System

Abstract

To provide a system for analyzing an operation of a user and presenting an ideal operation.SOLUTION: A system according to an embodiment comprises: a reception part; an analysis part; a generation part; and a projection part. The reception part images an operation of a user. The analysis part analyzes video imaged by the reception part, and makes motions of multiple joints into points. The generation part generates a reference operation on the basis of data analyzed by the analysis part. The projection part projects a reference shape generated by the generation part to the user.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to efficiently analyze sports and dance movements and present ideal movements to the user.

[0005] The system according to the embodiment aims to analyze the user's actions and present ideal actions. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a projection unit. The receiving unit captures a user's motion. The analyzing unit analyzes the video captured by the receiving unit and assigns points to the motions of a plurality of joints. The generating unit generates a reference motion based on the data analyzed by the analyzing unit. The projection unit projects the reference form generated by the generating unit onto the user. Effect of the Invention

[0007] The system according to the embodiment can analyze the user's actions and suggest ideal actions. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The sports motion analysis system according to the embodiment of the present invention is a system that efficiently analyzes the motion of a user and suggests ideal motion. In this system, a user shoots his / her own motion with a smartphone, and the generation AI analyzes the video, and points out the motion of 200 joints. The generation AI generates multiple ideal swings and motions using the motions of professional athletes as a benchmark. The generated ideal form is projected to the user using a projection, and the user can use it as a reference to correct his / her own motion. The generation AI also suggests a practice menu for mastering the ideal posture, movement of the hands and feet, position of the head, and how to use the trunk. For example, a user shoots his / her own motion with a smartphone. For example, a tennis swing, a golf swing, a baseball batting shot, a gymnastics performance, a dance movement, etc. are shot. This video is input to the generation AI. Next, the generation AI analyzes the input video and points out the motion of 200 joints. The generation AI analyzes the motion of each joint in detail and identifies which parts are different from the ideal motion. For example, in a tennis swing, if the shoulder movement or the rotation of the hips is different from the ideal motion, it is identified. The generation AI generates multiple ideal swings and movements using the movements of professional players as a benchmark. For example, an ideal swing is generated based on the swing of a professional tennis player. This allows the user to compare their own movements with those of a professional. The generated ideal form is projected to the user using a projection. For example, the user can use a smartphone to film their own movements and project the image on a projection screen to compare their own movements with the ideal movements. This allows the user to correct their own movements. In addition, the generation AI also suggests practice menus to master the ideal posture, movement of the hands and feet, position of the head, and how to use the core. For example, in a tennis swing, it suggests a practice menu to improve shoulder movement and hip rotation. This allows the user to efficiently master the ideal movements. This mechanism allows the user to improve their own movements while referring to the movements of professionals. For example, it can be used in various sports scenes such as tennis swings, golf swings, baseball batting, gymnastics performances, and dance movements.This enables the sports motion analysis system to efficiently analyze the user's motion and suggest ideal motions.

[0029] The sports motion analysis system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a projection unit. The reception unit photographs the user's motion. The user's motion includes, but is not limited to, a tennis swing, a golf swing, a baseball batting, a gymnastics routine, a dance motion, and the like. The reception unit photographs the user's motion using, for example, a smartphone. The reception unit can also receive a video photographed by the user. The analysis unit uses a generation AI to analyze the video photographed by the reception unit and assigns points to the motions of 200 joints. The generation AI analyzes the motion of each joint in detail and identifies which part is different from the ideal motion. For example, in a tennis swing, if the shoulder motion or the rotation of the hips is different from the ideal motion, it identifies it. The generation unit uses the generation AI to generate an ideal motion based on the data analyzed by the analysis unit. The generation AI generates multiple ideal swings and motions using the motions of professional players as a benchmark. For example, an ideal swing is generated based on the swing of a professional tennis player. The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to film his / her own motion using a smartphone and project the video to compare the ideal motion with the user's own motion. This allows the user to correct his / her own motion. This allows the sports motion analysis system according to the embodiment to efficiently analyze the user's motion and propose the ideal motion.

[0030] The reception unit photographs the user's movements. Examples of the user's movements include, but are not limited to, tennis swings, golf swings, baseball batting, gymnastics routines, and dance movements. The reception unit photographs the user's movements using, for example, a smartphone. Smartphone cameras have high resolution and can capture even the finest details of the movements clearly. Furthermore, smartphone cameras have high shutter speeds and can capture fast-moving sports movements without blurring. The reception unit can also accept videos taken by the user. When the user uploads videos taken with their own smartphones or cameras to the system, the reception unit receives the videos and transmits them to the analysis unit. This allows the user to take videos of their own movements anywhere, such as at home or at a practice field, and import them into the system. Furthermore, the reception unit can also take videos simultaneously from different angles using multiple cameras, which enables three-dimensional analysis of the movements. For example, videos from the front, side, and above can be taken simultaneously to grasp the overall picture of the movements. This allows the reception unit to capture the user's movements from multiple angles and provide high-quality data for detailed analysis.

[0031] The analysis unit uses the generation AI to analyze the video captured by the reception unit and assigns points to the movements of 200 joints. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the ideal movement. Specifically, the generation AI uses deep learning technology to automatically detect the joint positions in the video and quantifies the movement of each joint. For example, in a tennis swing, if the shoulder movement or hip rotation differs from the ideal movement, it identifies it. The generation AI has learned past data and the movement data of professional athletes, and has an ideal movement pattern. This makes it possible to compare the user's movement with the ideal movement and specifically indicate which parts need to be improved. In addition, the analysis unit analyzes the speed, timing, and force of the movement to perform a comprehensive movement evaluation. For example, in a golf swing, if the club's swing speed or impact timing differs from the ideal movement, it identifies it and provides feedback to the user. This allows the analysis unit to analyze the user's movement in detail and from multiple angles and provide specific improvements.

[0032] The generation unit uses the generation AI to generate ideal movements based on the data analyzed by the analysis unit. The generation AI generates multiple ideal swings and movements using the movements of professional players as a benchmark. Specifically, the generation AI learns the movement data of professional players and generates ideal movements based on the movement patterns. For example, an ideal swing is generated based on the swing of a professional tennis player. The generation AI compares the user's movement data with the movement data of professional players and provides specific advice to bring the user's movements closer to the ideal movements. In addition, the generation unit can customize optimal movements by taking into account individual characteristics such as the user's body type, muscle strength, and flexibility. For example, the ideal swing form differs between tall users and short users, so the generation AI generates movements suitable for each. This allows the generation unit to suggest optimal movements to the user and support performance improvement.

[0033] The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare his / her own motion with the ideal motion by using a smartphone to shoot his / her own motion and projecting the image by projection. Specifically, the projection unit displays the ideal motion and the user's motion superimposed on the smartphone screen to visually indicate which parts are different. This allows the user to correct his / her own motion. Furthermore, the projection unit can project the user's motion in real time and provide instant feedback during the motion. For example, if the position of the shoulders is misaligned compared to the ideal swing while the user is performing a tennis swing, the projection unit can indicate the correction points on the spot. This allows the user to immediately correct his / her motion during practice and efficiently master the ideal motion. The projection unit can also save the user's motion history and compare past motion with current motion. This allows the user to check his / her own progress and maintain motivation. This allows the projection unit to provide the user with visual and instant feedback to support the improvement of motion.

