System

Abstract

An object of a system according to an embodiment is to evaluate a state of a body of a user and provide an individual training menu.SOLUTION: A system includes a sensor part, an analysis part, a generation part, and a monitoring part. The sensor unit measures a physical condition of a user using a sensor. The analysis unit performs analysis based on the data collected by the sensor unit. The generation unit generates a training menu based on the evaluation obtained by the analysis unit. The monitoring unit monitors the progress of the user based on the menu generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to provide an individual training menu based on the user's physical condition, and there is room for improvement.

[0005] The system according to the embodiment aims to evaluate the physical condition of the user and provide an individual training menu. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a monitoring unit. The sensor unit measures the user's physical condition using a sensor. The analysis unit performs analysis based on data collected by the sensor unit. The generation unit generates a training menu based on the evaluation obtained by the analysis unit. The monitoring unit monitors the user's progress based on the menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate the physical condition of the user and provide an individual training menu. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training system according to an embodiment of the present invention evaluates a user's physical condition, generates an appropriate training menu, and monitors the user's progress. The training system uses sensors to precisely measure the user's physical condition, and AI analyzes the measurement data to generate an individual training menu. The user can receive personalized training tailored to their physical condition and progress. The training system can also monitor the user's progress and changes in health status through long-term data tracking. For example, the training system collects data such as heart rate, muscle activity, and body fat percentage using a heart rate sensor, an electromyography sensor, and a body fat scale. The training system then analyzes the collected data using AI to propose an optimal training menu for the user. For example, the training menu may include strength training, stretching to improve flexibility, and aerobic exercise to enhance cardiopulmonary function. Furthermore, the training system can increase the load or change the menu depending on the user's training progress. The training system can also check the user's muscle strength improvement and changes in body fat percentage based on past training data. This allows the user to feel their progress and maintain their motivation. This allows the training system to automatically assess the user's physical condition, generate appropriate training menus, and monitor progress. For example, promotions can be developed that will appeal to a wide range of people as a new training method that users can use anytime, anywhere, at home or at the gym.

[0029] The training system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a monitoring unit. The sensor unit measures the user's physical condition. The user's physical condition may include, but is not limited to, heart rate, muscle electrical activity, and body fat percentage. The sensor unit may measure the heart rate using a heart rate sensor. The sensor unit may also measure muscle electrical activity using an electromyography sensor. The sensor unit may also measure the body fat percentage using a body fat scale. For example, the heart rate sensor may measure the heart rate using an optical sensor or an electrical sensor. The electromyography sensor may measure muscle electrical activity using surface electrodes or needle electrodes. The body fat scale may measure the body fat percentage using an impedance method or a caliper method. The analysis unit performs analysis based on the data collected by the sensor unit. The analysis may be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit may evaluate the user's physical condition based on the collected data. The analysis unit may also score the user's health status or perform risk assessment. The analysis unit can also analyze the data using a machine learning algorithm to evaluate the user's physical condition. The generation unit generates a training menu based on the evaluation obtained by the analysis unit. The training menu includes, but is not limited to, examples of strength training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. For example, the generation unit can suggest weight training or bodyweight training as strength training. The generation unit can also suggest static stretching or dynamic stretching as stretching to improve flexibility. The generation unit can also suggest running or cycling as aerobic exercise to improve cardiopulmonary function. The monitoring unit monitors the user's progress based on the menu generated by the generation unit. Monitoring can be performed, for example, by storing the user's training data over the long term and periodically analyzing it, but is not limited to such examples. For example, the monitoring unit can store the user's training data in cloud storage and analyze the data weekly or monthly.The monitoring unit can also evaluate the user's progress based on the user's training data. The monitoring unit can also evaluate the frequency, intensity, and achievement of training based on the user's training data. This allows the training system according to the embodiment to evaluate the user's physical condition, generate an appropriate training menu, and monitor the user's progress.

[0030] The sensor unit may include a heart rate sensor, an electromyogram sensor, and a body fat scale. Heart rate sensors include, for example, optical sensors and electrical sensors. Optical sensors use light to measure blood flow and measure heart rate. Electrical sensors measure heart rate using electrical signals. Electromyogram sensors include, for example, surface electrodes and needle electrodes. Surface electrodes are attached to the surface of the skin to measure electrical activity in muscles. Needle electrodes are inserted directly into muscles to measure electrical activity. Body fat scales include, for example, impedance methods and caliper methods. The impedance method measures electrical resistance within the body to calculate body fat percentage. The caliper method measures skin thickness to calculate body fat percentage. This allows the use of a variety of sensors to measure the user's physical condition in more detail.

[0031] The analysis unit can evaluate the user's physical condition based on the collected data. The evaluation includes, for example, health condition scoring and risk assessment. Health condition scoring quantifies the user's health condition based on the collected data. Risk assessment evaluates the user's health risk based on the collected data. For example, the analysis unit evaluates the user's cardiopulmonary function based on heart rate data. The analysis unit can also evaluate the user's muscle strength based on electromyogram data. The analysis unit can also evaluate the user's body fat percentage based on body fat percentage data. This allows the user's physical condition to be accurately evaluated based on the collected data.

[0032] The generation unit can generate a training menu that includes strength training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. Strength training includes, for example, weight training and bodyweight training. Weight training uses dumbbells and barbells to build muscle strength. Bodyweight training uses one's own body weight to build muscle strength. Stretching to improve flexibility includes, for example, static stretching and dynamic stretching. Static stretching stretches muscles while maintaining a certain posture. Dynamic stretching stretches muscles while moving. Aerobic exercise includes, for example, running and cycling. Running improves cardiopulmonary function by running at a certain pace. Cycling improves cardiopulmonary function by pedaling a bicycle. This makes it possible to generate a variety of training menus that suit the user's goals.

[0033] The monitoring unit can store the user's training data for a long period of time and analyze it periodically. For example, cloud storage is used for long-term storage. Cloud storage is a service that stores data via the Internet and can store data for long periods of time. For example, regular analysis can be performed once a week or once a month. Weekly analysis involves analyzing the data on a specific day each week. Monthly analysis involves analyzing the data on a specific day each month. For example, the monitoring unit can store the user's training data in cloud storage and analyze the data weekly or monthly. The monitoring unit can also evaluate the user's progress based on the user's training data. In this way, by storing the user's training data for a long period of time and analyzing it periodically, the progress can be understood in detail.

[0034] The monitoring unit can analyze the data weekly or monthly to evaluate the user's progress. For weekly or monthly analysis, for example, the data is analyzed every Monday or the 1st of each month. For weekly or monthly analysis, the data is analyzed every Monday. For monthly analysis, the data is analyzed on the 1st of each month. For example, the monitoring unit analyzes the user's training data every Monday to evaluate the progress. The monitoring unit can also analyze the user's training data on the 1st of each month to evaluate the progress. This makes it easier to evaluate the user's progress by analyzing the data periodically.

[0035] The sensor unit can select the appropriate measurement timing by referring to the user's past measurement data. Past measurement data includes, for example, data from the past month and data from specific events. The data from the past month is a long-term record of the user's physical condition. Data from specific events is data from specific training sessions or health checkups. For example, the sensor unit performs measurements based on the user's past heart rate data during times when the heart rate is stable. The sensor unit can also perform measurements based on the user's past electromyogram data during times when muscle activity is most active. The sensor unit can also perform measurements based on the user's past body fat percentage data during times when the body fat percentage is likely to fluctuate. This allows for more accurate data collection by selecting the optimal measurement timing based on past data.

[0036] The sensor unit can automatically switch measurement items according to the user's activity level. Activity levels include, for example, the number of steps and exercise intensity. The number of steps indicates the number of steps the user takes in a day. Exercise intensity indicates the intensity of the exercise the user performs. For example, when the user is exercising, the sensor unit prioritizes the use of the heart rate sensor and electromyogram sensor. Furthermore, when the user is resting, the sensor unit can also measure the body fat percentage using a body fat scale. Furthermore, when the user is engaged in light exercise, the sensor unit can also perform measurements using both the heart rate sensor and the body fat scale. This allows appropriate data collection by switching measurement items according to the user's activity level.

[0037] The sensor unit can optimize the timing of measurement based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the time periods and quality of the user's sleep. Meal times indicate the time periods when the user eats meals. For example, if the user has a habit of exercising in the morning, the sensor unit can use the heart rate sensor and electromyogram sensor in the morning. Also, if the user has a habit of relaxing in the evening, the sensor unit can use the body fat scale in the evening. Also, if the user has a habit of exercising during their lunch break, the sensor unit can use the heart rate sensor and electromyogram sensor in the daytime. This allows for more effective data collection by optimizing the timing of measurement based on the user's lifestyle rhythm.

[0038] The sensor unit can correct the measurement data based on the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the sensor unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the sensor unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the sensor unit can correct the body fat percentage data to obtain accurate measurement values. This allows for accurate data collection by correcting the measurement data taking into account the geographical location information.

[0039] The sensor unit can analyze a user's social media activity and acquire related measurement data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates what the user has posted on social media. The number of likes indicates the number of likes other users have given to the user's post. For example, if the user posts about exercise on social media, the sensor unit can prioritize collecting heart rate data. Also, if the user posts about relaxation, the sensor unit can prioritize collecting electromyogram data. Also, if the user posts about dieting, the sensor unit can prioritize collecting body fat percentage data. In this way, related measurement data can be efficiently collected by analyzing social media activity.

