system

Abstract

The system according to this embodiment aims to propose an optimal training plan based on the user's exercise history and physical condition data. [Solution] The system according to the embodiment comprises a collection unit, a proposal unit, and an advice unit. The collection unit collects the user's exercise history and physical condition data. The proposal unit analyzes the data collected by the collection unit and proposes a training plan. The advice unit provides advice on post-training recovery methods and diet based on the training plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, an optimal training plan has not been sufficiently proposed individually based on a user's exercise history and physical condition data, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal training plan based on a user's exercise history and physical condition data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a proposal unit, and an advice unit. The data collection unit collects the user's exercise history and physical condition data. The proposal unit analyzes the data collected by the data collection unit and proposes a training plan. The advice unit provides advice on post-training recovery methods and diet based on the training plan. [Effects of the Invention]

[0007] The system according to this embodiment can suggest an optimal training plan based on the user's exercise history and physical condition data. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI assistant system according to an embodiment of the present invention is a system that proposes an optimal training plan based on the user's exercise history and physical condition data. This AI assistant system collects and analyzes the user's exercise history and physical condition data to propose an optimal training plan. Furthermore, it also provides advice on recovery methods after training and dietary advice. For example, the AI ​​assistant system collects the user's exercise history and physical condition data. For example, it collects data including the type and frequency of training the user has done in the past, and changes in physical condition. Next, it analyzes the collected data and proposes an optimal training plan to the user. If the user trains based on the proposed training plan, it also provides advice on recovery methods after training and dietary advice. Furthermore, it uses sensors and cameras attached to the user to check the form during training in real time and provides advice. This allows the user to train with the correct form, enabling effective training. In addition, the proposal unit proposes a new training plan based on the user's biometric information after performing the proposed training. This provides an optimal plan according to the user's physical condition and training progress. Furthermore, the proposal unit and the advice unit estimate the user's emotions and provide training plans, recovery methods, and dietary advice based on the estimated emotions. For example, if the user is tired, the system can suggest a recovery-focused plan, and if they are energetic, it can suggest a more challenging training plan. In this way, the AI ​​assistant system supports effective training by suggesting the optimal training plan based on the user's exercise history and physical condition data, and by providing form checks during training, recovery methods after training, and dietary advice.

[0029] The AI ​​assistant system according to the embodiment comprises a data collection unit, a proposal unit, and an advice unit. The data collection unit collects the user's exercise history and physical condition data. The data collection unit collects data such as the type and frequency of training the user has performed in the past, and changes in physical condition. The data collection unit can collect data using, for example, a wearable device or a smartphone application. The proposal unit analyzes the data collected by the data collection unit and proposes a training plan. The proposal unit analyzes the data using, for example, AI and generates an optimal training plan for the user. The proposal unit can propose an individually customized training plan based on, for example, the user's exercise history and physical condition data. The advice unit provides advice on recovery methods and diet after training based on the training plan. The advice unit analyzes the user's physical condition data using, for example, AI and provides optimal recovery methods and dietary advice. The advice unit can suggest stretches, massages, and nutritionally balanced meals according to the user's physical condition and training progress. As a result, the AI ​​assistant system according to this embodiment can propose an optimal training plan based on the user's exercise history and physical condition data, and provide advice on post-training recovery methods and diet.

[0030] The data collection unit collects the user's exercise history and physical condition data. For example, it collects data such as the type and frequency of past training and changes in the user's physical condition. Specifically, data can be collected using wearable devices and smartphone apps. Wearable devices are equipped with heart rate monitors, accelerometers, GPS functions, etc., and through these devices, detailed data such as the user's exercise intensity, distance traveled, and calories burned are collected in real time. Smartphone apps can collect data manually entered by the user or automatically acquired in conjunction with other health management applications. For example, they can record in detail the type of training the user performed (running, cycling, yoga, etc.), its frequency, heart rate fluctuations during training, and changes in physical condition after training (fatigue, muscle soreness, etc.). Furthermore, the data collection unit can also collect the user's sleep data, daily activity levels, and dietary information to understand their overall health status. This allows the data collection unit to comprehensively collect the user's exercise history and physical condition data, providing the information necessary for the next steps: data analysis and training plan suggestions. The collected data is securely stored on a cloud server and can be linked with other systems and departments as needed. For example, the collected data will be made accessible to the proposal and advice departments, forming the foundation for providing more accurate services to users.

[0031] The proposal department analyzes the data collected by the data collection department and proposes a training plan. For example, the proposal department uses AI to analyze the data and generate an optimal training plan for the user. Specifically, the AI ​​uses machine learning algorithms to analyze the user's exercise history and physical condition data and proposes a individually customized training plan. For example, based on past training data, it analyzes the user's exercise patterns and changes in physical condition to calculate the optimal training intensity and frequency. Furthermore, the AI ​​can adjust the training plan considering the user's goals (weight loss, muscle strengthening, endurance improvement, etc.) and constraints (time constraints, inability to perform certain exercises, etc.). The proposal department collects user feedback, evaluates the effectiveness of the training plan, and modifies the plan as needed. For example, after the user exercises according to the training plan, the results and changes in physical condition are collected as feedback, and the AI ​​analyzes this data and reflects it in the next training plan. In this way, the proposal department can always provide the user with the optimal training plan and support effective exercise. Furthermore, the proposal department can anonymize and aggregate user data and analyze overall trends and patterns to help improve more general training plans.

[0032] The advice department provides post-workout recovery methods and dietary advice based on the training plan. For example, it uses AI to analyze the user's physical condition data and provide optimal recovery methods and dietary advice. Specifically, the AI ​​analyzes the user's post-workout physical condition data (heart rate recovery rate, muscle fatigue level, sleep quality, etc.) and suggests stretching and massage techniques necessary for recovery. For instance, if a specific muscle is fatigued after training, it will specifically indicate stretching and massage techniques to effectively recover that muscle. The AI ​​also suggests balanced meals based on the user's nutritional status and training goals. For example, it suggests a protein-rich diet for users aiming to increase muscle strength and a diet with appropriate carbohydrate intake for users aiming to improve endurance. Furthermore, the advice department provides actionable advice considering the user's lifestyle and dietary preferences. For example, it suggests easy-to-prepare healthy meal recipes for busy users and provides recipes using alternative ingredients for users with allergies to specific foods. In this way, the advice department supports users in achieving proper recovery after training and effectively improving their physical condition. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can provide feedback on the results of users following the advice, and the AI ​​can analyze this data and incorporate it into future advice. This allows the advice unit to consistently provide users with the most optimal recovery methods and dietary advice, maximizing the effectiveness of their training.

