system

Abstract

The system according to this embodiment aims to naturally guide the wearer to the optimal form of a professional athlete, thereby improving form and preventing injuries. [Solution] The system according to the embodiment comprises an analysis unit, a guidance unit, an optimization unit, and a guidance mechanism unit. The analysis unit analyzes video data of professional athletes and extracts the optimal form. The guidance unit naturally guides the form of the wearer based on the optimal form extracted by the analysis unit. The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. The guidance mechanism unit guides the form optimized by the optimization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to learn the form of professional athletes and naturally acquire it, and there are problems in improving the form and preventing injuries.

[0005] The system according to the embodiment aims to naturally guide the optimal form of professional athletes to the wearer and improve the form and prevent injuries.

Means for Solving the Problems

[0006] [[ID=The system according to this embodiment comprises an analysis unit, a guidance unit, an optimization unit, and a guidance mechanism unit. The analysis unit analyzes video data of professional athletes and extracts the optimal form. The guidance unit naturally guides the form of the wearer based on the optimal form extracted by the analysis unit. The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. The guidance mechanism unit guides the form optimized by the optimization unit. [Effects of the Invention]

[0007] The system according to this embodiment can naturally guide the wearer to the optimal form for professional athletes, thereby improving form and preventing injuries. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The sports form guidance system according to an embodiment of the present invention is a system in which an optimized form is input from video data of various professional athletes, and a suit worn by the customer naturally guides the wearer's form. This sports form guidance system aims to improve sports technique naturally, improve form, and prevent injuries. For example, the sports form guidance system collects video data of professional athletes in sports such as baseball, soccer, dance, and muscle training, and an AI analyzes this data to extract the optimal form. In this process, the AI ​​identifies the optimal form considering the characteristics of each sport and the details of the movements. Next, the sports form guidance system has a suit worn by the customer that naturally guides the wearer's form. This suit has the function of further optimizing the incorporated optimized form to the wearer's body size. Specifically, using heat-hardening fiber technology, the wearer's movements are naturally guided to the optimized form. As a result, the wearer can adopt the correct form without strain. Furthermore, wearing this suit can be expected to improve sports technique naturally, improve form, and prevent injuries. For example, in various sports movements such as baseball swings, soccer kicks, and dance steps, mastering the correct form leads to improved technique. Furthermore, mastering the correct form reduces unnatural movements, which helps prevent injuries. Thus, the sports form guidance system uses AI to extract the optimal form from professional athletes and provides a suit that naturally guides the wearer's form, thereby improving sports technique, refining form, and preventing injuries. In this way, the sports form guidance system can achieve improved sports technique, refined form, and injury prevention for the wearer.

[0029] The sports form guidance system according to this embodiment comprises an analysis unit, a guidance unit, an optimization unit, and a guidance mechanism unit. The analysis unit analyzes video data of professional athletes and extracts the optimal form. Video data of professional athletes includes, but is not limited to, baseball, soccer, dance, and muscle training. For example, the analysis unit analyzes a baseball swing motion and extracts the optimal form. The analysis unit can also analyze a soccer kicking motion and extract the optimal form. Furthermore, the analysis unit can analyze a dance step motion and extract the optimal form. For example, the analysis unit uses AI to analyze video data of professional athletes and extract the optimal form. The AI ​​identifies the optimal form considering the characteristics of each sport and the details of the movements. The guidance unit naturally guides the form of the wearer based on the optimal form extracted by the analysis unit. The guidance unit uses, for example, heat-hardening fiber technology to naturally guide the wearer's movements to the optimized form. The guidance unit monitors the wearer's movements in real time and guides them to the optimal form. Furthermore, the guidance unit can analyze the wearer's movements and guide them to the optimal form. In addition, the guidance unit can correct the wearer's movements and guide them to the optimal form. For example, the guidance unit uses AI to analyze the wearer's movements and guide them to the optimal form. The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. For example, the optimization unit adjusts the optimal form based on the wearer's body size. Furthermore, the optimization unit can adjust the optimal form to match the wearer's body size. In addition, the optimization unit can adjust the optimal form based on the wearer's body size. For example, the optimization unit uses AI to measure the wearer's body size and adjust the optimal form. The guidance mechanism unit guides the form optimized by the optimization unit. For example, the guidance mechanism unit naturally guides the wearer's movements to the optimized form. For example, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form.Furthermore, the guidance mechanism can analyze the wearer's movements and guide them to the optimal form. It can also correct the wearer's movements and guide them to the optimal form. For example, the guidance mechanism uses AI to analyze the wearer's movements and guide them to the optimal form. As a result, the sports form guidance system according to this embodiment can improve the wearer's sports skills, refine their form, and prevent injuries.

[0030] The analysis unit analyzes video data of professional athletes and extracts the optimal form. This video data includes, but is not limited to, examples of sports such as baseball, soccer, dance, and muscle training. For example, the analysis unit can analyze a baseball swing and extract the optimal form. It can also analyze a soccer kick and extract the optimal form. Furthermore, it can analyze dance step movements and extract the optimal form. For instance, the analysis unit uses AI to analyze video data of professional athletes and extract the optimal form. The AI ​​identifies the optimal form by considering the characteristics of each sport and the details of the movements. Specifically, the AI ​​uses deep learning technology to extract the movements of the athlete's skeleton and joints from the video data and analyze the movement patterns. For example, in a baseball swing, it analyzes elements such as the bat trajectory, weight transfer, and hip rotation to identify the optimal swing form. In a soccer kick, it analyzes the way the leg is swung up, the timing of impact with the ball, and body balance to identify the optimal kick form. In dance step movements, the system analyzes the rhythm of the steps, foot position, and the coordination of body movements to identify the optimal step form. This allows the analysis unit to accurately extract the optimal form for each sport and provide it to the user. Furthermore, by analyzing video data not only from professional athletes but also from amateur athletes and beginners, the analysis unit can cater to a wide range of user levels. Through the analysis and optimization of sports forms, the analysis unit can support the improvement of users' skills.

[0031] The guidance unit naturally guides the wearer's form based on the optimal form extracted by the analysis unit. The guidance unit uses, for example, heat-curing fiber technology to naturally guide the wearer's movements to an optimized form. Specifically, the guidance unit monitors the wearer's movements in real time and guides them to the optimal form. For example, when a wearer swings a baseball, the guidance unit monitors the wearer's arm and waist movements and makes fine adjustments to approach the optimal swing form. The guidance unit uses AI to analyze the wearer's movements and guide them to the optimal form. The AI ​​analyzes the wearer's motion data in real time and detects deviations from the optimal form. For example, if the wearer's swing motion differs from the optimal form, the guidance unit corrects the wearer's movements and guides them to the optimal form. This allows the guidance unit to naturally optimize the wearer's movements and support the improvement of their sports skills. Furthermore, the guidance unit can not only correct the wearer's movements but also provide motion feedback. For example, when the wearer approaches the optimal form, feedback is provided through vibrations or sounds to make them aware of the correct form. In this way, the guidance unit can promote the wearer's form improvement and support the enhancement of their sports technique.

[0032] The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. Specifically, the optimization unit acquires data such as the wearer's height, weight, and body type, and adjusts the optimal form. For example, since the optimal form differs for tall and short wearers, the optimization unit adjusts the form based on the wearer's body size. The optimization unit uses AI to measure the wearer's body size and adjust the optimal form. The AI ​​analyzes the wearer's body data and identifies the optimal form. For example, it adjusts the optimal swing form or kick form based on the wearer's arm length and leg length. In this way, the optimization unit can provide the optimal form tailored to the wearer's body size and support the improvement of their sports technique. Furthermore, the optimization unit can adjust the optimal form by considering not only the wearer's body size but also their physical abilities such as muscle strength and flexibility. In this way, the optimization unit can provide the optimal form for each individual wearer and support the improvement of their sports technique.

