system

Abstract

An object of a system according to an embodiment is to provide optimal fitness training and nutritional guidance to an individual user and increase motivation.SOLUTION: A system according to an embodiment includes a data collector, an analyzer, a training plan generator, a VR provider, a progress tracker, and a gamification unit. The data collection unit collects data of the past diet experience, goal, and ability of the user. The analysis unit analyzes the data collected by the data collection unit. The training plan generation unit generates a customized training plan based on the result analyzed by the analysis unit. The VR providing unit provides the training plan generated by the training plan generating unit in a VR environment. The progress tracker tracks training progress of the user. A gamification unit provides gamification elements based on the progress tracked by the progress tracking unit.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] Conventional technology has made it difficult to provide optimal fitness training and nutritional guidance for individual users, making it difficult to maintain motivation.

[0005] The system according to the embodiment aims to provide optimal fitness training and nutritional guidance to individual users and increase their motivation. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a training plan generation unit, a VR provision unit, a progress tracking unit, and a gamification unit. The data collection unit collects data on a user's past dieting experiences, goals, and abilities. The analysis unit analyzes the data collected by the data collection unit. The training plan generation unit generates a customized training plan based on the results of the analysis by the analysis unit. The VR provision unit provides the training plan generated by the training plan generation unit in a VR environment. The progress tracking unit tracks the user's training progress. The gamification unit provides gamification elements based on the progress tracked by the progress tracking unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal fitness training and nutritional guidance to individual users, thereby increasing their motivation. [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 fitness training system according to an embodiment of the present invention provides a customized training plan that takes into account a user's past dieting experience, goals, and abilities, allows training in a VR environment, tracks progress, and incorporates gamification elements, thereby enabling the fitness training system to provide personalized fitness training and nutritional guidance to individual health-conscious individuals.

[0029] A fitness training system according to an embodiment includes a data collection unit, an analysis unit, a training plan generation unit, a VR provision unit, a progress tracking unit, and a gamification unit. The data collection unit collects data on a user's past dieting experiences, goals, and abilities. For example, it records the user's past successful dieting methods and the reasons for their failure. The user can also set goals such as weight loss and muscle gain. The data collection unit collects the user's physical fitness test results and exercise experience. The analysis unit analyzes the data collected by the data collection unit. For example, it may analyze the user's data using data mining techniques to generate basic data for a training plan. It may also analyze data trends using statistical analysis techniques. The training plan generation unit generates a customized training plan based on the results of the analysis by the analysis unit. For example, it may create a training menu based on the user's fitness level. It may also adjust the training plan according to the user's goals. The VR provision unit provides the training plan generated by the training plan generation unit in a VR environment. For example, the user can train in a virtual gym or experience exercise in a virtual natural environment. The progress tracking unit tracks the user's training progress. For example, the system records the user's exercise volume and achievements. It can also track the duration of training. The gamification unit provides gamification elements based on the progress tracked by the progress tracking unit. For example, it allows the user to level up or earn achievements when a certain goal is achieved. It can also provide a point system and badges. This allows the fitness training system according to the embodiment to provide personalized fitness training and nutritional guidance to individual health-conscious individuals. For example, it can provide a training plan based on a user's past successful diet methods, allowing the user to enjoy exercising on a virtual beach. Furthermore, for a user interested in soccer, it can provide a soccer-specific training menu, and track the user's progress to earn level ups and achievements, enhancing the sense of accomplishment.This is expected to support long-term training continuation.

[0030] The data collection unit can also generate a more comprehensive training plan based on the user's food log and sleep patterns. For example, the data collection unit uses an app to record the user's daily meals, and AI analyzes that data to evaluate nutritional balance. For example, the training plan can be adjusted based on protein, carbohydrate, and fat intake. The data collection unit also monitors the user's sleep patterns using a device such as a smartwatch, and the AI ​​analyzes the data to suggest optimal training times and intensity. For example, it can increase training intensity on days when the user has had deep sleep. The data collection unit also integrates the food log and sleep patterns, and the AI ​​comprehensively evaluates the user's physical condition and energy level to customize the training plan. For example, it can suggest lighter training on days when the user is low in energy. This allows the system to provide a more comprehensive training plan by taking the user's food log and sleep patterns into account.

[0031] The data collection unit can collect the user's real-time biometric data and dynamically adjust the training plan based on that data. For example, the data collection unit uses a smartwatch or fitness tracker to monitor the user's heart rate and blood pressure in real time and adjust the intensity based on the data during training. The data collection unit also transmits the user's biometric data to the cloud, where AI analyzes the data and dynamically updates the training plan. For example, if the heart rate is high, it suggests taking a break. The data collection unit also displays the biometric data during training in real time, allowing the user to monitor their physical condition while training. For example, it checks whether the heart rate is within the target range. This allows the training plan to be dynamically adjusted by taking the user's real-time biometric data into consideration.

[0032] The training plan generation unit can incorporate recreational activities that match the user's hobbies and lifestyle. For example, the training plan generation unit can incorporate the user's favorite outdoor activities (hiking, cycling, etc.) into the training plan, allowing the user to exercise while having fun. The training plan generation unit also suggests short exercises that can be done in between housework or work, tailored to the user's lifestyle. For example, it can incorporate stretching or light exercise that can be done while doing desk work. The training plan generation unit also takes into account the user's hobbies (dance, yoga, etc.) and provides training menus related to those hobbies. For example, it can suggest dance exercises for a user who likes dancing. In this way, the enjoyment of training is increased by incorporating recreational activities that match the user's hobbies and lifestyle.

[0033] The training plan generation unit can generate a joint training plan with other users and promote social interaction. For example, the training plan generation unit matches users with the same goals and provides a joint training plan. For example, it sets up an online group training session. The training plan generation unit also incorporates a competitive element with other users into the training plan to increase motivation. For example, it introduces a ranking system where users compete against each other on their training progress. The training plan generation unit also provides a platform where users can share their training results with each other, creating an environment where they can encourage each other. For example, it adds a social networking function that allows users to post their training results. This makes it possible to generate a joint training plan with other users and promote social interaction.