[0033] The service provider acquires the user's training form based on sensor information and provides real-time advice. The service provider detects the user's movements using, for example, an accelerometer or gyroscope and analyzes the training form. The service provider can, for example, film the user's movements using a camera and evaluate the accuracy of the form using AI. The service provider can, for example, provide real-time feedback to the user and advise them to train with the correct form. This allows the user to train with the correct form, enabling effective training. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input sensor information into AI and have the AI ​​perform the analysis of the training form.

[0034] The data collection unit can collect data on the types and frequency of training the user has performed in the past, as well as changes in their physical condition. For example, the data collection unit can record the types and frequency of training the user has performed and monitor changes in their physical condition. For example, the data collection unit can collect physical condition data such as heart rate, blood pressure, and body temperature using a wearable device. For example, the data collection unit can record the user's training history and track changes in their physical condition using a smartphone app. By collecting data on the user's past training history and changes in their physical condition, it is possible to propose a more accurate training plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into an AI and have the AI ​​perform data analysis.

[0035] The suggestion unit can propose a new training plan based on the biometric information of the user who has performed the suggested training. The suggestion unit can, for example, monitor the user's biometric information such as heart rate, blood pressure, and oxygen saturation, and evaluate the effectiveness of the training. The suggestion unit can, for example, use AI to analyze the biometric information and generate a new training plan that is tailored to the user's physical condition and training progress. The suggestion unit can, for example, propose a plan that adjusts the intensity and frequency of training based on the user's biometric information. This allows the suggestion unit to provide an optimal plan that is tailored to the user's physical condition and training progress. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's biometric information into AI and have the AI ​​generate a new training plan.

[0036] The data collection unit can analyze the user's past exercise history and select the optimal data collection method. For example, the data collection unit selects appropriate sensors based on the types of training the user has performed in the past. For example, the data collection unit adjusts the data collection interval based on the user's exercise frequency. For example, the data collection unit prioritizes a specific data collection method based on changes in the user's physical condition. This allows the optimal data collection method to be selected by analyzing the user's past exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise history data into AI and have AI select the data collection method.

[0037] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, if the user is at work, the data collection unit will refrain from collecting exercise data and collect only health data. For example, if the user is engaged in a hobby activity, the data collection unit will prioritize collecting data related to that activity. For example, if the user is on vacation, the data collection unit will collect data on relaxation. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the data filtering.

[0038] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit will prioritize the collection of training data. For example, if the user is at home, the data collection unit will prioritize the collection of relaxation data. For example, if the user is out, the data collection unit will prioritize the collection of walking data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI determine the data prioritization.

[0039] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will collect exercise data. For example, if the user posts about health, the data collection unit will collect physical condition data. For example, if the user posts about relaxation, the data collection unit will collect relaxation state data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0040] The suggestion unit can adjust the level of detail in a training plan based on the user's physical condition and exercise history. For example, the suggestion unit can suggest a detailed plan based on the types of training the user has done in the past. For example, the suggestion unit can adjust the intensity of the plan based on changes in the user's physical condition. For example, the suggestion unit can adjust the frequency of the plan based on the user's exercise frequency. By adjusting the level of detail in the plan based on the user's physical condition and exercise history, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the plan.

[0041] The suggestion unit can apply different suggestion algorithms depending on the user's goals and objectives when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will propose a plan specializing in strength training. If the user aims to lose weight, the suggestion unit will propose a plan centered on aerobic exercise. If the user aims to relax, the suggestion unit will propose a plan centered on yoga and stretching. By applying different suggestion algorithms according to the user's goals and objectives, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0042] The suggestion unit can prioritize training plans based on when the user's exercise history is submitted. For example, the suggestion unit might suggest the next plan based on the user's most recent training. For example, if the user hasn't trained for a long period, the suggestion unit might suggest a basic plan. For example, if the user has been training intensively during a specific period, the suggestion unit might suggest a plan tailored to that period. This allows the suggestion unit to provide a more appropriate plan by prioritizing plans based on when the user's exercise history is submitted. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit could input the user's exercise history data into an AI and have the AI ​​determine the priority of the plans.

[0043] The suggestion unit can adjust the order of training plans based on the user's relevance when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will suggest strength training first. For example, if the user aims to lose weight, the suggestion unit will suggest aerobic exercise first. For example, if the user aims to relax, the suggestion unit will suggest yoga or stretching first. By adjusting the order of plans based on the user's relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0044] The suggestion unit can adjust the order of training plans based on the user's relevance when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will suggest strength training first. For example, if the user aims to lose weight, the suggestion unit will suggest aerobic exercise first. For example, if the user aims to relax, the suggestion unit will suggest yoga or stretching first. By adjusting the order of plans based on the user's relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0045] The advice unit can adjust the level of detail of the advice based on the user's physical condition and exercise history when providing advice. For example, the advice unit can provide detailed advice based on the types of training the user has done in the past. For example, the advice unit can adjust the content of the advice based on changes in the user's physical condition. For example, the advice unit can adjust the frequency of advice based on the user's exercise frequency. By adjusting the level of detail of the advice based on the user's physical condition and exercise history, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the advice.

[0046] The advice unit can apply different advice algorithms depending on the user's goals and objectives when providing advice. For example, if the user aims to increase muscle strength, the advice unit will provide advice specifically focused on strength training. If the user aims to lose weight, the advice unit will provide advice focused on aerobic exercise. If the user aims to relax, the advice unit will provide advice focused on yoga and stretching. By applying different advice algorithms according to the user's goals and objectives, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the advice algorithm.

[0047] The advice unit can prioritize advice based on when the user's exercise history is submitted. For example, the advice unit may provide the following advice based on the user's most recent training. For example, if the user has not trained for a long period, the advice unit may provide basic advice. For example, if the user has concentrated their training during a specific period, the advice unit may provide advice tailored to that period. This allows for the provision of more appropriate advice by prioritizing advice based on when the user's exercise history is submitted. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit may input the user's exercise history data into AI and have the AI ​​determine the priority of advice.

[0048] The advice unit can adjust the order of advice based on the user's relevance when providing advice. For example, if the user is aiming to increase muscle strength, the advice unit will first provide advice on strength training. For example, if the user is aiming to lose weight, the advice unit will first provide advice on aerobic exercise. For example, if the user is aiming to relax, the advice unit will first provide advice on yoga or stretching. By adjusting the order of advice based on the user's relevance, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user relevance data into AI and have the AI ​​perform the adjustment of the order of advice.

