system

Abstract

The system according to this embodiment aims to generate and execute creative robot dance motions based on music analysis data. [Solution] The system according to the embodiment comprises a receiving unit, a generating unit, and an execution unit. The receiving unit receives music analysis data. The generating unit generates robot dance motion based on the music analysis data received by the receiving unit. The execution unit executes the robot dance motion generated by the generating unit.

Description

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

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2022-180282 <00000​​​​​​​​​​​​​​​​​​​​ [Effects of the Invention]

[0007] The system according to this embodiment can generate and execute creative robot dance motions based on music analysis data. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The robot dance generation system according to an embodiment of the present invention is a system that utilizes multiple existing Web APIs for music analysis, inputs data analyzed for beat, rhythm, song atmosphere, and chorus into the robot dance generation system, and generates and executes creative robot dance motions. The robot dance generation system utilizes multiple existing Web APIs for music analysis. These Web APIs have the function of analyzing the beat, rhythm, song atmosphere, chorus, etc. of music. Next, this analyzed data is input into the robot dance generation system. The robot dance generation system generates creative robot dance motions based on this data. Finally, the generated robot dance motions are executed. The robot dances according to the motions generated by the robot dance generation system. This enables creative robot dances synchronized with music in real time. This system eliminates the need for pre-programming and enables robot dances synchronized with music in real time. For example, robot dances can be performed spontaneously to music at live performances and events. It can also be used for home entertainment. As a result, the robot dance generation system can generate and execute creative robot dance motions based on music analysis data.

[0029] The robot dance generation system according to this embodiment comprises a receiving unit, a generating unit, and an execution unit. The receiving unit receives music analysis data. The receiving unit can receive analysis data such as the beat, rhythm, atmosphere of the song, and chorus of the music. The receiving unit uses multiple existing Web APIs to obtain this data for music analysis. The generating unit generates robot dance motions based on the music analysis data received by the receiving unit. The generating unit generates creative robot dance motions based on the received data such as the beat, rhythm, atmosphere of the song, and chorus. The generating unit analyzes the music analysis data using a generation AI and generates the optimal dance motion. The generation AI generates dance motions based on the music analysis data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The execution unit executes the robot dance motion generated by the generation unit. The execution unit executes the generated dance motion in real time, for example, so that the robot dances in time with the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. The adjustment unit, for example, adjusts the generated motion in real time to optimize the robot's movements. As a result, the robot dance generation system according to the embodiment can generate and execute creative robot dance motions based on music analysis data.

[0030] The receiving unit receives music analysis data. For example, the receiving unit can receive analysis data such as the beat, rhythm, atmosphere of the song, and chorus. Specifically, the receiving unit acquires music data from music streaming services or music files and sends it to a Web API for analysis. The Web API analyzes the beat, rhythm, tempo, melody, harmony, and song structure of the music and sends this data back to the receiving unit. The receiving unit centrally manages this analysis data and provides it to the generation unit. Furthermore, the receiving unit also acquires metadata such as music genre, artist information, and lyrics, and can perform more detailed analysis based on this information. For example, by considering rhythm patterns and dance styles specific to a particular genre, it can provide basic data for generating more appropriate dance motions. Also, because the receiving unit can continuously receive music data in real time, it can handle live performances and music being streamed. This allows the receiving unit to quickly and accurately acquire music analysis data and provide it to the generation unit, thereby improving the overall system performance.

[0031] The generation unit generates robot dance motions based on music analysis data received by the receiver unit. For example, the generation unit generates creative robot dance motions based on received data such as beat, rhythm, song atmosphere, and chorus. Specifically, the generation unit uses a generation AI to analyze the music analysis data and generate the optimal dance motion. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to generate dance motions based on the music analysis data. The generation AI designs the robot's movements to match the music's beat and rhythm, incorporating movements that are particularly emphasized in the song's atmosphere and chorus. For example, in songs with fast beats, the robot's movements are made faster, and in songs with complex rhythms, complex steps and movements are incorporated. It can also generate smooth or powerful movements to match the song's atmosphere. The generation unit simulates these movements to verify their feasibility as actual robot movements. Furthermore, the generation unit can utilize past dance performance data and training data to generate more natural and engaging dance motions. This allows the generation unit to generate creative and optimal robot dance motions based on music analysis data and provide them to the execution unit.