[0040] The sensor unit can customize the measurement method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about their experience using the sensor. The survey results indicate the results of a survey in which the user answered about their experience using the sensor. For example, if the user is dissatisfied with the accuracy of the heart rate sensor, the sensor unit can adjust the measurement method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of the electromyogram sensor, the sensor unit can adjust the measurement method to make it easier to use. Furthermore, if the user is dissatisfied with the results of the body fat scale, the sensor unit can adjust the measurement method to obtain accurate results. In this way, the measurement method can be optimized for the user by reflecting past feedback.

[0041] The analysis unit can analyze the collected data in real time and provide immediate feedback. Real-time analysis includes, for example, streaming analysis of data. Streaming analysis is a method of analyzing data as it is generated. Immediate feedback includes, for example, real-time notification and immediate alert. Real-time notification is a method of notifying the user of the analysis results in real time. Immediate alert is a method of issuing an alert immediately based on the analysis results. For example, the analysis unit can analyze heart rate data in real time and provide the user with immediate feedback on their stress level. The analysis unit can also analyze electromyogram data in real time and provide the user with immediate feedback on their muscle activity status. The analysis unit can also analyze body fat percentage data in real time and provide the user with immediate feedback on changes in their body fat percentage. This allows the user to respond quickly by analyzing in real time and providing immediate feedback.

[0042] The analysis unit can refer to the user's past training data to improve the accuracy of the analysis. Past training data includes, for example, training records from the past year and data from specific training sessions. Training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the analysis unit can refer to the user's past heart rate data to improve the analysis accuracy of current heart rate data. The analysis unit can also refer to the user's past electromyogram data to improve the analysis accuracy of current electromyogram data. The analysis unit can also refer to the user's past body fat percentage data to improve the analysis accuracy of current body fat percentage data. In this way, by referring to past training data, the accuracy of the analysis can be improved.

[0043] The analysis unit can integrate data from different sensors to evaluate an overall physical condition. Data from different sensors includes, for example, heart rate data, electromyogram data, and body fat data. Heart rate data refers to data collected from a heart rate sensor. Electromyogram data refers to data collected from an electromyogram sensor. Body fat data refers to data collected from a body fat scale. For example, the analysis unit can integrate data from the heart rate sensor and the electromyogram sensor to evaluate the user's overall stress level. The analysis unit can also integrate data from the electromyogram sensor and the body fat scale to evaluate the user's overall muscle condition. The analysis unit can also integrate data from the heart rate sensor and the body fat scale to evaluate the user's overall health condition. In this way, the overall physical condition can be evaluated by integrating data from different sensors.

[0044] The analysis unit can correct the analysis results by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the analysis unit corrects the analysis results of the heart rate data to bring it closer to a standard value. Furthermore, if the user is in a humid location, the analysis unit can correct the analysis results of the electromyogram data to perform an accurate evaluation. Furthermore, if the user is in a cold region, the analysis unit can correct the analysis results of the body fat percentage data to perform an accurate evaluation. Thus, correcting the analysis results by taking into account the geographical location information enables an accurate evaluation.

[0045] The analysis unit can analyze a user's social media activity and provide related analysis results. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the analysis unit can provide analysis results of heart rate data. Furthermore, if a user posts about relaxation, the analysis unit can also provide analysis results of electromyogram data. Furthermore, if a user posts about dieting, the analysis unit can provide analysis results of body fat percentage data. In this way, related analysis results can be efficiently provided by analyzing social media activity.

[0046] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the analysis results. The survey results indicate the results of a survey to which the user answered about the analysis results. For example, if the user is dissatisfied with the analysis results of heart rate data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of electromyogram data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of body fat percentage data, the analysis unit can adjust the analysis algorithm to improve accuracy. In this way, the analysis algorithm can be optimized for the user by reflecting past feedback.

[0047] The generation unit can refer to the user's past training history and generate an optimal training menu. The past training history includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training the user has performed over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the generation unit can refer to the user's past strength training history and generate a training menu according to the user's current muscle strength. The generation unit can also refer to the user's past stretching history and generate a training menu for improving flexibility. The generation unit can also refer to the user's past aerobic exercise history and generate a training menu for improving cardiopulmonary function. In this way, by referring to the past training history, it is possible to generate an optimal training menu for the user.

[0048] The generation unit can update the training menu in real time based on the user's current physical condition. The current physical condition includes, for example, heart rate, muscle electrical activity, and body fat percentage. The heart rate indicates data collected from a heart rate sensor. The muscle electrical activity indicates data collected from an electromyogram sensor. The body fat percentage indicates data collected from a body fat scale. For example, if the user's heart rate is high, the generation unit reduces the training menu and updates it in real time. The generation unit can also adjust the training menu according to the muscle fatigue level indicated by the user's electromyogram data. The generation unit can also reevaluate and update the training menu if the user's body fat percentage changes suddenly. This allows for more appropriate training by updating the training menu in real time based on the user's current physical condition.

[0049] The generation unit can automatically adjust the intensity of the training menu according to the user's goal. User goals include, for example, weight loss, muscle strength increase, and endurance improvement. Weight loss indicates a goal where the user aims to lose weight. Muscle strength increase indicates a goal where the user aims to increase muscle strength. Endurance improvement indicates a goal where the user aims to improve endurance. For example, the generation unit increases the intensity of the training menu if the user aims to improve muscle strength. The generation unit can also adjust the intensity of aerobic exercise if the user aims to lose weight. The generation unit can also adjust the intensity of stretching if the user aims to improve flexibility. In this way, adjusting the intensity of the training menu according to the user's goal enables effective training toward achieving the goal.

[0050] The generation unit can customize a training menu taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at high altitude, the generation unit generates a training menu that takes into account oxygen supply. Furthermore, if the user is in a humid location, the generation unit can also generate a training menu that prevents dehydration. Furthermore, if the user is in a cold region, the generation unit can also generate a training menu that takes into account maintaining body temperature. In this way, customizing a training menu taking into account geographical location information enables more appropriate training.

[0051] The generation unit can analyze the user's social media activity and suggest a related training menu. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if the user posts about strength training on social media, the generation unit can suggest a strength training menu. Furthermore, if the user posts about relaxation, the generation unit can also suggest a stretching menu. Furthermore, if the user posts about aerobic exercise, the generation unit can also suggest an aerobic exercise menu. In this way, by analyzing social media activity, related training menus can be efficiently suggested.

[0052] The generation unit can optimize the training menu by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the training menu. The survey results indicate the results of a survey answered by the user about the training menu. For example, if the user is dissatisfied with the strength training menu, the generation unit adjusts and optimizes the menu. Furthermore, if the user is dissatisfied with the stretching menu, the generation unit can also adjust and optimize the menu. Furthermore, if the user is dissatisfied with the aerobic exercise menu, the generation unit can also adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0053] The monitoring unit can refer to the user's past data to improve the accuracy of monitoring. Past data includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the monitoring unit can refer to the user's past heart rate data to improve the accuracy of current heart rate monitoring. The monitoring unit can also refer to the user's past electromyogram data to improve the accuracy of current electromyogram monitoring. The monitoring unit can also refer to the user's past body fat percentage data to improve the accuracy of current body fat percentage monitoring. In this way, the accuracy of monitoring can be improved by referring to past data.

[0054] The monitoring unit can automatically change the monitoring items according to the user's training progress. Training progress includes, for example, training frequency, intensity, and achievement level. Training frequency indicates the number of times the user has trained. Training intensity indicates the intensity of the training the user has performed. Achievement level indicates the degree to which the user has achieved their training goal. For example, the monitoring unit may increase the number of monitoring items for the electromyogram if the user's muscle strength improves. The monitoring unit may also increase the number of monitoring items for the heart rate if the user's cardiopulmonary function improves. The monitoring unit may also increase the number of monitoring items for the body fat percentage if the user's body fat percentage decreases. This allows for more appropriate data collection by changing the monitoring items according to the training progress.

[0055] The monitoring unit can optimize the monitoring timing based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the times and quality of the user's sleep. Meal times indicate the times when the user eats meals. For example, if the user has a habit of exercising in the morning, the monitoring unit may monitor the heart rate in the morning. Also, if the user has a habit of relaxing in the evening, the monitoring unit may monitor the electromyogram in the evening. Also, if the user has a habit of exercising during their lunch break, the monitoring unit may monitor the body fat percentage in the daytime. This allows for more effective data collection by optimizing the monitoring timing based on the user's lifestyle rhythm.

[0056] The monitoring unit can correct the monitoring data taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the monitoring unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the monitoring unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the monitoring unit can correct the body fat percentage data to obtain accurate measurement values. As a result, correcting the monitoring data taking into account the geographical location information enables accurate data collection.

[0057] The monitoring unit can analyze a user's social media activity and provide related monitoring data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the monitoring unit can prioritize collecting heart rate data. Also, if a user posts about relaxation, the monitoring unit can prioritize collecting electromyogram data. Also, if a user posts about dieting, the monitoring unit can prioritize collecting body fat percentage data. In this way, related monitoring data can be efficiently provided by analyzing social media activity.

[0058] The monitoring unit can customize the monitoring method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the monitoring method. The survey results indicate the results of a survey to which the user answered about the monitoring method. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback.

