System

Abstract

An object of the system according to the embodiment is to learn the characteristics of the choreographer and provide specific advice for improving the way the user dances.SOLUTION: A system includes a collection unit, a learning unit, an analysis unit, and a provision unit. The collection unit collects a practice scene or a swing moving image of an choreographer. The learning unit analyzes the moving image collected by the collection unit and learns a feature of the choreographer. The collection part collects how the user dances. The analysis unit analyzes the dance of the user collected by the collection unit. The providing unit provides advice for improving the dance of the user based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques do not adequately learn the characteristics of choreographers and provide specific advice to improve the user's dancing style, and there is room for improvement.

[0005] The system according to the embodiment aims to learn the characteristics of a choreographer and provide specific advice to improve the user's dancing style. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a learning unit, an analysis unit, and a provision unit. The collection unit collects practice footage or choreography videos of choreographers. The learning unit analyzes the videos collected by the collection unit and learns the characteristics of the choreographer. The collection unit collects users' dancing styles. The analysis unit analyzes the users' dancing styles collected by the collection unit. The provision unit provides advice to improve the user's dancing style based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can learn the characteristics of the choreographer and provide specific advice to improve the user's dancing style. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded 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, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A dance independent practice support system according to an embodiment of the present invention uses AI to analyze practice videos and choreographer choreography videos, learn the characteristics of each choreographer, and analyze a user's dancing style to provide advice. The dance independent practice support system uses AI to analyze practice videos and choreographer choreography videos and learn the characteristics of each choreographer. Next, the AI ​​analyzes the user's dancing style and provides advice for improving dancing. For example, the dance independent practice support system collects practice videos and choreographer choreography videos. For example, the dance independent practice support system analyzes each frame of the video and learns the choreographer's movements and style. Next, the dance independent practice support system collects the user's dancing style. For example, the dance independent practice support system collects videos of the user dancing. Next, the dance independent practice support system analyzes the user's dancing style. For example, the dance independent practice support system points out how the user's movements differ from the choreographer's movements. Next, the dance independent practice support system provides specific advice on how the user's movements can be improved. This allows the self-dance practice support system to efficiently analyze the user's dancing style and provide specific advice for improvement. This allows the self-dance practice support system to efficiently analyze the user's dancing style and provide specific advice for improvement. For example, if a user wants to learn the style of a particular choreographer, the AI ​​that has learned the characteristics of that choreographer can provide specific advice, allowing the user to master that style.

[0029] The dance independent practice support system according to the embodiment includes a collection unit, a learning unit, an analysis unit, and a providing unit. The collection unit collects practice footage or choreography videos of choreographers. For example, the collection unit may use a camera to capture practice footage and save the footage as video data. The collection unit may also download choreography videos of choreographers from the Internet. For example, the collection unit may collect videos from the official website of a specific choreographer. The learning unit analyzes the videos collected by the collection unit to learn the characteristics of the choreographer. For example, the learning unit may analyze each frame of the video to learn the choreographer's movements and style. For example, the learning unit may grasp the movements and rhythm of a specific choreographer. The learning unit may learn the characteristics of the choreographer using a machine learning algorithm, for example. The analysis unit collects the user's dancing style. For example, the analysis unit may capture a video of the user dancing with a camera and save the video data. The analysis unit may also download videos uploaded by the user to the Internet. For example, the analysis unit can collect dance videos posted by users on social media. The analysis unit analyzes the user's dancing style collected by the collection unit. For example, the analysis unit analyzes the user's movements and indicates how the user's movements differ from those of a choreographer. For example, the analysis unit can analyze discrepancies and differences in timing of the user's movements. For example, the analysis unit can analyze the user's movements using a motion analysis algorithm. The provision unit provides advice to improve the user's dancing style based on the analysis results obtained by the analysis unit. For example, the provision unit can provide specific advice on how to improve the user's movements. For example, the provision unit can point out how the user's movements differ from those of a choreographer and advise how to improve. The provision unit can provide, for example, text-based feedback. The provision unit can also provide instruction through videos. For example, the provision unit can provide videos showing areas for improvement in the user's movements. In this way, the independent dance practice support system according to the embodiment can efficiently analyze the user's dancing style and provide specific advice for improvement.

[0030] The learning unit can analyze each frame of a video to learn the choreographer's movements or style. For example, the learning unit can analyze each frame of a video to learn the choreographer's movements and style. For example, the learning unit can grasp what movements a particular choreographer performs and what rhythm they dance to. The learning unit can also set a frame rate and an analysis algorithm to analyze each frame of a video. For example, the learning unit can analyze a video at 30 frames per second and analyze the movement of each frame. In analyzing the movement, the learning unit can analyze hand movements, foot movements, whole-body movements, etc. For example, the learning unit can analyze hand movement patterns to learn the characteristics of the choreographer. In analyzing the style, the learning unit can analyze styles such as hip hop, ballet, and jazz dance. For example, the learning unit can analyze the movements of a hip hop choreographer and learn that style. This allows the learning unit to learn the choreographer's movements and style in detail.

[0031] The analysis unit can analyze the user's movements and point out differences between the user's movements and the choreographer's movements. For example, the analysis unit can analyze the user's movements and point out how the user's movements differ from the choreographer's movements. For example, the analysis unit can analyze discrepancies and differences in timing of the user's movements. In addition, the analysis unit can analyze hand movements, foot movements, whole-body movements, etc. in analyzing the movements. For example, the analysis unit can analyze hand movement patterns and point out how the user's movements differ from the choreographer's movements. In addition, the analysis unit can set a movement analysis algorithm to analyze discrepancies and differences in timing in analyzing the movements. For example, the analysis unit can use the movement analysis algorithm to analyze discrepancies in the user's movements. As a result, the analysis unit can specifically point out areas for improvement in the user's movements.

[0032] The providing unit can give advice on how to improve the user's movements. For example, the providing unit can give specific advice on how to improve the user's movements. For example, the providing unit can point out how the user's movements differ from the choreographer's movements and give advice on how to improve. In addition, the providing unit can provide text-based feedback when providing advice. For example, the providing unit can explain in text how to improve the user's movements. In addition, the providing unit can provide instruction through a video. For example, the providing unit can provide a video showing how to improve the user's movements. In this way, the providing unit can provide advice for specifically improving the user's movements.

