System

Abstract

An object of a system according to an embodiment is to analyze a dance video and to generate and propose an optimal choreography and behavior.SOLUTION: A system includes a collection unit, an analysis unit, a generation unit, and a proposal unit. The collection unit collects a dance video. The analysis unit analyzes the dance video that has been collected by the collection unit. The generation part generates dance choreography and behavior on the basis of a result analyzed by the analysis part. The proposal unit proposes the choreography and behavior generated by the generation 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 technology has the problem that it is difficult for dance choreographers to analyze many dance videos and generate optimal choreography and behaviors.

[0005] The system according to the embodiment aims to analyze dance videos and generate and suggest optimal choreography and behavior. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects dance videos. The analysis unit analyzes the dance videos collected by the collection unit. The generation unit generates dance choreography and behavior based on the results of the analysis by the analysis unit. The suggestion unit suggests the choreography and behavior generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze dance videos and generate and suggest optimal choreography and behavior. [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 system according to an embodiment of the present invention uses AI to analyze dance videos from around the world and generate and propose optimal dance choreography and behaviors for idol groups. This system collects and analyzes dance videos to generate and propose optimal choreography and behaviors. For example, the system collects and analyzes dance videos from online platforms. Based on the analysis results, AI then generates optimal dance choreography and behaviors for idol groups. The generated choreography and behaviors are then proposed to dance choreographers. This system helps dance choreographers bring variety and originality to idol groups' performances, supporting their work. For example, it can quickly create choreography for new song releases, supporting artists' activities. It also keeps up with dance trends and incorporates the latest styles. This ensures that idol groups' performances are always fresh and engaging.

[0029] A dance choreography generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects dance videos. For example, the collection unit can collect dance videos from online platforms. The collection unit can also filter the videos based on a specific dance style or genre. The analysis unit analyzes the dance videos collected by the collection unit. For example, the analysis unit can analyze dance movements and rhythms. The analysis unit can also apply an analysis algorithm specialized for a specific dance style. The generation unit generates dance choreography and behavior based on the results of the analysis by the analysis unit. For example, the generation unit can generate choreography that matches a specific song. The generation unit can also generate behavior that matches the personalities of group members. The suggestion unit proposes the choreography and behavior generated by the generation unit. For example, the suggestion unit can propose the generated choreography and behavior to a dance choreographer. As a result, the dance choreography generation system according to an embodiment can consistently perform processes from collecting dance videos to analyzing them, generating choreography, and suggesting them.

[0030] The analysis unit can analyze dance movements or rhythms. The analysis unit, for example, analyzes dance movements. For example, the analysis unit can analyze the speed and pattern of movements. The analysis unit can also analyze rhythms. For example, the analysis unit can analyze beats and tempos. This allows for detailed analysis of dance movements and rhythms, making it possible to generate choreography with higher accuracy.

[0031] The generation unit can generate choreography that matches a specific song. The generation unit generates choreography that matches a specific song, for example. For example, the generation unit can generate choreography based on the tempo and beat of the song. The generation unit can also customize choreography based on the lyrics and theme of the song. In this way, by generating choreography that matches a specific song, it is possible to provide an optimal performance for the song.

[0032] The generation unit can generate behaviors for the group members. The generation unit generates behaviors that are tailored to the individual personalities of the group members, for example. For example, the generation unit can generate behaviors based on the dance styles and expressiveness of the members. The generation unit can also generate behaviors by referencing the members' past performance data. This allows the group to provide a more appealing performance by generating behaviors that are tailored to the individual personalities of the group members.

[0033] The suggestion unit can suggest the generated choreography or behavior to a dance choreographer. The suggestion unit, for example, suggests the generated choreography or behavior to the dance choreographer. For example, the suggestion unit can suggest the generated choreography or behavior in a text format or a visual format. The suggestion unit can also suggest the generated choreography or behavior by adjusting the level of detail or the order of the suggestion. In this way, the suggestion unit can help the choreographer by suggesting the generated choreography or behavior to the dance choreographer.

[0034] The collection unit can collect dance videos from online platforms. For example, the collection unit can collect dance videos from platforms such as YouTube (registered trademark), Instagram (registered trademark), and TikTok (registered trademark). The collection unit can also filter based on a specific dance style or genre. This allows the latest dance videos to be obtained by collecting dance videos from online platforms.

[0035] The collection unit can filter dance videos based on a specific dance style or genre when collecting them. For example, the collection unit can filter dance videos based on a specific dance style or genre when collecting dance videos. For example, the collection unit can collect only hip-hop style dance videos. The collection unit can also prioritize collecting ballet and classical dance videos. Furthermore, the collection unit can collect videos of traditional dance styles from a specific region. In this way, by filtering based on a specific dance style or genre, dance videos that suit the purpose can be collected.

[0036] When collecting dance videos, the collection unit can determine the priority based on the number of views or ratings of the videos. For example, when collecting dance videos, the collection unit determines the priority based on the number of views or ratings of the videos. For example, the collection unit prioritizes collecting dance videos with a large number of views. The collection unit can also prioritize collecting dance videos with high ratings. Furthermore, the collection unit can prioritize collecting dance videos with a large number of comments. In this way, by determining the priority based on the number of views or ratings, popular dance videos can be preferentially collected.

[0037] The collection unit can analyze the user's past viewing history and select a collection method when collecting dance videos. For example, when collecting dance videos, the collection unit analyzes the user's past viewing history and selects the optimal collection method. For example, the collection unit collects videos of a user's favorite dance style based on the user's past viewing history. The collection unit can also prioritize collecting videos of dancers that the user frequently watches. Furthermore, the collection unit can collect unwatched related videos from the user's viewing history. In this way, by analyzing the user's past viewing history, dance videos that match the user's preferences can be collected.