[0033] The guidance mechanism unit guides the wearer to the form optimized by the optimization unit. For example, the guidance mechanism unit naturally guides the wearer's movements to the optimized form. Specifically, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form. For example, when a wearer performs a soccer kick, the guidance mechanism unit monitors the wearer's foot movements and makes fine adjustments to approach the optimal kick form. The guidance mechanism unit uses AI to analyze the wearer's movements and guide them to the optimal form. The AI ​​analyzes the wearer's movement data in real time and detects the difference from the optimal form. For example, if the wearer's kicking motion differs from the optimal form, the guidance mechanism unit corrects the wearer's movements and guides them to the optimal form. In this way, the guidance mechanism unit can naturally optimize the wearer's movements and support the improvement of sports skills. Furthermore, the guidance mechanism unit can not only correct the wearer's movements but also provide feedback on the movements. For example, when the wearer approaches the optimal form, it provides feedback through vibration or sound to make them aware of the correct form. This allows the guidance mechanism to promote improvement in the wearer's form and support the enhancement of sports techniques.

[0034] The analysis unit analyzes video data of professional athletes in sports such as baseball, soccer, dance, and muscle training, and extracts the optimal form. For example, the analysis unit can analyze a baseball swing motion and extract the optimal form. The analysis unit can also analyze a soccer kicking motion and extract the optimal form. The analysis unit can also analyze a dance step motion and extract the optimal form. In this way, the optimal form can be extracted by analyzing video data of professional athletes in various sports. The optimal form is evaluated based on criteria such as the efficiency of the movement and the reduction of the risk of injury. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0035] The guidance unit uses heat-curing fiber technology to naturally guide the wearer's movements into an optimized form. For example, the guidance unit uses heat-curing fiber technology to monitor the wearer's movements in real time and guide them to the optimal form. The guidance unit can also analyze the wearer's movements and guide them to the optimal form. The guidance unit can also correct the wearer's movements and guide them to the optimal form. In this way, by using heat-curing fiber technology, the wearer's movements can be naturally guided into an optimized form. The method of natural guidance is evaluated based on criteria such as the smoothness of movement and the wearer's comfort. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's movements into a generating AI and have the generating AI perform the guidance to the optimal form.

[0036] The optimization unit optimizes the garment to the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. The optimization unit can also adjust the optimal form based on the wearer's body size. The optimization unit can also adjust the optimal form to match the wearer's body size. This allows the wearer to adopt the correct form without strain by optimizing to their body size. The optimization method is evaluated based on criteria such as the method of matching body size and the details of the adjustment. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the wearer's body size into a generating AI and have the generating AI perform the adjustment to the optimal form.

[0037] The guidance mechanism unit naturally guides the wearer's movements to an optimized form. For example, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form. The guidance mechanism unit can also analyze the wearer's movements and guide them to the optimal form. The guidance mechanism unit can also correct the wearer's movements and guide them to the optimal form. In this way, by naturally guiding the wearer's movements to an optimized form, the wearer can acquire the correct form. The method of natural guidance is evaluated based on criteria such as the smoothness of movement and the wearer's comfort. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input the wearer's movements into a generating AI and have the generating AI perform the guidance to the optimal form.

[0038] The analysis unit extracts the optimal form by considering the speed and rhythm of the movements when analyzing video data of professional athletes. For example, the analysis unit compares the form at fast and slow speeds from the video data of professional athletes and extracts the form at the optimal speed. For example, the analysis unit analyzes the movements of professional athletes in accordance with their rhythm and extracts the form that is best suited to that rhythm. For example, the analysis unit analyzes the speed and rhythm of professional athletes in combination and extracts the optimal form. In this way, the optimal form can be extracted by considering the speed and rhythm of the movements. The speed and rhythm of the movements are evaluated based on criteria such as the timing and rhythm patterns of the movements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of professional athletes into a generating AI and have the generating AI perform the extraction of a form that takes into account the speed and rhythm of the movements.

[0039] The analysis unit extracts forms while considering the physique and muscle usage of professional athletes during analysis. For example, the analysis unit extracts the optimal form for a person with the same physique as a professional athlete based on their physique. For example, the analysis unit analyzes the muscle usage of professional athletes and extracts a form that matches the muscle usage of a person wearing the garment. For example, the analysis unit analyzes a combination of the physique and muscle usage of professional athletes and extracts the optimal form. In this way, by considering the physique and muscle usage of professional athletes, the optimal form for a person wearing the garment can be extracted. The physique and muscle usage are evaluated based on criteria such as muscle movement and physique characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the physique and muscle usage of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0040] The analysis unit improves analysis accuracy by considering the shooting angle and distance of the professional athlete's video data during analysis. For example, the analysis unit analyzes the shooting angle of the professional athlete's video data and extracts the form at the optimal angle. For example, the analysis unit analyzes the shooting distance of the professional athlete's video data and extracts the form at the optimal distance. For example, the analysis unit analyzes the shooting angle and distance of the professional athlete's video data in combination and extracts the optimal form. In this way, the analysis accuracy can be improved by considering the shooting angle and distance of the professional athlete's video data. The shooting angle and distance are evaluated based on criteria such as the camera position and shooting distance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shooting angle and distance data of the professional athlete's video data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0041] The analysis unit extracts forms by referring to the past performance data of professional athletes during the analysis. For example, the analysis unit analyzes the past performance data of professional athletes and extracts the optimal form. For example, the analysis unit refers to the past performance data of professional athletes and extracts a form to reproduce the same performance. For example, the analysis unit extracts the optimal form based on the past performance data of professional athletes. In this way, the optimal form can be extracted by referring to the past performance data of professional athletes. Past performance data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the past performance data of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0042] The guidance unit adjusts the guidance method during guidance, taking into account the degree of muscle tension and fatigue of the person wearing the garment. For example, the guidance unit analyzes the degree of muscle tension of the person wearing the garment and adjusts the optimal guidance method. For example, the guidance unit analyzes the degree of fatigue of the person wearing the garment and adjusts the optimal guidance method. For example, the guidance unit analyzes the degree of muscle tension and fatigue of the person wearing the garment in combination and adjusts the optimal guidance method. In this way, the optimal guidance method can be provided by taking into account the degree of muscle tension and fatigue of the person wearing the garment. The degree of muscle tension and fatigue is evaluated based on criteria such as electromyography or fatigue measurement methods. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI. For example, the guidance unit can input data on the degree of muscle tension and fatigue of the person wearing the garment into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0043] The guidance unit provides guidance while considering the timing and rhythm of the wearer's movements. For example, the guidance unit analyzes the timing of the wearer's movements and adjusts the optimal guidance method. For example, the guidance unit analyzes the rhythm of the wearer's movements and adjusts the optimal guidance method. For example, the guidance unit analyzes the timing and rhythm of the wearer's movements in combination and adjusts the optimal guidance method. This makes it possible to provide optimal guidance by considering the timing and rhythm of the wearer's movements. The timing and rhythm of movements are evaluated based on criteria such as patterns of timing and rhythm of movements. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the timing and rhythm of the wearer's movements into a generating AI and have the generating AI perform adjustments to the optimal guidance method.

