system

Abstract

The system according to this embodiment aims to personalize the user's fitness journey and maintain their motivation on a continuous basis. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a reproduction unit, an analysis unit, and a plan generation unit. The reception unit inputs the user's fitness journey as text. The generation unit analyzes the text received by the reception unit and generates personalized training. The reproduction unit reproduces the training generated by the generation unit in VR. The analysis unit analyzes the results of the training reproduced by the reproduction unit. The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to personalize a user's fitness journey and continuously maintain motivation.

[0005] The system according to the embodiment aims to personalize a user's fitness journey and continuously maintain motivation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a reproduction unit, an analysis unit, and a plan generation unit. The reception unit inputs the user's fitness journey as text. The generation unit analyzes the text received by the reception unit and generates personalized training. The reproduction unit reproduces the training generated by the generation unit in VR. The analysis unit analyzes the results of the training reproduced by the reproduction unit. The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can personalize the user's fitness journey and continuously maintain their motivation. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The fitness system according to an embodiment of the present invention is a system that utilizes LLM to generate a user's unique fitness journey from text and reproduces that experience in VR. When a user inputs their fitness journey as text, the generating AI analyzes the content and generates personalized training tailored to the user's interests and goals. The generated training is reproduced in VR, allowing the user to experience a fresh and varied training environment. Furthermore, the fitness system generates the next fitness and nutrition plan based on the user's exercise results, supporting the user's continued fitness motivation. For example, if a user inputs "I want to do strength training," the generating AI analyzes the content and generates a program suitable for strength training. The generated program is reproduced in VR, allowing the user to actually perform the training. After the training, the fitness system analyzes the exercise results and generates the next training plan and nutrition plan. This allows the user to always receive fresh and effective training and maintain their motivation. In this way, the fitness system can efficiently generate, reproduce, and analyze the user's fitness journey and provide the next plan.

[0029] The fitness system according to this embodiment comprises a reception unit, a generation unit, a reproduction unit, an analysis unit, and a plan generation unit. The reception unit inputs the user's fitness journey as text. The user's fitness journey includes, but is not limited to, the type, frequency, and duration of exercise. The reception unit can accept text input in, for example, a free-text format or a multiple-choice format. The generation unit uses a generation AI to analyze the text received by the reception unit and generate personalized training. The generated personalized training includes, for example, individual exercise plans and intensity adjustments, but is not limited to, individual exercise plans. The generation unit uses, for example, a generation AI to generate a training plan based on the user's interests and goals. The reproduction unit reproduces the training generated by the generation unit in VR. The reproduction unit can reproduce the training based, for example, the device used and the level of detail of the reproduction. The reproduction unit can reproduce the training environment using, for example, a VR headset. The analysis unit analyzes the results of the training reproduced by the reproduction unit. The analysis unit can analyze the training results based, for example, the data collection method and the analysis algorithm. The analysis unit analyzes, for example, heart rate and calories burned during training. The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. The plan generation unit can generate the next plan based on, for example, the details of the meal plan and exercise plan. The plan generation unit adjusts the next training plan based on, for example, the user's exercise results. In this way, the fitness system according to the embodiment can efficiently generate, reproduce, and analyze the user's fitness journey and provide the next plan.

[0030] The reception desk receives text input of the user's fitness journey. This includes, but is not limited to, the type, frequency, and duration of exercise. The reception desk can accept text input in either a free-text or multiple-choice format. Specifically, users access a dedicated application or website using their smartphone or computer to input their fitness goals, current exercise habits, and past exercise history. The free-text format allows users to provide detailed information in their own words, enabling customization to individual needs and goals. The multiple-choice format, on the other hand, is simpler to use, especially for fitness beginners, as users only need to select options from pre-set questions. Furthermore, the reception desk automatically saves the information entered by the user, allowing for later editing and additions. This allows users to update their information as their fitness journey progresses, keeping it always up-to-date. The reception desk also protects user privacy by encrypting and storing the entered data. This allows users to confidently provide their information.

[0031] The generation unit uses a generation AI to analyze text received by the reception unit and generate personalized training. The generated personalized training may include, but is not limited to, individual exercise plans and intensity adjustments. For example, the generation AI generates training plans based on the user's interests and goals. Specifically, the generation AI uses natural language processing technology to analyze the user's input text and creates an optimal training plan considering the user's fitness goals, current fitness level, and past exercise history. For example, if a user inputs "I want to jog for 30 minutes three times a week," the generation AI will use this information to suggest specific jogging routes, paces, and stretching methods. The generation AI can also adjust the training plan in real time based on user feedback. For example, if a user inputs "I'm tired today, so I want to do some light exercise," the generation AI will use this information to suggest a plan of light stretching or yoga. Furthermore, the generation AI can generate training plans that combine music and video content according to the user's interests and preferences. This allows users to continue training in an enjoyable and effective way.

[0032] The reproduction unit recreates the training generated by the generation unit in VR. The reproduction unit can recreate the training based on, for example, the device used and the level of detail of the recreation. For example, the reproduction unit can recreate the training environment using a VR headset. Specifically, the user can wear a VR headset and train in a virtual space. In the virtual space, various training environments are recreated, such as realistic gyms, natural landscapes, and sports stadiums, and the user can choose according to their preference. Furthermore, the reproduction unit tracks the user's movements in real time and accurately recreates the movements in the virtual space. This allows the user to perform effective training while moving their body just as they would in actual training. The reproduction unit also has a function to provide visual feedback on the progress and achievement of the training. For example, it can display how much progress the user has made towards their set goal in graphs and charts, which can help maintain motivation. In addition, the reproduction unit also provides a function that allows users to train together in the virtual space with other users. This allows users to enjoy training with friends and family and continue their fitness while feeling a sense of social connection.