[0038] The collection unit may filter dance videos based on a specific region or culture when collecting the dance videos. For example, the collection unit may filter dance videos based on a specific region or culture when collecting the dance videos. For example, the collection unit may collect traditional African dance videos. The collection unit may also collect dance videos based on Asian pop culture. The collection unit may also collect European folk dance videos. In this way, by filtering based on a specific region or culture, a variety of dance styles can be collected.

[0039] When collecting dance videos, the collection unit can prioritize collecting the most recent videos based on the upload date and time of the videos. For example, when collecting dance videos, the collection unit prioritizes collecting the most recent videos based on the upload date and time of the videos. For example, the collection unit prioritizes collecting the most recent dance videos. The collection unit can also collect dance videos uploaded within the past 24 hours. Furthermore, the collection unit can collect dance videos uploaded during a specific event period. In this way, by prioritizing the collection of the most recent videos, the latest dance trends can be grasped.

[0040] The collection unit can analyze the user's social media activity and collect related videos when collecting dance videos. For example, the collection unit analyzes the user's social media activity and collects related videos when collecting dance videos. For example, the collection unit collects dance videos shared by the user on social media. The collection unit can also collect dance videos watched by the user's followers. Furthermore, the collection unit can collect related dance videos based on the user's social media interests. In this way, by analyzing the user's social media activity, dance videos that match the user's interests can be collected.

[0041] The analysis unit can apply an analysis algorithm specialized for a specific dance style when analyzing dance movements and rhythms. For example, the analysis unit uses an analysis algorithm specialized for hip-hop style dance. The analysis unit can also use an analysis algorithm specialized for ballet style dance. Furthermore, the analysis unit can also use an analysis algorithm specialized for Latin dances such as salsa and tango. In this way, by applying an analysis algorithm specialized for a specific dance style, it is possible to provide analysis results that match the style.

[0042] When analyzing dance movements or rhythms, the analysis unit can adjust the analysis method based on the image quality or frame rate of the video. For example, when analyzing dance movements or rhythms, the analysis unit adjusts the analysis method based on the image quality or frame rate of the video. For example, the analysis unit performs a detailed analysis on high-image-quality video. The analysis unit can also perform a simplified analysis on low-image-quality video. Furthermore, the analysis unit can also strengthen movement analysis for video with a high frame rate. In this way, by adjusting the analysis method based on the image quality or frame rate of the video, appropriate analysis results can be provided.

[0043] When analyzing dance movements and rhythms, the analysis unit can compare multiple videos and extract common features. For example, when analyzing dance movements and rhythms, the analysis unit compares multiple videos and extracts common features. For example, the analysis unit compares multiple hip-hop dance videos and extracts common movements. The analysis unit can also compare ballet videos and extract common rhythms. Furthermore, the analysis unit can compare salsa dance videos and extract common steps. In this way, by comparing multiple videos and extracting common features, more general analysis results can be provided.

[0044] When analyzing dance movements or rhythms, the analysis unit can adjust the analysis method based on the tempo or beat of a specific song. For example, when analyzing dance movements or rhythms, the analysis unit adjusts the analysis method based on the tempo or beat of a specific song. For example, the analysis unit strengthens movement analysis for songs with a fast tempo. The analysis unit can also strengthen rhythm analysis for songs with a strong beat. Furthermore, the analysis unit can perform detailed movement analysis for songs with a slow tempo. In this way, by adjusting the analysis method based on the tempo or beat of a specific song, it is possible to provide analysis results that are suited to the song.

[0045] When analyzing dance movements or rhythms, the analysis unit can take into account the shooting angle or camera work of the video. For example, when analyzing dance movements or rhythms, the analysis unit can take into account the shooting angle and camera work of the video. For example, the analysis unit performs detailed movement analysis for videos shot from the front. The analysis unit can also strengthen rhythm analysis for videos shot from an oblique angle. Furthermore, the analysis unit can adjust movement analysis for videos with a lot of camera work. This allows for more accurate analysis results to be provided by taking into account the shooting angle and camera work of the video.

[0046] When analyzing dance movements or rhythms, the analysis unit can improve analysis accuracy by filtering background sounds or environmental sounds of the video. For example, when analyzing dance movements or rhythms, the analysis unit can improve analysis accuracy by filtering background sounds or environmental sounds of the video. For example, the analysis unit filters background sounds to analyze movements. The analysis unit can also filter environmental sounds to analyze rhythms. Furthermore, the analysis unit can remove noise to improve analysis accuracy. Thus, filtering background sounds or environmental sounds improves analysis accuracy.

[0047] When generating choreography for a specific song, the generation unit can customize the choreography based on the lyrics and theme of the song. For example, when generating choreography for a specific song, the generation unit customizes the choreography based on the lyrics and theme of the song. For example, the generation unit generates choreography based on the lyrics of the song. The generation unit can also customize the choreography based on the theme of the song. Furthermore, the generation unit can adjust the choreography to match the melody of the song. In this way, by customizing the choreography based on the lyrics and theme of the song, it is possible to provide choreography that matches the song.

[0048] The generation unit can refer to the past performance data of the group members when generating behaviors that match the individuality of the group members. For example, the generation unit refers to the past performance data of the group members when generating behaviors that match the individuality of the group members. For example, the generation unit generates behaviors based on the past performance data of the group members. The generation unit can also customize behaviors that match the individuality of the group members. Furthermore, the generation unit can also generate behaviors that incorporate the movements that the group members are good at. In this way, by referring to the past performance data of the group members, behaviors that match the individuality of the group members can be generated.