[0049] The service provider can adjust the level of detail of the training form check based on the user's physical condition and exercise history. For example, the service provider can perform a detailed check based on the types of training the user has done in the past. For example, the service provider can adjust the content of the check based on changes in the user's physical condition. For example, the service provider can adjust the frequency of the check based on the user's exercise frequency. By adjusting the level of detail of the check based on the user's physical condition and exercise history, a more appropriate check can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the check.

[0050] The service provider can apply different checking algorithms depending on the user's goals and objectives when checking training form. For example, if the user aims to increase muscle strength, the service provider will perform checks specifically focused on strength training. If the user aims to lose weight, the service provider will perform checks focused on aerobic exercise. If the user aims to relax, the service provider will perform checks focused on yoga and stretching. By applying different checking algorithms according to the user's goals and objectives, the service provider can provide more appropriate checks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the checking algorithms.

[0051] The service provider can prioritize checks based on when the user's exercise history was submitted when reviewing training forms. For example, the service provider might perform the following checks based on the user's most recent training. For example, if the user has not trained for a long period, the service provider might perform a basic check. For example, if the user has concentrated their training during a specific period, the service provider might perform checks tailored to that period. This allows for more appropriate checks to be provided by prioritizing checks based on when the user's exercise history was submitted. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider could input the user's exercise history data into an AI and have the AI ​​determine the priority of checks.

[0052] The service provider can adjust the order of checks based on the user's relevance when checking training form. For example, if the user is aiming to increase muscle strength, the service provider will first perform checks related to strength training. For example, if the user is aiming to lose weight, the service provider will first perform checks related to aerobic exercise. For example, if the user is aiming to relax, the service provider will first perform checks related to yoga and stretching. By adjusting the order of checks based on the user's relevance, a more appropriate check can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user relevance data into AI and have the AI ​​perform the adjustment of the order of checks.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The suggestion department can collect and analyze users' sleep data in addition to their exercise history and physical condition data. For example, it can monitor the user's sleep duration and quality and reflect this in the training plan. If a user is not getting enough sleep, it can suggest a lighter training plan; conversely, if they are getting good sleep, it can suggest a harder training plan. This allows the department to provide a training plan that takes into account the user's overall health. The suggestion department can also suggest the optimal time of day for training based on the user's sleep data. For example, if a user has a nocturnal lifestyle, it can recommend nighttime training. Furthermore, the suggestion department can adjust recovery methods and dietary advice based on the user's sleep data. For example, it can provide recovery-focused advice and recommend nutritious meals for users who are sleep-deprived.

[0055] The service provider can monitor the user's muscle fatigue level in real time when checking the user's training form and suggest corrections to the form. For example, if the muscle fatigue level is high, it can suggest correcting the form and advise avoiding excessive load. This allows the user to train effectively while reducing the risk of injury. The service provider can also adjust the intensity of training based on the user's muscle fatigue level. For example, if the muscle fatigue level is high, it can suggest lighter training, and conversely, if the fatigue level is low, it can suggest harder training. Furthermore, the service provider can adjust recovery methods and dietary advice based on the user's muscle fatigue level. For example, for users with high muscle fatigue, it can provide recovery-focused advice and recommend a diet high in protein.

[0056] The suggestion department can collect and analyze users' dietary data in addition to their exercise history and physical condition data. For example, it can monitor the types of meals and nutrients a user consumes and reflect this in their training plan. If a user is eating a balanced diet, it can suggest a standard training plan; conversely, if their diet is unbalanced, it can suggest a lighter training plan. This allows the department to provide training plans that take the user's nutritional status into consideration. The suggestion department can also suggest the optimal time of day for training based on the user's dietary data. For example, it can recommend training during the time after a meal when digestion is progressing. Furthermore, the suggestion department can adjust recovery methods and dietary advice based on the user's dietary data. For example, it can recommend a balanced diet and provide recovery-focused advice to users with unbalanced diets.

[0057] The data collection unit can monitor and collect data on the user's activity level, in addition to their exercise history and physical condition data. For example, it can record the number of steps taken and the number of times a user climbs or descends stairs in their daily life and incorporate this into their training plan. If a user is sufficiently active in their daily life, it can suggest a lighter training plan; conversely, if they are less active, it can suggest a more intense training plan. This allows the system to provide a training plan that takes into account the user's overall activity level. The data collection unit can also suggest the optimal time of day for training based on the user's activity level. For example, it can recommend training during times of low activity. Furthermore, the data collection unit can adjust recovery methods and dietary advice based on the user's activity level. For example, it can provide recovery-focused advice and recommend nutritious meals for users with low activity levels.

[0058] The service provider can monitor the user's joint movements in real time when checking their training form and suggest corrections. For example, if joint movement is unnatural, it can suggest form corrections to encourage correct movement. This allows users to train effectively while reducing the risk of injury. The service provider can also adjust the intensity of training based on the user's joint movements. For example, if joint movement is limited, it can suggest lighter training, and conversely, if movement is smooth, it can suggest harder training. Furthermore, the service provider can adjust recovery methods and dietary advice based on the user's joint movements. For example, for users with limited joint movement, it can provide recovery-focused advice and recommend a diet that is good for the joints.

[0059] The data collection unit can collect and analyze the user's environmental data in addition to their exercise history and physical condition data. For example, it can monitor the temperature, humidity, and air quality around the user and reflect this in the training plan. If the user is in a hot and humid environment, it can suggest a lighter training plan; conversely, if they are in a comfortable environment, it can suggest a normal training plan. This allows the system to provide a training plan that takes the user's environment into consideration. The data collection unit can also suggest the optimal training location based on the user's environmental data. For example, it can recommend training in a place with good air quality. Furthermore, the data collection unit can adjust recovery methods and dietary advice based on the user's environmental data. For example, it can provide recovery-focused advice and recommend hydration to users in hot and humid environments.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The data collection unit collects the user's exercise history and physical condition data. For example, the data collection unit collects data such as the type and frequency of training the user has done in the past, and changes in physical condition. The data collection unit can collect data using, for example, a wearable device or a smartphone app. Step 2: The proposal unit analyzes the data collected by the collection unit and proposes a training plan. The proposal unit can, for example, use AI to analyze the data and generate an optimal training plan for the user. The proposal unit can, for example, propose an individually customized training plan based on the user's exercise history and physical condition data. Step 3: The advice unit provides advice on post-training recovery methods and diet based on the training plan. For example, the advice unit uses AI to analyze the user's physical condition data and provide optimal recovery methods and dietary advice. For example, the advice unit can suggest stretches, massages, and nutritionally balanced meals according to the user's physical condition and training progress.