[0033] The analysis unit analyzes the results of the training reproduced by the reproduction unit. The analysis unit can analyze training results based on, for example, the data collection method and analysis algorithm. For example, the analysis unit analyzes heart rate and calories burned during training. Specifically, the analysis unit collects data such as heart rate, calories burned, exercise intensity, and exercise time from a wearable device worn by the user during VR training. This data is transmitted to a cloud server in real time and analyzed in detail by the analysis algorithm. The analysis algorithm evaluates the user's performance by comparing it with the user's past training data and a general fitness database. For example, it analyzes heart rate fluctuation patterns and calorie consumption trends to evaluate the user's fitness level and the effectiveness of the training. In addition, the analysis unit can issue warnings if abnormal data is detected during training using an anomaly detection algorithm. For example, if the heart rate rises sharply or the exercise intensity is excessively high, it can send a notification to the user prompting them to take a break. Furthermore, the analysis unit can collect user feedback and suggest improvements to the training plan based on the analysis results. This allows the user to accurately understand the effectiveness of their training and apply it to their next training session.

[0034] The planning unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. For example, the planning unit can generate the next plan based on the details of the meal plan and exercise plan. For example, the planning unit can adjust the next training plan based on the user's exercise results. Specifically, the planning unit creates an optimal fitness plan tailored to the user's fitness level and goals based on the data provided by the analysis unit. For example, if the user wants to focus on strength training, the planning unit will suggest an exercise plan and meal plan suitable for strength training. Also, if the user is aiming to lose weight, it will provide a plan that combines calorie restriction and aerobic exercise. Furthermore, the planning unit can flexibly adjust the plan according to the user's lifestyle and schedule. For example, it will suggest short, effective workouts on busy days and recommend longer workouts on days when the user has more time. The planning unit can also customize meal plans considering the user's preferences and allergy information. This ensures that the user has a fitness and nutrition plan that they can easily stick to. In addition, the planning unit regularly monitors the user's progress and updates the plan as needed. This allows users to always implement the most up-to-date fitness and nutrition plan based on the latest information.

[0035] The reception desk can analyze the user's past fitness journey history and select the optimal input method. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. For example, if the user has used voice input in the past, the reception desk can also prioritize suggesting voice input. For example, if the user has entered their fitness journey during a specific time period in the past, the reception desk can prompt them to enter their fitness journey during that time period. This allows the reception desk to suggest the optimal input method based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past fitness journey history into a generating AI and have the generating AI select the optimal input method.

[0036] The reception unit can filter the user's current health status and goals when they input their fitness journey. For example, if a user inputs their current health status, the reception unit can suggest an appropriate fitness journey based on that information. For example, if the user's goal is to increase muscle strength, the reception unit can suggest a fitness journey focused on strength training. For example, if the user's goal is weight loss, the reception unit can suggest a fitness journey that emphasizes calorie consumption. This allows the reception unit to suggest a fitness journey that is tailored to the user's health status and goals. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's health status data into a generating AI and have the generating AI perform the filtering.

[0037] The reception desk can prioritize highly relevant fitness journeys when the user inputs their fitness journey, taking into account their geographical location. For example, the reception desk can suggest fitness facilities near the user's current location. If the user is traveling, the reception desk can also suggest fitness journeys that can be done in that area. If the user is at home, the reception desk can also suggest fitness journeys that can be done at home. This allows the reception desk to suggest appropriate fitness journeys based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI select highly relevant journeys.

[0038] The reception desk can analyze the user's social media activity and input relevant journeys when the user enters their fitness journey. For example, the reception desk can suggest a journey based on the fitness goals the user has shared on social media. For example, the reception desk can also suggest training from fitness influencers the user follows. For example, the reception desk can also suggest a journey based on the trends of the fitness community the user participates in. This allows the reception desk to suggest an appropriate fitness journey based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant journeys.

[0039] The generation unit can adjust the level of detail of the training based on the user's fitness level when generating the training. For example, the generation unit can provide beginner users with training that focuses on basic movements. For example, the generation unit can also provide intermediate users with slightly more difficult training. For example, the generation unit can also provide advanced users with training that requires advanced skills. This allows the generation unit to provide training that is appropriate for the user's fitness level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's fitness level data into a generation AI and have the generation AI perform the adjustment of the level of detail of the training.

[0040] The generation unit can apply different generation algorithms depending on the user's goals when generating training. For example, for a user whose goal is to increase muscle strength, the generation unit can apply an algorithm specialized in muscle training. For example, for a user whose goal is to lose weight, the generation unit can apply an algorithm that emphasizes calorie consumption. For example, for a user whose goal is to improve flexibility, the generation unit can apply an algorithm that focuses on stretching. This allows the generation unit to provide training tailored to the user's goals. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's goal data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0041] The generation unit can determine training priorities based on the user's past training history when generating training. For example, the generation unit may prioritize suggesting training that was highly effective for the user in the past. The generation unit may also prioritize suggesting training that the user enjoyed in the past. The generation unit may also avoid suggesting training that the user found difficult in the past. This allows the generation unit to provide appropriate training based on the user's past training history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input the user's past training history into a generation AI and have the generation AI determine the training priorities.

[0042] The generation unit can adjust the order of training based on the user's interests when generating training. For example, the generation unit may suggest training that the user is interested in first. The generation unit may also suggest training that the user is interested in last. The generation unit may also suggest training that the user is interested in in the middle. This allows the generation unit to provide a training order that is tailored to the user's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI perform the adjustment of the training order.

[0043] The reproduction unit can adjust the level of detail of the VR reproduction based on the user's fitness level. For example, the reproduction unit can provide a VR reproduction focusing on basic movements to a beginner user. For example, the reproduction unit can also provide a VR reproduction that includes slightly more difficult movements to an intermediate user. For example, the reproduction unit can also provide a VR reproduction that includes movements requiring advanced skills to an advanced user. This allows for the provision of VR reproductions tailored to the user's fitness level. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's fitness level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reproduction.

[0044] The reproduction unit can apply different reproduction algorithms to VR reproductions according to the user's goals. For example, the reproduction unit can provide a VR reproduction specialized in strength training to a user whose goal is muscle strengthening. For example, the reproduction unit can also provide a VR reproduction that emphasizes calorie consumption to a user whose goal is weight loss. For example, the reproduction unit can provide a VR reproduction that focuses on stretching to a user whose goal is flexibility improvement. This allows the reproduction unit to provide VR reproductions tailored to the user's goals. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's goal data into a generating AI and have the generating AI execute the application of the reproduction algorithm.

