System

Abstract

An object of a system according to an embodiment is to propose an optimal muscle training menu to a user.SOLUTION: A system includes a data input unit, an analysis unit, an imaging unit, a learning unit, and a proposal unit. The data input unit inputs data from a user. The analysis unit analyzes the data input by the data input unit. The imaging unit captures an image of a user performing muscle training. The learning unit learns the video data captured by the imaging unit. The suggestion unit suggests an optimal muscle training menu to the user on the basis of the information obtained by the analysis unit and the learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze data and learn to suggest optimal muscle training menus to users, leaving room for improvement.

[0005] The system according to the embodiment aims to propose an optimal muscle training menu to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a data input unit, an analysis unit, a capture unit, a learning unit, and a suggestion unit. The data input unit inputs data from a user. The analysis unit analyzes the data input by the data input unit. The capture unit captures footage of the user performing muscle training. The learning unit learns from the video data captured by the capture unit. The suggestion unit suggests an optimal muscle training menu for the user based on the information obtained by the analysis unit and the learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose an optimal muscle training menu to the user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​system according to an embodiment of the present invention is a system that proposes an optimal muscle training menu for each individual based on data input by the user. This system films the user actually doing muscle training, and the AI ​​learns from the video data, allowing it to suggest posture correction and appropriate loads. This allows the AI ​​system to enable the user to continue muscle training more effectively and safely.

[0029] The AI ​​system according to the embodiment includes a data input unit, an analysis unit, a capture unit, a learning unit, and a suggestion unit. The data input unit inputs data from a user. For example, basic information such as age, gender, weight, height, exercise experience, and goals is input. The analysis unit analyzes the data input by the data input unit. For example, the generation AI generates an optimal muscle training menu based on the user's physical fitness level and goals. The capture unit captures footage of the user performing muscle training. For example, a camera is used to capture footage of the user's muscle training. The learning unit learns from the video data captured by the capture unit. For example, the generation AI analyzes the video data to evaluate the user's posture and movements. The suggestion unit suggests an optimal muscle training menu for the user based on the information obtained by the analysis unit and the learning unit. For example, the generation AI suggests a specific muscle training menu based on the user's data. This allows the AI ​​system according to the embodiment to suggest an optimal muscle training menu based on the user's data, enabling posture correction and appropriate load suggestions.

[0030] The suggestion unit can suggest basic exercises to beginners and more advanced training to experienced users. For example, the suggestion unit suggests basic exercises to beginners, such as squats and push-ups. The suggestion unit also suggests more advanced training to experienced users, such as weight training and interval training. This makes it possible to provide an appropriate training menu according to the user's experience level.

[0031] The suggestion unit can provide a detailed plan including a training schedule for three times a week and the number of sets, repetitions, rest times, etc. for each training session. The suggestion unit, for example, provides a training schedule for three times a week. For example, it proposes a schedule for training on Mondays, Wednesdays, and Fridays. The suggestion unit also provides a detailed plan including the number of sets, repetitions, rest times, etc. for each training session. For example, it provides a detailed plan such as three squat sets, 10 repetitions, and one minute of rest. This makes it possible to provide a specific training plan to the user.

[0032] The learning unit can check whether the knees are in the correct position when squatting, whether the back is straight, etc., based on video data of the user's muscle training. For example, the learning unit checks whether the knees are in the correct position when squatting, based on video data of the user's muscle training. For example, it checks whether the knees are in a position that does not extend beyond the toes. The learning unit also checks whether the back is straight when squatting. For example, it checks whether the back is not rounded. This allows the user's form to be accurately checked and proper posture to be maintained.

[0033] The suggestion unit can issue specific instructions to take care not to turn your knees inward when squatting. The suggestion unit issues specific instructions to take care not to turn your knees inward when squatting, for example. For example, the suggestion unit issues a specific instruction such as "take care not to turn your knees inward when squatting." This makes it possible to provide the user with specific instructions for correcting their posture.

[0034] When a user continues the same training for a certain period of time, the suggestion unit can evaluate the user's progress and suggest whether the load should be increased or whether the user should take a rest. For example, when a user continues the same training for a certain period of time, the suggestion unit evaluates the user's progress. For example, the suggestion unit evaluates the user's progress based on the number of training sessions or the number of sets. The suggestion unit also suggests whether the user should increase the load or whether the user should take a rest based on the results of the progress evaluation. For example, if the user's progress is good, the suggestion unit suggests increasing the load, and if the user's progress is stagnant, the suggestion unit suggests taking a rest. This makes it possible to suggest appropriate loads and rests according to the user's training progress.

[0035] The data input unit inputs the user's dietary data and sleep data, and the analysis unit analyzes the dietary data and sleep data to reflect it in the muscle training menu. The data input unit, for example, inputs the user's daily dietary details. For example, it inputs the amount of protein intake and calories. The data input unit also inputs the user's sleep data. For example, it inputs the amount of sleep and the quality of sleep. The analysis unit analyzes the dietary data and sleep data to reflect it in the muscle training menu. For example, it adjusts the muscle training menu based on the amount of protein intake. It also adjusts the training intensity based on the quality of sleep. This makes it possible to provide a muscle training menu that takes the user's dietary and sleep data into consideration.