[0049] When generating choreography, the generation unit can combine multiple dance styles to create a new style. For example, when generating choreography, the generation unit combines multiple dance styles to create a new style. For example, the generation unit generates choreography that combines hip hop and ballet. The generation unit can also generate choreography that combines salsa and jazz. Furthermore, the generation unit can also generate choreography that combines classical dance and modern dance. In this way, by combining multiple dance styles, choreography in a new style can be created.

[0050] When generating choreography for a specific song, the generation unit can generate choreography that corresponds to a remix version of the song. For example, when generating choreography for a specific song, the generation unit generates choreography that corresponds to a remix version of the song. For example, the generation unit generates choreography that corresponds to a remix version of the song. The generation unit can also generate choreography that corresponds to a different version of the song. Furthermore, the generation unit can adjust the choreography to match the tempo or beat of the song. In this way, by generating choreography that corresponds to a remix version of the song, it is possible to support various versions of the song.

[0051] When generating behaviors that match the individuality of the group members, the generation unit can adjust the behaviors based on the members' costumes or the stage set. For example, when generating behaviors that match the individuality of the group members, the generation unit adjusts the behaviors based on the members' costumes or the stage set. For example, the generation unit generates behaviors that match the members' costumes. The generation unit can also adjust the behaviors based on the stage set. Furthermore, the generation unit can customize the behaviors taking into account the individuality and costumes of the members. In this way, by adjusting the behaviors based on the members' costumes or the stage set, it is possible to provide a performance that feels more unified.

[0052] The generation unit can improve the generation algorithm by reflecting user feedback when generating choreography. For example, the generation unit improves the generation algorithm by reflecting user feedback when generating choreography. For example, the generation unit improves the choreography based on user feedback. The generation unit can also adjust the generation algorithm by reflecting user evaluations. Furthermore, the generation unit can customize the choreography by incorporating user opinions. In this way, the generation algorithm can be continuously improved by reflecting user feedback.

[0053] The suggestion unit can adjust the level of detail of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit adjusts the level of detail of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit makes a detailed suggestion when the user is relaxed. Also, the suggestion unit can make a simplified suggestion when the user is in a hurry. Furthermore, the suggestion unit can make a visually stimulating suggestion when the user is excited. In this way, by adjusting the level of detail of the suggestion, it is possible to make an appropriate suggestion according to the user's situation.

[0054] The suggestion unit can adjust the order of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the order of suggestions when suggesting the generated choreography or behavior. For example, when the user is relaxed, the suggestion unit makes suggestions in an orderly manner. Also, when the user is in a hurry, the suggestion unit can prioritize important suggestions. Furthermore, when the user is excited, the suggestion unit can make suggestions in a visually stimulating order. In this way, by adjusting the order of suggestions, suggestions can be made in an order that is easy for the user to understand.

[0055] The suggestion unit can adjust the frequency of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the frequency of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit makes frequent suggestions when the user is relaxed. Also, the suggestion unit can make the minimum necessary suggestions when the user is in a hurry. Furthermore, the suggestion unit can make suggestions at a visually stimulating frequency when the user is excited. In this way, by adjusting the frequency of suggestions, suggestions can be made at an appropriate frequency according to the user's situation.

[0056] The suggestion unit can customize the display method of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit customizes the display method of the suggestion when proposing the generated choreography or behavior. For example, if the user is relaxed, the suggestion unit can make the suggestion in a calm display method. Also, if the user is excited, the suggestion unit can make the suggestion in an energetic display method. Furthermore, if the user is sad, the suggestion unit can make the suggestion in an inspiring display method. In this way, by customizing the display method of the suggestion, the suggestion can be made in a form that is easy for the user to see.

[0057] The suggestion unit can adjust the timing of the suggestion when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the timing of the suggestion when suggesting the generated choreography or behavior. For example, when the user is relaxed, the suggestion unit makes the suggestion at an appropriate timing. Also, when the user is in a hurry, the suggestion unit can make the suggestion quickly. Furthermore, when the user is excited, the suggestion unit can make the suggestion at a visually stimulating timing. In this way, by adjusting the timing of the suggestion, it is possible to make the suggestion at an appropriate timing according to the user's situation.

[0058] The suggestion unit can improve the proposed content by reflecting the user's past feedback when proposing the generated choreography or behavior. For example, the suggestion unit improves the proposed content by reflecting the user's past feedback when proposing the generated choreography or behavior. For example, the suggestion unit improves the proposed content based on the user's past feedback. The suggestion unit can also adjust the proposed content by reflecting the user's evaluation. Furthermore, the suggestion unit can also customize the proposed content by incorporating the user's opinion. In this way, the proposed content can be continuously improved by reflecting the user's past feedback.

[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 also analyze the user's social media activity and collect dance videos that the user may be interested in. For example, the collection unit can prioritize collecting new videos of dancers the user follows. The collection unit can also collect videos of dance styles for which the user has left many likes and comments. Furthermore, the collection unit can analyze the user's social media trends and collect related dance videos. This allows for the collection of more personalized dance videos based on the user's social media activity.

[0061] When generating choreography for a specific song, the generation unit can also generate choreography that corresponds to a remix version of the song. For example, the generation unit generates choreography that corresponds to a remix version of the song. The generation unit can also generate choreography that corresponds to a different version of the song. Furthermore, the generation unit can adjust the choreography to match the tempo or beat of the song. In this way, by generating choreography that corresponds to a remix version of the song, it is possible to support various versions of the song.