[0034] The generation unit can generate a reference motion using the motion of a professional player as a benchmark. The generation unit uses a generation AI to generate a reference motion using the motion of a professional player as a benchmark. The generation AI learns the motion of a professional player and generates an ideal motion based on that motion. For example, the generation unit generates an ideal swing based on the swing of a professional tennis player. The generation unit can also generate an ideal swing based on the swing of a professional golf player. Furthermore, the generation unit can generate an ideal batting based on the batting of a professional baseball player. In this way, the generation unit generates ideal motions based on the motions of professional players, allowing the user to learn high-level motions.

[0035] The projection unit can project an image for comparing the user's motion with the ideal motion. The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare the ideal motion with the user's motion by shooting the user's motion using a smartphone and projecting the image by projection. This allows the user to correct his or her motion. The projection unit projects an image for comparing the user's motion with the ideal motion. For example, the projection unit projects the user's motion and the ideal motion side by side so that the user can visually compare them. The projection unit can also project the user's motion and the ideal motion so that the user can intuitively understand the difference between the motions. This allows the projection unit to visually understand the improvement points of the motion by comparing the user's motion with the ideal motion.

[0036] The generating unit can propose a practice menu for mastering a standard posture, movement of the hands and feet, position of the head, and how to use the trunk. The generating unit uses the generating AI to propose a practice menu for mastering a standard posture, movement of the hands and feet, position of the head, and how to use the trunk. The generating AI learns the movements of professional athletes and generates a practice menu based on the movements. For example, the generating unit proposes a practice menu for improving shoulder movement and hip rotation in a tennis swing. The generating unit can also propose a practice menu for improving wrist movement and hip rotation in a golf swing. Furthermore, the generating unit can also propose a practice menu for improving foot movement and arm usage in baseball batting. In this way, the generating unit can provide a specific practice menu for the user to master ideal movements.

[0037] The analysis unit can precisely analyze the movement of each joint and identify parts that differ from the reference movement. The analysis unit precisely analyzes the movement of each joint using the generation AI and identifies parts that differ from the reference movement. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the reference movement. For example, the analysis unit identifies when the shoulder movement or hip rotation differs from the reference movement in a tennis swing. The analysis unit can also identify when the wrist movement or hip rotation differs from the reference movement in a golf swing. Furthermore, the analysis unit can also identify when the foot movement or arm usage differs from the reference movement in baseball batting. This allows the analysis unit to know in detail which parts of the user's movement differ from the reference movement.

[0038] The reception unit can accept videos taken by the user with a mobile phone. The reception unit accepts videos taken by the user with a mobile phone. The user can use a smartphone to film their own movements and input the videos to the reception unit. For example, the user can film a tennis swing, a golf swing, a baseball batting, a gymnastics routine, a dance move, etc. with a smartphone. The reception unit accepts these videos and inputs them into the generation AI. This allows the reception unit to easily film the user's own movements with a smartphone and input them into the system.

[0039] The reception unit can analyze the user's past shooting history and select an appropriate shooting method. The reception unit analyzes the user's past shooting history and selects the optimal shooting method. For example, the reception unit preferentially suggests a shooting method that the user has been successful in the past. The reception unit can also adjust to avoid a shooting method that the user has failed in the past. Furthermore, the reception unit can also suggest an optimal shooting angle and timing from the user's past shooting history. As a result, the reception unit improves the accuracy of shooting by suggesting an optimal shooting method based on the user's past shooting history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The reception unit can select based on the user's current sports activities and fields of interest at the time of shooting. The reception unit performs filtering based on the user's current sports activities and fields of interest at the time of shooting. For example, if the user is interested in tennis, the reception unit can suggest a shooting method specialized for tennis movements. In addition, if the user is interested in golf, the reception unit can also suggest a shooting method specialized for golf swings. Furthermore, if the user is interested in dance, the reception unit can also suggest a shooting method specialized for dance movements. As a result, the reception unit can suggest a shooting method specialized for the user's field of interest, thereby enabling more effective shooting. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0041] The reception unit can photograph highly relevant actions in consideration of the geographical location information of the user when photographing. The reception unit preferentially photographs highly relevant actions in consideration of the geographical location information of the user when photographing. For example, when the user is on a tennis court, the reception unit preferentially photographs tennis actions. In addition, when the user is on a golf course, the reception unit can also preferentially photograph golf swings. Furthermore, when the user is in a dance studio, the reception unit can also preferentially photograph dance actions. This allows the reception unit to photograph optimal actions based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0042] The reception unit can analyze the online activities of the user and capture related actions at the time of shooting. The reception unit analyzes the social media activities of the user and captures related actions at the time of shooting. For example, if the user posts a lot of tennis on social media, the reception unit can preferentially capture tennis actions. In addition, if the user posts a lot of golf on social media, the reception unit can also preferentially capture golf swings. Furthermore, if the user posts a lot of dance on social media, the reception unit can also preferentially capture dance actions. This allows the reception unit to capture optimal actions based on the user's social media activities. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0043] The analysis unit can adjust the precision of the analysis based on the importance of the movement of each joint during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the movement of each joint during the analysis using the generation AI. For example, the analysis unit analyzes the movement of the shoulders and hips in detail in a tennis swing. The analysis unit can also analyze the movement of the wrists and hips in detail in a golf swing. Furthermore, the analysis unit can analyze the movement of the feet and arms in detail in a dance movement. This allows the analysis unit to obtain a more accurate analysis result by adjusting the level of detail of the analysis based on the importance of the movement of each joint. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The analysis unit can apply a specific analysis algorithm according to the type of sport during analysis. The analysis unit uses the generation AI to apply different analysis algorithms according to the type of sport during analysis. For example, the analysis unit applies an analysis algorithm dedicated to tennis to a tennis swing. The analysis unit can also apply an analysis algorithm dedicated to golf to a golf swing. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to dance to dance movements. In this way, the analysis unit can obtain more appropriate analysis results by applying an analysis algorithm according to the type of sport. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The analysis unit can determine the order of analysis based on the time when the action was shot during analysis. The analysis unit uses the generation AI to determine the priority of analysis based on the time when the action was shot during analysis. For example, the analysis unit preferentially analyzes the action that was recently shot. The analysis unit can also preferentially analyze the action that was shot before a specific event. Furthermore, the analysis unit can also preferentially analyze the action within a period specified by the user. Thereby, the analysis unit can preferentially analyze the more important action by determining the priority of analysis based on the time when the action was shot. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the movements during analysis. The analysis unit adjusts the order of analysis based on the relevance of the movements during analysis using the generation AI. For example, the analysis unit preferentially analyzes movements related to a tennis swing. The analysis unit can also preferentially analyze movements related to a golf swing. Furthermore, the analysis unit can also preferentially analyze movements related to dance movements. This allows the analysis unit to adjust the order of analysis based on the relevance of the movements, thereby enabling more effective analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The generation unit can adjust the precision of the ideal form based on the importance of the professional player's motion at the time of generation. The generation unit adjusts the level of detail of the ideal form based on the importance of the professional player's motion at the time of generation using a generation AI. For example, the generation unit generates detailed shoulder and hip movements in a tennis swing. The generation unit can also generate detailed wrist and hip movements in a golf swing. Furthermore, the generation unit can also generate detailed foot and arm movements in a dance movement. As a result, the generation unit can provide a more accurate ideal form by adjusting the level of detail of the ideal form based on the importance of the professional player's motion. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The generation unit can apply a specific generation algorithm according to the type of sport at the time of generation. The generation unit uses a generation AI to apply a different generation algorithm according to the type of sport at the time of generation. For example, the generation unit applies a generation algorithm dedicated to tennis to a tennis swing. The generation unit can also apply a generation algorithm dedicated to golf to a golf swing. Furthermore, the generation unit can also apply a generation algorithm dedicated to dance to a dance movement. In this way, the generation unit can provide a more appropriate ideal form by applying a generation algorithm according to the type of sport. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The generation unit can determine the order of ideal forms based on the time of filming the motion of the professional player at the time of generation. The generation unit uses the generation AI to determine the priority order of ideal forms based on the time of filming the motion of the professional player at the time of generation. For example, the generation unit preferentially generates the motion of the professional player that was recently filmed. The generation unit can also preferentially generate the motion of the professional player that was filmed before a specific tournament. Furthermore, the generation unit can also preferentially generate the motion of the time when the professional player mastered a specific technique. In this way, the generation unit can provide more important ideal forms preferentially by determining the priority order of ideal forms based on the time of filming the motion of the professional player. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The generation unit can adjust the order of the ideal forms based on the relevance of the movements of the professional players at the time of generation. The generation unit adjusts the order of the ideal forms based on the relevance of the movements of the professional players at the time of generation using the generation AI. For example, the generation unit generates movements related to a tennis swing with priority. The generation unit can also generate movements related to a golf swing with priority. Furthermore, the generation unit can also generate movements related to dance movements with priority. In this way, the generation unit can provide a more effective ideal form by adjusting the order of the ideal forms based on the relevance of the movements of the professional players. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The projection unit can select an appropriate display method by referring to the user's past operation history at the time of projection. The projection unit selects the optimal display method by referring to the user's past operation history at the time of projection. For example, the projection unit preferentially provides a display method that the user has preferred in the past. The projection unit can also exclude a display method that the user has avoided in the past. Furthermore, the projection unit can also suggest an optimal display angle and timing from the user's past operation history. This allows the projection unit to provide an optimal display method based on the user's past operation history, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The projection unit can adjust the means of projection based on the user's current sports activity at the time of projection. The projection unit customizes the means of projection based on the user's current sports activity at the time of projection. For example, when the user is playing tennis, the projection unit provides a projection method specialized for tennis movements. Also, when the user is playing golf, the projection unit can provide a projection method specialized for golf swings. Furthermore, when the user is dancing, the projection unit can provide a projection method specialized for dance movements. This allows the projection unit to provide an optimal projection method based on the user's current sports activity, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed using, for example, AI, or may be performed without using AI.