[0045] The reproduction unit can determine the priority of VR reproductions based on the user's past VR experience history. For example, the reproduction unit may prioritize suggesting VR experiences that were highly effective for the user in the past. It can also prioritize suggesting VR experiences that the user enjoyed in the past. It can also avoid suggesting VR experiences that the user disliked in the past. This allows the reproduction unit to provide appropriate VR reproductions based on the user's past VR experience history. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's past VR experience history into a generating AI and have the generating AI determine the priority of reproductions.

[0046] The reproduction unit can adjust the order of VR reproduction based on the user's interests. For example, the reproduction unit may suggest a VR experience that the user is interested in first. The reproduction unit may also suggest a VR experience that the user is interested in last. The reproduction unit may also suggest a VR experience that the user is interested in in the middle. This allows the reproduction unit to provide an order of VR reproduction that is tailored to the user's interests. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input user interest data into a generating AI and have the generating AI adjust the order of reproduction.

[0047] The analysis unit can adjust the level of detail of the analysis based on the user's fitness level. For example, the analysis unit provides basic analysis results to beginner users. For example, the analysis unit can also provide slightly more detailed analysis results to intermediate users. For example, the analysis unit can also provide detailed analysis results to advanced users. This allows the analysis unit to provide analysis results that are appropriate to the user's fitness level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's fitness level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the user's goals during the analysis. For example, for a user whose goal is to increase muscle strength, the analysis unit can perform an analysis that emphasizes the effects of muscle training. For example, for a user whose goal is to lose weight, the analysis unit can perform an analysis that emphasizes calorie consumption. For example, for a user whose goal is to improve flexibility, the analysis unit can perform an analysis that emphasizes the degree of flexibility improvement. This allows the analysis unit to provide analysis results that match the user's goals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0049] The analysis unit can determine the priority of analysis based on the user's past training results during the analysis process. For example, the analysis unit may prioritize analyzing training that was highly effective for the user in the past. The analysis unit may also prioritize analyzing training that the user enjoyed in the past. The analysis unit may also avoid analyzing training that the user struggled with in the past. This allows the analysis unit to provide appropriate analysis based on the user's past training results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past training results into a generating AI and have the generating AI determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the user's interests during the analysis process. For example, the analysis unit may analyze the training that the user is interested in first. The analysis unit may also analyze the training that the user is interested in last. The analysis unit may also analyze the training that the user is interested in in the middle. This allows the analysis to be performed in an order that suits the user's interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0051] The plan generation unit can adjust the level of detail of the plan based on the user's fitness level during plan generation. For example, the plan generation unit can provide a basic fitness and nutrition plan to beginner users. For example, the plan generation unit can also provide a slightly more detailed fitness and nutrition plan to intermediate users. For example, the plan generation unit can also provide a detailed fitness and nutrition plan to advanced users. This allows the system to provide fitness and nutrition plans tailored to the user's fitness level. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's fitness level data into a generation AI and have the generation AI adjust the level of detail of the plan.

[0052] The plan generation unit can apply different generation algorithms depending on the user's goals when generating a plan. For example, the plan generation unit can provide a fitness and nutrition plan specializing in strength training to a user whose goal is muscle strengthening. For example, the plan generation unit can also provide a fitness and nutrition plan that emphasizes calorie consumption to a user whose goal is weight loss. For example, the plan generation unit can provide a fitness and nutrition plan that focuses on stretching to a user whose goal is to improve flexibility. This allows the plan generation unit to provide a fitness and nutrition plan that is tailored to the user's goals. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's goal data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0053] The plan generation unit can determine the priority of plans based on the user's past fitness and nutrition plans when generating a plan. For example, the plan generation unit can prioritize providing plans that were highly effective in the user's past fitness and nutrition plans. For example, the plan generation unit can also prioritize providing plans that the user liked in their past fitness and nutrition plans. For example, the plan generation unit can also avoid providing plans that the user disliked in their past fitness and nutrition plans. This allows the system to provide an appropriate plan based on the user's past fitness and nutrition plans. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's past fitness and nutrition plans into a generation AI and have the generation AI determine the priority of the plans.

[0054] The plan generation unit can adjust the order of plans based on the user's interests during plan generation. For example, the plan generation unit may first provide the fitness and nutrition plan that the user is interested in. For example, the plan generation unit may also provide the fitness and nutrition plan that the user is interested in at the end. For example, the plan generation unit may also provide the fitness and nutrition plan that the user is interested in in the middle. This allows the system to provide a fitness and nutrition plan order that is tailored to the user's interests. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input user interest data into a generation AI and have the generation AI adjust the order of the plans.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The fitness system can also acquire user sleep data and incorporate it into the training plan. For example, if a user hasn't gotten enough sleep, it can suggest a lighter workout. Conversely, if a user has had good quality sleep, it can suggest a more challenging workout. Furthermore, it can adjust the training time based on sleep data. This allows the system to provide an optimal training plan tailored to the user's sleep patterns.

[0057] A fitness system can analyze a user's past fitness journey history and suggest the optimal training environment. For example, if a user previously preferred training in nature, a natural environment can be recreated using VR. Similarly, if a user preferred training at a gym, a gym environment can be recreated. Furthermore, if a user preferred training at home, a home environment can be recreated. This allows the system to provide the optimal training environment based on the user's past history.

[0058] The fitness system can adjust the difficulty of training based on the user's current health condition and goals. For example, if a user is not in good health, it can suggest lighter training. If the user aims to increase muscle strength, the difficulty of strength training can be increased. Furthermore, if the user aims to lose weight, it can suggest training that emphasizes calorie burning. This allows the system to provide optimal training tailored to the user's health condition and goals.

[0059] Fitness systems can incorporate region-specific elements into training plans by considering the user's geographical location. For example, if a user lives in a mountainous area, the system can suggest a training plan that includes mountain climbing and hiking. If a user lives in a coastal area, the system can suggest a plan that includes beach running and swimming. Furthermore, if a user lives in an urban area, the system can suggest a training plan that utilizes the urban environment. This allows the system to provide an optimal training plan based on the user's geographical location.

[0060] Fitness systems can analyze users' social media activity and incorporate social elements into training plans. For example, they can suggest training plans to do with friends based on fitness goals shared by users on social media. They can also incorporate training from fitness influencers that users follow. Furthermore, they can suggest training plans based on trends in the fitness communities that users participate in. This allows them to provide optimal training plans based on users' social media activity.

