System

Abstract

An object of a system according to an embodiment is to realize provision of an individually optimized training plan and checking of accuracy of a form in training in an unmanned gym or at home.SOLUTION: A system according to an embodiment includes a training plan generator, a video analyzer, and an evaluation feedback unit. The training plan generator includes a generation AI. The video analysis unit analyzes the training video of the user based on the training plan generated by the training plan generation unit. The evaluation feedback unit provides feedback to the user on the basis of the result analyzed by the video 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, it was difficult to provide individually optimized training plans or check the accuracy of form when training in unmanned gyms or at home.

[0005] The system according to the embodiment aims to provide individually optimized training plans and check the accuracy of form when training at an unmanned gym or at home. [Means for solving the problem]

[0006] The system according to the embodiment includes a training plan generation unit, a video analysis unit, and an evaluation feedback unit. The training plan generation unit is equipped with a generation AI. The video analysis unit analyzes the user's training video based on the training plan generated by the training plan generation unit. The evaluation feedback unit provides feedback to the user based on the results of the analysis by the video analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide individually optimized training plans and check the accuracy of form when training in an unmanned gym or at home. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training support system according to an embodiment of the present invention is a system that provides the benefits of a personal trainer via a smartphone app at unmanned gyms or at home. This system utilizes AI technology to generate and propose optimal training plans to users, taking into account the client's goals, physical fitness level, and past data. This allows the training support system to provide personalized training, enabling efficient training even at unmanned gyms, while also contributing to improved form and training effectiveness.

[0029] A training support system according to an embodiment includes a training plan generation unit, a video analysis unit, and an evaluation feedback unit. The training plan generation unit is equipped with a generation AI and generates an optimal training plan taking into account a user's goals, physical fitness level, and past training data. For example, if a user sets a goal of "increasing muscle strength," the generation AI suggests appropriate exercises, number of sets, and number of reps based on that goal. The generation AI receives input from a prompt containing the user's goals, physical fitness level, and past training data, and the generation AI generates a training plan based on the prompt. The video analysis unit analyzes the user's training video based on the training plan generated by the training plan generation unit. For example, if a user films a video of squats, the generation AI analyzes the user's knee angle, back position, and other factors to determine whether the user is performing the exercises with the correct form. If the form is incorrect, the generation AI provides specific advice on how to improve the user's workout. The generation AI receives input from a training video filmed by the user, and the generation AI analyzes the video. The evaluation feedback unit provides feedback to the user based on the results of the analysis by the video analysis unit. For example, when a user is running, the generation AI analyzes the user's running form and pace and provides appropriate advice in real time. This allows the user to immediately identify areas for improvement during training and perform effective training. The input to the generation AI is the user's real-time movement data, and the generation AI evaluates and provides feedback based on that data. This allows the training support system according to the embodiment to provide the user with a personalized training plan and feedback.

[0030] The training plan generation unit can generate a training plan that takes nutritional balance into consideration based on the user's dietary data. In the training plan generation unit, for example, the generation AI analyzes the user's dietary data and proposes a training plan that takes nutritional balance into consideration. For example, if protein intake is insufficient, a plan to strengthen strength training is generated. Furthermore, based on the dietary data, the generation AI evaluates the nutrient intake status and reflects it in the training plan. For example, if vitamin D is insufficient, outdoor training is recommended. Furthermore, based on the user's dietary data, the generation AI proposes a training plan that takes into consideration the balance between calorie consumption and intake. For example, on days when calorie intake is high, a plan to increase aerobic exercise is generated. In this way, a training plan that takes into consideration the user's nutritional balance can be provided.

[0031] The training plan generation unit can suggest optimal training time slots based on the user's sleep data. In the training plan generation unit, for example, the generation AI analyzes the user's sleep data and suggests optimal training time slots. For example, if the user is not getting deep sleep, training can be scheduled for the afternoon. Furthermore, based on the sleep data, the generation AI suggests a training plan that matches the user's biological rhythm. For example, training early in the morning can optimize energy levels. Furthermore, the generation AI analyzes the user's sleep data and suggests a training plan that takes fatigue recovery into consideration. For example, lighter training can be recommended on days when the user has not had enough sleep. This makes it possible to suggest optimal training time slots based on the user's sleep data.