[0062] (Example of form 2) An AI assistant system according to an embodiment of the present invention is a system that proposes an optimal training plan based on the user's exercise history and physical condition data. This AI assistant system collects and analyzes the user's exercise history and physical condition data to propose an optimal training plan. Furthermore, it also provides advice on recovery methods after training and dietary advice. For example, the AI ​​assistant system collects the user's exercise history and physical condition data. For example, it collects data including the type and frequency of training the user has done in the past, and changes in physical condition. Next, it analyzes the collected data and proposes an optimal training plan to the user. If the user trains based on the proposed training plan, it also provides advice on recovery methods after training and dietary advice. Furthermore, it uses sensors and cameras attached to the user to check the form during training in real time and provides advice. This allows the user to train with the correct form, enabling effective training. In addition, the proposal unit proposes a new training plan based on the user's biometric information after performing the proposed training. This provides an optimal plan according to the user's physical condition and training progress. Furthermore, the proposal unit and the advice unit estimate the user's emotions and provide training plans, recovery methods, and dietary advice based on the estimated emotions. For example, if the user is tired, the system can suggest a recovery-focused plan, and if they are energetic, it can suggest a more challenging training plan. In this way, the AI ​​assistant system supports effective training by suggesting the optimal training plan based on the user's exercise history and physical condition data, and by providing form checks during training, recovery methods after training, and dietary advice.

[0063] The AI ​​assistant system according to the embodiment comprises a data collection unit, a proposal unit, and an advice unit. The data collection unit collects the user's exercise history and physical condition data. The data collection unit collects data such as the type and frequency of training the user has performed in the past, and changes in physical condition. The data collection unit can collect data using, for example, a wearable device or a smartphone application. The proposal unit analyzes the data collected by the data collection unit and proposes a training plan. The proposal unit analyzes the data using, for example, AI and generates an optimal training plan for the user. The proposal unit can propose an individually customized training plan based on, for example, the user's exercise history and physical condition data. The advice unit provides advice on recovery methods and diet after training based on the training plan. The advice unit analyzes the user's physical condition data using, for example, AI and provides optimal recovery methods and dietary advice. The advice unit can suggest stretches, massages, and nutritionally balanced meals according to the user's physical condition and training progress. As a result, the AI ​​assistant system according to this embodiment can propose an optimal training plan based on the user's exercise history and physical condition data, and provide advice on post-training recovery methods and diet.

[0064] The data collection unit collects the user's exercise history and physical condition data. For example, it collects data such as the type and frequency of past training and changes in the user's physical condition. Specifically, data can be collected using wearable devices and smartphone apps. Wearable devices are equipped with heart rate monitors, accelerometers, GPS functions, etc., and through these devices, detailed data such as the user's exercise intensity, distance traveled, and calories burned are collected in real time. Smartphone apps can collect data manually entered by the user or automatically acquired in conjunction with other health management applications. For example, they can record in detail the type of training the user performed (running, cycling, yoga, etc.), its frequency, heart rate fluctuations during training, and changes in physical condition after training (fatigue, muscle soreness, etc.). Furthermore, the data collection unit can also collect the user's sleep data, daily activity levels, and dietary information to understand their overall health status. This allows the data collection unit to comprehensively collect the user's exercise history and physical condition data, providing the information necessary for the next steps: data analysis and training plan suggestions. The collected data is securely stored on a cloud server and can be linked with other systems and departments as needed. For example, the collected data will be made accessible to the proposal and advice departments, forming the foundation for providing more accurate services to users.

[0065] The proposal department analyzes the data collected by the data collection department and proposes a training plan. For example, the proposal department uses AI to analyze the data and generate an optimal training plan for the user. Specifically, the AI ​​uses machine learning algorithms to analyze the user's exercise history and physical condition data and proposes a individually customized training plan. For example, based on past training data, it analyzes the user's exercise patterns and changes in physical condition to calculate the optimal training intensity and frequency. Furthermore, the AI ​​can adjust the training plan considering the user's goals (weight loss, muscle strengthening, endurance improvement, etc.) and constraints (time constraints, inability to perform certain exercises, etc.). The proposal department collects user feedback, evaluates the effectiveness of the training plan, and modifies the plan as needed. For example, after the user exercises according to the training plan, the results and changes in physical condition are collected as feedback, and the AI ​​analyzes this data and reflects it in the next training plan. In this way, the proposal department can always provide the user with the optimal training plan and support effective exercise. Furthermore, the proposal department can anonymize and aggregate user data and analyze overall trends and patterns to help improve more general training plans.

[0066] The advice department provides post-workout recovery methods and dietary advice based on the training plan. For example, it uses AI to analyze the user's physical condition data and provide optimal recovery methods and dietary advice. Specifically, the AI ​​analyzes the user's post-workout physical condition data (heart rate recovery rate, muscle fatigue level, sleep quality, etc.) and suggests stretching and massage techniques necessary for recovery. For instance, if a specific muscle is fatigued after training, it will specifically indicate stretching and massage techniques to effectively recover that muscle. The AI ​​also suggests balanced meals based on the user's nutritional status and training goals. For example, it suggests a protein-rich diet for users aiming to increase muscle strength and a diet with appropriate carbohydrate intake for users aiming to improve endurance. Furthermore, the advice department provides actionable advice considering the user's lifestyle and dietary preferences. For example, it suggests easy-to-prepare healthy meal recipes for busy users and provides recipes using alternative ingredients for users with allergies to specific foods. In this way, the advice department supports users in achieving proper recovery after training and effectively improving their physical condition. Furthermore, the advice unit can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it can provide feedback on the results of users following the advice, and the AI ​​can analyze this data and incorporate it into future advice. This allows the advice unit to consistently provide users with the most optimal recovery methods and dietary advice, maximizing the effectiveness of their training.

[0067] The service provider acquires the user's training form based on sensor information and provides real-time advice. The service provider detects the user's movements using, for example, an accelerometer or gyroscope and analyzes the training form. The service provider can, for example, film the user's movements using a camera and evaluate the accuracy of the form using AI. The service provider can, for example, provide real-time feedback to the user and advise them to train with the correct form. This allows the user to train with the correct form, enabling effective training. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input sensor information into AI and have the AI ​​perform the analysis of the training form.