[0061] The fitness system can adjust the training pace based on the user's fitness level. For example, beginner users can be offered training at a slow pace, intermediate users at a slightly faster pace, and advanced users at a fast pace. This allows the system to provide training at an optimal pace according to the user's fitness level.

[0062] A fitness system can increase the variety of training options according to the user's goals. For example, a user aiming to increase muscle strength can be offered different strength training variations. Similarly, a user aiming to lose weight can be offered training variations that focus on calorie burning. Furthermore, a user aiming to improve flexibility can be offered variations of stretching exercises. This allows for the provision of diverse training tailored to the user's goals.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk enters the user's fitness journey in text. The user's fitness journey may include, but is not limited to, the type, frequency, and duration of exercise. The reception desk can accept text input in a free-text format or a multiple-choice format, for example. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate personalized training. The generated personalized training may include, but is not limited to, individual exercise plans and intensity adjustments. For example, the generation unit may use the generation AI to generate a training plan based on the user's interests and goals. Step 3: The reproduction unit reproduces the training generated by the generation unit in VR. The reproduction unit can reproduce the training based on, for example, the device used and the level of detail of the reproduction. The reproduction unit can reproduce the training environment using, for example, a VR headset. Step 4: The analysis unit analyzes the training results reproduced by the reproduction unit. The analysis unit can analyze the training results based on, for example, the data collection method and analysis algorithm. The analysis unit can analyze, for example, heart rate and calories burned during training. Step 5: The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. The plan generation unit can, for example, generate the next plan based on the details of the meal plan and exercise plan. The plan generation unit can, for example, adjust the next training plan based on the user's exercise results.

[0065] (Example of form 2) The fitness system according to an embodiment of the present invention is a system that utilizes LLM to generate a user's unique fitness journey from text and reproduces that experience in VR. When a user inputs their fitness journey as text, the generating AI analyzes the content and generates personalized training tailored to the user's interests and goals. The generated training is reproduced in VR, allowing the user to experience a fresh and varied training environment. Furthermore, the fitness system generates the next fitness and nutrition plan based on the user's exercise results, supporting the user's continued fitness motivation. For example, if a user inputs "I want to do strength training," the generating AI analyzes the content and generates a program suitable for strength training. The generated program is reproduced in VR, allowing the user to actually perform the training. After the training, the fitness system analyzes the exercise results and generates the next training plan and nutrition plan. This allows the user to always receive fresh and effective training and maintain their motivation. In this way, the fitness system can efficiently generate, reproduce, and analyze the user's fitness journey and provide the next plan.

[0066] The fitness system according to this embodiment comprises a reception unit, a generation unit, a reproduction unit, an analysis unit, and a plan generation unit. The reception unit inputs the user's fitness journey as text. The user's fitness journey includes, but is not limited to, the type, frequency, and duration of exercise. The reception unit can accept text input in, for example, a free-text format or a multiple-choice format. The generation unit uses a generation AI to analyze the text received by the reception unit and generate personalized training. The generated personalized training includes, for example, individual exercise plans and intensity adjustments, but is not limited to, individual exercise plans. The generation unit uses, for example, a generation AI to generate a training plan based on the user's interests and goals. The reproduction unit reproduces the training generated by the generation unit in VR. The reproduction unit can reproduce the training based, for example, the device used and the level of detail of the reproduction. The reproduction unit can reproduce the training environment using, for example, a VR headset. The analysis unit analyzes the results of the training reproduced by the reproduction unit. The analysis unit can analyze the training results based, for example, the data collection method and the analysis algorithm. The analysis unit analyzes, for example, heart rate and calories burned during training. The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. The plan generation unit can generate the next plan based on, for example, the details of the meal plan and exercise plan. The plan generation unit adjusts the next training plan based on, for example, the user's exercise results. In this way, the fitness system according to the embodiment can efficiently generate, reproduce, and analyze the user's fitness journey and provide the next plan.

[0067] The reception desk receives text input of the user's fitness journey. This includes, but is not limited to, the type, frequency, and duration of exercise. The reception desk can accept text input in either a free-text or multiple-choice format. Specifically, users access a dedicated application or website using their smartphone or computer to input their fitness goals, current exercise habits, and past exercise history. The free-text format allows users to provide detailed information in their own words, enabling customization to individual needs and goals. The multiple-choice format, on the other hand, is simpler to use, especially for fitness beginners, as users only need to select options from pre-set questions. Furthermore, the reception desk automatically saves the information entered by the user, allowing for later editing and additions. This allows users to update their information as their fitness journey progresses, keeping it always up-to-date. The reception desk also protects user privacy by encrypting and storing the entered data. This allows users to confidently provide their information.

[0068] The generation unit uses a generation AI to analyze text received by the reception unit and generate personalized training. The generated personalized training may include, but is not limited to, individual exercise plans and intensity adjustments. For example, the generation AI generates training plans based on the user's interests and goals. Specifically, the generation AI uses natural language processing technology to analyze the user's input text and creates an optimal training plan considering the user's fitness goals, current fitness level, and past exercise history. For example, if a user inputs "I want to jog for 30 minutes three times a week," the generation AI will use this information to suggest specific jogging routes, paces, and stretching methods. The generation AI can also adjust the training plan in real time based on user feedback. For example, if a user inputs "I'm tired today, so I want to do some light exercise," the generation AI will use this information to suggest a plan of light stretching or yoga. Furthermore, the generation AI can generate training plans that combine music and video content according to the user's interests and preferences. This allows users to continue training in an enjoyable and effective way.

[0069] The reproduction unit recreates the training generated by the generation unit in VR. The reproduction unit can recreate the training based on, for example, the device used and the level of detail of the recreation. For example, the reproduction unit can recreate the training environment using a VR headset. Specifically, the user can wear a VR headset and train in a virtual space. In the virtual space, various training environments are recreated, such as realistic gyms, natural landscapes, and sports stadiums, and the user can choose according to their preference. Furthermore, the reproduction unit tracks the user's movements in real time and accurately recreates the movements in the virtual space. This allows the user to perform effective training while moving their body just as they would in actual training. The reproduction unit also has a function to provide visual feedback on the progress and achievement of the training. For example, it can display how much progress the user has made towards their set goal in graphs and charts, which can help maintain motivation. In addition, the reproduction unit also provides a function that allows users to train together in the virtual space with other users. This allows users to enjoy training with friends and family, and continue their fitness while feeling a sense of social connection.

