System

Abstract

To propose optimum sports on the basis of body shapes, motor nerves, and physical strength of users.SOLUTION: A system according to an embodiment comprises a request unit, a scan unit, a determination unit, an identification processing unit, and a proposition unit. The request unit requests multiple types of motion by a user. The scan unit scans the multiple types of motion performed by the user on the basis of a request from the request unit. The determination unit determines the body shape, motor nerve, and physical strength of the user on the basis of information on user's motion acquired by the scan unit. The identification processing unit identifies an exercise that suits the user on the basis of the result of determination by the determination unit. The proposition unit proposes the exercise identified by the identification processing unit to the user.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 do not adequately suggest optimal sports based on a user's body shape, athletic ability, and physical strength, and there is room for improvement.

[0005] The system according to the embodiment aims to suggest the most suitable sport based on the user's body shape, athletic ability, and physical strength. [Means for solving the problem]

[0006] The system according to the embodiment includes a request unit, a scanning unit, a determination unit, a specific processing unit, and a suggestion unit. The request unit requests a plurality of types of movements from the user. The scanning unit scans the plurality of types of movements performed by the user in response to the request by the request unit. The determination unit determines the user's body shape, motor skills, and physical strength based on the information on the user's movements acquired by the scanning unit. The specific processing unit identifies an exercise suitable for the user based on the determination result by the determination unit. The suggestion unit suggests the exercise identified by the specific processing unit to the user. Effect of the Invention

[0007] The system according to the embodiment can suggest the most suitable sports based on the user's body shape, athletic ability, and physical strength. [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) The sports suggestion system according to the embodiment of the present invention is a system that grasps the user's body shape, motor nerves, and physical strength, and proposes the most suitable sports. This sports suggestion system scans the user with a camera or an infrared sensor, and requests the user to perform various movements. For example, the system makes the user perform movements such as "10 sit-ups, 10 side jumps, 100m sprint, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." This allows the system to grasp the user's body movements, dominant hand, skeletal balance, and physical strength. Next, the sports suggestion system analyzes the acquired data and determines the user's body shape, motor nerves, and physical strength. For example, the system measures the user's movement speed, endurance, reaction time, etc., and evaluates the user's athletic ability based on these data. Furthermore, the system generates information on the user's movements as a 3D model, and determines the skeletal balance. The sports suggestion system then creates a prompt and asks the generation AI, "The user has the following characteristics: left-handed, 100m sprint time ●● seconds, good at keeping the center of gravity low, faster side jumps than his peers, average endurance, etc. What sport is suitable for this?" If the generation AI answers, "Fencing. And left-handedness would be good," the sports suggestion system suggests fencing as the best sport. The sports suggestion system also provides detailed information about the identified exercise to the user's parent or guardian. In addition, the system stores the user's past exercise data, tracks changes in athletic ability, and continuously suggests suitable exercises. It also has the function of analyzing the user's movements in real time and providing feedback on various actions. This allows the sports suggestion system to understand the user's body shape, motor skills, and physical strength, and suggest the best sport.

[0029] The sports suggestion system according to the embodiment includes a request unit, a scanning unit, a determination unit, a specification processing unit, and a suggestion unit. The request unit requests a plurality of types of motions from the user. For example, the request unit can request the user to "do 10 sit-ups, 10 side jumps, run 100m, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." The scanning unit scans a plurality of types of motions performed by the user in response to the request by the request unit. For example, the scanning unit scans the user's motions using a camera or an infrared sensor to grasp the user's body shape, motor skills, and physical strength. The determination unit determines the user's body shape, motor skills, and physical strength based on the information on the user's motion acquired by the scanning unit. For example, the determination unit measures the user's motion speed, endurance, reaction time, and the like, and evaluates the user's athletic ability based on these data. The specification processing unit determines an exercise suitable for the user based on the determination result by the determination unit. For example, the identification processing unit uses the generation AI to identify an optimal sport based on the user's characteristics. The suggestion unit suggests the exercise identified by the identification processing unit to the user. For example, if the generation AI answers "Fencing. And it would be best if the user is left-handed," the suggestion unit suggests fencing to the user. In this way, the sports suggestion system according to the embodiment can grasp the user's body shape, motor skills, and physical strength, and suggest the optimal sport.

[0030] The request unit requests a plurality of types of movements from the user. For example, the request unit can request the user to do movements such as "10 sit-ups, 10 side jumps, 100m sprint, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." The request unit requests various exercises to evaluate the user's athletic ability and physical strength from various angles. This allows the user's muscle strength, endurance, agility, cooperation, and the like to be grasped comprehensively. The request unit generates an exercise request that is individually customized in consideration of basic information such as the user's age, sex, and exercise experience. For example, it requests high-intensity exercise for young users and low-intensity exercise for elderly users. The request unit can also receive feedback from the user and adjust the request content as appropriate. For example, if the user is not good at a particular exercise, it avoids that exercise or requests an alternative exercise. This allows the request unit to effectively evaluate the exercise while maintaining the user's motivation. Furthermore, the request unit provides audio guidance and video demonstrations to assist the user in performing the exercise correctly, so that the user can accurately understand and properly perform the requested exercise.

[0031] The scanning unit scans multiple types of movements made by the user in response to a request from the request unit. For example, the scanning unit scans the user's movements using a camera and an infrared sensor to grasp the user's body shape, motor nerves, and physical strength. The scanning unit uses a combination of multiple sensors to capture the user's movements with high accuracy. For example, a 3D camera is used to capture the user's movements in three dimensions, and an infrared sensor is used to detect muscle movements and changes in body temperature. This allows detailed data on the user's movements to be obtained and accurate evaluations to be made. The scanning unit collects data in real time and analyzes the speed, angle, and strength of the user's movements. This allows the user's athletic ability and physical strength to be evaluated from multiple angles. In addition, the scanning unit detects changes in the user's movement patterns and posture to evaluate the efficiency and accuracy of the exercise. For example, it analyzes the posture and smoothness of the movements when the user does abdominal exercises to check whether the exercise is being performed with the correct form. The scanning unit sends the collected data to a cloud server and shares it with other departments to strengthen cooperation throughout the system. This allows the scanning unit to efficiently collect the user's exercise data and provide a foundation for accurate evaluations.

[0032] The judgment unit judges the user's body shape, motor nerves, and physical strength based on the information on the user's movements acquired by the scan unit. For example, the judgment unit measures the speed, endurance, reaction time, etc. of the user's movements, and evaluates the user's athletic ability based on these data. The judgment unit uses an AI algorithm to analyze the collected data and evaluate the user's athletic ability from various angles. For example, a machine learning model is used to analyze the user's movement pattern, speed, and strength to identify strengths and weaknesses of athletic ability. The judgment unit evaluates the user's body shape and muscle balance and judges the efficiency and accuracy of exercise. For example, the judgment unit measures the user's body fat percentage and muscle mass and analyzes how they affect exercise performance. Furthermore, the judgment unit takes into account the user's exercise history and health condition to evaluate the overall athletic ability. For example, based on past exercise data and the results of a health check, it analyzes changes and trends in the user's athletic ability and predicts future athletic ability. The judgment unit visualizes the evaluation results and provides them to the user in an easy-to-understand manner. For example, the strengths and weaknesses of the user's athletic ability may be visually indicated using graphs or charts, and improvements or recommended exercises may be suggested. This allows the judgment unit to accurately evaluate the user's athletic ability and provide appropriate feedback.

[0033] The specific processing unit identifies an exercise suitable for the user based on the judgment result by the judgment unit. For example, the specific processing unit uses the generation AI to identify the optimal sport based on the user's characteristics. The generation AI receives data such as the user's athletic ability, body shape, and exercise history as input, and generates a prompt to identify the optimal sport. For example, a prompt such as "Please consider the user's athletic ability and suggest the optimal sport" is generated and input to the AI. The generation AI analyzes the input data and identifies the optimal sport for the user. For example, if the user has good reaction time and endurance, team sports such as soccer and basketball are suggested. Also, if the user has high muscle strength and flexibility, individual sports such as gymnastics and yoga are suggested. The specific processing unit identifies the optimal sport for the user based on the output result of the generation AI and provides it to the suggestion unit. As a result, the specific processing unit can identify the optimal sport based on the user's athletic ability and body shape, and suggest appropriate exercise to the user. Furthermore, the specific processing unit receives user feedback, evaluates the suitability of the identified sport, and reevaluates it as necessary. As a result, the specific processing unit can continuously provide the optimal sport to the user and maximize the effect of exercise.

[0034] The suggestion unit suggests the exercises specified by the specific processing unit to the user. For example, if the generation AI answers "Fencing. And left-handedness is good," the suggestion unit suggests fencing to the user. The suggestion unit provides detailed information about the proposed exercise to the user and explains the benefits and precautions of the exercise. For example, the suggestion unit provides detailed explanations of the basic rules and techniques of fencing, the necessary equipment, and training methods, and supports the user to start exercising. The suggestion unit emphasizes the appeal and fun of the proposed exercise to draw the user's interest and curiosity. For example, the suggestion unit explains the competitive and strategic aspects of fencing and the effects of improving physical fitness, and encourages the user to have a positive attitude toward exercise. Furthermore, the suggestion unit receives feedback from the user and continuously improves the accuracy and effectiveness of the proposed content. For example, the suggestion unit evaluates how the user responded to the proposed exercise and suggests other exercises as necessary. The suggestion unit also provides an environment and resources for the user to perform the proposed exercise. For example, the suggestion unit provides information about nearby sports facilities and trainers, and supports the user to start exercising. As a result, the suggestion unit can suggest optimal exercises to the user and provide support to maximize the effects of the exercise.

[0035] The specific processing unit includes an analysis unit that analyzes the information on the user's movements acquired by the scanning unit and extracts the elements of the user's body shape, motor nerves, and physical strength as data. The analysis unit analyzes the information on the user's movements acquired by the scanning unit and extracts the elements of the user's body shape, motor nerves, and physical strength as data. For example, the analysis unit measures the speed, stamina, reaction time, etc. of the user's movements and evaluates the user's athletic ability based on these data. The analysis unit can also generate the information on the user's movements as a 3D model and determine the balance of the skeleton. This allows the analysis unit to analyze the information on the user's movements in detail and extract the elements of the body shape, motor nerves, and physical strength as data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the information on the user's movements to a generation AI and extract data using the generation AI.

[0036] The identification processing unit includes an exercise identification unit that identifies an exercise suitable for the user based on the data extracted by the analysis unit. The exercise identification unit identifies an exercise suitable for the user based on the data extracted by the analysis unit. For example, the exercise identification unit identifies an optimal sport based on data on the user's body shape, motor nerves, and physical strength. This allows the exercise identification unit to identify an exercise suitable for the user based on the extracted data. A part or all of the above-mentioned processing in the exercise identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the exercise identification unit can input the data extracted by the analysis unit to a generation AI, and the generation AI can identify the exercise.