[0068] The data collection unit can collect data on the types and frequency of training the user has performed in the past, as well as changes in their physical condition. For example, the data collection unit can record the types and frequency of training the user has performed and monitor changes in their physical condition. For example, the data collection unit can collect physical condition data such as heart rate, blood pressure, and body temperature using a wearable device. For example, the data collection unit can record the user's training history and track changes in their physical condition using a smartphone app. By collecting data on the user's past training history and changes in their physical condition, it is possible to propose a more accurate training plan. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data acquired from a wearable device into an AI and have the AI ​​perform data analysis.

[0069] The suggestion unit can propose a new training plan based on the biometric information of the user who has performed the suggested training. The suggestion unit can, for example, monitor the user's biometric information such as heart rate, blood pressure, and oxygen saturation, and evaluate the effectiveness of the training. The suggestion unit can, for example, use AI to analyze the biometric information and generate a new training plan that is tailored to the user's physical condition and training progress. The suggestion unit can, for example, propose a plan that adjusts the intensity and frequency of training based on the user's biometric information. This allows the suggestion unit to provide an optimal plan that is tailored to the user's physical condition and training progress. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's biometric information into AI and have the AI ​​generate a new training plan.

[0070] The suggestion unit can estimate the user's emotions and propose a training plan based on those emotions. For example, the suggestion unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The suggestion unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is tired, the suggestion unit can propose a recovery-focused plan, and if the user is energetic, a more challenging training plan. This allows the suggestion unit to provide an optimal plan tailored to the user's condition by proposing a training plan based on their emotions. Some or all of the above-described processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​generate the training plan.

[0071] The advice unit can estimate the user's emotions and provide advice on post-training recovery methods and diet based on the estimated emotions. The advice unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The advice unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is tired, the advice unit can provide recovery-focused advice, and if the user is energetic, it can provide advice for more intense post-training. This enables more effective training support by providing recovery methods and dietary advice based on the user's emotions. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input the user's emotion data into an AI and have the AI ​​generate recovery methods and dietary advice.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of collecting exercise history and physical condition data based on the estimated emotions. The data collection unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The data collection unit is implemented using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the data collection unit reduces the collection timing and collects data when the user is relaxed. For example, if the user is relaxed, the data collection unit collects data frequently and records a detailed exercise history. For example, if the user is tired, the data collection unit adjusts the collection timing and also collects data during rest. This allows for the collection of more appropriate data by adjusting the data collection timing based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's emotion data into an AI and have the AI ​​adjust the data collection timing.

[0073] The data collection unit can analyze the user's past exercise history and select the optimal data collection method. For example, the data collection unit selects appropriate sensors based on the types of training the user has performed in the past. For example, the data collection unit adjusts the data collection interval based on the user's exercise frequency. For example, the data collection unit prioritizes a specific data collection method based on changes in the user's physical condition. This allows the optimal data collection method to be selected by analyzing the user's past exercise history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's exercise history data into AI and have AI select the data collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and areas of interest during collection. For example, if the user is at work, the data collection unit will refrain from collecting exercise data and collect only health data. For example, if the user is engaged in a hobby activity, the data collection unit will prioritize collecting data related to that activity. For example, if the user is on vacation, the data collection unit will collect data on relaxation. By filtering the data based on the user's lifestyle and areas of interest, more relevant data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's lifestyle and areas of interest into an AI and have the AI ​​perform the data filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, the data collection unit analyzes the user's facial expressions, voice, and text data to estimate emotions. The data collection unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the data collection unit will prioritize collecting physical condition data. For example, if the user is relaxed, the data collection unit will prioritize collecting exercise history data. For example, if the user is tired, the data collection unit will prioritize collecting rest data. This allows for the priority collection of more important data by determining data priority based on the user's emotions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into an AI and have the AI ​​perform the data priority determination.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is at a gym, the data collection unit will prioritize the collection of training data. For example, if the user is at home, the data collection unit will prioritize the collection of relaxation data. For example, if the user is out, the data collection unit will prioritize the collection of walking data. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI determine the data prioritization.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, if the user posts about exercise on social media, the data collection unit will collect exercise data. For example, if the user posts about health, the data collection unit will collect physical condition data. For example, if the user posts about relaxation, the data collection unit will collect relaxation state data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0078] The suggestion unit can estimate the user's emotions and adjust the way the training plan is presented based on the estimated emotions. The suggestion unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The suggestion unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the suggestion unit will suggest a simple and easy-to-understand plan. For example, if the user is relaxed, the suggestion unit will suggest a plan with detailed explanations. For example, if the user is tired, the suggestion unit will suggest a recovery-focused plan. This allows for the provision of a more appropriate plan by adjusting the way the training plan is presented based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the way the training plan is presented.

[0079] The suggestion unit can adjust the level of detail in a training plan based on the user's physical condition and exercise history. For example, the suggestion unit can suggest a detailed plan based on the types of training the user has done in the past. For example, the suggestion unit can adjust the intensity of the plan based on changes in the user's physical condition. For example, the suggestion unit can adjust the frequency of the plan based on the user's exercise frequency. By adjusting the level of detail in the plan based on the user's physical condition and exercise history, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail in the plan.

[0080] The suggestion unit can apply different suggestion algorithms depending on the user's goals and objectives when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will propose a plan specializing in strength training. If the user aims to lose weight, the suggestion unit will propose a plan centered on aerobic exercise. If the user aims to relax, the suggestion unit will propose a plan centered on yoga and stretching. By applying different suggestion algorithms according to the user's goals and objectives, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the suggestion algorithm.

[0081] The suggestion unit can estimate the user's emotions and adjust the length of the training plan based on the estimated emotions. The suggestion unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The suggestion unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the suggestion unit will suggest a short, effective plan. For example, if the user is relaxed, the suggestion unit will suggest a longer plan. For example, if the user is tired, the suggestion unit will suggest a short, recovery-focused plan. This allows for the provision of a more appropriate plan by adjusting the length of the training plan based on the user's emotions. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of the training plan.

[0082] The suggestion unit can prioritize training plans based on when the user's exercise history is submitted. For example, the suggestion unit might suggest the next plan based on the user's most recent training. For example, if the user hasn't trained for a long period, the suggestion unit might suggest a basic plan. For example, if the user has been training intensively during a specific period, the suggestion unit might suggest a plan tailored to that period. This allows the suggestion unit to provide a more appropriate plan by prioritizing plans based on when the user's exercise history is submitted. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit could input the user's exercise history data into an AI and have the AI ​​determine the priority of the plans.

