System

Abstract

An object of a system according to an embodiment is to analyze exercise content of a user and propose a training plan suitable for an individual purpose.SOLUTION: A system includes an exercise recording part, an analysis part, and a proposal part. The exercise recording unit records exercise contents of the user. The analysis unit analyzes the exercise content recorded by the exercise recording unit. The proposal unit proposes a training plan suitable for the purpose of the user on the basis of the result analyzed by the analysis 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, even if a user's exercise details are recorded, there is a problem in that the data is not effectively utilized to propose training plans that suit individual goals.

[0005] The system according to the embodiment aims to analyze the exercise content of the user and propose a training plan that suits the individual purpose. [Means for solving the problem]

[0006] The system according to the embodiment includes an exercise recording unit, an analysis unit, and a suggestion unit. The exercise recording unit records the exercise content of the user. The analysis unit analyzes the exercise content recorded by the exercise recording unit. The suggestion unit suggests a training plan that matches the user's objectives based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the exercise content of the user and propose a training plan that suits the individual purpose. [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) The training assistance system according to an embodiment of the present invention records the user's exercise, analyzes the recorded data using a generation AI, and proposes a training plan suited to the user's individual goals. This allows the training assistance system to provide an optimal training plan based on the user's exercise record and support effective training.

[0029] A training assistance system according to an embodiment includes an exercise recording unit, an analysis unit, and a suggestion unit. The exercise recording unit records the user's exercise details. For example, it records information such as which training machine the user used, how long the user exercised, and which body parts the user focused on training. The exercise recording unit can also analyze the user's posture and movements during exercise in real time and provide immediate feedback on improvements to their form. For example, a generation AI captures the user's posture during exercise with a camera and analyzes it in real time. The analysis unit analyzes the exercise details recorded by the exercise recording unit. For example, the generation AI analyzes the user's exercise record data and suggests a training plan optimal for the user's goal (such as weight loss or muscle building). The suggestion unit suggests a training plan tailored to the user's goal based on the results of the analysis by the analysis unit. For example, a plan combining aerobic exercise and strength training is suggested for a user aiming to lose weight, and a plan focusing on specific muscle groups is suggested for a user aiming to build muscle. As a result, the training assistance system according to the embodiment can provide effective training suited to individual goals by recording and analyzing the exercise content of the user and proposing an optimal training plan.

[0030] The exercise recording unit analyzes the user's posture and movements during exercise in real time and provides instant feedback on how to improve their form. For example, the generation AI captures the user's posture during exercise with a camera and analyzes it in real time. For example, during squats, it detects the angle of the knees and the degree of curvature of the back and provides feedback to correct form. The exercise recording unit also monitors the user's movements with sensors, and the generation AI analyzes the data to identify areas for improvement. For example, it analyzes the foot landing position and arm swing during running to suggest an efficient running form. The generation AI also analyzes the user's movements in real time and provides instant feedback on how to improve their form via audio and visual means. For example, it analyzes the shoulder position and bar movement during bench presses to suggest correct form. This allows the system to analyze the user's posture and movements during exercise in real time and provide instant feedback, supporting training with correct form.

[0031] The exercise recording unit also collects the user's dietary and sleep data to analyze their overall health. For example, the exercise recording unit allows the user to enter their daily dietary information into the app, and the generation AI analyzes that data. For example, the unit evaluates calorie intake and nutritional balance, and analyzes their overall health in conjunction with their exercise content. The exercise recording unit also uses a wearable device to collect the user's sleep data, and the generation AI analyzes that data. For example, the unit evaluates the quality and duration of sleep, and analyzes their health by linking it to their exercise content. The exercise recording unit also integrates the dietary and sleep data with their exercise records, and the generation AI analyzes their overall health. For example, the unit evaluates the impact of meal timing and content on exercise performance and proposes an optimal training plan. By collecting the user's dietary and sleep data and analyzing their overall health, the system can propose a more effective training plan.