[0034] The training plan generation unit can analyze the user's past sports performance data and suggest exercises aimed at improving specific skills. For example, the training plan generation unit collects the user's past sports performance data and uses AI to analyze the data to suggest exercises for improving skills. For example, it provides a training menu for improving soccer dribbling skills. The training plan generation unit also identifies the user's weaknesses based on the sports performance data and suggests exercises to overcome them. For example, it provides a practice menu for improving basketball shooting accuracy. The training plan generation unit also analyzes the user's sports performance data and customizes an exercise plan aimed at improving specific skills. For example, it suggests training to improve tennis serving power. In this way, by analyzing the user's past sports performance data, it is possible to suggest exercises aimed at improving specific skills.

[0035] The training plan generation unit can provide training plans tailored to sports seasons and events, thereby maximizing competitive ability. In the training plan generation unit, for example, AI customizes training plans to suit sports seasons and events. For example, it provides a training plan for a marathon. The training plan generation unit also proposes training plans to maximize competitive ability tailored to sports events the user plans to participate in. For example, it provides intensive training for a soccer game. The training plan generation unit also provides training plans tailored to sports seasons, thereby maximizing the user's competitive ability. For example, it proposes a training menu to be performed before basketball season. In this way, competitive ability can be maximized by providing training plans tailored to sports seasons and events.

[0036] The training plan generation unit can combine training elements of different sports to provide a cross-training plan. The training plan generation unit, for example, combines training elements of different sports to provide a cross-training plan. For example, a plan combining training elements of soccer and basketball is proposed. The training plan generation unit also provides a cross-training plan combining training elements of different sports in accordance with the user's interests and goals. For example, a plan combining running and swimming training is proposed. The training plan generation unit also provides a cross-training plan combining training elements of different sports to comprehensively improve the user's physical strength and skills. For example, a plan combining tennis and cycling training is proposed. In this way, a cross-training plan can be provided by combining training elements of different sports.

[0037] The training plan generation unit can incorporate the training menu of a user's favorite athlete. The training plan generation unit provides, for example, an exercise plan that incorporates the training menu of a user's favorite athlete. For example, it proposes a plan based on the training menu of a soccer player. The training plan generation unit also refers to the training menu of a user's favorite athlete and provides an exercise plan that incorporates elements of that menu. For example, it proposes a plan based on the training menu of a basketball player. The training plan generation unit also incorporates the training menu of an athlete the user admires and provides an exercise plan that incorporates elements of that menu. For example, it proposes a plan based on the training menu of a tennis player. In this way, incorporating the training menu of a user's favorite athlete can increase motivation for training.

[0038] The VR providing unit can automatically generate an optimal virtual environment based on the user's past training data. For example, the VR providing unit analyzes the user's past training data, and the AI ​​automatically generates an optimal virtual environment based on that data. For example, it suggests a training environment that the user prefers (such as a beach or a mountainous area). The VR providing unit also generates an optimal virtual environment based on the user's training history, maximizing the effectiveness of the training. For example, it identifies an environment in which the user can perform best based on past data. The VR providing unit also customizes the virtual environment based on the user's training data, providing a training environment that suits the user's preferences. For example, it generates an environment in which the user can relax. This maximizes the effectiveness of training by automatically generating an optimal virtual environment based on the user's past training data.

[0039] The VR providing unit can reflect user feedback in real time and dynamically change the virtual environment. For example, the VR providing unit collects feedback provided by the user during training in real time and dynamically changes the virtual environment. For example, it responds immediately when the user requests a change to the environment. Furthermore, the VR providing unit dynamically adjusts the virtual environment using AI based on the user feedback to provide an optimal training environment. For example, it changes the environment to one that allows the user to relax. Furthermore, the VR providing unit builds a system that reflects user feedback in real time and dynamically changes the virtual environment. For example, it adjusts the environment to reduce discomfort the user feels during training. In this way, the optimal training environment can be provided by reflecting user feedback in real time and dynamically changing the virtual environment.

[0040] The VR providing unit can incorporate scenes from the user's favorite movies or games into the virtual environment to enhance the entertainment value. The VR providing unit can incorporate, for example, scenes from the user's favorite movies or games into the virtual environment to enhance the entertainment value of training. For example, it can provide a training environment that recreates an action scene from a movie. The VR providing unit can also add entertainment elements to the virtual environment according to the user's preferences. For example, it can provide a stage from the user's favorite game as a training environment. The VR providing unit can also incorporate scenes from the user's favorite movies or games into the virtual environment to enhance the enjoyment of training. For example, it can provide a training environment that recreates an adventure scene from a movie. In this way, the entertainment value of training can be enhanced by incorporating scenes from the user's favorite movies or games into the virtual environment.

[0041] The VR providing unit enables joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit provides a function for joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit sets up a group training session in a virtual gym. The VR providing unit also provides a function for users to share their training in the virtual environment and encourage each other. For example, the VR providing unit realizes competitions and cooperative play on a virtual running course. The VR providing unit also enables joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit provides real-time communication in a virtual fitness class. This enables joint training with other users in a virtual environment, thereby promoting social interaction.

[0042] The VR providing unit can use the emotion estimation function to suggest a virtual event (e.g., a virtual marathon) that the user will enjoy most. For example, the VR providing unit can use the emotion estimation function to identify a virtual event that the user will enjoy most and suggest that event. For example, the VR providing unit can suggest a virtual marathon based on the user's emotion data. The VR providing unit can also analyze the user's emotional state in real time to provide the most enjoyable virtual event. For example, the VR providing unit can suggest a virtual sporting event that the user will find exciting. The VR providing unit can also use the emotion estimation function to suggest a virtual event that the user will enjoy most, thereby improving the enjoyment of training. For example, the VR providing unit can suggest a virtual triathlon based on the user's emotion data. In this way, the emotion estimation function can be used to suggest a virtual event that the user will enjoy most.