[0037] The determination unit can generate information on the user's movements as a 3D model and determine the balance of the user's skeleton based on the 3D model. For example, the determination unit can generate the user's movements as a 3D model using motion capture technology and evaluate the balance of the skeleton. The determination unit can also obtain detailed information on the user's skeleton using 3D scanning technology and determine the balance. This allows the determination unit to determine the balance of the user's skeleton based on the 3D model. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can cause the generation AI to generate the 3D model and determine the balance of the skeleton.

[0038] The determination unit can measure the speed, endurance, and reaction time of the user's movement, and determine the user's motor skills and physical strength based on the measurement results. For example, the determination unit measures the user's 100m sprint time and evaluates the speed. The determination unit can also conduct a running test for a certain period of time to evaluate the user's endurance. Furthermore, the determination unit can measure the reaction time to a visual stimulus to evaluate the user's reaction time. This allows the determination unit to measure the speed, endurance, and reaction time of the user's movement and determine the motor skills and physical strength. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data of the user's movement to the generation AI, and the generation AI can judge the motor skills and physical strength.

[0039] The suggestion unit can provide the parent or guardian of the user with detailed information about the exercise identified by the specific processing unit. For example, the suggestion unit can provide the parent or guardian with detailed information about a sport suitable for the user, and explain the purpose, method, and precautions of the sport. The suggestion unit can also provide the parent or guardian with an evaluation result of the user's athletic ability, and report the effect and progress of the exercise. This allows the suggestion unit to provide the parent or guardian of the user with detailed information about the identified exercise. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information about the identified exercise to a generation AI, and the generation AI can provide detailed information.

[0040] The system further includes a feedback unit that analyzes the user's movements in real time and provides feedback for various actions performed by the user. The feedback unit analyzes the user's movements in real time and provides feedback for various actions performed by the user. For example, the feedback unit analyzes the user's movements in real time using a camera or a sensor, and evaluates the accuracy and efficiency of the movements. The feedback unit can also provide immediate feedback for the user's movements and present improvements and advice. This allows the feedback unit to analyze the user's movements in real time and provide feedback for various actions. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's movement data to a generation AI, and the generation AI can provide feedback.

[0041] The system includes a storage unit that stores the user's past exercise data, and the suggestion unit can track the progress of the user's athletic ability based on the data stored in the storage unit and continuously suggest suitable exercises. The storage unit stores the user's past exercise data and tracks the progress of athletic ability. For example, the storage unit periodically records the user's exercise data and analyzes changes in athletic ability. The storage unit can also continuously suggest suitable exercises based on the user's exercise data. As a result, the storage unit can store the user's past exercise data, track changes in athletic ability, and continuously suggest suitable exercises. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the user's exercise data to a generation AI, and the generation AI can store and analyze the data.

[0042] The request unit can analyze the user's past exercise history and select the optimal request content. For example, the request unit preferentially requests movements that the user was good at in the past. The request unit can also request the user to avoid movements that the user was not good at in the past. Furthermore, the request unit can also request a balanced exercise from the user's past exercise history. This allows the request unit to analyze the user's past exercise history and select the optimal request content. A part or all of the above-mentioned processing in the request unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the request unit can input the user's exercise history data into a generation AI, and the generation AI can select the request content.

[0043] The request unit can customize the request content based on the user's current physical condition and mood at the time of the request. For example, if the user is not feeling well, the request unit requests light exercise. Also, if the user is in good health, the request unit can request harder exercise. Furthermore, if the user is feeling stressed, the request unit can request relaxing exercise. This allows the request unit to customize the request content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the request content.

[0044] At the time of the request, the request unit can preferentially request a highly relevant action in consideration of the geographical location information of the user. For example, when the user is outdoors, the request unit requests an action that requires a large space. Also, when the user is indoors, the request unit can request an action that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the request unit can also request an action suitable for that facility. This allows the request unit to preferentially request a highly relevant action in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI or may be performed without using AI. For example, the request unit can input the geographical location information of the user to the generation AI and select an action by the generation AI.

[0045] At the time of the request, the request unit can analyze the user's social media activity and request a related action. For example, the request unit requests an action related to a sport in which the user has shown interest on social media. The request unit can also request an action related to an exercise shared by the user with a friend. Furthermore, the request unit can also request an action related to the training of an athlete the user follows. This allows the request unit to analyze the user's social media activity and request a related action. Some or all of the above-mentioned processing in the request unit may be performed, for example, using AI or may be performed without using AI. For example, the request unit can input the user's social media data into the generation AI and select an action by the generation AI.

[0046] The scanning unit can apply high-precision scanning technology to detect minute changes in the user's movements during scanning. For example, the scanning unit can use a high-resolution camera to detect minute changes in the user's movements. The scanning unit can also use an infrared sensor to scan the user's muscle movements in detail. Furthermore, the scanning unit can also use a 3D scanner to detect minute changes in the user's skeleton. This allows the scanning unit to apply high-precision scanning technology to detect minute changes in the user's movements. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's movement data into the generation AI, and the generation AI can detect minute changes.

[0047] The scanning unit can use an additional sensor for measuring the speed and angle of the user's motion in detail during scanning. For example, the scanning unit uses an acceleration sensor to measure the speed of the user's motion in detail. The scanning unit can also use a gyro sensor to measure the angle of the user's motion in detail. Furthermore, the scanning unit can also use a pressure sensor to measure the force of the user's motion in detail. This allows the scanning unit to use an additional sensor for measuring the speed and angle of the user's motion in detail. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's motion data into the generation AI and measure the speed and angle using the generation AI.

[0048] The scanning unit can optimize the scan settings in consideration of the geographical location information of the user during scanning. For example, when the user is outdoors, the scanning unit can set the scan to scan a wide area. Also, when the user is indoors, the scanning unit can set the scan to scan a narrow area. Furthermore, when the user is in a specific sports facility, the scanning unit can set the scan settings to be suitable for that facility. This allows the scanning unit to optimize the scan settings in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the geographical location information of the user to the generation AI, and optimize the scan settings using the generation AI.

[0049] The scanning unit can improve the accuracy of the scan by referring to the user's past scan data during scanning. For example, the scanning unit optimizes the scan settings based on the user's past scan data. The scanning unit can also improve the accuracy of the scan by referring to the user's past scan data. Furthermore, the scanning unit can analyze the user's past scan data and adjust the scan parameters. This allows the scanning unit to improve the accuracy of the scan by referring to the user's past scan data. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scan data to the generation AI, and the generation AI can improve the accuracy of the scan.

[0050] The determination unit can apply an advanced algorithm for performing a detailed analysis of the user's motion at the time of determination. For example, the determination unit can perform a detailed analysis of the user's motion using a machine learning algorithm. The determination unit can also perform a detailed analysis of the user's motion using a deep learning algorithm. Furthermore, the determination unit can also perform a detailed analysis of the user's motion using a pattern recognition algorithm. This allows the determination unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0051] The determination unit can collect additional data for evaluating the consistency of the user's motion at the time of determination. For example, the determination unit collects scan data multiple times to evaluate the consistency of the user's motion. The determination unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the determination unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the determination unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's motion data to a generation AI and have the generation AI evaluate the consistency.

[0052] The determination unit can optimize the determination criteria in consideration of the geographical location information of the user at the time of determination. For example, when the user is at high altitude, the determination unit adjusts the determination criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the determination unit can apply a standard determination criterion. Furthermore, when the user is at a specific sports facility, the determination unit can also apply a determination criterion suitable for the facility. This allows the determination unit to optimize the determination criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the geographical location information of the user to the generation AI, and optimize the determination criterion by the generation AI.

[0053] The judgment unit can improve the accuracy of judgment by referring to the user's past judgment data at the time of judgment. For example, the judgment unit optimizes the judgment criteria based on the user's past judgment data. The judgment unit can also improve the accuracy of judgment by referring to the user's past judgment data. Furthermore, the judgment unit can analyze the user's past judgment data and adjust the judgment parameters. Thereby, the judgment unit can improve the accuracy of judgment by referring to the user's past judgment data. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the user's past judgment data to the generation AI, and the generation AI can improve the accuracy of judgment.

[0054] The specific processing unit can apply an advanced algorithm for performing a detailed analysis of the user's motion during the specific processing. For example, the specific processing unit can perform a detailed analysis of the user's motion using a machine learning algorithm. The specific processing unit can also perform a detailed analysis of the user's motion using a deep learning algorithm. Furthermore, the specific processing unit can also perform a detailed analysis of the user's motion using a pattern recognition algorithm. This allows the specific processing unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the specific processing unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0055] The specific processing unit can collect additional data for evaluating the consistency of the user's motion during the specific processing. For example, the specific processing unit collects scan data multiple times to evaluate the consistency of the user's motion. The specific processing unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the specific processing unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the specific processing unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the specific processing unit can input the user's motion data into a generation AI and have the generation AI evaluate the consistency.

[0056] The specific processing unit can optimize the specific criteria in consideration of the geographical location information of the user during the specific processing. For example, when the user is at high altitude, the specific processing unit adjusts the specific criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the specific processing unit can apply a standard specific criterion. Furthermore, when the user is at a specific sports facility, the specific processing unit can also apply a specific criterion suitable for the facility. This allows the specific processing unit to optimize the specific criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the specific processing unit may be performed, for example, using AI, or may be performed without using AI. For example, the specific processing unit can input the geographical location information of the user to the generation AI and optimize the specific criterion by the generation AI.

[0057] The specific processing unit can improve the accuracy of the specific processing by referring to the user's past specific data during the specific processing. For example, the specific processing unit optimizes the specific criteria based on the user's past specific data. The specific processing unit can also improve the accuracy of the specific processing by referring to the user's past specific data. Furthermore, the specific processing unit can analyze the user's past specific data and adjust specific parameters. This allows the specific processing unit to improve the accuracy of the specific processing by referring to the user's past specific data. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the specific processing unit can input the user's past specific data into the generation AI and improve the accuracy of the specific processing by the generation AI.

[0058] When making a proposal, the suggestion unit can select optimal proposal content by referring to the user's past exercise history. For example, the suggestion unit can suggest exercises that the user was good at in the past. The suggestion unit can also suggest to avoid exercises that the user was not good at in the past. Furthermore, the suggestion unit can also suggest balanced exercises from the user's past exercise history. This allows the suggestion unit to select optimal proposal content by referring to the user's past exercise history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise history data to the generation AI, and the generation AI can select the proposal content.

[0059] The suggestion unit can customize the suggested content based on the user's current physical condition and mood at the time of suggestion. For example, when the user is not feeling well, the suggestion unit can suggest light exercise. Also, when the user is in good health, the suggestion unit can suggest harder exercise. Furthermore, when the user is feeling stressed, the suggestion unit can also suggest relaxing exercise. This allows the suggestion unit to customize the suggested content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's physical condition and mood data into the generation AI, and the suggestion content can be customized by the generation AI.