[0032] The exercise recording unit uses a wearable device to record biometric data such as heart rate and calorie consumption in real time, which the generation AI can then analyze. For example, the user wears the wearable device and records heart rate and calorie consumption in real time. The generation AI analyzes the data and evaluates the effectiveness of the exercise. The exercise recording unit also analyzes the biometric data obtained from the wearable device using the generation AI to monitor changes in physical condition during exercise. For example, the exercise recording unit adjusts exercise intensity based on fluctuations in heart rate. The exercise recording unit also records biometric data in real time, which the generation AI then uses to evaluate exercise performance. For example, the generation AI analyzes the effectiveness of dieting based on calorie consumption. In this way, by recording and analyzing biometric data such as heart rate and calorie consumption in real time, the effectiveness of exercise can be evaluated and an optimal training plan can be proposed.

[0033] The exercise recording unit can also collect data on different exercise types and propose a comprehensive training plan. For example, the exercise recording unit records data on yoga or Pilates performed by the user, and the generation AI analyzes that data. For example, it evaluates the duration and accuracy of each pose and proposes a comprehensive training plan. The exercise recording unit also collects data on different exercise types, and the generation AI adjusts the training plan based on that data. For example, it proposes a plan that combines yoga and strength training. The exercise recording unit also integrates data on various exercise types performed by the user, and the generation AI generates a comprehensive training plan. For example, it proposes a plan that combines Pilates and aerobic exercise. In this way, by collecting data on different exercise types and proposing comprehensive training plans, it is possible to meet the diverse needs of users.

[0034] The suggestion unit can analyze the user's past exercise history and propose a long-term training plan. In the suggestion unit, for example, the generation AI analyzes the user's past exercise history and proposes a long-term training plan. For example, a plan that gradually increases intensity is created based on past exercise data. The suggestion unit also analyzes the user's exercise history and the generation AI proposes a training plan aimed at a long-term goal. For example, a plan is created for completing a marathon. The suggestion unit also analyzes the user's past exercise data and the generation AI proposes a long-term training plan. For example, an annual plan is created for improving muscle strength. In this way, by analyzing the user's past exercise history and proposing a long-term training plan, it is possible to support sustainable training effects.

[0035] The suggestion unit can periodically scan changes in the user's body shape and muscle mass and adjust the training plan based on that data. For example, the suggestion unit periodically scans the user's body shape and muscle mass, and the generation AI analyzes that data. For example, it evaluates changes in body fat percentage and muscle mass and adjusts the training plan. The suggestion unit also analyzes changes in the user's body shape and muscle mass using the generation AI to optimize the training plan. For example, if muscle mass increases, it increases the training intensity. The suggestion unit also periodically scans the user's body shape and muscle mass, and the generation AI adjusts the training plan based on that data. For example, it reduces the proportion of aerobic exercise if the body fat percentage decreases. In this way, effective training can be supported by periodically scanning changes in the user's body shape and muscle mass and adjusting the training plan based on that data.

[0036] The suggestion unit can propose plans according to different training goals. For example, the suggestion unit has the generation AI analyze the user's training goals and propose a plan aimed at completing a marathon. For example, a plan combining long-distance running and endurance training is created. The suggestion unit also has the generation AI propose a plan centered on strength training to a user aiming to participate in a bodybuilding competition. For example, a plan is created that focuses on training specific muscle groups. The suggestion unit also has the generation AI propose an optimal plan according to the user's training goals. For example, a plan aimed at completing a triathlon is created. This makes it possible to meet the diverse needs of users by proposing plans according to different training goals.

[0037] The suggestion unit can incorporate elements of stretching and relaxation into a training plan, aiming to maintain overall health. For example, the suggestion unit has a generation AI that incorporates elements of stretching and relaxation into a training plan, aiming to maintain overall health. For example, it suggests stretching or yoga after exercise. The suggestion unit also analyzes the user's health condition, and the generation AI suggests a training plan that includes elements of relaxation. For example, it creates a plan that incorporates meditation and deep breathing. The suggestion unit also incorporates elements of stretching and relaxation into a training plan, and the generation AI suggests a plan aimed at maintaining overall health. For example, it suggests a relaxation session once a week. In this way, by incorporating elements of stretching and relaxation into a training plan, it is possible to support the maintenance of overall health.