[0033] The collection unit can collect videos of users dancing. For example, the collection unit can capture videos of users dancing with a camera and save them as video data. The collection unit can also download videos that users have uploaded to the Internet. For example, the collection unit can collect dance videos that users have posted on social media. This allows the collection unit to collect detailed information about how users dance.

[0034] The collection unit can collect practice scenes or choreography videos of choreographers. For example, the collection unit can use a camera to capture practice scenes and save them as video data. The collection unit can also download choreography videos of choreographers from the Internet. For example, the collection unit can collect videos from the official website of a specific choreographer. This allows the collection unit to collect detailed information about the choreographer's characteristics.

[0035] The collection unit can analyze the user's past practice history and select the optimal collection method. The collection unit, for example, analyzes the user's past practice history and selects the optimal collection method. For example, the collection unit prioritizes collection of practice methods that the user has received high ratings for in the past. The collection unit can also prioritize collection of practice methods that the user has frequently performed in the past. The collection unit can also prioritize collection of practice methods that the user has indicated needing improvement in the past. This allows the collection unit to select the optimal collection method based on the user's past practice history.

[0036] The collection unit can filter videos based on the user's current practice status and areas of interest when collecting videos. For example, the collection unit filters videos based on the user's current practice status and areas of interest when collecting videos. For example, the collection unit prioritizes collecting videos related to choreography that the user is currently working on. The collection unit can also prioritize collecting videos related to a dance style in which the user is interested. The collection unit can also prioritize collecting videos of choreographers that the user has given high ratings to in the past. This allows the collection unit to collect optimal videos based on the user's current practice status and areas of interest.

[0037] The collection unit can select the optimal collection means depending on the user's input method when collecting videos. For example, when collecting videos, the collection unit selects the optimal collection means depending on the user's input method. For example, if the user inputs by voice, "I want to watch the latest choreography videos," the collection unit collects videos based on the voice input. Also, if the user inputs by text, "I want to watch hip-hop choreography videos," the collection unit can collect videos based on the text input. Also, if the user indicates a specific choreography using an image, the collection unit can collect related videos based on the image. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0038] When collecting videos, the collection unit can prioritize collecting highly relevant videos by taking into account the user's geographical location information. For example, when collecting videos, the collection unit prioritizes collecting highly relevant videos by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting choreography videos that are popular in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting videos related to dance events at the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting choreography videos that are easy to practice at home. This allows the collection unit to collect optimal videos based on the user's geographical location information.

[0039] The collection unit can analyze the user's social media activity and collect related videos when collecting videos. For example, the collection unit analyzes the user's social media activity and collects related videos when collecting videos. For example, the collection unit may prioritize collecting videos of choreographers the user follows on social media. The collection unit may also prioritize collecting videos related to dance styles that the user has "liked" on social media. The collection unit may also prioritize collecting videos related to dance events that the user has shared on social media. This allows the collection unit to collect optimal videos based on the user's social media activity.

[0040] The collection unit can customize the collection method by reflecting the user's past feedback when collecting videos. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting videos. For example, the collection unit prioritizes collection of styles of videos that the user has previously given high ratings to. The collection unit can also collect videos by avoiding styles of videos that the user has previously given low ratings to. The collection unit can also collect videos based on content that the user has previously requested in comments. This allows the collection unit to customize the optimal collection method based on the user's past feedback.

[0041] The learning unit can adjust the level of detail of learning based on the importance of the choreographer's movements during learning. For example, the learning unit adjusts the level of detail of learning based on the importance of the choreographer's movements during learning. For example, the learning unit emphasizes and learns particularly important parts of the choreographer's movements. The learning unit can also learn small parts of the choreographer's movements in detail. The learning unit can also learn basic parts of the choreographer's movements briefly. In this way, the learning unit can provide an optimal level of detail of learning depending on the importance of the choreographer's movements.

[0042] The learning unit can apply different learning algorithms depending on the category of the choreographer during learning. For example, the learning unit applies different learning algorithms depending on the category of the choreographer during learning. For example, the learning unit applies a learning algorithm that emphasizes rhythm to a hip-hop choreographer. The learning unit can also apply a learning algorithm that emphasizes posture to a ballet choreographer. The learning unit can also apply a learning algorithm that emphasizes expressiveness to a jazz dance choreographer. This allows the learning unit to provide an optimal learning algorithm depending on the category of the choreographer.

[0043] The learning unit can improve the accuracy of learning by referring to past learning results when learning. For example, the learning unit can improve the accuracy of learning by referring to past learning results when learning. For example, the learning unit can adjust the next learning content based on the content that the user has learned in the past. The learning unit can also focus on learning parts that the user was weak at in the past. The learning unit can also review parts that the user was strong at in the past. This allows the learning unit to improve the accuracy of learning based on past learning results.

[0044] The learning unit can determine learning priorities during learning based on the time of submission of the choreographer's videos. For example, during learning, the learning unit determines learning priorities based on the time of submission of the choreographer's videos. For example, the learning unit prioritizes learning the latest choreographer's videos. Furthermore, if the choreographer's videos are older, the learning unit can prioritize learning basic movements. Furthermore, if the choreographer's videos are more recent, the learning unit can prioritize learning movements that reflect the latest trends. This allows the learning unit to provide optimal learning priorities based on the time of submission of the choreographer's videos.

[0045] The learning unit can adjust the learning order based on the relevance of the choreographer during learning. The learning unit, for example, adjusts the learning order based on the relevance of the choreographer during learning. For example, the learning unit prioritizes learning videos of choreographers in which the user is interested. The learning unit can also prioritize learning videos of choreographers to which the user has given high ratings in the past. The learning unit can also prioritize learning videos of choreographers that the user follows. This allows the learning unit to provide an optimal learning order based on the relevance of the choreographer.