[0032] The training plan generation unit can analyze the user's stress level and propose a training plan aimed at stress reduction. In the training plan generation unit, for example, the generation AI analyzes the user's stress level and proposes a training plan aimed at stress reduction. For example, on days when stress is high, a plan including yoga or meditation is generated. The generation AI also suggests relaxation exercises based on stress data. For example, a training plan including deep breathing and stretching is provided. The generation AI also analyzes the user's stress level and proposes aerobic exercise that is effective at reducing stress. For example, walking or light jogging is recommended. This makes it possible to provide a training plan that matches the user's stress level.

[0033] The training plan generation unit can generate a training plan to reduce health risks based on the user's health checkup data. In the training plan generation unit, for example, the generation AI analyzes the user's health checkup data and generates a training plan to reduce health risks. For example, if there is a risk of high blood pressure, low-intensity aerobic exercise is recommended. Furthermore, based on the health checkup data, the generation AI proposes a training plan that addresses specific health risks. For example, if bone density is low, a plan to strengthen strength training is generated. Furthermore, the generation AI analyzes the user's health checkup data and proposes a balanced training plan to reduce health risks. For example, a plan including exercises to improve cardiopulmonary function is provided. In this way, a training plan to reduce the user's health risks can be provided.

[0034] The video analysis unit can analyze the user's muscle movements and provide form improvement advice focused on specific muscle groups. For example, the generation AI in the video analysis unit analyzes the user's muscle movements and provides form improvement advice focused on specific muscle groups. For example, the generation AI analyzes the movement of the quadriceps during squats and suggests appropriate form. Furthermore, based on the muscle movements, the generation AI provides form improvement advice that is effective for specific muscle groups. For example, the generation AI analyzes the movement of the pectoral muscles during bench presses and provides guidance on the correct form. Furthermore, the generation AI analyzes the user's muscle movements and provides training advice focused on specific muscle groups. For example, the generation AI analyzes the movement of the hamstrings during deadlifts and improves form. This makes it possible to provide form improvement advice focused on specific muscle groups.

[0035] The video analysis unit can analyze the movement of the user's joints and provide advice on improving form to reduce the strain on the joints. In the video analysis unit, for example, the generation AI analyzes the movement of the user's joints and provides advice on improving form to reduce the strain on the joints. For example, the movement of the knee joint during a lunge is analyzed to suggest a form that reduces the strain. Furthermore, the generation AI provides advice on improving form to reduce the strain on the joints based on the joint movement. For example, the movement of the shoulder joint during a push-up is analyzed to instruct the appropriate form. Furthermore, the generation AI analyzes the movement of the user's joints and provides training advice to reduce the strain on the joints. For example, the movement of the hip joint during a squat is analyzed to improve the form. In this way, advice on improving form to reduce the strain on the joints can be provided.

[0036] The video analysis unit can analyze the user's breathing patterns and provide advice on improving their breathing method. In the video analysis unit, for example, the generation AI analyzes the user's breathing patterns and provides advice on improving their breathing method. For example, the breathing pattern while running is analyzed and an efficient breathing method is suggested. The generation AI also provides advice on improving their breathing method based on the breathing pattern. For example, the breathing method while weightlifting is analyzed and appropriate breathing timing is instructed. The generation AI also analyzes the user's breathing patterns and provides advice on improving their breathing method. For example, the breathing method during a yoga pose is analyzed and suggestions are made to enhance the relaxation effect. This makes it possible to provide advice on improving their breathing method.

[0037] The video analysis unit can analyze the user's balance and suggest exercises to improve balance. In the video analysis unit, for example, the generation AI analyzes the user's balance and suggests exercises to improve balance. For example, it analyzes a video of standing on one leg and suggests exercises to improve balance. Furthermore, the generation AI suggests exercises to improve balance based on the balance data. For example, it recommends training using a balance ball. Furthermore, the generation AI analyzes the user's balance and provides a training plan to improve balance. For example, it suggests a plan that includes yoga balance poses. This makes it possible to suggest exercises to improve balance.

