System

Abstract

To provide a system according to an embodiment which provides the optimum training menu according to the health state of a user and properly traces the progress or the effect.SOLUTION: The system according to an embodiment includes a collection unit, a generation unit, a feedback unit, and a trace unit. The collection unit collects physical data of a user. The generation unit analyzes data collected by the collection unit and generates a training menu for a user. The feedback unit feeds back the progress of training on the basis of the training menu generated by the generation unit. The trace unit traces the improvement effect on the basis of data fed back by the feedback 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have the problem that they do not adequately provide users with an optimal training menu based on their health condition, or adequately track their progress and results.

[0005] The system according to the embodiment aims to provide an optimal training menu according to the user's health condition and to appropriately track the progress and effects of the training. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a feedback unit, and a tracking unit. The collection unit collects physical data of a user. The generation unit analyzes the data collected by the collection unit and generates a training menu for the user. The feedback unit provides feedback on the progress of training based on the training menu generated by the generation unit. The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. Effect of the Invention

[0007] The system according to the embodiment can provide an optimal training menu according to the user's health condition and can appropriately track the progress and effects of the training. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A health support system according to an embodiment of the present invention is a system that presents a training menu for muscle training or rehabilitation according to the health condition of a user. This health support system operates a health support AI that collects the user's physical data and generates an optimal training method based on the collected data. Furthermore, the health support system appropriately feeds back the progress of the user's training and tracks the improvement effect. For example, the health support system collects detailed data such as the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function. The user can use a smartphone or wearable device to record daily activity data and health data. This allows the user's physical condition to be accurately grasped. Next, based on the collected data, the health support AI generates an optimal training method for the user. The health support AI analyzes the collected data and proposes a training menu according to the user's physical condition. For example, a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function, can be generated. Furthermore, the health support system appropriately feeds back the progress of the user's training. The health support AI collects data on the training performed by the user and analyzes the progress. For example, the effectiveness of training can be evaluated based on data such as the load and time the user spent training. This allows the user to grasp the progress of his / her training and adjust the training menu as necessary. Finally, the health support system tracks and follows up on the improvement effect. The health support AI continuously collects the user's training data and tracks the improvement effect. For example, the effectiveness of training can be evaluated based on data such as the extent to which the user's muscle strength has improved and the extent to which flexibility has improved. This allows the user to grasp the improvement status of his / her health condition and maintain motivation. In this way, the health support system supports the user's health management by presenting the optimal training menu according to the user's health condition, feeding back the progress of training, and tracking the improvement effect.This enables the health support system to present the optimal training menu based on the user's health condition, provide feedback on the progress of training, and track the effects of improvement.

[0029] The health support system according to the embodiment includes a collection unit, a generation unit, a feedback unit, and a tracking unit. The collection unit collects physical data of the user. The physical data of the user includes, for example, age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, and the like, but is not limited to such examples. The collection unit can record daily activity data and health data, for example, using a smartphone or a wearable device. For example, the collection unit records the number of steps and heart rate of the user through a smartphone application. The collection unit can also record the sleep pattern and amount of exercise of the user using a wearable device. Furthermore, the collection unit can measure the blood pressure and body temperature of the user using a medical device. For example, the collection unit periodically measures the blood pressure of the user using a blood pressure monitor and records the data. The generation unit analyzes the data collected by the collection unit and generates a training menu optimal for the user. The generation unit analyzes the collected data using, for example, AI, and proposes a training menu according to the physical condition of the user. For example, the generation unit can generate a training method according to the purpose of the user, such as muscle training, stretching for improving flexibility, and aerobic exercise for improving cardiopulmonary function. The generation unit can, for example, use AI to analyze the muscle strength data of the user and generate a menu of muscle strength training. The generation unit can also use AI to analyze the flexibility data of the user and generate a menu of stretching. The generation unit can also use AI to analyze the cardiopulmonary function data of the user and generate a menu of aerobic exercise. The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. For example, the feedback unit can evaluate the effect of the training based on data such as the load at which the user performed the training and how long it took to perform the training. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The feedback unit can also suggest adjustments to the training menu based on the training data of the user.The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. For example, the tracking unit can evaluate the effect of training based on data such as the degree to which the user's muscle strength has improved and the degree to which the user's flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The tracking unit can also evaluate the effect of the training menu based on the training data of the user. As a result, the health support system according to the embodiment can present an optimal training menu according to the user's health condition, feed back the progress of the training, and track the improvement effect.

[0030] The collection unit collects the user's physical data. The user's physical data includes, for example, age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, and the like, but is not limited to such examples. The collection unit can record daily activity data and health data, for example, using a smartphone or a wearable device. Specifically, the number of steps and heart rate of the user are recorded through a smartphone application. This makes it possible to grasp the amount of daily activity and fluctuations in heart rate. The collection unit can also record the user's sleep pattern and amount of exercise using a wearable device. The wearable device is, for example, a type that is worn on the wrist, and can monitor movements and heart rate during sleep to evaluate the quality and depth of sleep. Furthermore, the collection unit can measure the user's blood pressure and body temperature using medical equipment. For example, the collection unit uses a blood pressure monitor to periodically measure the user's blood pressure and record the data. This makes it possible to monitor fluctuations in the user's blood pressure over the long term and detect changes in the health condition at an early stage. In addition, the collection unit can measure the user's body temperature using a thermometer and detect fever and abnormalities in body temperature. This allows the collection unit to use various devices to comprehensively collect the user's physical data and grasp the health condition in detail. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the generation unit and feedback unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The generating unit analyzes the data collected by the collecting unit and generates a training menu optimal for the user. The generating unit, for example, uses AI to analyze the collected data and proposes a training menu according to the user's physical condition. Specifically, the AI ​​generates an individual training menu based on data such as the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function. For example, a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function, can be generated. The generating unit, for example, uses AI to analyze the user's muscle strength data and generate a muscle strength training menu. Specifically, the AI ​​evaluates the strength and balance of the user's muscle strength and proposes an appropriate load, number of times, and number of sets. The generating unit can also use AI to analyze the user's flexibility data and generate a stretching menu. For example, the AI ​​evaluates the user's joint range of motion and muscle flexibility and proposes an appropriate stretching method and time. Furthermore, the generating unit can also use AI to analyze the user's cardiopulmonary function data and generate an aerobic exercise menu. For example, the AI ​​evaluates the user's heart rate and oxygen intake, and suggests appropriate exercise intensity and time. As a result, the generation unit can generate an optimal training menu according to the user's physical condition and support effective training. Furthermore, the generation unit provides the generated training menu to the user, so that the user can easily carry out the training. For example, the training menu is displayed through a smartphone application, and specific methods and precautions are explained. The generation unit can also monitor the progress of the training menu and adjust the menu as necessary. As a result, the generation unit can provide an optimal training menu according to the user's health condition and support effective training.

[0032] The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. Specifically, the feedback unit can evaluate the effectiveness of the training based on data such as the load under which the user trained and the time taken to train. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The AI ​​evaluates the effectiveness of the user's training based on the collected data and feeds back the progress. For example, the AI ​​analyzes the data of the user's muscle training and evaluates the degree of improvement in muscle strength and the effect of the training. The AI ​​can also analyze the flexibility data of the user and evaluate the degree of improvement in flexibility and the effect of stretching. Furthermore, the AI ​​can analyze the cardiopulmonary function data of the user and evaluate the effect of aerobic exercise. This allows the feedback unit to accurately grasp the progress of the user's training and provide effective feedback. Furthermore, the feedback unit can also suggest adjustments to the training menu based on the user's training data. For example, if the user's muscle strength has improved, the AI ​​suggests increasing the training load. In addition, if the user's flexibility improves, the AI ​​suggests adjusting the stretching method and time. As a result, the feedback unit can provide an optimal training menu based on the user's training progress and support effective training. Furthermore, the feedback unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the training menu. For example, the training menu is reviewed and adjusted based on feedback from the user. In addition, the feedback unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. As a result, the feedback unit can quickly and reliably provide feedback to the user and support effective training.