[0060] When making a proposal, the suggestion unit can give priority to a proposal with high relevance in consideration of the geographical location information of the user. For example, when the user is outdoors, the suggestion unit can suggest an exercise that requires a large space. Also, when the user is indoors, the suggestion unit can suggest an exercise that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the suggestion unit can also suggest an exercise suitable for the facility. This allows the suggestion unit to give priority to a proposal with high relevance in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user to the generation AI, and the generation AI can select the proposal content.

[0061] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and make a related proposal. For example, the suggestion unit can suggest an exercise related to a sport in which the user has shown an interest on social media. The suggestion unit can also make a proposal related to an exercise shared by the user with a friend. Furthermore, the suggestion unit can also suggest an exercise related to the training of an athlete the user follows. This allows the suggestion unit to analyze the user's social media activity and make a related proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI, and the generation AI can select the proposal content.

[0062] The analysis unit can apply an advanced algorithm for performing a detailed analysis of the user's motion during the analysis. For example, the analysis unit can use a machine learning algorithm to perform a detailed analysis of the user's motion. The analysis unit can also use a deep learning algorithm to perform a detailed analysis of the user's motion. Furthermore, the analysis unit can also use a pattern recognition algorithm to perform a detailed analysis of the user's motion. This allows the analysis unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0063] The analysis unit can collect additional data for evaluating the consistency of the user's movements during the analysis. For example, the analysis unit collects scan data multiple times to evaluate the consistency of the user's movements. The analysis unit can also collect scan data from different angles to evaluate the consistency of the user's movements. Furthermore, the analysis unit can also collect scan data at different time periods to evaluate the consistency of the user's movements. This allows the analysis unit to collect additional data for evaluating the consistency of the user's movements. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's movement data into a generation AI, and the generation AI can evaluate the consistency.

[0064] The analysis unit can optimize the analysis criteria in consideration of the geographical location information of the user during the analysis. For example, when the user is at high altitude, the analysis unit adjusts the analysis criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the analysis unit can apply standard analysis criteria. Furthermore, when the user is at a specific sports facility, the analysis unit can also apply analysis criteria suitable for the facility. This allows the analysis unit to optimize the analysis criteria in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI, and optimize the analysis criteria using the generation AI.

[0065] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis data during the analysis. For example, the analysis unit optimizes the analysis criteria based on the user's past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis data. Furthermore, the analysis unit can analyze the user's past analysis data and adjust the analysis parameters. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0066] The motion identification unit can apply an advanced algorithm for performing a detailed analysis of the user's motion at the time of identification. For example, the motion identification unit can use a machine learning algorithm to perform a detailed analysis of the user's motion. In addition, the motion identification unit can use a deep learning algorithm to perform a detailed analysis of the user's motion. Furthermore, the motion identification unit can also use a pattern recognition algorithm to perform a detailed analysis of the user's motion. This allows the motion identification unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the motion identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion identification unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0067] The motion identification unit can collect additional data for evaluating the consistency of the user's motion at the time of identification. For example, the motion identification unit collects scan data multiple times to evaluate the consistency of the user's motion. The motion identification unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the motion identification unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the motion identification unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the motion identification unit may be performed, for example, using AI or may be performed without using AI. For example, the motion identification unit can input the user's motion data to a generation AI and have the generation AI evaluate the consistency.

[0068] The exercise identification unit can optimize the identification criteria in consideration of the geographical location information of the user at the time of identification. For example, when the user is at high altitude, the exercise identification unit adjusts the identification criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the exercise identification unit can apply a standard identification criterion. Furthermore, when the user is at a specific sports facility, the exercise identification unit can also apply an identification criterion suitable for the facility. This allows the exercise identification unit to optimize the identification criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the exercise identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise identification unit can input the geographical location information of the user to the generation AI, and optimize the identification criterion by the generation AI.

[0069] The motion identification unit can improve the accuracy of identification by referring to the user's past identification data at the time of identification. For example, the motion identification unit optimizes the identification criteria based on the user's past identification data. The motion identification unit can also improve the accuracy of identification by referring to the user's past identification data. Furthermore, the motion identification unit can analyze the user's past identification data and adjust the identification parameters. Thereby, the motion identification unit can improve the accuracy of identification by referring to the user's past identification data. A part or all of the above-mentioned processing in the motion identification unit may be performed, for example, using AI or may be performed without using AI. For example, the motion identification unit can input the user's past identification data to the generation AI and improve the accuracy of identification by the generation AI.

[0070] The feedback unit can provide optimal feedback by referring to the user's past exercise history when providing feedback. For example, the feedback unit provides feedback regarding exercises that the user was good at in the past. The feedback unit can also provide feedback regarding exercises that the user was not good at in the past. Furthermore, the feedback unit can also provide balanced feedback from the user's past exercise history. This allows the feedback unit to provide optimal feedback by referring to the user's past exercise history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's exercise history data to a generation AI, and the generation AI can select the feedback content.

[0071] The feedback unit can customize the feedback content based on the user's current physical condition and mood at the time of feedback. For example, when the user is not feeling well, the feedback unit provides feedback on light exercise. Also, when the user is in good health, the feedback unit can provide feedback on harder exercise. Furthermore, when the user is feeling stressed, the feedback unit can also provide feedback on relaxing exercise. This allows the feedback unit to customize the feedback content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the feedback content.

[0072] The feedback unit can give priority to highly relevant feedback in consideration of the geographical location information of the user when providing feedback. For example, when the user is outdoors, the feedback unit provides feedback on exercises that require a large space. Also, when the user is indoors, the feedback unit can provide feedback on exercises that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the feedback unit can also provide feedback on exercises that are suitable for that facility. This allows the feedback unit to give priority to highly relevant feedback in consideration of the geographical location information of the user. 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 geographical location information of the user to the generation AI, and the generation AI can select the feedback content.

[0073] At the time of feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit provides feedback related to a sport in which the user has shown interest on social media. The feedback unit can also provide feedback related to an exercise the user has shared with a friend. Furthermore, the feedback unit can also provide feedback related to the training of an athlete the user follows. This allows the feedback unit to analyze the user's social media activity and provide relevant feedback. Some 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 social media data into a generation AI, and the generation AI can select the feedback content.

[0074] The storage unit can store optimal data by referring to the user's past exercise data when storing. For example, the storage unit preferentially stores exercise data that the user was good at in the past. The storage unit can also store exercise data that the user was not good at in the past. Furthermore, the storage unit can also store balanced data from the user's past exercise data. This allows the storage unit to store optimal data by referring to the user's past exercise data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past exercise data to the generation AI, and the generation AI can select the stored data.

[0075] The storage unit can customize the data to be stored based on the user's current physical condition and mood when storing. For example, when the user is not feeling well, the storage unit stores light exercise data. Also, when the user is in good health, the storage unit can store harder exercise data. Furthermore, when the user is feeling stressed, the storage unit can also store relaxing exercise data. This allows the storage unit to customize the data to be stored based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the stored data.

[0076] The storage unit can store highly relevant data preferentially in consideration of the user's geographical location information when storing. For example, when the user is outdoors, the storage unit stores exercise data that requires a large space. Also, when the user is indoors, the storage unit can store exercise data that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the storage unit can also store exercise data suitable for that facility. This allows the storage unit to store highly relevant data preferentially in consideration of the user's geographical location information. Part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information to the generation AI, and the generation AI can select the stored data.

[0077] The storage unit can analyze the user's social media activity and store the relevant data when storing. For example, the storage unit stores data related to a sport in which the user has shown interest on social media. The storage unit can also store data related to exercises shared by the user with friends. Furthermore, the storage unit can also store data related to training of athletes the user follows. This allows the storage unit to analyze the user's social media activity and store the relevant data. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using AI or may be performed without using AI. For example, the storage unit can input the user's social media data to a generation AI, and the generation AI can select the stored data.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The request unit can also adjust the intensity of the requested action based on the user's physical condition and mood. For example, if the user is tired, light exercise is requested. If the user is energetic, harder exercise can be requested. Furthermore, if the user is feeling stressed, relaxing exercise can be requested. This allows the request unit to adjust the intensity of the requested action based on the user's physical condition and mood.

[0080] The analysis unit can also analyze the pattern of the user's motion and evaluate the efficiency of the user's motion. For example, the analysis unit can analyze the repetitive pattern of the user's motion and identify efficient motion. The analysis unit can also analyze the timing of the user's motion and identify optimal timing. Furthermore, the analysis unit can analyze the energy consumption of the user's motion and identify efficient energy consumption. This allows the analysis unit to evaluate the efficiency of the user's motion.

[0081] The motion specification unit can also evaluate the performance of the user's movements and specify a training plan suitable for the user. For example, the motion specification unit can evaluate the speed and endurance of the user's movements and specify an optimal training plan. The motion specification unit can also evaluate the reaction time of the user's movements and specify a training plan to improve the reaction time. Furthermore, the motion specification unit can also evaluate the balance of the user's movements and specify a training plan to improve the balance. In this way, the motion specification unit can specify a training plan suitable for the user.

[0082] The determination unit can also evaluate the stability of the user's motion and identify areas for improvement in the user's motion. For example, the determination unit can evaluate the shaking of the user's motion and provide advice for improving the stability. The determination unit can also evaluate the consistency of the user's motion and provide advice for improving the consistency. Furthermore, the determination unit can evaluate the accuracy of the user's motion and provide advice for improving the accuracy. This allows the determination unit to identify areas for improvement in the user's motion.

[0083] The suggestion unit can also suggest a combination of exercises suitable for the user based on the user's exercise history. For example, the suggestion unit can analyze data on exercises the user has performed in the past and suggest an optimal combination of exercises. The suggestion unit can also suggest a balanced exercise plan based on the user's exercise history. Furthermore, the suggestion unit can also suggest an exercise plan aimed at a specific goal based on the user's exercise history. In this way, the suggestion unit can suggest a combination of exercises suitable for the user.

[0084] The process flow of the first embodiment will be briefly described below.

[0085] Step 1: The request unit requests a plurality of types of motions from the user. For example, the request unit can request the user to do motions such as "10 sit-ups, 10 side jumps, 100m sprint, right hand throwing motion, left hand throwing motion, rowing motion, right hand tennis swing, left hand tennis swing." Step 2: The scanning unit scans multiple types of movements made by the user in response to the request from the request unit. For example, the scanning unit scans the movements of the user using a camera or an infrared sensor to grasp the user's body shape, motor skills, and physical strength. Step 3: The determination unit determines the user's body shape, motor skills, and physical strength based on the information on the user's movements acquired by the scanning unit. For example, the determination unit measures the user's movement speed, endurance, reaction time, etc., and evaluates the user's athletic ability based on this data. Step 4: The identification processing unit identifies an exercise suitable for the user based on the result of the determination by the determination unit. For example, the identification processing unit uses a generation AI to identify an optimal sport based on the characteristics of the user. Step 5: The suggestion unit suggests the exercise identified by the identification processing unit to the user. For example, if the generation AI answers "Fencing. And left-handedness is better," the suggestion unit suggests fencing to the user.