[0062] When proposing the generated choreography or behavior, the suggestion unit can improve the suggestion content by reflecting the user's past feedback. For example, the suggestion unit improves the suggestion content based on the user's past feedback. The suggestion unit can also adjust the suggestion content by reflecting the user's evaluation. Furthermore, the suggestion unit can also customize the suggestion content by incorporating the user's opinion. In this way, the suggestion content can be continuously improved by reflecting the user's past feedback.

[0063] When collecting dance videos, the collection unit may filter them based on a specific region or culture. For example, the collection unit may collect traditional African dance videos. The collection unit may also collect dance videos based on Asian pop culture. The collection unit may also collect European folk dance videos. In this way, by filtering based on a specific region or culture, a variety of dance styles can be collected.

[0064] When analyzing dance movements and rhythms, the analysis unit can adjust the analysis method based on the image quality or frame rate of the video. For example, the analysis unit can perform detailed analysis on high-image-quality video. The analysis unit can also perform simplified analysis on low-image-quality video. Furthermore, the analysis unit can strengthen movement analysis on videos with high frame rates. This allows the analysis method to be adjusted based on the image quality and frame rate of the video, making it possible to provide appropriate analysis results.

[0065] When generating choreography, the generation unit can combine multiple dance styles to create a new style. For example, the generation unit can generate choreography that combines hip hop and ballet. The generation unit can also generate choreography that combines salsa and jazz. Furthermore, the generation unit can generate choreography that combines classical dance and modern dance. In this way, by combining multiple dance styles, new styles of choreography can be created.

[0066] The suggestion unit can adjust the timing of the suggestion when suggesting the generated choreography or behavior. For example, if the user is relaxed, the suggestion unit makes the suggestion at an appropriate timing. Also, if the user is in a hurry, the suggestion unit can make the suggestion quickly. Furthermore, if the user is excited, the suggestion unit can make the suggestion at a visually stimulating timing. In this way, by adjusting the timing of the suggestion, it is possible to make the suggestion at an appropriate timing according to the user's situation.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The collection unit collects dance videos. For example, the collection unit may collect dance videos from an online platform. The collection unit may also filter the dance videos based on a particular dance style or genre. Step 2: The analysis unit analyzes the dance video collected by the collection unit. For example, the analysis unit can analyze the dance movements and rhythm. The analysis unit can also apply analysis algorithms specialized for a particular dance style. Step 3: The generator generates dance choreography and behavior based on the results of the analysis by the analyzer. For example, the generator can generate choreography that matches a specific song. The generator can also generate behavior that matches the personalities of the group members. Step 4: The suggestion unit proposes the choreography or behavior generated by the generation unit. For example, the suggestion unit can propose the generated choreography or behavior to a dance choreographer.

[0069] (Example 2) A system according to an embodiment of the present invention uses AI to analyze dance videos from around the world and generate and propose optimal dance choreography and behaviors for idol groups. This system collects and analyzes dance videos to generate and propose optimal choreography and behaviors. For example, the system collects and analyzes dance videos from online platforms. Based on the analysis results, AI then generates optimal dance choreography and behaviors for idol groups. The generated choreography and behaviors are then proposed to dance choreographers. This system helps dance choreographers bring variety and originality to idol groups' performances, supporting their work. For example, it can quickly create choreography for new song releases, supporting artists' activities. It also keeps up with dance trends and incorporates the latest styles. This ensures that idol groups' performances are always fresh and engaging.

[0070] A dance choreography generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a suggestion unit. The collection unit collects dance videos. For example, the collection unit can collect dance videos from online platforms. The collection unit can also filter the videos based on a specific dance style or genre. The analysis unit analyzes the dance videos collected by the collection unit. For example, the analysis unit can analyze dance movements and rhythms. The analysis unit can also apply an analysis algorithm specialized for a specific dance style. The generation unit generates dance choreography and behavior based on the results of the analysis by the analysis unit. For example, the generation unit can generate choreography that matches a specific song. The generation unit can also generate behavior that matches the personalities of group members. The suggestion unit proposes the choreography and behavior generated by the generation unit. For example, the suggestion unit can propose the generated choreography and behavior to a dance choreographer. As a result, the dance choreography generation system according to an embodiment can consistently perform processes from collecting dance videos to analyzing them, generating choreography, and suggesting them.

[0071] The analysis unit can analyze dance movements or rhythms. The analysis unit, for example, analyzes dance movements. For example, the analysis unit can analyze the speed and pattern of movements. The analysis unit can also analyze rhythms. For example, the analysis unit can analyze beats and tempos. This allows for detailed analysis of dance movements and rhythms, making it possible to generate choreography with higher accuracy.

[0072] The generation unit can generate choreography that matches a specific song. The generation unit generates choreography that matches a specific song, for example. For example, the generation unit can generate choreography based on the tempo and beat of the song. The generation unit can also customize choreography based on the lyrics and theme of the song. In this way, by generating choreography that matches a specific song, it is possible to provide an optimal performance for the song.

[0073] The generation unit can generate behaviors for the group members. The generation unit generates behaviors that are tailored to the individual personalities of the group members, for example. For example, the generation unit can generate behaviors based on the dance styles and expressiveness of the members. The generation unit can also generate behaviors by referencing the members' past performance data. This allows the group to provide a more appealing performance by generating behaviors that are tailored to the individual personalities of the group members.