[0038] The analysis unit analyzes the user's exercise data and can predict signs of overtraining based on past training patterns. In the analysis unit, for example, the generation AI analyzes the user's exercise data and predicts signs of overtraining based on past training patterns. For example, it determines risk based on changes in exercise frequency and intensity. The analysis unit also analyzes the user's exercise history and the generation AI predicts signs of overtraining. For example, it issues a warning if there is a series of high-intensity training sessions. The analysis unit also predicts the risk of overtraining based on the user's exercise data. For example, it evaluates fatigue level and recovery time and suggests appropriate rest. In this way, the generation AI can analyze the user's exercise data and predict signs of overtraining based on past training patterns, thereby suggesting appropriate rest.

[0039] The analysis unit can monitor the user's biometric data in real time and determine the risk of overtraining. For example, the generation AI of the analysis unit monitors the user's heart rate variability in real time and determines the risk of overtraining. For example, it issues a warning if the heart rate is abnormally high. The analysis unit also measures the user's muscle fatigue level with a sensor, and the generation AI analyzes the data to determine the risk of overtraining. For example, it detects muscle stiffness and pain. The analysis unit also monitors the biometric data in real time, and the generation AI determines the risk of overtraining. For example, it suggests appropriate rest based on heart rate variability and muscle fatigue level. In this way, the user's biometric data can be monitored in real time, determining the risk of overtraining, and appropriate rest can be suggested.

[0040] The analysis unit can also analyze the user's lifestyle data and provide comprehensive advice to prevent overtraining. For example, the generation AI of the analysis unit analyzes the user's dietary data and provides advice to prevent overtraining. For example, it may suggest a nutritionally balanced diet. The analysis unit also analyzes the user's sleep data and the generation AI provides advice to prevent overtraining. For example, it may suggest ensuring sufficient sleep. The analysis unit also comprehensively analyzes the lifestyle data and the generation AI provides advice to prevent overtraining. For example, it may suggest balancing diet, sleep, and exercise. In this way, overtraining can be prevented by analyzing the user's lifestyle data and providing comprehensive advice.

[0041] The suggestion unit can incorporate regular rest days and recovery sessions into the training plan to prevent overtraining. For example, the suggestion unit allows the generation AI to incorporate regular rest days into the training plan to prevent overtraining. For example, the suggestion unit may suggest a rest day once a week. The suggestion unit also analyzes the user's exercise data, and the generation AI incorporates recovery sessions into the training plan. For example, the suggestion unit may suggest light stretching or yoga. The suggestion unit also incorporates regular rest days and recovery sessions into the training plan, and the generation AI prevents overtraining. For example, the suggestion unit may suggest recovery sessions to promote muscle recovery. In this way, by incorporating regular rest days and recovery sessions into the training plan, overtraining can be prevented.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The training assistance system can also provide advice to improve exercise performance based on the user's exercise history. For example, it can analyze the user's past training data and point out areas for improvement in form for a specific exercise. It can also provide feedback based on the user's exercise history to help them review their past achievements and maintain their motivation. It can also analyze the user's exercise history and suggest safe training plans that take into account any injuries or pain the user has experienced in the past.

[0044] The exercise recording unit can analyze the user's breathing patterns during exercise and suggest efficient breathing techniques. For example, it can analyze the breathing rhythm while running and provide guidance on optimal breathing timing. It can also suggest deep breathing and abdominal breathing techniques during yoga or Pilates to enhance relaxation. Furthermore, the exercise recording unit can provide real-time feedback on breathing techniques according to exercise intensity based on the user's breathing data.

[0045] The exercise recording unit can analyze the user's body temperature data during exercise and evaluate the effectiveness of the exercise. For example, it can monitor changes in body temperature during exercise and suggest appropriate times to hydrate. It can also adjust the user's exercise intensity based on the body temperature data to prevent excessive exercise. Furthermore, the exercise recording unit can analyze the body temperature data to evaluate the user's recovery status and suggest optimal rest periods.

[0046] The exercise recording unit can analyze the electromyogram data of the user during exercise to evaluate the state of muscle activity. For example, it can monitor the activity level of specific muscle groups based on the electromyogram data and suggest effective training methods. It can also analyze the electromyogram data to suggest rest if the user is using their muscles excessively. Furthermore, the exercise recording unit can evaluate the user's muscle fatigue level based on the electromyogram data and suggest optimal training intensity.