[0059] The analysis unit can analyze the collected data in real time and provide immediate feedback. Real-time analysis includes, for example, streaming analysis of data. Streaming analysis is a method of analyzing data as it is generated. Immediate feedback includes, for example, real-time notification and immediate alert. Real-time notification is a method of notifying the user of the analysis results in real time. Immediate alert is a method of issuing an alert immediately based on the analysis results. For example, the analysis unit can analyze heart rate data in real time and provide the user with immediate feedback on their stress level. The analysis unit can also analyze electromyogram data in real time and provide the user with immediate feedback on their muscle activity status. The analysis unit can also analyze body fat percentage data in real time and provide the user with immediate feedback on changes in their body fat percentage. This allows the user to respond quickly by analyzing in real time and providing immediate feedback.

[0060] The analysis unit can refer to the user's past training data to improve the accuracy of the analysis. Past training data includes, for example, training records from the past year and data from specific training sessions. Training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the analysis unit can refer to the user's past heart rate data to improve the analysis accuracy of current heart rate data. The analysis unit can also refer to the user's past electromyogram data to improve the analysis accuracy of current electromyogram data. The analysis unit can also refer to the user's past body fat percentage data to improve the analysis accuracy of current body fat percentage data. In this way, by referring to past training data, the accuracy of the analysis can be improved.

[0061] The analysis unit can integrate data from different sensors to evaluate an overall physical condition. Data from different sensors includes, for example, heart rate data, electromyogram data, and body fat data. Heart rate data refers to data collected from a heart rate sensor. Electromyogram data refers to data collected from an electromyogram sensor. Body fat data refers to data collected from a body fat scale. For example, the analysis unit can integrate data from the heart rate sensor and the electromyogram sensor to evaluate the user's overall stress level. The analysis unit can also integrate data from the electromyogram sensor and the body fat scale to evaluate the user's overall muscle condition. The analysis unit can also integrate data from the heart rate sensor and the body fat scale to evaluate the user's overall health condition. In this way, the overall physical condition can be evaluated by integrating data from different sensors.

[0062] The analysis unit can correct the analysis results by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the analysis unit corrects the analysis results of the heart rate data to bring it closer to a standard value. Furthermore, if the user is in a humid location, the analysis unit can correct the analysis results of the electromyogram data to perform an accurate evaluation. Furthermore, if the user is in a cold region, the analysis unit can correct the analysis results of the body fat percentage data to perform an accurate evaluation. Thus, correcting the analysis results by taking into account the geographical location information enables an accurate evaluation.

[0063] The analysis unit can analyze a user's social media activity and provide related analysis results. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the analysis unit can provide analysis results of heart rate data. Furthermore, if a user posts about relaxation, the analysis unit can also provide analysis results of electromyogram data. Furthermore, if a user posts about dieting, the analysis unit can provide analysis results of body fat percentage data. In this way, related analysis results can be efficiently provided by analyzing social media activity.

[0064] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the analysis results. The survey results indicate the results of a survey to which the user answered about the analysis results. For example, if the user is dissatisfied with the analysis results of heart rate data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of electromyogram data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of body fat percentage data, the analysis unit can adjust the analysis algorithm to improve accuracy. In this way, the analysis algorithm can be optimized for the user by reflecting past feedback.

[0065] The generation unit can refer to the user's past training history and generate an optimal training menu. The past training history includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training the user has performed over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the generation unit can refer to the user's past strength training history and generate a training menu according to the user's current muscle strength. The generation unit can also refer to the user's past stretching history and generate a training menu for improving flexibility. The generation unit can also refer to the user's past aerobic exercise history and generate a training menu for improving cardiopulmonary function. In this way, by referring to the past training history, it is possible to generate an optimal training menu for the user.

[0066] The generation unit can update the training menu in real time based on the user's current physical condition. The current physical condition includes, for example, heart rate, muscle electrical activity, and body fat percentage. The heart rate indicates data collected from a heart rate sensor. The muscle electrical activity indicates data collected from an electromyogram sensor. The body fat percentage indicates data collected from a body fat scale. For example, if the user's heart rate is high, the generation unit reduces the training menu and updates it in real time. The generation unit can also adjust the training menu according to the muscle fatigue level indicated by the user's electromyogram data. The generation unit can also reevaluate and update the training menu if the user's body fat percentage changes suddenly. This allows for more appropriate training by updating the training menu in real time based on the user's current physical condition.

[0067] The generation unit can automatically adjust the intensity of the training menu according to the user's goal. User goals include, for example, weight loss, muscle strength increase, and endurance improvement. Weight loss indicates a goal where the user aims to lose weight. Muscle strength increase indicates a goal where the user aims to increase muscle strength. Endurance improvement indicates a goal where the user aims to improve endurance. For example, the generation unit increases the intensity of the training menu if the user aims to improve muscle strength. The generation unit can also adjust the intensity of aerobic exercise if the user aims to lose weight. The generation unit can also adjust the intensity of stretching if the user aims to improve flexibility. In this way, adjusting the intensity of the training menu according to the user's goal enables effective training toward achieving the goal.

[0068] The generation unit can customize a training menu taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at high altitude, the generation unit generates a training menu that takes into account oxygen supply. Furthermore, if the user is in a humid location, the generation unit can also generate a training menu that prevents dehydration. Furthermore, if the user is in a cold region, the generation unit can also generate a training menu that takes into account maintaining body temperature. In this way, customizing a training menu taking into account geographical location information enables more appropriate training.

[0069] The generation unit can analyze the user's social media activity and suggest a related training menu. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if the user posts about strength training on social media, the generation unit can suggest a strength training menu. Furthermore, if the user posts about relaxation, the generation unit can also suggest a stretching menu. Furthermore, if the user posts about aerobic exercise, the generation unit can also suggest an aerobic exercise menu. In this way, by analyzing social media activity, related training menus can be efficiently suggested.

[0070] The generation unit can optimize the training menu by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the training menu. The survey results indicate the results of a survey answered by the user about the training menu. For example, if the user is dissatisfied with the strength training menu, the generation unit adjusts and optimizes the menu. Furthermore, if the user is dissatisfied with the stretching menu, the generation unit can also adjust and optimize the menu. Furthermore, if the user is dissatisfied with the aerobic exercise menu, the generation unit can also adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0071] The monitoring unit can refer to the user's past data to improve the accuracy of monitoring. Past data includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the monitoring unit can refer to the user's past heart rate data to improve the accuracy of current heart rate monitoring. The monitoring unit can also refer to the user's past electromyogram data to improve the accuracy of current electromyogram monitoring. The monitoring unit can also refer to the user's past body fat percentage data to improve the accuracy of current body fat percentage monitoring. In this way, the accuracy of monitoring can be improved by referring to past data.

[0072] The monitoring unit can automatically change the monitoring items according to the user's training progress. Training progress includes, for example, training frequency, intensity, and achievement level. Training frequency indicates the number of times the user has trained. Training intensity indicates the intensity of the training the user has performed. Achievement level indicates the degree to which the user has achieved their training goal. For example, the monitoring unit may increase the number of monitoring items for the electromyogram if the user's muscle strength improves. The monitoring unit may also increase the number of monitoring items for the heart rate if the user's cardiopulmonary function improves. The monitoring unit may also increase the number of monitoring items for the body fat percentage if the user's body fat percentage decreases. This allows for more appropriate data collection by changing the monitoring items according to the training progress.

[0073] The monitoring unit can optimize the monitoring timing based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the times and quality of the user's sleep. Meal times indicate the times when the user eats meals. For example, if the user has a habit of exercising in the morning, the monitoring unit may monitor the heart rate in the morning. Also, if the user has a habit of relaxing in the evening, the monitoring unit may monitor the electromyogram in the evening. Also, if the user has a habit of exercising during their lunch break, the monitoring unit may monitor the body fat percentage in the daytime. This allows for more effective data collection by optimizing the monitoring timing based on the user's lifestyle rhythm.

[0074] The monitoring unit can correct the monitoring data taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the monitoring unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the monitoring unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the monitoring unit can correct the body fat percentage data to obtain accurate measurement values. As a result, correcting the monitoring data taking into account the geographical location information enables accurate data collection.

[0075] The monitoring unit can analyze a user's social media activity and provide related monitoring data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the monitoring unit can prioritize collecting heart rate data. Also, if a user posts about relaxation, the monitoring unit can prioritize collecting electromyogram data. Also, if a user posts about dieting, the monitoring unit can prioritize collecting body fat percentage data. In this way, related monitoring data can be efficiently provided by analyzing social media activity.

[0076] The monitoring unit can customize the monitoring method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the monitoring method. The survey results indicate the results of a survey to which the user answered about the monitoring method. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The training system may also include a generation unit that customizes a training menu taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit may suggest a training menu that takes oxygen supply into account. Also, if the user is in a humid area, the generation unit may suggest a training menu to prevent dehydration. Furthermore, if the user is in a cold area, the generation unit may suggest a training menu that takes into account maintaining body temperature. This makes it possible to customize a training menu taking into account the user's geographical location information.

[0079] The training system may also include a generation unit that analyzes a user's social media activity and suggests a related training menu. For example, if a user posts about strength training on social media, the generation unit may suggest a strength training menu. Also, if a user posts about relaxation, the generation unit may suggest a stretching menu. Furthermore, if a user posts about aerobic exercise, the generation unit may suggest an aerobic exercise menu. In this way, related training menus can be efficiently suggested by analyzing social media activity.