[0086] (Example 2) The sports suggestion system according to the embodiment of the present invention is a system that grasps the user's body shape, motor nerves, and physical strength, and proposes the most suitable sports. This sports suggestion system scans the user with a camera or an infrared sensor, and requests the user to perform various movements. For example, the system makes the user perform movements such as "10 sit-ups, 10 side jumps, 100m sprint, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." This allows the system to grasp the user's body movements, dominant hand, skeletal balance, and physical strength. Next, the sports suggestion system analyzes the acquired data and determines the user's body shape, motor nerves, and physical strength. For example, the system measures the user's movement speed, endurance, reaction time, etc., and evaluates the user's athletic ability based on these data. Furthermore, the system generates information on the user's movements as a 3D model, and determines the skeletal balance. The sports suggestion system then creates a prompt and asks the generation AI, "The user has the following characteristics: left-handed, 100m sprint time ●● seconds, good at keeping the center of gravity low, faster side jumps than his peers, average endurance, etc. What sport is suitable for this?" If the generation AI answers, "Fencing. And left-handedness would be good," the sports suggestion system suggests fencing as the best sport. The sports suggestion system also provides detailed information about the identified exercise to the user's parent or guardian. In addition, the system stores the user's past exercise data, tracks changes in athletic ability, and continuously suggests suitable exercises. It also has the function of analyzing the user's movements in real time and providing feedback on various actions. This allows the sports suggestion system to understand the user's body shape, motor skills, and physical strength, and suggest the best sport.

[0087] The sports suggestion system according to the embodiment includes a request unit, a scanning unit, a determination unit, a specification processing unit, and a suggestion unit. The request unit requests a plurality of types of motions from the user. For example, the request unit can request the user to "do 10 sit-ups, 10 side jumps, run 100m, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." The scanning unit scans a plurality of types of motions performed by the user in response to the request by the request unit. For example, the scanning unit scans the user's motions using a camera or an infrared sensor to grasp the user's body shape, motor skills, and physical strength. The determination unit determines the user's body shape, motor skills, and physical strength based on the information on the user's motion acquired by the scanning unit. For example, the determination unit measures the user's motion speed, endurance, reaction time, and the like, and evaluates the user's athletic ability based on these data. The specification processing unit determines an exercise suitable for the user based on the determination result by the determination unit. For example, the identification processing unit uses the generation AI to identify an optimal sport based on the user's characteristics. The suggestion unit suggests the exercise identified by the identification processing unit to the user. For example, if the generation AI answers "Fencing. And it would be best if the user is left-handed," the suggestion unit suggests fencing to the user. In this way, the sports suggestion system according to the embodiment can grasp the user's body shape, motor skills, and physical strength, and suggest the optimal sport.

[0088] The request unit requests a plurality of types of movements from the user. For example, the request unit can request the user to do movements such as "10 sit-ups, 10 side jumps, 100m sprint, throw a ball with the right hand, throw a ball with the left hand, row a boat, swing like tennis with the right hand, and swing like tennis with the left hand." The request unit requests various exercises to evaluate the user's athletic ability and physical strength from various angles. This allows the user's muscle strength, endurance, agility, cooperation, and the like to be grasped comprehensively. The request unit generates an exercise request that is individually customized in consideration of basic information such as the user's age, sex, and exercise experience. For example, it requests high-intensity exercise for young users and low-intensity exercise for elderly users. The request unit can also receive feedback from the user and adjust the request content as appropriate. For example, if the user is not good at a particular exercise, it avoids that exercise or requests an alternative exercise. This allows the request unit to effectively evaluate the exercise while maintaining the user's motivation. Furthermore, the request unit provides audio guidance and video demonstrations to assist the user in performing the exercise correctly, so that the user can accurately understand and properly perform the requested exercise.

[0089] The scanning unit scans multiple types of movements made by the user in response to a request from the request unit. For example, the scanning unit scans the user's movements using a camera and an infrared sensor to grasp the user's body shape, motor nerves, and physical strength. The scanning unit uses a combination of multiple sensors to capture the user's movements with high accuracy. For example, a 3D camera is used to capture the user's movements in three dimensions, and an infrared sensor is used to detect muscle movements and changes in body temperature. This allows detailed data on the user's movements to be obtained and accurate evaluations to be made. The scanning unit collects data in real time and analyzes the speed, angle, and strength of the user's movements. This allows the user's athletic ability and physical strength to be evaluated from multiple angles. In addition, the scanning unit detects changes in the user's movement patterns and posture to evaluate the efficiency and accuracy of the exercise. For example, it analyzes the posture and smoothness of the movements when the user does abdominal exercises to check whether the exercise is being performed with the correct form. The scanning unit sends the collected data to a cloud server and shares it with other departments to strengthen cooperation throughout the system. This allows the scanning unit to efficiently collect the user's exercise data and provide a foundation for accurate evaluations.

[0090] The judgment unit judges the user's body shape, motor nerves, and physical strength based on the information on the user's movements acquired by the scan unit. For example, the judgment unit measures the speed, endurance, reaction time, etc. of the user's movements, and evaluates the user's athletic ability based on these data. The judgment unit uses an AI algorithm to analyze the collected data and evaluate the user's athletic ability from various angles. For example, a machine learning model is used to analyze the user's movement pattern, speed, and strength to identify strengths and weaknesses of athletic ability. The judgment unit evaluates the user's body shape and muscle balance and judges the efficiency and accuracy of exercise. For example, the judgment unit measures the user's body fat percentage and muscle mass and analyzes how they affect exercise performance. Furthermore, the judgment unit takes into account the user's exercise history and health condition to evaluate the overall athletic ability. For example, based on past exercise data and the results of a health check, it analyzes changes and trends in the user's athletic ability and predicts future athletic ability. The judgment unit visualizes the evaluation results and provides them to the user in an easy-to-understand manner. For example, the strengths and weaknesses of the user's athletic ability may be visually indicated using graphs or charts, and improvements or recommended exercises may be suggested. This allows the judgment unit to accurately evaluate the user's athletic ability and provide appropriate feedback.

[0091] The specific processing unit identifies an exercise suitable for the user based on the judgment result by the judgment unit. For example, the specific processing unit uses the generation AI to identify the optimal sport based on the user's characteristics. The generation AI receives data such as the user's athletic ability, body shape, and exercise history as input, and generates a prompt to identify the optimal sport. For example, a prompt such as "Please consider the user's athletic ability and suggest the optimal sport" is generated and input to the AI. The generation AI analyzes the input data and identifies the optimal sport for the user. For example, if the user has good reaction time and endurance, team sports such as soccer and basketball are suggested. Also, if the user has high muscle strength and flexibility, individual sports such as gymnastics and yoga are suggested. The specific processing unit identifies the optimal sport for the user based on the output result of the generation AI and provides it to the suggestion unit. As a result, the specific processing unit can identify the optimal sport based on the user's athletic ability and body shape, and suggest appropriate exercise to the user. Furthermore, the specific processing unit receives user feedback, evaluates the suitability of the identified sport, and reevaluates it as necessary. As a result, the specific processing unit can continuously provide the optimal sport to the user and maximize the effect of exercise.

[0092] The suggestion unit suggests the exercises specified by the specific processing unit to the user. For example, if the generation AI answers "Fencing. And left-handedness is good," the suggestion unit suggests fencing to the user. The suggestion unit provides detailed information about the proposed exercise to the user and explains the benefits and precautions of the exercise. For example, the suggestion unit provides detailed explanations of the basic rules and techniques of fencing, the necessary equipment, and training methods, and supports the user to start exercising. The suggestion unit emphasizes the appeal and fun of the proposed exercise to draw the user's interest and curiosity. For example, the suggestion unit explains the competitive and strategic aspects of fencing and the effects of improving physical fitness, and encourages the user to have a positive attitude toward exercise. Furthermore, the suggestion unit receives feedback from the user and continuously improves the accuracy and effectiveness of the proposed content. For example, the suggestion unit evaluates how the user responded to the proposed exercise and suggests other exercises as necessary. The suggestion unit also provides an environment and resources for the user to perform the proposed exercise. For example, the suggestion unit provides information about nearby sports facilities and trainers, and supports the user to start exercising. As a result, the suggestion unit can suggest optimal exercises to the user and provide support to maximize the effects of the exercise.

[0093] The specific processing unit includes an analysis unit that analyzes the information on the user's movements acquired by the scanning unit and extracts the elements of the user's body shape, motor nerves, and physical strength as data. The analysis unit analyzes the information on the user's movements acquired by the scanning unit and extracts the elements of the user's body shape, motor nerves, and physical strength as data. For example, the analysis unit measures the speed, stamina, reaction time, etc. of the user's movements and evaluates the user's athletic ability based on these data. The analysis unit can also generate the information on the user's movements as a 3D model and determine the balance of the skeleton. This allows the analysis unit to analyze the information on the user's movements in detail and extract the elements of the body shape, motor nerves, and physical strength as data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the information on the user's movements to a generation AI and extract data using the generation AI.

[0094] The identification processing unit includes an exercise identification unit that identifies an exercise suitable for the user based on the data extracted by the analysis unit. The exercise identification unit identifies an exercise suitable for the user based on the data extracted by the analysis unit. For example, the exercise identification unit identifies an optimal sport based on data on the user's body shape, motor nerves, and physical strength. This allows the exercise identification unit to identify an exercise suitable for the user based on the extracted data. A part or all of the above-mentioned processing in the exercise identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the exercise identification unit can input the data extracted by the analysis unit to a generation AI, and the generation AI can identify the exercise.

[0095] The determination unit can generate information on the user's movements as a 3D model and determine the balance of the user's skeleton based on the 3D model. For example, the determination unit can generate the user's movements as a 3D model using motion capture technology and evaluate the balance of the skeleton. The determination unit can also obtain detailed information on the user's skeleton using 3D scanning technology and determine the balance. This allows the determination unit to determine the balance of the user's skeleton based on the 3D model. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can cause the generation AI to generate the 3D model and determine the balance of the skeleton.

[0096] The determination unit can measure the speed, endurance, and reaction time of the user's movement, and determine the user's motor skills and physical strength based on the measurement results. For example, the determination unit measures the user's 100m sprint time and evaluates the speed. The determination unit can also conduct a running test for a certain period of time to evaluate the user's endurance. Furthermore, the determination unit can measure the reaction time to a visual stimulus to evaluate the user's reaction time. This allows the determination unit to measure the speed, endurance, and reaction time of the user's movement and determine the motor skills and physical strength. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data of the user's movement to the generation AI, and the generation AI can judge the motor skills and physical strength.

[0097] The suggestion unit can provide the parent or guardian of the user with detailed information about the exercise identified by the specific processing unit. For example, the suggestion unit can provide the parent or guardian with detailed information about a sport suitable for the user, and explain the purpose, method, and precautions of the sport. The suggestion unit can also provide the parent or guardian with an evaluation result of the user's athletic ability, and report the effect and progress of the exercise. This allows the suggestion unit to provide the parent or guardian of the user with detailed information about the identified exercise. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input information about the identified exercise to a generation AI, and the generation AI can provide detailed information.