[0032] The execution unit executes the robot dance motion generated by the generation unit. For example, the execution unit executes the generated dance motion in real time, allowing the robot to dance in time with the music. Specifically, the execution unit controls each joint and motor of the robot to accurately reproduce the generated motion. The execution unit is equipped with an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated motion in real time to optimize the robot's movements. The adjustment unit fine-tunes the movement of each joint and maintains balance so that the robot's movements are smooth and natural. It can also detect the surrounding environment with sensors to prevent the robot from bumping into obstacles and adjust its movements accordingly. Furthermore, the execution unit can adjust the dance motion in real time in response to changes in the tempo and rhythm of the music. For example, if the tempo of the song speeds up, it speeds up the robot's movements, and if the rhythm changes, it changes its steps accordingly. This allows the execution unit to accurately execute the generated dance motion, enabling the robot to perform an engaging dance performance in time with the music. Moreover, the execution unit can also handle cases where multiple robots dance in coordination, and by synchronizing the movements of each robot, it can achieve more complex and captivating performances.

[0033] The receiving unit can receive analysis data such as the beat, rhythm, atmosphere, and chorus of music. For example, the receiving unit can use a Web API to analyze the beat of music and receive beat analysis data. It can also use a Web API to analyze the rhythm and receive rhythm analysis data. Furthermore, it can use a Web API to analyze the atmosphere of music and receive atmosphere analysis data. For example, the receiving unit can use a Web API to analyze the chorus and receive chorus analysis data. In this way, the receiving unit can receive a variety of music analysis data. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can use AI to select the optimal Web API to acquire music analysis data and receive the data.

[0034] The generation unit can generate creative robot dance motions based on the received analysis data. For example, the generation unit can generate dance motions that match the beat based on the received beat analysis data. The generation unit can also generate dance motions that match the rhythm based on rhythm analysis data. Furthermore, the generation unit can generate dance motions that match the atmosphere of a song based on atmosphere analysis data. For example, the generation unit can generate dance motions that match the chorus based on chorus analysis data. In this way, the generation unit can generate creative robot dance motions based on the analysis data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates dance motions based on music analysis data, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes music analysis data as input and outputs creative robot dance motions.

[0035] The execution unit can execute the generated robot dance motion in real time. For example, the execution unit executes the generated dance motion in real time, causing the robot to dance in time with the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated motion in real time to optimize the robot's movements. This allows the execution unit to execute the generated robot dance motion in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can use AI to adjust the generated dance motion in real time to optimize the robot's movements.

[0036] The generation unit includes a specification unit that identifies the type of analysis data. The generation unit includes a specification unit that identifies the type of analysis data. The specification unit, for example, identifies the type of music analysis data received and generates the optimal dance motion based on it. The specification unit uses a generation AI to identify the type of music analysis data. The generation AI, for example, takes music analysis data as input and identifies the type of data. This allows the generation unit to identify the type of analysis data. Some or all of the above processing in the specification unit is performed using the generation AI. The generation AI identifies the type of data based on the music analysis data and generates the optimal dance motion.

[0037] The execution unit includes an adjustment unit that adjusts the generated motion in real time. The execution unit includes an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated dance motion in real time to optimize the robot's movement. The adjustment unit adjusts the generated motion in real time using AI. For example, the AI ​​takes the generated dance motion as input and outputs the motion adjusted in real time. This allows the execution unit to adjust the generated motion in real time. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can adjust the generated dance motion in real time using AI to optimize the robot's movement.

[0038] The receiving unit selects the most suitable Web API based on the music genre and receives the analysis data. For example, in the case of classical music, the receiving unit uses a specific Web API to analyze the types of instruments and playing styles. In the case of rock music, the receiving unit can use a different Web API to analyze guitar riffs and drum beats. In the case of jazz music, the receiving unit can use yet another Web API to analyze improvisation patterns. This allows the receiving unit to select the most suitable Web API according to the music genre and receive the analysis data. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can use AI to determine the music genre and select the most suitable Web API.