[0046] The learning unit can adjust the use of technical terms for learning depending on the choreographer's level of expertise during learning. For example, the learning unit can adjust the use of technical terms for learning depending on the choreographer's level of expertise during learning. For example, if the choreographer is a beginner, the learning unit can use simple technical terms. If the choreographer is an intermediate choreographer, the learning unit can use moderate technical terms. If the choreographer is an advanced choreographer, the learning unit can use detailed technical terms. This allows the learning unit to use optimal technical terms depending on the choreographer's level of expertise.

[0047] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can perform analysis by taking into account the interlocking of the user's limb movements. The analysis unit can also perform analysis by taking into account the relationship between the user's trunk movement and the limb movements. The analysis unit can also perform analysis by taking into account the user's sense of rhythm and the timing of the movements. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements.

[0048] The analysis unit can perform the analysis taking into account the user's attribute information. For example, the analysis unit performs the analysis taking into account the user's attribute information. For example, the analysis unit analyzes movements according to the user's age. The analysis unit can also analyze movements according to the user's gender. The analysis unit can also analyze movements according to the user's experience level. This allows the analysis unit to provide an optimal analysis based on the user's attribute information.

[0049] The analysis unit can weight the analysis based on the user's practice frequency during analysis. For example, the analysis unit weights the analysis based on the user's practice frequency during analysis. For example, if the user practices frequently, the analysis unit can perform a detailed analysis. If the user practices occasionally, the analysis unit can also perform a concise analysis. If the user does not practice often, the analysis unit can perform a concise analysis. This allows the analysis unit to provide optimal analysis weighting based on the user's practice frequency.

[0050] The analysis unit can perform the analysis taking into account the geographical distribution of users. For example, when performing the analysis, the analysis unit performs the analysis taking into account the geographical distribution of users. For example, if the user lives in an urban area, the analysis unit can perform the analysis based on the dance style in the urban area. Furthermore, if the user lives in a rural area, the analysis unit can also perform the analysis based on the dance style in the rural area. Furthermore, if the user lives overseas, the analysis unit can also perform the analysis based on the dance style of that region. This allows the analysis unit to provide an optimal analysis based on the geographical distribution of users.

[0051] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can perform the analysis by referring to the latest dance research papers. The analysis unit can also perform the analysis by referring to interview articles of choreographers. The analysis unit can also perform the analysis by referring to the historical background of dance. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0052] The analysis unit can perform the analysis taking into account the market value of the user. For example, the analysis unit performs the analysis taking into account the market value of the user. For example, if the user is a professional dancer, the analysis unit performs an analysis for professionals. Furthermore, if the user is an amateur dancer, the analysis unit can also perform an analysis for amateurs. Furthermore, if the user is a beginner, the analysis unit can also perform an analysis for beginners. This allows the analysis unit to provide an optimal analysis based on the market value of the user.

[0053] The providing unit can adjust the level of detail of the advice based on the importance of the user's movement when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the user's movement when providing advice. For example, the providing unit provides advice by emphasizing particularly important parts of the user's movement. The providing unit can also provide detailed advice on small parts of the user's movement. The providing unit can also provide concise advice on basic parts of the user's movement. This allows the providing unit to provide an optimal level of detail of advice according to the importance of the user's movement.

[0054] The providing unit can apply different advice algorithms depending on the user category when providing advice. For example, the providing unit can provide advice that emphasizes rhythm to a hip hop dancer. The providing unit can also provide advice that emphasizes posture to a ballet dancer. The providing unit can also provide advice that emphasizes expressiveness to a jazz dancer. This allows the providing unit to provide the optimal advice algorithm depending on the user category.

[0055] The providing unit can improve the accuracy of advice by referring to past advice results when providing advice. For example, the providing unit improves the accuracy of advice by referring to past advice results when providing advice. For example, the providing unit adjusts the next advice based on advice the user received in the past. The providing unit can also focus advice on areas where the user has shown improvement in the past. The providing unit can also give advice as a review on areas where the user has had difficulty in the past. This allows the providing unit to improve the accuracy of advice based on past advice results.

[0056] When providing advice, the providing unit can determine the priority of advice based on the time when the user's movements were submitted. When providing advice, the providing unit can determine the priority of advice based on, for example, the time when the user's movements were submitted. For example, the providing unit gives priority to the most recent movements when providing advice. Furthermore, if the movements were submitted earlier, the providing unit can also give priority to basic parts when providing advice. Furthermore, if the movements were submitted more recently, the providing unit can provide advice that reflects the latest trends. This allows the providing unit to provide optimal advice priority based on the time when the user's movements were submitted.

[0057] The providing unit can adjust the order of advice based on the relevance of the user when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of the user when providing advice. For example, the providing unit gives advice by giving priority to the style of a choreographer in which the user is interested. The providing unit can also give advice by giving priority to the style of a choreographer to whom the user has given a high rating in the past. The providing unit can also give advice by giving priority to the style of a choreographer that the user is following. In this way, the providing unit can provide an optimal order of advice based on the relevance of the user.

[0058] The providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise when providing advice. For example, the providing unit adjusts the use of technical terminology in the advice according to the user's level of expertise when providing advice. For example, if the user is a beginner, the providing unit can use simple technical terminology. If the user is an intermediate user, the providing unit can also use appropriate technical terminology. If the user is an advanced user, the providing unit can also use detailed technical terminology. This allows the providing unit to use optimal technical terminology according to the user's level of expertise.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's past practice history and select the optimal collection method. For example, the collection unit can prioritize collection of practice methods that the user has received high ratings for in the past. The collection unit can also prioritize collection of practice methods that the user has frequently performed in the past. The collection unit can also prioritize collection of practice methods that the user has previously indicated needing improvement. This allows the collection unit to select the optimal collection method based on the user's past practice history.

[0061] When collecting videos, the collection unit can prioritize collecting highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting choreography videos that are popular in that area. Also, if the user is traveling, the collection unit can prioritize collecting videos related to dance events at the user's travel destination. Also, if the user is at home, the collection unit can prioritize collecting choreography videos that are easy to practice at home. In this way, the collection unit can collect optimal videos based on the user's geographical location information.