[0053] The projection unit can select an appropriate projection method in consideration of the geographical location information of the user at the time of projection. The projection unit selects an optimal projection method in consideration of the geographical location information of the user at the time of projection. For example, when the user is on a tennis court, the projection unit preferentially projects tennis movements. Also, when the user is on a golf course, the projection unit can preferentially project golf swings. Furthermore, when the user is in a dance studio, the projection unit can preferentially project dance movements. This allows the projection unit to provide an optimal projection method based on the geographical location information of the user, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

[0054] The projection unit can analyze the user's online activities at the time of projection and suggest a means of projection. The projection unit analyzes the user's social media activities at the time of projection and suggest a means of projection. For example, if the user posts a lot of tennis on social media, the projection unit preferentially projects tennis movements. In addition, if the user posts a lot of golf on social media, the projection unit can also preferentially project golf swings. Furthermore, if the user posts a lot of dance on social media, the projection unit can also preferentially project dance movements. This allows the projection unit to provide an optimal projection method based on the user's social media activities, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can not only capture images of the user's movements, but also collect audio data related to the user's movements. For example, the reception unit can collect breathing sounds and footsteps when the user makes a tennis swing and provide the collected data to the analysis unit. The reception unit can also collect the sound of the club and the sound of the club contacting the ground when the user makes a golf swing. The reception unit can also collect the sound of the user's movements in sync with music when the user dances. This allows the reception unit to collect audio data related to the user's movements and provide the collected data to the analysis unit, enabling more detailed movement analysis.

[0057] The generation unit can refer to the user's past motion data when analyzing the user's motion. For example, the generation unit can refer to the user's past tennis swing data and compare it with the current swing. The generation unit can also refer to the user's past golf swing data and compare it with the current swing. The generation unit can also refer to the user's past dance motion data and compare it with the current motion. This allows the generation unit to perform more accurate motion analysis by referring to the user's past motion data.

[0058] The projection unit can not only project an image for comparing the user's motion with the ideal motion, but also provide feedback on the user's motion in real time. For example, when a user makes a tennis swing, if the shoulder motion differs from the ideal motion, correction points can be displayed on the spot. Also, when a user makes a golf swing, if the rotation of the hips differs from the ideal motion, correction points can be displayed on the spot. Furthermore, when a user dances, if the foot motion differs from the ideal motion, correction points can be displayed on the spot. In this way, the projection unit can provide feedback on the user's motion in real time, enabling more effective motion correction.

[0059] The generating unit can not only propose a practice menu for mastering the standard posture, the movement of the hands and feet, the position of the head, and the use of the trunk, but also propose a practice menu customized according to the physical strength and skill level of the user. For example, a practice menu focusing on basic movements is proposed for a beginner user, and a practice menu requiring advanced skills is proposed for an advanced user. In addition, the intensity and number of times of practice can be adjusted according to the physical strength of the user. Furthermore, a menu focusing on practicing a specific skill can be proposed according to the user's goal. In this way, the generating unit can provide a practice menu customized according to the physical strength and skill level of the user, enabling more effective practice.

[0060] The analysis unit can not only precisely analyze the movement of each joint and identify parts that differ from the reference movement, but also detect minute changes in the user's movements. For example, in a tennis swing, minute changes in the wrist movement can be detected and compared with the ideal movement. In a golf swing, minute changes in the foot movement can be detected and compared with the ideal movement. Furthermore, in dance movements, minute changes in the trunk movement can be detected and compared with the ideal movement. This allows the analysis unit to perform more detailed movement analysis by detecting minute changes in the user's movements.

