system

Abstract

The system according to this embodiment aims to efficiently support the acquisition of skateboarding tricks and provide feedback. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, an advice unit, and a feedback unit. The data collection unit automatically tracks and records the movement of the skateboard. The analysis unit analyzes the data collected by the data collection unit in detail. The advice unit provides real-time advice based on the data obtained by the analysis unit. The feedback unit provides feedback on the timing and posture of tricks based on the advice provided by the advice 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, there are problems that the acquisition of skateboarding skills is groping, it is difficult to obtain feedback, and it is difficult to understand the growth situation.

[0005] The system according to the embodiment aims to efficiently support the acquisition of skateboarding skills and provide feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an advice unit, and a feedback unit. The data collection unit automatically tracks and records the movement of the skateboard. The analysis unit analyzes the data collected by the data collection unit in detail. The advice unit provides real-time advice based on the data obtained by the analysis unit. The feedback unit provides feedback on the timing and posture of tricks based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support the learning of skateboarding tricks and provide feedback. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied 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 receiving 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 receiving 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 skateboarder skill improvement support system according to an embodiment of the present invention is an AI agent system for supporting the skill improvement of skateboarders. This system includes a camera tracking system that automatically tracks and records the movement of the skateboard. Sensors mounted on the skateboard analyze the movement and posture in detail. It provides real-time voice and app-based advice to improve the success rate of tricks. It analyzes the recorded data and provides feedback on the timing and posture of tricks. It performs learning level analysis to grasp the level of acquisition and determine the steps of the tricks to suggest. It includes data visualization and sharing functions that record distance traveled, jump height, and success rate, and make them shareable on social media. As a result, skateboarders can efficiently acquire skills, receive quick feedback, and improve their skills while having fun. Thus, the skateboarder skill improvement support system can efficiently support the skill improvement of skateboarders.

[0029] The skateboarder skill improvement support system according to this embodiment comprises a data collection unit, an analysis unit, an advice unit, and a feedback unit. The data collection unit automatically tracks and records the movement of the skateboard. The data collection unit automatically tracks and records the movement of the skateboard using, for example, a camera tracking system. The camera tracking system can use a fixed camera or a drone camera. The camera tracking system tracks the movement of the skateboard in real time and collects the recorded data. The analysis unit analyzes the data collected by the data collection unit in detail. The analysis unit analyzes the movement and posture in detail using, for example, sensors mounted on the skateboard. The sensors include acceleration sensors and gyroscope sensors. Based on the data obtained from the sensors, the analysis unit analyzes the movement and posture of the skateboard in detail. The advice unit provides advice in real time based on the data obtained by the analysis unit. The advice unit provides advice in real time using, for example, a voice assistant or app notifications. The advice unit provides specific advice to increase the success rate of tricks. The feedback unit provides feedback on the timing and posture of tricks based on the advice provided by the advice unit. The feedback unit, for example, analyzes recorded data and provides specific feedback on the timing and posture of tricks. The feedback unit grasps the level of mastery and determines the steps of the trick to suggest. The feedback unit records distance traveled, jump height, and success rate, and makes them shareable on social media. As a result, the skateboarder skill improvement support system according to this embodiment can efficiently support the skill improvement of skateboarders.

[0030] The data collection unit automatically tracks and records the movement of skateboards. For example, the unit uses a camera tracking system to automatically track and record skateboard movements. The camera tracking system can utilize fixed cameras or drone cameras. Fixed cameras are installed in specific locations within skate parks or practice areas, positioned to cover a wide area. Drone cameras track skateboarders' movements from above, recording from a more dynamic perspective. These cameras collect high-resolution video in real time, meticulously recording skateboard movements. The camera tracking system tracks skateboard movements in real time and collects recorded data. Image recognition technology and AI are used to accurately determine the skateboard's position and movement. For example, the system recognizes the skateboard's distinctive shape and color, allowing the camera to automatically track it. Furthermore, by linking multiple cameras, video from different angles can be collected simultaneously, obtaining more detailed data. The collected data is stored in a central database and used for subsequent analysis and feedback. This allows the data collection unit to efficiently and accurately record skateboarders' movements, providing foundational data for skill improvement.

[0031] The analysis unit performs a detailed analysis of the data collected by the data collection unit. For example, the analysis unit uses sensors mounted on the skateboard to analyze its movement and posture in detail. These sensors include accelerometers and gyroscopes. The accelerometer measures the skateboard's acceleration and deceleration, recording its movements in detail during jumps and tricks. The gyroscope measures the skateboard's rotation and tilt, analyzing its posture and balance during tricks. The data obtained from these sensors is collected in real time and transmitted to the analysis unit. Based on the data obtained from the sensors, the analysis unit performs a detailed analysis of the skateboard's movement and posture. For example, it evaluates the height of jumps, the angle of rotation, and the stability of landings to identify the success rate of tricks and areas for improvement. Furthermore, it can use AI to compare current data with past data and analyze the progress and trends of the technique. This allows the analysis unit to accurately grasp the skateboarder's skill level and suggest specific areas for improvement.

[0032] The Advice Department provides real-time advice based on data obtained by the Analysis Department. The Advice Department provides real-time advice using, for example, voice assistants and app notifications. The voice assistant provides specific advice to improve the success rate of tricks through earphones worn by skateboarders during practice. For example, it provides real-time instructions such as, "Try bending your knees a little more when you jump," or "Try speeding up your rotation timing a bit." App notifications are displayed on smartphones and smartwatches, providing detailed feedback after practice. The Advice Department provides specific advice to improve the success rate of tricks. For example, it suggests training methods to increase jump height and practice menus to improve rotation stability. This allows the Advice Department to support skateboarders in efficiently improving their skills.

[0033] The Feedback Department provides feedback on the timing and posture of tricks based on the advice provided by the Advice Department. For example, the Feedback Department analyzes video data to provide specific feedback on the timing and posture of tricks. The video data plays back footage of tricks actually performed by the skateboarder and highlights important points. For example, it provides detailed explanations of jump timing, rotation start position, landing posture, etc., and points out areas for improvement. The Feedback Department grasps the level of proficiency and determines the steps of the tricks to suggest. For example, once a basic trick can be performed consistently, it suggests a more advanced trick as the next step. The Feedback Department records distance traveled, jump height, and success rate, and makes them shareable on social media. This allows skateboarders to visualize their progress and maintain motivation. They can also share information with other skateboarders and inspire each other. In this way, the Feedback Department can efficiently support the improvement of skateboarders' skills and promote continuous growth.

[0034] The feedback unit can analyze recorded data and provide feedback on the timing and posture of techniques. For example, the feedback unit analyzes the recorded data using image analysis technology. The feedback unit extracts information about the timing and posture of techniques from the recorded data and provides specific feedback. For example, if the timing of a technique is too early or too late, the feedback unit will suggest how to correct it. Also, if the posture is inappropriate, the feedback unit will suggest how to correct it. In this way, by analyzing the recorded data, specific feedback on the timing and posture of techniques can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded data into a generating AI and have the generating AI perform the feedback on the timing and posture of techniques.

[0035] The advice unit can provide real-time voice and app-based advice to improve the success rate of techniques. For example, the advice unit can provide real-time voice advice using speech synthesis technology. The advice unit provides specific voice advice to improve the success rate of techniques. For example, the advice unit provides real-time advice on timing and posture. The advice unit can also provide real-time advice using app notifications. The advice unit provides specific app notifications to improve the success rate of techniques. For example, the advice unit provides app notifications on timing and posture. This allows for improved technique success rates by providing real-time advice via voice and app. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can have a generating AI generate advice to improve the success rate of techniques.

[0036] The analysis unit can analyze the movement and posture in detail using sensors mounted on the skateboard. For example, the analysis unit can analyze the movement in detail using an accelerometer mounted on the skateboard. Based on the data obtained from the accelerometer, the analysis unit analyzes the movement of the skateboard in detail. For example, the analysis unit analyzes the acceleration data of the skateboard and identifies movement patterns. The analysis unit can also analyze the posture in detail using a gyroscope sensor mounted on the skateboard. Based on the data obtained from the gyroscope, the analysis unit analyzes the posture of the skateboard in detail. For example, the analysis unit analyzes the rotation data of the skateboard and identifies changes in posture. In this way, by using sensors mounted on the skateboard, the movement and posture can be analyzed in detail. 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 sensor data into a generating AI and have the generating AI perform the analysis of movement and posture.