[0074] The suggestion unit can suggest the generated choreography or behavior to a dance choreographer. The suggestion unit, for example, suggests the generated choreography or behavior to the dance choreographer. For example, the suggestion unit can suggest the generated choreography or behavior in a text format or a visual format. The suggestion unit can also suggest the generated choreography or behavior by adjusting the level of detail or the order of the suggestion. In this way, the suggestion unit can help the choreographer by suggesting the generated choreography or behavior to the dance choreographer.

[0075] The collection unit can collect dance videos from online platforms. For example, the collection unit can collect dance videos from platforms such as YouTube, Instagram, and TikTok. The collection unit can also filter based on a specific dance style or genre. This allows the latest dance videos to be obtained by collecting dance videos from online platforms.

[0076] The collection unit can estimate the user's emotions and adjust the timing of collecting dance videos based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting dance videos based on the estimated user emotions. For example, the collection unit can set the collection of dance videos to take place at night if the user is relaxed. Furthermore, the collection unit can adjust the collection timing to suit the user's schedule if the user is busy. Furthermore, the collection unit can collect dance videos in real time if the user is excited. In this way, by adjusting the collection timing according to the user's emotions, dance videos can be collected at more appropriate times.

[0077] The collection unit can filter dance videos based on a specific dance style or genre when collecting them. For example, the collection unit can filter dance videos based on a specific dance style or genre when collecting dance videos. For example, the collection unit can collect only hip-hop style dance videos. The collection unit can also prioritize collecting ballet and classical dance videos. Furthermore, the collection unit can collect videos of traditional dance styles from a specific region. In this way, by filtering based on a specific dance style or genre, dance videos that suit the purpose can be collected.

[0078] When collecting dance videos, the collection unit can determine the priority based on the number of views or ratings of the videos. For example, when collecting dance videos, the collection unit determines the priority based on the number of views or ratings of the videos. For example, the collection unit prioritizes collecting dance videos with a large number of views. The collection unit can also prioritize collecting dance videos with high ratings. Furthermore, the collection unit can prioritize collecting dance videos with a large number of comments. In this way, by determining the priority based on the number of views or ratings, popular dance videos can be preferentially collected.

[0079] The collection unit can analyze the user's past viewing history and select a collection method when collecting dance videos. For example, when collecting dance videos, the collection unit analyzes the user's past viewing history and selects the optimal collection method. For example, the collection unit collects videos of a user's favorite dance style based on the user's past viewing history. The collection unit can also prioritize collecting videos of dancers that the user frequently watches. Furthermore, the collection unit can collect unwatched related videos from the user's viewing history. In this way, by analyzing the user's past viewing history, dance videos that match the user's preferences can be collected.

[0080] The collection unit can estimate the user's emotions and determine the priority of dance 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 dance videos to be collected based on the estimated user emotions. For example, if the user is relaxed, the collection unit can prioritize collecting relaxed dance videos. Also, if the user is excited, the collection unit can prioritize collecting energetic dance videos. Furthermore, if the user is sad, the collection unit can prioritize collecting moving dance videos. In this way, by determining the priority based on the user's emotions, dance videos that match the user's mood can be collected.

[0081] The collection unit may filter dance videos based on a specific region or culture when collecting the dance videos. For example, the collection unit may filter dance videos based on a specific region or culture when collecting the dance videos. For example, the collection unit may collect traditional African dance videos. The collection unit may also collect dance videos based on Asian pop culture. The collection unit may also collect European folk dance videos. In this way, by filtering based on a specific region or culture, a variety of dance styles can be collected.

[0082] When collecting dance videos, the collection unit can prioritize collecting the most recent videos based on the upload date and time of the videos. For example, when collecting dance videos, the collection unit prioritizes collecting the most recent videos based on the upload date and time of the videos. For example, the collection unit prioritizes collecting the most recent dance videos. The collection unit can also collect dance videos uploaded within the past 24 hours. Furthermore, the collection unit can collect dance videos uploaded during a specific event period. In this way, by prioritizing the collection of the most recent videos, the latest dance trends can be grasped.

[0083] The collection unit can analyze the user's social media activity and collect related videos when collecting dance videos. For example, the collection unit analyzes the user's social media activity and collects related videos when collecting dance videos. For example, the collection unit collects dance videos shared by the user on social media. The collection unit can also collect dance videos watched by the user's followers. Furthermore, the collection unit can collect related dance videos based on the user's social media interests. In this way, by analyzing the user's social media activity, dance videos that match the user's interests can be collected.

[0084] The analysis unit can estimate the user's emotions and adjust the level of detail of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the level of detail of the analysis based on the estimated user's emotions. For example, the analysis unit performs a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is in a hurry. Furthermore, the analysis unit can provide a visually stimulating analysis result when the user is excited. In this way, by adjusting the level of detail of the analysis based on the user's emotions, it is possible to provide an analysis result that meets the user's needs.

[0085] The analysis unit can apply an analysis algorithm specialized for a specific dance style when analyzing dance movements and rhythms. For example, the analysis unit uses an analysis algorithm specialized for hip-hop style dance. The analysis unit can also use an analysis algorithm specialized for ballet style dance. Furthermore, the analysis unit can also use an analysis algorithm specialized for Latin dances such as salsa and tango. In this way, by applying an analysis algorithm specialized for a specific dance style, it is possible to provide analysis results that match the style.

[0086] When analyzing dance movements or rhythms, the analysis unit can adjust the analysis method based on the image quality or frame rate of the video. For example, when analyzing dance movements or rhythms, the analysis unit adjusts the analysis method based on the image quality or frame rate of the video. For example, the analysis unit performs a detailed analysis on high-image-quality video. The analysis unit can also perform a simplified analysis on low-image-quality video. Furthermore, the analysis unit can also strengthen movement analysis for video with a high frame rate. In this way, by adjusting the analysis method based on the image quality or frame rate of the video, appropriate analysis results can be provided.