[0039] The receiving unit dynamically changes the type of data it receives based on the tempo and key of the music. For example, in the case of fast-tempo music, the receiving unit prioritizes receiving beat analysis data. In the case of slow-tempo music, the receiving unit can prioritize receiving mood and melody analysis data. The receiving unit can also change the type of analysis data each time the key changes, and receive the appropriate data. This allows the receiving unit to dynamically change the type of data it receives according to the tempo and key of the music. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the tempo and key of the music using AI and dynamically change the type of data it receives.

[0040] The receiving unit, upon receiving music analysis data, prioritizes receiving data that is highly relevant based on the user's past musical preferences. For example, the receiving unit may prioritize receiving relevant analysis data based on the genres of music the user has previously enjoyed listening to. The receiving unit may also prioritize receiving relevant analysis data based on songs by artists the user has previously enjoyed listening to. Furthermore, the receiving unit may prioritize receiving relevant analysis data based on playlists the user has previously enjoyed listening to. This allows the receiving unit to prioritize receiving data that is highly relevant based on the user's past musical preferences. Some or all of the above processing in the receiving unit may be performed using AI, or without AI. For example, the receiving unit may analyze the user's past musical preferences using AI and prioritize receiving highly relevant data.

[0041] The receiving unit receives region-specific music analysis data based on the user's geographical location information when receiving music analysis data. For example, if the user is in a specific region, the receiving unit prioritizes receiving analysis data on the traditional music of that region. If the user is traveling, the receiving unit can prioritize receiving music analysis data for the region they are visiting. If the user is in a specific city, the receiving unit can prioritize receiving analysis data related to the music scene of that city. This allows the receiving unit to receive region-specific music analysis data based on the user's geographical location information. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the user's geographical location information using AI and receive region-specific music analysis data.

[0042] The generation unit generates different dance styles based on the beat and rhythm of the music. For example, if the music has a strong beat, the generation unit can generate hip-hop style dance motions. If the music has a complex rhythm, the generation unit can generate contemporary dance style motions. If the music has a simple beat and rhythm, the generation unit can also generate classical ballet style motions. This allows the generation unit to generate different dance styles depending on the beat and rhythm of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal dance style based on the beat and rhythm of the music.

[0043] The generation unit customizes the robot's facial expressions and movements according to the mood of the music. For example, if the music has a cheerful mood, the generation unit can make the robot's facial expression smile and its movements lively. If the music has a calm mood, the generation unit can make the robot's facial expression gentle and its movements slow. If the music has a sad mood, the generation unit can make the robot's facial expression sad and its movements slow. In this way, the generation unit can customize the robot's facial expressions and movements according to the mood of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal facial expressions and movements for the robot based on the mood of the music.

[0044] The generation unit generates motions in which multiple robots dance in coordination based on music analysis data. The generation unit generates motions in which multiple robots dance in coordination based on music analysis data. For example, the generation unit generates dance motions in which multiple robots move in sync to the beat. The generation unit can generate coordinated dance motions in which different robots perform different movements based on the rhythm. The generation unit can also generate story-driven dance motions in which robots work together according to the atmosphere of the song. In this way, the generation unit can generate motions in which multiple robots dance in coordination. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal motion for multiple robots to dance in coordination based on music analysis data.

[0045] The generation unit dynamically adjusts the speed of the robot's movements according to the tempo of the music. For example, if the music has a fast tempo, the generation unit will increase the speed of the robot's movements. If the music has a slow tempo, the generation unit can decrease the speed of the robot's movements. The generation unit can also adjust the speed of the robot's movements each time the tempo changes. In this way, the generation unit can dynamically adjust the speed of the robot's movements according to the tempo of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI optimally adjusts the speed of the robot's movements based on the tempo of the music.

[0046] The execution unit provides real-time motion data feedback to improve the robot's motion accuracy. For example, the execution unit can provide real-time feedback of data from sensors during robot operation to improve motion accuracy. The execution unit can also provide real-time feedback of video data from cameras during robot operation to improve motion accuracy. Furthermore, the execution unit can incorporate real-time user feedback during robot operation to improve motion accuracy. This allows the execution unit to improve the robot's motion accuracy. Some or all of the above-described processes in the execution unit may be performed using AI or without AI. For example, the execution unit can analyze data from sensors and cameras using AI and provide real-time feedback.