[0036] The data input unit inputs the user's past exercise history and health checkup data, and the analysis unit analyzes the exercise history and health checkup data to perform a more precise analysis. The data input unit, for example, inputs the user's past exercise history. For example, it inputs past training content and frequency. The data input unit also inputs the user's health checkup data. For example, it inputs blood pressure and heart rate. The analysis unit analyzes the exercise history and health checkup data to perform a more precise analysis. For example, it adjusts the current training menu based on the past training content. It also adjusts the training intensity based on the health checkup data. This enables a precise analysis that takes into account the user's past exercise history and health checkup data.

[0037] The data input unit uses voice recognition technology to input data for the user, thereby reducing the amount of work required. For example, the data input unit allows the user to input basic information such as age, gender, weight, and height by voice, and then converts the data into text data using voice recognition technology. For example, the input content is converted into text data using voice recognition technology. This reduces the amount of work required for the user to input data.

[0038] The data input unit can work in conjunction with other fitness apps and wearable devices to automatically import data. The data input unit, for example, works in conjunction with other fitness apps to automatically import the user's exercise data. For example, the generation AI analyzes data from a running app and reflects it in a training menu. The data input unit also works in conjunction with wearable devices to automatically import the user's exercise data. For example, the generation AI analyzes data from a smartwatch and reflects it in a training menu. This makes it possible to work in conjunction with other fitness apps and wearable devices to automatically import data.

[0039] The suggestion unit can monitor the user's muscle growth and fatigue level in real time and dynamically adjust the menu based on that. The suggestion unit, for example, monitors muscle growth with a sensor, and the generation AI analyzes it in real time. For example, it measures muscle tone and size and adjusts the training menu based on that. The suggestion unit also monitors fatigue level with a sensor, and the generation AI analyzes it in real time. For example, it measures muscle fatigue level and adjusts the training menu based on that. This makes it possible to monitor the user's muscle growth and fatigue level in real time and dynamically adjust the menu.

[0040] The suggestion unit can suggest exercises that emphasize fun, taking into account the user's preferences and interests. For example, the suggestion unit collects the user's preferences and interests through a questionnaire, which the generation AI analyzes. For example, the suggestion unit suggests dance exercises to a user who likes dancing. The suggestion unit also suggests exercises that emphasize fun, taking into account the user's interests. For example, the suggestion unit suggests hiking or running to a user who likes the outdoors. This makes it possible to suggest exercises that emphasize fun, taking into account the user's preferences and interests.

[0041] The suggestion unit can work with the user's friends and family to suggest a joint exercise menu. For example, the suggestion unit inputs data on the user's friends and family, and the generation AI suggests a joint exercise menu. For example, it can suggest paired stretching or group circuit training. The suggestion unit can also work with the user's friends and family to suggest a joint exercise menu. For example, it can suggest a fitness challenge for the whole family. This makes it possible to suggest a joint exercise menu in collaboration with the user's friends and family.

[0042] The suggestion unit can suggest outdoor exercise menus according to the season and weather. For example, the suggestion unit takes in season and weather data, and the generation AI suggests outdoor exercise menus. For example, it suggests cherry blossom viewing runs in spring and beach exercises in summer. The suggestion unit also suggests outdoor exercise menus according to the season and weather. For example, it suggests indoor training when it rains and outdoor exercises when it's sunny. This makes it possible to suggest outdoor exercise menus according to the season and weather.

[0043] In addition to the video data, the filming unit can measure muscle movement with a sensor to perform more detailed analysis. For example, the filming unit measures the user's muscle movement with a sensor, and the generation AI analyzes the data. For example, the effectiveness of training can be evaluated based on data on muscle contraction and extension. In addition to the video data, the filming unit can also measure muscle movement with a sensor to perform more detailed analysis. For example, an electromyogram sensor can be used to measure muscle activity. This allows for more detailed analysis by measuring muscle movement with a sensor in addition to the video data.

[0044] The filming unit can analyze the video data in real time and provide instant feedback. For example, the filming unit analyzes video data in real time when a user is doing strength training, and the generation AI provides instant feedback. For example, if the user's form is compromised, the generation AI can give instructions such as "Keep your back straight." The filming unit can also analyze the video data in real time and provide instant feedback. For example, a real-time feedback system can be used to analyze the user's movements in real time and provide instant feedback. This allows the video data to be analyzed in real time and provide instant feedback.

[0045] The camera unit can convert the video data into a 3D model, allowing the user to check their movements from multiple angles. For example, the camera unit converts video data of the user's strength training into a 3D model, which is then analyzed by the generating AI. For example, the 3D model can be used to check the user's movements from multiple angles and identify areas for improvement in form. The camera unit can also convert the video data into a 3D model, allowing the user to check their movements from multiple angles. For example, the camera unit can use 3D scanning technology to convert the video data into a 3D model, allowing the user to check their movements from multiple angles.

[0046] The filming unit can share video data with other users and receive feedback within the community. For example, a user can share their muscle training video data within the community and receive feedback from other users. For example, they can receive advice such as "You should improve this a bit more." The filming unit can also share video data with other users and receive feedback within the community. For example, they can use the feedback function to receive advice from other users. This allows them to share video data with other users and receive feedback within the community.