[0038] The evaluation feedback unit can analyze the user's heart rate in real time and suggest adjustments to training intensity based on the heart rate. In the evaluation feedback unit, for example, the generation AI analyzes the user's heart rate in real time and suggests adjustments to training intensity based on the heart rate. For example, if the heart rate is high, it suggests lowering the training intensity. The generation AI also suggests adjustments to training intensity in real time based on the heart rate data. For example, if the heart rate is low, it recommends exercises with higher intensity. The generation AI also analyzes the user's heart rate in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the heart rate falls within the target range. This makes it possible to suggest adjustments to training intensity based on the heart rate.

[0039] The evaluation feedback unit can analyze the amount of sweat the user receives in real time and suggest appropriate times to hydrate. For example, the generation AI in the evaluation feedback unit analyzes the amount of sweat the user receives in real time and suggests appropriate times to hydrate. For example, if the amount of sweat is high, a notification is sent encouraging hydration. The generation AI also suggests the timing of hydration in real time based on the sweat amount data. For example, it may recommend hydration when a certain amount of sweat has been lost during training. The generation AI also analyzes the amount of sweat the user receives in real time and suggests the optimal timing to hydrate. For example, it may display an alert encouraging hydration when the amount of sweat increases. This makes it possible to suggest appropriate times to hydrate.

[0040] The evaluation feedback unit can analyze the user's body temperature in real time and suggest adjustments to training intensity based on body temperature. In the evaluation feedback unit, for example, the generation AI analyzes the user's body temperature in real time and suggests adjustments to training intensity based on body temperature. For example, if the body temperature is high, it suggests lowering the training intensity. The generation AI also suggests adjustments to training intensity in real time based on body temperature data. For example, if the body temperature is low, it recommends exercises with higher intensity. The generation AI also analyzes the user's body temperature in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the body temperature falls within the target range. This makes it possible to suggest adjustments to training intensity based on body temperature.

[0041] The evaluation feedback unit can analyze the user's muscle fatigue level in real time and suggest adjustments to training intensity based on the fatigue level. In the evaluation feedback unit, for example, the generation AI analyzes the user's muscle fatigue level in real time and suggests adjustments to training intensity based on the fatigue level. For example, if the fatigue level is high, it suggests lowering the training intensity. Furthermore, the generation AI suggests adjustments to training intensity in real time based on muscle fatigue level data. For example, if the fatigue level is low, it recommends exercises with higher intensity. Furthermore, the generation AI analyzes the user's muscle fatigue level in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the fatigue level falls within a target range. This makes it possible to suggest adjustments to training intensity based on the muscle fatigue level.

[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 plan generation unit can also generate a training plan that takes into account the user's hobbies and interests. For example, if the user is interested in dancing, it can suggest exercises that incorporate dancing. If the user likes outdoor activities, it can recommend outdoor training such as hiking or running. Furthermore, if the user enjoys music, it can provide rhythmic training that matches the music. This makes it possible to provide a training plan that suits the user's hobbies and interests.

[0044] The training plan generation unit can also generate training plans that fit the user's lifestyle. For example, it can suggest short, effective exercises for busy businessmen. It can also recommend workouts that parents can do together with their children. It can also provide low-impact exercises that are gentle on the joints for seniors. This makes it possible to provide training plans that fit the user's lifestyle.

[0045] The training plan generation unit can also generate a training plan that matches the weather based on the user's weather data. For example, it can suggest exercises that can be done indoors on rainy days, and recommend outdoor training on sunny days. It can also provide a plan that emphasizes warm-ups on cold days. This makes it possible to provide a training plan that matches the user's weather.