[0062] During learning, the learning unit can adjust the level of detail of learning based on the importance of the choreographer's movements. For example, the learning unit can learn by emphasizing particularly important parts of the choreographer's movements. The learning unit can also learn small parts of the choreographer's movements in detail. The learning unit can also learn basic parts of the choreographer's movements briefly. In this way, the learning unit can provide an optimal level of detail of learning depending on the importance of the choreographer's movements.

[0063] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can consider the interlocking of the user's limb movements during analysis. The analysis unit can also consider the relationship between the user's trunk movement and limb movements during analysis. The analysis unit can also consider the user's sense of rhythm and the timing of their movements during analysis. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements.

[0064] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the user's movements. For example, the providing unit can give priority to the most recent movements when providing advice. Furthermore, if the movements are submitted older, the providing unit can also give priority to basic advice. Furthermore, if the movements are submitted more recently, the providing unit can provide advice that reflects the latest trends. This allows the providing unit to provide optimal advice priority based on the time of submission of the user's movements.

[0065] When collecting videos, the collection unit can analyze the user's social media activity and collect related videos. For example, the collection unit can prioritize collecting videos of choreographers the user follows on social media. The collection unit can also prioritize collecting videos related to dance styles that the user has "liked" on social media. The collection unit can also prioritize collecting videos related to dance events that the user has shared on social media. This allows the collection unit to collect optimal videos based on the user's social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects practice scenes or choreography videos of choreographers. For example, the collection unit may use a camera to capture practice scenes and save them as video data. The collection unit may also download choreography videos of choreographers from the Internet. For example, the collection unit may collect videos from the official website of a specific choreographer. Step 2: The learning unit analyzes the video collected by the collection unit and learns the characteristics of the choreographer. For example, the learning unit analyzes each frame of the video and learns the choreographer's movements and style. For example, the learning unit can grasp what movements a particular choreographer makes and what rhythm they dance to. For example, the learning unit can learn the characteristics of the choreographer using a machine learning algorithm. Step 3: The collection unit collects the user's dancing style. For example, the collection unit may take a video of the user dancing with a camera and save the video data. The collection unit may also download videos that the user has uploaded to the Internet. For example, the collection unit may collect dance videos that the user has posted on social media. Step 4: The analysis unit analyzes the user's dancing style collected by the collection unit. The analysis unit, for example, analyzes the user's movements and points out how the user's movements differ from the choreographer's movements. For example, the analysis unit can analyze discrepancies or differences in timing of the user's movements. For example, the analysis unit can analyze the user's movements using a motion analysis algorithm. Step 5: The providing unit provides advice to improve the user's dancing style based on the analysis results obtained by the analyzing unit. The providing unit, for example, provides specific advice on how to improve the user's movements. For example, the providing unit can point out how the user's movements differ from the choreographer's movements and provide advice on how to improve. The providing unit can provide, for example, text-based feedback. The providing unit can also provide instruction through video. For example, the providing unit can provide a video showing how the user's movements can be improved.

[0068] (Example 2) A dance independent practice support system according to an embodiment of the present invention uses AI to analyze practice videos and choreographer choreography videos, learn the characteristics of each choreographer, and analyze a user's dancing style to provide advice. The dance independent practice support system uses AI to analyze practice videos and choreographer choreography videos and learn the characteristics of each choreographer. Next, the AI ​​analyzes the user's dancing style and provides advice for improving dancing. For example, the dance independent practice support system collects practice videos and choreographer choreography videos. For example, the dance independent practice support system analyzes each frame of the video and learns the choreographer's movements and style. Next, the dance independent practice support system collects the user's dancing style. For example, the dance independent practice support system collects videos of the user dancing. Next, the dance independent practice support system analyzes the user's dancing style. For example, the dance independent practice support system points out how the user's movements differ from the choreographer's movements. Next, the dance independent practice support system provides specific advice on how the user's movements can be improved. This allows the self-dance practice support system to efficiently analyze the user's dancing style and provide specific advice for improvement. This allows the self-dance practice support system to efficiently analyze the user's dancing style and provide specific advice for improvement. For example, if a user wants to learn the style of a particular choreographer, the AI ​​that has learned the characteristics of that choreographer can provide specific advice, allowing the user to master that style.

[0069] The dance independent practice support system according to the embodiment includes a collection unit, a learning unit, an analysis unit, and a providing unit. The collection unit collects practice footage or choreography videos of choreographers. For example, the collection unit may use a camera to capture practice footage and save the footage as video data. The collection unit may also download choreography videos of choreographers from the Internet. For example, the collection unit may collect videos from the official website of a specific choreographer. The learning unit analyzes the videos collected by the collection unit to learn the characteristics of the choreographer. For example, the learning unit may analyze each frame of the video to learn the choreographer's movements and style. For example, the learning unit may grasp the movements and rhythm of a specific choreographer. The learning unit may learn the characteristics of the choreographer using a machine learning algorithm, for example. The analysis unit collects the user's dancing style. For example, the analysis unit may capture a video of the user dancing with a camera and save the video data. The analysis unit may also download videos uploaded by the user to the Internet. For example, the analysis unit can collect dance videos posted by users on social media. The analysis unit analyzes the user's dancing style collected by the collection unit. For example, the analysis unit analyzes the user's movements and indicates how the user's movements differ from those of a choreographer. For example, the analysis unit can analyze discrepancies and differences in timing of the user's movements. For example, the analysis unit can analyze the user's movements using a motion analysis algorithm. The provision unit provides advice to improve the user's dancing style based on the analysis results obtained by the analysis unit. For example, the provision unit can provide specific advice on how to improve the user's movements. For example, the provision unit can point out how the user's movements differ from those of a choreographer and advise how to improve. The provision unit can provide, for example, text-based feedback. The provision unit can also provide instruction through videos. For example, the provision unit can provide videos showing areas for improvement in the user's movements. In this way, the independent dance practice support system according to the embodiment can efficiently analyze the user's dancing style and provide specific advice for improvement.