[0070] The analysis unit analyzes the results of the training reproduced by the reproduction unit. The analysis unit can analyze training results based on, for example, the data collection method and analysis algorithm. For example, the analysis unit analyzes heart rate and calories burned during training. Specifically, the analysis unit collects data such as heart rate, calories burned, exercise intensity, and exercise time from a wearable device worn by the user during VR training. This data is transmitted to a cloud server in real time and analyzed in detail by the analysis algorithm. The analysis algorithm evaluates the user's performance by comparing it with the user's past training data and a general fitness database. For example, it analyzes heart rate fluctuation patterns and calorie consumption trends to evaluate the user's fitness level and the effectiveness of the training. In addition, the analysis unit can issue warnings if abnormal data is detected during training using an anomaly detection algorithm. For example, if the heart rate rises sharply or the exercise intensity is excessively high, it can send a notification to the user prompting them to take a break. Furthermore, the analysis unit can collect user feedback and suggest improvements to the training plan based on the analysis results. This allows the user to accurately understand the effectiveness of their training and apply it to their next training session.

[0071] The planning unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. For example, the planning unit can generate the next plan based on the details of the meal plan and exercise plan. For example, the planning unit can adjust the next training plan based on the user's exercise results. Specifically, the planning unit creates an optimal fitness plan tailored to the user's fitness level and goals based on the data provided by the analysis unit. For example, if the user wants to focus on strength training, the planning unit will suggest an exercise plan and meal plan suitable for strength training. Also, if the user is aiming to lose weight, it will provide a plan that combines calorie restriction and aerobic exercise. Furthermore, the planning unit can flexibly adjust the plan according to the user's lifestyle and schedule. For example, it will suggest short, effective workouts on busy days and recommend longer workouts on days when the user has more time. The planning unit can also customize meal plans considering the user's preferences and allergy information. This ensures that the user has a fitness and nutrition plan that they can easily stick to. In addition, the planning unit regularly monitors the user's progress and updates the plan as needed. This allows users to always implement the most up-to-date fitness and nutrition plan based on the latest information.

[0072] The reception unit can estimate the user's emotions and adjust the timing of fitness journey input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit may prompt the user to input the fitness journey during a time when they can relax. For example, if the reception unit estimates the user to be highly motivated, it may prompt the user to input the fitness journey immediately. For example, if the user is tired, the reception unit may prompt the user to input the fitness journey after resting. This allows the system to prompt the user to input the fitness journey at the appropriate time according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI. For example, the reception unit may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The reception desk can analyze the user's past fitness journey history and select the optimal input method. For example, if the user has preferred using text input in the past, the reception desk will prioritize suggesting text input. For example, if the user has used voice input in the past, the reception desk can also prioritize suggesting voice input. For example, if the user has entered their fitness journey during a specific time period in the past, the reception desk can prompt them to enter their fitness journey during that time period. This allows the reception desk to suggest the optimal input method based on the user's past history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past fitness journey history into a generating AI and have the generating AI select the optimal input method.

[0074] The reception unit can filter the user's current health status and goals when they input their fitness journey. For example, if a user inputs their current health status, the reception unit can suggest an appropriate fitness journey based on that information. For example, if the user's goal is to increase muscle strength, the reception unit can suggest a fitness journey focused on strength training. For example, if the user's goal is weight loss, the reception unit can suggest a fitness journey that emphasizes calorie consumption. This allows the reception unit to suggest a fitness journey that is tailored to the user's health status and goals. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's health status data into a generating AI and have the generating AI perform the filtering.

[0075] The reception desk can estimate the user's emotions and determine the priority of the fitness journey to be entered based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may prioritize suggesting a relaxing fitness journey. For example, if the reception desk estimates that the user is highly motivated, it may also prioritize suggesting a challenging fitness journey. For example, if the user is tired, it may also prioritize suggesting a lighter fitness journey. This allows for the priority suggestion of an appropriate fitness journey according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception desk can prioritize highly relevant fitness journeys when the user inputs their fitness journey, taking into account their geographical location. For example, the reception desk can suggest fitness facilities near the user's current location. If the user is traveling, the reception desk can also suggest fitness journeys that can be done in that area. If the user is at home, the reception desk can also suggest fitness journeys that can be done at home. This allows the reception desk to suggest appropriate fitness journeys based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's geographical location into a generating AI and have the generating AI select highly relevant journeys.

[0077] The reception desk can analyze the user's social media activity and input relevant journeys when the user enters their fitness journey. For example, the reception desk can suggest a journey based on the fitness goals the user has shared on social media. For example, the reception desk can also suggest training from fitness influencers the user follows. For example, the reception desk can also suggest a journey based on the trends of the fitness community the user participates in. This allows the reception desk to suggest an appropriate fitness journey based on the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI select relevant journeys.

[0078] The generation unit can estimate the user's emotions and adjust the training presentation based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide training with calming music. If the user is excited, the generation unit can also provide training with energetic music. If the user is stressed, the generation unit can also provide training with relaxing music. This allows for the provision of appropriate training presentations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0079] The generation unit can adjust the level of detail of the training based on the user's fitness level when generating the training. For example, the generation unit can provide beginner users with training that focuses on basic movements. For example, the generation unit can also provide intermediate users with slightly more difficult training. For example, the generation unit can also provide advanced users with training that requires advanced skills. This allows the generation unit to provide training that is appropriate for the user's fitness level. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's fitness level data into a generation AI and have the generation AI perform the adjustment of the level of detail of the training.