[0098] The system further includes a feedback unit that analyzes the user's movements in real time and provides feedback for various actions performed by the user. The feedback unit analyzes the user's movements in real time and provides feedback for various actions performed by the user. For example, the feedback unit analyzes the user's movements in real time using a camera or a sensor, and evaluates the accuracy and efficiency of the movements. The feedback unit can also provide immediate feedback for the user's movements and present improvements and advice. This allows the feedback unit to analyze the user's movements in real time and provide feedback for various actions. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's movement data to a generation AI, and the generation AI can provide feedback.

[0099] The system includes a storage unit that stores the user's past exercise data, and the suggestion unit can track the progress of the user's athletic ability based on the data stored in the storage unit and continuously suggest suitable exercises. The storage unit stores the user's past exercise data and tracks the progress of athletic ability. For example, the storage unit periodically records the user's exercise data and analyzes changes in athletic ability. The storage unit can also continuously suggest suitable exercises based on the user's exercise data. As a result, the storage unit can store the user's past exercise data, track changes in athletic ability, and continuously suggest suitable exercises. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the user's exercise data to a generation AI, and the generation AI can store and analyze the data.

[0100] The request unit can estimate the user's emotion and adjust the order of requested actions based on the estimated user's emotion. For example, if the user is nervous, the request unit requests the user to start with an action that relaxes the user. Also, if the user is excited, the request unit can preferentially request an action that releases energy. Furthermore, if the user is tired, the request unit can request the user to start with light exercise. This allows the request unit to adjust the order of requested actions based on 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 request unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the request unit can input the user's emotion data into the generation AI, and the generation AI can adjust the order of actions.

[0101] The request unit can analyze the user's past exercise history and select the optimal request content. For example, the request unit preferentially requests movements that the user was good at in the past. The request unit can also request the user to avoid movements that the user was not good at in the past. Furthermore, the request unit can also request a balanced exercise from the user's past exercise history. This allows the request unit to analyze the user's past exercise history and select the optimal request content. A part or all of the above-mentioned processing in the request unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the request unit can input the user's exercise history data into a generation AI, and the generation AI can select the request content.

[0102] The request unit can customize the request content based on the user's current physical condition and mood at the time of the request. For example, if the user is not feeling well, the request unit requests light exercise. Also, if the user is in good health, the request unit can request harder exercise. Furthermore, if the user is feeling stressed, the request unit can request relaxing exercise. This allows the request unit to customize the request content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the request unit may be performed using, for example, AI, or may be performed without using AI. For example, the request unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the request content.

[0103] The request unit can estimate the user's emotion and adjust the difficulty of the requested action based on the estimated user's emotion. For example, if the user is nervous, the request unit requests the user to start with an easy action. Also, if the user is relaxed, the request unit can request a difficult action. Furthermore, if the user is excited, the request unit can also request a challenging action. This allows the request unit to adjust the difficulty of the requested action based on 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 request unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the request unit can input the user's emotion data into the generation AI, and the generation AI can adjust the difficulty of the action.

[0104] At the time of the request, the request unit can preferentially request a highly relevant action in consideration of the geographical location information of the user. For example, when the user is outdoors, the request unit requests an action that requires a large space. Also, when the user is indoors, the request unit can request an action that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the request unit can also request an action suitable for that facility. This allows the request unit to preferentially request a highly relevant action in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the request unit may be performed, for example, using AI or may be performed without using AI. For example, the request unit can input the geographical location information of the user to the generation AI and select an action by the generation AI.

[0105] At the time of the request, the request unit can analyze the user's social media activity and request a related action. For example, the request unit requests an action related to a sport in which the user has shown interest on social media. The request unit can also request an action related to an exercise shared by the user with a friend. Furthermore, the request unit can also request an action related to the training of an athlete the user follows. This allows the request unit to analyze the user's social media activity and request a related action. Some or all of the above-mentioned processing in the request unit may be performed, for example, using AI or may be performed without using AI. For example, the request unit can input the user's social media data into the generation AI and select an action by the generation AI.

[0106] The scanning unit can estimate the user's emotion and adjust the timing of the scan based on the estimated user's emotion. For example, the scanning unit can perform the scan slowly when the user is relaxed. Also, the scanning unit can perform the scan quickly when the user is nervous. Furthermore, the scanning unit can also perform the scan at a moderate speed when the user is excited. This allows the scanning unit to adjust the timing of the scan based on 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 scanning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scanning unit can input the user's emotion data to the generation AI and adjust the timing of the scan using the generation AI.

[0107] The scanning unit can apply high-precision scanning technology to detect minute changes in the user's movements during scanning. For example, the scanning unit can use a high-resolution camera to detect minute changes in the user's movements. The scanning unit can also use an infrared sensor to scan the user's muscle movements in detail. Furthermore, the scanning unit can also use a 3D scanner to detect minute changes in the user's skeleton. This allows the scanning unit to apply high-precision scanning technology to detect minute changes in the user's movements. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's movement data into the generation AI, and the generation AI can detect minute changes.

[0108] The scanning unit can use an additional sensor for measuring the speed and angle of the user's motion in detail during scanning. For example, the scanning unit uses an acceleration sensor to measure the speed of the user's motion in detail. The scanning unit can also use a gyro sensor to measure the angle of the user's motion in detail. Furthermore, the scanning unit can also use a pressure sensor to measure the force of the user's motion in detail. This allows the scanning unit to use an additional sensor for measuring the speed and angle of the user's motion in detail. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's motion data into the generation AI and measure the speed and angle using the generation AI.

[0109] The scanning unit can estimate the user's emotion and adjust the scanning range based on the estimated user's emotion. For example, the scanning unit scans a wide range when the user is relaxed. Also, the scanning unit can scan a narrow range when the user is nervous. Furthermore, the scanning unit can also scan a moderate range when the user is excited. This allows the scanning unit to adjust the scanning range based on 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 scanning unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the scanning unit can input the user's emotion data into the generation AI and adjust the scanning range by the generation AI.

[0110] The scanning unit can optimize the scan settings in consideration of the geographical location information of the user during scanning. For example, when the user is outdoors, the scanning unit can set the scan to scan a wide area. Also, when the user is indoors, the scanning unit can set the scan to scan a narrow area. Furthermore, when the user is in a specific sports facility, the scanning unit can set the scan settings to be suitable for that facility. This allows the scanning unit to optimize the scan settings in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the geographical location information of the user to the generation AI, and optimize the scan settings using the generation AI.

[0111] The scanning unit can improve the accuracy of the scan by referring to the user's past scan data during scanning. For example, the scanning unit optimizes the scan settings based on the user's past scan data. The scanning unit can also improve the accuracy of the scan by referring to the user's past scan data. Furthermore, the scanning unit can analyze the user's past scan data and adjust the scan parameters. This allows the scanning unit to improve the accuracy of the scan by referring to the user's past scan data. A part or all of the above-mentioned processing in the scanning unit may be performed using, for example, AI, or may be performed without using AI. For example, the scanning unit can input the user's past scan data to the generation AI, and the generation AI can improve the accuracy of the scan.

[0112] The determination unit can estimate the user's emotion and adjust the judgment criteria based on the estimated user's emotion. For example, the determination unit applies strict judgment criteria when the user is relaxed. Furthermore, the determination unit can apply lenient judgment criteria when the user is nervous. Furthermore, the determination unit can also apply moderate judgment criteria when the user is excited. This allows the determination unit to adjust the judgment criteria based on 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 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and adjust the judgment criteria using the generation AI.

[0113] The determination unit can apply an advanced algorithm for performing a detailed analysis of the user's motion at the time of determination. For example, the determination unit can perform a detailed analysis of the user's motion using a machine learning algorithm. The determination unit can also perform a detailed analysis of the user's motion using a deep learning algorithm. Furthermore, the determination unit can also perform a detailed analysis of the user's motion using a pattern recognition algorithm. This allows the determination unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0114] The determination unit can collect additional data for evaluating the consistency of the user's motion at the time of determination. For example, the determination unit collects scan data multiple times to evaluate the consistency of the user's motion. The determination unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the determination unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the determination unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's motion data to a generation AI and have the generation AI evaluate the consistency.

[0115] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user's emotion. For example, the determination unit displays a detailed determination result when the user is relaxed. Also, the determination unit can display a concise determination result when the user is nervous. Furthermore, the determination unit can display a visually stimulating determination result when the user is excited. This allows the determination unit to adjust the display method of the determination result based on the user's emotion. The estimation of the emotion 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's emotion data to the generation AI and adjust the display method using the generation AI.

[0116] The determination unit can optimize the determination criteria in consideration of the geographical location information of the user at the time of determination. For example, when the user is at high altitude, the determination unit adjusts the determination criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the determination unit can apply a standard determination criterion. Furthermore, when the user is at a specific sports facility, the determination unit can also apply a determination criterion suitable for the facility. This allows the determination unit to optimize the determination criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input the geographical location information of the user to the generation AI, and optimize the determination criterion by the generation AI.

[0117] The judgment unit can improve the accuracy of the judgment by referring to the past judgment data of the user at the time of judgment. The judgment unit can improve the accuracy of the judgment by referring to the past judgment data of the user at the time of judgment. For example, the judgment unit optimizes the judgment criteria based on the past judgment data of the user. The judgment unit can also improve the accuracy of the judgment by referring to the past judgment data of the user. Furthermore, the judgment unit can analyze the past judgment data of the user and adjust the parameters of the judgment. Thereby, the judgment unit can improve the accuracy of the judgment by referring to the past judgment data of the user. A part or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the past judgment data of the user to the generation AI, and the generation AI can improve the accuracy of the judgment.

[0118] The specific processing unit can estimate the user's emotion and determine the priority of the exercises to be specified based on the estimated user's emotion. For example, when the user is relaxed, the specific processing unit preferentially identifies an exercise that relaxes the user. Furthermore, when the user is tense, the specific processing unit can preferentially identify an exercise that relieves tension. Furthermore, when the user is excited, the specific processing unit can also preferentially identify an exercise that releases energy. This allows the specific processing unit to determine the priority of the exercises to be specified based on the user's emotion. The estimation of the 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 specific processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the specific processing unit can input the user's emotion data to the generation AI and use the generation AI to determine the priority of the exercises.

[0119] The specific processing unit can apply an advanced algorithm for performing a detailed analysis of the user's motion during the specific processing. For example, the specific processing unit can perform a detailed analysis of the user's motion using a machine learning algorithm. The specific processing unit can also perform a detailed analysis of the user's motion using a deep learning algorithm. Furthermore, the specific processing unit can also perform a detailed analysis of the user's motion using a pattern recognition algorithm. This allows the specific processing unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the specific processing unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0120] The specific processing unit can collect additional data for evaluating the consistency of the user's motion during the specific processing. For example, the specific processing unit collects scan data multiple times to evaluate the consistency of the user's motion. The specific processing unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the specific processing unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the specific processing unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the specific processing unit can input the user's motion data into a generation AI and have the generation AI evaluate the consistency.