[0061] The reception unit can not only receive images taken by the user with a mobile phone, but also data collected by the user with a wearable device. For example, the reception unit can provide the heart rate and step count data collected by the user with a smart watch. The reception unit can also provide the reception unit with calorie consumption and exercise time data collected by the user with a fitness tracker. Furthermore, the reception unit can provide the reception unit with foot movement data collected by the user with smart shoes. In this way, the reception unit can perform more multifaceted motion analysis by accepting data collected by the user with a wearable device.

[0062] The reception unit can analyze the user's past photography history and not only select an appropriate photography method but also evaluate the progress of the user's movements based on the user's past photography data. For example, the reception unit can evaluate the progress of the current swing based on the user's past tennis swing data. The reception unit can also evaluate the progress of the current swing based on the user's past golf swing data. The reception unit can also evaluate the progress of the current movement based on the user's past dance movement data. This allows the reception unit to more effectively improve the movements by evaluating the progress of the movements based on the user's past photography data.

[0063] The reception unit can select the user's current sports activities and areas of interest at the time of shooting, and can also adjust the timing of shooting based on the user's current physical condition. For example, if the user is tired, the shooting can be resumed after a break. If the user is not feeling well, the shooting can be postponed. Furthermore, if the user is in good physical condition, the shooting can be started immediately. This allows the reception unit to adjust the timing of shooting based on the user's current physical condition, enabling more effective shooting.

[0064] The process flow of the first embodiment will be briefly described below.

[0065] Step 1: The reception unit captures the user's movements. The user's movements include, but are not limited to, tennis swings, golf swings, baseball batting, gymnastics routines, dance movements, and the like. The reception unit captures the user's movements using, for example, a smartphone. The reception unit can also accept videos captured by the user. Step 2: The analysis unit uses the generation AI to analyze the video captured by the reception unit and assigns points to the movements of 200 joints. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the ideal movement. For example, in a tennis swing, if the shoulder movement or hip rotation differs from the ideal movement, it will identify this. Step 3: The generator uses the generation AI to generate ideal movements based on the data analyzed by the analysis unit. The generator AI generates multiple ideal swings and movements using the movements of professional athletes as a benchmark. For example, it generates an ideal swing based on the swing of a professional tennis player. Step 4: The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare their own movements with the ideal movements by filming their own movements using a smartphone and projecting the image on the projection unit. This allows the user to correct their own movements.

[0066] (Example 2) The sports motion analysis system according to the embodiment of the present invention is a system that efficiently analyzes the motion of a user and suggests ideal motion. In this system, a user shoots his / her own motion with a smartphone, and the generation AI analyzes the video, and points out the motion of 200 joints. The generation AI generates multiple ideal swings and motions using the motions of professional athletes as a benchmark. The generated ideal form is projected to the user using a projection, and the user can use it as a reference to correct his / her own motion. The generation AI also suggests a practice menu for mastering the ideal posture, movement of the hands and feet, position of the head, and how to use the trunk. For example, a user shoots his / her own motion with a smartphone. For example, a tennis swing, a golf swing, a baseball batting shot, a gymnastics performance, a dance movement, etc. are shot. This video is input to the generation AI. Next, the generation AI analyzes the input video and points out the motion of 200 joints. The generation AI analyzes the motion of each joint in detail and identifies which parts are different from the ideal motion. For example, in a tennis swing, if the shoulder movement or the rotation of the hips is different from the ideal motion, it is identified. The generation AI generates multiple ideal swings and movements using the movements of professional players as a benchmark. For example, an ideal swing is generated based on the swing of a professional tennis player. This allows the user to compare their own movements with those of a professional. The generated ideal form is projected to the user using a projection. For example, the user can use a smartphone to film their own movements and project the image on a projection screen to compare their own movements with the ideal movements. This allows the user to correct their own movements. In addition, the generation AI also suggests practice menus to master the ideal posture, movement of the hands and feet, position of the head, and how to use the core. For example, in a tennis swing, it suggests a practice menu to improve shoulder movement and hip rotation. This allows the user to efficiently master the ideal movements. This mechanism allows the user to improve their own movements while referring to the movements of professionals. For example, it can be used in various sports scenes such as tennis swings, golf swings, baseball batting, gymnastics performances, and dance movements.This enables the sports motion analysis system to efficiently analyze the user's motion and suggest ideal motions.

[0067] The sports motion analysis system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a projection unit. The reception unit photographs the user's motion. The user's motion includes, but is not limited to, a tennis swing, a golf swing, a baseball batting, a gymnastics routine, a dance motion, and the like. The reception unit photographs the user's motion using, for example, a smartphone. The reception unit can also receive a video photographed by the user. The analysis unit uses a generation AI to analyze the video photographed by the reception unit and assigns points to the motions of 200 joints. The generation AI analyzes the motion of each joint in detail and identifies which part is different from the ideal motion. For example, in a tennis swing, if the shoulder motion or the rotation of the hips is different from the ideal motion, it identifies it. The generation unit uses the generation AI to generate an ideal motion based on the data analyzed by the analysis unit. The generation AI generates multiple ideal swings and motions using the motions of professional players as a benchmark. For example, an ideal swing is generated based on the swing of a professional tennis player. The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to film his / her own motion using a smartphone and project the video to compare the ideal motion with the user's own motion. This allows the user to correct his / her own motion. This allows the sports motion analysis system according to the embodiment to efficiently analyze the user's motion and propose the ideal motion.

[0068] The reception unit photographs the user's movements. Examples of the user's movements include, but are not limited to, tennis swings, golf swings, baseball batting, gymnastics routines, and dance movements. The reception unit photographs the user's movements using, for example, a smartphone. Smartphone cameras have high resolution and can capture even the finest details of the movements clearly. Furthermore, smartphone cameras have high shutter speeds and can capture fast-moving sports movements without blurring. The reception unit can also accept videos taken by the user. When the user uploads videos taken with their own smartphones or cameras to the system, the reception unit receives the videos and transmits them to the analysis unit. This allows the user to take videos of their own movements anywhere, such as at home or at a practice field, and import them into the system. Furthermore, the reception unit can also take videos simultaneously from different angles using multiple cameras, which enables three-dimensional analysis of the movements. For example, videos from the front, side, and above can be taken simultaneously to grasp the overall picture of the movements. This allows the reception unit to capture the user's movements from multiple angles and provide high-quality data for detailed analysis.

[0069] The analysis unit uses the generation AI to analyze the video captured by the reception unit and assigns points to the movements of 200 joints. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the ideal movement. Specifically, the generation AI uses deep learning technology to automatically detect the joint positions in the video and quantifies the movement of each joint. For example, in a tennis swing, if the shoulder movement or hip rotation differs from the ideal movement, it identifies it. The generation AI has learned past data and the movement data of professional athletes, and has an ideal movement pattern. This makes it possible to compare the user's movement with the ideal movement and specifically indicate which parts need to be improved. In addition, the analysis unit analyzes the speed, timing, and force of the movement to perform a comprehensive movement evaluation. For example, in a golf swing, if the club's swing speed or impact timing differs from the ideal movement, it identifies it and provides feedback to the user. This allows the analysis unit to analyze the user's movement in detail and from multiple angles and provide specific improvements.