[0043] The progress tracking unit can provide individualized feedback and advice based on the user's progress data. For example, the progress tracking unit analyzes the user's progress data and uses AI to provide individualized feedback and advice. For example, it evaluates the results of training and suggests the next step. The progress tracking unit also uses AI to provide specific improvements and advice based on the user's progress data. For example, it makes suggestions to adjust the frequency or intensity of training. The progress tracking unit also analyzes the user's progress data and builds a system that provides individualized feedback and advice. For example, it visualizes the results of training to increase motivation. This makes it possible to maximize the effectiveness of training by providing individualized feedback and advice based on the user's progress data.

[0044] The progress tracking unit can provide an interactive dashboard to visualize the user's progress and maintain motivation. The progress tracking unit, for example, visualizes the user's progress data and provides an interactive dashboard. For example, it displays training results in graphs and charts. The progress tracking unit also updates the user's progress in real time and provides an interactive dashboard. For example, it provides a dashboard that allows the user to check training progress at a glance. The progress tracking unit also builds a system that provides an interactive dashboard to visualize the user's progress and maintain motivation. For example, it visually displays training results to increase motivation. This makes it possible to maximize the effectiveness of training by providing an interactive dashboard to visualize the user's progress and maintain motivation.

[0045] The progress tracking unit can introduce a function for comparing progress with other users to stimulate a competitive spirit. The progress tracking unit, for example, provides a function that allows users to compare their progress with other users to stimulate a competitive spirit. For example, it displays training progress in a ranking format. The progress tracking unit also provides a function that allows users to compare their progress with other users to enhance a competitive spirit. For example, it provides a platform where users can share their training results. The progress tracking unit also introduces a function for comparing progress with other users to build a system that stimulates a competitive spirit. For example, it provides a function that allows training progress to be compared in real time. By introducing a function for comparing progress with other users, the competitive spirit can be stimulated and the effectiveness of training can be maximized.

[0046] The progress tracking unit can provide real rewards according to the user's progress. The progress tracking unit, for example, builds a system that provides real rewards according to the user's progress. For example, fitness goods are given to users who achieve certain goals. The progress tracking unit also provides real rewards based on the user's progress data. For example, fitness goods or gift cards are provided according to training results. The progress tracking unit also develops a system that provides real rewards according to the user's progress. For example, a special benefit is provided to users who achieve their training goals. In this way, by providing real rewards according to the user's progress, it is possible to increase motivation for training.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The training plan generation unit can incorporate recreational activities that match the user's hobbies and lifestyle. For example, the user can incorporate their favorite outdoor activities (hiking, cycling, etc.) into the training plan, allowing them to exercise while having fun. The training plan generation unit also suggests short exercises that can be done between housework or work, tailored to the user's lifestyle. For example, it can incorporate stretching or light exercise that can be done while doing desk work. The training plan generation unit also takes into account the user's hobbies (dance, yoga, etc.) and provides training menus related to those hobbies. For example, it suggests dance exercises for a user who likes dancing. In this way, incorporating recreational activities that match the user's hobbies and lifestyle makes training more enjoyable.

[0049] The training plan generation unit can generate joint training plans with other users and promote social interaction. For example, it can match users with the same goals and provide them with a plan for joint training. For example, it can set up an online group training session. The training plan generation unit can also incorporate a competitive element with other users into the training plan to increase motivation. For example, it can introduce a ranking system that allows users to compete based on their training progress. The training plan generation unit can also provide a platform where users can share their training results, creating an environment where they can encourage each other. For example, it can add a social networking function that allows users to post their training results. This can promote social interaction by generating joint training plans with other users.

[0050] The training plan generation unit can analyze the user's past sports performance data and suggest exercises aimed at improving specific skills. For example, the unit collects the user's past sports performance data and analyzes the data using AI to suggest exercises to improve skills. For example, it can provide a training menu to improve soccer dribbling skills. The training plan generation unit can also identify the user's weaknesses based on the sports performance data and suggest exercises to overcome them. For example, it can provide a practice menu to improve basketball shooting accuracy. The training plan generation unit can also analyze the user's sports performance data and customize an exercise plan aimed at improving specific skills. For example, it can suggest training to improve tennis serving power. In this way, by analyzing the user's past sports performance data, it can suggest exercises aimed at improving specific skills.

[0051] The training plan generation unit can provide training plans that match sports seasons and events, thereby maximizing competitive ability. For example, AI customizes training plans to match sports seasons and events. For example, it provides a training plan for a marathon. The training plan generation unit also proposes training plans to maximize competitive ability that match sports events the user plans to participate in. For example, it provides intensive training for a soccer game. The training plan generation unit also provides training plans that match sports seasons, thereby maximizing the user's competitive ability. For example, it proposes a training menu that should be done before basketball season. In this way, competitive ability can be maximized by providing training plans that match sports seasons and events.

[0052] The training plan generation unit can combine training elements of different sports to provide a cross-training plan. For example, a cross-training plan is provided by combining training elements of different sports. For example, a plan combining training elements of soccer and basketball is proposed. The training plan generation unit also provides a cross-training plan by combining training elements of different sports according to the user's interests and goals. For example, a plan combining running and swimming training is proposed. The training plan generation unit also provides a cross-training plan by combining training elements of different sports to comprehensively improve the user's physical strength and skills. For example, a plan combining tennis and cycling training is proposed. In this way, a cross-training plan can be provided by combining training elements of different sports.