[0033] The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. Specifically, the tracking unit can evaluate the effect of the training based on data such as the degree to which the user's muscle strength has improved and the degree to which the flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The AI ​​evaluates the effect of the user's training based on the collected data and tracks the improvement effect. For example, the AI ​​analyzes the data of the user's muscle training and evaluates the degree of improvement in muscle strength and the effect of the training. The AI ​​can also analyze the flexibility data of the user and evaluate the degree of improvement in flexibility and the effect of stretching. Furthermore, the AI ​​can analyze the cardiopulmonary function data of the user and evaluate the effect of aerobic exercise. This allows the tracking unit to accurately grasp the effect of the user's training and track the improvement effect. Furthermore, the tracking unit can evaluate the effect of the training menu based on the training data of the user. For example, if the user's muscle strength has improved, the AI ​​suggests increasing the training load. In addition, if the user's flexibility improves, the AI ​​suggests adjusting the stretching method and time. This allows the tracking unit to accurately grasp the effect of the user's training and provide an optimal training menu. Furthermore, the tracking unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the training menu. For example, the training menu is reviewed and adjusted based on feedback from the user. In addition, the tracking unit can reliably transmit information using multiple communication methods. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the tracking unit to provide quick and reliable feedback to the user and support effective training.

[0034] The collection unit can collect data on the age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function of the user. For example, the collection unit provides an interface for inputting the age of the user. For example, the collection unit displays a form for the user to input the age. The collection unit can also provide an interface for inputting the gender of the user. For example, the collection unit displays a radio button for the user to select the gender. The collection unit can also provide an instrument for measuring the weight of the user. For example, the collection unit measures the weight of the user using a weight scale and records the data. The collection unit can also provide an instrument for measuring the height of the user. For example, the collection unit measures the height of the user using a height measuring device and records the data. The collection unit can also provide an instrument for measuring the muscle strength of the user. For example, the collection unit measures the muscle strength of the user using a dynamometer and records the data. The collection unit can also provide an instrument for measuring the flexibility of the user. For example, the collection unit performs a flexibility test and records the flexibility data of the user. The collection unit can also provide an instrument for measuring the cardiopulmonary function of the user. For example, the collection unit uses a heart rate monitor to measure the user's heart rate and record the data. This allows the collection unit to generate a more accurate training menu by collecting detailed physical data of the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function into AI, and have the AI ​​analyze the data.

[0035] The generating unit can generate a training menu according to the physical condition of the user based on the collected data. The generating unit, for example, analyzes the collected data and generates a training menu according to the physical condition of the user. For example, the generating unit uses AI to analyze the collected data and generates a muscle strength training menu for the user. The generating unit, for example, uses AI to analyze the muscle strength data of the user and generates a muscle strength training menu. The generating unit can also use AI to analyze the flexibility data of the user and generate a stretching menu. For example, the generating unit uses AI to analyze the flexibility data of the user and generate a stretching menu for improving flexibility. Furthermore, the generating unit can also use AI to analyze the cardiopulmonary function data of the user and generate an aerobic exercise menu. For example, the generating unit uses AI to analyze the cardiopulmonary function data of the user and generate an aerobic exercise menu for improving cardiopulmonary function. This allows the generating unit to generate an optimal training menu according to the physical condition of the user. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the generation unit can input collected data to a generation AI and cause the generation AI to generate a training menu.

[0036] The feedback unit can collect data on the training performed by the user and analyze the progress. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. For example, the feedback unit uses AI to analyze the training data of the user and evaluate the progress. The feedback unit can evaluate the effect of the training based on data such as the degree of load the user performed the training and the time spent on the training. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The feedback unit can also propose adjustment of the training menu based on the training data of the user. For example, the feedback unit analyzes the training data of the user and makes a proposal to increase or decrease the load of the training menu. Furthermore, the feedback unit can also make a proposal to adjust the frequency or time of the training based on the training data of the user. This allows the feedback unit to appropriately feedback the progress of the user's training. Part or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the training data of the user to the generation AI and cause the generation AI to execute an analysis of the progress.

[0037] The tracking unit can continuously collect the training data of the user and track the improvement effect. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. For example, the tracking unit uses AI to analyze the training data of the user and evaluate the improvement effect. The tracking unit can evaluate the effect of the training, for example, based on data such as the extent to which the user's muscle strength has improved and the extent to which the flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The tracking unit can also evaluate the effect of the training menu based on the training data of the user. For example, the tracking unit analyzes the muscle strength data of the user and evaluates the effect of muscle training. Furthermore, the tracking unit can analyze the flexibility data of the user and evaluate the effect of stretching. This allows the tracking unit to continuously track the improvement effect of the training of the user. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the training data of the user to the generation AI and cause the generation AI to execute tracking of the improvement effect.

[0038] The collection unit can analyze the user's past health data and select a data collection method. The collection unit, for example, analyzes the user's past health data and selects an optimal data collection method. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit can also analyze the user's past sleep data and collect data during sleep. Furthermore, the collection unit can analyze the user's past exercise data and collect data after exercise. This allows the collection unit to collect data in an optimal manner based on the past health data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI and cause AI to select a data collection method.

[0039] The collection unit can perform filtering based on the user's current activity level and lifestyle habits when collecting physical data. For example, the collection unit performs filtering based on the user's current activity level and lifestyle habits when collecting physical data. For example, when the user is exercising, the collection unit preferentially collects exercise data. In addition, when the user is resting, the collection unit can also collect data such as heart rate and respiratory rate. Furthermore, when the user is eating, the collection unit can also collect data related to the meal content. In this way, the collection unit can collect data according to the user's activity level and lifestyle habits. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity level and lifestyle habit data to AI and have the AI ​​perform filtering of the data.

[0040] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting physical data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the user when collecting physical data. For example, when the user is at a gym, the collection unit preferentially collects exercise data. In addition, when the user is at home, the collection unit can also preferentially collect data in a relaxed state. Furthermore, when the user is out, the collection unit can also preferentially collect walking data and heart rate data. This allows the collection unit to collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant data.

[0041] The collection unit can analyze the social media activity of the user when collecting the physical data, and collect related data. The collection unit, for example, analyzes the social media activity of the user when collecting the physical data, and collects related data. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. In addition, when the user posts about stress, the collection unit can also preferentially collect stress-related data. Furthermore, when the user posts about relaxation, the collection unit can also preferentially collect data about a relaxed state. This allows the collection unit to collect related data based on the social media activity of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to AI, and cause the AI ​​to collect related data.

[0042] The generating unit can generate a training menu based on the health goal of the user when generating the training menu. For example, the generating unit generates a training menu based on the health goal of the user when generating the training menu. For example, when the user's goal is to improve muscle strength, the generating unit generates a detailed menu of muscle strength training. In addition, when the user's goal is to improve flexibility, the generating unit can also generate a detailed menu of stretching. Furthermore, when the user's goal is to improve cardiopulmonary function, the generating unit can also generate a detailed menu of aerobic exercise. This allows the generating unit to generate a detailed training menu according to the user's health goal. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's health goal data to AI and cause AI to execute the generation of the training menu.

[0043] The generating unit can apply different algorithms according to the physical condition of the user when generating a training menu. For example, the generating unit applies different algorithms according to the physical condition of the user when generating a training menu. For example, when the user's muscle strength is low, the generating unit applies an algorithm for improving muscle strength. In addition, when the user's flexibility is low, the generating unit can also apply an algorithm for improving flexibility. Furthermore, when the user's cardiopulmonary function is low, the generating unit can also apply an algorithm for improving cardiopulmonary function. Thereby, the generating unit can generate a training menu with an optimal algorithm according to the user's physical condition. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's physical condition data to AI and cause AI to apply the algorithm.