[0047] The exercise recording unit can analyze the user's blood pressure data during exercise and evaluate the safety of the exercise. For example, it can monitor blood pressure fluctuations during exercise and issue a warning if abnormal fluctuations are detected. It can also adjust the user's exercise intensity based on the blood pressure data to support safe training. Furthermore, the exercise recording unit can analyze the blood pressure data to evaluate the user's health condition and propose an appropriate training plan.

[0048] The exercise recording unit can analyze the user's oxygen saturation data during exercise and evaluate the effectiveness of the exercise. For example, it can monitor oxygen saturation during exercise and suggest appropriate breathing techniques. It can also adjust the user's exercise intensity based on the oxygen saturation data to support effective training. Furthermore, the exercise recording unit can analyze the oxygen saturation data to evaluate the user's recovery state and suggest optimal rest periods.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The exercise recording unit records the user's exercise. For example, it records information such as which training machine the user used, how long they exercised, and which body parts they focused on training. The exercise recording unit can also analyze the user's posture and movements during exercise in real time and provide immediate feedback on areas for improvement in form. For example, the generative AI can capture the user's posture during exercise with a camera and analyze it in real time. Step 2: The analysis unit analyzes the exercise recorded by the exercise recording unit. For example, the generation AI analyzes the user's exercise record data and proposes a training plan that is optimal for the user's goals (weight loss or muscle building). Step 3: The suggestion unit proposes a training plan that matches the user's purpose based on the results of the analysis by the analysis unit. For example, a plan that combines aerobic exercise and strength training is proposed to a user who is trying to lose weight, and a plan that focuses on training specific muscle groups is proposed to a user who is trying to increase muscle strength.

[0051] (Example 2) The training assistance system according to an embodiment of the present invention records the user's exercise, analyzes the recorded data using a generation AI, and proposes a training plan suited to the user's individual goals. This allows the training assistance system to provide an optimal training plan based on the user's exercise record and support effective training.

[0052] A training assistance system according to an embodiment includes an exercise recording unit, an analysis unit, and a suggestion unit. The exercise recording unit records the user's exercise details. For example, it records information such as which training machine the user used, how long the user exercised, and which body parts the user focused on training. The exercise recording unit can also analyze the user's posture and movements during exercise in real time and provide immediate feedback on improvements to their form. For example, a generation AI captures the user's posture during exercise with a camera and analyzes it in real time. The analysis unit analyzes the exercise details recorded by the exercise recording unit. For example, the generation AI analyzes the user's exercise record data and suggests a training plan optimal for the user's goal (such as weight loss or muscle building). The suggestion unit suggests a training plan tailored to the user's goal based on the results of the analysis by the analysis unit. For example, a plan combining aerobic exercise and strength training is suggested for a user aiming to lose weight, and a plan focusing on specific muscle groups is suggested for a user aiming to build muscle. As a result, the training assistance system according to the embodiment can provide effective training suited to individual goals by recording and analyzing the exercise content of the user and proposing an optimal training plan.

[0053] The exercise recording unit analyzes the user's posture and movements during exercise in real time and provides instant feedback on how to improve their form. For example, the generation AI captures the user's posture during exercise with a camera and analyzes it in real time. For example, during squats, it detects the angle of the knees and the degree of curvature of the back and provides feedback to correct form. The exercise recording unit also monitors the user's movements with sensors, and the generation AI analyzes the data to identify areas for improvement. For example, it analyzes the foot landing position and arm swing during running to suggest an efficient running form. The generation AI also analyzes the user's movements in real time and provides instant feedback on how to improve their form via audio and visual means. For example, it analyzes the shoulder position and bar movement during bench presses to suggest correct form. This allows the system to analyze the user's posture and movements during exercise in real time and provide instant feedback, supporting training with correct form.

[0054] The exercise recording unit also collects the user's dietary and sleep data to analyze their overall health. For example, the exercise recording unit allows the user to enter their daily dietary information into the app, and the generation AI analyzes that data. For example, the unit evaluates calorie intake and nutritional balance, and analyzes their overall health in conjunction with their exercise content. The exercise recording unit also uses a wearable device to collect the user's sleep data, and the generation AI analyzes that data. For example, the unit evaluates the quality and duration of sleep, and analyzes their health by linking it to their exercise content. The exercise recording unit also integrates the dietary and sleep data with their exercise records, and the generation AI analyzes their overall health. For example, the unit evaluates the impact of meal timing and content on exercise performance and proposes an optimal training plan. By collecting the user's dietary and sleep data and analyzing their overall health, the system can propose a more effective training plan.