[0083] The suggestion unit can adjust the order of training plans based on the user's relevance when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will suggest strength training first. For example, if the user aims to lose weight, the suggestion unit will suggest aerobic exercise first. For example, if the user aims to relax, the suggestion unit will suggest yoga or stretching first. By adjusting the order of plans based on the user's relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0084] The suggestion unit can adjust the order of training plans based on the user's relevance when proposing a training plan. For example, if the user aims to increase muscle strength, the suggestion unit will suggest strength training first. For example, if the user aims to lose weight, the suggestion unit will suggest aerobic exercise first. For example, if the user aims to relax, the suggestion unit will suggest yoga or stretching first. By adjusting the order of plans based on the user's relevance, a more appropriate plan can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user relevance data into AI and have the AI ​​perform the adjustment of the plan order.

[0085] The advice unit can estimate the user's emotions and adjust the way recovery methods and dietary advice are expressed based on the estimated emotions. The advice unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The advice unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the advice unit will provide simple and easy-to-understand advice. For example, if the user is relaxed, the advice unit will provide advice with detailed explanations. For example, if the user is tired, the advice unit will provide recovery-focused advice. This allows for more appropriate advice to be provided by adjusting the way recovery methods and dietary advice are expressed based on the user's emotions. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input the user's emotion data into an AI and have the AI ​​adjust the way the advice is expressed.

[0086] The advice unit can adjust the level of detail of the advice based on the user's physical condition and exercise history when providing advice. For example, the advice unit can provide detailed advice based on the types of training the user has done in the past. For example, the advice unit can adjust the content of the advice based on changes in the user's physical condition. For example, the advice unit can adjust the frequency of advice based on the user's exercise frequency. By adjusting the level of detail of the advice based on the user's physical condition and exercise history, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the advice.

[0087] The advice unit can apply different advice algorithms depending on the user's goals and objectives when providing advice. For example, if the user aims to increase muscle strength, the advice unit will provide advice specifically focused on strength training. If the user aims to lose weight, the advice unit will provide advice focused on aerobic exercise. If the user aims to relax, the advice unit will provide advice focused on yoga and stretching. By applying different advice algorithms according to the user's goals and objectives, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the advice algorithm.

[0088] The advice unit can estimate the user's emotions and adjust the length of recovery methods and dietary advice based on the estimated emotions. The advice unit estimates emotions by, for example, analyzing the user's facial expressions, voice, and text data. The advice unit is implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the advice unit provides effective advice in a short amount of time. For example, if the user is relaxed, the advice unit provides longer advice with detailed explanations. For example, if the user is tired, the advice unit provides recovery-focused advice in a short amount of time. This allows for more appropriate advice to be provided by adjusting the length of recovery methods and dietary advice based on the user's emotions. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input user emotion data into AI and have the AI ​​adjust the length of the advice.

[0089] The advice unit can prioritize advice based on when the user's exercise history is submitted. For example, the advice unit may provide the following advice based on the user's most recent training. For example, if the user has not trained for a long period, the advice unit may provide basic advice. For example, if the user has concentrated their training during a specific period, the advice unit may provide advice tailored to that period. This allows for the provision of more appropriate advice by prioritizing advice based on when the user's exercise history is submitted. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit may input the user's exercise history data into AI and have the AI ​​determine the priority of advice.

[0090] The advice unit can adjust the order of advice based on the user's relevance when providing advice. For example, if the user is aiming to increase muscle strength, the advice unit will first provide advice on strength training. For example, if the user is aiming to lose weight, the advice unit will first provide advice on aerobic exercise. For example, if the user is aiming to relax, the advice unit will first provide advice on yoga or stretching. By adjusting the order of advice based on the user's relevance, more appropriate advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user relevance data into AI and have the AI ​​perform the adjustment of the order of advice.

[0091] The service provider can estimate the user's emotions and adjust the training form checking method based on the estimated user emotions. The service provider can estimate emotions by, for example, analyzing the user's facial expressions, voice, and text data. The service provider can be implemented using emotion estimation functionality, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand checking method. For example, if the user is relaxed, the service provider can provide a checking method that includes detailed explanations. For example, if the user is tired, the service provider can provide a recovery-focused checking method. This allows for more appropriate checking by adjusting the training form checking method based on the user's emotions. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the checking method.

[0092] The service provider can adjust the level of detail of the training form check based on the user's physical condition and exercise history. For example, the service provider can perform a detailed check based on the types of training the user has done in the past. For example, the service provider can adjust the content of the check based on changes in the user's physical condition. For example, the service provider can adjust the frequency of the check based on the user's exercise frequency. By adjusting the level of detail of the check based on the user's physical condition and exercise history, a more appropriate check can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's physical condition data and exercise history data into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the check.

[0093] The service provider can apply different checking algorithms depending on the user's goals and objectives when checking training form. For example, if the user aims to increase muscle strength, the service provider will perform checks specifically focused on strength training. If the user aims to lose weight, the service provider will perform checks focused on aerobic exercise. If the user aims to relax, the service provider will perform checks focused on yoga and stretching. By applying different checking algorithms according to the user's goals and objectives, the service provider can provide more appropriate checks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's goals and objectives into the AI ​​and have the AI ​​execute the application of the checking algorithms.

[0094] The service provider can estimate the user's emotions and adjust the display method of the training form check results based on the estimated user emotions. The service provider can estimate emotions by, for example, analyzing the user's facial expressions, voice, and text data. The service provider can implement this emotion estimation function using, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. For example, if the user is stressed, the service provider can provide a simple and easy-to-understand display method. For example, if the user is relaxed, the service provider can provide a display method that includes detailed explanations. For example, if the user is tired, the service provider can provide a recovery-focused display method. This allows for more appropriate display by adjusting the display method of the training form check results based on the user's emotions. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0095] The service provider can prioritize checks based on when the user's exercise history was submitted when reviewing training forms. For example, the service provider might perform the following checks based on the user's most recent training. For example, if the user has not trained for a long period, the service provider might perform a basic check. For example, if the user has concentrated their training during a specific period, the service provider might perform checks tailored to that period. This allows for more appropriate checks to be provided by prioritizing checks based on when the user's exercise history was submitted. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider could input the user's exercise history data into an AI and have the AI ​​determine the priority of checks.