[0121] The specific processing unit can estimate the user's emotion and adjust the display method of the specific exercise based on the estimated user's emotion. For example, the specific processing unit displays detailed exercise information when the user is relaxed. Also, the specific processing unit can display concise exercise information when the user is nervous. Furthermore, the specific processing unit can display visually stimulating exercise information when the user is excited. This allows the specific processing unit to adjust the display method of the specific exercise based on 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 specific processing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the specific processing unit can input the user's emotion data to the generation AI and adjust the display method using the generation AI.

[0122] The specific processing unit can optimize the specific criteria in consideration of the geographical location information of the user during the specific processing. For example, when the user is at high altitude, the specific processing unit adjusts the specific criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the specific processing unit can apply a standard specific criterion. Furthermore, when the user is at a specific sports facility, the specific processing unit can also apply a specific criterion suitable for the facility. This allows the specific processing unit to optimize the specific criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the specific processing unit may be performed, for example, using AI, or may be performed without using AI. For example, the specific processing unit can input the geographical location information of the user to the generation AI and optimize the specific criterion by the generation AI.

[0123] The specific processing unit can improve the accuracy of the specific processing by referring to the user's past specific data during the specific processing. For example, the specific processing unit optimizes the specific criteria based on the user's past specific data. The specific processing unit can also improve the accuracy of the specific processing by referring to the user's past specific data. Furthermore, the specific processing unit can analyze the user's past specific data and adjust specific parameters. This allows the specific processing unit to improve the accuracy of the specific processing by referring to the user's past specific data. A part or all of the above-mentioned processing in the specific processing unit may be performed using, for example, AI, or may be performed without using AI. For example, the specific processing unit can input the user's past specific data into the generation AI and improve the accuracy of the specific processing by the generation AI.

[0124] The suggestion unit can estimate the user's emotion and adjust the expression method of the suggestion based on the estimated user's emotion. For example, the suggestion unit provides detailed suggested content when the user is relaxed. In addition, the suggestion unit can provide concise suggested content when the user is nervous. Furthermore, the suggestion unit can also provide visually stimulating suggested content when the user is excited. This allows the suggestion unit to adjust the expression method of the suggestion based on the user's emotion. The estimation of the emotion 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, and adjust the expression method by the generation AI.

[0125] When making a proposal, the suggestion unit can select optimal proposal content by referring to the user's past exercise history. For example, the suggestion unit can suggest exercises that the user was good at in the past. The suggestion unit can also suggest to avoid exercises that the user was not good at in the past. Furthermore, the suggestion unit can also suggest balanced exercises from the user's past exercise history. This allows the suggestion unit to select optimal proposal content by referring to the user's past exercise history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's exercise history data to the generation AI, and the generation AI can select the proposal content.

[0126] The suggestion unit can customize the suggested content based on the user's current physical condition and mood at the time of suggestion. For example, when the user is not feeling well, the suggestion unit can suggest light exercise. Also, when the user is in good health, the suggestion unit can suggest harder exercise. Furthermore, when the user is feeling stressed, the suggestion unit can also suggest relaxing exercise. This allows the suggestion unit to customize the suggested content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's physical condition and mood data into the generation AI, and the suggestion content can be customized by the generation AI.

[0127] The suggestion unit can estimate the user's emotion and determine the priority of suggestions based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit preferentially suggests relaxing exercises. In addition, when the user is tense, the suggestion unit can preferentially suggest exercises that relieve tension. Furthermore, when the user is excited, the suggestion unit can preferentially suggest exercises that release energy. This allows the suggestion unit to determine the priority of suggestions based on 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 suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of suggestions.

[0128] When making a proposal, the suggestion unit can give priority to a proposal with high relevance in consideration of the geographical location information of the user. For example, when the user is outdoors, the suggestion unit can suggest an exercise that requires a large space. Also, when the user is indoors, the suggestion unit can suggest an exercise that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the suggestion unit can also suggest an exercise suitable for the facility. This allows the suggestion unit to give priority to a proposal with high relevance in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the geographical location information of the user to the generation AI, and the generation AI can select the proposal content.

[0129] At the time of making a proposal, the suggestion unit can analyze the user's social media activity and make a related proposal. For example, the suggestion unit can suggest an exercise related to a sport in which the user has shown an interest on social media. The suggestion unit can also make a proposal related to an exercise shared by the user with a friend. Furthermore, the suggestion unit can also suggest an exercise related to the training of an athlete the user follows. This allows the suggestion unit to analyze the user's social media activity and make a related proposal. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input the user's social media data into a generation AI, and the generation AI can select the proposal content.

[0130] The analysis unit can estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. For example, the analysis unit applies strict analysis criteria when the user is relaxed. Furthermore, the analysis unit can apply loose analysis criteria when the user is nervous. Furthermore, the analysis unit can also apply moderate analysis criteria when the user is excited. This allows the analysis unit to adjust the analysis criteria based on 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the analysis criteria using the generation AI.

[0131] The analysis unit can apply an advanced algorithm for performing a detailed analysis of the user's motion during the analysis. For example, the analysis unit can use a machine learning algorithm to perform a detailed analysis of the user's motion. The analysis unit can also use a deep learning algorithm to perform a detailed analysis of the user's motion. Furthermore, the analysis unit can also use a pattern recognition algorithm to perform a detailed analysis of the user's motion. This allows the analysis unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0132] The analysis unit can collect additional data for evaluating the consistency of the user's movements during the analysis. For example, the analysis unit collects scan data multiple times to evaluate the consistency of the user's movements. The analysis unit can also collect scan data from different angles to evaluate the consistency of the user's movements. Furthermore, the analysis unit can also collect scan data at different time periods to evaluate the consistency of the user's movements. This allows the analysis unit to collect additional data for evaluating the consistency of the user's movements. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's movement data into a generation AI, and the generation AI can evaluate the consistency.

[0133] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit displays a detailed analysis result when the user is relaxed. Also, the analysis unit can display a concise analysis result when the user is nervous. Furthermore, the analysis unit can display a visually stimulating analysis result when the user is excited. This allows the analysis unit to adjust the display method of the analysis result based on 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 analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and adjust the display method using the generation AI.

[0134] The analysis unit can optimize the analysis criteria in consideration of the geographical location information of the user during the analysis. For example, when the user is at high altitude, the analysis unit adjusts the analysis criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the analysis unit can apply standard analysis criteria. Furthermore, when the user is at a specific sports facility, the analysis unit can also apply analysis criteria suitable for the facility. This allows the analysis unit to optimize the analysis criteria in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the geographical location information of the user to the generation AI, and optimize the analysis criteria using the generation AI.

[0135] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis data during the analysis. For example, the analysis unit optimizes the analysis criteria based on the user's past analysis data. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis data. Furthermore, the analysis unit can analyze the user's past analysis data and adjust the analysis parameters. This allows the analysis unit to improve the accuracy of the analysis by referring to the user's past analysis data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI, and the generation AI can improve the accuracy of the analysis.

[0136] The motion identification unit can estimate the user's emotion and determine the priority of the identified exercises based on the estimated user's emotion. For example, when the user is relaxed, the motion identification unit preferentially identifies an exercise that relaxes the user. Furthermore, when the user is tense, the motion identification unit can preferentially identify an exercise that relieves tension. Furthermore, when the user is excited, the motion identification unit can preferentially identify an exercise that releases energy. This allows the motion identification unit to determine the priority of the identified exercises based on the user's emotion. The estimation of the 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 motion identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the motion identification unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the exercises.

[0137] The motion identification unit can apply an advanced algorithm for performing a detailed analysis of the user's motion at the time of identification. For example, the motion identification unit can use a machine learning algorithm to perform a detailed analysis of the user's motion. In addition, the motion identification unit can use a deep learning algorithm to perform a detailed analysis of the user's motion. Furthermore, the motion identification unit can also use a pattern recognition algorithm to perform a detailed analysis of the user's motion. This allows the motion identification unit to apply an advanced algorithm for performing a detailed analysis of the user's motion. A part or all of the above-mentioned processing in the motion identification unit may be performed using, for example, AI, or may be performed without using AI. For example, the motion identification unit can input the user's motion data to a generation AI, and the generation AI can perform a detailed analysis.

[0138] The motion identification unit can collect additional data for evaluating the consistency of the user's motion at the time of identification. For example, the motion identification unit collects scan data multiple times to evaluate the consistency of the user's motion. The motion identification unit can also collect scan data from different angles to evaluate the consistency of the user's motion. Furthermore, the motion identification unit can also collect scan data at different time periods to evaluate the consistency of the user's motion. This allows the motion identification unit to collect additional data for evaluating the consistency of the user's motion. A part or all of the above-mentioned processing in the motion identification unit may be performed, for example, using AI or may be performed without using AI. For example, the motion identification unit can input the user's motion data to a generation AI and have the generation AI evaluate the consistency.

[0139] The motion identification unit can estimate the user's emotion and adjust the display method of the identified motion based on the estimated user's emotion. For example, the motion identification unit displays detailed motion information when the user is relaxed. Also, the motion identification unit can display concise motion information when the user is nervous. Furthermore, the motion identification unit can display visually stimulating motion information when the user is excited. This allows the motion identification unit to adjust the display method of the identified motion based on 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 motion identification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the motion identification unit can input the user's emotion data to the generation AI, and the generation AI can adjust the display method.

[0140] The exercise identification unit can optimize the identification criteria in consideration of the geographical location information of the user at the time of identification. For example, when the user is at high altitude, the exercise identification unit adjusts the identification criteria in consideration of the oxygen concentration. In addition, when the user is at zero meters above sea level, the exercise identification unit can apply a standard identification criterion. Furthermore, when the user is at a specific sports facility, the exercise identification unit can also apply an identification criterion suitable for the facility. This allows the exercise identification unit to optimize the identification criterion in consideration of the geographical location information of the user. A part or all of the above-mentioned processing in the exercise identification unit may be performed, for example, using AI, or may be performed without using AI. For example, the exercise identification unit can input the geographical location information of the user to the generation AI, and optimize the identification criterion by the generation AI.

[0141] The motion identification unit can improve the accuracy of identification by referring to the user's past identification data at the time of identification. For example, the motion identification unit optimizes the identification criteria based on the user's past identification data. The motion identification unit can also improve the accuracy of identification by referring to the user's past identification data. Furthermore, the motion identification unit can analyze the user's past identification data and adjust the identification parameters. Thereby, the motion identification unit can improve the accuracy of identification by referring to the user's past identification data. A part or all of the above-mentioned processing in the motion identification unit may be performed, for example, using AI or may be performed without using AI. For example, the motion identification unit can input the user's past identification data to the generation AI and improve the accuracy of identification by the generation AI.