[0055] The exercise recording unit uses the emotion estimation function to analyze the user's emotions during exercise and provide advice that helps maintain and improve motivation. For example, the generation AI in the exercise recording unit analyzes the user's facial expressions and voice to estimate their emotions during exercise. For example, it provides advice on relaxing if the user is feeling tired or stressed. The exercise recording unit also uses the emotion estimation function to analyze the user's motivation level in real time and provide appropriate encouragement and advice. For example, it displays positive messages during exercise. The exercise recording unit also uses the generation AI to propose a training plan to maintain motivation based on the user's emotional data. For example, it adjusts exercise intensity according to emotional fluctuations. This allows the unit to analyze the user's emotions during exercise and provide advice that helps maintain and improve motivation, thereby supporting effective training.

[0056] The exercise recording unit uses a wearable device to record biometric data such as heart rate and calorie consumption in real time, which the generation AI can then analyze. For example, the user wears the wearable device and records heart rate and calorie consumption in real time. The generation AI analyzes the data and evaluates the effectiveness of the exercise. The exercise recording unit also analyzes the biometric data obtained from the wearable device using the generation AI to monitor changes in physical condition during exercise. For example, the exercise recording unit adjusts exercise intensity based on fluctuations in heart rate. The exercise recording unit also records biometric data in real time, which the generation AI then uses to evaluate exercise performance. For example, the generation AI analyzes the effectiveness of dieting based on calorie consumption. In this way, by recording and analyzing biometric data such as heart rate and calorie consumption in real time, the effectiveness of exercise can be evaluated and an optimal training plan can be proposed.

[0057] The exercise recording unit can also collect data on different exercise types and propose a comprehensive training plan. For example, the exercise recording unit records data on yoga or Pilates performed by the user, and the generation AI analyzes that data. For example, it evaluates the duration and accuracy of each pose and proposes a comprehensive training plan. The exercise recording unit also collects data on different exercise types, and the generation AI adjusts the training plan based on that data. For example, it proposes a plan that combines yoga and strength training. The exercise recording unit also integrates data on various exercise types performed by the user, and the generation AI generates a comprehensive training plan. For example, it proposes a plan that combines Pilates and aerobic exercise. In this way, by collecting data on different exercise types and proposing comprehensive training plans, it is possible to meet the diverse needs of users.

[0058] The exercise recording unit can use the emotion estimation function to analyze the user's emotional changes before and after exercise and suggest the optimal timing for exercise. In the exercise recording unit, for example, the generation AI analyzes the user's emotions before and after exercise and evaluates the emotional changes. For example, it identifies the timing when positive emotions increase after exercise. The exercise recording unit also uses the emotion estimation function to monitor the user's emotional changes in real time and suggest the optimal timing for exercise. For example, it advises the user to exercise during times of low stress. In addition, the exercise recording unit uses the generation AI to analyze the emotional changes before and after exercise based on the user's emotional data and suggest effective timing for exercise. For example, it recommends exercising during times of stable emotions. This allows for effective training support by analyzing the user's emotional changes before and after exercise and suggesting the optimal timing for exercise.

[0059] The suggestion unit can analyze the user's past exercise history and propose a long-term training plan. In the suggestion unit, for example, the generation AI analyzes the user's past exercise history and proposes a long-term training plan. For example, a plan that gradually increases intensity is created based on past exercise data. The suggestion unit also analyzes the user's exercise history and the generation AI proposes a training plan aimed at a long-term goal. For example, a plan is created for completing a marathon. The suggestion unit also analyzes the user's past exercise data and the generation AI proposes a long-term training plan. For example, an annual plan is created for improving muscle strength. In this way, by analyzing the user's past exercise history and proposing a long-term training plan, it is possible to support sustainable training effects.