[0096] The service provider can adjust the order of checks based on the user's relevance when checking training form. For example, if the user is aiming to increase muscle strength, the service provider will first perform checks related to strength training. For example, if the user is aiming to lose weight, the service provider will first perform checks related to aerobic exercise. For example, if the user is aiming to relax, the service provider will first perform checks related to yoga and stretching. By adjusting the order of checks based on the user's relevance, a more appropriate check can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user relevance data into AI and have the AI ​​perform the adjustment of the order of checks.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The suggestion department can collect and analyze users' sleep data in addition to their exercise history and physical condition data. For example, it can monitor the user's sleep duration and quality and reflect this in the training plan. If a user is not getting enough sleep, it can suggest a lighter training plan; conversely, if they are getting good sleep, it can suggest a harder training plan. This allows the department to provide a training plan that takes into account the user's overall health. The suggestion department can also suggest the optimal time of day for training based on the user's sleep data. For example, if a user has a nocturnal lifestyle, it can recommend nighttime training. Furthermore, the suggestion department can adjust recovery methods and dietary advice based on the user's sleep data. For example, it can provide recovery-focused advice and recommend nutritious meals for users who are sleep-deprived.

[0099] The service provider can monitor the user's muscle fatigue level in real time when checking the user's training form and suggest corrections to the form. For example, if the muscle fatigue level is high, it can suggest correcting the form and advise avoiding excessive load. This allows the user to train effectively while reducing the risk of injury. The service provider can also adjust the intensity of training based on the user's muscle fatigue level. For example, if the muscle fatigue level is high, it can suggest lighter training, and conversely, if the fatigue level is low, it can suggest harder training. Furthermore, the service provider can adjust recovery methods and dietary advice based on the user's muscle fatigue level. For example, for users with high muscle fatigue, it can provide recovery-focused advice and recommend a diet high in protein.

[0100] The data collection unit can monitor and collect data on the user's stress level, in addition to their exercise history and physical condition. For example, it can measure the user's heart rate variability and skin electrical activity to assess their stress level. Based on the user's stress level, the data collection unit can adjust the suggested training plan. For example, if the stress level is high, it can suggest a training plan with a relaxing effect, and if the stress level is low, it can suggest a normal training plan. This allows for the provision of a training plan that takes the user's mental health into consideration. The data collection unit can also adjust recovery methods and dietary advice based on the user's stress level. For example, it can recommend relaxing recovery methods and diets to users with high stress levels. Furthermore, the data collection unit can adjust the timing of data collection based on the user's stress level. For example, if the stress level is high, it can reduce the frequency of data collection, allowing data to be collected in a relaxed state.

[0101] The suggestion department can collect and analyze users' dietary data in addition to their exercise history and physical condition data. For example, it can monitor the types of meals and nutrients a user consumes and reflect this in their training plan. If a user is eating a balanced diet, it can suggest a standard training plan; conversely, if their diet is unbalanced, it can suggest a lighter training plan. This allows the department to provide training plans that take the user's nutritional status into consideration. The suggestion department can also suggest the optimal time of day for training based on the user's dietary data. For example, it can recommend training during the time after a meal when digestion is progressing. Furthermore, the suggestion department can adjust recovery methods and dietary advice based on the user's dietary data. For example, it can recommend a balanced diet and provide recovery-focused advice to users with unbalanced diets.

[0102] The advice unit can estimate the user's emotions and, based on those estimates, provide advice to maintain motivation during training. For example, if the user is tired, it can provide encouraging messages to boost motivation. Conversely, if the user is energetic, it can set challenging goals to encourage further effort. This allows for motivational advice tailored to the user's emotions. The advice unit can also adjust the music and ambient sounds during training based on the user's emotions. For example, if the user wants to relax, it can play calming music; conversely, if they want an energetic workout, it can play upbeat music. Furthermore, the advice unit can suggest break times during training based on the user's emotions. For example, if the user is tired, it can advise taking frequent breaks; if they are energetic, it can suggest fewer breaks.

[0103] The data collection unit can monitor and collect data on the user's activity level, in addition to their exercise history and physical condition data. For example, it can record the number of steps taken and the number of times a user climbs or descends stairs in their daily life and incorporate this into their training plan. If a user is sufficiently active in their daily life, it can suggest a lighter training plan; conversely, if they are less active, it can suggest a more intense training plan. This allows the system to provide a training plan that takes into account the user's overall activity level. The data collection unit can also suggest the optimal time of day for training based on the user's activity level. For example, it can recommend training during times of low activity. Furthermore, the data collection unit can adjust recovery methods and dietary advice based on the user's activity level. For example, it can provide recovery-focused advice and recommend nutritious meals for users with low activity levels.

[0104] The proposal department can collect and analyze users' mental health data in addition to their exercise history and physical condition data. For example, it can monitor users' moods and stress levels and reflect them in training plans. If a user is feeling stressed, it can suggest a training plan with relaxing effects; conversely, if they are feeling good, it can suggest a normal training plan. This allows for the provision of training plans that take the user's mental health into consideration. Furthermore, the proposal department can suggest the optimal time of day for training based on the user's mental health data. For example, it can recommend training during times of low stress. In addition, the proposal department can adjust recovery methods and dietary advice based on the user's mental health data. For example, it can recommend recovery methods and diets with relaxing effects to users who are feeling stressed.

[0105] The service provider can monitor the user's joint movements in real time when checking their training form and suggest corrections. For example, if joint movement is unnatural, it can suggest form corrections to encourage correct movement. This allows users to train effectively while reducing the risk of injury. The service provider can also adjust the intensity of training based on the user's joint movements. For example, if joint movement is limited, it can suggest lighter training, and conversely, if movement is smooth, it can suggest harder training. Furthermore, the service provider can adjust recovery methods and dietary advice based on the user's joint movements. For example, for users with limited joint movement, it can provide recovery-focused advice and recommend a diet that is good for the joints.

[0106] The data collection unit can collect and analyze the user's environmental data in addition to their exercise history and physical condition data. For example, it can monitor the temperature, humidity, and air quality around the user and reflect this in the training plan. If the user is in a hot and humid environment, it can suggest a lighter training plan; conversely, if they are in a comfortable environment, it can suggest a normal training plan. This allows the system to provide a training plan that takes the user's environment into consideration. The data collection unit can also suggest the optimal training location based on the user's environmental data. For example, it can recommend training in a place with good air quality. Furthermore, the data collection unit can adjust recovery methods and dietary advice based on the user's environmental data. For example, it can provide recovery-focused advice and recommend hydration to users in hot and humid environments.