[0047] The execution unit adds a function to detect obstacles during robot operation and automatically avoid them. The execution unit adds a function to detect obstacles during robot operation and automatically avoid them. For example, the execution unit can detect obstacles with sensors during robot operation and automatically avoid them. The execution unit can detect obstacles with cameras during robot operation and automatically avoid them. The execution unit can also avoid obstacles based on user instructions during robot operation. This allows the execution unit to avoid obstacles during robot operation. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze data from sensors and cameras using AI to detect and avoid obstacles.

[0048] The execution unit adjusts the robot's movements in real time in response to changes in the music while the robot is operating. For example, if the tempo of the music speeds up, the execution unit can speed up the robot's movements. If the tempo of the music slows down, the execution unit can slow down the robot's movements. If the mood of the music changes, the execution unit can adjust the robot's movements accordingly. In this way, the execution unit can adjust the robot's movements in real time in response to changes in the music. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze changes in the music using AI and adjust the robot's movements in real time.

[0049] The execution unit performs coordinated actions in conjunction with other robots and devices while the robot is operating. For example, the execution unit can enable multiple robots to operate in sync and perform a coordinated dance performance. The execution unit can also enable the robot to operate in time with music while coordinating with lighting and sound devices to enhance the performance. Furthermore, the execution unit can enable the robot to cooperate with other smart devices and perform coordinated actions based on user instructions. This allows the execution unit to perform coordinated actions in conjunction with other robots and devices. Some or all of the above-described processes in the execution unit may be performed using AI or not. For example, the execution unit can control its coordination with other robots and devices using AI to achieve coordinated action.

[0050] The identification unit dynamically changes the type of analysis data it identifies based on the genre of music. For example, in the case of classical music, the identification unit identifies analysis data on instrument types and playing styles. In the case of rock music, the identification unit can identify analysis data on guitar riffs and drum beats. In the case of jazz music, the identification unit can also identify analysis data on improvisation patterns. This allows the identification unit to dynamically change the type of analysis data according to the genre of music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the genre of music using AI and dynamically change the type of analysis data it identifies.

[0051] The identification unit improves the accuracy of the analysis data it identifies based on the tempo and key of the music. The identification unit improves the accuracy of the analysis data it identifies based on the tempo and key of the music. For example, in the case of fast-tempo music, the identification unit can improve the accuracy of the beat analysis data. In the case of slow-tempo music, the identification unit can improve the accuracy of the mood and melody analysis data. The identification unit can also adjust the accuracy of the analysis data each time the key changes. In this way, the identification unit can improve the accuracy of the analysis data according to the tempo and key of the music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the tempo and key of the music using AI to improve the accuracy of the analysis data it identifies.

[0052] The identification unit dynamically changes the type of data to identify based on the music analysis data. For example, if the music has a prominent beat, the identification unit may prioritize identifying beat analysis data. If the music has a complex rhythm, the identification unit may prioritize identifying rhythm analysis data. If the music's atmosphere is important, the identification unit may also prioritize identifying atmosphere analysis data. This allows the identification unit to dynamically change the type of data to identify based on the music analysis data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit may analyze the music analysis data using AI and dynamically change the type of data to identify.

[0053] The identification unit customizes the types of analysis data to identify according to the mood of the music. For example, in the case of music with a cheerful mood, the identification unit identifies beat and rhythm analysis data. In the case of music with a calm mood, the identification unit can identify mood analysis data for the song. In the case of music with a sad mood, the identification unit can also identify chorus analysis data. In this way, the identification unit can customize the types of analysis data according to the mood of the music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the mood of the music using AI and customize the types of analysis data to identify.

[0054] The adjustment unit adjusts the robot's movements in real time based on the beat and rhythm of the music while it is operating. For example, if the music has a strong beat, the adjustment unit adjusts the robot's movements to match the beat. If the music has a complex rhythm, the adjustment unit can adjust the robot's movements to match the rhythm. The adjustment unit can also adjust the robot's movements whenever the tempo of the song changes. This allows the adjustment unit to adjust the robot's movements in real time according to the beat and rhythm of the music. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can analyze the beat and rhythm of the music using AI and adjust the robot's movements in real time.