[0044] The guidance unit optimizes the guidance method by referring to the wearer's past movement data during guidance. For example, the guidance unit analyzes the wearer's past movement data and adjusts the optimal guidance method. For example, the guidance unit refers to the wearer's past movement data and adjusts the guidance method to reproduce the same movement. For example, the guidance unit adjusts the optimal guidance method based on the wearer's past movement data. In this way, the optimal guidance method can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0045] The guidance unit monitors the wearer's body temperature and heart rate during guidance and adjusts the guidance method accordingly. For example, the guidance unit monitors the wearer's body temperature and adjusts the optimal guidance method. For example, the guidance unit monitors the wearer's heart rate and adjusts the optimal guidance method. For example, the guidance unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal guidance method. This allows the system to provide the optimal guidance method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0046] The optimization unit performs optimization while considering the wearer's body shape and muscle usage. For example, the optimization unit analyzes the wearer's body shape and optimizes the optimal form. For example, the optimization unit analyzes the wearer's muscle usage and optimizes the optimal form. For example, the optimization unit analyzes the wearer's body shape and muscle usage in combination and optimizes the optimal form. In this way, the optimal form can be provided by considering the wearer's body shape and muscle usage. Body shape and muscle usage are evaluated based on criteria such as muscle movement and body shape characteristics. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the wearer's body shape and muscle usage into a generating AI and have the generating AI perform the optimization of the optimal form.

[0047] The optimization unit performs optimization while considering the speed and rhythm of the wearer's movements. For example, the optimization unit analyzes the speed of the wearer's movements and optimizes the optimal form. For example, the optimization unit analyzes the rhythm of the wearer's movements and optimizes the optimal form. For example, the optimization unit analyzes the speed and rhythm of the wearer's movements in combination and optimizes the optimal form. In this way, the optimal form can be provided by considering the speed and rhythm of the wearer's movements. The speed and rhythm of movements are evaluated based on criteria such as the timing and rhythm patterns of the movements. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the speed and rhythm of the wearer's movements into a generating AI and have the generating AI perform the optimization of the optimal form.

[0048] The optimization unit optimizes the optimization method by referring to the wearer's past movement data during optimization. The optimization unit, for example, analyzes the wearer's past movement data and adjusts the optimal optimization method. The optimization unit, for example, refers to the wearer's past movement data and adjusts the optimization method to reproduce the same movement. The optimization unit, for example, adjusts the optimal optimization method based on the wearer's past movement data. In this way, the optimal optimization method can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal optimization method.

[0049] The optimization unit monitors the wearer's body temperature and heart rate during optimization and adjusts the optimization method accordingly. For example, the optimization unit monitors the wearer's body temperature and adjusts the optimal optimization method. For example, the optimization unit monitors the wearer's heart rate and adjusts the optimal optimization method. For example, the optimization unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal optimization method. This allows the system to provide the optimal optimization method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal optimization method.

[0050] The induction mechanism unit adjusts its operation considering the muscle tension and fatigue level of the wearer during the operation of the induction mechanism. For example, the induction mechanism unit analyzes the muscle tension level of the wearer and adjusts the operation of the induction mechanism to the optimal level. For example, the induction mechanism unit analyzes the fatigue level of the wearer and adjusts the operation of the induction mechanism to the optimal level. For example, the induction mechanism unit analyzes the muscle tension level and fatigue level of the wearer in combination and adjusts the operation of the induction mechanism to the optimal level. In this way, by considering the muscle tension level and fatigue level of the wearer, the optimal operation of the induction mechanism can be provided. Muscle tension level and fatigue level are evaluated based on criteria such as electromyography or fatigue measurement methods. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input data on the muscle tension level and fatigue level of the wearer into a generating AI and have the generating AI perform the adjustment of the optimal operation of the induction mechanism.

[0051] The guidance mechanism unit operates while considering the timing and rhythm of the wearer's movements. For example, the guidance mechanism unit analyzes the timing of the wearer's movements and adjusts the optimal operation of the guidance mechanism. For example, the guidance mechanism unit analyzes the rhythm of the wearer's movements and adjusts the optimal operation of the guidance mechanism. For example, the guidance mechanism unit analyzes the timing and rhythm of the wearer's movements in combination and adjusts the optimal operation of the guidance mechanism. In this way, by considering the timing and rhythm of the wearer's movements, the optimal operation of the guidance mechanism can be provided. The timing and rhythm of the movements are evaluated based on criteria such as patterns of timing and rhythm of movements. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input data on the timing and rhythm of the wearer's movements into a generating AI and have the generating AI perform the adjustment of the optimal operation of the guidance mechanism.

[0052] The guidance mechanism unit optimizes its operation method by referring to the wearer's past movement data when the guidance mechanism is in operation. For example, the guidance mechanism unit analyzes the wearer's past movement data and adjusts the optimal operation method of the guidance mechanism. For example, the guidance mechanism unit refers to the wearer's past movement data and adjusts the operation method of the guidance mechanism to reproduce the same movement. For example, the guidance mechanism unit adjusts the optimal operation method of the guidance mechanism based on the wearer's past movement data. In this way, the optimal operation method of the guidance mechanism can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal operation method of the guidance mechanism.

[0053] The induction mechanism unit monitors the wearer's body temperature and heart rate during the operation of the induction mechanism and adjusts its operation method accordingly. For example, the induction mechanism unit monitors the wearer's body temperature and adjusts the optimal operation method of the induction mechanism. For example, the induction mechanism unit monitors the wearer's heart rate and adjusts the optimal operation method of the induction mechanism. For example, the induction mechanism unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal operation method of the induction mechanism. This allows the induction mechanism to provide an optimal operation method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal operation method of the induction mechanism.

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

[0055] The analysis unit can also extract the optimal form by considering the athlete's fitness level when analyzing video data of professional athletes. For example, it can compare the form when an athlete is fatigued and when they are not, and provide the optimal form according to the wearer's fitness level. The analysis unit can also evaluate the efficiency and endurance of the form based on the athlete's fitness level. Furthermore, the analysis unit can identify areas for improvement in the form, taking the athlete's fitness level into consideration, and provide feedback to the wearer. As a result, the wearer can adopt the optimal form according to their fitness level, and an improvement in performance can be expected.

[0056] The optimization unit can also optimize the garment by considering the wearer's body shape and muscle usage. For example, it can analyze the wearer's body shape and optimize the optimal form. It can also analyze the wearer's muscle usage and optimize the optimal form. Furthermore, it can analyze the wearer's body shape and muscle usage in combination to optimize the optimal form. As a result, by considering the wearer's body shape and muscle usage, it can provide the optimal form and is expected to improve performance.

[0057] The analysis unit can also extract the optimal form by considering the speed and rhythm of the athlete's movements when analyzing video data of professional athletes. For example, it can compare the form of a professional athlete at fast and slow speeds from video data and extract the form at the optimal speed. It can also analyze the movements in accordance with the professional athlete's rhythm and extract the form that is best suited to that rhythm. Furthermore, it can analyze the professional athlete's movement speed and rhythm in combination to extract the optimal form. By considering the speed and rhythm of movements, it is possible to extract the optimal form, which is expected to improve performance.

[0058] The optimization unit can also perform optimization while considering the speed and rhythm of the wearer's movements. For example, it can analyze the wearer's movement speed and optimize the optimal form. It can also analyze the wearer's movement rhythm and optimize the optimal form. Furthermore, it can analyze a combination of the wearer's movement speed and rhythm and optimize the optimal form. As a result, by considering the wearer's movement speed and rhythm, it can provide the optimal form, and an improvement in performance can be expected.