[0070] The learning unit can analyze each frame of a video to learn the choreographer's movements or style. For example, the learning unit can analyze each frame of a video to learn the choreographer's movements and style. For example, the learning unit can grasp what movements a particular choreographer performs and what rhythm they dance to. The learning unit can also set a frame rate and an analysis algorithm to analyze each frame of a video. For example, the learning unit can analyze a video at 30 frames per second and analyze the movement of each frame. In analyzing the movement, the learning unit can analyze hand movements, foot movements, whole-body movements, etc. For example, the learning unit can analyze hand movement patterns to learn the characteristics of the choreographer. In analyzing the style, the learning unit can analyze styles such as hip hop, ballet, and jazz dance. For example, the learning unit can analyze the movements of a hip hop choreographer and learn that style. This allows the learning unit to learn the choreographer's movements and style in detail.

[0071] The analysis unit can analyze the user's movements and point out differences between the user's movements and the choreographer's movements. For example, the analysis unit can analyze the user's movements and point out how the user's movements differ from the choreographer's movements. For example, the analysis unit can analyze discrepancies and differences in timing of the user's movements. In addition, the analysis unit can analyze hand movements, foot movements, whole-body movements, etc. in analyzing the movements. For example, the analysis unit can analyze hand movement patterns and point out how the user's movements differ from the choreographer's movements. In addition, the analysis unit can set a movement analysis algorithm to analyze discrepancies and differences in timing in analyzing the movements. For example, the analysis unit can use the movement analysis algorithm to analyze discrepancies in the user's movements. As a result, the analysis unit can specifically point out areas for improvement in the user's movements.

[0072] The providing unit can give advice on how to improve the user's movements. For example, the providing unit can give specific advice on how to improve the user's movements. For example, the providing unit can point out how the user's movements differ from the choreographer's movements and give advice on how to improve. In addition, the providing unit can provide text-based feedback when providing advice. For example, the providing unit can explain in text how to improve the user's movements. In addition, the providing unit can provide instruction through a video. For example, the providing unit can provide a video showing how to improve the user's movements. In this way, the providing unit can provide advice for specifically improving the user's movements.

[0073] The collection unit can collect videos of users dancing. For example, the collection unit can capture videos of users dancing with a camera and save them as video data. The collection unit can also download videos that users have uploaded to the Internet. For example, the collection unit can collect dance videos that users have posted on social media. This allows the collection unit to collect detailed information about how users dance.

[0074] The collection unit can collect practice scenes or choreography videos of choreographers. For example, the collection unit can use a camera to capture practice scenes and save them as video data. The collection unit can also download choreography videos of choreographers from the Internet. For example, the collection unit can collect videos from the official website of a specific choreographer. This allows the collection unit to collect detailed information about the choreographer's characteristics.

[0075] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of video collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit collects videos at the beginning of practice. If the user is concentrating, the collection unit can also collect videos in the middle of practice. If the user is tired, the collection unit can also collect videos at the end of practice. This allows the collection unit to collect videos at the optimal timing depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0076] The collection unit can analyze the user's past practice history and select the optimal collection method. The collection unit, for example, analyzes the user's past practice history and selects the optimal collection method. For example, the collection unit prioritizes collection of practice methods that the user has received high ratings for in the past. The collection unit can also prioritize collection of practice methods that the user has frequently performed in the past. The collection unit can also prioritize collection of practice methods that the user has indicated needing improvement in the past. This allows the collection unit to select the optimal collection method based on the user's past practice history.

[0077] The collection unit can filter videos based on the user's current practice status and areas of interest when collecting videos. For example, the collection unit filters videos based on the user's current practice status and areas of interest when collecting videos. For example, the collection unit prioritizes collecting videos related to choreography that the user is currently working on. The collection unit can also prioritize collecting videos related to a dance style in which the user is interested. The collection unit can also prioritize collecting videos of choreographers that the user has given high ratings to in the past. This allows the collection unit to collect optimal videos based on the user's current practice status and areas of interest.

[0078] The collection unit can select the optimal collection means depending on the user's input method when collecting videos. For example, when collecting videos, the collection unit selects the optimal collection means depending on the user's input method. For example, if the user inputs by voice, "I want to watch the latest choreography videos," the collection unit collects videos based on the voice input. Also, if the user inputs by text, "I want to watch hip-hop choreography videos," the collection unit can collect videos based on the text input. Also, if the user indicates a specific choreography using an image, the collection unit can collect related videos based on the image. This allows the collection unit to select the optimal collection means depending on the user's input method.

[0079] The collection unit can estimate the user's emotions and determine the priority of videos to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of videos to be collected based on the estimated user emotions. For example, when the user is relaxed, the collection unit prioritizes collecting videos with a relaxing atmosphere. Furthermore, when the user is concentrating, the collection unit can prioritize collecting videos that enhance concentration. Furthermore, when the user is tired, the collection unit can prioritize collecting videos that are refreshing. In this way, the collection unit can prioritize collecting optimal videos according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0080] When collecting videos, the collection unit can prioritize collecting highly relevant videos by taking into account the user's geographical location information. For example, when collecting videos, the collection unit prioritizes collecting highly relevant videos by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting choreography videos that are popular in that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting videos related to dance events at the user's travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting choreography videos that are easy to practice at home. This allows the collection unit to collect optimal videos based on the user's geographical location information.

[0081] The collection unit can analyze the user's social media activity and collect related videos when collecting videos. For example, the collection unit analyzes the user's social media activity and collects related videos when collecting videos. For example, the collection unit may prioritize collecting videos of choreographers the user follows on social media. The collection unit may also prioritize collecting videos related to dance styles that the user has "liked" on social media. The collection unit may also prioritize collecting videos related to dance events that the user has shared on social media. This allows the collection unit to collect optimal videos based on the user's social media activity.

[0082] The collection unit can customize the collection method by reflecting the user's past feedback when collecting videos. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting videos. For example, the collection unit prioritizes collection of styles of videos that the user has previously given high ratings to. The collection unit can also collect videos by avoiding styles of videos that the user has previously given low ratings to. The collection unit can also collect videos based on content that the user has previously requested in comments. This allows the collection unit to customize the optimal collection method based on the user's past feedback.