[0047] The suggestion unit can suggest specific exercises for posture correction so that the user can put them into practice. For example, the suggestion unit analyzes the user's posture data, and the generation AI suggests specific exercises. For example, it suggests stretching and strength training to improve slouching. The suggestion unit also suggests specific exercises for posture correction so that the user can put them into practice. For example, it specifically explains how to do the exercises for posture correction. This suggests specific exercises for posture correction so that the user can put them into practice.

[0048] The suggestion unit can monitor the user's posture over the long term and evaluate the progress of improvement. For example, the suggestion unit monitors the user's posture data over the long term, and the generation AI analyzes the data. For example, it periodically takes photos of the posture and evaluates the progress of improvement. The suggestion unit also monitors the user's posture over the long term and evaluates the progress of improvement. For example, it evaluates the progress based on changes in posture and improvements in muscle strength. This makes it possible to monitor the user's posture over the long term and evaluate the progress of improvement.

[0049] The suggestion unit can cooperate with a wearable device for posture correction and provide feedback in real time. The suggestion unit, for example, uses a wearable device to collect posture data of the user in real time, which is then analyzed by the generation AI. For example, a sensor on the back can be used to detect poor posture and provide immediate feedback. The suggestion unit can also cooperate with a wearable device for posture correction and provide feedback in real time. For example, a posture sensor can be used to detect poor posture in real time and provide immediate feedback. This allows the suggestion unit to cooperate with a wearable device for posture correction and provide feedback in real time.

[0050] The suggestion unit can integrate the user's posture data with other health data to provide comprehensive health management. For example, the suggestion unit integrates the user's posture data with other health data (e.g., heart rate and sleep data), and the generation AI performs a comprehensive analysis. For example, it evaluates the impact of improving posture on heart rate and sleep quality. The suggestion unit also integrates the user's posture data with other health data to provide comprehensive health management. For example, it integrates the data with data such as blood pressure and body fat percentage for analysis. This allows the user's posture data to be integrated with other health data to provide comprehensive health management.

[0051] The suggestion unit can monitor the recovery state of the user's muscles and suggest the optimal load. For example, the suggestion unit monitors the recovery state of the user's muscles with a sensor, which is then analyzed by the generation AI. For example, it measures muscle stiffness and fatigue level and adjusts the training load based on that. The suggestion unit also monitors the recovery state of the user's muscles and suggests the optimal load. For example, it adjusts the training intensity based on the muscle recovery state. This makes it possible to monitor the recovery state of the user's muscles and suggest the optimal load.

[0052] The suggestion unit can analyze the user's training history and adjust the load based on past data. For example, the suggestion unit analyzes the user's training history, and the generation AI adjusts the load based on past data. For example, the current training load is adjusted based on past training content and frequency. The suggestion unit also analyzes the user's training history and adjusts the load based on past data. For example, the intensity and number of times of training are adjusted based on past training data. This makes it possible to analyze the user's training history and adjust the load based on past data.

[0053] The suggestion unit can suggest a load that suits the user's lifestyle and work schedule. For example, the suggestion unit analyzes the user's lifestyle and work schedule, and the generation AI adjusts the load. For example, it suggests lighter training during busy periods. The suggestion unit also suggests a load that suits the user's lifestyle and work schedule. For example, it adjusts the intensity and number of training sessions according to working hours and break times. This makes it possible to suggest a load that suits the user's lifestyle and work schedule.

[0054] The suggestion unit can suggest a load in a competitive format with other users to increase motivation. The suggestion unit, for example, suggests a training load in a competitive format with other users. For example, the users compete for training progress in a ranking format. The suggestion unit can also suggest a load in a competitive format with other users to increase motivation. For example, the users compete for training progress using a battle mode. This allows the suggestion unit to suggest a load in a competitive format with other users to increase motivation.

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

[0056] The suggestion unit can also analyze the user's training history and adjust the training menu based on past data. For example, it can optimize the current training menu based on the frequency and intensity of past training. It can also adjust the menu to avoid exercises that the user found difficult in the past. Furthermore, it can maintain the user's motivation by re-suggesting training menus that the user found successful in the past. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0057] The suggestion unit can also suggest a training menu that suits the user's lifestyle and work schedule. For example, when the user is busy, it can suggest a short but effective training menu. Also, when the user is on vacation and has more time, it can suggest a long training menu. Furthermore, it can adjust the timing of training to suit the user's working hours and break times. This makes it possible to provide a training menu that suits the user's lifestyle and work schedule and support continued training.

[0058] The suggestion unit can also work with the user's friends and family to suggest exercise menus to do together. For example, it can suggest paired stretching or group circuit training. It can also suggest fitness challenges for the whole family. Furthermore, training in a competitive format with friends and family can increase motivation. This allows the suggestion unit to suggest exercise menus to do together with the user's friends and family, making training more enjoyable.

[0059] The suggestion unit can also suggest outdoor exercise menus according to the season and weather. For example, it can suggest cherry blossom viewing runs in spring and beach exercises in summer. It can also suggest indoor training when it rains and outdoor exercises when it's sunny. It can also suggest training menus tailored to seasonal events and activities. This allows for outdoor exercise menus to be provided according to the season and weather, increasing the variety of training options.