[0070] The generation unit uses the generation AI to generate ideal movements based on the data analyzed by the analysis unit. The generation AI generates multiple ideal swings and movements using the movements of professional players as a benchmark. Specifically, the generation AI learns the movement data of professional players and generates ideal movements based on the movement patterns. For example, an ideal swing is generated based on the swing of a professional tennis player. The generation AI compares the user's movement data with the movement data of professional players and provides specific advice to bring the user's movements closer to the ideal movements. In addition, the generation unit can customize optimal movements by taking into account individual characteristics such as the user's body type, muscle strength, and flexibility. For example, the ideal swing form differs between tall users and short users, so the generation AI generates movements suitable for each. This allows the generation unit to suggest optimal movements to the user and support performance improvement.

[0071] The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare his / her own motion with the ideal motion by using a smartphone to shoot his / her own motion and projecting the image by projection. Specifically, the projection unit displays the ideal motion and the user's motion superimposed on the smartphone screen to visually indicate which parts are different. This allows the user to correct his / her own motion. Furthermore, the projection unit can project the user's motion in real time and provide instant feedback during the motion. For example, if the position of the shoulders is misaligned compared to the ideal swing while the user is performing a tennis swing, the projection unit can indicate the correction points on the spot. This allows the user to immediately correct his / her motion during practice and efficiently master the ideal motion. The projection unit can also save the user's motion history and compare past motion with current motion. This allows the user to check his / her own progress and maintain motivation. This allows the projection unit to provide the user with visual and instant feedback to support the improvement of motion.

[0072] The generation unit can generate a reference motion using the motion of a professional player as a benchmark. The generation unit uses a generation AI to generate a reference motion using the motion of a professional player as a benchmark. The generation AI learns the motion of a professional player and generates an ideal motion based on that motion. For example, the generation unit generates an ideal swing based on the swing of a professional tennis player. The generation unit can also generate an ideal swing based on the swing of a professional golf player. Furthermore, the generation unit can generate an ideal batting based on the batting of a professional baseball player. In this way, the generation unit generates ideal motions based on the motions of professional players, allowing the user to learn high-level motions.

[0073] The projection unit can project an image for comparing the user's motion with the ideal motion. The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare the ideal motion with the user's motion by shooting the user's motion using a smartphone and projecting the image by projection. This allows the user to correct his or her motion. The projection unit projects an image for comparing the user's motion with the ideal motion. For example, the projection unit projects the user's motion and the ideal motion side by side so that the user can visually compare them. The projection unit can also project the user's motion and the ideal motion so that the user can intuitively understand the difference between the motions. This allows the projection unit to visually understand the improvement points of the motion by comparing the user's motion with the ideal motion.

[0074] The generating unit can propose a practice menu for mastering a standard posture, movement of the hands and feet, position of the head, and how to use the trunk. The generating unit uses the generating AI to propose a practice menu for mastering a standard posture, movement of the hands and feet, position of the head, and how to use the trunk. The generating AI learns the movements of professional athletes and generates a practice menu based on the movements. For example, the generating unit proposes a practice menu for improving shoulder movement and hip rotation in a tennis swing. The generating unit can also propose a practice menu for improving wrist movement and hip rotation in a golf swing. Furthermore, the generating unit can also propose a practice menu for improving foot movement and arm usage in baseball batting. In this way, the generating unit can provide a specific practice menu for the user to master ideal movements.

[0075] The analysis unit can precisely analyze the movement of each joint and identify parts that differ from the reference movement. The analysis unit precisely analyzes the movement of each joint using the generation AI and identifies parts that differ from the reference movement. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the reference movement. For example, the analysis unit identifies when the shoulder movement or hip rotation differs from the reference movement in a tennis swing. The analysis unit can also identify when the wrist movement or hip rotation differs from the reference movement in a golf swing. Furthermore, the analysis unit can also identify when the foot movement or arm usage differs from the reference movement in baseball batting. This allows the analysis unit to know in detail which parts of the user's movement differ from the reference movement.

[0076] The reception unit can accept videos taken by the user with a mobile phone. The reception unit accepts videos taken by the user with a mobile phone. The user can use a smartphone to film their own movements and input the videos to the reception unit. For example, the user can film a tennis swing, a golf swing, a baseball batting, a gymnastics routine, a dance move, etc. with a smartphone. The reception unit accepts these videos and inputs them into the generation AI. This allows the reception unit to easily film the user's own movements with a smartphone and input them into the system.

[0077] The reception unit can estimate the user's emotion and adjust the timing of shooting based on the estimated user's emotion. The reception unit estimates the user's emotion and adjusts the timing of shooting based on the estimated user's emotion. For example, when the user is nervous, the reception unit waits until the user relaxes before starting shooting. In addition, when the user is concentrating, the reception unit can also immediately start shooting to maintain the user's concentration. Furthermore, when the user is tired, the reception unit can also resume shooting after a break. This allows the reception unit to shoot at the optimal timing according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0078] The reception unit can analyze the user's past shooting history and select an appropriate shooting method. The reception unit analyzes the user's past shooting history and selects the optimal shooting method. For example, the reception unit preferentially suggests a shooting method that the user has been successful in the past. The reception unit can also adjust to avoid a shooting method that the user has failed in the past. Furthermore, the reception unit can also suggest an optimal shooting angle and timing from the user's past shooting history. As a result, the reception unit improves the accuracy of shooting by suggesting an optimal shooting method based on the user's past shooting history. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The reception unit can select based on the user's current sports activities and fields of interest at the time of shooting. The reception unit performs filtering based on the user's current sports activities and fields of interest at the time of shooting. For example, if the user is interested in tennis, the reception unit can suggest a shooting method specialized for tennis movements. In addition, if the user is interested in golf, the reception unit can also suggest a shooting method specialized for golf swings. Furthermore, if the user is interested in dance, the reception unit can also suggest a shooting method specialized for dance movements. As a result, the reception unit can suggest a shooting method specialized for the user's field of interest, thereby enabling more effective shooting. Part or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0080] The reception unit can estimate the user's emotion and determine the order of actions to be photographed based on the estimated user's emotion. The reception unit estimates the user's emotion and determines the priority order of actions to be photographed based on the estimated user's emotion. For example, when the user is excited, the reception unit preferentially photographs the most important action. Also, when the user is relaxed, the reception unit can also photograph the entire action in a balanced manner. Furthermore, when the user is tired, the reception unit can also preferentially photograph an action that is effective in a short time. Thereby, the reception unit can preferentially photograph the optimal action according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0081] The reception unit can photograph highly relevant actions in consideration of the geographical location information of the user when photographing. The reception unit preferentially photographs highly relevant actions in consideration of the geographical location information of the user when photographing. For example, when the user is on a tennis court, the reception unit preferentially photographs tennis actions. In addition, when the user is on a golf course, the reception unit can also preferentially photograph golf swings. Furthermore, when the user is in a dance studio, the reception unit can also preferentially photograph dance actions. This allows the reception unit to photograph optimal actions based on the geographical location information of the user. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0082] The reception unit can analyze the online activities of the user and capture related actions at the time of shooting. The reception unit analyzes the social media activities of the user and captures related actions at the time of shooting. For example, if the user posts a lot of tennis on social media, the reception unit can preferentially capture tennis actions. In addition, if the user posts a lot of golf on social media, the reception unit can also preferentially capture golf swings. Furthermore, if the user posts a lot of dance on social media, the reception unit can also preferentially capture dance actions. This allows the reception unit to capture optimal actions based on the user's social media activities. A part or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI.