[0059] The guidance mechanism can also optimize its operation by referring to the wearer's past movement data. For example, it can analyze the wearer's past movement data and adjust the optimal operation method of the guidance mechanism. It can also refer to the wearer's past movement data and adjust the operation method of the guidance mechanism to reproduce the same movement. Furthermore, it can adjust the optimal operation method of the guidance mechanism based on the wearer's past movement data. As a result, by referring to the wearer's past movement data, the optimal operation method of the guidance mechanism can be provided, and improved performance can be expected.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The analysis unit analyzes video data of professional athletes and extracts the optimal form. The video data of professional athletes includes baseball, soccer, dance, muscle training, etc. The analysis unit uses AI to consider the characteristics and details of movements of each sport and identifies the optimal form. Step 2: The guidance unit naturally guides the wearer's form based on the optimal form extracted by the analysis unit. The guidance unit uses heat-curing fiber technology and real-time monitoring to guide the wearer's movements to the optimal form. Step 3: The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. The optimization unit measures the wearer's body size and adjusts the form to the optimal size. Step 4: The guidance mechanism unit guides the wearer to the form optimized by the optimization unit. The guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form.

[0062] (Example of form 2) The sports form guidance system according to an embodiment of the present invention is a system in which an optimized form is input from video data of various professional athletes, and a suit worn by the customer naturally guides the wearer's form. This sports form guidance system aims to improve sports technique naturally, improve form, and prevent injuries. For example, the sports form guidance system collects video data of professional athletes in sports such as baseball, soccer, dance, and muscle training, and an AI analyzes this data to extract the optimal form. In this process, the AI ​​identifies the optimal form considering the characteristics of each sport and the details of the movements. Next, the sports form guidance system has a suit worn by the customer that naturally guides the wearer's form. This suit has the function of further optimizing the incorporated optimized form to the wearer's body size. Specifically, using heat-hardening fiber technology, the wearer's movements are naturally guided to the optimized form. As a result, the wearer can adopt the correct form without strain. Furthermore, wearing this suit can be expected to improve sports technique naturally, improve form, and prevent injuries. For example, in various sports movements such as baseball swings, soccer kicks, and dance steps, mastering the correct form leads to improved technique. Furthermore, mastering the correct form reduces unnatural movements, which helps prevent injuries. Thus, the sports form guidance system uses AI to extract the optimal form from professional athletes and provides a suit that naturally guides the wearer's form, thereby improving sports technique, refining form, and preventing injuries. In this way, the sports form guidance system can achieve improved sports technique, refined form, and injury prevention for the wearer.

[0063] The sports form guidance system according to this embodiment comprises an analysis unit, a guidance unit, an optimization unit, and a guidance mechanism unit. The analysis unit analyzes video data of professional athletes and extracts the optimal form. Video data of professional athletes includes, but is not limited to, baseball, soccer, dance, and muscle training. For example, the analysis unit analyzes a baseball swing motion and extracts the optimal form. The analysis unit can also analyze a soccer kicking motion and extract the optimal form. Furthermore, the analysis unit can analyze a dance step motion and extract the optimal form. For example, the analysis unit uses AI to analyze video data of professional athletes and extract the optimal form. The AI ​​identifies the optimal form considering the characteristics of each sport and the details of the movements. The guidance unit naturally guides the form of the wearer based on the optimal form extracted by the analysis unit. The guidance unit uses, for example, heat-hardening fiber technology to naturally guide the wearer's movements to the optimized form. The guidance unit monitors the wearer's movements in real time and guides them to the optimal form. Furthermore, the guidance unit can analyze the wearer's movements and guide them to the optimal form. In addition, the guidance unit can correct the wearer's movements and guide them to the optimal form. For example, the guidance unit uses AI to analyze the wearer's movements and guide them to the optimal form. The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. For example, the optimization unit adjusts the optimal form based on the wearer's body size. Furthermore, the optimization unit can adjust the optimal form to match the wearer's body size. In addition, the optimization unit can adjust the optimal form based on the wearer's body size. For example, the optimization unit uses AI to measure the wearer's body size and adjust the optimal form. The guidance mechanism unit guides the form optimized by the optimization unit. For example, the guidance mechanism unit naturally guides the wearer's movements to the optimized form. For example, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form.Furthermore, the guidance mechanism can analyze the wearer's movements and guide them to the optimal form. It can also correct the wearer's movements and guide them to the optimal form. For example, the guidance mechanism uses AI to analyze the wearer's movements and guide them to the optimal form. As a result, the sports form guidance system according to this embodiment can improve the wearer's sports skills, refine their form, and prevent injuries.

[0064] The analysis unit analyzes video data of professional athletes and extracts the optimal form. This video data includes, but is not limited to, examples of sports such as baseball, soccer, dance, and muscle training. For example, the analysis unit can analyze a baseball swing and extract the optimal form. It can also analyze a soccer kick and extract the optimal form. Furthermore, it can analyze dance step movements and extract the optimal form. For instance, the analysis unit uses AI to analyze video data of professional athletes and extract the optimal form. The AI ​​identifies the optimal form by considering the characteristics of each sport and the details of the movements. Specifically, the AI ​​uses deep learning technology to extract the movements of the athlete's skeleton and joints from the video data and analyze the movement patterns. For example, in a baseball swing, it analyzes elements such as the bat trajectory, weight transfer, and hip rotation to identify the optimal swing form. In a soccer kick, it analyzes the way the leg is swung up, the timing of impact with the ball, and body balance to identify the optimal kick form. In dance step movements, the system analyzes the rhythm of the steps, foot position, and the coordination of body movements to identify the optimal step form. This allows the analysis unit to accurately extract the optimal form for each sport and provide it to the user. Furthermore, by analyzing video data not only from professional athletes but also from amateur athletes and beginners, the analysis unit can cater to a wide range of user levels. Through the analysis and optimization of sports forms, the analysis unit can support the improvement of users' skills.

[0065] The guidance unit naturally guides the wearer's form based on the optimal form extracted by the analysis unit. The guidance unit uses, for example, heat-curing fiber technology to naturally guide the wearer's movements to an optimized form. Specifically, the guidance unit monitors the wearer's movements in real time and guides them to the optimal form. For example, when a wearer swings a baseball, the guidance unit monitors the wearer's arm and waist movements and makes fine adjustments to approach the optimal swing form. The guidance unit uses AI to analyze the wearer's movements and guide them to the optimal form. The AI ​​analyzes the wearer's motion data in real time and detects deviations from the optimal form. For example, if the wearer's swing motion differs from the optimal form, the guidance unit corrects the wearer's movements and guides them to the optimal form. This allows the guidance unit to naturally optimize the wearer's movements and support the improvement of their sports skills. Furthermore, the guidance unit can not only correct the wearer's movements but also provide motion feedback. For example, when the wearer approaches the optimal form, feedback is provided through vibrations or sounds to make them aware of the correct form. In this way, the guidance unit can promote the wearer's form improvement and support the enhancement of their sports technique.

[0066] The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. Specifically, the optimization unit acquires data such as the wearer's height, weight, and body type, and adjusts the optimal form. For example, since the optimal form differs for tall and short wearers, the optimization unit adjusts the form based on the wearer's body size. The optimization unit uses AI to measure the wearer's body size and adjust the optimal form. The AI ​​analyzes the wearer's body data and identifies the optimal form. For example, it adjusts the optimal swing form or kick form based on the wearer's arm length and leg length. In this way, the optimization unit can provide the optimal form tailored to the wearer's body size and support the improvement of their sports technique. Furthermore, the optimization unit can adjust the optimal form by considering not only the wearer's body size but also their physical abilities such as muscle strength and flexibility. In this way, the optimization unit can provide the optimal form for each individual wearer and support the improvement of their sports technique.