[0060] The suggestion unit can also analyze the user's training history and adjust the training menu based on past data. For example, it can optimize the current training menu based on the frequency and intensity of past training. It can also adjust the menu to avoid exercises that the user found difficult in the past. Furthermore, it can maintain the user's motivation by re-suggesting training menus that the user found successful in the past. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The data input unit inputs data from the user, such as basic information such as age, sex, weight, height, exercise experience, and goals. Step 2: The analysis unit analyzes the data entered by the data input unit. For example, the generation AI generates an optimal muscle training menu based on the user's physical fitness level and goals. Step 3: The photographing unit photographs the user doing muscle training. For example, the photographing unit photographs the user doing muscle training using a camera. Step 4: The learning unit learns from the video data captured by the capture unit. For example, the generation AI analyzes the video data and evaluates the user's posture and movements. Step 5: The suggestion unit proposes the optimal muscle training menu for the user based on the information obtained by the analysis unit and learning unit. For example, the generation AI proposes a specific muscle training menu based on the user's data.

[0063] (Example 2) The AI ​​system according to an embodiment of the present invention is a system that proposes an optimal muscle training menu for each individual based on data input by the user. This system films the user actually doing muscle training, and the AI ​​learns from the video data, allowing it to suggest posture correction and appropriate loads. This allows the AI ​​system to enable the user to continue muscle training more effectively and safely.

[0064] The AI ​​system according to the embodiment includes a data input unit, an analysis unit, a capture unit, a learning unit, and a suggestion unit. The data input unit inputs data from a user. For example, basic information such as age, gender, weight, height, exercise experience, and goals is input. The analysis unit analyzes the data input by the data input unit. For example, the generation AI generates an optimal muscle training menu based on the user's physical fitness level and goals. The capture unit captures footage of the user performing muscle training. For example, a camera is used to capture footage of the user's muscle training. The learning unit learns from the video data captured by the capture unit. For example, the generation AI analyzes the video data to evaluate the user's posture and movements. The suggestion unit suggests an optimal muscle training menu for the user based on the information obtained by the analysis unit and the learning unit. For example, the generation AI suggests a specific muscle training menu based on the user's data. This allows the AI ​​system according to the embodiment to suggest an optimal muscle training menu based on the user's data, enabling posture correction and appropriate load suggestions.

[0065] The suggestion unit can suggest basic exercises to beginners and more advanced training to experienced users. For example, the suggestion unit suggests basic exercises to beginners, such as squats and push-ups. The suggestion unit also suggests more advanced training to experienced users, such as weight training and interval training. This makes it possible to provide an appropriate training menu according to the user's experience level.

[0066] The suggestion unit can provide a detailed plan including a training schedule for three times a week and the number of sets, repetitions, rest times, etc. for each training session. The suggestion unit, for example, provides a training schedule for three times a week. For example, it proposes a schedule for training on Mondays, Wednesdays, and Fridays. The suggestion unit also provides a detailed plan including the number of sets, repetitions, rest times, etc. for each training session. For example, it provides a detailed plan such as three squat sets, 10 repetitions, and one minute of rest. This makes it possible to provide a specific training plan to the user.

[0067] The learning unit can check whether the knees are in the correct position when squatting, whether the back is straight, etc., based on video data of the user's muscle training. For example, the learning unit checks whether the knees are in the correct position when squatting, based on video data of the user's muscle training. For example, it checks whether the knees are in a position that does not extend beyond the toes. The learning unit also checks whether the back is straight when squatting. For example, it checks whether the back is not rounded. This allows the user's form to be accurately checked and proper posture to be maintained.

[0068] The suggestion unit can issue specific instructions to take care not to turn your knees inward when squatting. The suggestion unit issues specific instructions to take care not to turn your knees inward when squatting, for example. For example, the suggestion unit issues a specific instruction such as "take care not to turn your knees inward when squatting." This makes it possible to provide the user with specific instructions for correcting their posture.

[0069] When a user continues the same training for a certain period of time, the suggestion unit can evaluate the user's progress and suggest whether the load should be increased or whether the user should take a rest. For example, when a user continues the same training for a certain period of time, the suggestion unit evaluates the user's progress. For example, the suggestion unit evaluates the user's progress based on the number of training sessions or the number of sets. The suggestion unit also suggests whether the user should increase the load or whether the user should take a rest based on the results of the progress evaluation. For example, if the user's progress is good, the suggestion unit suggests increasing the load, and if the user's progress is stagnant, the suggestion unit suggests taking a rest. This makes it possible to suggest appropriate loads and rests according to the user's training progress.

[0070] The data input unit inputs the user's dietary data and sleep data, and the analysis unit analyzes the dietary data and sleep data to reflect it in the muscle training menu. The data input unit, for example, inputs the user's daily dietary details. For example, it inputs the amount of protein intake and calories. The data input unit also inputs the user's sleep data. For example, it inputs the amount of sleep and the quality of sleep. The analysis unit analyzes the dietary data and sleep data to reflect it in the muscle training menu. For example, it adjusts the muscle training menu based on the amount of protein intake. It also adjusts the training intensity based on the quality of sleep. This makes it possible to provide a muscle training menu that takes the user's dietary and sleep data into consideration.