[0083] The learning unit can estimate the user's emotions and adjust the learning expression method based on the estimated user emotions. For example, the learning unit estimates the user's emotions and adjusts the learning expression method based on the estimated user emotions. For example, when the user is relaxed, the learning unit proceeds with the learning at a leisurely pace. When the user is concentrating, the learning unit can provide learning that includes detailed explanations. When the user is tired, the learning unit can provide learning that is concise and to the point. This allows the learning unit to provide an optimal learning expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 these examples. Some or all of the above-mentioned processing in the learning unit may be performed using an AI, for example, or without an AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0084] The learning unit can adjust the level of detail of learning based on the importance of the choreographer's movements during learning. For example, the learning unit adjusts the level of detail of learning based on the importance of the choreographer's movements during learning. For example, the learning unit emphasizes and learns particularly important parts of the choreographer's movements. The learning unit can also learn small parts of the choreographer's movements in detail. The learning unit can also learn basic parts of the choreographer's movements briefly. In this way, the learning unit can provide an optimal level of detail of learning depending on the importance of the choreographer's movements.

[0085] The learning unit can apply different learning algorithms depending on the category of the choreographer during learning. For example, the learning unit applies different learning algorithms depending on the category of the choreographer during learning. For example, the learning unit applies a learning algorithm that emphasizes rhythm to a hip-hop choreographer. The learning unit can also apply a learning algorithm that emphasizes posture to a ballet choreographer. The learning unit can also apply a learning algorithm that emphasizes expressiveness to a jazz dance choreographer. This allows the learning unit to provide an optimal learning algorithm depending on the category of the choreographer.

[0086] The learning unit can improve the accuracy of learning by referring to past learning results when learning. For example, the learning unit can improve the accuracy of learning by referring to past learning results when learning. For example, the learning unit can adjust the next learning content based on the content that the user has learned in the past. The learning unit can also focus on learning parts that the user was weak at in the past. The learning unit can also review parts that the user was strong at in the past. This allows the learning unit to improve the accuracy of learning based on past learning results.

[0087] The learning unit can estimate the user's emotions and adjust the length of the learning based on the estimated user emotions. For example, the learning unit can estimate the user's emotions and adjust the length of the learning based on the estimated user emotions. For example, the learning unit can provide a longer learning session when the user is relaxed. The learning unit can also provide a short, concise learning session when the user is focused. The learning unit can also provide a short, concise learning session when the user is tired. This allows the learning unit to provide an optimal length of learning depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-mentioned processing in the learning unit can be performed using AI, for example, or without AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0088] The learning unit can determine learning priorities during learning based on the time of submission of the choreographer's videos. For example, during learning, the learning unit determines learning priorities based on the time of submission of the choreographer's videos. For example, the learning unit prioritizes learning the latest choreographer's videos. Furthermore, if the choreographer's videos are older, the learning unit can prioritize learning basic movements. Furthermore, if the choreographer's videos are more recent, the learning unit can prioritize learning movements that reflect the latest trends. This allows the learning unit to provide optimal learning priorities based on the time of submission of the choreographer's videos.

[0089] The learning unit can adjust the learning order based on the relevance of the choreographer during learning. The learning unit, for example, adjusts the learning order based on the relevance of the choreographer during learning. For example, the learning unit prioritizes learning videos of choreographers in which the user is interested. The learning unit can also prioritize learning videos of choreographers to which the user has given high ratings in the past. The learning unit can also prioritize learning videos of choreographers that the user follows. This allows the learning unit to provide an optimal learning order based on the relevance of the choreographer.

[0090] The learning unit can adjust the use of technical terms for learning depending on the choreographer's level of expertise during learning. For example, the learning unit can adjust the use of technical terms for learning depending on the choreographer's level of expertise during learning. For example, if the choreographer is a beginner, the learning unit can use simple technical terms. If the choreographer is an intermediate choreographer, the learning unit can use moderate technical terms. If the choreographer is an advanced choreographer, the learning unit can use detailed technical terms. This allows the learning unit to use optimal technical terms depending on the choreographer's level of expertise.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is focused. The analysis unit can also perform a concise analysis when the user is tired. This allows the analysis unit to provide optimal analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0092] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can perform analysis by taking into account the interlocking of the user's limb movements. The analysis unit can also perform analysis by taking into account the relationship between the user's trunk movement and the limb movements. The analysis unit can also perform analysis by taking into account the user's sense of rhythm and the timing of the movements. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements.

[0093] The analysis unit can perform the analysis taking into account the user's attribute information. For example, the analysis unit performs the analysis taking into account the user's attribute information. For example, the analysis unit analyzes movements according to the user's age. The analysis unit can also analyze movements according to the user's gender. The analysis unit can also analyze movements according to the user's experience level. This allows the analysis unit to provide an optimal analysis based on the user's attribute information.

[0094] The analysis unit can weight the analysis based on the user's practice frequency during analysis. For example, the analysis unit weights the analysis based on the user's practice frequency during analysis. For example, if the user practices frequently, the analysis unit can perform a detailed analysis. If the user practices occasionally, the analysis unit can also perform a concise analysis. If the user does not practice often, the analysis unit can perform a concise analysis. This allows the analysis unit to provide optimal analysis weighting based on the user's practice frequency.

[0095] The analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the order in which the analysis results are displayed based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed results first. Also, if the user is concentrating, the analysis unit can display concise results first. Also, if the user is tired, the analysis unit can display concise results first. This allows the analysis unit to display the analysis results in an optimal order depending on the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate emotions.

[0096] The analysis unit can perform the analysis taking into account the geographical distribution of users. For example, when performing the analysis, the analysis unit performs the analysis taking into account the geographical distribution of users. For example, if the user lives in an urban area, the analysis unit can perform the analysis based on the dance style in the urban area. Furthermore, if the user lives in a rural area, the analysis unit can also perform the analysis based on the dance style in the rural area. Furthermore, if the user lives overseas, the analysis unit can also perform the analysis based on the dance style of that region. This allows the analysis unit to provide an optimal analysis based on the geographical distribution of users.

[0097] The analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to related literature during the analysis. For example, the analysis unit can perform the analysis by referring to the latest dance research papers. The analysis unit can also perform the analysis by referring to interview articles of choreographers. The analysis unit can also perform the analysis by referring to the historical background of dance. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature.