[0080] The training system may also include a generation unit that optimizes a training menu by reflecting the user's past feedback. For example, if the user is dissatisfied with a strength training menu, the generation unit adjusts and optimizes the menu. Also, if the user is dissatisfied with a stretching menu, the generation unit can adjust and optimize the menu. Furthermore, if the user is dissatisfied with an aerobic exercise menu, the generation unit can adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0081] The training system may also include a monitoring unit that corrects monitoring data taking into account the user's geographical location information. For example, if the user is at high altitude, the monitoring unit corrects heart rate data to bring it closer to a standard value. Also, if the user is in a humid area, the monitoring unit may correct electromyogram data to obtain accurate measurement values. Furthermore, if the user is in a cold area, the monitoring unit may correct body fat percentage data to obtain accurate measurement values. This makes it possible to correct monitoring data taking into account geographical location information.

[0082] The training system may also include a monitoring unit that analyzes a user's social media activity and provides related monitoring data. For example, if a user posts about exercise on social media, the monitoring unit may prioritize collecting heart rate data. Alternatively, if a user posts about relaxation, the monitoring unit may prioritize collecting electromyogram data. Furthermore, if a user posts about dieting, the monitoring unit may prioritize collecting body fat percentage data. This allows related monitoring data to be provided efficiently by analyzing social media activity.

[0083] The training system may also include a monitoring unit that customizes the monitoring method by reflecting the user's past feedback. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve accuracy. Also, if the user is dissatisfied with the ease of use of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback.

[0084] The processing flow of the first embodiment will be briefly explained below.

[0085] Step 1: The sensor unit measures the user's physical condition. The user's physical condition includes heart rate, electrical muscle activity, and body fat percentage. For example, a heart rate sensor measures heart rate using an optical or electrical sensor, an electromyogram sensor measures electrical muscle activity using surface or needle electrodes, and a body fat scale measures body fat percentage using an impedance method or a caliper method. Step 2: The analysis unit performs analysis based on the data collected by the sensor unit. The analysis is performed using statistical analysis of the data and machine learning algorithms to evaluate the user's physical condition. For example, it can score the user's health status and perform risk assessment. Step 3: The generator generates a training menu based on the evaluation obtained by the analyzer. The training menu includes strength training, stretching to improve flexibility, aerobic exercise to improve cardiopulmonary function, etc. For example, it suggests weight training, bodyweight training, static stretching, dynamic stretching, running, and cycling. Step 4: The monitoring unit monitors the user's progress based on the menu generated by the generation unit. This monitoring is performed by storing the user's training data over the long term and periodically analyzing it. For example, the data may be stored in cloud storage, analyzed weekly or monthly, and evaluated for frequency, intensity, and achievement of training.

[0086] (Example 2) A training system according to an embodiment of the present invention evaluates a user's physical condition, generates an appropriate training menu, and monitors the user's progress. The training system uses sensors to precisely measure the user's physical condition, and AI analyzes the measurement data to generate an individual training menu. The user can receive personalized training tailored to their physical condition and progress. The training system can also monitor the user's progress and changes in health status through long-term data tracking. For example, the training system collects data such as heart rate, muscle activity, and body fat percentage using a heart rate sensor, an electromyography sensor, and a body fat scale. The training system then analyzes the collected data using AI to propose an optimal training menu for the user. For example, the training menu may include strength training, stretching to improve flexibility, and aerobic exercise to enhance cardiopulmonary function. Furthermore, the training system can increase the load or change the menu depending on the user's training progress. The training system can also check the user's muscle strength improvement and changes in body fat percentage based on past training data. This allows the user to feel their progress and maintain their motivation. This allows the training system to automatically assess the user's physical condition, generate appropriate training menus, and monitor progress. For example, promotions can be developed that will appeal to a wide range of people as a new training method that users can use anytime, anywhere, at home or at the gym.

[0087] The training system according to the embodiment includes a sensor unit, an analysis unit, a generation unit, and a monitoring unit. The sensor unit measures the user's physical condition. The user's physical condition may include, but is not limited to, heart rate, muscle electrical activity, and body fat percentage. The sensor unit may measure the heart rate using a heart rate sensor. The sensor unit may also measure muscle electrical activity using an electromyography sensor. The sensor unit may also measure the body fat percentage using a body fat scale. For example, the heart rate sensor may measure the heart rate using an optical sensor or an electrical sensor. The electromyography sensor may measure muscle electrical activity using surface electrodes or needle electrodes. The body fat scale may measure the body fat percentage using an impedance method or a caliper method. The analysis unit performs analysis based on the data collected by the sensor unit. The analysis may be performed using, for example, statistical analysis of data or a machine learning algorithm, but is not limited to, these examples. For example, the analysis unit may evaluate the user's physical condition based on the collected data. The analysis unit may also score the user's health status or perform risk assessment. The analysis unit can also analyze the data using a machine learning algorithm to evaluate the user's physical condition. The generation unit generates a training menu based on the evaluation obtained by the analysis unit. The training menu includes, but is not limited to, examples of strength training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. For example, the generation unit can suggest weight training or bodyweight training as strength training. The generation unit can also suggest static stretching or dynamic stretching as stretching to improve flexibility. The generation unit can also suggest running or cycling as aerobic exercise to improve cardiopulmonary function. The monitoring unit monitors the user's progress based on the menu generated by the generation unit. Monitoring can be performed, for example, by storing the user's training data over the long term and periodically analyzing it, but is not limited to such examples. For example, the monitoring unit can store the user's training data in cloud storage and analyze the data weekly or monthly.The monitoring unit can also evaluate the user's progress based on the user's training data. The monitoring unit can also evaluate the frequency, intensity, and achievement of training based on the user's training data. This allows the training system according to the embodiment to evaluate the user's physical condition, generate an appropriate training menu, and monitor the user's progress.

[0088] The sensor unit may include a heart rate sensor, an electromyogram sensor, and a body fat scale. Heart rate sensors include, for example, optical sensors and electrical sensors. Optical sensors use light to measure blood flow and measure heart rate. Electrical sensors measure heart rate using electrical signals. Electromyogram sensors include, for example, surface electrodes and needle electrodes. Surface electrodes are attached to the surface of the skin to measure electrical activity in muscles. Needle electrodes are inserted directly into muscles to measure electrical activity. Body fat scales include, for example, impedance methods and caliper methods. The impedance method measures electrical resistance within the body to calculate body fat percentage. The caliper method measures skin thickness to calculate body fat percentage. This allows the use of a variety of sensors to measure the user's physical condition in more detail.

[0089] The analysis unit can evaluate the user's physical condition based on the collected data. The evaluation includes, for example, health condition scoring and risk assessment. Health condition scoring quantifies the user's health condition based on the collected data. Risk assessment evaluates the user's health risk based on the collected data. For example, the analysis unit evaluates the user's cardiopulmonary function based on heart rate data. The analysis unit can also evaluate the user's muscle strength based on electromyogram data. The analysis unit can also evaluate the user's body fat percentage based on body fat percentage data. This allows the user's physical condition to be accurately evaluated based on the collected data.

[0090] The generation unit can generate a training menu that includes strength training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. Strength training includes, for example, weight training and bodyweight training. Weight training uses dumbbells and barbells to build muscle strength. Bodyweight training uses one's own body weight to build muscle strength. Stretching to improve flexibility includes, for example, static stretching and dynamic stretching. Static stretching stretches muscles while maintaining a certain posture. Dynamic stretching stretches muscles while moving. Aerobic exercise includes, for example, running and cycling. Running improves cardiopulmonary function by running at a certain pace. Cycling improves cardiopulmonary function by pedaling a bicycle. This makes it possible to generate a variety of training menus that suit the user's goals.

[0091] The monitoring unit can store the user's training data for a long period of time and analyze it periodically. For example, cloud storage is used for long-term storage. Cloud storage is a service that stores data via the Internet and can store data for long periods of time. For example, regular analysis can be performed once a week or once a month. Weekly analysis involves analyzing the data on a specific day each week. Monthly analysis involves analyzing the data on a specific day each month. For example, the monitoring unit can store the user's training data in cloud storage and analyze the data weekly or monthly. The monitoring unit can also evaluate the user's progress based on the user's training data. In this way, by storing the user's training data for a long period of time and analyzing it periodically, the progress can be understood in detail.

[0092] The monitoring unit can analyze the data weekly or monthly to evaluate the user's progress. For weekly or monthly analysis, for example, the data is analyzed every Monday or the 1st of each month. For weekly or monthly analysis, the data is analyzed every Monday. For monthly analysis, the data is analyzed on the 1st of each month. For example, the monitoring unit analyzes the user's training data every Monday to evaluate the progress. The monitoring unit can also analyze the user's training data on the 1st of each month to evaluate the progress. This makes it easier to evaluate the user's progress by analyzing the data periodically.