[0071] The data input unit inputs the user's past exercise history and health checkup data, and the analysis unit analyzes the exercise history and health checkup data to perform a more precise analysis. The data input unit, for example, inputs the user's past exercise history. For example, it inputs past training content and frequency. The data input unit also inputs the user's health checkup data. For example, it inputs blood pressure and heart rate. The analysis unit analyzes the exercise history and health checkup data to perform a more precise analysis. For example, it adjusts the current training menu based on the past training content. It also adjusts the training intensity based on the health checkup data. This enables a precise analysis that takes into account the user's past exercise history and health checkup data.

[0072] The data input unit uses the emotion estimation function to analyze the emotional state of the user at the time of input, and the analysis unit analyzes the emotional state and can suggest a menu that takes into account fluctuations in stress and motivation. The data input unit, for example, uses a camera to analyze the facial expression of the user when inputting data and estimates the emotional state using the emotion estimation function. For example, if stress is high, it suggests training that has a relaxing effect. The analysis unit analyzes the emotional state and suggests a menu that takes into account fluctuations in stress and motivation. For example, if stress is high, it suggests training that has a relaxing effect, and if motivation is high, it suggests high-intensity training. This makes it possible to provide a training menu that takes into account the user's emotional state.

[0073] The data input unit uses voice recognition technology to input data for the user, thereby reducing the amount of work required. For example, the data input unit allows the user to input basic information such as age, gender, weight, and height by voice, and then converts the data into text data using voice recognition technology. For example, the input content is converted into text data using voice recognition technology. This reduces the amount of work required for the user to input data.

[0074] The data input unit can work in conjunction with other fitness apps and wearable devices to automatically import data. The data input unit, for example, works in conjunction with other fitness apps to automatically import the user's exercise data. For example, the generation AI analyzes data from a running app and reflects it in a training menu. The data input unit also works in conjunction with wearable devices to automatically import the user's exercise data. For example, the generation AI analyzes data from a smartwatch and reflects it in a training menu. This makes it possible to work in conjunction with other fitness apps and wearable devices to automatically import data.

[0075] The data input unit can use the emotion estimation function to analyze the user's emotions in real time when entering data and provide positive feedback. For example, the data input unit uses a camera to analyze the user's facial expressions when entering data and estimates the user's emotional state in real time using the emotion estimation function. For example, when the user is smiling, positive feedback such as "That's great!" is provided. This allows the user's emotions to be analyzed in real time and positive feedback to be provided.

[0076] The suggestion unit can monitor the user's muscle growth and fatigue level in real time and dynamically adjust the menu based on that. The suggestion unit, for example, monitors muscle growth with a sensor, and the generation AI analyzes it in real time. For example, it measures muscle tone and size and adjusts the training menu based on that. The suggestion unit also monitors fatigue level with a sensor, and the generation AI analyzes it in real time. For example, it measures muscle fatigue level and adjusts the training menu based on that. This makes it possible to monitor the user's muscle growth and fatigue level in real time and dynamically adjust the menu.

[0077] The suggestion unit can suggest exercises that emphasize fun, taking into account the user's preferences and interests. For example, the suggestion unit collects the user's preferences and interests through a questionnaire, which the generation AI analyzes. For example, the suggestion unit suggests dance exercises to a user who likes dancing. The suggestion unit also suggests exercises that emphasize fun, taking into account the user's interests. For example, the suggestion unit suggests hiking or running to a user who likes the outdoors. This makes it possible to suggest exercises that emphasize fun, taking into account the user's preferences and interests.

[0078] The suggestion unit uses the emotion estimation function to suggest a menu that corresponds to the user's emotional state, thereby maintaining motivation. For example, the suggestion unit estimates the user's emotional state through facial expression analysis, and the generation AI suggests a menu based on that data. For example, when the user is tired, it suggests exercises that have a relaxing effect. The suggestion unit also suggests a menu that corresponds to the user's emotional state, thereby maintaining motivation. For example, when the user is motivated, it suggests high-intensity training. In this way, it is possible to suggest a menu that corresponds to the user's emotional state and maintain motivation.

[0079] The suggestion unit can work with the user's friends and family to suggest a joint exercise menu. For example, the suggestion unit inputs data on the user's friends and family, and the generation AI suggests a joint exercise menu. For example, it can suggest paired stretching or group circuit training. The suggestion unit can also work with the user's friends and family to suggest a joint exercise menu. For example, it can suggest a fitness challenge for the whole family. This makes it possible to suggest a joint exercise menu in collaboration with the user's friends and family.

[0080] The suggestion unit can suggest outdoor exercise menus according to the season and weather. For example, the suggestion unit takes in season and weather data, and the generation AI suggests outdoor exercise menus. For example, it suggests cherry blossom viewing runs in spring and beach exercises in summer. The suggestion unit also suggests outdoor exercise menus according to the season and weather. For example, it suggests indoor training when it rains and outdoor exercises when it's sunny. This makes it possible to suggest outdoor exercise menus according to the season and weather.