[0107] The suggestion department can collect and analyze users' social data in addition to their exercise history and physical condition data. For example, it can monitor the activities of groups and communities that users participate in and reflect this in training plans. If a user is highly social, it can suggest a group training plan; conversely, if they are more solitary, it can suggest an individual training plan. This allows for the provision of training plans that take into account the user's social nature. Furthermore, the suggestion department can suggest the optimal time of day for training based on the user's social data. For example, it can recommend training during times when group activities are less frequent. In addition, the suggestion department can adjust recovery methods and dietary advice based on the user's social data. For example, it can provide recovery-focused advice and recommend energy replenishment for users who are highly social.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects the user's exercise history and physical condition data. For example, the data collection unit collects data such as the type and frequency of training the user has done in the past, and changes in physical condition. The data collection unit can collect data using, for example, a wearable device or a smartphone app. Step 2: The proposal unit analyzes the data collected by the collection unit and proposes a training plan. The proposal unit can, for example, use AI to analyze the data and generate an optimal training plan for the user. The proposal unit can, for example, propose an individually customized training plan based on the user's exercise history and physical condition data. Step 3: The advice unit provides advice on post-training recovery methods and diet based on the training plan. For example, the advice unit uses AI to analyze the user's physical condition data and provide optimal recovery methods and dietary advice. For example, the advice unit can suggest stretches, massages, and nutritionally balanced meals according to the user's physical condition and training progress.

[0110] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0113] For example, the data collection unit can collect the user's exercise history and physical condition data using the camera 42 and microphone 38B of the smart device 14. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data to generate an optimal training plan. The advice unit is implemented by the control unit 46A of the smart device 14, which provides advice on post-training recovery methods and diet. The provision unit uses the camera 42 and acceleration sensor of the smart device 14 to check the user's training form in real time and provides advice to ensure that the user trains with the correct form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the data collection unit can collect the user's exercise history and physical condition data using the camera 42 and microphone 238 of the smart glasses 214. The suggestion unit is implemented by the identification processing unit 290 of the data processing device 12, which analyzes the collected data and generates an optimal training plan. The advice unit is implemented by the control unit 46A of the smart glasses 214, which provides advice on post-training recovery methods and diet. The provision unit checks the user's training form in real time using the camera 42 and acceleration sensor of the smart glasses 214 and advises the user to train with the correct form. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the data collection unit can collect the user's exercise history and physical condition data using the camera 42 and microphone 238 of the headset terminal 314. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data and generates an optimal training plan. The advice unit is implemented by the control unit 46A of the headset terminal 314, which provides advice on post-training recovery methods and diet. The provision unit uses the camera 42 and acceleration sensor of the headset terminal 314 to check the user's training form in real time and provides advice on how to train with the correct form. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

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

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

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

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

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

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

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

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

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

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

[0160] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

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

[0162] For example, the data collection unit can collect the user's exercise history and physical condition data using the camera 42 and microphone 238 of the robot 414. The suggestion unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes the collected data to generate an optimal training plan. The advice unit is implemented by the control unit 46A of the robot 414, which provides advice on post-training recovery methods and diet. The provision unit uses the camera 42 and acceleration sensor of the robot 414 to check the user's training form in real time and advises the user to train with the correct form. The correspondence between each unit and the device and control unit is not limited to the example described above, and various modifications are possible.

[0163] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) A collection unit that collects the user's exercise history and physical condition data, The collection unit analyzes the data collected and proposes a training plan, The system includes an advice unit that provides advice on post-training recovery methods and dietary advice when training is performed based on the aforementioned training plan. A system characterized by the following features. (Note 2) The system further includes a provisioning unit that acquires the user's training form based on sensor information and provides advice in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is The system collects data on the types and frequency of training the user has done in the past, as well as changes in their physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the biometric information of users who have performed the proposed training, a new training plan will be suggested. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, It estimates the user's emotions and proposes a training plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned advice section, It estimates the user's emotions and provides post-training recovery methods and dietary advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting exercise history and health data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past exercise history and select a data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, the data is filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts how the training plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When proposing a training plan, adjust the level of detail based on the user's physical condition and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When proposing a training plan, different suggestion algorithms are applied depending on the user's goals and objectives. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the training plan based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When proposing a training plan, the plan's priority is determined based on when the user submitted their exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When proposing a training plan, adjust the order of the plan based on the user's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When proposing a training plan, adjust the order of the plan based on the user's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, The system estimates the user's emotions and adjusts the way recovery methods and dietary advice are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, the level of detail in the advice is adjusted based on the user's physical condition and exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's goals and objectives. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, The system estimates the user's emotions and adjusts the length of recovery methods and dietary advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, the priority of advice is determined based on when the user submitted their exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, the order of advice is adjusted based on the relevance to the user. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the training form checking method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When checking training form, the level of detail of the check is adjusted based on the user's physical condition and exercise history. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When checking training form, different checking algorithms are applied depending on the user's goals and objectives. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the training form check results are displayed based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When checking training form, the system prioritizes checks based on when the user's exercise history was submitted. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When checking training forms, the order of checks is adjusted based on user relevance. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the user's exercise history and physical condition data, The collection unit analyzes the data collected and proposes a training plan, The system includes an advice unit that provides advice on post-training recovery methods and dietary advice when training is performed based on the aforementioned training plan. A system characterized by the following features.

2. The system further includes a provisioning unit that acquires the user's training form based on sensor information and provides advice in real time. The system according to feature 1.

3. The aforementioned collection unit is The system collects data on the types and frequency of training the user has performed in the past, as well as changes in their physical condition. The system according to feature 1.

4. The aforementioned proposal section is, Based on the biometric information of the user who performed the proposed training, a new training plan will be proposed. The system according to feature 1.

5. The aforementioned proposal section is, The system estimates the user's emotions and proposes a training plan based on the estimated emotions of the user. The system according to feature 1.

6. The aforementioned advice section, The system estimates the user's emotions and provides advice on post-training recovery methods and diet based on the estimated emotions of the user. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of collecting exercise history and physical condition data based on the estimated emotions of the user. The system according to feature 1.

8. The aforementioned collection unit is The user's past exercise history is analyzed, and a data collection method is selected. The system according to feature 1.

9. The aforementioned collection unit is During data collection, the data is filtered based on the user's current lifestyle and areas of interest. The system according to feature 1.

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