[0044] The generating unit can determine the priority order of the menu based on the user's past training history when generating the training menu. For example, the generating unit determines the priority order of the menu based on the user's past training history when generating the training menu. For example, the generating unit suggests a menu to be preferentially implemented based on the training menu implemented by the user in the past. The generating unit can also preferentially suggest a menu that has been highly effective based on the user's past training history. Furthermore, the generating unit can analyze the user's past training history and determine the optimal menu order. Thereby, the generating unit can provide the training menu in the optimal order based on the past training history. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's past training history data to AI and cause the AI ​​to execute the determination of the menu priority order.

[0045] The generating unit can adjust the order of the menu based on the user's related health data when generating the training menu. For example, the generating unit adjusts the order of the menu based on the user's related health data when generating the training menu. For example, the generating unit proposes a menu to be performed during a time period when the heart rate is stable based on the user's heart rate data. The generating unit can also propose a menu to be performed after sleep based on the user's sleep data. Furthermore, the generating unit can also propose a menu to be performed after exercise based on the user's exercise data. This allows the generating unit to provide a training menu in an optimal order based on the related health data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's related health data into AI and cause AI to adjust the order of the menu.

[0046] The feedback unit can adjust the level of detail of the feedback based on the training data of the user at the time of feedback. For example, the feedback unit adjusts the level of detail of the feedback based on the training data of the user at the time of feedback. For example, the feedback unit provides detailed feedback based on data of the training performed by the user. The feedback unit can also provide feedback by emphasizing the effective parts from the training data of the user. Furthermore, the feedback unit can analyze the training data of the user and specifically feed back the points to be improved. This allows the feedback unit to provide feedback with an optimal level of detail based on the training data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the training data of the user to AI and cause the AI ​​to adjust the level of detail of the feedback.

[0047] The feedback unit can apply different feedback methods according to the training goal of the user at the time of feedback. For example, the feedback unit applies different feedback methods according to the training goal of the user at the time of feedback. For example, when the user's goal is to improve muscle strength, the feedback unit provides feedback by emphasizing the effect of muscle strength training. In addition, when the user's goal is to improve flexibility, the feedback unit can also provide feedback by emphasizing the effect of improved flexibility. Furthermore, when the user's goal is to improve cardiopulmonary function, the feedback unit can also provide feedback by emphasizing the effect of improved cardiopulmonary function. This allows the feedback unit to provide an optimal feedback method according to the training goal of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's training goal data to the AI ​​and cause the AI ​​to apply the feedback method.

[0048] The feedback unit can adjust the order of feedback based on the training history of the user at the time of feedback. For example, the feedback unit adjusts the order of feedback based on the training history of the user at the time of feedback. For example, the feedback unit preferentially feeds back the parts that were effective based on the past training history of the user. The feedback unit can also preferentially feed back the improvement points from the training history of the user. Furthermore, the feedback unit can analyze the training history of the user and determine the optimal feedback order. Thereby, the feedback unit can provide feedback in the optimal order based on the training history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the training history data of the user to AI and cause the AI ​​to adjust the feedback order.

[0049] The feedback unit can improve the accuracy of the feedback by referring to the user's related health data at the time of feedback. The feedback unit can improve the accuracy of the feedback by referring to the user's related health data at the time of feedback, for example. For example, the feedback unit provides feedback during a time period when the heart rate is stable based on the user's heart rate data. The feedback unit can also provide feedback after sleep based on the user's sleep data. Furthermore, the feedback unit can also provide feedback after exercise based on the user's exercise data. This allows the feedback unit to improve the accuracy of the feedback based on the related health data. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's related health data to AI and cause AI to improve the accuracy of the feedback.

[0050] The tracking unit can optimize the tracking algorithm by referring to the user's past training data during tracking. The tracking unit optimizes the tracking algorithm by referring to the user's past training data during tracking, for example. For example, the tracking unit applies an optimal tracking algorithm based on the user's past training data. The tracking unit can also emphasize and track a part that was effective from the user's past training data. Furthermore, the tracking unit can analyze the user's past training data and determine an optimal tracking method. This allows the tracking unit to apply an optimal tracking algorithm based on the past training data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past training data to AI and cause AI to execute optimization of the tracking algorithm.

[0051] The tracking unit can adjust the level of detail of the tracking data based on the health goal of the user during tracking. For example, the tracking unit adjusts the level of detail of the tracking data based on the health goal of the user during tracking. For example, when the user's goal is to improve muscle strength, the tracking unit collects detailed tracking data of muscle training. In addition, when the user's goal is to improve flexibility, the tracking unit can also collect detailed tracking data of stretching. Furthermore, when the user's goal is to improve cardiopulmonary function, the tracking unit can also collect detailed tracking data of aerobic exercise. This allows the tracking unit to collect detailed tracking data according to the user's health goal. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's health goal data into AI and cause AI to adjust the level of detail of the tracking data.

[0052] The tracking unit can weight the tracking data based on the training history of the user during tracking. The tracking unit, for example, weights the tracking data based on the training history of the user during tracking. For example, the tracking unit preferentially tracks the parts that were effective based on the user's past training history. The tracking unit can also preferentially track the improvement points from the user's training history. Furthermore, the tracking unit can analyze the user's training history and determine the optimal weighting of the tracking data. This allows the tracking unit to collect tracking data with optimal weighting based on the training history. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's training history data to AI and cause the AI ​​to perform weighting of the tracking data.

[0053] The tracking unit can improve the accuracy of tracking by referring to the user's related health data during tracking. The tracking unit can improve the accuracy of tracking by referring to the user's related health data during tracking, for example. For example, the tracking unit performs tracking during a time period when the heart rate is stable based on the user's heart rate data. The tracking unit can also perform tracking after sleep based on the user's sleep data. Furthermore, the tracking unit can perform tracking after exercise based on the user's exercise data. This allows the tracking unit to improve the accuracy of tracking based on the related health data. Part or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's related health data to AI and cause AI to improve the accuracy of tracking.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The health support system may further include a motivation unit. The motivation unit may provide the user with a message or reward to increase motivation based on the user's training data or progress. For example, the motivation unit may display an encouraging message or a badge when the user achieves a goal. The motivation unit may also provide points or rewards as an incentive for the user to continue training. Furthermore, the motivation unit may analyze the user's training data and transmit a message to increase motivation at an appropriate time. In this way, the motivation unit may maintain the user's motivation and support the user in continuing training.

[0056] The collection unit can collect dietary data of the user and evaluate the nutritional balance. For example, the collection unit can record the contents of meals consumed by the user and calculate the intake of calories and nutrients. The collection unit can also evaluate the nutritional balance based on the dietary data of the user and suggest supplementing necessary nutrients. Furthermore, the collection unit can analyze the dietary data of the user and provide feedback on improvements to the diet. In this way, the collection unit can collect dietary data of the user and support the evaluation and improvement of nutritional balance.

[0057] The feedback unit can visually display the effect of training based on the training data of the user. For example, the feedback unit can display the improvement in the user's muscle strength and flexibility in the form of a graph or chart. The feedback unit can also visually display data showing the improvement in the user's cardiopulmonary function. Furthermore, the feedback unit can display the progress of the user's training in a timeline format. This allows the feedback unit to easily and intuitively understand the effect of the training.

[0058] The collection unit can improve the accuracy of data collection based on the user's past health data. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit can also collect data during sleep based on the user's past sleep data. Furthermore, the collection unit can collect data after exercise based on the user's past exercise data. This allows the collection unit to collect data in an optimal manner based on the past health data.

[0059] The collection unit can adjust the timing of data collection based on the user's current activity level and lifestyle. For example, when the user is exercising, the collection unit prioritizes collection of exercise data. When the user is resting, the collection unit can also collect data such as heart rate and respiratory rate. Furthermore, when the user is eating, the collection unit can also collect data related to meal content. This allows the collection unit to collect data according to the user's activity level and lifestyle.