[0081] The suggestion unit uses the emotion estimation function to suggest the exercise that the user will enjoy most, making it easier for them to continue. For example, the suggestion unit estimates the user's emotional state through facial expression analysis, and the generation AI suggests the exercise that the user will enjoy most. For example, if the user smiles a lot, it suggests a dance exercise. The suggestion unit also suggests the exercise that the user will enjoy most, making it easier for them to continue. For example, it suggests exercises that take the user's preferences and interests into consideration. This suggests the exercise that the user will enjoy most, making it easier for them to continue.

[0082] In addition to the video data, the filming unit can measure muscle movement with a sensor to perform more detailed analysis. For example, the filming unit measures the user's muscle movement with a sensor, and the generation AI analyzes the data. For example, the effectiveness of training can be evaluated based on data on muscle contraction and extension. In addition to the video data, the filming unit can also measure muscle movement with a sensor to perform more detailed analysis. For example, an electromyogram sensor can be used to measure muscle activity. This allows for more detailed analysis by measuring muscle movement with a sensor in addition to the video data.

[0083] The filming unit can analyze the video data in real time and provide instant feedback. For example, the filming unit analyzes video data in real time when a user is doing strength training, and the generation AI provides instant feedback. For example, if the user's form is compromised, the generation AI can give instructions such as "Keep your back straight." The filming unit can also analyze the video data in real time and provide instant feedback. For example, a real-time feedback system can be used to analyze the user's movements in real time and provide instant feedback. This allows the video data to be analyzed in real time and provide instant feedback.

[0084] The camera unit uses the emotion estimation function to analyze the user's emotions from their facial expressions and tone of voice, and can adjust the progress of the training. For example, the camera unit analyzes the user's facial expressions with a camera, and the generation AI estimates their emotional state. For example, if it detects a tired expression, it will suggest lowering the intensity of the training. The camera unit also analyzes the tone of the user's voice, and the generation AI estimates their emotional state. For example, if the voice tone is low, it will suggest lowering the intensity of the training. This allows the user's emotions to be analyzed and the progress of the training to be adjusted.

[0085] The camera unit can convert the video data into a 3D model, allowing the user to check their movements from multiple angles. For example, the camera unit converts video data of the user's strength training into a 3D model, which is then analyzed by the generating AI. For example, the 3D model can be used to check the user's movements from multiple angles and identify areas for improvement in form. The camera unit can also convert the video data into a 3D model, allowing the user to check their movements from multiple angles. For example, the camera unit can use 3D scanning technology to convert the video data into a 3D model, allowing the user to check their movements from multiple angles.

[0086] The filming unit can share video data with other users and receive feedback within the community. For example, a user can share their muscle training video data within the community and receive feedback from other users. For example, they can receive advice such as "You should improve this a bit more." The filming unit can also share video data with other users and receive feedback within the community. For example, they can use the feedback function to receive advice from other users. This allows them to share video data with other users and receive feedback within the community.

[0087] The filming unit uses the emotion estimation function to analyze the user's emotional state along with the video data, maximizing the effectiveness of the training. For example, the filming unit analyzes the user's facial expressions with a camera, and the generation AI estimates the emotional state. For example, if the user smiles a lot, it will suggest increasing the intensity of the training. The filming unit also uses the emotion estimation function to analyze the user's emotional state along with the video data, maximizing the effectiveness of the training. For example, it will associate and analyze the emotional state with the effectiveness of the training, and suggest an optimal training menu. This allows the user's emotional state to be analyzed along with the video data, maximizing the effectiveness of the training.

[0088] The suggestion unit can suggest specific exercises for posture correction so that the user can put them into practice. For example, the suggestion unit analyzes the user's posture data, and the generation AI suggests specific exercises. For example, it suggests stretching and strength training to improve slouching. The suggestion unit also suggests specific exercises for posture correction so that the user can put them into practice. For example, it specifically explains how to do the exercises for posture correction. This suggests specific exercises for posture correction so that the user can put them into practice.

[0089] The suggestion unit can monitor the user's posture over the long term and evaluate the progress of improvement. For example, the suggestion unit monitors the user's posture data over the long term, and the generation AI analyzes the data. For example, it periodically takes photos of the posture and evaluates the progress of improvement. The suggestion unit also monitors the user's posture over the long term and evaluates the progress of improvement. For example, it evaluates the progress based on changes in posture and improvements in muscle strength. This makes it possible to monitor the user's posture over the long term and evaluate the progress of improvement.

[0090] The suggestion unit can use the emotion estimation function to provide posture correction advice according to the user's emotional state. For example, the suggestion unit analyzes the user's facial expressions using a camera, and the generation AI estimates the emotional state. For example, when stress is high, the suggestion unit suggests posture correction exercises that have a relaxing effect. The suggestion unit also uses the emotion estimation function to provide posture correction advice according to the user's emotional state. For example, it suggests exercise methods and points to be careful of in daily life according to the emotional state. This makes it possible to provide posture correction advice according to the user's emotional state.