[0067] The guidance mechanism unit guides the wearer to the form optimized by the optimization unit. For example, the guidance mechanism unit naturally guides the wearer's movements to the optimized form. Specifically, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form. For example, when a wearer performs a soccer kick, the guidance mechanism unit monitors the wearer's foot movements and makes fine adjustments to approach the optimal kick form. The guidance mechanism unit uses AI to analyze the wearer's movements and guide them to the optimal form. The AI ​​analyzes the wearer's movement data in real time and detects the difference from the optimal form. For example, if the wearer's kicking motion differs from the optimal form, the guidance mechanism unit corrects the wearer's movements and guides them to the optimal form. In this way, the guidance mechanism unit can naturally optimize the wearer's movements and support the improvement of sports skills. Furthermore, the guidance mechanism unit can not only correct the wearer's movements but also provide feedback on the movements. For example, when the wearer approaches the optimal form, it provides feedback through vibration or sound to make them aware of the correct form. This allows the guidance mechanism to promote improvement in the wearer's form and support the enhancement of sports techniques.

[0068] The analysis unit analyzes video data of professional athletes in sports such as baseball, soccer, dance, and muscle training, and extracts the optimal form. For example, the analysis unit can analyze a baseball swing motion and extract the optimal form. The analysis unit can also analyze a soccer kicking motion and extract the optimal form. The analysis unit can also analyze a dance step motion and extract the optimal form. In this way, the optimal form can be extracted by analyzing video data of professional athletes in various sports. The optimal form is evaluated based on criteria such as the efficiency of the movement and the reduction of the risk of injury. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0069] The guidance unit uses heat-curing fiber technology to naturally guide the wearer's movements into an optimized form. For example, the guidance unit uses heat-curing fiber technology to monitor the wearer's movements in real time and guide them to the optimal form. The guidance unit can also analyze the wearer's movements and guide them to the optimal form. The guidance unit can also correct the wearer's movements and guide them to the optimal form. In this way, by using heat-curing fiber technology, the wearer's movements can be naturally guided into an optimized form. The method of natural guidance is evaluated based on criteria such as the smoothness of movement and the wearer's comfort. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's movements into a generating AI and have the generating AI perform the guidance to the optimal form.

[0070] The optimization unit optimizes the garment to the wearer's body size. For example, the optimization unit measures the wearer's body size and adjusts the optimal form. The optimization unit can also adjust the optimal form based on the wearer's body size. The optimization unit can also adjust the optimal form to match the wearer's body size. This allows the wearer to adopt the correct form without strain by optimizing to their body size. The optimization method is evaluated based on criteria such as the method of matching body size and the details of the adjustment. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the wearer's body size into a generating AI and have the generating AI perform the adjustment to the optimal form.

[0071] The guidance mechanism unit naturally guides the wearer's movements to an optimized form. For example, the guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form. The guidance mechanism unit can also analyze the wearer's movements and guide them to the optimal form. The guidance mechanism unit can also correct the wearer's movements and guide them to the optimal form. In this way, by naturally guiding the wearer's movements to an optimized form, the wearer can acquire the correct form. The method of natural guidance is evaluated based on criteria such as the smoothness of movement and the wearer's comfort. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input the wearer's movements into a generating AI and have the generating AI perform the guidance to the optimal form.

[0072] The analysis unit estimates the emotions of the person wearing the garment and selects video data to analyze based on the estimated emotions. For example, if the person wearing the garment is relaxed, the analysis unit prioritizes analyzing video data of professional athletes in a relaxed state. For example, if the person wearing the garment is tense, the analysis unit analyzes video data that includes relaxing actions to relieve tension. For example, if the person wearing the garment is focused, the analysis unit analyzes video data of professional athletes that enhance concentration. By selecting video data to analyze based on the emotions of the person wearing the garment, a more appropriate form can be extracted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the person's emotion data into the generative AI and have the generative AI perform the selection of video data based on emotions.

[0073] The analysis unit extracts the optimal form by considering the speed and rhythm of the movements when analyzing video data of professional athletes. For example, the analysis unit compares the form at fast and slow speeds from the video data of professional athletes and extracts the form at the optimal speed. For example, the analysis unit analyzes the movements of professional athletes in accordance with their rhythm and extracts the form that is best suited to that rhythm. For example, the analysis unit analyzes the speed and rhythm of professional athletes in combination and extracts the optimal form. In this way, the optimal form can be extracted by considering the speed and rhythm of the movements. The speed and rhythm of the movements are evaluated based on criteria such as the timing and rhythm patterns of the movements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of professional athletes into a generating AI and have the generating AI perform the extraction of a form that takes into account the speed and rhythm of the movements.

[0074] The analysis unit extracts forms while considering the physique and muscle usage of professional athletes during analysis. For example, the analysis unit extracts the optimal form for a person with the same physique as a professional athlete based on their physique. For example, the analysis unit analyzes the muscle usage of professional athletes and extracts a form that matches the muscle usage of a person wearing the garment. For example, the analysis unit analyzes a combination of the physique and muscle usage of professional athletes and extracts the optimal form. In this way, by considering the physique and muscle usage of professional athletes, the optimal form for a person wearing the garment can be extracted. The physique and muscle usage are evaluated based on criteria such as muscle movement and physique characteristics. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the physique and muscle usage of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0075] The analysis unit estimates the wearer's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the wearer is relaxed, the analysis unit displays the analysis results for a relaxed state. For example, if the wearer is tense, the analysis unit displays the analysis results for relaxation to relieve tension. For example, if the wearer is concentrating, the analysis unit displays the analysis results to enhance concentration. In this way, by adjusting the display method of the analysis results based on the wearer's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the display method of the analysis results based on emotions.

[0076] The analysis unit improves analysis accuracy by considering the shooting angle and distance of the professional athlete's video data during analysis. For example, the analysis unit analyzes the shooting angle of the professional athlete's video data and extracts the form at the optimal angle. For example, the analysis unit analyzes the shooting distance of the professional athlete's video data and extracts the form at the optimal distance. For example, the analysis unit analyzes the shooting angle and distance of the professional athlete's video data in combination and extracts the optimal form. In this way, the analysis accuracy can be improved by considering the shooting angle and distance of the professional athlete's video data. The shooting angle and distance are evaluated based on criteria such as the camera position and shooting distance. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the shooting angle and distance data of the professional athlete's video data into a generating AI and have the generating AI perform the improvement of analysis accuracy.

[0077] The analysis unit extracts forms by referring to the past performance data of professional athletes during the analysis. For example, the analysis unit analyzes the past performance data of professional athletes and extracts the optimal form. For example, the analysis unit refers to the past performance data of professional athletes and extracts a form to reproduce the same performance. For example, the analysis unit extracts the optimal form based on the past performance data of professional athletes. In this way, the optimal form can be extracted by referring to the past performance data of professional athletes. Past performance data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the past performance data of professional athletes into a generating AI and have the generating AI perform the extraction of the optimal form.

[0078] The guidance unit estimates the wearer's emotions and adjusts the intensity of the guidance based on the estimated emotions. For example, if the wearer is relaxed, the guidance unit provides relaxed guidance. For example, if the wearer is tense, the guidance unit provides relaxing guidance to relieve tension. For example, if the wearer is concentrating, the guidance unit provides guidance to enhance concentration. By adjusting the intensity of the guidance based on the wearer's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the guidance intensity based on emotions.