[0060] The suggestion unit can periodically scan changes in the user's body shape and muscle mass and adjust the training plan based on that data. For example, the suggestion unit periodically scans the user's body shape and muscle mass, and the generation AI analyzes that data. For example, it evaluates changes in body fat percentage and muscle mass and adjusts the training plan. The suggestion unit also analyzes changes in the user's body shape and muscle mass using the generation AI to optimize the training plan. For example, if muscle mass increases, it increases the training intensity. The suggestion unit also periodically scans the user's body shape and muscle mass, and the generation AI adjusts the training plan based on that data. For example, it reduces the proportion of aerobic exercise if the body fat percentage decreases. In this way, effective training can be supported by periodically scanning changes in the user's body shape and muscle mass and adjusting the training plan based on that data.

[0061] The suggestion unit can use the emotion estimation function to suggest a training plan that matches the user's motivation level. For example, the generation AI in the suggestion unit analyzes the user's emotions and suggests a training plan that matches the user's motivation level. For example, when motivation is low, it suggests light exercise. The suggestion unit also uses the emotion estimation function to analyze the user's motivation level in real time and suggest an appropriate training plan. For example, when motivation is high, it suggests high-intensity exercise. The suggestion unit also uses the generation AI to suggest a training plan that matches the user's motivation level based on the user's emotion data. For example, when motivation is low, it suggests enjoyable exercise. This makes it possible to support effective training by suggesting a training plan that matches the user's motivation level.

[0062] The suggestion unit can propose plans according to different training goals. For example, the suggestion unit has the generation AI analyze the user's training goals and propose a plan aimed at completing a marathon. For example, a plan combining long-distance running and endurance training is created. The suggestion unit also has the generation AI propose a plan centered on strength training to a user aiming to participate in a bodybuilding competition. For example, a plan is created that focuses on training specific muscle groups. The suggestion unit also has the generation AI propose an optimal plan according to the user's training goals. For example, a plan aimed at completing a triathlon is created. This makes it possible to meet the diverse needs of users by proposing plans according to different training goals.

[0063] The suggestion unit can incorporate elements of stretching and relaxation into a training plan, aiming to maintain overall health. For example, the suggestion unit has a generation AI that incorporates elements of stretching and relaxation into a training plan, aiming to maintain overall health. For example, it suggests stretching or yoga after exercise. The suggestion unit also analyzes the user's health condition, and the generation AI suggests a training plan that includes elements of relaxation. For example, it creates a plan that incorporates meditation and deep breathing. The suggestion unit also incorporates elements of stretching and relaxation into a training plan, and the generation AI suggests a plan aimed at maintaining overall health. For example, it suggests a relaxation session once a week. In this way, by incorporating elements of stretching and relaxation into a training plan, it is possible to support the maintenance of overall health.

[0064] The suggestion unit can use the emotion estimation function to adjust the training plan in real time according to the user's emotional state. For example, the generation AI in the suggestion unit analyzes the user's emotional state in real time and adjusts the training plan. For example, when stress is high, it suggests relaxing exercises. The suggestion unit also uses the emotion estimation function to adjust the training plan in real time according to the user's emotional state. For example, when positive emotions are strong, it suggests high-intensity exercises. The suggestion unit also uses the generation AI to adjust the training plan in real time based on the user's emotional data. For example, it suggests lighter exercises when emotions are unstable. This allows the training plan to be adjusted in real time according to the user's emotional state, supporting effective training.

[0065] The analysis unit analyzes the user's exercise data and can predict signs of overtraining based on past training patterns. In the analysis unit, for example, the generation AI analyzes the user's exercise data and predicts signs of overtraining based on past training patterns. For example, it determines risk based on changes in exercise frequency and intensity. The analysis unit also analyzes the user's exercise history and the generation AI predicts signs of overtraining. For example, it issues a warning if there is a series of high-intensity training sessions. The analysis unit also predicts the risk of overtraining based on the user's exercise data. For example, it evaluates fatigue level and recovery time and suggests appropriate rest. In this way, the generation AI can analyze the user's exercise data and predict signs of overtraining based on past training patterns, thereby suggesting appropriate rest.

[0066] The analysis unit can monitor the user's biometric data in real time and determine the risk of overtraining. For example, the generation AI of the analysis unit monitors the user's heart rate variability in real time and determines the risk of overtraining. For example, it issues a warning if the heart rate is abnormally high. The analysis unit also measures the user's muscle fatigue level with a sensor, and the generation AI analyzes the data to determine the risk of overtraining. For example, it detects muscle stiffness and pain. The analysis unit also monitors the biometric data in real time, and the generation AI determines the risk of overtraining. For example, it suggests appropriate rest based on heart rate variability and muscle fatigue level. In this way, the user's biometric data can be monitored in real time, determining the risk of overtraining, and appropriate rest can be suggested.