[0046] The training plan generation unit can also generate training plans that take the user's travel schedule into consideration. For example, it can suggest exercises that can be done at a hotel gym during a business trip, or recommend walking or cycling in tourist spots. It can also provide stretching or light exercises before and after long flights. This makes it possible to provide training plans that suit the user's travel schedule.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The training plan generator is equipped with a generation AI that generates an optimal training plan taking into account the user's goals, physical fitness level, and past training data. For example, if the user sets a goal of "increasing muscle strength," the generation AI will suggest appropriate exercises, number of sets, and number of reps based on that goal. The input to the generation AI is prompts that include the user's goals, physical fitness level, and past training data, and the generation AI generates a training plan based on those prompts. Step 2: The video analysis unit analyzes the user's training video based on the training plan generated by the training plan generation unit. For example, if a user films a video of themselves doing squats, the generation AI analyzes the angle of the knees, the position of the back, etc., to determine whether the form is correct. If the form is incorrect, the generation AI provides specific advice on how to improve. The input to the generation AI is the training video filmed by the user, and the generation AI performs analysis based on that video. Step 3: The evaluation and feedback unit provides feedback to the user based on the results of the analysis by the video analysis unit. For example, if the user is running, the generation AI analyzes their running form and pace and provides appropriate advice in real time. This allows the user to immediately identify areas for improvement during training and perform effective training. The input to the generation AI is the user's real-time movement data, and the generation AI uses that data to provide evaluation and feedback.

[0049] (Example 2) A training support system according to an embodiment of the present invention is a system that provides the benefits of a personal trainer via a smartphone app at unmanned gyms or at home. This system utilizes AI technology to generate and propose optimal training plans to users, taking into account the client's goals, physical fitness level, and past data. This allows the training support system to provide personalized training, enabling efficient training even at unmanned gyms, while also contributing to improved form and training effectiveness.

[0050] A training support system according to an embodiment includes a training plan generation unit, a video analysis unit, and an evaluation feedback unit. The training plan generation unit is equipped with a generation AI and generates an optimal training plan taking into account a user's goals, physical fitness level, and past training data. For example, if a user sets a goal of "increasing muscle strength," the generation AI suggests appropriate exercises, number of sets, and number of reps based on that goal. The generation AI receives input from a prompt containing the user's goals, physical fitness level, and past training data, and the generation AI generates a training plan based on the prompt. The video analysis unit analyzes the user's training video based on the training plan generated by the training plan generation unit. For example, if a user films a video of squats, the generation AI analyzes the user's knee angle, back position, and other factors to determine whether the user is performing the exercises with the correct form. If the form is incorrect, the generation AI provides specific advice on how to improve the user's workout. The generation AI receives input from a training video filmed by the user, and the generation AI analyzes the video. The evaluation feedback unit provides feedback to the user based on the results of the analysis by the video analysis unit. For example, when a user is running, the generation AI analyzes the user's running form and pace and provides appropriate advice in real time. This allows the user to immediately identify areas for improvement during training and perform effective training. The input to the generation AI is the user's real-time movement data, and the generation AI evaluates and provides feedback based on that data. This allows the training support system according to the embodiment to provide the user with a personalized training plan and feedback.

[0051] The training plan generation unit can generate a training plan that takes nutritional balance into consideration based on the user's dietary data. In the training plan generation unit, for example, the generation AI analyzes the user's dietary data and proposes a training plan that takes nutritional balance into consideration. For example, if protein intake is insufficient, a plan to strengthen strength training is generated. Furthermore, based on the dietary data, the generation AI evaluates the nutrient intake status and reflects it in the training plan. For example, if vitamin D is insufficient, outdoor training is recommended. Furthermore, based on the user's dietary data, the generation AI proposes a training plan that takes into consideration the balance between calorie consumption and intake. For example, on days when calorie intake is high, a plan to increase aerobic exercise is generated. In this way, a training plan that takes into consideration the user's nutritional balance can be provided.

[0052] The training plan generation unit can suggest optimal training time slots based on the user's sleep data. In the training plan generation unit, for example, the generation AI analyzes the user's sleep data and suggests optimal training time slots. For example, if the user is not getting deep sleep, training can be scheduled for the afternoon. Furthermore, based on the sleep data, the generation AI suggests a training plan that matches the user's biological rhythm. For example, training early in the morning can optimize energy levels. Furthermore, the generation AI analyzes the user's sleep data and suggests a training plan that takes fatigue recovery into consideration. For example, lighter training can be recommended on days when the user has not had enough sleep. This makes it possible to suggest optimal training time slots based on the user's sleep data.