[0079] The guidance unit adjusts the guidance method during guidance, taking into account the degree of muscle tension and fatigue of the person wearing the garment. For example, the guidance unit analyzes the degree of muscle tension of the person wearing the garment and adjusts the optimal guidance method. For example, the guidance unit analyzes the degree of fatigue of the person wearing the garment and adjusts the optimal guidance method. For example, the guidance unit analyzes the degree of muscle tension and fatigue of the person wearing the garment in combination and adjusts the optimal guidance method. In this way, the optimal guidance method can be provided by taking into account the degree of muscle tension and fatigue of the person wearing the garment. The degree of muscle tension and fatigue is evaluated based on criteria such as electromyography or fatigue measurement methods. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without using AI. For example, the guidance unit can input data on the degree of muscle tension and fatigue of the person wearing the garment into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0080] The guidance unit provides guidance while considering the timing and rhythm of the wearer's movements. For example, the guidance unit analyzes the timing of the wearer's movements and adjusts the optimal guidance method. For example, the guidance unit analyzes the rhythm of the wearer's movements and adjusts the optimal guidance method. For example, the guidance unit analyzes the timing and rhythm of the wearer's movements in combination and adjusts the optimal guidance method. This makes it possible to provide optimal guidance by considering the timing and rhythm of the wearer's movements. The timing and rhythm of movements are evaluated based on criteria such as patterns of timing and rhythm of movements. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the timing and rhythm of the wearer's movements into a generating AI and have the generating AI perform adjustments to the optimal guidance method.

[0081] The guidance unit estimates the wearer's emotions and adjusts the timing of guidance based on the estimated emotions. For example, if the wearer is relaxed, the guidance unit adjusts the guidance timing to a relaxed state. For example, if the wearer is tense, the guidance unit adjusts the guidance timing to a relaxed state to relieve tension. For example, if the wearer is concentrating, the guidance unit adjusts the guidance timing to enhance concentration. By adjusting the timing of guidance based on the wearer's emotions, more appropriate guidance becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the guidance timing based on emotions.

[0082] The guidance unit optimizes the guidance method by referring to the wearer's past movement data during guidance. For example, the guidance unit analyzes the wearer's past movement data and adjusts the optimal guidance method. For example, the guidance unit refers to the wearer's past movement data and adjusts the guidance method to reproduce the same movement. For example, the guidance unit adjusts the optimal guidance method based on the wearer's past movement data. In this way, the optimal guidance method can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0083] The guidance unit monitors the wearer's body temperature and heart rate during guidance and adjusts the guidance method accordingly. For example, the guidance unit monitors the wearer's body temperature and adjusts the optimal guidance method. For example, the guidance unit monitors the wearer's heart rate and adjusts the optimal guidance method. For example, the guidance unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal guidance method. This allows the system to provide the optimal guidance method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above processing in the guidance unit may be performed using AI, for example, or without AI. For example, the guidance unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal guidance method.

[0084] The optimization unit estimates the wearer's emotions and adjusts the optimization parameters based on the estimated emotions. For example, if the wearer is relaxed, the optimization unit adjusts the optimization parameters for a relaxed state. For example, if the wearer is tense, the optimization unit adjusts the optimization parameters for relaxation to alleviate tension. For example, if the wearer is focused, the optimization unit adjusts the optimization parameters to enhance concentration. By adjusting the optimization parameters based on the wearer's emotions, more appropriate optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the optimization parameters based on the emotions.

[0085] The optimization unit performs optimization while considering the wearer's body shape and muscle usage. For example, the optimization unit analyzes the wearer's body shape and optimizes the optimal form. For example, the optimization unit analyzes the wearer's muscle usage and optimizes the optimal form. For example, the optimization unit analyzes the wearer's body shape and muscle usage in combination and optimizes the optimal form. In this way, the optimal form can be provided by considering the wearer's body shape and muscle usage. Body shape and muscle usage are evaluated based on criteria such as muscle movement and body shape characteristics. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the wearer's body shape and muscle usage into a generating AI and have the generating AI perform the optimization of the optimal form.

[0086] The optimization unit performs optimization while considering the speed and rhythm of the wearer's movements. For example, the optimization unit analyzes the speed of the wearer's movements and optimizes the optimal form. For example, the optimization unit analyzes the rhythm of the wearer's movements and optimizes the optimal form. For example, the optimization unit analyzes the speed and rhythm of the wearer's movements in combination and optimizes the optimal form. In this way, the optimal form can be provided by considering the speed and rhythm of the wearer's movements. The speed and rhythm of movements are evaluated based on criteria such as the timing and rhythm patterns of the movements. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the speed and rhythm of the wearer's movements into a generating AI and have the generating AI perform the optimization of the optimal form.

[0087] The optimization unit estimates the wearer's emotions and adjusts the optimization frequency based on the estimated emotions. For example, if the wearer is relaxed, the optimization unit adjusts the optimization frequency for a relaxed state. For example, if the wearer is tense, the optimization unit adjusts the optimization frequency for relaxation to alleviate tension. For example, if the wearer is concentrating, the optimization unit adjusts the optimization frequency to enhance concentration. By adjusting the optimization frequency based on the wearer's emotions, more appropriate optimization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the optimization frequency based on emotions.

[0088] The optimization unit optimizes the optimization method by referring to the wearer's past movement data during optimization. The optimization unit, for example, analyzes the wearer's past movement data and adjusts the optimal optimization method. The optimization unit, for example, refers to the wearer's past movement data and adjusts the optimization method to reproduce the same movement. The optimization unit, for example, adjusts the optimal optimization method based on the wearer's past movement data. In this way, the optimal optimization method can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without using AI. For example, the optimization unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal optimization method.

[0089] The optimization unit monitors the wearer's body temperature and heart rate during optimization and adjusts the optimization method accordingly. For example, the optimization unit monitors the wearer's body temperature and adjusts the optimal optimization method. For example, the optimization unit monitors the wearer's heart rate and adjusts the optimal optimization method. For example, the optimization unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal optimization method. This allows the system to provide the optimal optimization method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above-described processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal optimization method.

[0090] The induction mechanism unit estimates the wearer's emotions and adjusts the operation of the induction mechanism based on the estimated emotions. For example, if the wearer is relaxed, the induction mechanism unit adjusts the operation of the induction mechanism to a relaxed state. For example, if the wearer is tense, the induction mechanism unit adjusts the operation of the induction mechanism to a relaxed state to relieve tension. For example, if the wearer is concentrating, the induction mechanism unit adjusts the operation of the induction mechanism to enhance concentration. By adjusting the operation of the induction mechanism based on the wearer's emotions, more appropriate induction becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the operation of the induction mechanism based on emotions.

[0091] The induction mechanism unit adjusts its operation considering the muscle tension and fatigue level of the wearer during the operation of the induction mechanism. For example, the induction mechanism unit analyzes the muscle tension level of the wearer and adjusts the operation of the induction mechanism to the optimal level. For example, the induction mechanism unit analyzes the fatigue level of the wearer and adjusts the operation of the induction mechanism to the optimal level. For example, the induction mechanism unit analyzes the muscle tension level and fatigue level of the wearer in combination and adjusts the operation of the induction mechanism to the optimal level. In this way, by considering the muscle tension level and fatigue level of the wearer, the optimal operation of the induction mechanism can be provided. Muscle tension level and fatigue level are evaluated based on criteria such as electromyography or fatigue measurement methods. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input data on the muscle tension level and fatigue level of the wearer into a generating AI and have the generating AI perform the adjustment of the optimal operation of the induction mechanism.