[0093] The training system includes a sensor unit that estimates a user's emotions and adjusts the sensor measurement frequency based on the estimated user emotions. The sensor unit estimates a user's emotions and adjusts the sensor measurement frequency based on the estimated user emotions. Emotion estimation can be achieved, for example, by facial expression recognition or voice analysis. Facial expression recognition uses a camera to analyze the user's facial expressions and estimate emotions. Voice analysis uses a microphone to analyze the user's voice and estimate emotions. For example, if the user is feeling stressed, the sensor unit increases the measurement frequency of the heart rate sensor to monitor the stress level in detail. Furthermore, if the user is relaxed, the sensor unit can reduce the measurement frequency of the electromyogram sensor and limit data collection to the minimum necessary. Furthermore, if the user is excited, the sensor unit can increase the measurement frequency of the body fat scale and quickly grasp changes in the body. This allows for more appropriate data collection by adjusting the sensor measurement frequency according to the user's emotions.

[0094] The sensor unit can select the appropriate measurement timing by referring to the user's past measurement data. Past measurement data includes, for example, data from the past month and data from specific events. The data from the past month is a long-term record of the user's physical condition. Data from specific events is data from specific training sessions or health checkups. For example, the sensor unit performs measurements based on the user's past heart rate data during times when the heart rate is stable. The sensor unit can also perform measurements based on the user's past electromyogram data during times when muscle activity is most active. The sensor unit can also perform measurements based on the user's past body fat percentage data during times when the body fat percentage is likely to fluctuate. This allows for more accurate data collection by selecting the optimal measurement timing based on past data.

[0095] The sensor unit can automatically switch measurement items according to the user's activity level. Activity levels include, for example, the number of steps and exercise intensity. The number of steps indicates the number of steps the user takes in a day. Exercise intensity indicates the intensity of the exercise the user performs. For example, when the user is exercising, the sensor unit prioritizes the use of the heart rate sensor and electromyogram sensor. Furthermore, when the user is resting, the sensor unit can also measure the body fat percentage using a body fat scale. Furthermore, when the user is engaged in light exercise, the sensor unit can also perform measurements using both the heart rate sensor and the body fat scale. This allows appropriate data collection by switching measurement items according to the user's activity level.

[0096] The sensor unit can optimize the timing of measurement based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the time periods and quality of the user's sleep. Meal times indicate the time periods when the user eats meals. For example, if the user has a habit of exercising in the morning, the sensor unit can use the heart rate sensor and electromyogram sensor in the morning. Also, if the user has a habit of relaxing in the evening, the sensor unit can use the body fat scale in the evening. Also, if the user has a habit of exercising during their lunch break, the sensor unit can use the heart rate sensor and electromyogram sensor in the daytime. This allows for more effective data collection by optimizing the timing of measurement based on the user's lifestyle rhythm.

[0097] The training system includes a sensor unit that estimates a user's emotions and prioritizes measurement data based on the estimated user emotions. The sensor unit estimates the user's emotions and prioritizes measurement data based on the estimated user emotions. Emotion estimation can be achieved, for example, by facial expression recognition or voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, the sensor unit prioritizes collection of heart rate data when the user is stressed. The sensor unit can also prioritize collection of electromyogram data when the user is relaxed. The sensor unit can also prioritize collection of body fat percentage data when the user is excited. In this way, important data can be collected preferentially by prioritizing measurement data according to the user's emotions.

[0098] The sensor unit can correct the measurement data based on the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the sensor unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the sensor unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the sensor unit can correct the body fat percentage data to obtain accurate measurement values. This allows for accurate data collection by correcting the measurement data taking into account the geographical location information.

[0099] The sensor unit can analyze a user's social media activity and acquire related measurement data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates what the user has posted on social media. The number of likes indicates the number of likes other users have given to the user's post. For example, if the user posts about exercise on social media, the sensor unit can prioritize collecting heart rate data. Also, if the user posts about relaxation, the sensor unit can prioritize collecting electromyogram data. Also, if the user posts about dieting, the sensor unit can prioritize collecting body fat percentage data. In this way, related measurement data can be efficiently collected by analyzing social media activity.

[0100] The sensor unit can customize the measurement method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about their experience using the sensor. The survey results indicate the results of a survey in which the user answered about their experience using the sensor. For example, if the user is dissatisfied with the accuracy of the heart rate sensor, the sensor unit can adjust the measurement method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of the electromyogram sensor, the sensor unit can adjust the measurement method to make it easier to use. Furthermore, if the user is dissatisfied with the results of the body fat scale, the sensor unit can adjust the measurement method to obtain accurate results. In this way, the measurement method can be optimized for the user by reflecting past feedback.

[0101] The training system includes an analysis unit that estimates a user's emotions and adjusts an analysis algorithm based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, facial expression recognition and voice analysis are used to estimate emotions. Facial expression recognition uses a camera to analyze the user's facial expressions and estimate emotions. Voice analysis uses a microphone to analyze the user's voice and estimate emotions. For example, if the user is feeling stressed, the analysis unit adjusts the analysis algorithm of heart rate data to evaluate the stress level in detail. Furthermore, if the user is relaxed, the analysis unit can adjust the analysis algorithm of electromyogram data to evaluate the degree of muscle relaxation. Furthermore, if the user is excited, the analysis unit can adjust the analysis algorithm of body fat percentage data to quickly evaluate changes in the body. This allows for more accurate analysis by adjusting the analysis algorithm according to the user's emotions.

[0102] The analysis unit can analyze the collected data in real time and provide immediate feedback. Real-time analysis includes, for example, streaming analysis of data. Streaming analysis is a method of analyzing data as it is generated. Immediate feedback includes, for example, real-time notification and immediate alert. Real-time notification is a method of notifying the user of the analysis results in real time. Immediate alert is a method of issuing an alert immediately based on the analysis results. For example, the analysis unit can analyze heart rate data in real time and provide the user with immediate feedback on their stress level. The analysis unit can also analyze electromyogram data in real time and provide the user with immediate feedback on their muscle activity status. The analysis unit can also analyze body fat percentage data in real time and provide the user with immediate feedback on changes in their body fat percentage. This allows the user to respond quickly by analyzing in real time and providing immediate feedback.

[0103] The analysis unit can refer to the user's past training data to improve the accuracy of the analysis. Past training data includes, for example, training records from the past year and data from specific training sessions. Training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the analysis unit can refer to the user's past heart rate data to improve the analysis accuracy of current heart rate data. The analysis unit can also refer to the user's past electromyogram data to improve the analysis accuracy of current electromyogram data. The analysis unit can also refer to the user's past body fat percentage data to improve the analysis accuracy of current body fat percentage data. In this way, by referring to past training data, the accuracy of the analysis can be improved.

[0104] The analysis unit can integrate data from different sensors to evaluate an overall physical condition. Data from different sensors includes, for example, heart rate data, electromyogram data, and body fat data. Heart rate data refers to data collected from a heart rate sensor. Electromyogram data refers to data collected from an electromyogram sensor. Body fat data refers to data collected from a body fat scale. For example, the analysis unit can integrate data from the heart rate sensor and the electromyogram sensor to evaluate the user's overall stress level. The analysis unit can also integrate data from the electromyogram sensor and the body fat scale to evaluate the user's overall muscle condition. The analysis unit can also integrate data from the heart rate sensor and the body fat scale to evaluate the user's overall health condition. In this way, the overall physical condition can be evaluated by integrating data from different sensors.

[0105] The training system includes an analysis unit that estimates a user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition or voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is excited, the analysis unit can also provide a visually stimulating display method. This allows for more appropriate feedback to be provided by adjusting the display method of the analysis results according to the user's emotion.

[0106] The analysis unit can correct the analysis results by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the analysis unit corrects the analysis results of the heart rate data to bring it closer to a standard value. Furthermore, if the user is in a humid location, the analysis unit can correct the analysis results of the electromyogram data to perform an accurate evaluation. Furthermore, if the user is in a cold region, the analysis unit can correct the analysis results of the body fat percentage data to perform an accurate evaluation. Thus, correcting the analysis results by taking into account the geographical location information enables an accurate evaluation.

[0107] The analysis unit can analyze a user's social media activity and provide related analysis results. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the analysis unit can provide analysis results of heart rate data. Furthermore, if a user posts about relaxation, the analysis unit can also provide analysis results of electromyogram data. Furthermore, if a user posts about dieting, the analysis unit can provide analysis results of body fat percentage data. In this way, related analysis results can be efficiently provided by analyzing social media activity.

[0108] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the analysis results. The survey results indicate the results of a survey to which the user answered about the analysis results. For example, if the user is dissatisfied with the analysis results of heart rate data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of electromyogram data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of body fat percentage data, the analysis unit can adjust the analysis algorithm to improve accuracy. In this way, the analysis algorithm can be optimized for the user by reflecting past feedback.

[0109] The training system includes a generation unit that estimates a user's emotion and adjusts the content of a training menu based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the content of the training menu based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the generation unit generates a training menu that includes stretches that have a relaxing effect. If the user is relaxed, the generation unit can also generate a training menu that focuses on strength training. If the user is excited, the generation unit can also generate a training menu that includes aerobic exercise. This allows for more effective training by adjusting the content of the training menu according to the user's emotion.

[0110] The generation unit can refer to the user's past training history and generate an optimal training menu. The past training history includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training the user has performed over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the generation unit can refer to the user's past strength training history and generate a training menu according to the user's current muscle strength. The generation unit can also refer to the user's past stretching history and generate a training menu for improving flexibility. The generation unit can also refer to the user's past aerobic exercise history and generate a training menu for improving cardiopulmonary function. In this way, by referring to the past training history, it is possible to generate an optimal training menu for the user.