[0037] The data collection unit can automatically track and record the movement of a skateboard using a camera tracking system. For example, the data collection unit can automatically track and record the movement of a skateboard using a fixed camera. The data collection unit can install a fixed camera at a specific location and track the movement of the skateboard in real time. For example, the data collection unit tracks the movement of the skateboard within the camera's field of view and collects recorded data. Alternatively, the data collection unit can automatically track and record the movement of a skateboard using a drone camera. The data collection unit can place a drone camera above the skateboard and track its movement in real time. For example, the data collection unit tracks the movement of the skateboard using a drone camera and collects recorded data. This allows the camera tracking system to automatically track and record the movement of a skateboard. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have a generative AI control the camera tracking system.

[0038] The feedback unit can understand the level of proficiency and determine the steps of the techniques to suggest. For example, the feedback unit can understand the level of proficiency based on past performance data. The feedback unit analyzes the success rate and failure rate of past techniques and evaluates the level of proficiency. For example, if the success rate of past techniques is high, the feedback unit will determine that the level of proficiency is high. The feedback unit will also determine the steps of the techniques to suggest. The feedback unit will suggest the next steps of the techniques to be learned according to the level of proficiency. For example, the feedback unit will suggest basic techniques to beginners and advanced techniques to intermediate users. In this way, by understanding the level of proficiency, it can suggest appropriate steps of techniques. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the evaluation of the level of proficiency and the suggestion of the steps of the techniques.

[0039] The feedback unit can record distance traveled, jump height, and success rate, and make them shareable on social media. For example, the feedback unit records distance traveled using GPS data. The feedback unit calculates and records the distance traveled by the skateboard from GPS data. For example, the feedback unit measures the distance traveled by the skateboard in real time and saves it to a database. The feedback unit can also record jump height using sensor measurements. The feedback unit calculates and records the jump height of the skateboard from sensor data. For example, the feedback unit measures the jump height of the skateboard in real time and saves it to a database. Furthermore, the feedback unit can record the success rate of tricks and share it on social media. The feedback unit calculates the success rate of tricks and generates data for sharing on social media. For example, the feedback unit displays the success rate of tricks as a percentage and shares it on social media. This allows for increased user motivation by recording distance traveled, jump height, and success rate and sharing them on social media. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have the generating AI calculate the distance traveled, jump height, and success rate, and then share that information on social media.

[0040] The data collection unit can analyze the user's past recording data and select the optimal recording method. For example, the data collection unit can analyze past recording data and select the recording method for successful tricks. The data collection unit identifies the recording methods for successful tricks from past recording data and selects a similar recording method. For example, the data collection unit selects a similar recording method based on the user's past successful trick recording data. The data collection unit can also analyze past recording data and improve the recording methods for unsuccessful tricks. The data collection unit identifies the recording methods for unsuccessful tricks from past recording data and selects a different recording method. For example, the data collection unit selects a different recording method based on the user's past unsuccessful trick recording data. Furthermore, the data collection unit can select the recording method with the highest visibility from past recording data. The data collection unit identifies the recording method with the highest visibility from past recording data and selects the optimal recording method. For example, the data collection unit selects the recording method with the highest visibility from the user's past recording data. In this way, the optimal recording method can be selected by analyzing past recording data. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past recorded data into a generating AI and have the generating AI select the optimal recording method.

[0041] The recording unit can change the camera tracking pattern during recording based on the user's current skill level. For example, for beginners, the unit will use a wide-angle tracking pattern that captures the whole scene. The unit will select a wide-angle tracking pattern that captures the whole scene according to the beginner's skill level. For example, the unit will use a wide-angle tracking pattern that captures the whole scene based on the beginner's skill level. The unit can also use a tracking pattern that zooms in to capture the details of the technique for intermediate users. The unit will select a tracking pattern that zooms in to capture the details of the technique according to the intermediate user's skill level. For example, the unit will use a zoom-in tracking pattern based on the intermediate user's skill level. Furthermore, for advanced users, the unit can adjust the camera tracking speed to match the speed of the technique. The unit will adjust the camera tracking speed to match the speed of the technique according to the advanced user's skill level. For example, the unit will adjust the camera tracking speed based on the advanced user's skill level. This allows for optimal recording by changing the camera tracking pattern according to the user's skill level. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's skill level into a generating AI and cause the generating AI to modify the camera tracking pattern.

[0042] The data collection unit can select the optimal recording settings while recording, taking into account the user's geographical location information. The data collection unit can acquire the user's geographical location information, for example, using GPS data. Based on the user's geographical location information, the data collection unit selects the optimal recording settings. For example, when recording indoors, the data collection unit can select settings that match the lighting conditions. The data collection unit can also select settings that match the weather conditions when recording outdoors. Furthermore, when recording at a specific skate park, the data collection unit can select settings that are optimal for that location. In this way, the optimal recording settings can be selected by taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI select the optimal recording settings.

[0043] The data collection unit can analyze the user's social media activity during recording and prioritize the collection of relevant recording data. For example, the data collection unit can analyze the user's social media activity and identify techniques that the user wants to share. The data collection unit identifies techniques that the user wants to share from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user wants to share on social media. The data collection unit can also analyze the user's social media activity and identify techniques that have received high ratings in the past. The data collection unit identifies techniques that have received high ratings in the past from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user has received high ratings for on social media in the past. Furthermore, the data collection unit can analyze the user's social media activity and identify techniques that their followers might be interested in. The data collection unit identifies techniques that their followers might be interested in from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user's followers might be interested in. In this way, by analyzing social media activity, the data collection unit can prioritize the collection of recording data that the user wants to share. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activity.

[0044] The analysis unit can improve the accuracy of its analysis by considering the movement patterns of the skateboard during the analysis. For example, the analysis unit performs analysis by considering the rotation patterns of the skateboard. Based on the rotation data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the rotation patterns of the skateboard. The analysis unit can also perform analysis by considering the jump patterns of the skateboard. Based on the jump data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the jump patterns of the skateboard. Furthermore, the analysis unit can also perform analysis by considering the slide patterns of the skateboard. Based on the slide data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the slide patterns of the skateboard. In this way, the accuracy of the analysis can be improved by considering the movement patterns of the skateboard. 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 skateboard movement patterns into a generation AI and have the generation AI perform tasks to improve the accuracy of the analysis.

[0045] The analysis unit can optimize its analysis algorithm by referring to the user's past performance data during analysis. For example, the analysis unit optimizes its analysis algorithm based on the user's past successful technique data. The analysis unit analyzes the user's past success data and optimizes its analysis algorithm. For example, the analysis unit improves the accuracy of analyzing similar techniques based on the user's past successful technique data. The analysis unit can also optimize its analysis algorithm based on the user's past failed technique data. The analysis unit analyzes the user's past failure data and optimizes its analysis algorithm. For example, the analysis unit improves the accuracy of analyzing similar techniques based on the user's past failed technique data. Furthermore, the analysis unit can optimize its analysis algorithm comprehensively by referring to the user's entire past performance data. The analysis unit analyzes the user's entire past performance data and optimizes its analysis algorithm comprehensively. For example, the analysis unit improves the accuracy of technique analysis based on the user's entire past performance data. This allows the analysis algorithm to be optimized by referring to past performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past performance data into a generation AI and have the generation AI optimize the analysis algorithm.

[0046] The analysis unit can perform analysis while considering data on the geographical movement of the skateboard. For example, the analysis unit can perform analysis while considering the distance traveled by the skateboard. The analysis unit can perform analysis while considering geographical movement based on the distance traveled by the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the distance traveled by the skateboard. The analysis unit can also perform analysis while considering the speed of the skateboard. The analysis unit can perform analysis while considering geographical movement based on the speed of the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the speed of the skateboard. Furthermore, the analysis unit can also perform analysis while considering the direction of movement of the skateboard. The analysis unit can perform analysis while considering geographical movement based on the direction of movement data of the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the direction of movement of the skateboard. In this way, the accuracy of the analysis can be improved by considering data on geographical movement. 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 department can input geographical movement data into a generating AI and have the AI ​​perform tasks to improve the accuracy of the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on skateboarding. For example, the analysis unit performs analysis by referring to papers on skateboarding techniques. The analysis unit improves the accuracy of its analysis based on data from papers on skateboarding techniques. For example, the analysis unit evaluates the success rate of tricks by referring to papers on skateboarding techniques. The analysis unit can also perform analysis by referring to research on skateboarding movements. The analysis unit improves the accuracy of its analysis based on research data on skateboarding movements. For example, the analysis unit evaluates the success rate of tricks by referring to research on skateboarding movements. Furthermore, the analysis unit can also perform analysis by referring to data on skateboarding performance. The analysis unit improves the accuracy of its analysis based on data on skateboarding performance. For example, the analysis unit evaluates the success rate of tricks by referring to data on skateboarding performance. In this way, the accuracy of the analysis can be improved by referring to relevant literature. 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 have a generating AI perform the referencing of relevant literature.