[0053] The training plan generation unit can use the emotion estimation function to evaluate the user's motivation level and generate a training plan tailored to periods when motivation is high. The training plan generation unit, for example, uses the emotion estimation function to evaluate the user's motivation level and generate a training plan tailored to periods when motivation is high. For example, high-intensity training is set for days when positive emotions are strong. The generation AI also analyzes the user's emotion data and suggests lighter training when motivation is low. For example, relaxation exercises are recommended on days when stress is high. The emotion estimation function also evaluates the user's motivation level in real time and dynamically adjusts the training plan. For example, a new challenge is suggested the moment motivation rises. This makes it possible to provide a training plan tailored to the user's motivation level.

[0054] The training plan generation unit can analyze the user's stress level and propose a training plan aimed at stress reduction. In the training plan generation unit, for example, the generation AI analyzes the user's stress level and proposes a training plan aimed at stress reduction. For example, on days when stress is high, a plan including yoga or meditation is generated. The generation AI also suggests relaxation exercises based on stress data. For example, a training plan including deep breathing and stretching is provided. The generation AI also analyzes the user's stress level and proposes aerobic exercise that is effective at reducing stress. For example, walking or light jogging is recommended. This makes it possible to provide a training plan that matches the user's stress level.

[0055] The training plan generation unit can generate a training plan to reduce health risks based on the user's health checkup data. In the training plan generation unit, for example, the generation AI analyzes the user's health checkup data and generates a training plan to reduce health risks. For example, if there is a risk of high blood pressure, low-intensity aerobic exercise is recommended. Furthermore, based on the health checkup data, the generation AI proposes a training plan that addresses specific health risks. For example, if bone density is low, a plan to strengthen strength training is generated. Furthermore, the generation AI analyzes the user's health checkup data and proposes a balanced training plan to reduce health risks. For example, a plan including exercises to improve cardiopulmonary function is provided. In this way, a training plan to reduce the user's health risks can be provided.

[0056] The training plan generation unit can use the emotion estimation function to suggest relaxation exercises according to the user's emotional state. The training plan generation unit, for example, uses the emotion estimation function to suggest relaxation exercises according to the user's emotional state. For example, on days when stress is high, meditation or deep breathing may be recommended. The generation AI also analyzes the user's emotional data and suggests relaxation exercises. For example, on days when positive emotions are low, a plan including yoga and stretching may be provided. The emotion estimation function also evaluates the user's emotional state in real time and dynamically adjusts the relaxation exercises. For example, relaxation music may be suggested when emotions are unstable. This makes it possible to provide relaxation exercises according to the user's emotional state.

[0057] The video analysis unit can analyze the user's muscle movements and provide form improvement advice focused on specific muscle groups. For example, the generation AI in the video analysis unit analyzes the user's muscle movements and provides form improvement advice focused on specific muscle groups. For example, the generation AI analyzes the movement of the quadriceps during squats and suggests appropriate form. Furthermore, based on the muscle movements, the generation AI provides form improvement advice that is effective for specific muscle groups. For example, the generation AI analyzes the movement of the pectoral muscles during bench presses and provides guidance on the correct form. Furthermore, the generation AI analyzes the user's muscle movements and provides training advice focused on specific muscle groups. For example, the generation AI analyzes the movement of the hamstrings during deadlifts and improves form. This makes it possible to provide form improvement advice focused on specific muscle groups.

[0058] The video analysis unit can analyze the movement of the user's joints and provide advice on improving form to reduce the strain on the joints. In the video analysis unit, for example, the generation AI analyzes the movement of the user's joints and provides advice on improving form to reduce the strain on the joints. For example, the movement of the knee joint during a lunge is analyzed to suggest a form that reduces the strain. Furthermore, the generation AI provides advice on improving form to reduce the strain on the joints based on the joint movement. For example, the movement of the shoulder joint during a push-up is analyzed to instruct the appropriate form. Furthermore, the generation AI analyzes the movement of the user's joints and provides training advice to reduce the strain on the joints. For example, the movement of the hip joint during a squat is analyzed to improve the form. In this way, advice on improving form to reduce the strain on the joints can be provided.