[0083] The analysis unit can estimate the user's emotion and adjust the level of detail of the analysis based on the estimated user's emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the accuracy of the analysis based on the estimated user's emotion. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can perform an analysis focusing on a specific action when the user is excited. This allows the analysis unit to obtain a more appropriate analysis result by adjusting the accuracy of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0084] The analysis unit can adjust the precision of the analysis based on the importance of the movement of each joint during the analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the movement of each joint during the analysis using the generation AI. For example, the analysis unit analyzes the movement of the shoulders and hips in detail in a tennis swing. The analysis unit can also analyze the movement of the wrists and hips in detail in a golf swing. Furthermore, the analysis unit can analyze the movement of the feet and arms in detail in a dance movement. This allows the analysis unit to obtain a more accurate analysis result by adjusting the level of detail of the analysis based on the importance of the movement of each joint. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The analysis unit can apply a specific analysis algorithm according to the type of sport during analysis. The analysis unit uses the generation AI to apply different analysis algorithms according to the type of sport during analysis. For example, the analysis unit applies an analysis algorithm dedicated to tennis to a tennis swing. The analysis unit can also apply an analysis algorithm dedicated to golf to a golf swing. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to dance to dance movements. In this way, the analysis unit can obtain more appropriate analysis results by applying an analysis algorithm according to the type of sport. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0086] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis result based on the estimated user's emotion. The analysis unit uses the generation AI to estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, the analysis unit provides a simple and highly visible display method when the user is nervous. The analysis unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis result according to the user's emotion, thereby enabling a more understandable display. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The analysis unit can determine the order of analysis based on the time when the action was shot during analysis. The analysis unit uses the generation AI to determine the priority of analysis based on the time when the action was shot during analysis. For example, the analysis unit preferentially analyzes the action that was recently shot. The analysis unit can also preferentially analyze the action that was shot before a specific event. Furthermore, the analysis unit can also preferentially analyze the action within a period specified by the user. Thereby, the analysis unit can preferentially analyze the more important action by determining the priority of analysis based on the time when the action was shot. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the movements during analysis. The analysis unit adjusts the order of analysis based on the relevance of the movements during analysis using the generation AI. For example, the analysis unit preferentially analyzes movements related to a tennis swing. The analysis unit can also preferentially analyze movements related to a golf swing. Furthermore, the analysis unit can also preferentially analyze movements related to dance movements. This allows the analysis unit to adjust the order of analysis based on the relevance of the movements, thereby enabling more effective analysis. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The generation unit can estimate the user's emotion and adjust the presentation method of the ideal form to be generated based on the estimated user's emotion. The generation unit uses the generation AI to estimate the user's emotion and adjust the expression method of the ideal form to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates an ideal form that proceeds at a leisurely pace. In addition, when the user is in a hurry, the generation unit can also generate an ideal form that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can also generate an ideal form with a visually stimulating effect added. Thereby, the generation unit can provide a more effective ideal form by adjusting the expression method of the ideal form according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] The generation unit can adjust the precision of the ideal form based on the importance of the professional player's motion at the time of generation. The generation unit adjusts the level of detail of the ideal form based on the importance of the professional player's motion at the time of generation using a generation AI. For example, the generation unit generates detailed shoulder and hip movements in a tennis swing. The generation unit can also generate detailed wrist and hip movements in a golf swing. Furthermore, the generation unit can also generate detailed foot and arm movements in a dance movement. As a result, the generation unit can provide a more accurate ideal form by adjusting the level of detail of the ideal form based on the importance of the professional player's motion. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The generation unit can apply a specific generation algorithm according to the type of sport at the time of generation. The generation unit uses a generation AI to apply a different generation algorithm according to the type of sport at the time of generation. For example, the generation unit applies a generation algorithm dedicated to tennis to a tennis swing. The generation unit can also apply a generation algorithm dedicated to golf to a golf swing. Furthermore, the generation unit can also apply a generation algorithm dedicated to dance to a dance movement. In this way, the generation unit can provide a more appropriate ideal form by applying a generation algorithm according to the type of sport. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The generation unit can estimate the user's emotion and adjust the duration of the ideal form to be generated based on the estimated user's emotion. The generation unit uses the generation AI to estimate the user's emotion and adjust the length of the ideal form to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates a short ideal form that is to the point. In addition, when the user is relaxed, the generation unit can also generate a longer ideal form that includes detailed explanations. Furthermore, when the user is excited, the generation unit can also generate an ideal form with a visually stimulating effect. In this way, the generation unit can provide a more effective ideal form by adjusting the length of the ideal form according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The generation unit can determine the order of ideal forms based on the time of filming the motion of the professional player at the time of generation. The generation unit uses the generation AI to determine the priority order of ideal forms based on the time of filming the motion of the professional player at the time of generation. For example, the generation unit preferentially generates the motion of the professional player that was recently filmed. The generation unit can also preferentially generate the motion of the professional player that was filmed before a specific tournament. Furthermore, the generation unit can also preferentially generate the motion of the time when the professional player mastered a specific technique. In this way, the generation unit can provide more important ideal forms preferentially by determining the priority order of ideal forms based on the time of filming the motion of the professional player. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0094] The generation unit can adjust the order of the ideal forms based on the relevance of the movements of the professional players at the time of generation. The generation unit adjusts the order of the ideal forms based on the relevance of the movements of the professional players at the time of generation using the generation AI. For example, the generation unit generates movements related to a tennis swing with priority. The generation unit can also generate movements related to a golf swing with priority. Furthermore, the generation unit can also generate movements related to dance movements with priority. In this way, the generation unit can provide a more effective ideal form by adjusting the order of the ideal forms based on the relevance of the movements of the professional players. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0095] The projection unit can estimate the user's emotion and adjust the presentation method of the projection based on the estimated user's emotion. The projection unit uses the generation AI to estimate the user's emotion and adjust the display method of the projection based on the estimated user's emotion. For example, the projection unit provides a simple and highly visible display method when the user is nervous. In addition, the projection unit can also provide a display method including detailed information when the user is relaxed. Furthermore, the projection unit can also provide a display method that focuses on the main points when the user is in a hurry. This allows the projection unit to adjust the display method of the projection according to the user's emotion, thereby enabling a more highly visible display. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The projection unit can select an appropriate display method by referring to the user's past operation history at the time of projection. The projection unit selects the optimal display method by referring to the user's past operation history at the time of projection. For example, the projection unit preferentially provides a display method that the user has preferred in the past. The projection unit can also exclude a display method that the user has avoided in the past. Furthermore, the projection unit can also suggest an optimal display angle and timing from the user's past operation history. This allows the projection unit to provide an optimal display method based on the user's past operation history, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

[0097] The projection unit can adjust the means of projection based on the user's current sports activity at the time of projection. The projection unit customizes the means of projection based on the user's current sports activity at the time of projection. For example, when the user is playing tennis, the projection unit provides a projection method specialized for tennis movements. Also, when the user is playing golf, the projection unit can provide a projection method specialized for golf swings. Furthermore, when the user is dancing, the projection unit can provide a projection method specialized for dance movements. This allows the projection unit to provide an optimal projection method based on the user's current sports activity, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed using, for example, AI, or may be performed without using AI.