[0048] The advice unit can adjust the level of detail in its advice based on the importance of the technique. For example, the advice unit provides detailed advice for important techniques. The advice unit evaluates the importance of techniques and provides detailed advice for important techniques. For example, the advice unit provides detailed advice based on the importance of the technique. The advice unit can also provide concise advice for basic techniques. The advice unit evaluates the importance of techniques and provides concise advice for basic techniques. For example, the advice unit provides concise advice based on the importance of the technique. Furthermore, the advice unit can provide focused advice for techniques that the user particularly wants to learn. The advice unit evaluates the importance of techniques and provides focused advice for techniques that the user particularly wants to learn. For example, the advice unit provides focused advice based on the importance of the technique. This allows the advice unit to provide optimal advice by adjusting the level of detail in its advice based on the importance of the technique. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of a technique into the generating AI and have the generating AI adjust the level of detail in the advice.

[0049] The advice unit can apply different advice algorithms depending on the category of the technique when providing advice. For example, in the case of a jumping technique, the advice unit applies an advice algorithm specialized for jumping. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for jumping techniques. For example, the advice unit applies an advice algorithm specialized for jumping based on the category of the technique. The advice unit can also apply an advice algorithm specialized for sliding techniques. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for sliding techniques. For example, the advice unit applies an advice algorithm specialized for sliding based on the category of the technique. Furthermore, the advice unit can also apply an advice algorithm specialized for rotational techniques. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for rotational techniques. For example, the advice unit applies an advice algorithm specialized for rotation based on the category of the technique. In this way, by applying different advice algorithms depending on the category of the technique, the optimal advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input the category of the technique into the generating AI and have the generating AI execute the application of the advice algorithm.

[0050] The advice unit can determine the priority of advice based on the timing of the technique's execution. For example, the advice unit can provide important advice immediately before the technique is executed. The advice unit evaluates the timing of the technique's execution and provides important advice immediately before the technique is executed. For example, the advice unit provides important advice immediately before the technique is executed based on the timing of the technique's execution. The advice unit can also provide feedback after the technique is executed. The advice unit evaluates the timing of the technique's execution and provides feedback after the technique is executed. For example, the advice unit provides feedback after the technique is executed based on the timing of the technique's execution. Furthermore, the advice unit can provide advice in real time during the technique's practice. The advice unit evaluates the timing of the technique's execution and provides advice in real time during the technique's practice. For example, the advice unit provides advice in real time during the technique's practice based on the timing of the technique's execution. This allows the advice unit to provide advice at the optimal timing by determining the priority of advice based on the timing of the technique's execution. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the timing of the technique's execution into a generating AI and have the generating AI determine the priority of the advice.

[0051] The advice unit can adjust the order of advice based on the relationships between techniques. For example, the advice unit can provide advice in a sequential order from basic techniques to advanced techniques. The advice unit evaluates the relationships between techniques and provides advice in a sequential order from basic techniques to advanced techniques. For example, the advice unit provides advice in a sequential order from basic techniques to advanced techniques based on the relationships between techniques. The advice unit can also provide advice on related techniques consecutively. The advice unit evaluates the relationships between techniques and provides advice on related techniques consecutively. For example, the advice unit provides advice on related techniques consecutively based on the relationships between techniques. Furthermore, the advice unit can provide advice in the optimal order according to the user's learning progress. The advice unit evaluates the relationships between techniques and provides advice in the optimal order according to the user's learning progress. For example, the advice unit provides advice in the optimal order according to the user's learning progress based on the relationships between techniques. In this way, by adjusting the order of advice based on the relationships between techniques, advice can be provided in the optimal order. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input the relationships between techniques into the generating AI and have the generating AI adjust the order of the advice.

[0052] The feedback unit can analyze the user's past performance data to select the optimal feedback method when providing feedback. For example, the feedback unit can provide feedback based on the user's past successful technique data. The feedback unit analyzes the user's past success data and selects the optimal feedback method. For example, the feedback unit provides feedback on similar techniques based on the user's past successful technique data. The feedback unit can also provide feedback based on the user's past unsuccessful technique data. The feedback unit analyzes the user's past failure data and selects the optimal feedback method. For example, the feedback unit provides feedback on similar techniques based on the user's past unsuccessful technique data. Furthermore, the feedback unit can refer to the user's entire past performance data to provide comprehensive feedback. The feedback unit analyzes the user's entire past performance data and selects the optimal feedback method comprehensively. For example, the feedback unit provides feedback on techniques based on the user's entire past performance data. This allows the optimal feedback method to be selected by analyzing past performance data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past performance data into the generating AI and have the AI ​​select the optimal feedback method.

[0053] The feedback unit can customize the content of the feedback based on the user's current skill level. For example, the feedback unit provides basic feedback to beginners. The feedback unit evaluates the user's skill level and provides basic feedback to beginners. For example, the feedback unit provides basic feedback to beginners based on the user's skill level. The feedback unit can also provide detailed technique feedback to intermediate users. The feedback unit evaluates the user's skill level and provides detailed technique feedback to intermediate users. For example, the feedback unit provides detailed technique feedback to intermediate users based on the user's skill level. Furthermore, the feedback unit can provide feedback on fine-tuning techniques to advanced users. The feedback unit evaluates the user's skill level and provides feedback on fine-tuning techniques to advanced users. For example, the feedback unit provides feedback on fine-tuning techniques to advanced users based on the user's skill level. This allows for the provision of optimal feedback by customizing the content of the feedback based on the user's skill level. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's skill level into a generating AI and have the AI ​​customize the content of the feedback.

[0054] The feedback unit can select the optimal feedback method by considering the user's geographical location information during feedback. For example, the feedback unit can acquire the user's geographical location information using GPS data. Based on the user's geographical location information, the feedback unit selects the optimal feedback method. For example, when providing feedback indoors, the feedback unit selects a method suited to a quiet environment. When providing feedback outdoors, the feedback unit can also select a method that takes ambient noise into account. Furthermore, when providing feedback at a specific skate park, the feedback unit can select a method best suited to that location. In this way, the optimal feedback method can be selected by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0055] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback. For example, the feedback unit can analyze the user's social media activity and identify feedback that the user wants to share. The feedback unit identifies feedback that the user wants to share from the user's social media activity and provides it with priority. For example, the feedback unit provides feedback that the user wants to share on social media with priority. The feedback unit can also analyze the user's social media activity and identify feedback that has received high ratings in the past. The feedback unit identifies feedback that has received high ratings in the past from the user's social media activity and provides it with priority. For example, the feedback unit provides feedback that has received high ratings in the past on social media with priority. Furthermore, the feedback unit can analyze the user's social media activity and identify feedback that the user's followers might be interested in. The feedback unit identifies feedback that the user's followers might be interested in and provides it with priority. For example, the feedback unit provides feedback that the user's followers might be interested in with priority. In this way, by analyzing social media activity, the feedback unit can suggest the most suitable method for providing feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generative AI perform the analysis of social media activity.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The data collection unit not only automatically tracks and records skateboard movements, but can also monitor the user's heart rate and respiration rate. For example, the unit measures the user's heart rate and respiration rate in real time using the user's heart rate sensor and respiration sensor. This allows the unit to understand the user's physical condition and fatigue level, and prompt them to take breaks at appropriate times. The data collection unit can also evaluate the physical strain of performing tricks based on the user's heart rate and respiration rate data. For example, the unit analyzes changes in heart rate and respiration rate to evaluate the strain of performing tricks. Furthermore, the data collection unit can share heart rate and respiration rate data on social media. This allows users to improve their skills while managing their own physical condition.