[0087] When analyzing dance movements and rhythms, the analysis unit can compare multiple videos and extract common features. For example, when analyzing dance movements and rhythms, the analysis unit compares multiple videos and extracts common features. For example, the analysis unit compares multiple hip-hop dance videos and extracts common movements. The analysis unit can also compare ballet videos and extract common rhythms. Furthermore, the analysis unit can compare salsa dance videos and extract common steps. In this way, by comparing multiple videos and extracting common features, more general analysis results can be provided.

[0088] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user's emotions. For example, the analysis unit can display detailed analysis results when the user is relaxed. Furthermore, the analysis unit can display simplified analysis results when the user is in a hurry. Furthermore, the analysis unit can display visually stimulating analysis results when the user is excited. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0089] When analyzing dance movements or rhythms, the analysis unit can adjust the analysis method based on the tempo or beat of a specific song. For example, when analyzing dance movements or rhythms, the analysis unit adjusts the analysis method based on the tempo or beat of a specific song. For example, the analysis unit strengthens movement analysis for songs with a fast tempo. The analysis unit can also strengthen rhythm analysis for songs with a strong beat. Furthermore, the analysis unit can perform detailed movement analysis for songs with a slow tempo. In this way, by adjusting the analysis method based on the tempo or beat of a specific song, it is possible to provide analysis results that are suited to the song.

[0090] When analyzing dance movements or rhythms, the analysis unit can take into account the shooting angle or camera work of the video. For example, when analyzing dance movements or rhythms, the analysis unit can take into account the shooting angle and camera work of the video. For example, the analysis unit performs detailed movement analysis for videos shot from the front. The analysis unit can also strengthen rhythm analysis for videos shot from an oblique angle. Furthermore, the analysis unit can adjust movement analysis for videos with a lot of camera work. This allows for more accurate analysis results to be provided by taking into account the shooting angle and camera work of the video.

[0091] When analyzing dance movements or rhythms, the analysis unit can improve analysis accuracy by filtering background sounds or environmental sounds of the video. For example, when analyzing dance movements or rhythms, the analysis unit can improve analysis accuracy by filtering background sounds or environmental sounds of the video. For example, the analysis unit filters background sounds to analyze movements. The analysis unit can also filter environmental sounds to analyze rhythms. Furthermore, the analysis unit can remove noise to improve analysis accuracy. Thus, filtering background sounds or environmental sounds improves analysis accuracy.

[0092] The generation unit can estimate the user's emotion and adjust the style of the choreography to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the style of the choreography to be generated based on the estimated user's emotion. For example, the generation unit can generate relaxed choreography when the user is relaxed. Furthermore, the generation unit can generate energetic choreography when the user is excited. Furthermore, the generation unit can generate moving choreography when the user is sad. In this way, by adjusting the style of the choreography based on the user's emotion, choreography that matches the user's mood can be generated.

[0093] When generating choreography for a specific song, the generation unit can customize the choreography based on the lyrics and theme of the song. For example, when generating choreography for a specific song, the generation unit customizes the choreography based on the lyrics and theme of the song. For example, the generation unit generates choreography based on the lyrics of the song. The generation unit can also customize the choreography based on the theme of the song. Furthermore, the generation unit can adjust the choreography to match the melody of the song. In this way, by customizing the choreography based on the lyrics and theme of the song, it is possible to provide choreography that matches the song.

[0094] The generation unit can refer to the past performance data of the group members when generating behaviors that match the individuality of the group members. For example, the generation unit refers to the past performance data of the group members when generating behaviors that match the individuality of the group members. For example, the generation unit generates behaviors based on the past performance data of the group members. The generation unit can also customize behaviors that match the individuality of the group members. Furthermore, the generation unit can also generate behaviors that incorporate the movements that the group members are good at. In this way, by referring to the past performance data of the group members, behaviors that match the individuality of the group members can be generated.

[0095] When generating choreography, the generation unit can combine multiple dance styles to create a new style. For example, when generating choreography, the generation unit combines multiple dance styles to create a new style. For example, the generation unit generates choreography that combines hip hop and ballet. The generation unit can also generate choreography that combines salsa and jazz. Furthermore, the generation unit can also generate choreography that combines classical dance and modern dance. In this way, by combining multiple dance styles, choreography in a new style can be created.

[0096] The generation unit can estimate the user's emotions and adjust the difficulty of the choreography to be generated based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the difficulty of the choreography to be generated based on the estimated user's emotions. For example, the generation unit generates easy choreography when the user is relaxed. The generation unit can also generate difficult choreography when the user is excited. Furthermore, the generation unit can generate moving choreography when the user is sad. In this way, by adjusting the difficulty of the choreography based on the user's emotions, choreography that matches the user's skills can be generated.

[0097] When generating choreography for a specific song, the generation unit can generate choreography that corresponds to a remix version of the song. For example, when generating choreography for a specific song, the generation unit generates choreography that corresponds to a remix version of the song. For example, the generation unit generates choreography that corresponds to a remix version of the song. The generation unit can also generate choreography that corresponds to a different version of the song. Furthermore, the generation unit can adjust the choreography to match the tempo or beat of the song. In this way, by generating choreography that corresponds to a remix version of the song, it is possible to support various versions of the song.