[0098] The projection unit can estimate the user's emotion and determine the order of projection based on the estimated user's emotion. The projection unit uses the generation AI to estimate the user's emotion and determine the priority of projection based on the estimated user's emotion. For example, when the user is excited, the projection unit preferentially projects the most important action. In addition, when the user is relaxed, the projection unit can also project the entire action in a balanced manner. Furthermore, when the user is tired, the projection unit can also preferentially project an action that is effective in a short time. This allows the projection unit to preferentially project the optimal action according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0099] The projection unit can select an appropriate projection method in consideration of the geographical location information of the user at the time of projection. The projection unit selects an optimal projection method in consideration of the geographical location information of the user at the time of projection. For example, when the user is on a tennis court, the projection unit preferentially projects tennis movements. Also, when the user is on a golf course, the projection unit can preferentially project golf swings. Furthermore, when the user is in a dance studio, the projection unit can preferentially project dance movements. This allows the projection unit to provide an optimal projection method based on the geographical location information of the user, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

[0100] The projection unit can analyze the user's online activities at the time of projection and suggest a means of projection. The projection unit analyzes the user's social media activities at the time of projection and suggest a means of projection. For example, if the user posts a lot of tennis on social media, the projection unit preferentially projects tennis movements. In addition, if the user posts a lot of golf on social media, the projection unit can also preferentially project golf swings. Furthermore, if the user posts a lot of dance on social media, the projection unit can also preferentially project dance movements. This allows the projection unit to provide an optimal projection method based on the user's social media activities, thereby enabling more effective projection. A part or all of the above-mentioned processing in the projection unit may be performed, for example, using AI, or may be performed without using AI.

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

[0102] The reception unit can not only capture images of the user's movements, but also collect audio data related to the user's movements. For example, the reception unit can collect breathing sounds and footsteps when the user makes a tennis swing and provide the collected data to the analysis unit. The reception unit can also collect the sound of the club and the sound of the club contacting the ground when the user makes a golf swing. The reception unit can also collect the sound of the user's movements in sync with music when the user dances. This allows the reception unit to collect audio data related to the user's movements and provide the collected data to the analysis unit, enabling more detailed movement analysis.

[0103] The generation unit can refer to the user's past motion data when analyzing the user's motion. For example, the generation unit can refer to the user's past tennis swing data and compare it with the current swing. The generation unit can also refer to the user's past golf swing data and compare it with the current swing. The generation unit can also refer to the user's past dance motion data and compare it with the current motion. This allows the generation unit to perform more accurate motion analysis by referring to the user's past motion data.

[0104] The projection unit can not only project an image for comparing the user's motion with the ideal motion, but also provide feedback on the user's motion in real time. For example, when a user makes a tennis swing, if the shoulder motion differs from the ideal motion, correction points can be displayed on the spot. Also, when a user makes a golf swing, if the rotation of the hips differs from the ideal motion, correction points can be displayed on the spot. Furthermore, when a user dances, if the foot motion differs from the ideal motion, correction points can be displayed on the spot. In this way, the projection unit can provide feedback on the user's motion in real time, enabling more effective motion correction.

[0105] The generating unit can not only propose a practice menu for mastering the standard posture, the movement of the hands and feet, the position of the head, and the use of the trunk, but also propose a practice menu customized according to the physical strength and skill level of the user. For example, a practice menu focusing on basic movements is proposed for a beginner user, and a practice menu requiring advanced skills is proposed for an advanced user. In addition, the intensity and number of times of practice can be adjusted according to the physical strength of the user. Furthermore, a menu focusing on practicing a specific skill can be proposed according to the user's goal. In this way, the generating unit can provide a practice menu customized according to the physical strength and skill level of the user, enabling more effective practice.

[0106] The analysis unit can not only precisely analyze the movement of each joint and identify parts that differ from the reference movement, but also detect minute changes in the user's movements. For example, in a tennis swing, minute changes in the wrist movement can be detected and compared with the ideal movement. In a golf swing, minute changes in the foot movement can be detected and compared with the ideal movement. Furthermore, in dance movements, minute changes in the trunk movement can be detected and compared with the ideal movement. This allows the analysis unit to perform more detailed movement analysis by detecting minute changes in the user's movements.

[0107] The reception unit can not only receive images taken by the user with a mobile phone, but also data collected by the user with a wearable device. For example, the reception unit can provide the heart rate and step count data collected by the user with a smart watch. The reception unit can also provide the reception unit with calorie consumption and exercise time data collected by the user with a fitness tracker. Furthermore, the reception unit can provide the reception unit with foot movement data collected by the user with smart shoes. In this way, the reception unit can perform more multifaceted motion analysis by accepting data collected by the user with a wearable device.

[0108] The reception unit can estimate the user's emotions and adjust the timing of shooting based on the estimated user's emotions, as well as provide a message to improve the user's motivation according to the user's emotions. For example, if the user is nervous, a message to relax can be displayed. Also, if the user is concentrating, a message of encouragement to maintain the user's concentration can be displayed. Furthermore, if the user is tired, a message encouraging the user to take a break can be displayed. In this way, the reception unit can provide a message to improve the user's motivation according to the user's emotions, enabling more effective shooting.

[0109] The reception unit can analyze the user's past photography history and not only select an appropriate photography method but also evaluate the progress of the user's movements based on the user's past photography data. For example, the reception unit can evaluate the progress of the current swing based on the user's past tennis swing data. The reception unit can also evaluate the progress of the current swing based on the user's past golf swing data. The reception unit can also evaluate the progress of the current movement based on the user's past dance movement data. This allows the reception unit to more effectively improve the movements by evaluating the progress of the movements based on the user's past photography data.

[0110] The reception unit can select the user's current sports activities and areas of interest at the time of shooting, and can also adjust the timing of shooting based on the user's current physical condition. For example, if the user is tired, the shooting can be resumed after a break. If the user is not feeling well, the shooting can be postponed. Furthermore, if the user is in good physical condition, the shooting can be started immediately. This allows the reception unit to adjust the timing of shooting based on the user's current physical condition, enabling more effective shooting.