[0059] The video analysis unit can use the emotion estimation function to evaluate the user's level of fatigue and provide advice to improve form when fatigue is accumulating. The video analysis unit, for example, uses the emotion estimation function to evaluate the user's level of fatigue and provide advice to improve form when fatigue is accumulating. For example, lighter training is recommended on days when fatigue is high. In addition, the user's emotion data is analyzed, and the generation AI evaluates the level of fatigue and provides advice to improve form. For example, if fatigue is accumulating, it suggests simplifying form. In addition, the emotion estimation function is used to evaluate the user's level of fatigue in real time and provide training advice when fatigue is accumulating. For example, recovery exercises are suggested when fatigue is high. This makes it possible to provide advice to improve form when fatigue is accumulating.

[0060] The video analysis unit can analyze the user's breathing patterns and provide advice on improving their breathing method. In the video analysis unit, for example, the generation AI analyzes the user's breathing patterns and provides advice on improving their breathing method. For example, the breathing pattern while running is analyzed and an efficient breathing method is suggested. The generation AI also provides advice on improving their breathing method based on the breathing pattern. For example, the breathing method while weightlifting is analyzed and appropriate breathing timing is instructed. The generation AI also analyzes the user's breathing patterns and provides advice on improving their breathing method. For example, the breathing method during a yoga pose is analyzed and suggestions are made to enhance the relaxation effect. This makes it possible to provide advice on improving their breathing method.

[0061] The video analysis unit can analyze the user's balance and suggest exercises to improve balance. In the video analysis unit, for example, the generation AI analyzes the user's balance and suggests exercises to improve balance. For example, it analyzes a video of standing on one leg and suggests exercises to improve balance. Furthermore, the generation AI suggests exercises to improve balance based on the balance data. For example, it recommends training using a balance ball. Furthermore, the generation AI analyzes the user's balance and provides a training plan to improve balance. For example, it suggests a plan that includes yoga balance poses. This makes it possible to suggest exercises to improve balance.

[0062] The video analysis unit can use the emotion estimation function to evaluate the user's stress level and provide advice on improving form to reduce stress. The video analysis unit, for example, uses the emotion estimation function to evaluate the user's stress level and provide advice on improving form to reduce stress. For example, on days when stress is high, it may suggest a form that has a relaxing effect. In addition, the user's emotion data is analyzed, and the generation AI evaluates the stress level and provides advice on improving form. For example, if stress is high, it may suggest simplifying the form. In addition, the emotion estimation function is used to evaluate the user's stress level in real time and provide training advice to reduce stress. For example, if stress is high, it may suggest recovery exercises. This makes it possible to provide advice on improving form to reduce stress.

[0063] The evaluation feedback unit can analyze the user's heart rate in real time and suggest adjustments to training intensity based on the heart rate. In the evaluation feedback unit, for example, the generation AI analyzes the user's heart rate in real time and suggests adjustments to training intensity based on the heart rate. For example, if the heart rate is high, it suggests lowering the training intensity. The generation AI also suggests adjustments to training intensity in real time based on the heart rate data. For example, if the heart rate is low, it recommends exercises with higher intensity. The generation AI also analyzes the user's heart rate in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the heart rate falls within the target range. This makes it possible to suggest adjustments to training intensity based on the heart rate.

[0064] The evaluation feedback unit can analyze the amount of sweat the user receives in real time and suggest appropriate times to hydrate. For example, the generation AI in the evaluation feedback unit analyzes the amount of sweat the user receives in real time and suggests appropriate times to hydrate. For example, if the amount of sweat is high, a notification is sent encouraging hydration. The generation AI also suggests the timing of hydration in real time based on the sweat amount data. For example, it may recommend hydration when a certain amount of sweat has been lost during training. The generation AI also analyzes the amount of sweat the user receives in real time and suggests the optimal timing to hydrate. For example, it may display an alert encouraging hydration when the amount of sweat increases. This makes it possible to suggest appropriate times to hydrate.