[0058] The analytics department can not only analyze skateboarding movements and posture in detail, but also take into account the user's diet and sleep data. For example, based on the user's diet data, the analytics department can identify nutrients that affect the success rate of tricks. This allows users to improve their trick success rate by eating a proper diet. The analytics department can also evaluate the quality of sleep, which affects trick success rate, based on the user's sleep data. For example, the analytics department can analyze the user's sleep data and evaluate the impact of sleep quality on trick success rate. Furthermore, based on diet and sleep data, the analytics department can suggest the optimal timing for performing tricks. This allows users to improve their skills while improving their lifestyle habits.

[0059] The analysis unit not only uses sensors mounted on the skateboard to analyze movement and posture in detail, but can also analyze the user's muscle movements and load in real time. For example, the analysis unit uses electromyography sensors to analyze the user's muscle movements in detail. This allows it to understand which muscles are being used and to what extent when performing tricks. The analysis unit can also evaluate muscle load and suggest appropriate rest if excessive load is being placed on the muscles. Furthermore, based on the muscle movement and load data, the analysis unit can suggest the optimal way to use muscles when performing tricks. This allows users to improve their skills while using their muscles efficiently.

[0060] The data collection unit not only automatically tracks and records the skateboard's movements using a camera tracking system, but can also collect and analyze ambient sounds around the user. For example, the data collection unit uses microphones to collect ambient sounds around the user in real time. This allows the system to understand the ambient sounds the user is hearing while performing tricks. The data collection unit can also identify factors that affect the success rate of tricks based on the ambient sound data. For example, it can evaluate the impact of noise on the success rate of tricks. Furthermore, based on the ambient sound data, the data collection unit can suggest the optimal environment for performing tricks. This allows the user to perform tricks in the optimal environment and improve their skills.

[0061] The data collection unit not only analyzes the user's past recording data and selects the optimal recording method, but can also predict the success rate of techniques based on the user's past recording data. For example, the data collection unit analyzes past recording data and applies an algorithm to predict the success rate of techniques. This allows the user to know the success rate of techniques in advance. Furthermore, the data collection unit can also suggest the optimal timing for executing techniques based on the data predicting the success rate of techniques. In addition, the data collection unit can share the data predicting the success rate of techniques on social media. This allows users to improve their skills while predicting the success rate of techniques.

[0062] The recording unit can not only change the camera tracking pattern based on the user's current skill level during recording, but also adjust the recording frame rate based on the user's skill level. For example, the unit can record at a low frame rate for beginners, saving data size. It can also record at a medium frame rate for intermediate users, capturing details of techniques while managing data size appropriately. Furthermore, it can record at a high frame rate for advanced users, capturing even the finest details of techniques clearly. By adjusting the recording frame rate according to the user's skill level, optimal recording data can be obtained.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit automatically tracks and records the skateboard's movements. The collection unit automatically tracks and records the skateboard's movements using, for example, a camera tracking system. The camera tracking system can use fixed cameras or drone cameras. The camera tracking system tracks the skateboard's movements in real time and collects the recorded data. Step 2: The analysis unit performs a detailed analysis of the data collected by the collection unit. For example, the analysis unit uses sensors mounted on the skateboard to perform a detailed analysis of its movement and posture. These sensors include accelerometers and gyroscopes. Based on the data obtained from the sensors, the analysis unit performs a detailed analysis of the skateboard's movement and posture. Step 3: The advice unit provides real-time advice based on the data obtained by the analysis unit. The advice unit provides real-time advice using, for example, a voice assistant or app notifications. The advice unit provides specific advice to increase the success rate of the technique. Step 4: The Feedback Department provides feedback on the timing and posture of the technique based on the advice provided by the Advice Department. For example, the Feedback Department analyzes video data to provide specific feedback on the timing and posture of the technique. The Feedback Department assesses the level of mastery and determines the steps of the technique to suggest. The Feedback Department records the distance traveled, jump height, and success rate, and makes them shareable on social media.

[0065] (Example of form 2) The skateboarder skill improvement support system according to an embodiment of the present invention is an AI agent system for supporting the skill improvement of skateboarders. This system includes a camera tracking system that automatically tracks and records the movement of the skateboard. Sensors mounted on the skateboard analyze the movement and posture in detail. It provides real-time voice and app-based advice to improve the success rate of tricks. It analyzes the recorded data and provides feedback on the timing and posture of tricks. It performs learning level analysis to grasp the level of acquisition and determine the steps of the tricks to suggest. It includes data visualization and sharing functions that record distance traveled, jump height, and success rate, and make them shareable on social media. As a result, skateboarders can efficiently acquire skills, receive quick feedback, and improve their skills while having fun. Thus, the skateboarder skill improvement support system can efficiently support the skill improvement of skateboarders.

[0066] The skateboarder skill improvement support system according to this embodiment comprises a data collection unit, an analysis unit, an advice unit, and a feedback unit. The data collection unit automatically tracks and records the movement of the skateboard. The data collection unit automatically tracks and records the movement of the skateboard using, for example, a camera tracking system. The camera tracking system can use a fixed camera or a drone camera. The camera tracking system tracks the movement of the skateboard in real time and collects the recorded data. The analysis unit analyzes the data collected by the data collection unit in detail. The analysis unit analyzes the movement and posture in detail using, for example, sensors mounted on the skateboard. The sensors include acceleration sensors and gyroscope sensors. Based on the data obtained from the sensors, the analysis unit analyzes the movement and posture of the skateboard in detail. The advice unit provides advice in real time based on the data obtained by the analysis unit. The advice unit provides advice in real time using, for example, a voice assistant or app notifications. The advice unit provides specific advice to increase the success rate of tricks. The feedback unit provides feedback on the timing and posture of tricks based on the advice provided by the advice unit. The feedback unit, for example, analyzes recorded data and provides specific feedback on the timing and posture of tricks. The feedback unit grasps the level of mastery and determines the steps of the trick to suggest. The feedback unit records distance traveled, jump height, and success rate, and makes them shareable on social media. As a result, the skateboarder skill improvement support system according to this embodiment can efficiently support the skill improvement of skateboarders.

[0067] The data collection unit automatically tracks and records the movement of skateboards. For example, the unit uses a camera tracking system to automatically track and record skateboard movements. The camera tracking system can utilize fixed cameras or drone cameras. Fixed cameras are installed in specific locations within skate parks or practice areas, positioned to cover a wide area. Drone cameras track skateboarders' movements from above, recording from a more dynamic perspective. These cameras collect high-resolution video in real time, meticulously recording skateboard movements. The camera tracking system tracks skateboard movements in real time and collects recorded data. Image recognition technology and AI are used to accurately determine the skateboard's position and movement. For example, the system recognizes the skateboard's distinctive shape and color, allowing the camera to automatically track it. Furthermore, by linking multiple cameras, video from different angles can be collected simultaneously, obtaining more detailed data. The collected data is stored in a central database and used for subsequent analysis and feedback. This allows the data collection unit to efficiently and accurately record skateboarders' movements, providing foundational data for skill improvement.

[0068] The analysis unit performs a detailed analysis of the data collected by the data collection unit. For example, the analysis unit uses sensors mounted on the skateboard to analyze its movement and posture in detail. These sensors include accelerometers and gyroscopes. The accelerometer measures the skateboard's acceleration and deceleration, recording its movements in detail during jumps and tricks. The gyroscope measures the skateboard's rotation and tilt, analyzing its posture and balance during tricks. The data obtained from these sensors is collected in real time and transmitted to the analysis unit. Based on the data obtained from the sensors, the analysis unit performs a detailed analysis of the skateboard's movement and posture. For example, it evaluates the height of jumps, the angle of rotation, and the stability of landings to identify the success rate of tricks and areas for improvement. Furthermore, it can use AI to compare current data with past data and analyze the progress and trends of the technique. This allows the analysis unit to accurately grasp the skateboarder's skill level and suggest specific areas for improvement.

[0069] The Advice Department provides real-time advice based on data obtained by the Analysis Department. The Advice Department provides real-time advice using, for example, voice assistants and app notifications. The voice assistant provides specific advice to improve the success rate of tricks through earphones worn by skateboarders during practice. For example, it provides real-time instructions such as, "Try bending your knees a little more when you jump," or "Try speeding up your rotation timing a bit." App notifications are displayed on smartphones and smartwatches, providing detailed feedback after practice. The Advice Department provides specific advice to improve the success rate of tricks. For example, it suggests training methods to increase jump height and practice menus to improve rotation stability. This allows the Advice Department to support skateboarders in efficiently improving their skills.