[0098] When generating behaviors that match the individuality of the group members, the generation unit can adjust the behaviors based on the members' costumes or the stage set. For example, when generating behaviors that match the individuality of the group members, the generation unit adjusts the behaviors based on the members' costumes or the stage set. For example, the generation unit generates behaviors that match the members' costumes. The generation unit can also adjust the behaviors based on the stage set. Furthermore, the generation unit can customize the behaviors taking into account the individuality and costumes of the members. In this way, by adjusting the behaviors based on the members' costumes or the stage set, it is possible to provide a performance that feels more unified.

[0099] The generation unit can improve the generation algorithm by reflecting user feedback when generating choreography. For example, the generation unit improves the generation algorithm by reflecting user feedback when generating choreography. For example, the generation unit improves the choreography based on user feedback. The generation unit can also adjust the generation algorithm by reflecting user evaluations. Furthermore, the generation unit can customize the choreography by incorporating user opinions. In this way, the generation algorithm can be continuously improved by reflecting user feedback.

[0100] The suggestion unit can estimate the user's emotion and adjust the way in which the suggestion is expressed based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion and adjusts the way in which the suggestion is expressed based on the estimated user's emotion. For example, if the user is relaxed, the suggestion unit can make the suggestion using calm expression. Also, if the user is excited, the suggestion unit can make the suggestion using energetic expression. Furthermore, if the user is sad, the suggestion unit can make the suggestion using moving expression. In this way, by adjusting the way in which the suggestion is expressed based on the user's emotion, it is possible to make a suggestion that is easy for the user to accept.

[0101] The suggestion unit can adjust the level of detail of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit adjusts the level of detail of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit makes a detailed suggestion when the user is relaxed. Also, the suggestion unit can make a simplified suggestion when the user is in a hurry. Furthermore, the suggestion unit can make a visually stimulating suggestion when the user is excited. In this way, by adjusting the level of detail of the suggestion, it is possible to make an appropriate suggestion according to the user's situation.

[0102] The suggestion unit can adjust the order of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the order of suggestions when suggesting the generated choreography or behavior. For example, when the user is relaxed, the suggestion unit makes suggestions in an orderly manner. Also, when the user is in a hurry, the suggestion unit can prioritize important suggestions. Furthermore, when the user is excited, the suggestion unit can make suggestions in a visually stimulating order. In this way, by adjusting the order of suggestions, suggestions can be made in an order that is easy for the user to understand.

[0103] The suggestion unit can adjust the frequency of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the frequency of suggestions when suggesting the generated choreography or behavior. For example, the suggestion unit makes frequent suggestions when the user is relaxed. Also, the suggestion unit can make the minimum necessary suggestions when the user is in a hurry. Furthermore, the suggestion unit can make suggestions at a visually stimulating frequency when the user is excited. In this way, by adjusting the frequency of suggestions, suggestions can be made at an appropriate frequency according to the user's situation.

[0104] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated user's emotions. The suggestion unit, for example, estimates the user's emotions and determines the priority of suggestions based on the estimated user's emotions. For example, the suggestion unit can prioritize calm suggestions when the user is relaxed. Also, the suggestion unit can prioritize energetic suggestions when the user is excited. Furthermore, the suggestion unit can prioritize moving suggestions when the user is sad. In this way, by determining the priority of suggestions based on the user's emotions, suggestions that match the user's mood can be made.

[0105] The suggestion unit can customize the display method of the suggestion when proposing the generated choreography or behavior. For example, the suggestion unit customizes the display method of the suggestion when proposing the generated choreography or behavior. For example, if the user is relaxed, the suggestion unit can make the suggestion in a calm display method. Also, if the user is excited, the suggestion unit can make the suggestion in an energetic display method. Furthermore, if the user is sad, the suggestion unit can make the suggestion in an inspiring display method. In this way, by customizing the display method of the suggestion, the suggestion can be made in a form that is easy for the user to see.

[0106] The suggestion unit can adjust the timing of the suggestion when suggesting the generated choreography or behavior. For example, the suggestion unit adjusts the timing of the suggestion when suggesting the generated choreography or behavior. For example, when the user is relaxed, the suggestion unit makes the suggestion at an appropriate timing. Also, when the user is in a hurry, the suggestion unit can make the suggestion quickly. Furthermore, when the user is excited, the suggestion unit can make the suggestion at a visually stimulating timing. In this way, by adjusting the timing of the suggestion, it is possible to make the suggestion at an appropriate timing according to the user's situation.

[0107] The suggestion unit can improve the proposed content by reflecting the user's past feedback when proposing the generated choreography or behavior. For example, the suggestion unit improves the proposed content by reflecting the user's past feedback when proposing the generated choreography or behavior. For example, the suggestion unit improves the proposed content based on the user's past feedback. The suggestion unit can also adjust the proposed content by reflecting the user's evaluation. Furthermore, the suggestion unit can also customize the proposed content by incorporating the user's opinion. In this way, the proposed content can be continuously improved by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and suggestion 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 collects dance videos from an online platform using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected dance videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dance choreography and behavior based on the analysis results. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14 and suggests the generated choreography and behavior to a dance choreographer. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and suggestion 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 collects dance videos from an online platform using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected dance videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dance choreography and behavior based on the analysis results. The suggestion unit is realized, for example, by the control unit 46A of the smart glasses 214 and suggests the generated choreography and behavior to a dance choreographer. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and suggestion 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 collects dance videos from an online platform using the camera 42 and communication I / F 44 of the headset-type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected dance videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dance choreography and behavior based on the analysis results. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and suggests the generated choreography and behavior to a dance choreographer. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, and suggestion 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 collects dance videos from an online platform using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected dance videos. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates dance choreography and behavior based on the analysis results. The suggestion unit is realized, for example, by the control unit 46A of the robot 414 and suggests the generated choreography and behavior to a dance choreographer.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The collection unit can also analyze the user's social media activity and collect dance videos that the user may be interested in. For example, the collection unit can prioritize collecting new videos of dancers the user follows. The collection unit can also collect videos of dance styles for which the user has left many likes and comments. Furthermore, the collection unit can analyze the user's social media trends and collect related dance videos. This allows for the collection of more personalized dance videos based on the user's social media activity.