[0053] The training plan generation unit can incorporate the training menu of a user's favorite athlete. For example, it can provide an exercise plan that incorporates the training menu of the user's favorite athlete. For example, it can propose a plan based on the training menu of a soccer player. The training plan generation unit can also refer to the training menu of the user's favorite athlete and provide an exercise plan that incorporates elements of that menu. For example, it can propose a plan based on the training menu of a basketball player. The training plan generation unit can also incorporate the training menu of an athlete the user admires and provide an exercise plan that incorporates elements of that menu. For example, it can propose a plan based on the training menu of a tennis player. In this way, incorporating the training menu of the user's favorite athlete can increase motivation for training.

[0054] The VR providing unit can automatically generate an optimal virtual environment based on the user's past training data. For example, the AI ​​analyzes the user's past training data and automatically generates an optimal virtual environment based on that data. For example, it suggests a training environment that the user prefers (e.g., a beach or mountainous area). The VR providing unit also generates an optimal virtual environment based on the user's training history, maximizing the effectiveness of training. For example, it identifies an environment in which the user can perform best based on past data. The VR providing unit also customizes the virtual environment based on the user's training data and provides a training environment that suits the user's preferences. For example, it generates an environment in which the user can relax. This maximizes the effectiveness of training by automatically generating an optimal virtual environment based on the user's past training data.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The data collection unit collects data on the user's past dieting experience, goals, and abilities. For example, the user records the dieting methods that were successful in the past and the reasons for failure, and sets goals such as weight loss and muscle gain. The data collection unit also collects the user's physical fitness test results and exercise experience. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the user's data using data mining techniques to generate basic data for a training plan. It also analyzes data trends using statistical analysis techniques. Step 3: The training plan generation unit generates a customized training plan based on the results of the analysis by the analysis unit. For example, it creates a training menu according to the user's fitness level and adjusts the training plan according to the user's goals. Step 4: The VR providing unit provides the training plan generated by the training plan generating unit in a VR environment. For example, the user can train in a virtual gym or experience exercise in a virtual natural environment. Step 5: The progress tracking unit tracks the user's training progress, for example, by recording the user's exercise volume and achievements and tracking the duration of the training. Step 6: The gamification unit provides gamification elements based on the progress tracked by the progress tracking unit, such as allowing the user to level up or earn achievements upon achieving certain goals, and providing a points system and badges.

[0057] (Example 2) A fitness training system according to an embodiment of the present invention provides a customized training plan that takes into account a user's past dieting experience, goals, and abilities, allows training in a VR environment, tracks progress, and incorporates gamification elements, thereby enabling the fitness training system to provide personalized fitness training and nutritional guidance to individual health-conscious individuals.

[0058] A fitness training system according to an embodiment includes a data collection unit, an analysis unit, a training plan generation unit, a VR provision unit, a progress tracking unit, and a gamification unit. The data collection unit collects data on a user's past dieting experiences, goals, and abilities. For example, it records the user's past successful dieting methods and the reasons for their failure. The user can also set goals such as weight loss and muscle gain. The data collection unit collects the user's physical fitness test results and exercise experience. The analysis unit analyzes the data collected by the data collection unit. For example, it may analyze the user's data using data mining techniques to generate basic data for a training plan. It may also analyze data trends using statistical analysis techniques. The training plan generation unit generates a customized training plan based on the results of the analysis by the analysis unit. For example, it may create a training menu based on the user's fitness level. It may also adjust the training plan according to the user's goals. The VR provision unit provides the training plan generated by the training plan generation unit in a VR environment. For example, the user can train in a virtual gym or experience exercise in a virtual natural environment. The progress tracking unit tracks the user's training progress. For example, the system records the user's exercise volume and achievements. It can also track the duration of training. The gamification unit provides gamification elements based on the progress tracked by the progress tracking unit. For example, it allows the user to level up or earn achievements when a certain goal is achieved. It can also provide a point system and badges. This allows the fitness training system according to the embodiment to provide personalized fitness training and nutritional guidance to individual health-conscious individuals. For example, it can provide a training plan based on a user's past successful diet methods, allowing the user to enjoy exercising on a virtual beach. Furthermore, for a user interested in soccer, it can provide a soccer-specific training menu, and track the user's progress to earn level ups and achievements, enhancing the sense of accomplishment.This is expected to support long-term training continuation.

[0059] The data collection unit can also generate a more comprehensive training plan based on the user's food log and sleep patterns. For example, the data collection unit uses an app to record the user's daily meals, and AI analyzes that data to evaluate nutritional balance. For example, the training plan can be adjusted based on protein, carbohydrate, and fat intake. The data collection unit also monitors the user's sleep patterns using a device such as a smartwatch, and the AI ​​analyzes the data to suggest optimal training times and intensity. For example, it can increase training intensity on days when the user has had deep sleep. The data collection unit also integrates the food log and sleep patterns, and the AI ​​comprehensively evaluates the user's physical condition and energy level to customize the training plan. For example, it can suggest lighter training on days when the user is low in energy. This allows the system to provide a more comprehensive training plan by taking the user's food log and sleep patterns into account.

[0060] The analysis unit can analyze the user's psychological state based on the emotion estimation function and provide a training plan that includes mental support to maintain motivation. For example, the analysis unit can use the emotion estimation function to analyze the stress and anxiety the user feels during training in real time and incorporate breathing techniques and meditation for relaxation into the training plan. The analysis unit can also use the emotion estimation function to evaluate whether the user has positive emotions toward training and, if motivation is declining, present encouraging messages or success stories. The analysis unit can also periodically check the user's psychological state and provide mental support to maintain motivation. For example, the analysis unit can evaluate the user's emotional state weekly and adjust the training plan as needed. This allows the analysis unit to provide mental support to maintain motivation by taking the user's psychological state into consideration.