[0070] The Feedback Department provides feedback on the timing and posture of tricks based on the advice provided by the Advice Department. For example, the Feedback Department analyzes video data to provide specific feedback on the timing and posture of tricks. The video data plays back footage of tricks actually performed by the skateboarder and highlights important points. For example, it provides detailed explanations of jump timing, rotation start position, landing posture, etc., and points out areas for improvement. The Feedback Department grasps the level of proficiency and determines the steps of the tricks to suggest. For example, once a basic trick can be performed consistently, it suggests a more advanced trick as the next step. The Feedback Department records distance traveled, jump height, and success rate, and makes them shareable on social media. This allows skateboarders to visualize their progress and maintain motivation. They can also share information with other skateboarders and inspire each other. In this way, the Feedback Department can efficiently support the improvement of skateboarders' skills and promote continuous growth.

[0071] The feedback unit can analyze recorded data and provide feedback on the timing and posture of techniques. For example, the feedback unit analyzes the recorded data using image analysis technology. The feedback unit extracts information about the timing and posture of techniques from the recorded data and provides specific feedback. For example, if the timing of a technique is too early or too late, the feedback unit will suggest how to correct it. Also, if the posture is inappropriate, the feedback unit will suggest how to correct it. In this way, by analyzing the recorded data, specific feedback on the timing and posture of techniques can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input recorded data into a generating AI and have the generating AI perform the feedback on the timing and posture of techniques.

[0072] The advice unit can provide real-time voice and app-based advice to improve the success rate of techniques. For example, the advice unit can provide real-time voice advice using speech synthesis technology. The advice unit provides specific voice advice to improve the success rate of techniques. For example, the advice unit provides real-time advice on timing and posture. The advice unit can also provide real-time advice using app notifications. The advice unit provides specific app notifications to improve the success rate of techniques. For example, the advice unit provides app notifications on timing and posture. This allows for improved technique success rates by providing real-time advice via voice and app. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can have a generating AI generate advice to improve the success rate of techniques.

[0073] The analysis unit can analyze the movement and posture in detail using sensors mounted on the skateboard. For example, the analysis unit can analyze the movement in detail using an accelerometer mounted on the skateboard. Based on the data obtained from the accelerometer, the analysis unit analyzes the movement of the skateboard in detail. For example, the analysis unit analyzes the acceleration data of the skateboard and identifies movement patterns. The analysis unit can also analyze the posture in detail using a gyroscope sensor mounted on the skateboard. Based on the data obtained from the gyroscope, the analysis unit analyzes the posture of the skateboard in detail. For example, the analysis unit analyzes the rotation data of the skateboard and identifies changes in posture. In this way, by using sensors mounted on the skateboard, the movement and posture can be analyzed in detail. 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 sensor data into a generating AI and have the generating AI perform the analysis of movement and posture.

[0074] The data collection unit can automatically track and record the movement of a skateboard using a camera tracking system. For example, the data collection unit can automatically track and record the movement of a skateboard using a fixed camera. The data collection unit can install a fixed camera at a specific location and track the movement of the skateboard in real time. For example, the data collection unit tracks the movement of the skateboard within the camera's field of view and collects recorded data. Alternatively, the data collection unit can automatically track and record the movement of a skateboard using a drone camera. The data collection unit can place a drone camera above the skateboard and track its movement in real time. For example, the data collection unit tracks the movement of the skateboard using a drone camera and collects recorded data. This allows the camera tracking system to automatically track and record the movement of a skateboard. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can have a generative AI control the camera tracking system.

[0075] The feedback unit can understand the level of proficiency and determine the steps of the techniques to suggest. For example, the feedback unit can understand the level of proficiency based on past performance data. The feedback unit analyzes the success rate and failure rate of past techniques and evaluates the level of proficiency. For example, if the success rate of past techniques is high, the feedback unit will determine that the level of proficiency is high. The feedback unit will also determine the steps of the techniques to suggest. The feedback unit will suggest the next steps of the techniques to be learned according to the level of proficiency. For example, the feedback unit will suggest basic techniques to beginners and advanced techniques to intermediate users. In this way, by understanding the level of proficiency, it can suggest appropriate steps of techniques. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generating AI perform the evaluation of the level of proficiency and the suggestion of the steps of the techniques.

[0076] The feedback unit can record distance traveled, jump height, and success rate, and make them shareable on social media. For example, the feedback unit records distance traveled using GPS data. The feedback unit calculates and records the distance traveled by the skateboard from GPS data. For example, the feedback unit measures the distance traveled by the skateboard in real time and saves it to a database. The feedback unit can also record jump height using sensor measurements. The feedback unit calculates and records the jump height of the skateboard from sensor data. For example, the feedback unit measures the jump height of the skateboard in real time and saves it to a database. Furthermore, the feedback unit can record the success rate of tricks and share it on social media. The feedback unit calculates the success rate of tricks and generates data for sharing on social media. For example, the feedback unit displays the success rate of tricks as a percentage and shares it on social media. This allows for increased user motivation by recording distance traveled, jump height, and success rate and sharing them on social media. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have the generating AI calculate the distance traveled, jump height, and success rate, and then share that information on social media.

[0077] The data collection unit can estimate the user's emotions and adjust the recording start time based on the estimated emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit analyzes the user's facial expression data and estimates the emotions. For example, if the data collection unit is tense, it will not start recording until the user is relaxed. The data collection unit can also start recording immediately if the user is focused. Furthermore, if the data collection unit is excited, it can delay recording until the user's emotions have calmed down. This allows for optimal timing for recording by adjusting the recording start time based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0078] The data collection unit can analyze the user's past recording data and select the optimal recording method. For example, the data collection unit can analyze past recording data and select the recording method for successful tricks. The data collection unit identifies the recording methods for successful tricks from past recording data and selects a similar recording method. For example, the data collection unit selects a similar recording method based on the user's past successful trick recording data. The data collection unit can also analyze past recording data and improve the recording methods for unsuccessful tricks. The data collection unit identifies the recording methods for unsuccessful tricks from past recording data and selects a different recording method. For example, the data collection unit selects a different recording method based on the user's past unsuccessful trick recording data. Furthermore, the data collection unit can select the recording method with the highest visibility from past recording data. The data collection unit identifies the recording method with the highest visibility from past recording data and selects the optimal recording method. For example, the data collection unit selects the recording method with the highest visibility from the user's past recording data. In this way, the optimal recording method can be selected by analyzing past recording data. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input past recorded data into a generating AI and have the generating AI select the optimal recording method.

[0079] The recording unit can change the camera tracking pattern during recording based on the user's current skill level. For example, for beginners, the unit will use a wide-angle tracking pattern that captures the whole scene. The unit will select a wide-angle tracking pattern that captures the whole scene according to the beginner's skill level. For example, the unit will use a wide-angle tracking pattern that captures the whole scene based on the beginner's skill level. The unit can also use a tracking pattern that zooms in to capture the details of the technique for intermediate users. The unit will select a tracking pattern that zooms in to capture the details of the technique according to the intermediate user's skill level. For example, the unit will use a zoom-in tracking pattern based on the intermediate user's skill level. Furthermore, for advanced users, the unit can adjust the camera tracking speed to match the speed of the technique. The unit will adjust the camera tracking speed to match the speed of the technique according to the advanced user's skill level. For example, the unit will adjust the camera tracking speed based on the advanced user's skill level. This allows for optimal recording by changing the camera tracking pattern according to the user's skill level. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's skill level into a generating AI and cause the generating AI to modify the camera tracking pattern.

[0080] The data collection unit can estimate the user's emotions and adjust the recording viewpoint based on the estimated user emotions. The data collection unit estimates the user's emotions, for example, using facial recognition technology. The data collection unit analyzes the user's facial expression data and estimates the emotions. For example, if the user is relaxed, the data collection unit uses a wide-angle viewpoint that captures the whole scene. If the user is focused, the data collection unit can also use a zoomed-in viewpoint to capture the details of the technique. Furthermore, if the user is excited, the data collection unit can use a visually stimulating viewpoint. This allows for recording from the optimal viewpoint by adjusting the recording viewpoint based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0081] The data collection unit can select the optimal recording settings while recording, taking into account the user's geographical location information. The data collection unit can acquire the user's geographical location information, for example, using GPS data. Based on the user's geographical location information, the data collection unit selects the optimal recording settings. For example, when recording indoors, the data collection unit can select settings that match the lighting conditions. The data collection unit can also select settings that match the weather conditions when recording outdoors. Furthermore, when recording at a specific skate park, the data collection unit can select settings that are optimal for that location. In this way, the optimal recording settings can be selected by taking geographical location information into consideration. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI. For example, the data collection unit can input geographical location information into a generating AI and have the generating AI select the optimal recording settings.