[0142] The feedback unit can estimate the user's emotion and adjust the content of the feedback based on the estimated user's emotion. For example, the feedback unit can provide detailed feedback when the user is relaxed. Also, the feedback unit can provide concise feedback when the user is nervous. Furthermore, the feedback unit can also provide visually stimulating feedback when the user is excited. This allows the feedback unit to adjust the content of the feedback based on 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, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI, and the generation AI can adjust the feedback content.

[0143] The feedback unit can provide optimal feedback by referring to the user's past exercise history when providing feedback. For example, the feedback unit provides feedback regarding exercises that the user was good at in the past. The feedback unit can also provide feedback regarding exercises that the user was not good at in the past. Furthermore, the feedback unit can also provide balanced feedback from the user's past exercise history. This allows the feedback unit to provide optimal feedback by referring to the user's past exercise history. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's exercise history data to a generation AI, and the generation AI can select the feedback content.

[0144] The feedback unit can customize the feedback content based on the user's current physical condition and mood at the time of feedback. For example, when the user is not feeling well, the feedback unit provides feedback on light exercise. Also, when the user is in good health, the feedback unit can provide feedback on harder exercise. Furthermore, when the user is feeling stressed, the feedback unit can also provide feedback on relaxing exercise. This allows the feedback unit to customize the feedback content based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the feedback content.

[0145] The feedback unit can estimate the user's emotion and determine the priority of the feedback based on the estimated user's emotion. For example, when the user is relaxed, the feedback unit can provide feedback on relaxing exercises preferentially. Furthermore, when the user is tense, the feedback unit can provide feedback on relieving exercises preferentially. Furthermore, when the user is excited, the feedback unit can also provide feedback on energy-relaxing exercises preferentially. This allows the feedback unit to determine the priority of the feedback based on 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, an AI, or may be performed without using an AI. For example, the feedback unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the feedback.

[0146] The feedback unit can give priority to highly relevant feedback in consideration of the geographical location information of the user when providing feedback. For example, when the user is outdoors, the feedback unit provides feedback on exercises that require a large space. Also, when the user is indoors, the feedback unit can provide feedback on exercises that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the feedback unit can also provide feedback on exercises that are suitable for that facility. This allows the feedback unit to give priority to highly relevant feedback in consideration of the geographical location information of the user. 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 geographical location information of the user to the generation AI, and the generation AI can select the feedback content.

[0147] At the time of feedback, the feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit provides feedback related to a sport in which the user has shown interest on social media. The feedback unit can also provide feedback related to an exercise the user has shared with a friend. Furthermore, the feedback unit can also provide feedback related to the training of an athlete the user follows. This allows the feedback unit to analyze the user's social media activity and provide relevant feedback. Some 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 social media data into a generation AI, and the generation AI can select the feedback content.

[0148] The storage unit can estimate the user's emotion and select data to be stored based on the estimated user's emotion. For example, the storage unit stores detailed exercise data when the user is relaxed. Also, the storage unit can store concise exercise data when the user is nervous. Furthermore, the storage unit can store visually stimulating exercise data when the user is excited. This allows the storage unit to select data to be stored based on 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI and select the stored data by the generation AI.

[0149] The storage unit can store optimal data by referring to the user's past exercise data when storing. For example, the storage unit preferentially stores exercise data that the user was good at in the past. The storage unit can also store exercise data that the user was not good at in the past. Furthermore, the storage unit can also store balanced data from the user's past exercise data. This allows the storage unit to store optimal data by referring to the user's past exercise data. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's past exercise data to the generation AI, and the generation AI can select the stored data.

[0150] The storage unit can customize the data to be stored based on the user's current physical condition and mood when storing. For example, when the user is not feeling well, the storage unit stores light exercise data. Also, when the user is in good health, the storage unit can store harder exercise data. Furthermore, when the user is feeling stressed, the storage unit can also store relaxing exercise data. This allows the storage unit to customize the data to be stored based on the user's current physical condition and mood. A part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's physical condition and mood data into the generation AI, and the generation AI can customize the stored data.

[0151] The storage unit can estimate the user's emotion and determine the priority of the stored data based on the estimated user's emotion. For example, when the user is relaxed, the storage unit preferentially stores the exercise data that relaxes the user. Also, when the user is tense, the storage unit can preferentially store the exercise data that relieves tension. Furthermore, when the user is excited, the storage unit can also preferentially store the exercise data that releases energy. This allows the storage unit to determine the priority of the stored data based on 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the user's emotion data to the generation AI, and the generation AI can determine the priority of the stored data.

[0152] The storage unit can store highly relevant data preferentially in consideration of the user's geographical location information when storing. For example, when the user is outdoors, the storage unit stores exercise data that requires a large space. Also, when the user is indoors, the storage unit can store exercise data that can be performed in a limited space. Furthermore, when the user is in a specific sports facility, the storage unit can also store exercise data suitable for that facility. This allows the storage unit to store highly relevant data preferentially in consideration of the user's geographical location information. Part or all of the above-mentioned processing in the storage unit may be performed using, for example, AI, or may be performed without using AI. For example, the storage unit can input the user's geographical location information to the generation AI, and the generation AI can select the stored data.

[0153] The storage unit can analyze the user's social media activity and store the relevant data when storing. For example, the storage unit stores data related to a sport in which the user has shown interest on social media. The storage unit can also store data related to exercises shared by the user with friends. Furthermore, the storage unit can also store data related to training of athletes the user follows. This allows the storage unit to analyze the user's social media activity and store the relevant data. Some or all of the above-mentioned processing in the storage unit may be performed, for example, using AI or may be performed without using AI. For example, the storage unit can input the user's social media data to a generation AI, and the generation AI can select the stored data.

[0154] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0155] The request unit can also adjust the intensity of the requested action based on the user's physical condition and mood. For example, if the user is tired, light exercise is requested. If the user is energetic, harder exercise can be requested. Furthermore, if the user is feeling stressed, relaxing exercise can be requested. This allows the request unit to adjust the intensity of the requested action based on the user's physical condition and mood.

[0156] The analysis unit can also analyze the pattern of the user's motion and evaluate the efficiency of the user's motion. For example, the analysis unit can analyze the repetitive pattern of the user's motion and identify efficient motion. The analysis unit can also analyze the timing of the user's motion and identify optimal timing. Furthermore, the analysis unit can analyze the energy consumption of the user's motion and identify efficient energy consumption. This allows the analysis unit to evaluate the efficiency of the user's motion.

[0157] The motion specification unit can also evaluate the performance of the user's movements and specify a training plan suitable for the user. For example, the motion specification unit can evaluate the speed and endurance of the user's movements and specify an optimal training plan. The motion specification unit can also evaluate the reaction time of the user's movements and specify a training plan to improve the reaction time. Furthermore, the motion specification unit can also evaluate the balance of the user's movements and specify a training plan to improve the balance. In this way, the motion specification unit can specify a training plan suitable for the user.

[0158] The determination unit can also evaluate the stability of the user's motion and identify areas for improvement in the user's motion. For example, the determination unit can evaluate the shaking of the user's motion and provide advice for improving the stability. The determination unit can also evaluate the consistency of the user's motion and provide advice for improving the consistency. Furthermore, the determination unit can evaluate the accuracy of the user's motion and provide advice for improving the accuracy. This allows the determination unit to identify areas for improvement in the user's motion.

[0159] The suggestion unit can also suggest a combination of exercises suitable for the user based on the user's exercise history. For example, the suggestion unit can analyze data on exercises the user has performed in the past and suggest an optimal combination of exercises. The suggestion unit can also suggest a balanced exercise plan based on the user's exercise history. Furthermore, the suggestion unit can also suggest an exercise plan aimed at a specific goal based on the user's exercise history. In this way, the suggestion unit can suggest a combination of exercises suitable for the user.

[0160] The request unit can also estimate the user's emotion and adjust the type of action to be requested based on the estimated user's emotion. For example, if the user is relaxed, an action that helps the user to relax can be requested. If the user is tense, an action that relieves tension can be requested. Furthermore, if the user is excited, an action that releases energy can be requested. This allows the request unit to adjust the type of action to be requested based on the user's emotion.

[0161] The scanning unit can also estimate the user's emotion and adjust the accuracy of the scan based on the estimated emotion of the user. For example, if the user is relaxed, a detailed scan can be performed. If the user is nervous, a simple scan can be performed. Furthermore, if the user is excited, a scan with moderate accuracy can be performed. This allows the scanning unit to adjust the accuracy of the scan based on the emotion of the user.

[0162] The determination unit can also estimate the user's emotion and adjust the method of feedback of the determination result based on the estimated user's emotion. For example, when the user is relaxed, detailed feedback can be provided. When the user is nervous, brief feedback can be provided. Furthermore, when the user is excited, visually stimulating feedback can be provided. This allows the determination unit to adjust the method of feedback of the determination result based on the user's emotion.

[0163] The identification processing unit can also estimate the user's emotion and adjust the type of exercise to be identified based on the estimated user's emotion. For example, when the user is relaxed, an exercise that relaxes the user can be identified. When the user is tense, an exercise that relieves tension can be identified. Furthermore, when the user is excited, an exercise that releases energy can be identified. This allows the identification processing unit to adjust the type of exercise to be identified based on the user's emotion.

[0164] The suggestion unit can also estimate the user's emotion and adjust the level of detail of the suggested content based on the estimated user's emotion. For example, when the user is relaxed, detailed suggested content can be provided. When the user is nervous, concise suggested content can be provided. Furthermore, when the user is excited, visually stimulating suggested content can be provided. In this way, the suggestion unit can adjust the level of detail of the suggested content based on the user's emotion.

[0165] The process flow of the second embodiment will be briefly described below.

[0166] Step 1: The request unit requests a plurality of types of motions from the user. For example, the request unit can request the user to do motions such as "10 sit-ups, 10 side jumps, 100m sprint, right hand throwing motion, left hand throwing motion, rowing motion, right hand tennis swing, left hand tennis swing." Step 2: The scanning unit scans multiple types of movements made by the user in response to the request from the request unit. For example, the scanning unit scans the movements of the user using a camera or an infrared sensor to grasp the user's body shape, motor skills, and physical strength. Step 3: The determination unit determines the user's body shape, motor skills, and physical strength based on the information on the user's movements acquired by the scanning unit. For example, the determination unit measures the user's movement speed, endurance, reaction time, etc., and evaluates the user's athletic ability based on this data. Step 4: The identification processing unit identifies an exercise suitable for the user based on the result of the determination by the determination unit. For example, the identification processing unit uses a generation AI to identify an optimal sport based on the characteristics of the user. Step 5: The suggestion unit suggests the exercise identified by the identification processing unit to the user. For example, if the generation AI answers "Fencing. And left-handedness is better," the suggestion unit suggests fencing to the user.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] For example, the request unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the scan unit is realized using the camera 42 or an infrared sensor of the smart device 14. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the specific processing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0171] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] For example, the request unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the scanning unit is realized using the camera 42 of the smart glasses 214 or an infrared sensor. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the specific processing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0187] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] For example, the request unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the scanning unit is realized using the camera 42 or an infrared sensor of the headset type terminal 314. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the specific processing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0203] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] For example, the request unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the scanning unit is realized using the camera 42 or an infrared sensor of the robot 414. For example, the determination unit is realized by the specific processing unit 290 of the data processing device 12. For example, the specific processing unit is realized by the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0220] 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.