[0065] The evaluation feedback unit can use the emotion estimation function to evaluate the user's motivation level in real time and provide feedback to maintain motivation. The evaluation feedback unit, for example, uses the emotion estimation function to evaluate the user's motivation level in real time and provide feedback to maintain motivation. For example, it can send an encouraging message when motivation is low. In addition, the evaluation feedback unit analyzes the user's emotion data, and the generation AI evaluates the motivation level in real time and provides feedback. For example, it can provide advice to increase motivation when positive emotions are low. In addition, the emotion estimation function can be used to evaluate the user's motivation level in real time and provide training advice to maintain motivation. For example, it can suggest easy exercises when motivation is low. This makes it possible to provide feedback to maintain motivation.

[0066] The evaluation feedback unit can analyze the user's body temperature in real time and suggest adjustments to training intensity based on body temperature. In the evaluation feedback unit, for example, the generation AI analyzes the user's body temperature in real time and suggests adjustments to training intensity based on body temperature. For example, if the body temperature is high, it suggests lowering the training intensity. The generation AI also suggests adjustments to training intensity in real time based on body temperature data. For example, if the body temperature is low, it recommends exercises with higher intensity. The generation AI also analyzes the user's body temperature in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the body temperature falls within the target range. This makes it possible to suggest adjustments to training intensity based on body temperature.

[0067] The evaluation feedback unit can analyze the user's muscle fatigue level in real time and suggest adjustments to training intensity based on the fatigue level. In the evaluation feedback unit, for example, the generation AI analyzes the user's muscle fatigue level in real time and suggests adjustments to training intensity based on the fatigue level. For example, if the fatigue level is high, it suggests lowering the training intensity. Furthermore, the generation AI suggests adjustments to training intensity in real time based on muscle fatigue level data. For example, if the fatigue level is low, it recommends exercises with higher intensity. Furthermore, the generation AI analyzes the user's muscle fatigue level in real time and suggests optimizing the training intensity. For example, it adjusts the training plan so that the fatigue level falls within a target range. This makes it possible to suggest adjustments to training intensity based on the muscle fatigue level.

[0068] The evaluation feedback unit can use the emotion estimation function to evaluate the user's stress level in real time and provide feedback for stress reduction. The evaluation feedback unit, for example, uses the emotion estimation function to evaluate the user's stress level in real time and provide feedback for stress reduction. For example, when stress is high, relaxation exercises are suggested. The evaluation feedback unit also analyzes the user's emotion data, and the generation AI evaluates the stress level in real time and provides feedback. For example, when stress is high, advice that has a relaxing effect is given. The emotion estimation function can also be used to evaluate the user's stress level in real time and provide training advice for stress reduction. For example, when stress is high, light exercises are suggested. This makes it possible to provide feedback for stress reduction.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The training plan generation unit can also generate a training plan that takes into account the user's hobbies and interests. For example, if the user is interested in dancing, it can suggest exercises that incorporate dancing. If the user likes outdoor activities, it can recommend outdoor training such as hiking or running. Furthermore, if the user enjoys music, it can provide rhythmic training that matches the music. This makes it possible to provide a training plan that suits the user's hobbies and interests.

[0071] The training plan generation unit can also generate training plans that fit the user's lifestyle. For example, it can suggest short, effective exercises for busy businessmen. It can also recommend workouts that parents can do together with their children. It can also provide low-impact exercises that are gentle on the joints for seniors. This makes it possible to provide training plans that fit the user's lifestyle.

[0072] The training plan generation unit can also generate a training plan that matches the weather based on the user's weather data. For example, it can suggest exercises that can be done indoors on rainy days, and recommend outdoor training on sunny days. It can also provide a plan that emphasizes warm-ups on cold days. This makes it possible to provide a training plan that matches the user's weather.

[0073] The training plan generation unit can also generate training plans that take the user's travel schedule into consideration. For example, it can suggest exercises that can be done at a hotel gym during a business trip, or recommend walking or cycling in tourist spots. It can also provide stretching or light exercises before and after long flights. This makes it possible to provide training plans that suit the user's travel schedule.