[0111] The generation unit can update the training menu in real time based on the user's current physical condition. The current physical condition includes, for example, heart rate, muscle electrical activity, and body fat percentage. The heart rate indicates data collected from a heart rate sensor. The muscle electrical activity indicates data collected from an electromyogram sensor. The body fat percentage indicates data collected from a body fat scale. For example, if the user's heart rate is high, the generation unit reduces the training menu and updates it in real time. The generation unit can also adjust the training menu according to the muscle fatigue level indicated by the user's electromyogram data. The generation unit can also reevaluate and update the training menu if the user's body fat percentage changes suddenly. This allows for more appropriate training by updating the training menu in real time based on the user's current physical condition.

[0112] The generation unit can automatically adjust the intensity of the training menu according to the user's goal. User goals include, for example, weight loss, muscle strength increase, and endurance improvement. Weight loss indicates a goal where the user aims to lose weight. Muscle strength increase indicates a goal where the user aims to increase muscle strength. Endurance improvement indicates a goal where the user aims to improve endurance. For example, the generation unit increases the intensity of the training menu if the user aims to improve muscle strength. The generation unit can also adjust the intensity of aerobic exercise if the user aims to lose weight. The generation unit can also adjust the intensity of stretching if the user aims to improve flexibility. In this way, adjusting the intensity of the training menu according to the user's goal enables effective training toward achieving the goal.

[0113] The training system includes a generation unit that estimates a user's emotion and adjusts the order of a training menu based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the order of the training menu based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the generation unit may first perform stretching, which has a relaxing effect. If the user is relaxed, the generation unit may also perform strength training first. If the user is excited, the generation unit may also perform aerobic exercise first. This allows for more effective training by adjusting the order of the training menu according to the user's emotion.

[0114] The generation unit can customize a training menu taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at high altitude, the generation unit generates a training menu that takes into account oxygen supply. Furthermore, if the user is in a humid location, the generation unit can also generate a training menu that prevents dehydration. Furthermore, if the user is in a cold region, the generation unit can also generate a training menu that takes into account maintaining body temperature. In this way, customizing a training menu taking into account geographical location information enables more appropriate training.

[0115] The generation unit can analyze the user's social media activity and suggest a related training menu. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if the user posts about strength training on social media, the generation unit can suggest a strength training menu. Furthermore, if the user posts about relaxation, the generation unit can also suggest a stretching menu. Furthermore, if the user posts about aerobic exercise, the generation unit can also suggest an aerobic exercise menu. In this way, by analyzing social media activity, related training menus can be efficiently suggested.

[0116] The generation unit can optimize the training menu by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the training menu. The survey results indicate the results of a survey answered by the user about the training menu. For example, if the user is dissatisfied with the strength training menu, the generation unit adjusts and optimizes the menu. Furthermore, if the user is dissatisfied with the stretching menu, the generation unit can also adjust and optimize the menu. Furthermore, if the user is dissatisfied with the aerobic exercise menu, the generation unit can also adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0117] The training system includes a monitoring unit that estimates a user's emotion and adjusts the monitoring frequency based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts the monitoring frequency based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, the monitoring unit increases the monitoring frequency of the heart rate when the user is feeling stressed. The monitoring unit can also decrease the monitoring frequency of the electromyogram when the user is relaxed. The monitoring unit can also increase the monitoring frequency of the body fat percentage when the user is excited. This allows for more appropriate data collection by adjusting the monitoring frequency according to the user's emotion.

[0118] The monitoring unit can refer to the user's past data to improve the accuracy of monitoring. Past data includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the monitoring unit can refer to the user's past heart rate data to improve the accuracy of current heart rate monitoring. The monitoring unit can also refer to the user's past electromyogram data to improve the accuracy of current electromyogram monitoring. The monitoring unit can also refer to the user's past body fat percentage data to improve the accuracy of current body fat percentage monitoring. In this way, the accuracy of monitoring can be improved by referring to past data.

[0119] The monitoring unit can automatically change the monitoring items according to the user's training progress. Training progress includes, for example, training frequency, intensity, and achievement level. Training frequency indicates the number of times the user has trained. Training intensity indicates the intensity of the training the user has performed. Achievement level indicates the degree to which the user has achieved their training goal. For example, the monitoring unit may increase the number of monitoring items for the electromyogram if the user's muscle strength improves. The monitoring unit may also increase the number of monitoring items for the heart rate if the user's cardiopulmonary function improves. The monitoring unit may also increase the number of monitoring items for the body fat percentage if the user's body fat percentage decreases. This allows for more appropriate data collection by changing the monitoring items according to the training progress.

[0120] The monitoring unit can optimize the monitoring timing based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the times and quality of the user's sleep. Meal times indicate the times when the user eats meals. For example, if the user has a habit of exercising in the morning, the monitoring unit may monitor the heart rate in the morning. Also, if the user has a habit of relaxing in the evening, the monitoring unit may monitor the electromyogram in the evening. Also, if the user has a habit of exercising during their lunch break, the monitoring unit may monitor the body fat percentage in the daytime. This allows for more effective data collection by optimizing the monitoring timing based on the user's lifestyle rhythm.

[0121] The training system includes a monitoring unit that estimates a user's emotion and adjusts a display method of the monitoring results based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts a display method of the monitoring results based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the monitoring unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the monitoring unit can also provide a visually stimulating display method. In this way, more appropriate feedback can be provided by adjusting the display method of the monitoring results according to the user's emotion.

[0122] The monitoring unit can correct the monitoring data taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the monitoring unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the monitoring unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the monitoring unit can correct the body fat percentage data to obtain accurate measurement values. As a result, correcting the monitoring data taking into account the geographical location information enables accurate data collection.

[0123] The monitoring unit can analyze a user's social media activity and provide related monitoring data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the monitoring unit can prioritize collecting heart rate data. Also, if a user posts about relaxation, the monitoring unit can prioritize collecting electromyogram data. Also, if a user posts about dieting, the monitoring unit can prioritize collecting body fat percentage data. In this way, related monitoring data can be efficiently provided by analyzing social media activity.

[0124] The monitoring unit can customize the monitoring method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the monitoring method. The survey results indicate the results of a survey to which the user answered about the monitoring method. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback.

[0125] The training system includes an analysis unit that estimates a user's emotions and adjusts an analysis algorithm based on the estimated user emotions. The analysis unit estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. For example, facial expression recognition and voice analysis are used to estimate emotions. Facial expression recognition uses a camera to analyze the user's facial expressions and estimate emotions. Voice analysis uses a microphone to analyze the user's voice and estimate emotions. For example, if the user is feeling stressed, the analysis unit adjusts the analysis algorithm of heart rate data to evaluate the stress level in detail. Furthermore, if the user is relaxed, the analysis unit can adjust the analysis algorithm of electromyogram data to evaluate the degree of muscle relaxation. Furthermore, if the user is excited, the analysis unit can adjust the analysis algorithm of body fat percentage data to quickly evaluate changes in the body. This allows for more accurate analysis by adjusting the analysis algorithm according to the user's emotions.

[0126] The analysis unit can analyze the collected data in real time and provide immediate feedback. Real-time analysis includes, for example, streaming analysis of data. Streaming analysis is a method of analyzing data as it is generated. Immediate feedback includes, for example, real-time notification and immediate alert. Real-time notification is a method of notifying the user of the analysis results in real time. Immediate alert is a method of issuing an alert immediately based on the analysis results. For example, the analysis unit can analyze heart rate data in real time and provide the user with immediate feedback on their stress level. The analysis unit can also analyze electromyogram data in real time and provide the user with immediate feedback on their muscle activity status. The analysis unit can also analyze body fat percentage data in real time and provide the user with immediate feedback on changes in their body fat percentage. This allows the user to respond quickly by analyzing in real time and providing immediate feedback.

[0127] The analysis unit can refer to the user's past training data to improve the accuracy of the analysis. Past training data includes, for example, training records from the past year and data from specific training sessions. Training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the analysis unit can refer to the user's past heart rate data to improve the analysis accuracy of current heart rate data. The analysis unit can also refer to the user's past electromyogram data to improve the analysis accuracy of current electromyogram data. The analysis unit can also refer to the user's past body fat percentage data to improve the analysis accuracy of current body fat percentage data. In this way, by referring to past training data, the accuracy of the analysis can be improved.

[0128] The analysis unit can integrate data from different sensors to evaluate an overall physical condition. Data from different sensors includes, for example, heart rate data, electromyogram data, and body fat data. Heart rate data refers to data collected from a heart rate sensor. Electromyogram data refers to data collected from an electromyogram sensor. Body fat data refers to data collected from a body fat scale. For example, the analysis unit can integrate data from the heart rate sensor and the electromyogram sensor to evaluate the user's overall stress level. The analysis unit can also integrate data from the electromyogram sensor and the body fat scale to evaluate the user's overall muscle condition. The analysis unit can also integrate data from the heart rate sensor and the body fat scale to evaluate the user's overall health condition. In this way, the overall physical condition can be evaluated by integrating data from different sensors.

[0129] The training system includes an analysis unit that estimates a user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. The analysis unit estimates the user's emotion and adjusts the display method of the analysis results based on the estimated user's emotion. Emotion estimation can be performed using, for example, facial expression recognition or voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is excited, the analysis unit can also provide a visually stimulating display method. This allows for more appropriate feedback to be provided by adjusting the display method of the analysis results according to the user's emotion.