[0055] The adjustment unit automatically adjusts the robot's movements in response to changes in the environment while it is operating. For example, the adjustment unit adjusts the robot's movements in response to changes in lighting. The adjustment unit can also adjust the robot's movements in response to changes in sound. The adjustment unit can also adjust the robot's movements in response to the movements of people in the surrounding environment. This allows the adjustment unit to automatically adjust the robot's movements in response to changes in the environment. Some or all of the above-described processes in the adjustment unit may be performed using AI, or they may not. For example, the adjustment unit can analyze environmental changes using AI and automatically adjust the robot's movements.

[0056] The adjustment unit adjusts the robot's movements in coordination with other robots and devices while the robot is operating. The adjustment unit adjusts the robot's movements in coordination with other robots and devices while the robot is operating. For example, the adjustment unit adjusts the movements so that multiple robots operate in sync. The adjustment unit can adjust the robot's movements in coordination with lighting and sound devices while the robot is operating in time with music. The adjustment unit can also adjust the robot's movements in coordination with other smart devices based on user instructions. This allows the adjustment unit to adjust movements in coordination with other robots and devices. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can control coordination with other robots and devices using AI to adjust movements.

[0057] The adjustment unit adjusts the robot's movements in real time in response to changes in the music while the robot is operating. For example, if the tempo of the music speeds up, the adjustment unit can speed up the robot's movements. If the tempo of the music slows down, the adjustment unit can slow down the robot's movements. If the mood of the music changes, the adjustment unit can adjust the robot's movements accordingly. In this way, the adjustment unit can adjust the robot's movements in real time in response to changes in the music. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can analyze changes in the music using AI and adjust the robot's movements in real time.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The generation unit can add effects to the robot's movements based on music analysis data. For example, it can add light effects to the robot's movements in time with the beat. It can also add color changes to the robot's movements in time with the rhythm. Furthermore, it can add smoke or fog effects to the robot's movements to match the mood of the song. In this way, the generation unit can add effects to the robot's movements based on music analysis data, enhancing the visual presentation. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal effects based on the music analysis data.

[0060] The receiving unit can prioritize receiving highly relevant data based on the user's past dance performance data when receiving music analysis data. For example, it can prioritize receiving relevant analysis data based on the dance styles the user has preferred to dance in the past. The receiving unit can also prioritize receiving relevant analysis data based on the beats and rhythms of songs the user has preferred to dance to in the past. The receiving unit can also prioritize receiving relevant analysis data based on the atmosphere of performances the user has preferred to dance in the past. In this way, the receiving unit can prioritize receiving highly relevant data based on the user's past dance performance data. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the user's past dance performance data using AI and prioritize receiving highly relevant data.

[0061] The generation unit can add interactive elements to the robot's movements based on music analysis data. For example, it can add a movement where the robot waves to the user in time with the beat. It can also add a movement where the robot claps to the user in time with the rhythm. Furthermore, it can add a movement where the robot bows to the user in accordance with the mood of the song. In this way, the generation unit can add interactive elements to the robot's movements based on music analysis data, thereby increasing engagement with the user. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal interactive movements based on the music analysis data.

[0062] The execution unit can analyze the user's movements in real time while the robot is operating and synchronize the robot's movements with the user's movements. For example, if the user raises their hand, the robot will also raise its hand. Similarly, if the user takes a step, the robot can also take a step. Furthermore, if the user rotates, the robot can also rotate. In this way, the execution unit can execute robot movements that are synchronized with the user's movements in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze the user's movements using AI and synchronize the robot's movements in real time.