[0098] The analysis unit can perform the analysis taking into account the market value of the user. For example, the analysis unit performs the analysis taking into account the market value of the user. For example, if the user is a professional dancer, the analysis unit performs an analysis for professionals. Furthermore, if the user is an amateur dancer, the analysis unit can also perform an analysis for amateurs. Furthermore, if the user is a beginner, the analysis unit can also perform an analysis for beginners. This allows the analysis unit to provide an optimal analysis based on the market value of the user.

[0099] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide advice in a gentle tone. If the user is concentrating, the providing unit can also provide specific and detailed advice. If the user is tired, the providing unit can also provide concise and to-the-point advice. This allows the providing unit to provide the optimal way of expressing advice according to the user's emotions. Emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0100] The providing unit can adjust the level of detail of the advice based on the importance of the user's movement when providing advice. For example, the providing unit adjusts the level of detail of the advice based on the importance of the user's movement when providing advice. For example, the providing unit provides advice by emphasizing particularly important parts of the user's movement. The providing unit can also provide detailed advice on small parts of the user's movement. The providing unit can also provide concise advice on basic parts of the user's movement. This allows the providing unit to provide an optimal level of detail of advice according to the importance of the user's movement.

[0101] The providing unit can apply different advice algorithms depending on the user category when providing advice. For example, the providing unit can provide advice that emphasizes rhythm to a hip hop dancer. The providing unit can also provide advice that emphasizes posture to a ballet dancer. The providing unit can also provide advice that emphasizes expressiveness to a jazz dancer. This allows the providing unit to provide the optimal advice algorithm depending on the user category.

[0102] The providing unit can improve the accuracy of advice by referring to past advice results when providing advice. For example, the providing unit improves the accuracy of advice by referring to past advice results when providing advice. For example, the providing unit adjusts the next advice based on advice the user received in the past. The providing unit can also focus advice on areas where the user has shown improvement in the past. The providing unit can also give advice as a review on areas where the user has had difficulty in the past. This allows the providing unit to improve the accuracy of advice based on past advice results.

[0103] The providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated user's emotion. For example, the providing unit provides longer advice when the user is relaxed. The providing unit can also provide advice that is brief and to the point when the user is concentrating. The providing unit can also provide short and concise advice when the user is tired. This allows the providing unit to provide the optimal length of advice depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0104] When providing advice, the providing unit can determine the priority of advice based on the time when the user's movements were submitted. When providing advice, the providing unit can determine the priority of advice based on, for example, the time when the user's movements were submitted. For example, the providing unit gives priority to the most recent movements when providing advice. Furthermore, if the movements were submitted earlier, the providing unit can also give priority to basic parts when providing advice. Furthermore, if the movements were submitted more recently, the providing unit can provide advice that reflects the latest trends. This allows the providing unit to provide optimal advice priority based on the time when the user's movements were submitted.

[0105] The providing unit can adjust the order of advice based on the relevance of the user when providing advice. For example, the providing unit adjusts the order of advice based on the relevance of the user when providing advice. For example, the providing unit gives advice by giving priority to the style of a choreographer in which the user is interested. The providing unit can also give advice by giving priority to the style of a choreographer to whom the user has given a high rating in the past. The providing unit can also give advice by giving priority to the style of a choreographer that the user is following. In this way, the providing unit can provide an optimal order of advice based on the relevance of the user.

[0106] The providing unit can adjust the use of technical terminology in the advice according to the user's level of expertise when providing advice. For example, the providing unit adjusts the use of technical terminology in the advice according to the user's level of expertise when providing advice. For example, if the user is a beginner, the providing unit can use simple technical terminology. If the user is an intermediate user, the providing unit can also use appropriate technical terminology. If the user is an advanced user, the providing unit can also use detailed technical terminology. This allows the providing unit to use optimal technical terminology according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, learning unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can capture practice scenes using the camera 42 of the smart device 14 and save the captured footage as video data. For example, the learning unit can analyze the videos collected by the specific processing unit 290 of the data processing device 12 to learn the characteristics of the choreographer. For example, the analysis unit can collect data on the user's dancing style using the camera 42 of the smart device 14 and analyze the data by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide advice to improve the user's dancing style based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, learning unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can capture practice scenes using the camera 42 of the smart glasses 214 and save them as video data. For example, the learning unit can analyze the videos collected by the specific processing unit 290 of the data processing device 12 and learn the characteristics of the choreographer. For example, the analysis unit can collect the user's dancing style using the camera 42 of the smart glasses 214 and analyze it using the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide advice to improve the user's dancing style based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, learning unit, analysis unit, and provision unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can capture practice scenes using the camera 42 of the headset-type terminal 314 and save the captured footage as video data. For example, the learning unit can analyze the videos collected by the specific processing unit 290 of the data processing device 12 and learn the characteristics of the choreographer. For example, the analysis unit can collect data on the user's dancing style using the camera 42 of the headset-type terminal 314 and analyze the data by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide advice for improving the user's dancing style based on the analysis results obtained by the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, learning unit, analysis unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can capture practice scenes using the camera 42 of the robot 414 and save the captured footage as video data. For example, the learning unit can analyze the videos collected by the specific processing unit 290 of the data processing device 12 to learn the characteristics of the choreographer. For example, the analysis unit can collect data on the user's dancing style using the camera 42 of the robot 414 and analyze the data by the specific processing unit 290 of the data processing device 12. For example, the provision unit can provide advice for improving the user's dancing style based on the analysis results obtained by the specific processing unit 290 of the data processing device 12.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The providing unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide advice in a gentle tone. Furthermore, if the user is concentrating, the providing unit can provide specific and detailed advice. Furthermore, if the user is tired, the providing unit can provide concise and to-the-point advice. This allows the providing unit to provide the optimal way of expressing advice according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0109] The collection unit can analyze the user's past practice history and select the optimal collection method. For example, the collection unit can prioritize collection of practice methods that the user has received high ratings for in the past. The collection unit can also prioritize collection of practice methods that the user has frequently performed in the past. The collection unit can also prioritize collection of practice methods that the user has previously indicated needing improvement. This allows the collection unit to select the optimal collection method based on the user's past practice history.

