group training menu to be done with the user's friends and family to the training suggestion. For example, the training suggestion unit adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a fitness challenge or group exercise that the whole family can participate in. The training suggestion unit also adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a running challenge in which the user competes with friends or a training menu for team sports. The training suggestion unit also adds a group training menu to be done with the user's friends and family to the training suggestion. For example, it suggests a virtual fitness session that the user can participate in online with friends and family. In this way, adding a group training menu to be done with the user's friends and family can increase the enjoyment and motivation of training.

[0070] The training suggestion unit can incorporate activities related to the user's hobbies and interests into the training suggestion. For example, the training suggestion unit incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes dancing, it proposes a dance exercise menu or dance fitness menu. The training suggestion unit also incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes the outdoors, it proposes a hiking menu or trail running menu. The training suggestion unit also incorporates activities related to the user's hobbies and interests into the training suggestion. For example, for a user who likes music, it proposes an exercise menu or rhythmic training menu set to music. In this way, by incorporating activities related to the user's hobbies and interests, it is possible to improve the enjoyment and continuity of training.

[0071] The training suggestion unit can use the emotion estimation function to introduce game elements that allow the user to enjoy training. The training suggestion unit, for example, uses the emotion estimation function to introduce game elements that allow the user to enjoy training. For example, a system is provided in which points are earned according to the progress of training, and the user can level up and receive rewards. The training suggestion unit also uses the emotion estimation function to introduce game elements that allow the user to enjoy training. For example, a mini-game or challenge is inserted during training to increase the user's motivation. The training suggestion unit also uses the emotion estimation function to introduce game elements that allow the user to enjoy training. For example, a system is provided in which virtual items or avatars can be customized according to the results of training. In this way, introducing game elements that allow the user to enjoy training can improve motivation and continuity of training.

[0072] The dietary support unit can analyze the user's dietary data and automatically generate recipes for optimizing nutritional balance. The dietary support unit, for example, analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes for supplementing necessary nutrients based on the user's dietary history. The dietary support unit also analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes suitable for building muscle or reducing body fat according to the user's goals. The dietary support unit also analyzes the user's dietary data and automatically generates recipes for optimizing nutritional balance. For example, it suggests recipes that accommodate the user's allergies and dietary restrictions. In this way, by analyzing the user's dietary data and automatically generating recipes for optimizing nutritional balance, it is possible to support the creation of an ideal body.

[0073] The meal support unit can add an ingredient barcode scanning function to simplify the input of meal details. The meal support unit, for example, adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which meal details are automatically input by the user simply scanning the barcodes of ingredients. The meal support unit also adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which the user scans the barcodes of food items purchased and automatically records nutritional information and calories. The meal support unit also adds an ingredient barcode scanning function to simplify the input of meal details. For example, a system is provided in which meal details are automatically input by the user simply scanning the ingredients of a recipe. By adding an ingredient barcode scanning function to simplify the input of meal details, the burden on the user can be reduced.

[0074] The meal support unit can use the emotion estimation function to evaluate the user's satisfaction with the meal and make suggestions for improvement. The meal support unit, for example, uses the emotion estimation function to evaluate the user's satisfaction with the meal and make suggestions for improvement. For example, the meal support unit analyzes the satisfaction the user feels after a meal and makes suggestions for the next meal. The meal support unit also uses the emotion estimation function to evaluate the user's satisfaction with the meal and make suggestions for improvement. For example, the meal support unit analyzes the satisfaction the user feels with specific ingredients or dishes and suggests recipes that suit their preferences. The meal support unit also uses the emotion estimation function to evaluate the user's satisfaction with the meal and make suggestions for improvement. For example, the meal support unit analyzes the satisfaction the user feels during a meal in real time and makes suggestions for improving the quality of the meal. In this way, the quality of the meal can be improved by evaluating the user's satisfaction with the meal and making suggestions for improvement.

[0075] The meal support unit can add a community function for sharing recipes with other users based on the user's meal data. The meal support unit, for example, adds a community function for sharing recipes with other users based on the user's meal data. For example, a user publishes their own meal data and shares recipes and meal plans with other users. The meal support unit also adds a community function for sharing recipes with other users based on the user's meal data. For example, a user posts recipes created based on their own meal data and receives feedback from other users. The meal support unit also adds a community function for sharing recipes with other users based on the user's meal data. For example, a user shares recipes created based on their own meal data and exchanges meal plans with other users. In this way, adding a community function for sharing recipes with other users based on the user's meal data can increase the enjoyment and variety of meals.

[0076] The meal support unit can add a customization function that takes into account the user's culture and regional eating habits to the input of meal details. The meal support unit, for example, adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, ingredients and dishes specific to the user's region are registered in a database to simplify the input of meal details. The meal support unit also adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, input options are provided that accommodate dietary restrictions and preferences based on the user's culture or religion. The meal support unit also adds a customization function that takes into account the user's culture and regional eating habits to the input of meal details. For example, recipes are suggested based on ingredients and dishes specific to the user's region to simplify the input of meal details. In this way, adding a customization function that takes into account the user's culture and regional eating habits to the input of meal details simplifies the input of meal details.