[0061] The data collection unit can collect the user's real-time biometric data and dynamically adjust the training plan based on that data. For example, the data collection unit uses a smartwatch or fitness tracker to monitor the user's heart rate and blood pressure in real time and adjust the intensity based on the data during training. The data collection unit also transmits the user's biometric data to the cloud, where AI analyzes the data and dynamically updates the training plan. For example, if the heart rate is high, it suggests taking a break. The data collection unit also displays the biometric data during training in real time, allowing the user to monitor their physical condition while training. For example, it checks whether the heart rate is within the target range. This allows the training plan to be dynamically adjusted by taking the user's real-time biometric data into consideration.

[0062] The training plan generation unit can incorporate recreational activities that match the user's hobbies and lifestyle. For example, the training plan generation unit can incorporate the user's favorite outdoor activities (hiking, cycling, etc.) into the training plan, allowing the user to exercise while having fun. The training plan generation unit also suggests short exercises that can be done in between housework or work, tailored to the user's lifestyle. For example, it can incorporate stretching or light exercise that can be done while doing desk work. The training plan generation unit also takes into account the user's hobbies (dance, yoga, etc.) and provides training menus related to those hobbies. For example, it can suggest dance exercises for a user who likes dancing. In this way, the enjoyment of training is increased by incorporating recreational activities that match the user's hobbies and lifestyle.

[0063] The training plan generation unit can generate a joint training plan with other users and promote social interaction. For example, the training plan generation unit matches users with the same goals and provides a joint training plan. For example, it sets up an online group training session. The training plan generation unit also incorporates a competitive element with other users into the training plan to increase motivation. For example, it introduces a ranking system where users compete against each other on their training progress. The training plan generation unit also provides a platform where users can share their training results with each other, creating an environment where they can encourage each other. For example, it adds a social networking function that allows users to post their training results. This makes it possible to generate a joint training plan with other users and promote social interaction.

[0064] The training plan generation unit can use the emotion estimation function to suggest the training format (music, video, etc.) that the user will enjoy most. For example, the training plan generation unit uses the emotion estimation function to suggest music that will most relax the user during training. For example, it analyzes heart rate and facial expressions to generate an appropriate playlist. The training plan generation unit also analyzes the user's emotional state in real time to suggest video content to watch during training. For example, it may provide videos of natural scenery or motivational videos. The training plan generation unit also customizes the training format that the user will enjoy most based on the emotion estimation data. For example, it may adjust the type and intensity of exercises to match the user's emotional state. In this way, the emotion estimation function can suggest the training format that the user will enjoy most.

[0065] The training plan generation unit can analyze the user's past sports performance data and suggest exercises aimed at improving specific skills. For example, the training plan generation unit collects the user's past sports performance data and uses AI to analyze the data to suggest exercises for improving skills. For example, it provides a training menu for improving soccer dribbling skills. The training plan generation unit also identifies the user's weaknesses based on the sports performance data and suggests exercises to overcome them. For example, it provides a practice menu for improving basketball shooting accuracy. The training plan generation unit also analyzes the user's sports performance data and customizes an exercise plan aimed at improving specific skills. For example, it suggests training to improve tennis serving power. In this way, by analyzing the user's past sports performance data, it is possible to suggest exercises aimed at improving specific skills.

[0066] The training plan generation unit can provide training plans tailored to sports seasons and events, thereby maximizing competitive ability. In the training plan generation unit, for example, AI customizes training plans to suit sports seasons and events. For example, it provides a training plan for a marathon. The training plan generation unit also proposes training plans to maximize competitive ability tailored to sports events the user plans to participate in. For example, it provides intensive training for a soccer game. The training plan generation unit also provides training plans tailored to sports seasons, thereby maximizing the user's competitive ability. For example, it proposes a training menu to be performed before basketball season. In this way, competitive ability can be maximized by providing training plans tailored to sports seasons and events.

[0067] The training plan generation unit uses the emotion estimation function to recreate the sports scene that excites the user most and allows the user to exercise in that scene. The training plan generation unit, for example, uses the emotion estimation function to identify the sports scene that excites the user most and recreates that scene in VR. For example, a goal scene from a soccer game is recreated and the user exercises in that scene. The training plan generation unit also selects the most exciting sports scene based on the user's emotion data and provides that scene as a training environment. For example, the climax scene from a basketball game is recreated. The training plan generation unit also uses the emotion estimation function to recreate the sports scene that excites the user most and allows the user to exercise in that scene. For example, a rally scene from a tennis game is recreated and the user trains in that scene. In this way, by using the emotion estimation function, the user can recreate the sports scene that excites the user most and exercise in that scene.

[0068] The training plan generation unit can combine training elements of different sports to provide a cross-training plan. The training plan generation unit, for example, combines training elements of different sports to provide a cross-training plan. For example, a plan combining training elements of soccer and basketball is proposed. The training plan generation unit also provides a cross-training plan combining training elements of different sports in accordance with the user's interests and goals. For example, a plan combining running and swimming training is proposed. The training plan generation unit also provides a cross-training plan combining training elements of different sports to comprehensively improve the user's physical strength and skills. For example, a plan combining tennis and cycling training is proposed. In this way, a cross-training plan can be provided by combining training elements of different sports.

[0069] The training plan generation unit can incorporate the training menu of a user's favorite athlete. The training plan generation unit provides, for example, an exercise plan that incorporates the training menu of a user's favorite athlete. For example, it proposes a plan based on the training menu of a soccer player. The training plan generation unit also refers to the training menu of a user's favorite athlete and provides an exercise plan that incorporates elements of that menu. For example, it proposes a plan based on the training menu of a basketball player. The training plan generation unit also incorporates the training menu of an athlete the user admires and provides an exercise plan that incorporates elements of that menu. For example, it proposes a plan based on the training menu of a tennis player. In this way, incorporating the training menu of a user's favorite athlete can increase motivation for training.