[0060] The collection unit can improve the accuracy of data collection based on the geographical location information of the user. For example, when the user is at the gym, the collection unit prioritizes collection of exercise data. Also, when the user is at home, the collection unit can prioritize collection of data in a relaxed state. Furthermore, when the user is out, the collection unit can prioritize collection of walking data and heart rate data. This allows the collection unit to collect highly relevant data based on the geographical location information of the user.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects the user's physical data. The user's physical data includes age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, etc. The collection unit records daily activity data and health data using a smartphone or wearable device. For example, the number of steps and heart rate are recorded through a smartphone application, and sleep patterns and exercise amount are recorded using a wearable device. In addition, blood pressure and body temperature can be measured using medical equipment. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal training menu for the user. The generation unit analyzes the collected data using AI and proposes a training menu according to the user's physical condition. For example, it generates a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. Step 3: The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit collects data on the training performed by the user and analyzes the progress. For example, the effectiveness of the training is evaluated based on data such as the degree of load under which the user trained and the amount of time spent training. Step 4: The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit continuously collects the training data of the user and tracks the improvement effect. For example, the effect of training is evaluated based on data such as the degree to which the user's muscle strength has improved and the degree to which flexibility has improved.

[0063] (Example 2) A health support system according to an embodiment of the present invention is a system that presents a training menu for muscle training or rehabilitation according to the health condition of a user. This health support system operates a health support AI that collects the user's physical data and generates an optimal training method based on the collected data. Furthermore, the health support system appropriately feeds back the progress of the user's training and tracks the improvement effect. For example, the health support system collects detailed data such as the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function. The user can use a smartphone or wearable device to record daily activity data and health data. This allows the user's physical condition to be accurately grasped. Next, based on the collected data, the health support AI generates an optimal training method for the user. The health support AI analyzes the collected data and proposes a training menu according to the user's physical condition. For example, a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function, can be generated. Furthermore, the health support system appropriately feeds back the progress of the user's training. The health support AI collects data on the training performed by the user and analyzes the progress. For example, the effectiveness of training can be evaluated based on data such as the load and time the user spent training. This allows the user to grasp the progress of his / her training and adjust the training menu as necessary. Finally, the health support system tracks and follows up on the improvement effect. The health support AI continuously collects the user's training data and tracks the improvement effect. For example, the effectiveness of training can be evaluated based on data such as the extent to which the user's muscle strength has improved and the extent to which flexibility has improved. This allows the user to grasp the improvement status of his / her health condition and maintain motivation. In this way, the health support system supports the user's health management by presenting the optimal training menu according to the user's health condition, feeding back the progress of training, and tracking the improvement effect.This enables the health support system to present the optimal training menu based on the user's health condition, provide feedback on the progress of training, and track the effects of improvement.

[0064] The health support system according to the embodiment includes a collection unit, a generation unit, a feedback unit, and a tracking unit. The collection unit collects physical data of the user. The physical data of the user includes, for example, age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, and the like, but is not limited to such examples. The collection unit can record daily activity data and health data, for example, using a smartphone or a wearable device. For example, the collection unit records the number of steps and heart rate of the user through a smartphone application. The collection unit can also record the sleep pattern and amount of exercise of the user using a wearable device. Furthermore, the collection unit can measure the blood pressure and body temperature of the user using a medical device. For example, the collection unit periodically measures the blood pressure of the user using a blood pressure monitor and records the data. The generation unit analyzes the data collected by the collection unit and generates a training menu optimal for the user. The generation unit analyzes the collected data using, for example, AI, and proposes a training menu according to the physical condition of the user. For example, the generation unit can generate a training method according to the purpose of the user, such as muscle training, stretching for improving flexibility, and aerobic exercise for improving cardiopulmonary function. The generation unit can, for example, use AI to analyze the muscle strength data of the user and generate a menu of muscle strength training. The generation unit can also use AI to analyze the flexibility data of the user and generate a menu of stretching. The generation unit can also use AI to analyze the cardiopulmonary function data of the user and generate a menu of aerobic exercise. The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. For example, the feedback unit can evaluate the effect of the training based on data such as the load at which the user performed the training and how long it took to perform the training. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The feedback unit can also suggest adjustments to the training menu based on the training data of the user.The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. For example, the tracking unit can evaluate the effect of training based on data such as the degree to which the user's muscle strength has improved and the degree to which the user's flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The tracking unit can also evaluate the effect of the training menu based on the training data of the user. As a result, the health support system according to the embodiment can present an optimal training menu according to the user's health condition, feed back the progress of the training, and track the improvement effect.

[0065] The collection unit collects the user's physical data. The user's physical data includes, for example, age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, and the like, but is not limited to such examples. The collection unit can record daily activity data and health data, for example, using a smartphone or a wearable device. Specifically, the number of steps and heart rate of the user are recorded through a smartphone application. This makes it possible to grasp the amount of daily activity and fluctuations in heart rate. The collection unit can also record the user's sleep pattern and amount of exercise using a wearable device. The wearable device is, for example, a type that is worn on the wrist, and can monitor movements and heart rate during sleep to evaluate the quality and depth of sleep. Furthermore, the collection unit can measure the user's blood pressure and body temperature using medical equipment. For example, the collection unit uses a blood pressure monitor to periodically measure the user's blood pressure and record the data. This makes it possible to monitor fluctuations in the user's blood pressure over the long term and detect changes in the health condition at an early stage. In addition, the collection unit can measure the user's body temperature using a thermometer and detect fever and abnormalities in body temperature. This allows the collection unit to use various devices to comprehensively collect the user's physical data and grasp the health condition in detail. Furthermore, the collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server so that the generation unit and feedback unit can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses according to specific situations and conditions are possible. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0066] The generating unit analyzes the data collected by the collecting unit and generates a training menu optimal for the user. The generating unit, for example, uses AI to analyze the collected data and proposes a training menu according to the user's physical condition. Specifically, the AI ​​generates an individual training menu based on data such as the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function. For example, a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function, can be generated. The generating unit, for example, uses AI to analyze the user's muscle strength data and generate a muscle strength training menu. Specifically, the AI ​​evaluates the strength and balance of the user's muscle strength and proposes an appropriate load, number of times, and number of sets. The generating unit can also use AI to analyze the user's flexibility data and generate a stretching menu. For example, the AI ​​evaluates the user's joint range of motion and muscle flexibility and proposes an appropriate stretching method and time. Furthermore, the generating unit can also use AI to analyze the user's cardiopulmonary function data and generate an aerobic exercise menu. For example, the AI ​​evaluates the user's heart rate and oxygen intake, and suggests appropriate exercise intensity and time. As a result, the generation unit can generate an optimal training menu according to the user's physical condition and support effective training. Furthermore, the generation unit provides the generated training menu to the user, so that the user can easily carry out the training. For example, the training menu is displayed through a smartphone application, and specific methods and precautions are explained. The generation unit can also monitor the progress of the training menu and adjust the menu as necessary. As a result, the generation unit can provide an optimal training menu according to the user's health condition and support effective training.

[0067] The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. Specifically, the feedback unit can evaluate the effectiveness of the training based on data such as the load under which the user trained and the time taken to train. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The AI ​​evaluates the effectiveness of the user's training based on the collected data and feeds back the progress. For example, the AI ​​analyzes the data of the user's muscle training and evaluates the degree of improvement in muscle strength and the effect of the training. The AI ​​can also analyze the flexibility data of the user and evaluate the degree of improvement in flexibility and the effect of stretching. Furthermore, the AI ​​can analyze the cardiopulmonary function data of the user and evaluate the effect of aerobic exercise. This allows the feedback unit to accurately grasp the progress of the user's training and provide effective feedback. Furthermore, the feedback unit can also suggest adjustments to the training menu based on the user's training data. For example, if the user's muscle strength has improved, the AI ​​suggests increasing the training load. In addition, if the user's flexibility improves, the AI ​​suggests adjusting the stretching method and time. As a result, the feedback unit can provide an optimal training menu based on the user's training progress and support effective training. Furthermore, the feedback unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the training menu. For example, the training menu is reviewed and adjusted based on feedback from the user. In addition, the feedback unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. As a result, the feedback unit can quickly and reliably provide feedback to the user and support effective training.