[0091] The suggestion unit can cooperate with a wearable device for posture correction and provide feedback in real time. The suggestion unit, for example, uses a wearable device to collect posture data of the user in real time, which is then analyzed by the generation AI. For example, a sensor on the back can be used to detect poor posture and provide immediate feedback. The suggestion unit can also cooperate with a wearable device for posture correction and provide feedback in real time. For example, a posture sensor can be used to detect poor posture in real time and provide immediate feedback. This allows the suggestion unit to cooperate with a wearable device for posture correction and provide feedback in real time.

[0092] The suggestion unit can integrate the user's posture data with other health data to provide comprehensive health management. For example, the suggestion unit integrates the user's posture data with other health data (e.g., heart rate and sleep data), and the generation AI performs a comprehensive analysis. For example, it evaluates the impact of improving posture on heart rate and sleep quality. The suggestion unit also integrates the user's posture data with other health data to provide comprehensive health management. For example, it integrates the data with data such as blood pressure and body fat percentage for analysis. This allows the user's posture data to be integrated with other health data to provide comprehensive health management.

[0093] The suggestion unit can use the emotion estimation function to provide advice that helps the user to have positive feelings about posture correction. For example, the suggestion unit analyzes the user's facial expressions using a camera, and the generation AI estimates their emotional state. For example, if the user smiles a lot, it provides positive feedback such as "That's great!". The suggestion unit also uses the emotion estimation function to provide advice that helps the user to have positive feelings about posture correction. For example, it provides specific explanations about exercise methods and things to be careful about in daily life. This makes it possible to provide advice that helps the user to have positive feelings about posture correction.

[0094] The suggestion unit can monitor the recovery state of the user's muscles and suggest the optimal load. For example, the suggestion unit monitors the recovery state of the user's muscles with a sensor, which is then analyzed by the generation AI. For example, it measures muscle stiffness and fatigue level and adjusts the training load based on that. The suggestion unit also monitors the recovery state of the user's muscles and suggests the optimal load. For example, it adjusts the training intensity based on the muscle recovery state. This makes it possible to monitor the recovery state of the user's muscles and suggest the optimal load.

[0095] The suggestion unit can analyze the user's training history and adjust the load based on past data. For example, the suggestion unit analyzes the user's training history, and the generation AI adjusts the load based on past data. For example, the current training load is adjusted based on past training content and frequency. The suggestion unit also analyzes the user's training history and adjusts the load based on past data. For example, the intensity and number of times of training are adjusted based on past training data. This makes it possible to analyze the user's training history and adjust the load based on past data.

[0096] The suggestion unit uses the emotion estimation function to suggest a load that corresponds to the user's emotional state, thereby maximizing the effectiveness of the training. For example, the suggestion unit analyzes the user's facial expression using a camera, and the generation AI estimates the emotional state. For example, if a tired expression is detected, the suggestion unit suggests lowering the training load. The suggestion unit also uses the emotion estimation function to suggest a load that corresponds to the user's emotional state, thereby maximizing the effectiveness of the training. For example, the intensity and number of times of training are adjusted according to the emotional state. This makes it possible to suggest a load that corresponds to the user's emotional state, thereby maximizing the effectiveness of the training.

[0097] The suggestion unit can suggest a load that suits the user's lifestyle and work schedule. For example, the suggestion unit analyzes the user's lifestyle and work schedule, and the generation AI adjusts the load. For example, it suggests lighter training during busy periods. The suggestion unit also suggests a load that suits the user's lifestyle and work schedule. For example, it adjusts the intensity and number of training sessions according to working hours and break times. This makes it possible to suggest a load that suits the user's lifestyle and work schedule.

[0098] The suggestion unit can suggest a load in a competitive format with other users to increase motivation. The suggestion unit, for example, suggests a training load in a competitive format with other users. For example, the users compete for training progress in a ranking format. The suggestion unit can also suggest a load in a competitive format with other users to increase motivation. For example, the users compete for training progress using a battle mode. This allows the suggestion unit to suggest a load in a competitive format with other users to increase motivation.

[0099] The suggestion unit uses the emotion estimation function to suggest the load that will most motivate the user, thereby encouraging continuation of training. For example, the suggestion unit analyzes the user's facial expression using a camera, and the generation AI estimates the emotional state. For example, if a motivated expression is detected, the suggestion unit suggests increasing the training load. The suggestion unit also uses the emotion estimation function to suggest the load that will most motivate the user, thereby encouraging continuation of training. For example, the intensity and number of times of training are adjusted according to the emotional state. This suggests the load that will most motivate the user, thereby encouraging continuation of training.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The suggestion unit can also estimate the user's emotional state and suggest types of training based on the estimated emotion. For example, if the user is feeling stressed, it can suggest relaxing yoga or stretching. If the user is highly motivated, it can suggest high-intensity interval training. Furthermore, if the user is feeling fatigued, it can suggest light aerobic exercise or recovery training. This makes it possible to provide a training menu that matches the user's emotional state and support effective training.