[0077] The meal support unit can use the emotion estimation function to propose a meal plan for the user to enjoy meals. The meal support unit, for example, uses the emotion estimation function to propose a meal plan for the user to enjoy meals. For example, the meal support unit can propose a meal plan based on the user's favorite ingredients and dishes, thereby improving the enjoyment of meals. The meal support unit also uses the emotion estimation function to propose a meal plan for the user to enjoy meals. For example, the meal support unit can propose a relaxing meal or a meal that increases energy depending on the user's emotional state. The meal support unit also uses the emotion estimation function to propose a meal plan for the user to enjoy meals. For example, the meal support unit can propose new dishes and ingredients based on the user's eating history and preferences, thereby increasing the variety of meals. In this way, meal plans can be proposed for the user to enjoy meals, thereby improving meal satisfaction.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The training support system can also acquire the user's sleep data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user is not getting enough sleep, it can suggest a lighter training menu, and conversely, if the user is getting enough sleep, it can suggest a more challenging training menu. The system can also suggest the user's optimal training time based on the sleep data. This makes it possible to maximize the effectiveness of training by providing a training menu that suits the user's sleep state.

[0080] The training support system also acquires the user's heart rate data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user's heart rate is high, it will suggest a recovery menu or light exercises, and if the heart rate is stable, it will suggest a more intense training menu. It can also monitor the user's training progress in real time based on the heart rate data and provide appropriate feedback. This allows the system to maximize the effectiveness of training by providing a training menu based on the user's heart rate data.

[0081] The training support system also acquires the user's body temperature data, and the training suggestion unit can adjust the training menu based on that data. For example, if the user's body temperature is high, it can suggest a cool-down menu or light exercises, and if the user's body temperature is normal, it can suggest a regular training menu. The system can also monitor the user's training progress in real time based on the body temperature data and provide appropriate feedback. This makes it possible to maximize the effectiveness of training by providing a training menu based on the user's body temperature data.

[0082] The training support system can also estimate the user's emotional state, and the training suggestion unit can adjust the training menu based on that data. For example, if the user is feeling stressed, it can suggest a relaxation or stretching menu, and if the user is low-motivation, it can suggest fun exercises or menus incorporating game elements. It can also suggest light exercises or recovery menus if the user is tired. This makes it possible to maximize the effectiveness of training by providing a training menu that matches the user's emotional state.

[0083] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0084] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0085] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0086] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0087] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0088] The training support system can also estimate the user's emotional state, and the training suggestion unit can visualize the training progress based on that data. For example, if the user feels a sense of accomplishment, the progress can be displayed in graphs or charts, and the next goal can be set. Also, if the user's motivation is low, encouraging messages or success stories can be displayed to increase motivation. This makes it possible to maximize the effectiveness of training by visualizing the progress according to the user's emotional state.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The personal trainer avatar generation unit uses generation AI to create an avatar of a personal trainer. The generation AI creates a personal trainer avatar based on the user's sport and training needs. For example, using deep learning or GAN (generative artificial network), it generates an avatar based on the knowledge and experience of an actual famous trainer. If a user requests soccer training, the generation AI creates an avatar of a famous soccer trainer and provides the user with an appropriate training menu. Step 2: The Curriculum Programming Department uses AR technology to program the curriculum of a famous personal trainer. For example, in basketball training, the generative AI recreates the famous trainer's curriculum in AR and instructs the user on specific movements and techniques. Step 3: The training suggestion unit proposes training menus tailored to each individual level based on the user's current training level and goals. For example, it offers basic training for beginners, applied training for intermediate users, and advanced training for advanced users. Step 4: The dietary support section helps users build their ideal body by inputting their daily dietary information. The generation AI analyzes the user's dietary data, evaluates nutritional balance and calorie intake, and proposes appropriate meal plans and nutritional supplements.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0158] 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 dedicated trainer avatar generation unit that uses generation AI to create an avatar of a dedicated trainer; The Curriculum Programming Department programs the curriculum of famous exclusive trainers using AR, A training proposal department that proposes training tailored to each individual stage; It also has a dietary support section that supports the creation of an ideal body by inputting daily meal contents. A system characterized by:

2. The dedicated trainer avatar generation unit: Mimics user actions in real time and provides feedback 2. The system of claim 1.

3. The dedicated trainer avatar generation unit: Analyzes the user's past training data and proposes long-term training plans 2. The system of claim 1.

4. The dedicated trainer avatar generation unit: Proposing training menus according to the user's emotional state 2. The system of claim 1.

5. The dedicated trainer avatar generation unit: Generate multiple trainer avatars to accommodate different sports and fitness programs.

2. The system of claim 1.

Images