[0074] The training plan generation unit can also use the user's emotion estimation function to suggest music that matches the user's emotional state. For example, energetic music can be set for days when positive emotions are strong. Relaxing music can also be recommended for days when stress is high. Furthermore, motivational music can be provided when emotions are unstable. This makes it possible to provide music that matches the user's emotional state.

[0075] The training plan generation unit can also use the user's emotion estimation function to suggest training partners that match the user's emotional state. For example, on days when positive emotions are strong, a partner that stimulates competitive spirit can be set. On days when stress is high, a partner with a relaxing effect can be recommended. Furthermore, a partner that provides support when emotions are unstable can be provided. This makes it possible to provide training partners that match the user's emotional state.

[0076] The training plan generation unit can also use the user's emotion estimation function to set training goals according to the user's emotional state. For example, it can set a high goal on days when positive emotions are strong, or recommend a more realistic goal on days when stress is high. It can also provide short-term goals when emotions are unstable. This makes it possible to provide training goals according to the user's emotional state.

[0077] The training plan generation unit can also use the user's emotion estimation function to suggest a training environment that matches the user's emotional state. For example, it can set up a lively gym on days when positive emotions are strong, or recommend a quiet environment on days when stress is high. It can also offer training in nature when emotions are unstable. This makes it possible to provide a training environment that matches the user's emotional state.

[0078] The training plan generation unit can also use the user's emotion estimation function to set training intervals according to the user's emotional state. For example, it can set shorter intervals on days when positive emotions are strong, or recommend longer intervals on days when stress is high. It can also provide flexible intervals when emotions are unstable. This makes it possible to provide training intervals according to the user's emotional state.

[0079] The training plan generation unit can also use the user's emotion estimation function to set training rewards according to the user's emotional state. For example, a high reward can be set on days when positive emotions are strong, and a small reward can be recommended on days when stress is high. Furthermore, immediate rewards can be provided when emotions are unstable. This makes it possible to provide training rewards according to the user's emotional state.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The training plan generator is equipped with a generation AI that generates an optimal training plan taking into account the user's goals, physical fitness level, and past training data. For example, if the user sets a goal of "increasing muscle strength," the generation AI will suggest appropriate exercises, number of sets, and number of reps based on that goal. The input to the generation AI is prompts that include the user's goals, physical fitness level, and past training data, and the generation AI generates a training plan based on those prompts. Step 2: The video analysis unit analyzes the user's training video based on the training plan generated by the training plan generation unit. For example, if a user films a video of themselves doing squats, the generation AI analyzes the angle of the knees, the position of the back, etc., to determine whether the form is correct. If the form is incorrect, the generation AI provides specific advice on how to improve. The input to the generation AI is the training video filmed by the user, and the generation AI performs analysis based on that video. Step 3: The evaluation and feedback unit provides feedback to the user based on the results of the analysis by the video analysis unit. For example, if the user is running, the generation AI analyzes their running form and pace and provides appropriate advice in real time. This allows the user to immediately identify areas for improvement during training and perform effective training. The input to the generation AI is the user's real-time movement data, and the generation AI uses that data to provide evaluation and feedback.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

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

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

[0126] 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.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A training plan generation unit equipped with generation AI, a video analysis unit that analyzes a user's training video based on the training plan generated by the training plan generation unit; an evaluation feedback unit that provides feedback to the user based on the results of the analysis by the video analysis unit. A system characterized by:

2. The training plan generation unit The training plan is generated based on the dietary data of the user, taking into consideration nutritional balance.

2. The system of claim 1.

3. The training plan generation unit Based on the user's sleep data, the system suggests the optimal training time.

2. The system of claim 1.

4. The training plan generation unit Evaluating the user's motivation level and generating the training plan tailored to periods when motivation is high 2. The system of claim 1.

5. The training plan generation unit Analyzing the user's stress level and proposing the training plan aimed at reducing stress 2. The system of claim 1.

6. The training plan generation unit The training plan for reducing health risks is generated based on the health checkup data of the user.

2. The system of claim 1.

7. The training plan generation unit Suggesting relaxation exercises according to the user's emotional state 2. The system of claim 1.

8. The video analysis unit Analyzing the user's muscle movements and providing form improvement advice focused on specific muscle groups 2. The system of claim 1.

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