[0068] The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. Specifically, the tracking unit can evaluate the effect of the training based on data such as the degree to which the user's muscle strength has improved and the degree to which the flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The AI ​​evaluates the effect of the user's training based on the collected data and tracks the improvement effect. For example, the AI ​​analyzes the data of the user's muscle training and evaluates the degree of improvement in muscle strength and the effect of the training. The AI ​​can also analyze the flexibility data of the user and evaluate the degree of improvement in flexibility and the effect of stretching. Furthermore, the AI ​​can analyze the cardiopulmonary function data of the user and evaluate the effect of aerobic exercise. This allows the tracking unit to accurately grasp the effect of the user's training and track the improvement effect. Furthermore, the tracking unit can evaluate the effect of the training menu based on the training data of the user. For example, if the user's muscle strength has improved, the AI ​​suggests increasing the training load. In addition, if the user's flexibility improves, the AI ​​suggests adjusting the stretching method and time. This allows the tracking unit to accurately grasp the effect of the user's training and provide an optimal training menu. Furthermore, the tracking unit can collect feedback from the user and continuously improve the accuracy and effectiveness of the training menu. For example, the training menu is reviewed and adjusted based on feedback from the user. In addition, the tracking unit can reliably transmit information using multiple communication methods. For example, important information is reliably delivered by using not only smartphone notifications but also voice calls, SMS, emails, etc. in combination. This allows the tracking unit to provide quick and reliable feedback to the user and support effective training.

[0069] The collection unit can collect data on the age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function of the user. For example, the collection unit provides an interface for inputting the age of the user. For example, the collection unit displays a form for the user to input the age. The collection unit can also provide an interface for inputting the gender of the user. For example, the collection unit displays a radio button for the user to select the gender. The collection unit can also provide an instrument for measuring the weight of the user. For example, the collection unit measures the weight of the user using a weight scale and records the data. The collection unit can also provide an instrument for measuring the height of the user. For example, the collection unit measures the height of the user using a height measuring device and records the data. The collection unit can also provide an instrument for measuring the muscle strength of the user. For example, the collection unit measures the muscle strength of the user using a dynamometer and records the data. The collection unit can also provide an instrument for measuring the flexibility of the user. For example, the collection unit performs a flexibility test and records the flexibility data of the user. The collection unit can also provide an instrument for measuring the cardiopulmonary function of the user. For example, the collection unit uses a heart rate monitor to measure the user's heart rate and record the data. This allows the collection unit to generate a more accurate training menu by collecting detailed physical data of the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data on the user's age, sex, weight, height, muscle strength, flexibility, and cardiopulmonary function into AI, and have the AI ​​analyze the data.

[0070] The generating unit can generate a training menu according to the physical condition of the user based on the collected data. The generating unit, for example, analyzes the collected data and generates a training menu according to the physical condition of the user. For example, the generating unit uses AI to analyze the collected data and generates a muscle strength training menu for the user. The generating unit, for example, uses AI to analyze the muscle strength data of the user and generates a muscle strength training menu. The generating unit can also use AI to analyze the flexibility data of the user and generate a stretching menu. For example, the generating unit uses AI to analyze the flexibility data of the user and generate a stretching menu for improving flexibility. Furthermore, the generating unit can also use AI to analyze the cardiopulmonary function data of the user and generate an aerobic exercise menu. For example, the generating unit uses AI to analyze the cardiopulmonary function data of the user and generate an aerobic exercise menu for improving cardiopulmonary function. This allows the generating unit to generate an optimal training menu according to the physical condition of the user. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using the generating AI, or may be performed without using the generating AI. For example, the generation unit can input collected data to a generation AI and cause the generation AI to generate a training menu.

[0071] The feedback unit can collect data on the training performed by the user and analyze the progress. The feedback unit, for example, collects data on the training performed by the user and analyzes the progress. For example, the feedback unit uses AI to analyze the training data of the user and evaluate the progress. The feedback unit can evaluate the effect of the training based on data such as the degree of load the user performed the training and the time spent on the training. The feedback unit, for example, collects the training data of the user and analyzes the progress using AI. The feedback unit can also propose adjustment of the training menu based on the training data of the user. For example, the feedback unit analyzes the training data of the user and makes a proposal to increase or decrease the load of the training menu. Furthermore, the feedback unit can also make a proposal to adjust the frequency or time of the training based on the training data of the user. This allows the feedback unit to appropriately feedback the progress of the user's training. Part or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the training data of the user to the generation AI and cause the generation AI to execute an analysis of the progress.

[0072] The tracking unit can continuously collect the training data of the user and track the improvement effect. The tracking unit, for example, continuously collects the training data of the user and tracks the improvement effect. For example, the tracking unit uses AI to analyze the training data of the user and evaluate the improvement effect. The tracking unit can evaluate the effect of the training, for example, based on data such as the extent to which the user's muscle strength has improved and the extent to which the flexibility has improved. The tracking unit, for example, collects the training data of the user and tracks the improvement effect using AI. The tracking unit can also evaluate the effect of the training menu based on the training data of the user. For example, the tracking unit analyzes the muscle strength data of the user and evaluates the effect of muscle training. Furthermore, the tracking unit can analyze the flexibility data of the user and evaluate the effect of stretching. This allows the tracking unit to continuously track the improvement effect of the training of the user. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the training data of the user to the generation AI and cause the generation AI to execute tracking of the improvement effect.

[0073] The collection unit can estimate the user's emotion and adjust the collection timing of the physical data based on the user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the collection timing of the physical data based on the estimated emotion of the user. For example, when the user is stressed, the collection unit sets the collection timing at night in order to collect data in a relaxed state. In addition, when the user is relaxed, the collection unit can also collect data during daytime activities. Furthermore, when the user is in a hurry, the collection unit can adjust the collection timing in order to collect necessary data in a short time. This allows the collection unit to collect physical data at an optimal timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0074] The collection unit can analyze the user's past health data and select a data collection method. The collection unit, for example, analyzes the user's past health data and selects an optimal data collection method. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit can also analyze the user's past sleep data and collect data during sleep. Furthermore, the collection unit can analyze the user's past exercise data and collect data after exercise. This allows the collection unit to collect data in an optimal manner based on the past health data. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's past health data into AI and cause AI to select a data collection method.

[0075] The collection unit can perform filtering based on the user's current activity level and lifestyle habits when collecting physical data. For example, the collection unit performs filtering based on the user's current activity level and lifestyle habits when collecting physical data. For example, when the user is exercising, the collection unit preferentially collects exercise data. In addition, when the user is resting, the collection unit can also collect data such as heart rate and respiratory rate. Furthermore, when the user is eating, the collection unit can also collect data related to the meal content. In this way, the collection unit can collect data according to the user's activity level and lifestyle habits. Part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's activity level and lifestyle habit data to AI and have the AI ​​perform filtering of the data.

[0076] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the user's emotion. The collection unit, for example, estimates the user's emotion and determines the priority of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects stress-related data. In addition, when the user is relaxed, the collection unit can also preferentially collect data related to a relaxed state. Furthermore, when the user is in a hurry, the collection unit can also preferentially collect data that can be collected in a short time. This allows the collection unit to determine the data to be preferentially collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The collection unit can preferentially collect highly relevant data based on the geographical location information of the user when collecting physical data. For example, the collection unit preferentially collects highly relevant data based on the geographical location information of the user when collecting physical data. For example, when the user is at a gym, the collection unit preferentially collects exercise data. In addition, when the user is at home, the collection unit can also preferentially collect data in a relaxed state. Furthermore, when the user is out, the collection unit can also preferentially collect walking data and heart rate data. This allows the collection unit to collect highly relevant data based on the geographical location information of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to AI and cause the AI ​​to collect highly relevant data.