[0092] The guidance mechanism unit operates while considering the timing and rhythm of the wearer's movements. For example, the guidance mechanism unit analyzes the timing of the wearer's movements and adjusts the optimal operation of the guidance mechanism. For example, the guidance mechanism unit analyzes the rhythm of the wearer's movements and adjusts the optimal operation of the guidance mechanism. For example, the guidance mechanism unit analyzes the timing and rhythm of the wearer's movements in combination and adjusts the optimal operation of the guidance mechanism. In this way, by considering the timing and rhythm of the wearer's movements, the optimal operation of the guidance mechanism can be provided. The timing and rhythm of the movements are evaluated based on criteria such as patterns of timing and rhythm of movements. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input data on the timing and rhythm of the wearer's movements into a generating AI and have the generating AI perform the adjustment of the optimal operation of the guidance mechanism.

[0093] The induction mechanism unit estimates the wearer's emotions and adjusts the timing of the induction mechanism's operation based on the estimated emotions. For example, if the wearer is relaxed, the induction mechanism unit adjusts the timing of the induction mechanism's operation for a relaxed state. For example, if the wearer is tense, the induction mechanism unit adjusts the timing of the induction mechanism's operation for relaxation to relieve tension. For example, if the wearer is concentrating, the induction mechanism unit adjusts the timing of the induction mechanism's operation to enhance concentration. By adjusting the timing of the induction mechanism's operation based on the wearer's emotions, more appropriate induction becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input the wearer's emotion data into the generative AI and have the generative AI perform the adjustment of the induction mechanism's operation timing based on emotions.

[0094] The guidance mechanism unit optimizes its operation method by referring to the wearer's past movement data when the guidance mechanism is in operation. For example, the guidance mechanism unit analyzes the wearer's past movement data and adjusts the optimal operation method of the guidance mechanism. For example, the guidance mechanism unit refers to the wearer's past movement data and adjusts the operation method of the guidance mechanism to reproduce the same movement. For example, the guidance mechanism unit adjusts the optimal operation method of the guidance mechanism based on the wearer's past movement data. In this way, the optimal operation method of the guidance mechanism can be provided by referring to the wearer's past movement data. Past movement data is evaluated based on criteria such as the type of data and the method of reference. Some or all of the above processing in the guidance mechanism unit may be performed using AI, for example, or without AI. For example, the guidance mechanism unit can input the wearer's past movement data into a generating AI and have the generating AI perform the adjustment of the optimal operation method of the guidance mechanism.

[0095] The induction mechanism unit monitors the wearer's body temperature and heart rate during the operation of the induction mechanism and adjusts its operation method accordingly. For example, the induction mechanism unit monitors the wearer's body temperature and adjusts the optimal operation method of the induction mechanism. For example, the induction mechanism unit monitors the wearer's heart rate and adjusts the optimal operation method of the induction mechanism. For example, the induction mechanism unit monitors the wearer's body temperature and heart rate in combination and adjusts the optimal operation method of the induction mechanism. This allows the induction mechanism to provide an optimal operation method by monitoring the wearer's body temperature and heart rate. Body temperature and heart rate are evaluated based on criteria such as the type of sensor and measurement method. Some or all of the above processing in the induction mechanism unit may be performed using AI, for example, or without AI. For example, the induction mechanism unit can input data on the wearer's body temperature and heart rate into a generating AI and have the generating AI perform the adjustment of the optimal operation method of the induction mechanism.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The analysis unit can also extract the optimal form by considering the athlete's psychological state when analyzing video data of professional athletes. For example, it can compare the form of an athlete when they are tense and relaxed during a match and provide the optimal form according to the wearer's psychological state. The analysis unit can also evaluate the stability and consistency of the form based on the athlete's psychological state. Furthermore, the analysis unit can identify areas for improvement in the form, taking the athlete's psychological state into consideration, and provide feedback to the wearer. As a result, the wearer can adopt the optimal form according to their psychological state, and an improvement in performance can be expected.

[0098] The analysis unit can also extract the optimal form by considering the athlete's fitness level when analyzing video data of professional athletes. For example, it can compare the form when an athlete is fatigued and when they are not, and provide the optimal form according to the wearer's fitness level. The analysis unit can also evaluate the efficiency and endurance of the form based on the athlete's fitness level. Furthermore, the analysis unit can identify areas for improvement in the form, taking the athlete's fitness level into consideration, and provide feedback to the wearer. As a result, the wearer can adopt the optimal form according to their fitness level, and an improvement in performance can be expected.

[0099] The guidance unit can estimate the wearer's emotions and adjust the timing of guidance based on those emotions. For example, if the wearer is relaxed, the guidance timing will be adjusted to promote relaxation. If the wearer is tense, the guidance timing will be adjusted to help them relax. Furthermore, if the wearer is focused, the guidance timing will be adjusted to enhance their concentration. By adjusting the guidance timing based on the wearer's emotions, more appropriate guidance becomes possible, and improved performance can be expected.

[0100] The optimization unit can also optimize the garment by considering the wearer's body shape and muscle usage. For example, it can analyze the wearer's body shape and optimize the optimal form. It can also analyze the wearer's muscle usage and optimize the optimal form. Furthermore, it can analyze the wearer's body shape and muscle usage in combination to optimize the optimal form. As a result, by considering the wearer's body shape and muscle usage, it can provide the optimal form and is expected to improve performance.

[0101] The induction mechanism can also estimate the wearer's emotions and adjust its operation based on those emotions. For example, if the wearer is relaxed, the induction mechanism's operation will be adjusted to reflect that relaxed state. If the wearer is tense, the induction mechanism's operation will be adjusted to promote relaxation. Furthermore, if the wearer is focused, the induction mechanism's operation will be adjusted to enhance their concentration. By adjusting the induction mechanism's operation based on the wearer's emotions, more appropriate induction becomes possible, leading to improved performance.

[0102] The analysis unit can also extract the optimal form by considering the speed and rhythm of the athlete's movements when analyzing video data of professional athletes. For example, it can compare the form of a professional athlete at fast and slow speeds from video data and extract the form at the optimal speed. It can also analyze the movements in accordance with the professional athlete's rhythm and extract the form that is best suited to that rhythm. Furthermore, it can analyze the professional athlete's movement speed and rhythm in combination to extract the optimal form. By considering the speed and rhythm of movements, it is possible to extract the optimal form, which is expected to improve performance.

[0103] The guidance unit can estimate the wearer's emotions and adjust the intensity of the guidance based on those emotions. For example, if the wearer is relaxed, it will provide guidance in a relaxed state. If the wearer is tense, it can provide relaxing guidance to alleviate tension. Furthermore, if the wearer is concentrating, it can provide guidance to enhance concentration. By adjusting the intensity of the guidance based on the wearer's emotions, more appropriate guidance becomes possible, and improved performance can be expected.

[0104] The optimization unit can also perform optimization while considering the speed and rhythm of the wearer's movements. For example, it can analyze the wearer's movement speed and optimize the optimal form. It can also analyze the wearer's movement rhythm and optimize the optimal form. Furthermore, it can analyze a combination of the wearer's movement speed and rhythm and optimize the optimal form. As a result, by considering the wearer's movement speed and rhythm, it can provide the optimal form, and an improvement in performance can be expected.