[0111] The reception unit can estimate the user's emotion and determine the order of the shooting operations based on the estimated user's emotion, as well as adjust the shooting environment according to the user's emotion. For example, if the user is nervous, the reception unit can prepare an environment in which the user can relax. Also, if the user is concentrating, the reception unit can provide a quiet environment to help the user maintain their concentration. Furthermore, if the user is tired, the reception unit can provide a comfortable environment to allow the user to shoot. This allows the reception unit to adjust the shooting environment according to the user's emotion, thereby enabling more effective shooting.

[0112] The process flow of the second embodiment will be briefly described below.

[0113] Step 1: The reception unit captures the user's movements. The user's movements include, but are not limited to, tennis swings, golf swings, baseball batting, gymnastics routines, dance movements, and the like. The reception unit captures the user's movements using, for example, a smartphone. The reception unit can also accept videos captured by the user. Step 2: The analysis unit uses the generation AI to analyze the video captured by the reception unit and assigns points to the movements of 200 joints. The generation AI analyzes the movement of each joint in detail and identifies which parts differ from the ideal movement. For example, in a tennis swing, if the shoulder movement or hip rotation differs from the ideal movement, it will identify this. Step 3: The generator uses the generation AI to generate ideal movements based on the data analyzed by the analysis unit. The generator AI generates multiple ideal swings and movements using the movements of professional athletes as a benchmark. For example, it generates an ideal swing based on the swing of a professional tennis player. Step 4: The projection unit projects the ideal form generated by the generation unit to the user. For example, the projection unit allows the user to compare their own movements with the ideal movements by filming their own movements using a smartphone and projecting the image on the projection unit. This allows the user to correct their own movements.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0116] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit photographs the user's movements using the camera 42 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and points the joint movements using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates ideal movements using the movements of professional athletes as benchmarks. The projection unit projects the ideal form generated using the output device 40 of the smart device 14 to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0125] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit photographs the user's movements using the camera 42 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and points the joint movements using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates ideal movements using the movements of professional athletes as a benchmark. The projection unit projects the ideal form generated using the output device of the smart glasses 214 to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0144] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

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

[0149] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit photographs the user's movements using the camera 42 of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and points the joint movements using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates ideal movements using the movements of professional athletes as benchmarks. The projection unit projects the ideal form generated using the display 343 of the headset type terminal 314 to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0161] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

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

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and projection unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit photographs the user's movements using the camera 42 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and points the joint movements using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12, and generates ideal movements using the movements of professional athletes as benchmarks. The projection unit projects the ideal form generated using the display device of the robot 414 to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0168] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the three o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0172] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0179] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0183] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0185] (Appendix 1) A reception unit that captures a user's actions; an analysis unit that analyzes the video captured by the reception unit and converts the movements of a plurality of joints into points; A generation unit that generates a reference motion based on the data analyzed by the analysis unit; A projection unit that projects the reference form generated by the generation unit to a user. A system characterized by: (Appendix 2) The generation unit is Generate reference movements by benchmarking the movements of professional athletes 2. The system of claim 1, (Appendix 3) The projection unit includes: Projecting images to compare user movements with ideal movements 2. The system of claim 1, (Appendix 4) The generation unit is We propose a training menu to help you master the standard posture, movement of your hands and feet, head position, and how to use your core. 2. The system of claim 1, (Appendix 5) The analysis unit is Precisely analyze the movement of each joint and identify any deviations from the standard movement 2. The system of claim 1, (Appendix 6) The reception unit is Accepts videos taken by users on their mobile phones 2. The system of claim 1, (Appendix 7) The reception unit is Estimate the user's emotions and adjust the timing of shooting based on the estimated user's emotions 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past shooting history and select the appropriate shooting method 2. The system of claim 1, (Appendix 9) The reception unit is At the time of capture, filtering is performed based on the user's current sports activities and interests 2. The system of claim 1, (Appendix 10) The reception unit is A user's emotion is estimated, and a sequence of actions to be taken is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 11) The reception unit is When taking a photo, the geographic location of the user is taken into account to capture the most relevant actions. 2. The system of claim 1, (Appendix 12) The reception unit is When capturing a photo, the system analyzes the user's online activities and captures relevant actions. 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the level of analysis detail based on the estimated user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the accuracy of the analysis based on the importance of each joint's movement 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, specific analysis algorithms are applied depending on the type of sport 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user emotions and adjust the presentation of analysis results based on the inferred user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, the order of analysis is determined based on when the action was captured. 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the analysis order based on behavior relevance 2. The system of claim 1, (Appendix 19) The generation unit is Estimate the user's emotions and adjust the presentation method of the ideal form based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The generation unit is During generation, the precision of the ideal form is adjusted based on the importance of the movements of professional players. 2. The system of claim 1, (Appendix 21) The generation unit is During generation, specific generation algorithms are applied depending on the type of sport. 2. The system of claim 1, (Appendix 22) The generation unit is The user's emotions are estimated, and the duration of the ideal form to be generated is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 23) The generation unit is During generation, the order of ideal movements is determined based on the time of filming of professional athletes' movements. 2. The system of claim 1, (Appendix 24) The generation unit is During generation, the order of ideal forms is adjusted based on the relevance of professional player movements. 2. The system of claim 1, (Appendix 25) The projection unit includes: Estimating a user's emotion and adjusting a projection presentation method based on the estimated user's emotion 2. The system of claim 1, (Appendix 26) The projection unit includes: When projecting, the system selects the appropriate display method by referring to the user's past operation history. 2. The system of claim 1, (Appendix 27) The projection unit includes: At the time of projection, the means of projection is adjusted based on the user's current sports activity. 2. The system of claim 1, (Appendix 28) The projection unit includes: Estimate the user's emotion and determine the order of projection based on the estimated user's emotion. 2. The system of claim 1, (Appendix 29) The projection unit includes: When projecting, the appropriate projection method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 30) The projection unit includes: When projecting, the system analyzes the user's online activity to suggest projection methods. 2. The system of claim 1, [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that captures a user's actions; an analysis unit that analyzes the video captured by the reception unit and converts the movements of a plurality of joints into points; A generation unit that generates a reference motion based on the data analyzed by the analysis unit; A projection unit that projects the reference form generated by the generation unit to a user. A system characterized by:

2. The generation unit is Generate reference movements by benchmarking the movements of professional athletes 2. The system of claim 1 .

3. The projection unit includes: Projecting images to compare user movements with ideal movements 2. The system of claim 1 .

4. The generation unit is We propose a training menu to help you master the standard posture, movement of your hands and feet, head position, and how to use your core.

2. The system of claim 1 .

5. The analysis unit is Precisely analyze the movement of each joint and identify any deviations from the standard movement 2. The system of claim 1 .

6. The reception unit is Accepts videos taken by users on their mobile phones 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the timing of shooting based on the estimated user's emotions 2. The system of claim 1 .

8. The reception unit is Analyze the user's past shooting history and select the appropriate shooting method 2. The system of claim 1 .

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