[0070] The training plan generation unit can use the emotion estimation function to suggest a sports environment in which the user can be most relaxed and provide a recovery plan. For example, the training plan generation unit can use the emotion estimation function to identify a sports environment in which the user can be most relaxed and provide a recovery plan in that environment. For example, the training plan generation unit can suggest yoga or stretching in nature. The training plan generation unit can also suggest a sports environment in which the user can be most relaxed based on the user's emotion data and provide a recovery plan in that environment. For example, the training plan generation unit can suggest relaxation exercises on the beach. The training plan generation unit can also use the emotion estimation function to suggest a sports environment in which the user can be most relaxed and provide a recovery plan in that environment. For example, the training plan generation unit can suggest recovery training in a mountainous area. In this way, the emotion estimation function can be used to suggest a sports environment in which the user can be most relaxed and provide a recovery plan.

[0071] The VR providing unit can automatically generate an optimal virtual environment based on the user's past training data. For example, the VR providing unit analyzes the user's past training data, and the AI ​​automatically generates an optimal virtual environment based on that data. For example, it suggests a training environment that the user prefers (such as a beach or a mountainous area). The VR providing unit also generates an optimal virtual environment based on the user's training history, maximizing the effectiveness of the training. For example, it identifies an environment in which the user can perform best based on past data. The VR providing unit also customizes the virtual environment based on the user's training data, providing a training environment that suits the user's preferences. For example, it generates an environment in which the user can relax. This maximizes the effectiveness of training by automatically generating an optimal virtual environment based on the user's past training data.

[0072] The VR providing unit uses the emotion estimation function to provide a virtual environment in which the user can be most relaxed, thereby reducing stress. For example, the VR providing unit uses the emotion estimation function to identify a virtual environment in which the user can be most relaxed and provide that environment. For example, it generates a natural environment in which the user can be relaxed based on the user's emotion data. The VR providing unit also analyzes the user's emotional state in real time and provides a virtual environment for stress reduction. For example, it generates an environment that incorporates music and scenery that the user can relax in. The VR providing unit also uses the emotion estimation function to provide a virtual environment in which the user can be most relaxed, thereby reducing stress. For example, it generates an environment in which relaxation exercises can be performed based on the user's emotion data. In this way, by using the emotion estimation function, it is possible to provide a virtual environment in which the user can be most relaxed, thereby reducing stress.

[0073] The VR providing unit can reflect user feedback in real time and dynamically change the virtual environment. For example, the VR providing unit collects feedback provided by the user during training in real time and dynamically changes the virtual environment. For example, it responds immediately when the user requests a change to the environment. Furthermore, the VR providing unit dynamically adjusts the virtual environment using AI based on the user feedback to provide an optimal training environment. For example, it changes the environment to one that allows the user to relax. Furthermore, the VR providing unit builds a system that reflects user feedback in real time and dynamically changes the virtual environment. For example, it adjusts the environment to reduce discomfort the user feels during training. In this way, the optimal training environment can be provided by reflecting user feedback in real time and dynamically changing the virtual environment.

[0074] The VR providing unit can incorporate scenes from the user's favorite movies or games into the virtual environment to enhance the entertainment value. The VR providing unit can incorporate, for example, scenes from the user's favorite movies or games into the virtual environment to enhance the entertainment value of training. For example, it can provide a training environment that recreates an action scene from a movie. The VR providing unit can also add entertainment elements to the virtual environment according to the user's preferences. For example, it can provide a stage from the user's favorite game as a training environment. The VR providing unit can also incorporate scenes from the user's favorite movies or games into the virtual environment to enhance the enjoyment of training. For example, it can provide a training environment that recreates an adventure scene from a movie. In this way, the entertainment value of training can be enhanced by incorporating scenes from the user's favorite movies or games into the virtual environment.

[0075] The VR providing unit enables joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit provides a function for joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit sets up a group training session in a virtual gym. The VR providing unit also provides a function for users to share their training in the virtual environment and encourage each other. For example, the VR providing unit realizes competitions and cooperative play on a virtual running course. The VR providing unit also enables joint training with other users in a virtual environment, thereby promoting social interaction. For example, the VR providing unit provides real-time communication in a virtual fitness class. This enables joint training with other users in a virtual environment, thereby promoting social interaction.

[0076] The VR providing unit can use the emotion estimation function to suggest a virtual event (e.g., a virtual marathon) that the user will enjoy most. For example, the VR providing unit can use the emotion estimation function to identify a virtual event that the user will enjoy most and suggest that event. For example, the VR providing unit can suggest a virtual marathon based on the user's emotion data. The VR providing unit can also analyze the user's emotional state in real time to provide the most enjoyable virtual event. For example, the VR providing unit can suggest a virtual sporting event that the user will find exciting. The VR providing unit can also use the emotion estimation function to suggest a virtual event that the user will enjoy most, thereby improving the enjoyment of training. For example, the VR providing unit can suggest a virtual triathlon based on the user's emotion data. In this way, the emotion estimation function can be used to suggest a virtual event that the user will enjoy most.

[0077] The progress tracking unit can provide individualized feedback and advice based on the user's progress data. For example, the progress tracking unit analyzes the user's progress data and uses AI to provide individualized feedback and advice. For example, it evaluates the results of training and suggests the next step. The progress tracking unit also uses AI to provide specific improvements and advice based on the user's progress data. For example, it makes suggestions to adjust the frequency or intensity of training. The progress tracking unit also analyzes the user's progress data and builds a system that provides individualized feedback and advice. For example, it visualizes the results of training to increase motivation. This makes it possible to maximize the effectiveness of training by providing individualized feedback and advice based on the user's progress data.

[0078] The progress tracking unit can use the emotion estimation function to set a goal that gives the user the greatest sense of accomplishment. The progress tracking unit, for example, uses the emotion estimation function to identify a goal that gives the user the greatest sense of accomplishment and set that goal. For example, it suggests a goal that will increase the sense of accomplishment based on the user's emotion data. The progress tracking unit also analyzes the user's emotional state in real time and sets a goal that gives the user the greatest sense of accomplishment. For example, it suggests a training goal that will give the user a sense of accomplishment. The progress tracking unit also uses the emotion estimation function to set a goal that gives the user the greatest sense of accomplishment and increase motivation for training. For example, it sets a goal that will increase the sense of accomplishment based on the user's emotion data. In this way, by using the emotion estimation function, it is possible to set a goal that gives the user the greatest sense of accomplishment.