[0080] The generation unit can apply different generation algorithms depending on the user's goals when generating training. For example, for a user whose goal is to increase muscle strength, the generation unit can apply an algorithm specialized in muscle training. For example, for a user whose goal is to lose weight, the generation unit can apply an algorithm that emphasizes calorie consumption. For example, for a user whose goal is to improve flexibility, the generation unit can apply an algorithm that focuses on stretching. This allows the generation unit to provide training tailored to the user's goals. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's goal data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0081] The generation unit can estimate the user's emotions and adjust the training length based on the estimated emotions. For example, if the user is tired, the generation unit can provide short, effective training. For example, if the user is relaxed, the generation unit can also provide longer training. For example, if the user is in a hurry, the generation unit can also provide short, focused training. This allows for providing an appropriate training length according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0082] The generation unit can determine training priorities based on the user's past training history when generating training. For example, the generation unit may prioritize suggesting training that was highly effective for the user in the past. The generation unit may also prioritize suggesting training that the user enjoyed in the past. The generation unit may also avoid suggesting training that the user found difficult in the past. This allows the generation unit to provide appropriate training based on the user's past training history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit may input the user's past training history into a generation AI and have the generation AI determine the training priorities.

[0083] The generation unit can adjust the order of training based on the user's interests when generating training. For example, the generation unit may suggest training that the user is interested in first. The generation unit may also suggest training that the user is interested in last. The generation unit may also suggest training that the user is interested in in the middle. This allows the generation unit to provide a training order that is tailored to the user's interests. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user interest data into a generation AI and have the generation AI perform the adjustment of the training order.

[0084] The VR reproduction unit can estimate the user's emotions and adjust the VR reproduction method based on the estimated user emotions. For example, if the user is relaxed, the reproduction unit can provide a VR reproduction in a calm environment. For example, if the user is excited, the reproduction unit can also provide a VR reproduction in an energetic environment. For example, if the user is stressed, the reproduction unit can also provide a VR reproduction in a relaxing environment. This allows for the provision of a VR reproduction method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The reproduction unit can adjust the level of detail of the VR reproduction based on the user's fitness level. For example, the reproduction unit can provide a VR reproduction focusing on basic movements to a beginner user. For example, the reproduction unit can also provide a VR reproduction that includes slightly more difficult movements to an intermediate user. For example, the reproduction unit can also provide a VR reproduction that includes movements requiring advanced skills to an advanced user. This allows for the provision of VR reproductions tailored to the user's fitness level. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's fitness level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the reproduction.

[0086] The reproduction unit can apply different reproduction algorithms to VR reproductions according to the user's goals. For example, the reproduction unit can provide a VR reproduction specialized in strength training to a user whose goal is muscle strengthening. For example, the reproduction unit can also provide a VR reproduction that emphasizes calorie consumption to a user whose goal is weight loss. For example, the reproduction unit can provide a VR reproduction that focuses on stretching to a user whose goal is flexibility improvement. This allows the reproduction unit to provide VR reproductions tailored to the user's goals. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's goal data into a generating AI and have the generating AI execute the application of the reproduction algorithm.

[0087] The VR simulation unit can estimate the user's emotions and adjust the length of the VR simulation based on the estimated emotions. For example, if the user is tired, the simulation unit can provide a short but effective VR simulation. For example, if the user is relaxed, the simulation unit can also provide a longer VR simulation. For example, if the user is in a hurry, the simulation unit can also provide a short but focused VR simulation. This allows for providing a VR simulation length that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0088] The reproduction unit can determine the priority of VR reproductions based on the user's past VR experience history. For example, the reproduction unit may prioritize suggesting VR experiences that were highly effective for the user in the past. It can also prioritize suggesting VR experiences that the user enjoyed in the past. It can also avoid suggesting VR experiences that the user disliked in the past. This allows the reproduction unit to provide appropriate VR reproductions based on the user's past VR experience history. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input the user's past VR experience history into a generating AI and have the generating AI determine the priority of reproductions.

[0089] The reproduction unit can adjust the order of VR reproduction based on the user's interests. For example, the reproduction unit may suggest a VR experience that the user is interested in first. The reproduction unit may also suggest a VR experience that the user is interested in last. The reproduction unit may also suggest a VR experience that the user is interested in in the middle. This allows the reproduction unit to provide an order of VR reproduction that is tailored to the user's interests. Some or all of the above processing in the reproduction unit may be performed using AI, for example, or without AI. For example, the reproduction unit can input user interest data into a generating AI and have the generating AI adjust the order of reproduction.

[0090] The analysis unit can estimate the user's emotions and adjust the analysis method of the training results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit can also provide analysis results that focus on positive feedback. This allows for the provision of an analysis method of the training results that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the user's fitness level. For example, the analysis unit provides basic analysis results to beginner users. For example, the analysis unit can also provide slightly more detailed analysis results to intermediate users. For example, the analysis unit can also provide detailed analysis results to advanced users. This allows the analysis unit to provide analysis results that are appropriate to the user's fitness level. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's fitness level data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the user's goals during the analysis. For example, for a user whose goal is to increase muscle strength, the analysis unit can perform an analysis that emphasizes the effects of muscle training. For example, for a user whose goal is to lose weight, the analysis unit can perform an analysis that emphasizes calorie consumption. For example, for a user whose goal is to improve flexibility, the analysis unit can perform an analysis that emphasizes the degree of flexibility improvement. This allows the analysis unit to provide analysis results that match the user's goals. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's goal data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results. For example, if the user is in a hurry, the analysis unit can also display concise analysis results that get straight to the point. For example, if the user is stressed, the analysis unit can also display analysis results that focus on positive feedback. This provides a method of displaying analysis results that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on the user's past training results during the analysis process. For example, the analysis unit may prioritize analyzing training that was highly effective for the user in the past. The analysis unit may also prioritize analyzing training that the user enjoyed in the past. The analysis unit may also avoid analyzing training that the user struggled with in the past. This allows the analysis unit to provide appropriate analysis based on the user's past training results. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past training results into a generating AI and have the generating AI determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the user's interests during the analysis process. For example, the analysis unit may analyze the training that the user is interested in first. The analysis unit may also analyze the training that the user is interested in last. The analysis unit may also analyze the training that the user is interested in in the middle. This allows the analysis to be performed in an order that suits the user's interests. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interest data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0096] The plan generation unit can estimate the user's emotions and adjust the method of generating the next fitness and nutrition plan based on the estimated user emotions. For example, if the user is relaxed, the plan generation unit can provide a detailed fitness and nutrition plan. For example, if the user is in a hurry, the plan generation unit can provide a concise fitness and nutrition plan that gets straight to the point. For example, if the user is stressed, the plan generation unit can provide a fitness and nutrition plan that focuses on positive feedback. This allows for the provision of a fitness and nutrition plan that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the plan generation unit may be performed using AI, or not using AI. For example, the plan generation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0097] The plan generation unit can adjust the level of detail of the plan based on the user's fitness level during plan generation. For example, the plan generation unit can provide a basic fitness and nutrition plan to beginner users. For example, the plan generation unit can also provide a slightly more detailed fitness and nutrition plan to intermediate users. For example, the plan generation unit can also provide a detailed fitness and nutrition plan to advanced users. This allows the system to provide fitness and nutrition plans tailored to the user's fitness level. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's fitness level data into a generation AI and have the generation AI adjust the level of detail of the plan.