[0082] The data collection unit can analyze the user's social media activity during recording and prioritize the collection of relevant recording data. For example, the data collection unit can analyze the user's social media activity and identify techniques that the user wants to share. The data collection unit identifies techniques that the user wants to share from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user wants to share on social media. The data collection unit can also analyze the user's social media activity and identify techniques that have received high ratings in the past. The data collection unit identifies techniques that have received high ratings in the past from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user has received high ratings for on social media in the past. Furthermore, the data collection unit can analyze the user's social media activity and identify techniques that their followers might be interested in. The data collection unit identifies techniques that their followers might be interested in from the user's social media activity and prioritizes recording them. For example, the data collection unit prioritizes recording techniques that the user's followers might be interested in. In this way, by analyzing social media activity, the data collection unit can prioritize the collection of recording data that the user wants to share. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have a generative AI perform the analysis of social media activity.

[0083] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions using facial recognition technology. The analysis unit analyzes the user's facial expression data and estimates the emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually stimulating analysis results. This allows for the provision of optimal analysis results by adjusting the level of detail of the analysis based on the user's emotions. 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 not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0084] The analysis unit can improve the accuracy of its analysis by considering the movement patterns of the skateboard during the analysis. For example, the analysis unit performs analysis by considering the rotation patterns of the skateboard. Based on the rotation data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the rotation patterns of the skateboard. The analysis unit can also perform analysis by considering the jump patterns of the skateboard. Based on the jump data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the jump patterns of the skateboard. Furthermore, the analysis unit can also perform analysis by considering the slide patterns of the skateboard. Based on the slide data of the skateboard, the analysis unit identifies movement patterns and improves the accuracy of the analysis. For example, the analysis unit evaluates the success rate of tricks by considering the slide patterns of the skateboard. In this way, the accuracy of the analysis can be improved by considering the movement patterns of the skateboard. 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 skateboard movement patterns into a generation AI and have the generation AI perform tasks to improve the accuracy of the analysis.

[0085] The analysis unit can optimize its analysis algorithm by referring to the user's past performance data during analysis. For example, the analysis unit optimizes its analysis algorithm based on the user's past successful technique data. The analysis unit analyzes the user's past success data and optimizes its analysis algorithm. For example, the analysis unit improves the accuracy of analyzing similar techniques based on the user's past successful technique data. The analysis unit can also optimize its analysis algorithm based on the user's past failed technique data. The analysis unit analyzes the user's past failure data and optimizes its analysis algorithm. For example, the analysis unit improves the accuracy of analyzing similar techniques based on the user's past failed technique data. Furthermore, the analysis unit can optimize its analysis algorithm comprehensively by referring to the user's entire past performance data. The analysis unit analyzes the user's entire past performance data and optimizes its analysis algorithm comprehensively. For example, the analysis unit improves the accuracy of technique analysis based on the user's entire past performance data. This allows the analysis algorithm to be optimized by referring to past performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past performance data into a generation AI and have the generation AI optimize the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit estimates the user's emotions using, for example, facial recognition technology. The analysis unit analyzes the user's facial expression data and estimates the emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided. 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 not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0087] The analysis unit can perform analysis while considering data on the geographical movement of the skateboard. For example, the analysis unit can perform analysis while considering the distance traveled by the skateboard. The analysis unit can perform analysis while considering geographical movement based on the distance traveled by the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the distance traveled by the skateboard. The analysis unit can also perform analysis while considering the speed of the skateboard. The analysis unit can perform analysis while considering geographical movement based on the speed of the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the speed of the skateboard. Furthermore, the analysis unit can also perform analysis while considering the direction of movement of the skateboard. The analysis unit can perform analysis while considering geographical movement based on the direction of movement data of the skateboard. For example, the analysis unit can evaluate the success rate of tricks while considering the direction of movement of the skateboard. In this way, the accuracy of the analysis can be improved by considering data on geographical movement. 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 department can input geographical movement data into a generating AI and have the AI ​​perform tasks to improve the accuracy of the analysis.

[0088] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on skateboarding. For example, the analysis unit performs analysis by referring to papers on skateboarding techniques. The analysis unit improves the accuracy of its analysis based on data from papers on skateboarding techniques. For example, the analysis unit evaluates the success rate of tricks by referring to papers on skateboarding techniques. The analysis unit can also perform analysis by referring to research on skateboarding movements. The analysis unit improves the accuracy of its analysis based on research data on skateboarding movements. For example, the analysis unit evaluates the success rate of tricks by referring to research on skateboarding movements. Furthermore, the analysis unit can also perform analysis by referring to data on skateboarding performance. The analysis unit improves the accuracy of its analysis based on data on skateboarding performance. For example, the analysis unit evaluates the success rate of tricks by referring to data on skateboarding performance. In this way, the accuracy of the analysis can be improved by referring to relevant literature. 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 have a generating AI perform the referencing of relevant literature.

[0089] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on the estimated emotions. For example, the advice unit might use facial recognition technology to estimate the user's emotions. The advice unit analyzes the user's facial expression data to estimate emotions. For example, if the user is tense, the advice unit might provide advice in a calm voice. It might also provide advice in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the advice unit might provide quick and concise advice. This allows the system to provide optimal advice by adjusting the way it expresses advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the advice unit may be performed using AI, or not. For example, the advice unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0090] The advice unit can adjust the level of detail in its advice based on the importance of the technique. For example, the advice unit provides detailed advice for important techniques. The advice unit evaluates the importance of techniques and provides detailed advice for important techniques. For example, the advice unit provides detailed advice based on the importance of the technique. The advice unit can also provide concise advice for basic techniques. The advice unit evaluates the importance of techniques and provides concise advice for basic techniques. For example, the advice unit provides concise advice based on the importance of the technique. Furthermore, the advice unit can provide focused advice for techniques that the user particularly wants to learn. The advice unit evaluates the importance of techniques and provides focused advice for techniques that the user particularly wants to learn. For example, the advice unit provides focused advice based on the importance of the technique. This allows the advice unit to provide optimal advice by adjusting the level of detail in its advice based on the importance of the technique. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the importance of a technique into the generating AI and have the generating AI adjust the level of detail in the advice.

[0091] The advice unit can apply different advice algorithms depending on the category of the technique when providing advice. For example, in the case of a jumping technique, the advice unit applies an advice algorithm specialized for jumping. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for jumping techniques. For example, the advice unit applies an advice algorithm specialized for jumping based on the category of the technique. The advice unit can also apply an advice algorithm specialized for sliding techniques. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for sliding techniques. For example, the advice unit applies an advice algorithm specialized for sliding based on the category of the technique. Furthermore, the advice unit can also apply an advice algorithm specialized for rotational techniques. The advice unit evaluates the category of the technique and applies an advice algorithm specialized for rotational techniques. For example, the advice unit applies an advice algorithm specialized for rotation based on the category of the technique. In this way, by applying different advice algorithms depending on the category of the technique, the optimal advice can be provided. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input the category of the technique into the generating AI and have the generating AI execute the application of the advice algorithm.

[0092] The advice unit can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. The advice unit estimates the user's emotions, for example, using facial recognition technology. The advice unit analyzes the user's facial expression data and estimates the emotions. For example, if the user is tense, the advice unit provides short, concise advice. The advice unit can also provide detailed advice if the user is relaxed. Furthermore, if the user is in a hurry, the advice unit can provide quick and concise advice. This allows the system to provide optimal advice by adjusting the length of the advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0093] The advice unit can determine the priority of advice based on the timing of the technique's execution. For example, the advice unit can provide important advice immediately before the technique is executed. The advice unit evaluates the timing of the technique's execution and provides important advice immediately before the technique is executed. For example, the advice unit provides important advice immediately before the technique is executed based on the timing of the technique's execution. The advice unit can also provide feedback after the technique is executed. The advice unit evaluates the timing of the technique's execution and provides feedback after the technique is executed. For example, the advice unit provides feedback after the technique is executed based on the timing of the technique's execution. Furthermore, the advice unit can provide advice in real time during the technique's practice. The advice unit evaluates the timing of the technique's execution and provides advice in real time during the technique's practice. For example, the advice unit provides advice in real time during the technique's practice based on the timing of the technique's execution. This allows the advice unit to provide advice at the optimal timing by determining the priority of advice based on the timing of the technique's execution. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the timing of the technique's execution into a generating AI and have the generating AI determine the priority of the advice.