[0063] The generation unit can add storytelling elements to the robot's movements based on music analysis data. For example, it can add movements where the robot acts out a part of a story in time with the beat. It can also add movements where the robot acts out characters from a story in time with the rhythm. Furthermore, it can add movements where the robot recreates scenes from a story in accordance with the mood of the music. In this way, the generation unit can add storytelling elements to the robot's movements based on music analysis data, thereby enhancing the visual presentation. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal storytelling elements based on the music analysis data.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The receiving unit receives music analysis data. The receiving unit can receive analysis data such as the beat, rhythm, mood of the song, and chorus. The receiving unit uses multiple existing Web APIs to obtain this data for music analysis. Step 2: The generation unit generates robot dance motions based on the music analysis data received by the receiving unit. The generation unit generates creative robot dance motions based on data such as the received beat, rhythm, song atmosphere, and chorus. The generation unit uses a generation AI to analyze the music analysis data and generate the optimal dance motion. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to generate dance motions based on the music analysis data. Step 3: The execution unit executes the robot dance motion generated by the generation unit. The execution unit executes the generated dance motion in real time, for example, so that the robot dances to the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. The adjustment unit adjusts the generated motion in real time, for example, to optimize the robot's movements.

[0066] (Example of form 2) The robot dance generation system according to an embodiment of the present invention is a system that utilizes multiple existing Web APIs for music analysis, inputs data analyzed for beat, rhythm, song atmosphere, and chorus into the robot dance generation system, and generates and executes creative robot dance motions. The robot dance generation system utilizes multiple existing Web APIs for music analysis. These Web APIs have the function of analyzing the beat, rhythm, song atmosphere, chorus, etc. of music. Next, this analyzed data is input into the robot dance generation system. The robot dance generation system generates creative robot dance motions based on this data. Finally, the generated robot dance motions are executed. The robot dances according to the motions generated by the robot dance generation system. This enables creative robot dances synchronized with music in real time. This system eliminates the need for pre-programming and enables robot dances synchronized with music in real time. For example, robot dances can be performed spontaneously to music at live performances and events. It can also be used for home entertainment. As a result, the robot dance generation system can generate and execute creative robot dance motions based on music analysis data.

[0067] The robot dance generation system according to this embodiment comprises a receiving unit, a generating unit, and an execution unit. The receiving unit receives music analysis data. The receiving unit can receive analysis data such as the beat, rhythm, atmosphere of the song, and chorus of the music. The receiving unit uses multiple existing Web APIs to obtain this data for music analysis. The generating unit generates robot dance motions based on the music analysis data received by the receiving unit. The generating unit generates creative robot dance motions based on the received data such as the beat, rhythm, atmosphere of the song, and chorus. The generating unit analyzes the music analysis data using a generation AI and generates the optimal dance motion. The generation AI generates dance motions based on the music analysis data using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The execution unit executes the robot dance motion generated by the generation unit. The execution unit executes the generated dance motion in real time, for example, so that the robot dances in time with the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. The adjustment unit, for example, adjusts the generated motion in real time to optimize the robot's movements. As a result, the robot dance generation system according to the embodiment can generate and execute creative robot dance motions based on music analysis data.

[0068] The receiving unit receives music analysis data. For example, the receiving unit can receive analysis data such as the beat, rhythm, atmosphere of the song, and chorus. Specifically, the receiving unit acquires music data from music streaming services or music files and sends it to a Web API for analysis. The Web API analyzes the beat, rhythm, tempo, melody, harmony, and song structure of the music and sends this data back to the receiving unit. The receiving unit centrally manages this analysis data and provides it to the generation unit. Furthermore, the receiving unit also acquires metadata such as music genre, artist information, and lyrics, and can perform more detailed analysis based on this information. For example, by considering rhythm patterns and dance styles specific to a particular genre, it can provide basic data for generating more appropriate dance motions. Also, because the receiving unit can continuously receive music data in real time, it can handle live performances and music being streamed. This allows the receiving unit to quickly and accurately acquire music analysis data and provide it to the generation unit, thereby improving the overall system performance.

[0069] The generation unit generates robot dance motions based on music analysis data received by the receiver unit. For example, the generation unit generates creative robot dance motions based on received data such as beat, rhythm, song atmosphere, and chorus. Specifically, the generation unit uses a generation AI to analyze the music analysis data and generate the optimal dance motion. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to generate dance motions based on the music analysis data. The generation AI designs the robot's movements to match the music's beat and rhythm, incorporating movements that are particularly emphasized in the song's atmosphere and chorus. For example, in songs with fast beats, the robot's movements are made faster, and in songs with complex rhythms, complex steps and movements are incorporated. It can also generate smooth or powerful movements to match the song's atmosphere. The generation unit simulates these movements to verify their feasibility as actual robot movements. Furthermore, the generation unit can utilize past dance performance data and training data to generate more natural and engaging dance motions. This allows the generation unit to generate creative and optimal robot dance motions based on music analysis data and provide them to the execution unit.