[0102] The suggestion unit can also analyze the user's training history and adjust the training menu based on past data. For example, it can optimize the current training menu based on the frequency and intensity of past training. It can also adjust the menu to avoid exercises that the user found difficult in the past. Furthermore, it can maintain the user's motivation by re-suggesting training menus that the user found successful in the past. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0103] The suggestion unit can also suggest a training menu that suits the user's lifestyle and work schedule. For example, when the user is busy, it can suggest a short but effective training menu. Also, when the user is on vacation and has more time, it can suggest a long training menu. Furthermore, it can adjust the timing of training to suit the user's working hours and break times. This makes it possible to provide a training menu that suits the user's lifestyle and work schedule and support continued training.

[0104] The suggestion unit can also work with the user's friends and family to suggest exercise menus to do together. For example, it can suggest paired stretching or group circuit training. It can also suggest fitness challenges for the whole family. Furthermore, training in a competitive format with friends and family can increase motivation. This allows the suggestion unit to suggest exercise menus to do together with the user's friends and family, making training more enjoyable.

[0105] The suggestion unit can also suggest outdoor exercise menus according to the season and weather. For example, it can suggest cherry blossom viewing runs in spring and beach exercises in summer. It can also suggest indoor training when it rains and outdoor exercises when it's sunny. It can also suggest training menus tailored to seasonal events and activities. This allows for outdoor exercise menus to be provided according to the season and weather, increasing the variety of training options.

[0106] The suggestion unit can also estimate the user's emotional state and adjust the progress of the training based on the estimated emotion. For example, if the user looks tired, the suggestion unit can suggest lowering the intensity of the training. Also, if the user is highly motivated, the suggestion unit can suggest increasing the intensity of the training. Furthermore, if the user is feeling stressed, the suggestion unit can suggest exercises that have a relaxing effect. This allows the progress of the training to be adjusted according to the user's emotional state, supporting effective training.

[0107] The suggestion unit can also estimate the user's emotional state and provide positive feedback based on the estimated emotion. For example, if the user smiles, positive feedback such as "That's great!" can be provided. If the user looks tired, encouraging words such as "Let's try a little harder!" can be provided. Furthermore, if the user is feeling stressed, relaxing feedback such as "Relax and keep going" can be provided. This allows the user to receive positive feedback according to their emotional state and maintain their motivation for training.

[0108] The suggestion unit can also estimate the user's emotional state and provide advice to maximize the effectiveness of training based on the estimated emotion. For example, if the user is highly motivated, the suggestion unit can suggest increasing the intensity of training. Also, if the user looks tired, the suggestion unit can suggest decreasing the intensity of training. Furthermore, if the user is feeling stressed, the suggestion unit can suggest exercises that have a relaxing effect. In this way, advice can be provided according to the user's emotional state, maximizing the effectiveness of training.

[0109] The suggestion unit can also estimate the user's emotional state and provide advice to encourage training continuation based on the estimated emotion. For example, if the user feels motivated, the suggestion unit can suggest increasing the intensity of training. Also, if the user looks tired, the suggestion unit can suggest lowering the intensity of training. Furthermore, if the user feels stressed, the suggestion unit can suggest exercises that have a relaxing effect. In this way, advice can be provided according to the user's emotional state, encouraging training continuation.

[0110] The suggestion unit can also analyze the user's training history and adjust the training menu based on past data. For example, it can optimize the current training menu based on the frequency and intensity of past training. It can also adjust the menu to avoid exercises that the user found difficult in the past. Furthermore, it can maintain the user's motivation by re-suggesting training menus that the user found successful in the past. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The data input unit inputs data from the user, such as basic information such as age, sex, weight, height, exercise experience, and goals. Step 2: The analysis unit analyzes the data entered by the data input unit. For example, the generation AI generates an optimal muscle training menu based on the user's physical fitness level and goals. Step 3: The photographing unit photographs the user doing muscle training. For example, the photographing unit photographs the user doing muscle training using a camera. Step 4: The learning unit learns from the video data captured by the capture unit. For example, the generation AI analyzes the video data and evaluates the user's posture and movements. Step 5: The suggestion unit proposes the optimal muscle training menu for the user based on the information obtained by the analysis unit and learning unit. For example, the generation AI proposes a specific muscle training menu based on the user's data.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0124] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0148] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0150] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0152] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0153] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0154] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0155] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0157] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0158] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0159] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0161] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0162] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data input unit for inputting data from a user; an analysis unit that analyzes the data input by the data input unit; A photographing unit that photographs the user performing muscle training; a learning unit that learns the video data captured by the imaging unit; a suggestion unit that suggests an optimal muscle training menu to the user based on the information obtained by the analysis unit and the learning unit. A system characterized by:

2. The data input unit The user's data entry is performed using voice recognition technology, reducing the time and effort required.

2. The system of claim 1.

3. The proposal unit The user's muscle growth and fatigue level are monitored in real time, and the menu is dynamically adjusted based on that.

2. The system of claim 1.

4. The imaging unit is In addition to the video data, muscle movements are measured with sensors for more detailed analysis.

2. The system of claim 1.

5. The proposal unit Monitor the user's muscle recovery status and suggest optimal loads 2. The system of claim 1.

6. The data input unit an emotion estimation function for analyzing the emotional state of the user at the time of input; The analysis unit Analyze the emotional state and propose a menu that takes into account fluctuations in stress or motivation.

2. The system of claim 1.

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