[0094] The advice unit can adjust the order of advice based on the relationships between techniques. For example, the advice unit can provide advice in a sequential order from basic techniques to advanced techniques. The advice unit evaluates the relationships between techniques and provides advice in a sequential order from basic techniques to advanced techniques. For example, the advice unit provides advice in a sequential order from basic techniques to advanced techniques based on the relationships between techniques. The advice unit can also provide advice on related techniques consecutively. The advice unit evaluates the relationships between techniques and provides advice on related techniques consecutively. For example, the advice unit provides advice on related techniques consecutively based on the relationships between techniques. Furthermore, the advice unit can provide advice in the optimal order according to the user's learning progress. The advice unit evaluates the relationships between techniques and provides advice in the optimal order according to the user's learning progress. For example, the advice unit provides advice in the optimal order according to the user's learning progress based on the relationships between techniques. In this way, by adjusting the order of advice based on the relationships between techniques, advice can be provided in the optimal order. Some or all of the above processing in the advice unit may be performed using AI, for example, or without using AI. For example, the advice unit can input the relationships between techniques into the generating AI and have the generating AI adjust the order of the advice.

[0095] The feedback unit can estimate the user's emotions and adjust the feedback method based on the estimated emotions. For example, the feedback unit might use facial recognition technology to estimate the user's emotions. The feedback unit analyzes the user's facial expression data to estimate emotions. For example, if the user is tense, the feedback unit might provide feedback in a calm voice. It might also provide feedback in a cheerful voice if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit might provide quick and concise feedback. This allows for optimal feedback by adjusting the feedback method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0096] The feedback unit can analyze the user's past performance data to select the optimal feedback method when providing feedback. For example, the feedback unit can provide feedback based on the user's past successful technique data. The feedback unit analyzes the user's past success data and selects the optimal feedback method. For example, the feedback unit provides feedback on similar techniques based on the user's past successful technique data. The feedback unit can also provide feedback based on the user's past unsuccessful technique data. The feedback unit analyzes the user's past failure data and selects the optimal feedback method. For example, the feedback unit provides feedback on similar techniques based on the user's past unsuccessful technique data. Furthermore, the feedback unit can refer to the user's entire past performance data to provide comprehensive feedback. The feedback unit analyzes the user's entire past performance data and selects the optimal feedback method comprehensively. For example, the feedback unit provides feedback on techniques based on the user's entire past performance data. This allows the optimal feedback method to be selected by analyzing past performance data. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input past performance data into the generating AI and have the AI ​​select the optimal feedback method.

[0097] The feedback unit can customize the content of the feedback based on the user's current skill level. For example, the feedback unit provides basic feedback to beginners. The feedback unit evaluates the user's skill level and provides basic feedback to beginners. For example, the feedback unit provides basic feedback to beginners based on the user's skill level. The feedback unit can also provide detailed technique feedback to intermediate users. The feedback unit evaluates the user's skill level and provides detailed technique feedback to intermediate users. For example, the feedback unit provides detailed technique feedback to intermediate users based on the user's skill level. Furthermore, the feedback unit can provide feedback on fine-tuning techniques to advanced users. The feedback unit evaluates the user's skill level and provides feedback on fine-tuning techniques to advanced users. For example, the feedback unit provides feedback on fine-tuning techniques to advanced users based on the user's skill level. This allows for the provision of optimal feedback by customizing the content of the feedback based on the user's skill level. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's skill level into a generating AI and have the AI ​​customize the content of the feedback.

[0098] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, the feedback unit might use facial recognition technology to estimate the user's emotions. The feedback unit analyzes the user's facial expression data to estimate emotions. For example, if the user is tense, the feedback unit prioritizes providing important feedback. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback. This allows for the provision of optimal feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0099] The feedback unit can select the optimal feedback method by considering the user's geographical location information during feedback. For example, the feedback unit can acquire the user's geographical location information using GPS data. Based on the user's geographical location information, the feedback unit selects the optimal feedback method. For example, when providing feedback indoors, the feedback unit selects a method suited to a quiet environment. When providing feedback outdoors, the feedback unit can also select a method that takes ambient noise into account. Furthermore, when providing feedback at a specific skate park, the feedback unit can select a method best suited to that location. In this way, the optimal feedback method can be selected by considering geographical location information. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information into a generating AI and have the generating AI select the optimal feedback method.

[0100] The feedback unit can analyze the user's social media activity and suggest methods for providing feedback. For example, the feedback unit can analyze the user's social media activity and identify feedback that the user wants to share. The feedback unit identifies feedback that the user wants to share from the user's social media activity and provides it with priority. For example, the feedback unit provides feedback that the user wants to share on social media with priority. The feedback unit can also analyze the user's social media activity and identify feedback that has received high ratings in the past. The feedback unit identifies feedback that has received high ratings in the past from the user's social media activity and provides it with priority. For example, the feedback unit provides feedback that has received high ratings in the past on social media with priority. Furthermore, the feedback unit can analyze the user's social media activity and identify feedback that the user's followers might be interested in. The feedback unit identifies feedback that the user's followers might be interested in and provides it with priority. For example, the feedback unit provides feedback that the user's followers might be interested in with priority. In this way, by analyzing social media activity, the feedback unit can suggest the most suitable method for providing feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can have a generative AI perform the analysis of social media activity.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The data collection unit not only automatically tracks and records skateboard movements, but can also monitor the user's heart rate and respiration rate. For example, the unit measures the user's heart rate and respiration rate in real time using the user's heart rate sensor and respiration sensor. This allows the unit to understand the user's physical condition and fatigue level, and prompt them to take breaks at appropriate times. The data collection unit can also evaluate the physical strain of performing tricks based on the user's heart rate and respiration rate data. For example, the unit analyzes changes in heart rate and respiration rate to evaluate the strain of performing tricks. Furthermore, the data collection unit can share heart rate and respiration rate data on social media. This allows users to improve their skills while managing their own physical condition.

[0103] The analytics department can not only analyze skateboarding movements and posture in detail, but also take into account the user's diet and sleep data. For example, based on the user's diet data, the analytics department can identify nutrients that affect the success rate of tricks. This allows users to improve their trick success rate by eating a proper diet. The analytics department can also evaluate the quality of sleep, which affects trick success rate, based on the user's sleep data. For example, the analytics department can analyze the user's sleep data and evaluate the impact of sleep quality on trick success rate. Furthermore, based on diet and sleep data, the analytics department can suggest the optimal timing for performing tricks. This allows users to improve their skills while improving their lifestyle habits.

[0104] The advice system not only provides real-time voice and app-based advice to improve the success rate of techniques, but it can also estimate the user's emotions and adjust the advice based on those emotions. For example, if the user is nervous, the advice system can provide advice on breathing techniques and stretches to help them relax. If the user is feeling down, the advice system can provide words of encouragement to boost their motivation. Furthermore, if the user is excited, the advice system can provide advice to help them calm down. By providing appropriate advice tailored to the user's emotions, the success rate of techniques can be improved.

[0105] The analysis unit not only uses sensors mounted on the skateboard to analyze movement and posture in detail, but can also analyze the user's muscle movements and load in real time. For example, the analysis unit uses electromyography sensors to analyze the user's muscle movements in detail. This allows it to understand which muscles are being used and to what extent when performing tricks. The analysis unit can also evaluate muscle load and suggest appropriate rest if excessive load is being placed on the muscles. Furthermore, based on the muscle movement and load data, the analysis unit can suggest the optimal way to use muscles when performing tricks. This allows users to improve their skills while using their muscles efficiently.

[0106] The data collection unit not only automatically tracks and records the skateboard's movements using a camera tracking system, but can also collect and analyze ambient sounds around the user. For example, the data collection unit uses microphones to collect ambient sounds around the user in real time. This allows the system to understand the ambient sounds the user is hearing while performing tricks. The data collection unit can also identify factors that affect the success rate of tricks based on the ambient sound data. For example, it can evaluate the impact of noise on the success rate of tricks. Furthermore, based on the ambient sound data, the data collection unit can suggest the optimal environment for performing tricks. This allows the user to perform tricks in the optimal environment and improve their skills.