[0221] 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.

[0222] These emotions are distributed in the 3 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.

[0223] 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).

[0224] 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.

[0225] 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."

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] (Appendix 1) A request unit that requests a plurality of types of movements from a user; a scanning unit configured to scan the plurality of types of movements performed by the user in response to a request from the request unit; A determination unit that determines the user's body shape, motor skills, and physical strength based on the information on the user's movement acquired by the scan unit; A specific processing unit that identifies an exercise suitable for the user based on a result of the determination by the determination unit; A suggestion unit that suggests the exercise identified by the identification processing unit to the user. A system characterized by: (Appendix 2) The specific processing unit is Analyzing the information on the user's movement acquired by the scanning unit; An analysis unit that extracts data on each element of the user's body shape, motor skills, and physical strength 2. The system of claim 1, (Appendix 3) The specific processing unit is Based on the data extracted by the analysis unit, an exercise specification unit that specifies an exercise suitable for the user; 3. The system of claim 2, (Appendix 4) The determination unit is Generate a 3D model of the user's movement information; Determining the balance of the user's skeleton based on the 3D model 2. The system of claim 1, (Appendix 5) The determination unit is measuring the user's speed of movement, endurance, and reaction time; The motor skills and physical strength of the user are determined based on the measurement results. 2. The system of claim 1, (Appendix 6) The suggestion unit, Detailed information regarding the exercise identified by the identification processing unit, Provide to the user's parent or guardian 2. The system of claim 1, (Appendix 7) The system comprises: Real-time analysis of the user's movements; The feedback unit further provides feedback to the user on various actions. 2. The system of claim 1, (Appendix 8) The system comprises: A storage unit that stores past exercise data of the user, Based on the data stored in the storage unit, Tracking the progress of the user's athletic ability; Continuously recommend appropriate exercise 2. The system of claim 1, (Appendix 9) The request unit is Estimate the user's emotions, Adjust the order of requested actions based on the estimated user emotion 2. The system of claim 1, (Appendix 10) The request unit is Analyze the user's past exercise history, Select the best request 2. The system of claim 1, (Appendix 11) The request unit is Upon request, Customize requests based on the user's current health and mood 2. The system of claim 1, (Appendix 12) The request unit is Estimate the user's emotions, Adjust the difficulty of the requested action based on the estimated user emotion 2. The system of claim 1, (Appendix 13) The request unit is Upon request, Prioritize requests for actions that are more relevant based on the user's geographic location 2. The system of claim 1, (Appendix 14) The request unit is Upon request, Analyze your social media activity; Request the relevant action 2. The system of claim 1, (Appendix 15) The scanning unit includes: Estimate the user's emotions, Adjusting scan timing based on estimated user emotions 2. The system of claim 1, (Appendix 16) The scanning unit includes: When scanning, Applying high-precision scanning technology to detect subtle changes in user behavior 2. The system of claim 1, (Appendix 17) The scanning unit includes: When scanning, Use additional sensors to measure the speed and angle of user movements in detail 2. The system of claim 1, (Appendix 18) The scanning unit includes: Estimate the user's emotions, Adjusting the scan range based on estimated user emotions 2. The system of claim 1, (Appendix 19) The scanning unit includes: When scanning, Optimize scan settings by taking into account the user's geographic location 2. The system of claim 1, (Appendix 20) The scanning unit includes: When scanning, Improve scan accuracy by referencing users' past scan data 2. The system of claim 1, (Appendix 21) The determination unit is Estimate the user's emotions, Adjusting the criteria based on the estimated user's emotions 2. The system of claim 1, (Appendix 22) The determination unit is At the time of judgment, Applying advanced algorithms to perform detailed analysis of user behavior 2. The system of claim 1, (Appendix 23) The determination unit is At the time of judgment, Collect additional data to evaluate the consistency of user behavior 2. The system of claim 1, (Appendix 24) The determination unit is Estimate the user's emotions, Adjust the way judgment results are displayed based on the estimated user's emotions 2. The system of claim 1, (Appendix 25) The determination unit is At the time of judgment, Optimize the criteria by considering the user's geographic location 2. The system of claim 1, (Appendix 26) The determination unit is At the time of judgment, Improve accuracy of judgment by referring to the user's past judgment data 2. The system of claim 1, (Appendix 27) The specific processing unit is Estimate the user's emotions, Prioritize exercises to be identified based on estimated user emotions 2. The system of claim 1, (Appendix 28) The specific processing unit is During specific processing, Applying advanced algorithms to perform detailed analysis of user behavior 2. The system of claim 1, (Appendix 29) The specific processing unit is During specific processing, Collect additional data to evaluate the consistency of user behavior 2. The system of claim 1, (Appendix 30) The specific processing unit is Estimate the user's emotions, Adjusting the display method of specific exercises based on the estimated user emotion 2. The system of claim 1, (Appendix 31) The specific processing unit is During specific processing, Optimize specific criteria by taking into account the user's geographic location 2. The system of claim 1, (Appendix 32) The specific processing unit is During specific processing, Improve the accuracy of identification by referring to the user's past identification data 2. The system of claim 1, (Appendix 33) The suggestion unit, Estimate the user's emotions, Adjust the presentation of suggestions based on estimated user sentiment 2. The system of claim 1, (Appendix 34) The suggestion unit, When proposing, Select optimal suggestions by referring to the user's past exercise history 2. The system of claim 1, (Appendix 35) The suggestion unit, When proposing, Customize suggestions based on the user's current state and mood 2. The system of claim 1, (Appendix 36) The suggestion unit, Estimate the user's emotions, Prioritize suggestions based on estimated user sentiment 2. The system of claim 1, (Appendix 37) The suggestion unit, When proposing, Prioritize relevant suggestions by taking into account the user's geographic location 2. The system of claim 1, (Appendix 38) The suggestion unit, When proposing, Analyze your social media activity; Make relevant suggestions 2. The system of claim 1, (Appendix 39) The analysis unit is Estimate the user's emotions, Adjust analysis criteria based on estimated user sentiment 2. The system of claim 1, (Appendix 40) The analysis unit is During analysis, Applying advanced algorithms to perform detailed analysis of user behavior 2. The system of claim 1, (Appendix 41) The analysis unit is During analysis, Collect additional data to evaluate the consistency of user behavior 2. The system of claim 1, (Appendix 42) The analysis unit is Estimate the user's emotions, Adjust the way analysis results are displayed based on estimated user emotions 2. The system of claim 1, (Appendix 43) The analysis unit is During analysis, Optimize analysis criteria by considering the user's geographic location 2. The system of claim 1, (Appendix 44) The analysis unit is During analysis, Improve the accuracy of analysis by referring to the user's past analysis data 2. The system of claim 1, (Appendix 45) The exercise specification unit is Estimate the user's emotions, Prioritize exercises to be identified based on estimated user emotions 4. The system of claim 3. (Appendix 46) The exercise specification unit is At the specific time, Applying advanced algorithms to perform detailed analysis of user behavior 4. The system of claim 3. (Appendix 47) The exercise specification unit is At the specific time, Collect additional data to evaluate the consistency of user behavior 4. The system of claim 3. (Appendix 48) The exercise specification unit is Estimate the user's emotions, Adjusting the display method of specific exercises based on the estimated user emotion 4. The system of claim 3. (Appendix 49) The exercise specification unit is At the specific time, Optimize specific criteria by taking into account the user's geographic location 4. The system of claim 3. (Appendix 50) The exercise specification unit is At the specific time, Improve the accuracy of identification by referring to the user's past identification data 4. The system of claim 3. (Appendix 51) The feedback unit is Estimate the user's emotions, Adjusting feedback based on estimated user emotions 8. The system of claim 7, (Appendix 52) The feedback unit is When giving feedback, Provide optimal feedback by referencing the user's past exercise history 8. The system of claim 7, (Appendix 53) The feedback unit is When giving feedback, Customize feedback based on the user's current state or mood 8. The system of claim 7, (Appendix 54) The feedback unit is Estimate the user's emotions, Prioritize feedback based on estimated user sentiment 8. The system of claim 7, (Appendix 55) The feedback unit is When giving feedback, Prioritize relevant feedback by taking into account the user's geographic location 8. The system of claim 7, (Appendix 56) The feedback unit is When giving feedback, Analyze your social media activity; Give relevant feedback 8. The system of claim 7, (Appendix 57) The storage unit is Estimate the user's emotions, Select data to be memorized based on estimated user emotions 9. The system of claim 8, (Appendix 58) The storage unit is When memorizing, Refer to the user's past exercise data and memorize the optimal data 9. The system of claim 8, (Appendix 59) The storage unit is When memorizing, Customize the data you remember based on your current physical condition or mood 9. The system of claim 8, (Appendix 60) The storage unit is Estimate the user's emotions, Prioritize stored data based on estimated user emotions 9. The system of claim 8, (Appendix 61) The storage unit is When memorizing, Prioritize storage of relevant data based on the user's geographic location 9. The system of claim 8, (Appendix 62) The storage unit is When memorizing, Analyze your social media activity; Remembering relevant data 9. The system of claim 8, [Explanation of symbols]

[0239] 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 request unit that requests a plurality of types of movements from a user; a scanning unit configured to scan the plurality of types of movements performed by the user in response to a request from the request unit; A determination unit that determines the user's body shape, motor skills, and physical strength based on the information on the user's movement acquired by the scan unit; A specific processing unit that identifies an exercise suitable for the user based on a result of the determination by the determination unit; A suggestion unit that suggests the exercise identified by the identification processing unit to the user. A system characterized by:

2. The specific processing unit is An analysis unit that analyzes the information on the user's movements acquired by the scanning unit and extracts each element of the user's body shape, motor skills, and physical strength as data.

2. The system of claim 1 .

3. The specific processing unit is An exercise specification unit that specifies an exercise suitable for the user based on the data extracted by the analysis unit.

3. The system of claim 2.

4. The determination unit is The information on the user's movements is generated as a 3D model, and the balance of the user's skeleton is determined based on the 3D model.

2. The system of claim 1 .

5. The determination unit is Measure the user's movement speed, endurance, and reaction time, and determine the user's motor skills and physical strength based on the measurement results.

2. The system of claim 1 .

6. The suggestion unit, Providing detailed information regarding the exercise identified by the identification processing unit to a parent or guardian of the user.

2. The system of claim 1 .

7. The system comprises: The present invention further includes a feedback unit that analyzes the user's movements in real time and provides feedback on various actions performed by the user.

2. The system of claim 1 .

8. The system comprises: A storage unit that stores past exercise data of the user, The suggestion unit, Based on the data stored in the storage unit, the progress of the user's athletic ability is tracked and suitable exercises are continuously suggested.

2. The system of claim 1 .

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