[0067] The analysis unit can use the emotion estimation function to provide recovery advice when the user feels stressed or fatigued. For example, the generation AI in the analysis unit analyzes the user's emotions and provides recovery advice when the user feels stressed or fatigued. For example, it may suggest stretching or resting to relax. The analysis unit also uses the emotion estimation function to provide recovery advice in real time when the user feels stressed or fatigued. For example, it may suggest meditation or deep breathing. The analysis unit also uses the generation AI to provide recovery advice based on the user's emotion data. For example, it may play relaxing music when stress is high. This allows the analysis unit to provide recovery advice when the user feels stressed or fatigued, suggesting appropriate rest.

[0068] The analysis unit can also analyze the user's lifestyle data and provide comprehensive advice to prevent overtraining. For example, the generation AI of the analysis unit analyzes the user's dietary data and provides advice to prevent overtraining. For example, it may suggest a nutritionally balanced diet. The analysis unit also analyzes the user's sleep data and the generation AI provides advice to prevent overtraining. For example, it may suggest ensuring sufficient sleep. The analysis unit also comprehensively analyzes the lifestyle data and the generation AI provides advice to prevent overtraining. For example, it may suggest balancing diet, sleep, and exercise. In this way, overtraining can be prevented by analyzing the user's lifestyle data and providing comprehensive advice.

[0069] The suggestion unit can incorporate regular rest days and recovery sessions into the training plan to prevent overtraining. For example, the suggestion unit allows the generation AI to incorporate regular rest days into the training plan to prevent overtraining. For example, the suggestion unit may suggest a rest day once a week. The suggestion unit also analyzes the user's exercise data, and the generation AI incorporates recovery sessions into the training plan. For example, the suggestion unit may suggest light stretching or yoga. The suggestion unit also incorporates regular rest days and recovery sessions into the training plan, and the generation AI prevents overtraining. For example, the suggestion unit may suggest recovery sessions to promote muscle recovery. In this way, by incorporating regular rest days and recovery sessions into the training plan, overtraining can be prevented.

[0070] The suggestion unit uses the emotion estimation function to propose a recovery plan based on the user's emotional state, thereby maintaining motivation. In the suggestion unit, for example, the generation AI analyzes the user's emotional state and proposes a recovery plan. For example, when stress is high, it proposes relaxation exercises. The suggestion unit also uses the emotion estimation function to propose recovery plans based on the user's emotional state in real time. For example, when positive emotions are strong, it suggests light exercises. The suggestion unit also uses the generation AI to propose recovery plans based on the user's emotional data, thereby maintaining motivation. For example, when emotions are unstable, it suggests activities to relax. In this way, by proposing a recovery plan based on the user's emotional state, motivation can be maintained and effective training can be supported.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The training assistance system can also provide advice to improve exercise performance based on the user's exercise history. For example, it can analyze the user's past training data and point out areas for improvement in form for a specific exercise. It can also provide feedback based on the user's exercise history to help them review their past achievements and maintain their motivation. It can also analyze the user's exercise history and suggest safe training plans that take into account any injuries or pain the user has experienced in the past.

[0073] The exercise recording unit can analyze the user's breathing patterns during exercise and suggest efficient breathing techniques. For example, it can analyze the breathing rhythm while running and provide guidance on optimal breathing timing. It can also suggest deep breathing and abdominal breathing techniques during yoga or Pilates to enhance relaxation. Furthermore, the exercise recording unit can provide real-time feedback on breathing techniques according to exercise intensity based on the user's breathing data.

[0074] The exercise recording unit can analyze the user's heart rate variability during exercise and evaluate their stress level. For example, it can monitor their stress level during exercise in real time based on heart rate variability and suggest appropriate rest. It can also evaluate the user's recovery status and suggest optimal training intensity based on heart rate variability data. Furthermore, the exercise recording unit can analyze heart rate variability data and provide advice for the user's stress management.