[0105] The induction mechanism can also estimate the wearer's emotions and adjust the timing of its operation based on those emotions. For example, if the wearer is relaxed, the timing of the induction mechanism's operation will be adjusted to accommodate a relaxed state. If the wearer is tense, the timing of the induction mechanism's operation will be adjusted to help them relax. Furthermore, if the wearer is focused, the timing of the induction mechanism's operation will be adjusted to enhance their concentration. By adjusting the timing of the induction mechanism's operation based on the wearer's emotions, more appropriate induction becomes possible, and improved performance can be expected.

[0106] The guidance mechanism can also optimize its operation by referring to the wearer's past movement data. For example, it can analyze the wearer's past movement data and adjust the optimal operation method of the guidance mechanism. It can also refer to the wearer's past movement data and adjust the operation method of the guidance mechanism to reproduce the same movement. Furthermore, it can adjust the optimal operation method of the guidance mechanism based on the wearer's past movement data. As a result, by referring to the wearer's past movement data, the optimal operation method of the guidance mechanism can be provided, and improved performance can be expected.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The analysis unit analyzes video data of professional athletes and extracts the optimal form. The video data of professional athletes includes baseball, soccer, dance, muscle training, etc. The analysis unit uses AI to consider the characteristics and details of movements of each sport and identifies the optimal form. Step 2: The guidance unit naturally guides the wearer's form based on the optimal form extracted by the analysis unit. The guidance unit uses heat-curing fiber technology and real-time monitoring to guide the wearer's movements to the optimal form. Step 3: The optimization unit optimizes the form guided by the guidance unit to match the wearer's body size. The optimization unit measures the wearer's body size and adjusts the form to the optimal size. Step 4: The guidance mechanism unit guides the wearer to the form optimized by the optimization unit. The guidance mechanism unit monitors the wearer's movements in real time and guides them to the optimal form.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the analysis unit, guidance unit, optimization unit, and guidance mechanism unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes video data of professional athletes to extract the optimal form. The guidance unit is implemented by the control unit 46A of the smart device 14, which naturally guides the wearer's movements to the optimized form. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the optimal form to match the wearer's body size. The guidance mechanism unit is implemented by the control unit 46A of the smart device 14, which monitors the wearer's movements in real time and guides them to the optimal form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the analysis unit, guidance unit, optimization unit, and guidance mechanism unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12, which analyzes video data of professional athletes to extract the optimal form. The guidance unit is implemented by the control unit 46A of the smart glasses 214, which naturally guides the wearer's movements to the optimized form. The optimization unit is implemented by the specific processing unit 290 of the data processing device 12, which adjusts the optimal form to match the wearer's body size. The guidance mechanism unit is implemented by the control unit 46A of the smart glasses 214, which monitors the wearer's movements in real time and guides them to the optimal form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the analysis unit, guidance unit, optimization unit, and guidance mechanism unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes video data of professional athletes to extract the optimal form. The guidance unit is implemented by the control unit 46A of the headset terminal 314, which naturally guides the wearer's movements to the optimized form. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the optimal form to match the wearer's body size. The guidance mechanism unit is implemented by the control unit 46A of the headset terminal 314, which monitors the wearer's movements in real time and guides them to the optimal form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the 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 user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the analysis unit, guidance unit, optimization unit, and guidance mechanism unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes video data of professional athletes to extract the optimal form. The guidance unit is implemented by the control unit 46A of the robot 414, which naturally guides the wearer's movements to the optimized form. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12, which adjusts the optimal form to match the wearer's body size. The guidance mechanism unit is implemented by the control unit 46A of the robot 414, which monitors the wearer's movements in real time and guides them to the optimal form. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) The analysis unit analyzes video data of professional athletes and extracts the optimal form, Based on the optimal form extracted by the aforementioned analysis unit, a guidance unit naturally guides the wearer's form, An optimization unit that optimizes the form guided by the aforementioned guidance unit to match the wearer's body size, The system includes a guidance mechanism that guides the form optimized by the optimization unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze video data of professional athletes in baseball, soccer, dance, muscle training, etc., to extract the optimal form. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned induction unit is Using heat-setting fiber technology, the garment naturally guides the wearer's movements into an optimized form. The system described in Appendix 1, characterized by the features described herein. (Note 4) The optimization unit, Optimized to fit the wearer's body size. The system described in Appendix 1, characterized by the features described herein. (Note 5) The induction mechanism section is, It naturally guides the wearer's movements into an optimized form. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the emotions of the person wearing the clothing and selects video data to analyze based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing video data of professional athletes, the optimal form is extracted by considering the speed and rhythm of their movements. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, During analysis, the form is extracted while taking into account the physique and muscle usage of professional athletes. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, The system estimates the wearer's emotions and adjusts the display method of the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by taking into account the shooting angle and distance of the professional athletes' video data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the system extracts form by referencing past performance data of professional athletes. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned induction unit is The system estimates the wearer's emotions and adjusts the intensity of the induction based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned induction unit is During the guidance process, the guidance method is adjusted considering the degree of muscle tension and fatigue of the person wearing the clothing. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned induction unit is When guiding people, the timing and rhythm of their movements should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned induction unit is The system estimates the wearer's emotions and adjusts the timing of the guidance based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned induction unit is During guidance, the guidance method is optimized by referring to the wearer's past movement data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned induction unit is During induction, monitor the wearer's body temperature and heart rate to adjust the induction method. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, It estimates the wearer's emotions and adjusts the optimization parameters based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, During optimization, the wearer's body shape and muscle usage are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, During optimization, the speed and rhythm of the wearer's movements are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, It estimates the wearer's emotions and adjusts the optimization frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The optimization unit, During optimization, the optimization method is optimized by referring to the wearer's past movement data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The optimization unit, During optimization, the body temperature and heart rate of the wearer are monitored, and the optimization method is adjusted accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 24) The induction mechanism section is, It estimates the wearer's emotions and adjusts the operation of the induction mechanism based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The induction mechanism section is, During the operation of the induction mechanism, the movement is adjusted considering the wearer's muscle tension and fatigue level. The system described in Appendix 1, characterized by the features described herein. (Note 26) The induction mechanism section is, When the induction mechanism operates, it takes into account the timing and rhythm of the wearer's movements. The system described in Appendix 1, characterized by the features described herein. (Note 27) The induction mechanism section is, The system estimates the wearer's emotions and adjusts the timing of the induction mechanism's operation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The induction mechanism section is, When the induction mechanism is activated, it optimizes the operation method by referring to the wearer's past movement data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The induction mechanism section is, During the operation of the induction mechanism, the wearer's body temperature and heart rate are monitored to adjust the operation method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes video data of professional athletes and extracts the optimal form, Based on the optimal form extracted by the aforementioned analysis unit, a guidance unit naturally guides the wearer's form, An optimization unit that optimizes the form guided by the aforementioned guidance unit to match the wearer's body size, The system includes a guidance mechanism that guides the form optimized by the optimization unit. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze video data of professional athletes in baseball, soccer, dance, muscle training, etc., to extract the optimal form. The system according to feature 1.

3. The aforementioned induction unit is Using heat-setting fiber technology, the garment naturally guides the wearer's movements into an optimized form. The system according to feature 1.

4. The optimization unit, Optimized to fit the wearer's body size. The system according to feature 1.

5. The induction mechanism section is, It naturally guides the wearer's movements into an optimized form. The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the emotions of the person wearing the clothing and selects video data to analyze based on those estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing video data of professional athletes, the optimal form is extracted by considering the speed and rhythm of their movements. The system according to feature 1.

8. The aforementioned analysis unit, During analysis, the form is extracted while taking into account the physique and muscle usage of professional athletes. The system according to feature 1.

Images