[0098] The plan generation unit can apply different generation algorithms depending on the user's goals when generating a plan. For example, the plan generation unit can provide a fitness and nutrition plan specializing in strength training to a user whose goal is muscle strengthening. For example, the plan generation unit can also provide a fitness and nutrition plan that emphasizes calorie consumption to a user whose goal is weight loss. For example, the plan generation unit can provide a fitness and nutrition plan that focuses on stretching to a user whose goal is to improve flexibility. This allows the plan generation unit to provide a fitness and nutrition plan that is tailored to the user's goals. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's goal data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0099] The plan generation unit can estimate the user's emotions and determine the priority of plans based on the estimated emotions. For example, if the user is relaxed, the plan generation unit may prioritize providing a detailed fitness and nutrition plan. If the user is in a hurry, the plan generation unit may also prioritize providing a concise fitness and nutrition plan that gets straight to the point. If the user is stressed, the plan generation unit may also prioritize providing a fitness and nutrition plan that focuses on positive feedback. This allows for the provision of fitness and nutrition plan priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or not using AI. For example, the plan generation unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0100] The plan generation unit can determine the priority of plans based on the user's past fitness and nutrition plans when generating a plan. For example, the plan generation unit can prioritize providing plans that were highly effective in the user's past fitness and nutrition plans. For example, the plan generation unit can also prioritize providing plans that the user liked in their past fitness and nutrition plans. For example, the plan generation unit can also avoid providing plans that the user disliked in their past fitness and nutrition plans. This allows the system to provide an appropriate plan based on the user's past fitness and nutrition plans. Some or all of the above processing in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input the user's past fitness and nutrition plans into a generation AI and have the generation AI determine the priority of the plans.

[0101] The plan generation unit can adjust the order of plans based on the user's interests during plan generation. For example, the plan generation unit may first provide the fitness and nutrition plan that the user is interested in. For example, the plan generation unit may also provide the fitness and nutrition plan that the user is interested in at the end. For example, the plan generation unit may also provide the fitness and nutrition plan that the user is interested in in the middle. This allows the system to provide a fitness and nutrition plan order that is tailored to the user's interests. Some or all of the above-described processes in the plan generation unit may be performed using AI, for example, or without AI. For example, the plan generation unit can input user interest data into a generation AI and have the generation AI adjust the order of the plans.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The fitness system can also acquire user sleep data and incorporate it into the training plan. For example, if a user hasn't gotten enough sleep, it can suggest a lighter workout. Conversely, if a user has had good quality sleep, it can suggest a more challenging workout. Furthermore, it can adjust the training time based on sleep data. This allows the system to provide an optimal training plan tailored to the user's sleep patterns.

[0104] A fitness system can estimate a user's emotions and adjust the training interaction based on those estimates. For example, if a user is feeling stressed, it can provide relaxing guidance during training. If a user is estimated to be highly motivated, it can frequently display encouraging messages. Furthermore, if a user is tired, it can minimize interaction during training. This allows for interaction tailored to the user's emotions.

[0105] A fitness system can analyze a user's past fitness journey history and suggest the optimal training environment. For example, if a user previously preferred training in nature, a natural environment can be recreated using VR. Similarly, if a user preferred training at a gym, a gym environment can be recreated. Furthermore, if a user preferred training at home, a home environment can be recreated. This allows the system to provide the optimal training environment based on the user's past history.

[0106] The fitness system can adjust the difficulty of training based on the user's current health condition and goals. For example, if a user is not in good health, it can suggest lighter training. If the user aims to increase muscle strength, the difficulty of strength training can be increased. Furthermore, if the user aims to lose weight, it can suggest training that emphasizes calorie burning. This allows the system to provide optimal training tailored to the user's health condition and goals.

[0107] A fitness system can estimate a user's emotions and adjust the training feedback based on those emotions. For example, if the user is relaxed, it can provide detailed feedback. If the user is in a hurry, it can provide concise, to-the-point feedback. Furthermore, if the user is stressed, it can provide feedback centered on positive feedback. This allows for the provision of optimal feedback tailored to the user's emotions.

[0108] Fitness systems can incorporate region-specific elements into training plans by considering the user's geographical location. For example, if a user lives in a mountainous area, the system can suggest a training plan that includes mountain climbing and hiking. If a user lives in a coastal area, the system can suggest a plan that includes beach running and swimming. Furthermore, if a user lives in an urban area, the system can suggest a training plan that utilizes the urban environment. This allows the system to provide an optimal training plan based on the user's geographical location.

[0109] Fitness systems can analyze users' social media activity and incorporate social elements into training plans. For example, they can suggest training plans to do with friends based on fitness goals shared by users on social media. They can also incorporate training from fitness influencers that users follow. Furthermore, they can suggest training plans based on trends in the fitness communities that users participate in. This allows them to provide optimal training plans based on users' social media activity.

[0110] The fitness system can estimate the user's emotions and adjust its training motivation based on those estimates. For example, if the system estimates the user to be unmotivated, it can provide encouraging messages and rewards. If the user is estimated to be highly motivated, it can set challenging goals. Furthermore, if the user is feeling stressed, it can suggest relaxing workouts. This allows the system to provide the optimal motivational approach tailored to the user's emotions.