[0078] The collection unit can analyze the social media activity of the user when collecting the physical data, and collect related data. The collection unit, for example, analyzes the social media activity of the user when collecting the physical data, and collects related data. For example, when the user posts about exercise on social media, the collection unit preferentially collects exercise data. In addition, when the user posts about stress, the collection unit can also preferentially collect stress-related data. Furthermore, when the user posts about relaxation, the collection unit can also preferentially collect data about a relaxed state. This allows the collection unit to collect related data based on the social media activity of the user. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the social media activity data of the user to AI, and cause the AI ​​to collect related data.

[0079] The generation unit can estimate the user's emotion and adjust the expression method of the training menu based on the user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the training menu based on the estimated user's emotion. For example, the generation unit generates a simple and easy-to-understand training menu when the user is stressed. In addition, the generation unit can also generate a training menu including detailed explanations when the user is relaxed. Furthermore, the generation unit can also generate a training menu that can be performed in a short time when the user is in a hurry. This allows the generation unit to provide a training menu with an optimal expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the expression method of the training menu.

[0080] The generating unit can generate a training menu based on the health goal of the user when generating the training menu. For example, the generating unit generates a training menu based on the health goal of the user when generating the training menu. For example, when the user's goal is to improve muscle strength, the generating unit generates a detailed menu of muscle strength training. In addition, when the user's goal is to improve flexibility, the generating unit can also generate a detailed menu of stretching. Furthermore, when the user's goal is to improve cardiopulmonary function, the generating unit can also generate a detailed menu of aerobic exercise. This allows the generating unit to generate a detailed training menu according to the user's health goal. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's health goal data to AI and cause AI to execute the generation of the training menu.

[0081] The generating unit can apply different algorithms according to the physical condition of the user when generating a training menu. For example, the generating unit applies different algorithms according to the physical condition of the user when generating a training menu. For example, when the user's muscle strength is low, the generating unit applies an algorithm for improving muscle strength. In addition, when the user's flexibility is low, the generating unit can also apply an algorithm for improving flexibility. Furthermore, when the user's cardiopulmonary function is low, the generating unit can also apply an algorithm for improving cardiopulmonary function. Thereby, the generating unit can generate a training menu with an optimal algorithm according to the user's physical condition. A part or all of the above-mentioned processing in the generating unit may be performed, for example, using AI, or may be performed without using AI. For example, the generating unit can input the user's physical condition data to AI and cause AI to apply the algorithm.

[0082] The generation unit can estimate the user's emotion and adjust the length of the training menu based on the user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the training menu based on the estimated user's emotion. For example, the generation unit generates a training menu that can be performed in a short time when the user is stressed. The generation unit can also generate a long training menu when the user is relaxed. Furthermore, the generation unit can also generate an effective training menu in a short time when the user is in a hurry. This allows the generation unit to provide a training menu of optimal length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the training menu.

[0083] The generating unit can determine the priority order of the menu based on the user's past training history when generating the training menu. For example, the generating unit determines the priority order of the menu based on the user's past training history when generating the training menu. For example, the generating unit suggests a menu to be preferentially implemented based on the training menu implemented by the user in the past. The generating unit can also preferentially suggest a menu that has been highly effective based on the user's past training history. Furthermore, the generating unit can analyze the user's past training history and determine the optimal menu order. Thereby, the generating unit can provide the training menu in the optimal order based on the past training history. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's past training history data to AI and cause the AI ​​to execute the determination of the menu priority order.

[0084] The generating unit can adjust the order of the menu based on the user's related health data when generating the training menu. For example, the generating unit adjusts the order of the menu based on the user's related health data when generating the training menu. For example, the generating unit proposes a menu to be performed during a time period when the heart rate is stable based on the user's heart rate data. The generating unit can also propose a menu to be performed after sleep based on the user's sleep data. Furthermore, the generating unit can also propose a menu to be performed after exercise based on the user's exercise data. This allows the generating unit to provide a training menu in an optimal order based on the related health data. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, AI, or may be performed without using AI. For example, the generating unit can input the user's related health data into AI and cause AI to adjust the order of the menu.

[0085] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the user's emotion. The feedback unit, for example, estimates the user's emotion and adjusts the feedback expression method based on the estimated user's emotion. For example, the feedback unit provides simple and easy-to-understand feedback when the user is stressed. The feedback unit can also provide feedback including detailed explanations when the user is relaxed. Furthermore, the feedback unit can provide feedback that can be understood in a short time when the user is in a hurry. This allows the feedback unit to provide feedback in an optimal expression method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the feedback expression method.

[0086] The feedback unit can adjust the level of detail of the feedback based on the training data of the user at the time of feedback. For example, the feedback unit adjusts the level of detail of the feedback based on the training data of the user at the time of feedback. For example, the feedback unit provides detailed feedback based on data of the training performed by the user. The feedback unit can also provide feedback by emphasizing the effective parts from the training data of the user. Furthermore, the feedback unit can analyze the training data of the user and specifically feed back the points to be improved. This allows the feedback unit to provide feedback with an optimal level of detail based on the training data. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the training data of the user to AI and cause the AI ​​to adjust the level of detail of the feedback.

[0087] The feedback unit can apply different feedback methods according to the training goal of the user at the time of feedback. For example, the feedback unit applies different feedback methods according to the training goal of the user at the time of feedback. For example, when the user's goal is to improve muscle strength, the feedback unit provides feedback by emphasizing the effect of muscle strength training. In addition, when the user's goal is to improve flexibility, the feedback unit can also provide feedback by emphasizing the effect of improved flexibility. Furthermore, when the user's goal is to improve cardiopulmonary function, the feedback unit can also provide feedback by emphasizing the effect of improved cardiopulmonary function. This allows the feedback unit to provide an optimal feedback method according to the training goal of the user. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the user's training goal data to the AI ​​and cause the AI ​​to apply the feedback method.

[0088] The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the user's emotion. The feedback unit, for example, estimates the user's emotion and determines the priority of the feedback based on the estimated user's emotion. For example, when the user is stressed, the feedback unit can preferentially provide feedback related to stress reduction. Also, when the user is relaxed, the feedback unit can also preferentially provide feedback related to the relaxed state. Furthermore, when the user is in a hurry, the feedback unit can also preferentially provide feedback that can be understood in a short time. This allows the feedback unit to provide feedback preferentially according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the feedback unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the feedback.

[0089] The feedback unit can adjust the order of feedback based on the training history of the user at the time of feedback. For example, the feedback unit adjusts the order of feedback based on the training history of the user at the time of feedback. For example, the feedback unit preferentially feeds back the parts that were effective based on the past training history of the user. The feedback unit can also preferentially feed back the improvement points from the training history of the user. Furthermore, the feedback unit can analyze the training history of the user and determine the optimal feedback order. Thereby, the feedback unit can provide feedback in the optimal order based on the training history. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the training history data of the user to AI and cause the AI ​​to adjust the feedback order.

[0090] The feedback unit can improve the accuracy of the feedback by referring to the user's related health data at the time of feedback. The feedback unit can improve the accuracy of the feedback by referring to the user's related health data at the time of feedback, for example. For example, the feedback unit provides feedback during a time period when the heart rate is stable based on the user's heart rate data. The feedback unit can also provide feedback after sleep based on the user's sleep data. Furthermore, the feedback unit can also provide feedback after exercise based on the user's exercise data. This allows the feedback unit to improve the accuracy of the feedback based on the related health data. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's related health data to AI and cause AI to improve the accuracy of the feedback.