[0110] The learning unit can estimate the user's emotions and adjust the learning presentation method based on the estimated user emotions. For example, if the user is relaxed, the learning unit can proceed with the learning at a leisurely pace. Furthermore, if the user is concentrating, the learning unit can provide learning with detailed explanations. Furthermore, if the user is tired, the learning unit can provide learning that is concise and to the point. This allows the learning unit to provide the optimal learning presentation method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the learning unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0111] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a concise analysis when the user is focused. The analysis unit can also perform a concise analysis when the user is tired. This allows the analysis unit to provide optimal analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, 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 these examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0112] The providing unit can estimate the user's emotions and adjust the length of advice based on the estimated user's emotions. For example, the providing unit can provide longer advice when the user is relaxed. Furthermore, the providing unit can provide short, concise advice when the user is concentrating. Furthermore, the providing unit can provide short, concise advice when the user is tired. This allows the providing unit to provide advice of an optimal length depending on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0113] When collecting videos, the collection unit can prioritize collecting highly relevant videos by taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting choreography videos that are popular in that area. Also, if the user is traveling, the collection unit can prioritize collecting videos related to dance events at the user's travel destination. Also, if the user is at home, the collection unit can prioritize collecting choreography videos that are easy to practice at home. In this way, the collection unit can collect optimal videos based on the user's geographical location information.

[0114] During learning, the learning unit can adjust the level of detail of learning based on the importance of the choreographer's movements. For example, the learning unit can learn by emphasizing particularly important parts of the choreographer's movements. The learning unit can also learn small parts of the choreographer's movements in detail. The learning unit can also learn basic parts of the choreographer's movements briefly. In this way, the learning unit can provide an optimal level of detail of learning depending on the importance of the choreographer's movements.

[0115] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements during analysis. For example, the analysis unit can consider the interlocking of the user's limb movements during analysis. The analysis unit can also consider the relationship between the user's trunk movement and limb movements during analysis. The analysis unit can also consider the user's sense of rhythm and the timing of their movements during analysis. In this way, the analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between the user's movements.

[0116] When providing advice, the providing unit can determine the priority of advice based on the time of submission of the user's movements. For example, the providing unit can give priority to the most recent movements when providing advice. Furthermore, if the movements are submitted older, the providing unit can also give priority to basic advice. Furthermore, if the movements are submitted more recently, the providing unit can provide advice that reflects the latest trends. This allows the providing unit to provide optimal advice priority based on the time of submission of the user's movements.

[0117] When collecting videos, the collection unit can analyze the user's social media activity and collect related videos. For example, the collection unit can prioritize collecting videos of choreographers the user follows on social media. The collection unit can also prioritize collecting videos related to dance styles that the user has "liked" on social media. The collection unit can also prioritize collecting videos related to dance events that the user has shared on social media. This allows the collection unit to collect optimal videos based on the user's social media activity.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The collection unit collects practice scenes or choreography videos of choreographers. For example, the collection unit may use a camera to capture practice scenes and save them as video data. The collection unit may also download choreography videos of choreographers from the Internet. For example, the collection unit may collect videos from the official website of a specific choreographer. Step 2: The learning unit analyzes the video collected by the collection unit and learns the characteristics of the choreographer. For example, the learning unit analyzes each frame of the video and learns the choreographer's movements and style. For example, the learning unit can grasp what movements a particular choreographer makes and what rhythm they dance to. For example, the learning unit can learn the characteristics of the choreographer using a machine learning algorithm. Step 3: The collection unit collects the user's dancing style. For example, the collection unit may take a video of the user dancing with a camera and save the video data. The collection unit may also download videos that the user has uploaded to the Internet. For example, the collection unit may collect dance videos that the user has posted on social media. Step 4: The analysis unit analyzes the user's dancing style collected by the collection unit. The analysis unit, for example, analyzes the user's movements and points out how the user's movements differ from the choreographer's movements. For example, the analysis unit can analyze discrepancies or differences in timing of the user's movements. For example, the analysis unit can analyze the user's movements using a motion analysis algorithm. Step 5: The providing unit provides advice to improve the user's dancing style based on the analysis results obtained by the analyzing unit. The providing unit, for example, provides specific advice on how to improve the user's movements. For example, the providing unit can point out how the user's movements differ from the choreographer's movements and provide advice on how to improve. The providing unit can provide, for example, text-based feedback. The providing unit can also provide instruction through video. For example, the providing unit can provide a video showing how the user's movements can be improved.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0121] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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, RAM 30, and 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.

[0127] 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.

[0128] 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 audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] 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 carried out in a secure state.

[0131] Fig. 4 shows an example of the 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.

[0132] 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.

[0133] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0135] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0137] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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, RAM 30, and 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.

[0143] 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.

[0144] 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 audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] 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 carried out in a secure state.

[0147] 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.

[0148] 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.

[0149] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0151] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0153] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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, RAM 30, and 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.

[0159] 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.

[0160] 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 audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] 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 carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0164] 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.

[0165] 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.

[0166] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0168] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] 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 voice indicating a user input regarding 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.

[0170] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] 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.

[0181] 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 USB (Universal Serial Bus) 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.

[0182] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0183] 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.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0186] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects practice videos or choreographer dance videos; a learning unit that analyzes the videos collected by the collecting unit and learns the characteristics of the choreographer; a collection unit that collects the user's dancing style; an analysis unit that analyzes the user's dancing style collected by the collection unit; a providing unit that provides advice to the user to improve their dancing style based on the analysis results obtained by the analysis unit. A system characterized by:

2. The learning unit Analyzes each frame of video and learns the choreographer's movements or style The system of claim 1 .

3. The analysis unit Analyze the user's movements and identify differences between the user's movements and the choreographer's movements The system of claim 1 .

4. The providing unit Advising users on how to improve their actions The system of claim 1 .

5. The collecting unit Collect videos of users dancing The system of claim 1 .

6. The collecting unit Collect videos of practice sessions or choreographers' moves The system of claim 1 .

7. The collecting unit Estimate user emotions and adjust video collection timing based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze the user's past practice history and select the optimal collection method The system of claim 1 .

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