[0110] When analyzing the dance movements and rhythms, the analysis unit can estimate the user's emotions and adjust the focus of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can focus on slow movements and rhythms. If the user is excited, the analysis unit can also focus on energetic movements and rhythms. Furthermore, if the user is sad, the analysis unit can also focus on moving movements and rhythms. In this way, by adjusting the focus of the analysis based on the user's emotions, more appropriate analysis results can be provided.

[0111] When generating choreography for a specific song, the generation unit can also generate choreography that corresponds to a remix version of the song. For example, the generation unit generates choreography that corresponds to a remix version of the song. The generation unit can also generate choreography that corresponds to a different version of the song. Furthermore, the generation unit can adjust the choreography to match the tempo or beat of the song. In this way, by generating choreography that corresponds to a remix version of the song, it is possible to support various versions of the song.

[0112] The generation unit can estimate the user's emotions and adjust the style of the choreography to be generated based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a relaxed choreography. If the user is excited, the generation unit can generate an energetic choreography. Furthermore, if the user is sad, the generation unit can generate an emotional choreography. In this way, by adjusting the style of the choreography based on the user's emotions, choreography that matches the user's mood can be generated.

[0113] When proposing the generated choreography or behavior, the suggestion unit can improve the suggestion content by reflecting the user's past feedback. For example, the suggestion unit improves the suggestion content based on the user's past feedback. The suggestion unit can also adjust the suggestion content by reflecting the user's evaluation. Furthermore, the suggestion unit can also customize the suggestion content by incorporating the user's opinion. In this way, the suggestion content can be continuously improved by reflecting the user's past feedback.

[0114] When collecting dance videos, the collection unit may filter them based on a specific region or culture. For example, the collection unit may collect traditional African dance videos. The collection unit may also collect dance videos based on Asian pop culture. The collection unit may also collect European folk dance videos. In this way, by filtering based on a specific region or culture, a variety of dance styles can be collected.

[0115] When analyzing dance movements and rhythms, the analysis unit can adjust the analysis method based on the image quality or frame rate of the video. For example, the analysis unit can perform detailed analysis on high-image-quality video. The analysis unit can also perform simplified analysis on low-image-quality video. Furthermore, the analysis unit can strengthen movement analysis on videos with high frame rates. This allows the analysis method to be adjusted based on the image quality and frame rate of the video, making it possible to provide appropriate analysis results.

[0116] When generating choreography, the generation unit can combine multiple dance styles to create a new style. For example, the generation unit can generate choreography that combines hip hop and ballet. The generation unit can also generate choreography that combines salsa and jazz. Furthermore, the generation unit can generate choreography that combines classical dance and modern dance. In this way, by combining multiple dance styles, new styles of choreography can be created.

[0117] The suggestion unit can estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can make the suggestion using calm expressions. If the user is excited, the suggestion unit can also make the suggestion using energetic expressions. Furthermore, if the user is sad, the suggestion unit can also make the suggestion using moving expressions. In this way, by adjusting the way the suggestion is expressed based on the user's emotions, it is possible to make suggestions that are easy for the user to accept.

[0118] The suggestion unit can adjust the timing of the suggestion when suggesting the generated choreography or behavior. For example, if the user is relaxed, the suggestion unit makes the suggestion at an appropriate timing. Also, if the user is in a hurry, the suggestion unit can make the suggestion quickly. Furthermore, if the user is excited, the suggestion unit can make the suggestion at a visually stimulating timing. In this way, by adjusting the timing of the suggestion, it is possible to make the suggestion at an appropriate timing according to the user's situation.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects dance videos. For example, the collection unit may collect dance videos from an online platform. The collection unit may also filter the dance videos based on a particular dance style or genre. Step 2: The analysis unit analyzes the dance video collected by the collection unit. For example, the analysis unit can analyze the dance movements and rhythm. The analysis unit can also apply analysis algorithms specialized for a particular dance style. Step 3: The generator generates dance choreography and behavior based on the results of the analysis by the analyzer. For example, the generator can generate choreography that matches a specific song. The generator can also generate behavior that matches the personalities of the group members. Step 4: The suggestion unit proposes the choreography or behavior generated by the generation unit. For example, the suggestion unit can propose the generated choreography or behavior to a dance choreographer.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 AI 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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 AI 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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 AI 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 dance videos, an analysis unit that analyzes the dance videos collected by the collection unit; a generation unit that generates dance choreography and behavior based on the results of the analysis by the analysis unit; a suggestion unit that suggests the choreography and behavior generated by the generation unit. A system characterized by:

2. The analysis unit Analyzing dance movements or rhythms 2. The system of claim 1.

3. The generation unit Generate choreography for a specific song 2. The system of claim 1.

4. The generation unit Generate behavior for members of a group 2. The system of claim 1.

5. The proposal unit Suggesting the generated choreography or behavior to a dance choreographer 2. The system of claim 1.

6. The collecting unit Collect dance videos from online platforms 2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and adjust the timing of collecting dance videos based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting dance videos, filter them based on a specific dance style or genre.

2. The system of claim 1.

9. The collecting unit When collecting dance videos, prioritize them based on the number of views or ratings of the videos.

2. The system of claim 1.

Images