[0075] The exercise recording unit can analyze the user's facial expressions while exercising and monitor changes in emotions in real time. For example, it can identify when the user's smile increases during exercise and recommend that exercise. It can also provide advice on how to relax if the user is feeling stressed based on the facial expression data during exercise. Furthermore, the exercise recording unit can analyze the facial expression data and adjust the training plan according to the user's emotional state.

[0076] The exercise recording unit can analyze the user's voice data during exercise and evaluate changes in emotions. For example, it can analyze the tone and tempo of the user's voice during exercise to estimate the user's motivation level. It can also provide encouraging messages based on the voice data if the user feels tired. Furthermore, the exercise recording unit can analyze the voice data and suggest training plans based on the user's emotional state.

[0077] The exercise recording unit can analyze the user's body temperature data during exercise and evaluate the effectiveness of the exercise. For example, it can monitor changes in body temperature during exercise and suggest appropriate times to hydrate. It can also adjust the user's exercise intensity based on the body temperature data to prevent excessive exercise. Furthermore, the exercise recording unit can analyze the body temperature data to evaluate the user's recovery status and suggest optimal rest periods.

[0078] The exercise recording unit can analyze the electromyogram data of the user during exercise to evaluate the state of muscle activity. For example, it can monitor the activity level of specific muscle groups based on the electromyogram data and suggest effective training methods. It can also analyze the electromyogram data to suggest rest if the user is using their muscles excessively. Furthermore, the exercise recording unit can evaluate the user's muscle fatigue level based on the electromyogram data and suggest optimal training intensity.

[0079] The exercise recording unit can analyze the user's blood pressure data during exercise and evaluate the safety of the exercise. For example, it can monitor blood pressure fluctuations during exercise and issue a warning if abnormal fluctuations are detected. It can also adjust the user's exercise intensity based on the blood pressure data to support safe training. Furthermore, the exercise recording unit can analyze the blood pressure data to evaluate the user's health condition and propose an appropriate training plan.

[0080] The exercise recording unit can analyze the user's oxygen saturation data during exercise and evaluate the effectiveness of the exercise. For example, it can monitor oxygen saturation during exercise and suggest appropriate breathing techniques. It can also adjust the user's exercise intensity based on the oxygen saturation data to support effective training. Furthermore, the exercise recording unit can analyze the oxygen saturation data to evaluate the user's recovery state and suggest optimal rest periods.

[0081] The exercise recording unit can analyze the electrodermal activity data of the user during exercise and evaluate the stress level. For example, the electrodermal activity data can be used to monitor the stress level during exercise in real time and suggest appropriate rest. The electrodermal activity data can also be analyzed to recommend exercise if the user is relaxed. Furthermore, the exercise recording unit can provide advice for stress management to the user based on the electrodermal activity data.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The exercise recording unit records the user's exercise. For example, it records information such as which training machine the user used, how long they exercised, and which body parts they focused on training. The exercise recording unit can also analyze the user's posture and movements during exercise in real time and provide immediate feedback on areas for improvement in form. For example, the generative AI can capture the user's posture during exercise with a camera and analyze it in real time. Step 2: The analysis unit analyzes the exercise recorded by the exercise recording unit. For example, the generation AI analyzes the user's exercise record data and proposes a training plan that is optimal for the user's goals (weight loss or muscle building). Step 3: The suggestion unit proposes a training plan that matches the user's purpose based on the results of the analysis by the analysis unit. For example, a plan that combines aerobic exercise and strength training is proposed to a user who is trying to lose weight, and a plan that focuses on training specific muscle groups is proposed to a user who is trying to increase muscle strength.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 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. an exercise recording unit that records the exercise content of the user; an analysis unit that analyzes the exercise content recorded by the exercise recording unit; a suggestion unit that suggests a training plan that matches the user's purpose based on the results of the analysis by the analysis unit. A system characterized by:

2. The exercise recording unit Analyzes the user's posture and movements during exercise in real time, providing immediate feedback on improvements to form.

2. The system of claim 1.

3. The exercise recording unit It also collects the user's diet and sleep data to analyze their overall health.

2. The system of claim 1.

4. The exercise recording unit Analyzes the user's emotions during exercise and provides advice to help maintain and improve motivation 2. The system of claim 1.

5. The exercise recording unit Wearable devices will be used to record biometric data such as heart rate and calorie consumption in real time, which will then be analyzed by generative AI.

2. The system of claim 1.

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