[0079] The progress tracking unit can provide an interactive dashboard to visualize the user's progress and maintain motivation. The progress tracking unit, for example, visualizes the user's progress data and provides an interactive dashboard. For example, it displays training results in graphs and charts. The progress tracking unit also updates the user's progress in real time and provides an interactive dashboard. For example, it provides a dashboard that allows the user to check training progress at a glance. The progress tracking unit also builds a system that provides an interactive dashboard to visualize the user's progress and maintain motivation. For example, it visually displays training results to increase motivation. This makes it possible to maximize the effectiveness of training by providing an interactive dashboard to visualize the user's progress and maintain motivation.

[0080] The progress tracking unit can introduce a function for comparing progress with other users to stimulate a competitive spirit. The progress tracking unit, for example, provides a function that allows users to compare their progress with other users to stimulate a competitive spirit. For example, it displays training progress in a ranking format. The progress tracking unit also provides a function that allows users to compare their progress with other users to enhance a competitive spirit. For example, it provides a platform where users can share their training results. The progress tracking unit also introduces a function for comparing progress with other users to build a system that stimulates a competitive spirit. For example, it provides a function that allows training progress to be compared in real time. By introducing a function for comparing progress with other users, the competitive spirit can be stimulated and the effectiveness of training can be maximized.

[0081] The progress tracking unit can provide real rewards according to the user's progress. The progress tracking unit, for example, builds a system that provides real rewards according to the user's progress. For example, fitness goods are given to users who achieve certain goals. The progress tracking unit also provides real rewards based on the user's progress data. For example, fitness goods or gift cards are provided according to training results. The progress tracking unit also develops a system that provides real rewards according to the user's progress. For example, a special benefit is provided to users who achieve their training goals. In this way, by providing real rewards according to the user's progress, it is possible to increase motivation for training.

[0082] The progress tracking unit can use the emotion estimation function to suggest the gamification element (e.g., raising a virtual pet) that the user will enjoy most. The progress tracking unit, for example, uses the emotion estimation function to identify the gamification element that the user will enjoy most and suggest that element. For example, it suggests raising a virtual pet based on the user's emotion data. The progress tracking unit also analyzes the user's emotional state in real time to provide the most enjoyable gamification element. For example, it incorporates game elements that the user will enjoy into training. The progress tracking unit also uses the emotion estimation function to suggest the gamification element that the user will enjoy most and improve the enjoyment of training. For example, it suggests raising a virtual pet based on the user's emotion data. In this way, the emotion estimation function can be used to suggest the most enjoyable gamification element for the user.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The training plan generation unit can incorporate recreational activities that match the user's hobbies and lifestyle. For example, the user can incorporate their favorite outdoor activities (hiking, cycling, etc.) into the training plan, allowing them to exercise while having fun. The training plan generation unit also suggests short exercises that can be done between housework or work, tailored to the user's lifestyle. For example, it can incorporate stretching or light exercise that can be done while doing desk work. The training plan generation unit also takes into account the user's hobbies (dance, yoga, etc.) and provides training menus related to those hobbies. For example, it suggests dance exercises for a user who likes dancing. In this way, incorporating recreational activities that match the user's hobbies and lifestyle makes training more enjoyable.

[0085] The training plan generation unit can generate joint training plans with other users and promote social interaction. For example, it can match users with the same goals and provide them with a plan for joint training. For example, it can set up an online group training session. The training plan generation unit can also incorporate a competitive element with other users into the training plan to increase motivation. For example, it can introduce a ranking system that allows users to compete based on their training progress. The training plan generation unit can also provide a platform where users can share their training results, creating an environment where they can encourage each other. For example, it can add a social networking function that allows users to post their training results. This can promote social interaction by generating joint training plans with other users.

[0086] The training plan generation unit can use the emotion estimation function to suggest the training format (music, video, etc.) that the user will enjoy most. For example, the emotion estimation function can be used to suggest music that will most relax the user during training. For example, an appropriate playlist can be generated by analyzing heart rate and facial expressions. The training plan generation unit can also analyze the user's emotional state in real time to suggest video content to watch during training. For example, it can provide videos of natural scenery or videos that will increase motivation. The training plan generation unit can also customize the training format that the user will enjoy most based on the emotion estimation data. For example, it can adjust the type and intensity of exercises to match the user's emotional state. In this way, the emotion estimation function can suggest the training format that the user will enjoy most.

[0087] The training plan generation unit can analyze the user's past sports performance data and suggest exercises aimed at improving specific skills. For example, the unit collects the user's past sports performance data and analyzes the data using AI to suggest exercises to improve skills. For example, it can provide a training menu to improve soccer dribbling skills. The training plan generation unit can also identify the user's weaknesses based on the sports performance data and suggest exercises to overcome them. For example, it can provide a practice menu to improve basketball shooting accuracy. The training plan generation unit can also analyze the user's sports performance data and customize an exercise plan aimed at improving specific skills. For example, it can suggest training to improve tennis serving power. In this way, by analyzing the user's past sports performance data, it can suggest exercises aimed at improving specific skills.

[0088] The training plan generation unit can provide training plans that match sports seasons and events, thereby maximizing competitive ability. For example, AI customizes training plans to match sports seasons and events. For example, it provides a training plan for a marathon. The training plan generation unit also proposes training plans to maximize competitive ability that match sports events the user plans to participate in. For example, it provides intensive training for a soccer game. The training plan generation unit also provides training plans that match sports seasons, thereby maximizing the user's competitive ability. For example, it proposes a training menu that should be done before basketball season. In this way, competitive ability can be maximized by providing training plans that match sports seasons and events.