[0091] The tracking unit can estimate the user's emotion and adjust the method of collecting tracking data based on the user's emotion. The tracking unit, for example, estimates the user's emotion and adjusts the method of collecting tracking data based on the estimated user's emotion. For example, when the user is feeling stressed, the tracking unit preferentially collects stress-related data. In addition, when the user is relaxed, the tracking unit can also preferentially collect data related to a relaxed state. Furthermore, when the user is in a hurry, the tracking unit can also preferentially collect data that can be collected in a short time. This allows the tracking unit to collect tracking data in an optimal manner according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the method of collecting tracking data.

[0092] The tracking unit can optimize the tracking algorithm by referring to the user's past training data during tracking. The tracking unit optimizes the tracking algorithm by referring to the user's past training data during tracking, for example. For example, the tracking unit applies an optimal tracking algorithm based on the user's past training data. The tracking unit can also emphasize and track a part that was effective from the user's past training data. Furthermore, the tracking unit can analyze the user's past training data and determine an optimal tracking method. This allows the tracking unit to apply an optimal tracking algorithm based on the past training data. A part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's past training data to AI and cause AI to execute optimization of the tracking algorithm.

[0093] The tracking unit can adjust the level of detail of the tracking data based on the health goal of the user during tracking. For example, the tracking unit adjusts the level of detail of the tracking data based on the health goal of the user during tracking. For example, when the user's goal is to improve muscle strength, the tracking unit collects detailed tracking data of muscle training. In addition, when the user's goal is to improve flexibility, the tracking unit can also collect detailed tracking data of stretching. Furthermore, when the user's goal is to improve cardiopulmonary function, the tracking unit can also collect detailed tracking data of aerobic exercise. This allows the tracking unit to collect detailed tracking data according to the user's health goal. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's health goal data into AI and cause AI to adjust the level of detail of the tracking data.

[0094] The tracking unit can estimate the user's emotion and determine the priority of the tracking data based on the user's emotion. The tracking unit, for example, estimates the user's emotion and determines the priority of the tracking data based on the estimated user's emotion. For example, when the user is stressed, the tracking unit preferentially collects tracking data related to stress reduction. Also, when the user is relaxed, the tracking unit can preferentially collect tracking data related to a relaxed state. Furthermore, when the user is in a hurry, the tracking unit can preferentially collect tracking data that can be collected in a short time. This allows the tracking unit to collect tracking data preferentially according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the tracking unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the tracking unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the tracking data.

[0095] The tracking unit can weight the tracking data based on the training history of the user during tracking. The tracking unit, for example, weights the tracking data based on the training history of the user during tracking. For example, the tracking unit preferentially tracks the parts that were effective based on the user's past training history. The tracking unit can also preferentially track the improvement points from the user's training history. Furthermore, the tracking unit can analyze the user's training history and determine the optimal weighting of the tracking data. This allows the tracking unit to collect tracking data with optimal weighting based on the training history. Part or all of the above-mentioned processing in the tracking unit may be performed, for example, using AI, or may be performed without using AI. For example, the tracking unit can input the user's training history data to AI and cause the AI ​​to perform weighting of the tracking data.

[0096] The tracking unit can improve the accuracy of tracking by referring to the user's related health data during tracking. The tracking unit can improve the accuracy of tracking by referring to the user's related health data during tracking, for example. For example, the tracking unit performs tracking during a time period when the heart rate is stable based on the user's heart rate data. The tracking unit can also perform tracking after sleep based on the user's sleep data. Furthermore, the tracking unit can perform tracking after exercise based on the user's exercise data. This allows the tracking unit to improve the accuracy of tracking based on the related health data. Part or all of the above-mentioned processing in the tracking unit may be performed using, for example, AI, or may be performed without using AI. For example, the tracking unit can input the user's related health data to AI and cause AI to improve the accuracy of tracking.

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

[0098] The health support system may further include a motivation unit. The motivation unit may provide the user with a message or reward to increase motivation based on the user's training data or progress. For example, the motivation unit may display an encouraging message or a badge when the user achieves a goal. The motivation unit may also provide points or rewards as an incentive for the user to continue training. Furthermore, the motivation unit may analyze the user's training data and transmit a message to increase motivation at an appropriate time. In this way, the motivation unit may maintain the user's motivation and support the user in continuing training.

[0099] The collection unit can collect dietary data of the user and evaluate the nutritional balance. For example, the collection unit can record the contents of meals consumed by the user and calculate the intake of calories and nutrients. The collection unit can also evaluate the nutritional balance based on the dietary data of the user and suggest supplementing necessary nutrients. Furthermore, the collection unit can analyze the dietary data of the user and provide feedback on improvements to the diet. In this way, the collection unit can collect dietary data of the user and support the evaluation and improvement of nutritional balance.

[0100] The generation unit can estimate the user's emotions and adjust the difficulty of the training menu based on the user's emotions. For example, the generation unit generates a training menu with a low level of difficulty when the user is stressed. The generation unit can also generate a training menu with a high level of difficulty when the user is relaxed. Furthermore, the generation unit can also generate a training menu that can be performed in a short time when the user is in a hurry. In this way, the generation unit can provide a training menu with an optimal level of difficulty according to the user's emotions.

[0101] The feedback unit can visually display the effect of training based on the training data of the user. For example, the feedback unit can display the improvement in the user's muscle strength and flexibility in the form of a graph or chart. The feedback unit can also visually display data showing the improvement in the user's cardiopulmonary function. Furthermore, the feedback unit can display the progress of the user's training in a timeline format. This allows the feedback unit to easily and intuitively understand the effect of the training.

[0102] The tracking unit can estimate the user's emotions and adjust the display method of the tracking data based on the user's emotions. For example, the tracking unit can adopt a simple and easy-to-understand display method when the user is stressed. The tracking unit can also display detailed data when the user is relaxed. Furthermore, the tracking unit can adopt a display method that can be understood in a short time when the user is in a hurry. In this way, the tracking unit can provide tracking data in an optimal display method according to the user's emotions.

[0103] The collection unit can estimate the user's emotions and adjust the frequency of data collection based on the user's emotions. For example, the collection unit can reduce the frequency of data collection when the user is stressed. The collection unit can also increase the frequency of data collection when the user is relaxed. Furthermore, the collection unit can adjust the collection frequency to collect necessary data in a short period of time when the user is in a hurry. This allows the collection unit to collect data at an optimal frequency according to the user's emotions.

[0104] The collection unit can improve the accuracy of data collection based on the user's past health data. For example, the collection unit analyzes the user's past heart rate data and collects data during a time period when the heart rate is stable. The collection unit can also collect data during sleep based on the user's past sleep data. Furthermore, the collection unit can collect data after exercise based on the user's past exercise data. This allows the collection unit to collect data in an optimal manner based on the past health data.

[0105] The collection unit can adjust the timing of data collection based on the user's current activity level and lifestyle. For example, when the user is exercising, the collection unit prioritizes collection of exercise data. When the user is resting, the collection unit can also collect data such as heart rate and respiratory rate. Furthermore, when the user is eating, the collection unit can also collect data related to meal content. This allows the collection unit to collect data according to the user's activity level and lifestyle.

[0106] The collection unit can estimate the user's emotions and determine the priority of data collection based on the user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting stress-related data. Also, when the user is relaxed, the collection unit can also prioritize collecting data related to a relaxed state. Furthermore, when the user is in a hurry, the collection unit can also prioritize collecting data that can be collected in a short time. In this way, the collection unit can determine the data to be collected with priority according to the user's emotions.

[0107] The collection unit can improve the accuracy of data collection based on the geographical location information of the user. For example, when the user is at the gym, the collection unit prioritizes collection of exercise data. Also, when the user is at home, the collection unit can prioritize collection of data in a relaxed state. Furthermore, when the user is out, the collection unit can prioritize collection of walking data and heart rate data. This allows the collection unit to collect highly relevant data based on the geographical location information of the user.

[0108] The process flow of the second embodiment will be briefly described below.