[0130] The analysis unit can correct the analysis results by taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the analysis unit corrects the analysis results of the heart rate data to bring it closer to a standard value. Furthermore, if the user is in a humid location, the analysis unit can correct the analysis results of the electromyogram data to perform an accurate evaluation. Furthermore, if the user is in a cold region, the analysis unit can correct the analysis results of the body fat percentage data to perform an accurate evaluation. Thus, correcting the analysis results by taking into account the geographical location information enables an accurate evaluation.

[0131] The analysis unit can analyze a user's social media activity and provide related analysis results. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the analysis unit can provide analysis results of heart rate data. Furthermore, if a user posts about relaxation, the analysis unit can also provide analysis results of electromyogram data. Furthermore, if a user posts about dieting, the analysis unit can provide analysis results of body fat percentage data. In this way, related analysis results can be efficiently provided by analyzing social media activity.

[0132] The analysis unit can customize the analysis algorithm by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the analysis results. The survey results indicate the results of a survey to which the user answered about the analysis results. For example, if the user is dissatisfied with the analysis results of heart rate data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of electromyogram data, the analysis unit can adjust the analysis algorithm to improve accuracy. Also, if the user is dissatisfied with the analysis results of body fat percentage data, the analysis unit can adjust the analysis algorithm to improve accuracy. In this way, the analysis algorithm can be optimized for the user by reflecting past feedback.

[0133] The training system includes a generation unit that estimates a user's emotion and adjusts the content of a training menu based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the content of the training menu based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the generation unit generates a training menu that includes stretches that have a relaxing effect. If the user is relaxed, the generation unit can also generate a training menu that focuses on strength training. If the user is excited, the generation unit can also generate a training menu that includes aerobic exercise. This allows for more effective training by adjusting the content of the training menu according to the user's emotion.

[0134] The generation unit can refer to the user's past training history and generate an optimal training menu. The past training history includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training the user has performed over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the generation unit can refer to the user's past strength training history and generate a training menu according to the user's current muscle strength. The generation unit can also refer to the user's past stretching history and generate a training menu for improving flexibility. The generation unit can also refer to the user's past aerobic exercise history and generate a training menu for improving cardiopulmonary function. In this way, by referring to the past training history, it is possible to generate an optimal training menu for the user.

[0135] The generation unit can update the training menu in real time based on the user's current physical condition. The current physical condition includes, for example, heart rate, muscle electrical activity, and body fat percentage. The heart rate indicates data collected from a heart rate sensor. The muscle electrical activity indicates data collected from an electromyogram sensor. The body fat percentage indicates data collected from a body fat scale. For example, if the user's heart rate is high, the generation unit reduces the training menu and updates it in real time. The generation unit can also adjust the training menu according to the muscle fatigue level indicated by the user's electromyogram data. The generation unit can also reevaluate and update the training menu if the user's body fat percentage changes suddenly. This allows for more appropriate training by updating the training menu in real time based on the user's current physical condition.

[0136] The generation unit can automatically adjust the intensity of the training menu according to the user's goal. User goals include, for example, weight loss, muscle strength increase, and endurance improvement. Weight loss indicates a goal where the user aims to lose weight. Muscle strength increase indicates a goal where the user aims to increase muscle strength. Endurance improvement indicates a goal where the user aims to improve endurance. For example, the generation unit increases the intensity of the training menu if the user aims to improve muscle strength. The generation unit can also adjust the intensity of aerobic exercise if the user aims to lose weight. The generation unit can also adjust the intensity of stretching if the user aims to improve flexibility. In this way, adjusting the intensity of the training menu according to the user's goal enables effective training toward achieving the goal.

[0137] The training system includes a generation unit that estimates a user's emotion and adjusts the order of a training menu based on the estimated user's emotion. The generation unit estimates the user's emotion and adjusts the order of the training menu based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the generation unit may first perform stretching, which has a relaxing effect. If the user is relaxed, the generation unit may also perform strength training first. If the user is excited, the generation unit may also perform aerobic exercise first. This allows for more effective training by adjusting the order of the training menu according to the user's emotion.

[0138] The generation unit can customize a training menu taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at high altitude, the generation unit generates a training menu that takes into account oxygen supply. Furthermore, if the user is in a humid location, the generation unit can also generate a training menu that prevents dehydration. Furthermore, if the user is in a cold region, the generation unit can also generate a training menu that takes into account maintaining body temperature. In this way, customizing a training menu taking into account geographical location information enables more appropriate training.

[0139] The generation unit can analyze the user's social media activity and suggest a related training menu. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if the user posts about strength training on social media, the generation unit can suggest a strength training menu. Furthermore, if the user posts about relaxation, the generation unit can also suggest a stretching menu. Furthermore, if the user posts about aerobic exercise, the generation unit can also suggest an aerobic exercise menu. In this way, by analyzing social media activity, related training menus can be efficiently suggested.

[0140] The generation unit can optimize the training menu by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the training menu. The survey results indicate the results of a survey answered by the user about the training menu. For example, if the user is dissatisfied with the strength training menu, the generation unit adjusts and optimizes the menu. Furthermore, if the user is dissatisfied with the stretching menu, the generation unit can also adjust and optimize the menu. Furthermore, if the user is dissatisfied with the aerobic exercise menu, the generation unit can also adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0141] The training system includes a monitoring unit that estimates a user's emotion and adjusts the monitoring frequency based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts the monitoring frequency based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, the monitoring unit increases the monitoring frequency of the heart rate when the user is feeling stressed. The monitoring unit can also decrease the monitoring frequency of the electromyogram when the user is relaxed. The monitoring unit can also increase the monitoring frequency of the body fat percentage when the user is excited. This allows for more appropriate data collection by adjusting the monitoring frequency according to the user's emotion.

[0142] The monitoring unit can refer to the user's past data to improve the accuracy of monitoring. Past data includes, for example, training records from the past year and data from specific training sessions. The training records from the past year indicate records of training performed by the user over the past year. Data from specific training sessions indicates data collected in specific training sessions. For example, the monitoring unit can refer to the user's past heart rate data to improve the accuracy of current heart rate monitoring. The monitoring unit can also refer to the user's past electromyogram data to improve the accuracy of current electromyogram monitoring. The monitoring unit can also refer to the user's past body fat percentage data to improve the accuracy of current body fat percentage monitoring. In this way, the accuracy of monitoring can be improved by referring to past data.

[0143] The monitoring unit can automatically change the monitoring items according to the user's training progress. Training progress includes, for example, training frequency, intensity, and achievement level. Training frequency indicates the number of times the user has trained. Training intensity indicates the intensity of the training the user has performed. Achievement level indicates the degree to which the user has achieved their training goal. For example, the monitoring unit may increase the number of monitoring items for the electromyogram if the user's muscle strength improves. The monitoring unit may also increase the number of monitoring items for the heart rate if the user's cardiopulmonary function improves. The monitoring unit may also increase the number of monitoring items for the body fat percentage if the user's body fat percentage decreases. This allows for more appropriate data collection by changing the monitoring items according to the training progress.

[0144] The monitoring unit can optimize the monitoring timing based on the user's lifestyle rhythm. Lifestyle rhythms include, for example, sleep patterns and meal times. Sleep patterns indicate the times and quality of the user's sleep. Meal times indicate the times when the user eats meals. For example, if the user has a habit of exercising in the morning, the monitoring unit may monitor the heart rate in the morning. Also, if the user has a habit of relaxing in the evening, the monitoring unit may monitor the electromyogram in the evening. Also, if the user has a habit of exercising during their lunch break, the monitoring unit may monitor the body fat percentage in the daytime. This allows for more effective data collection by optimizing the monitoring timing based on the user's lifestyle rhythm.

[0145] The training system includes a monitoring unit that estimates a user's emotion and adjusts a display method of the monitoring results based on the estimated user's emotion. The monitoring unit estimates the user's emotion and adjusts a display method of the monitoring results based on the estimated user's emotion. Emotion estimation uses, for example, facial expression recognition and voice analysis. Facial expression recognition uses a camera to analyze the user's facial expression and estimate the emotion. Voice analysis uses a microphone to analyze the user's voice and estimate the emotion. For example, if the user is feeling stressed, the monitoring unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. Furthermore, if the user is excited, the monitoring unit can also provide a visually stimulating display method. In this way, more appropriate feedback can be provided by adjusting the display method of the monitoring results according to the user's emotion.

[0146] The monitoring unit can correct the monitoring data taking into account the user's geographical location information. Geographical location information includes, for example, GPS data and location information services. GPS data indicates the user's current location. Location information services are services that obtain the user's location via the Internet. For example, if the user is at a high altitude, the monitoring unit can correct the heart rate data to bring it closer to a standard value. Also, if the user is in a humid location, the monitoring unit can correct the electromyogram data to obtain accurate measurement values. Also, if the user is in a cold location, the monitoring unit can correct the body fat percentage data to obtain accurate measurement values. As a result, correcting the monitoring data taking into account the geographical location information enables accurate data collection.