[0089] The training plan generation unit uses the emotion estimation function to recreate the sports scene that excites the user most and allows the user to exercise in that scene. For example, the emotion estimation function is used to identify the sports scene that excites the user most and recreate that scene in VR. For example, a goal scene from a soccer game is recreated and the user exercises in that scene. The training plan generation unit also selects the most exciting sports scene based on the user's emotion data and provides that scene as a training environment. For example, the climax scene from a basketball game is recreated. The training plan generation unit also uses the emotion estimation function to recreate the sports scene that excites the user most and allows the user to exercise in that scene. For example, a rally scene from a tennis game is recreated and the user trains in that scene. In this way, the emotion estimation function allows the user to recreate the sports scene that excites the user most and allow the user to exercise in that scene.

[0090] The training plan generation unit can combine training elements of different sports to provide a cross-training plan. For example, a cross-training plan is provided by combining training elements of different sports. For example, a plan combining training elements of soccer and basketball is proposed. The training plan generation unit also provides a cross-training plan by combining training elements of different sports according to the user's interests and goals. For example, a plan combining running and swimming training is proposed. The training plan generation unit also provides a cross-training plan by combining training elements of different sports to comprehensively improve the user's physical strength and skills. For example, a plan combining tennis and cycling training is proposed. In this way, a cross-training plan can be provided by combining training elements of different sports.

[0091] The training plan generation unit can incorporate the training menu of a user's favorite athlete. For example, it can provide an exercise plan that incorporates the training menu of the user's favorite athlete. For example, it can propose a plan based on the training menu of a soccer player. The training plan generation unit can also refer to the training menu of the user's favorite athlete and provide an exercise plan that incorporates elements of that menu. For example, it can propose a plan based on the training menu of a basketball player. The training plan generation unit can also incorporate the training menu of an athlete the user admires and provide an exercise plan that incorporates elements of that menu. For example, it can propose a plan based on the training menu of a tennis player. In this way, incorporating the training menu of the user's favorite athlete can increase motivation for training.

[0092] The training plan generation unit can use the emotion estimation function to suggest a sports environment in which the user can be most relaxed and provide a recovery plan. For example, the emotion estimation function can be used to identify a sports environment in which the user can be most relaxed and provide a recovery plan in that environment. For example, yoga or stretching in nature can be suggested. The training plan generation unit can also suggest a sports environment in which the user can be most relaxed based on the user's emotion data and provide a recovery plan in that environment. For example, relaxation exercises can be suggested on the beach. The training plan generation unit can also use the emotion estimation function to suggest a sports environment in which the user can be most relaxed and provide a recovery plan in that environment. For example, recovery training in a mountainous area can be suggested. In this way, the emotion estimation function can be used to suggest a sports environment in which the user can be most relaxed and provide a recovery plan.

[0093] The VR providing unit can automatically generate an optimal virtual environment based on the user's past training data. For example, the AI ​​analyzes the user's past training data and automatically generates an optimal virtual environment based on that data. For example, it suggests a training environment that the user prefers (e.g., a beach or mountainous area). The VR providing unit also generates an optimal virtual environment based on the user's training history, maximizing the effectiveness of training. For example, it identifies an environment in which the user can perform best based on past data. The VR providing unit also customizes the virtual environment based on the user's training data and provides a training environment that suits the user's preferences. For example, it generates an environment in which the user can relax. This maximizes the effectiveness of training by automatically generating an optimal virtual environment based on the user's past training data.

[0094] The processing flow of the second embodiment will be briefly explained below.

[0095] Step 1: The data collection unit collects data on the user's past dieting experience, goals, and abilities. For example, the user records the dieting methods that were successful in the past and the reasons for failure, and sets goals such as weight loss and muscle gain. The data collection unit also collects the user's physical fitness test results and exercise experience. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the user's data using data mining techniques to generate basic data for a training plan. It also analyzes data trends using statistical analysis techniques. Step 3: The training plan generation unit generates a customized training plan based on the results of the analysis by the analysis unit. For example, it creates a training menu according to the user's fitness level and adjusts the training plan according to the user's goals. Step 4: The VR providing unit provides the training plan generated by the training plan generating unit in a VR environment. For example, the user can train in a virtual gym or experience exercise in a virtual natural environment. Step 5: The progress tracking unit tracks the user's training progress, for example, by recording the user's exercise volume and achievements and tracking the duration of the training. Step 6: The gamification unit provides gamification elements based on the progress tracked by the progress tracking unit, such as allowing the user to level up or earn achievements upon achieving certain goals, and providing a points system and badges.

[0096] 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.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0098] 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.

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

[0100] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

[0112] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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 AI 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.

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

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 AI 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.

[0128] 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.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 AI 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0150] 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."

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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]

[0163] 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 data collection unit that collects data on the user's past dieting experience, goals, and abilities; an analysis unit that analyzes the data collected by the data collection unit; a training plan generation unit that generates a customized training plan based on the results of the analysis by the analysis unit; a VR providing unit that provides the training plan generated by the training plan generating unit in a VR environment; a progress tracker for tracking the user's training progress; a gamification unit that provides gamification elements based on the progress tracked by the progress tracking unit. A system characterized by:

2. The data collection unit Generate a more comprehensive training plan based on the user's food log and sleep patterns as well.

2. The system of claim 1.

3. The analysis unit The psychological state of the user is analyzed based on an emotion estimation function, and the training plan is provided, which includes mental support for maintaining motivation.

2. The system of claim 1.

4. The data collection unit Collecting real-time biometric data of the user and dynamically adjusting the training plan based thereon.

2. The system of claim 1.

5. The training plan generation unit Incorporating recreational activities tailored to the user's hobbies and lifestyle 2. The system of claim 1.

6. The training plan generation unit Suggest the training format that the user will enjoy most 2. The system of claim 1.

Images