[0109] Step 1: The collection unit collects the user's physical data. The user's physical data includes age, sex, weight, height, muscle strength, flexibility, cardiopulmonary function, etc. The collection unit records daily activity data and health data using a smartphone or wearable device. For example, the number of steps and heart rate are recorded through a smartphone application, and sleep patterns and exercise amount are recorded using a wearable device. In addition, blood pressure and body temperature can be measured using medical equipment. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal training menu for the user. The generation unit analyzes the collected data using AI and proposes a training menu according to the user's physical condition. For example, it generates a training method according to the user's purpose, such as muscle training, stretching to improve flexibility, and aerobic exercise to improve cardiopulmonary function. Step 3: The feedback unit feeds back the progress of the training based on the training menu generated by the generation unit. The feedback unit collects data on the training performed by the user and analyzes the progress. For example, the effectiveness of the training is evaluated based on data such as the degree of load under which the user trained and the amount of time spent training. Step 4: The tracking unit tracks the improvement effect based on the data fed back by the feedback unit. The tracking unit continuously collects the training data of the user and tracks the improvement effect. For example, the effect of training is evaluated based on data such as the degree to which the user's muscle strength has improved and the degree to which flexibility has improved.

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

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0112] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0113] Each of the multiple elements including the above-mentioned collection unit, generation unit, feedback unit, and tracking unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the user's body data using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing device 12 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal training menu. The feedback unit is realized, for example, by the control unit 46A of the smart device 14, and collects the user's training data and analyzes the progress. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and continuously collects the user's training data and tracks the improvement effect. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0115] 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.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0117] 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.

[0118] 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 the voice according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0120] 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 performed in a secure state.

[0121] Fig. 4 shows an example of 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0125] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0127] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0129] Each of the multiple elements including the above-mentioned collection unit, generation unit, feedback unit, and tracking unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the user's physical data using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing device 12 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal training menu. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214, and collects the user's training data and analyzes the progress. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and continuously collects the user's training data and tracks the improvement effect. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0131] 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.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0133] 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.

[0134] 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 the voice according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0136] 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 performed in a secure state.

[0137] 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.

[0138] 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.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0141] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0142] 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 voice indicating a user input for 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.

[0143] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0144] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements including the above-mentioned collection unit, generation unit, feedback unit, and tracking unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the user's body data using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the data to the data processing device 12 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal training menu. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314, and collects the user's training data and analyzes the progress. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and continuously collects the user's training data and tracks the improvement effect. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

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

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

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

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

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

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

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

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

[0156] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0157] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 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.

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

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

[0160] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0162] Each of the multiple elements including the above-mentioned collection unit, generation unit, feedback unit, and tracking unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the user's body data using the camera 42 and microphone 238 of the robot 414, and transmits the data to the data processing device 12 by the control unit 46A. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to generate an optimal training menu. The feedback unit is realized, for example, by the control unit 46A of the robot 414, and collects the user's training data and analyzes the progress. The tracking unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and continuously collects the user's training data and tracks the improvement effect. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0164] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0165] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0166] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0167] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

[0169] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0170] 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.

[0171] 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 Universal Serial Bus (USB) 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.

[0172] In addition, 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 upon request from the data processing device 12.

[0173] 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.

[0174] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0175] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0176] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0177] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0178] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0179] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0180] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0181] (Appendix 1) A collection unit that collects physical data of a user; A generation unit that analyzes the data collected by the collection unit and generates a training menu for a user; A feedback unit that feeds back a progress degree of training based on the training menu generated by the generation unit; A tracking unit that tracks the improvement effect based on the data fed back by the feedback unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on the user's age, sex, weight, height, strength, flexibility, and cardiopulmonary function 2. The system of claim 1, (Appendix 3) The generation unit is Based on the collected data, a training menu is created according to the user's physical condition. 2. The system of claim 1, (Appendix 4) The feedback unit is Collect data on the training conducted by users and analyze their progress 2. The system of claim 1, (Appendix 5) The tracking unit is Continuously collect user training data and track improvements 2. The system of claim 1, (Appendix 6) The collecting unit includes: Estimate the user's emotions and adjust the timing of collecting physical data based on the user's emotions. 2. The system of claim 1, (Appendix 7) The collecting unit includes: Analyze users' past health data and select data collection methods 2. The system of claim 1, (Appendix 8) The collecting unit includes: When collecting physical data, it is filtered based on the user's current activity level and lifestyle habits. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Infer user emotions and prioritize data collection based on user emotions 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting physical data, the most relevant data is collected based on the user's geographic location. 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting physical data, we analyze your social media activity and collect relevant data. 2. The system of claim 1, (Appendix 12) The generation unit is Estimate the user's emotions and adjust the presentation of the training menu based on the user's emotions 2. The system of claim 1, (Appendix 13) The generation unit is When creating a training menu, create a training menu based on the user's health goals 2. The system of claim 1, (Appendix 14) The generation unit is When generating training menus, different algorithms are applied depending on the user's physical condition. 2. The system of claim 1, (Appendix 15) The generation unit is Estimate the user's emotions and adjust the length of the training menu based on the user's emotions 2. The system of claim 1, (Appendix 16) The generation unit is When generating a training menu, the priority of the menu is determined based on the user's past training history. 2. The system of claim 1, (Appendix 17) The generation unit is When generating training menus, adjust the menu order based on the user's relevant health data 2. The system of claim 1, (Appendix 18) The feedback unit is Estimate the user's emotions and adjust the way feedback is presented based on the user's emotions 2. The system of claim 1, (Appendix 19) The feedback unit is When providing feedback, adjust the level of detail of the feedback based on the user's training data 2. The system of claim 1, (Appendix 20) The feedback unit is During feedback, different feedback methods are applied according to the user's training goals. 2. The system of claim 1, (Appendix 21) The feedback unit is Estimate user emotions and prioritize feedback based on user emotions 2. The system of claim 1, (Appendix 22) The feedback unit is When providing feedback, adjust the order of feedback based on the user's training history 2. The system of claim 1, (Appendix 23) The feedback unit is When providing feedback, the accuracy of the feedback is improved by referring to the user's relevant health data. 2. The system of claim 1, (Appendix 24) The tracking unit is Inferring user emotions and adjusting how tracking data is collected based on the user emotions 2. The system of claim 1, (Appendix 25) The tracking unit is During tracking, the tracking algorithm is optimized by referencing the user's past training data. 2. The system of claim 1, (Appendix 26) The tracking unit is When tracking, adjust the granularity of your tracking data based on your health goals 2. The system of claim 1, (Appendix 27) The tracking unit is Inferring user emotions and prioritizing tracking data based on user emotions 2. The system of claim 1, (Appendix 28) The tracking unit is During tracking, the tracking data is weighted based on the user's training history. 2. The system of claim 1, (Appendix 29) The tracking unit is During tracking, the user's relevant health data is referenced to improve tracking accuracy 2. The system of claim 1, [Explanation of symbols]

[0182] 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 collection unit that collects physical data of a user; A generation unit that analyzes the data collected by the collection unit and generates a training menu for a user; A feedback unit that feeds back a progress degree of training based on the training menu generated by the generation unit; A tracking unit that tracks the improvement effect based on the data fed back by the feedback unit. A system characterized by:

2. The collecting unit includes: Collect data on the user's age, sex, weight, height, strength, flexibility, and cardiopulmonary function 2. The system of claim 1 .

3. The generation unit is Based on the collected data, a training menu is created according to the user's physical condition.

2. The system of claim 1 .

4. The feedback unit is Collect data on the training conducted by users and analyze their progress 2. The system of claim 1 .

5. The tracking unit is Continuously collect user training data and track improvements 2. The system of claim 1 .

6. The collecting unit includes: Estimate the user's emotions and adjust the timing of collecting physical data based on the user's emotions.

2. The system of claim 1 .

7. The collecting unit includes: Analyze users' past health data and select data collection methods 2. The system of claim 1 .

8. The collecting unit includes: When collecting physical data, it is filtered based on the user's current activity level and lifestyle habits.

2. The system of claim 1 .

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