[0147] The monitoring unit can analyze a user's social media activity and provide related monitoring data. Social media activity includes, for example, the content of posts and the number of likes. The content of posts indicates the content posted by the user on social media. The number of likes indicates the number of likes given by other users to the user's post. For example, if a user posts about exercise on social media, the monitoring unit can prioritize collecting heart rate data. Also, if a user posts about relaxation, the monitoring unit can prioritize collecting electromyogram data. Also, if a user posts about dieting, the monitoring unit can prioritize collecting body fat percentage data. In this way, related monitoring data can be efficiently provided by analyzing social media activity.

[0148] The monitoring unit can customize the monitoring method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments and survey results. The evaluation comments indicate comments written by the user about the monitoring method. The survey results indicate the results of a survey to which the user answered about the monitoring method. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve the accuracy. Furthermore, if the user is dissatisfied with the usability of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the sensor unit, analysis unit, generation unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit estimates the user's emotions using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A can adjust the measurement frequency of the sensor. In addition, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates a training menu based on the analysis results. The monitoring unit can monitor the user's progress using the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the sensor unit, analysis unit, generation unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit estimates the user's emotions using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A can adjust the measurement frequency of the sensor. In addition, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates a training menu based on the analysis results. The monitoring unit can monitor the user's progress by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the sensor unit, analysis unit, generation unit, and monitoring unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the sensor unit estimates the user's emotions using the camera 42 and microphone 238 of the headset type terminal 314, and the control unit 46A can adjust the measurement frequency of the sensor. Furthermore, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates a training menu based on the analysis results. The monitoring unit can monitor the user's progress by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the sensor unit, analysis unit, generation unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit estimates the user's emotions using the camera 42 and microphone 238 of the robot 414, and the control unit 46A can adjust the measurement frequency of the sensor. In addition, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12, and the generation unit generates a training menu based on the analysis results. The monitoring unit can monitor the user's progress by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0149] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0150] The training system may also include a generation unit that estimates the user's emotions and adjusts the difficulty of the training menu based on the estimated emotions. For example, if the user is feeling stressed, the generation unit may lower the difficulty of the training menu and suggest exercises that have a relaxing effect. Alternatively, if the user is relaxed, the generation unit may increase the difficulty and suggest more challenging training. Furthermore, if the user is excited, the generation unit may increase the intensity of aerobic exercise and suggest training that will release energy. This makes it possible to adjust the difficulty of the training menu according to the user's emotions.

[0151] The training system may also include a generation unit that customizes a training menu taking into account the user's geographical location information. For example, if the user is at high altitude, the generation unit may suggest a training menu that takes oxygen supply into account. Also, if the user is in a humid area, the generation unit may suggest a training menu to prevent dehydration. Furthermore, if the user is in a cold area, the generation unit may suggest a training menu that takes into account maintaining body temperature. This makes it possible to customize a training menu taking into account the user's geographical location information.

[0152] The training system may also include a generation unit that analyzes a user's social media activity and suggests a related training menu. For example, if a user posts about strength training on social media, the generation unit may suggest a strength training menu. Also, if a user posts about relaxation, the generation unit may suggest a stretching menu. Furthermore, if a user posts about aerobic exercise, the generation unit may suggest an aerobic exercise menu. In this way, related training menus can be efficiently suggested by analyzing social media activity.

[0153] The training system may also include a generation unit that estimates the user's emotions and adjusts the order of the training menu based on the estimated emotions. For example, if the user is feeling stressed, the generation unit may adjust the order so that relaxing stretching is performed first. Alternatively, if the user is relaxed, the generation unit may adjust the order so that strength training is performed first. Furthermore, if the user is excited, the generation unit may adjust the order so that aerobic exercise is performed first. This makes it possible to adjust the order of the training menu according to the user's emotions.

[0154] The training system may also include a generation unit that optimizes a training menu by reflecting the user's past feedback. For example, if the user is dissatisfied with a strength training menu, the generation unit adjusts and optimizes the menu. Also, if the user is dissatisfied with a stretching menu, the generation unit can adjust and optimize the menu. Furthermore, if the user is dissatisfied with an aerobic exercise menu, the generation unit can adjust and optimize the menu. In this way, the training menu can be optimized for the user by reflecting past feedback.

[0155] The training system may also include a monitoring unit that estimates the user's emotions and adjusts the monitoring frequency based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit may increase the frequency of monitoring the heart rate. Also, if the user is relaxed, the monitoring unit may decrease the frequency of monitoring the electromyogram. Furthermore, if the user is excited, the monitoring unit may increase the frequency of monitoring the body fat percentage. This makes it possible to adjust the monitoring frequency according to the user's emotions.

[0156] The training system may also include a monitoring unit that corrects monitoring data taking into account the user's geographical location information. For example, if the user is at high altitude, the monitoring unit corrects heart rate data to bring it closer to a standard value. Also, if the user is in a humid area, the monitoring unit may correct electromyogram data to obtain accurate measurement values. Furthermore, if the user is in a cold area, the monitoring unit may correct body fat percentage data to obtain accurate measurement values. This makes it possible to correct monitoring data taking into account geographical location information.

[0157] The training system may also include a monitoring unit that analyzes a user's social media activity and provides related monitoring data. For example, if a user posts about exercise on social media, the monitoring unit may prioritize collecting heart rate data. Alternatively, if a user posts about relaxation, the monitoring unit may prioritize collecting electromyogram data. Furthermore, if a user posts about dieting, the monitoring unit may prioritize collecting body fat percentage data. This allows related monitoring data to be provided efficiently by analyzing social media activity.

[0158] The training system may also include a monitoring unit that estimates the user's emotions and adjusts the display method of the monitoring results based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit may provide a simple, highly visible display method. If the user is relaxed, the monitoring unit may provide a display method that includes detailed information. Furthermore, if the user is excited, the monitoring unit may provide a visually stimulating display method. This makes it possible to adjust the display method of the monitoring results according to the user's emotions.

[0159] The training system may also include a monitoring unit that customizes the monitoring method by reflecting the user's past feedback. For example, if the user is dissatisfied with the accuracy of heart rate monitoring, the monitoring unit can adjust the monitoring method to improve accuracy. Also, if the user is dissatisfied with the ease of use of electromyogram monitoring, the monitoring unit can adjust the monitoring method to make it easier to use. Furthermore, if the user is dissatisfied with the results of body fat percentage monitoring, the monitoring unit can adjust the monitoring method to obtain accurate results. In this way, the monitoring method can be optimized for the user by reflecting past feedback.

[0160] The processing flow of the second embodiment will be briefly explained below.

[0161] Step 1: The sensor unit measures the user's physical condition. The user's physical condition includes heart rate, electrical muscle activity, and body fat percentage. For example, a heart rate sensor measures heart rate using an optical or electrical sensor, an electromyogram sensor measures electrical muscle activity using surface or needle electrodes, and a body fat scale measures body fat percentage using an impedance method or a caliper method. Step 2: The analysis unit performs analysis based on the data collected by the sensor unit. The analysis is performed using statistical analysis of the data and machine learning algorithms to evaluate the user's physical condition. For example, it can score the user's health status and perform risk assessment. Step 3: The generator generates a training menu based on the evaluation obtained by the analyzer. The training menu includes strength training, stretching to improve flexibility, aerobic exercise to improve cardiopulmonary function, etc. For example, it suggests weight training, bodyweight training, static stretching, dynamic stretching, running, and cycling. Step 4: The monitoring unit monitors the user's progress based on the menu generated by the generation unit. This monitoring is performed by storing the user's training data over the long term and periodically analyzing it. For example, the data may be stored in cloud storage, analyzed weekly or monthly, and evaluated for frequency, intensity, and achievement of training.

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0164] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0166] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0167] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0168] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0169] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0170] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0172] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0173] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0174] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0180] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0182] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0183] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0184] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0185] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0186] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0188] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0189] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0190] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0191] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0192] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0193] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0194] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0195] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0196] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0197] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0198] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0199] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0200] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0201] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0202] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0203] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0204] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0205] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0206] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0207] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0209] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0210] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0211] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0212] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0213] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0214] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0215] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0216] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0217] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0218] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0219] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0220] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0221] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0222] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0223] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0224] 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.

[0225] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0226] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0227] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0228] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0229] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0230] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0231] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0232] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0233] [Explanation of symbols]

[0234] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a sensor unit that measures the physical state of the user using a sensor; an analysis unit that performs analysis based on the data collected by the sensor unit; a generation unit that generates a training menu based on the evaluation obtained by the analysis unit; a monitoring unit that monitors the progress of the user based on the menu generated by the generation unit; A system characterized by:

2. The sensor unit Includes heart rate sensor, electromyogram sensor, and body fat scale 2. The system of claim 1.

3. The analysis unit Evaluate the user's physical condition based on the collected data 2. The system of claim 1.

4. The generation unit Generate workouts that include strength training, stretching to improve flexibility, and cardio to improve cardiovascular fitness 2. The system of claim 1.

5. The monitoring unit Store user training data over the long term and analyze it periodically 2. The system of claim 1.

6. The monitoring unit Analyze data on a weekly or monthly basis to assess user progress 2. The system of claim 1.

7. The sensor unit The user's emotion is estimated, and the measurement frequency of the sensor is adjusted based on the estimated user's emotion.

2. The system of claim 1.

8. The sensor unit Refer to the user's past measurement data and select the appropriate measurement timing 2. The system of claim 1.

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