[0107] The feedback unit not only understands the user's level of mastery and determines the steps to suggest, but it can also estimate the user's emotions and adjust the content of the feedback based on those emotions. For example, if the user is nervous, the feedback unit can provide feedback to help them relax. It can also provide feedback to boost motivation if the user is feeling down. Furthermore, if the user is excited, it can provide feedback to help them calm down. By providing appropriate feedback tailored to the user's emotions, the success rate of the technique can be improved.

[0108] The feedback system not only records distance traveled, jump height, and success rate, making them shareable on social media, but it can also estimate the user's emotions and adjust the content shared based on those emotions. For example, if the user is happy, the feedback system will share positive content that reflects that emotion. If the user is down, the feedback system can share encouraging messages that take that emotion into consideration. Furthermore, if the user is excited, the feedback system can share energetic content that reflects that emotion. By sharing appropriate content according to the user's emotions, it is possible to increase engagement on social media.

[0109] The recording unit can estimate the user's emotions and adjust not only the start time of recording based on the estimated emotions, but also the end time of recording based on the user's emotions. For example, if the user is tired, the recording unit will end the recording earlier. Conversely, if the user is focused, the recording unit can continue recording for a longer period. Furthermore, if the user is excited, the recording unit can pause the recording and wait for their emotions to calm down. This allows for optimal recording data to be obtained by ending the recording at the appropriate time according to the user's emotions.

[0110] The data collection unit not only analyzes the user's past recording data and selects the optimal recording method, but can also predict the success rate of techniques based on the user's past recording data. For example, the data collection unit analyzes past recording data and applies an algorithm to predict the success rate of techniques. This allows the user to know the success rate of techniques in advance. Furthermore, the data collection unit can also suggest the optimal timing for executing techniques based on the data predicting the success rate of techniques. In addition, the data collection unit can share the data predicting the success rate of techniques on social media. This allows users to improve their skills while predicting the success rate of techniques.

[0111] The recording unit can not only change the camera tracking pattern based on the user's current skill level during recording, but also adjust the recording frame rate based on the user's skill level. For example, the unit can record at a low frame rate for beginners, saving data size. It can also record at a medium frame rate for intermediate users, capturing details of techniques while managing data size appropriately. Furthermore, it can record at a high frame rate for advanced users, capturing even the finest details of techniques clearly. By adjusting the recording frame rate according to the user's skill level, optimal recording data can be obtained.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The collection unit automatically tracks and records the skateboard's movements. The collection unit automatically tracks and records the skateboard's movements using, for example, a camera tracking system. The camera tracking system can use fixed cameras or drone cameras. The camera tracking system tracks the skateboard's movements in real time and collects the recorded data. Step 2: The analysis unit performs a detailed analysis of the data collected by the collection unit. For example, the analysis unit uses sensors mounted on the skateboard to perform a detailed analysis of its movement and posture. These sensors include accelerometers and gyroscopes. Based on the data obtained from the sensors, the analysis unit performs a detailed analysis of the skateboard's movement and posture. Step 3: The advice unit provides real-time advice based on the data obtained by the analysis unit. The advice unit provides real-time advice using, for example, a voice assistant or app notifications. The advice unit provides specific advice to increase the success rate of the technique. Step 4: The Feedback Department provides feedback on the timing and posture of the technique based on the advice provided by the Advice Department. For example, the Feedback Department analyzes video data to provide specific feedback on the timing and posture of the technique. The Feedback Department assesses the level of mastery and determines the steps of the technique to suggest. The Feedback Department records the distance traveled, jump height, and success rate, and makes them shareable on social media.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit automatically tracks and records the skateboard's movements using the camera 42 of the smart device 14 or a drone camera. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12, based on data obtained from sensors mounted on the skateboard. The advice unit is implemented in real time by the control unit 46A of the smart device 14, using a voice assistant or app notifications. The feedback unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, analyzing the recorded data and providing specific feedback on the timing and posture of tricks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit automatically tracks and records the movement of the skateboard using the camera 42 of the smart glasses 214 or a drone camera. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12, based on data obtained from sensors mounted on the skateboard. The advice unit is implemented in real time by the control unit 46A of the smart glasses 214, using a voice assistant or app notifications. The feedback unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, analyzing the recorded data and providing specific feedback on the timing and posture of tricks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit automatically tracks and records the movement of the skateboard using the camera 42 of the headset terminal 314 or a drone camera. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12, based on data obtained from sensors mounted on the skateboard. The advice unit is implemented in real time by the control unit 46A of the headset terminal 314, using a voice assistant or app notifications. The feedback unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, analyzing the recorded data and providing specific feedback on the timing and posture of tricks. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the data collection unit, analysis unit, advice unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit automatically tracks and records the movement of the skateboard using the camera 42 of the robot 414 or a drone camera. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12, based on data obtained from sensors mounted on the skateboard. The advice unit is implemented in real time by the control unit 46A of the robot 414, using a voice assistant or app notifications. The feedback unit is implemented in real time by the specific processing unit 290 of the data processing unit 12, analyzing the recorded data and providing specific feedback on the timing and posture of tricks. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) A collection unit that automatically tracks and records the movement of the skateboard, An analysis unit that analyzes the data collected by the aforementioned collection unit in detail, An advice unit provides real-time advice based on the data obtained by the analysis unit, The system includes a feedback unit that provides feedback on the timing and posture of techniques based on the advice provided by the aforementioned advice unit. A system characterized by the following features. (Note 2) The aforementioned feedback unit is The video data is analyzed to provide feedback on the timing and posture of techniques. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned advice section, Provides real-time audio and app-based advice to improve the success rate of tricks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Sensors mounted on the skateboard analyze movement and posture in detail. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The camera tracking system automatically tracks and records the skateboard's movements. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Assess the level of proficiency and determine the steps to suggest for the technique. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned feedback unit is The system will record distance traveled, jump height, and success rate, and allow users to share this information on social media. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the recording start time based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is The system analyzes the user's past recording data and selects the optimal recording method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During recording, the camera tracking pattern is changed based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and adjusts the recording perspective based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During recording, the system selects the optimal recording settings by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During recording, the system analyzes the user's social media activity and prioritizes collecting relevant recording data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates the user's emotions and adjusts the level of detail of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, consider the patterns of skateboard movement to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referencing the user's past performance data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During the analysis, data on the geographical movement of skateboards will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, we refer to relevant skateboarding literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When giving advice, adjust the level of detail based on the importance of the technique. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the category of the technique. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When giving advice, prioritize the advice based on when the technique should be performed. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When giving advice, adjust the order of advice based on the relevance of the techniques. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is It estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is During the feedback process, the system analyzes the user's past performance data to select the most suitable feedback method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, the optimal feedback method is selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest ways to provide feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0186] 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. A collection unit that automatically tracks and records the movement of the skateboard, An analysis unit that analyzes in detail the data collected by the aforementioned collection unit, An advice unit provides real-time advice based on the data obtained by the analysis unit, The system includes a feedback unit that provides feedback on the timing and posture of techniques based on the advice provided by the aforementioned advice unit. A system characterized by the following features.

2. The aforementioned feedback unit is The video data is analyzed to provide feedback on the timing and posture of the techniques. The system according to feature 1.

3. The aforementioned advice section, Provides real-time audio and app-based advice to improve the success rate of tricks. The system according to feature 1.

4. The aforementioned analysis unit is Sensors mounted on the skateboard analyze movement and posture in detail. The system according to feature 1.

5. The aforementioned collection unit is The camera tracking system automatically tracks and records the skateboard's movements. The system according to feature 1.

6. The aforementioned feedback unit is Assess the level of proficiency and determine the steps to suggest for the technique. The system according to feature 1.

7. The aforementioned feedback unit is The system will record distance traveled, jump height, and success rate, and allow users to share this information on social media. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the recording start time based on the estimated emotions. The system according to feature 1.

9. The aforementioned collection unit is The system analyzes the user's past recording data and selects the optimal recording method. The system according to feature 1.

10. The aforementioned collection unit is During recording, the camera tracking pattern is changed based on the user's current skill level. The system according to feature 1.

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