[0111] The fitness system can adjust the training pace based on the user's fitness level. For example, beginner users can be offered training at a slow pace, intermediate users at a slightly faster pace, and advanced users at a fast pace. This allows the system to provide training at an optimal pace according to the user's fitness level.

[0112] A fitness system can increase the variety of training options according to the user's goals. For example, a user aiming to increase muscle strength can be offered different strength training variations. Similarly, a user aiming to lose weight can be offered training variations that focus on calorie burning. Furthermore, a user aiming to improve flexibility can be offered variations of stretching exercises. This allows for the provision of diverse training tailored to the user's goals.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk enters the user's fitness journey in text. The user's fitness journey may include, but is not limited to, the type, frequency, and duration of exercise. The reception desk can accept text input in a free-text format or a multiple-choice format, for example. Step 2: The generation unit uses a generation AI to analyze the text received by the reception unit and generate personalized training. The generated personalized training may include, but is not limited to, individual exercise plans and intensity adjustments. For example, the generation unit may use the generation AI to generate a training plan based on the user's interests and goals. Step 3: The reproduction unit reproduces the training generated by the generation unit in VR. The reproduction unit can reproduce the training based on, for example, the device used and the level of detail of the reproduction. The reproduction unit can reproduce the training environment using, for example, a VR headset. Step 4: The analysis unit analyzes the training results reproduced by the reproduction unit. The analysis unit can analyze the training results based on, for example, the data collection method and analysis algorithm. The analysis unit can analyze, for example, heart rate and calories burned during training. Step 5: The plan generation unit generates the next fitness and nutrition plan based on the results analyzed by the analysis unit. The plan generation unit can, for example, generate the next plan based on the details of the meal plan and exercise plan. The plan generation unit can, for example, adjust the next training plan based on the user's exercise results.

[0115] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0116] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0118] Each of the multiple elements described above, including the reception unit, generation unit, reproduction unit, analysis unit, and plan generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit inputs the user's fitness journey as text using the touch panel 38A and microphone 38B of the smart device 14. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the text using a generation AI to generate personalized training. The reproduction unit is implemented in the specific processing unit 46A of the smart device 14, for example, and reproduces the training environment using a VR headset. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the training results. The plan generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates the next fitness and nutrition plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0123] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, generation unit, reproduction unit, analysis unit, and plan generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit inputs the user's fitness journey as text using the microphone 238 of the smart glasses 214. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates personalized training. The reproduction unit is implemented, for example, by the control unit 46A of the smart glasses 214, which reproduces the training environment using a VR headset. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the training results. The plan generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates the next fitness and nutrition plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, generation unit, reproduction unit, analysis unit, and plan generation unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the reception unit inputs the user's fitness journey as text using the microphone 238 of the headset terminal 314. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates personalized training. The reproduction unit is implemented, for example, by the control unit 46A of the headset terminal 314, which reproduces the training environment using a VR headset. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the training results. The plan generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates the next fitness and nutrition plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0154] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0158] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, generation unit, reproduction unit, analysis unit, and plan generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit inputs the user's fitness journey as text using the microphone 238 of the robot 414. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the text using a generation AI and generates personalized training. The reproduction unit is implemented by, for example, the control unit 46A of the robot 414, which reproduces the training environment using a VR headset. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the training results. The plan generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates the next fitness and nutrition plan based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0168] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0169] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0170] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0171] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0172] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0173] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0174] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0175] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0177] 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.

[0178] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0179] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0180] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0181] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0182] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0185] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0186] (Note 1) A reception desk where users input their fitness journey as text, A generation unit analyzes the text received by the reception unit and generates personalized training, A reproduction unit that reproduces the training generated by the generation unit in VR, An analysis unit analyzes the results of the training reproduced by the reproduction unit, The system comprises a planning generation unit that generates the next fitness and nutrition plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputs in the fitness journey based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past fitness journey history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their fitness journey data, filtering is performed based on their current health status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the fitness journey based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users input their fitness journey, the system prioritizes inputting the most relevant journeys by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering a fitness journey, the system analyzes the user's social media activity and inputs relevant journey data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is It estimates the user's emotions and adjusts the training's expression based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is When generating training, adjust the level of detail based on the user's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is When generating training data, different generation algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is It estimates the user's emotions and adjusts the training length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating training, the system prioritizes training based on the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During training generation, the training order is adjusted based on user interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The reproduction unit is, It estimates the user's emotions and adjusts the VR reproduction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The reproduction unit is, When recreating a VR experience, the level of detail is adjusted based on the user's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 16) The reproduction unit is, When creating a VR simulation, different simulation algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 17) The reproduction unit is, It estimates the user's emotions and adjusts the length of the VR experience based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The reproduction unit is, When recreating VR experiences, the system prioritizes the recreation based on the user's past VR experience history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The reproduction unit is, When recreating in VR, the order of recreation is adjusted based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method of the training results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the user's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, the analysis priority is determined based on the user's past training results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the user's interests. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned plan generation unit, It estimates the user's emotions and adjusts how the next fitness and nutrition plan is generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned plan generation unit, When generating a plan, adjust the level of detail based on the user's fitness level. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned plan generation unit, When generating a plan, different generation algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned plan generation unit, It estimates user sentiment and prioritizes plans based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned plan generation unit, When generating a plan, the system prioritizes the plan based on the user's past fitness and nutrition plans. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned plan generation unit, During plan generation, the order of the plan is adjusted based on the user's interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users input their fitness journey as text, A generation unit analyzes the text received by the reception unit and generates personalized training, A reproduction unit that reproduces the training generated by the generation unit in VR, An analysis unit analyzes the results of the training reproduced by the reproduction unit, The system comprises a planning generation unit that generates the next fitness and nutrition plan based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of inputs in the fitness journey based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past fitness journey history and select the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter their fitness journey data, filtering is performed based on their current health status and goals. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the fitness journey based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users input their fitness journey, the system prioritizes inputting the most relevant journeys by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering a fitness journey, the system analyzes the user's social media activity and inputs relevant journey data. The system according to feature 1.

8. The generating unit is It estimates the user's emotions and adjusts the training's expression based on the estimated user emotions. The system according to feature 1.

Images