[0070] The execution unit executes the robot dance motion generated by the generation unit. For example, the execution unit executes the generated dance motion in real time, allowing the robot to dance in time with the music. Specifically, the execution unit controls each joint and motor of the robot to accurately reproduce the generated motion. The execution unit is equipped with an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated motion in real time to optimize the robot's movements. The adjustment unit fine-tunes the movement of each joint and maintains balance so that the robot's movements are smooth and natural. It can also detect the surrounding environment with sensors to prevent the robot from bumping into obstacles and adjust its movements accordingly. Furthermore, the execution unit can adjust the dance motion in real time in response to changes in the tempo and rhythm of the music. For example, if the tempo of the song speeds up, it speeds up the robot's movements, and if the rhythm changes, it changes its steps accordingly. This allows the execution unit to accurately execute the generated dance motion, enabling the robot to perform an engaging dance performance in time with the music. Moreover, the execution unit can also handle cases where multiple robots dance in coordination, and by synchronizing the movements of each robot, it can achieve more complex and captivating performances.

[0071] The receiving unit can receive analysis data such as the beat, rhythm, atmosphere, and chorus of music. For example, the receiving unit can use a Web API to analyze the beat of music and receive beat analysis data. It can also use a Web API to analyze the rhythm and receive rhythm analysis data. Furthermore, it can use a Web API to analyze the atmosphere of music and receive atmosphere analysis data. For example, the receiving unit can use a Web API to analyze the chorus and receive chorus analysis data. In this way, the receiving unit can receive a variety of music analysis data. Some or all of the above processing in the receiving unit may be performed using AI, for example, or without AI. For example, the receiving unit can use AI to select the optimal Web API to acquire music analysis data and receive the data.

[0072] The generation unit can generate creative robot dance motions based on the received analysis data. For example, the generation unit can generate dance motions that match the beat based on the received beat analysis data. The generation unit can also generate dance motions that match the rhythm based on rhythm analysis data. Furthermore, the generation unit can generate dance motions that match the atmosphere of a song based on atmosphere analysis data. For example, the generation unit can generate dance motions that match the chorus based on chorus analysis data. In this way, the generation unit can generate creative robot dance motions based on the analysis data. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates dance motions based on music analysis data, for example, using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation AI takes music analysis data as input and outputs creative robot dance motions.

[0073] The execution unit can execute the generated robot dance motion in real time. For example, the execution unit executes the generated dance motion in real time, causing the robot to dance in time with the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated motion in real time to optimize the robot's movements. This allows the execution unit to execute the generated robot dance motion in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can use AI to adjust the generated dance motion in real time to optimize the robot's movements.

[0074] The generation unit includes a specification unit that identifies the type of analysis data. The generation unit includes a specification unit that identifies the type of analysis data. The specification unit, for example, identifies the type of music analysis data received and generates the optimal dance motion based on it. The specification unit uses a generation AI to identify the type of music analysis data. The generation AI, for example, takes music analysis data as input and identifies the type of data. This allows the generation unit to identify the type of analysis data. Some or all of the above processing in the specification unit is performed using the generation AI. The generation AI identifies the type of data based on the music analysis data and generates the optimal dance motion.

[0075] The execution unit includes an adjustment unit that adjusts the generated motion in real time. The execution unit includes an adjustment unit that adjusts the generated motion in real time. For example, the adjustment unit adjusts the generated dance motion in real time to optimize the robot's movement. The adjustment unit adjusts the generated motion in real time using AI. For example, the AI ​​takes the generated dance motion as input and outputs the motion adjusted in real time. This allows the execution unit to adjust the generated motion in real time. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can adjust the generated dance motion in real time using AI to optimize the robot's movement.

[0076] The receiving unit estimates the user's emotions and adjusts the timing of receiving music analysis data based on the estimated emotions. The receiving unit estimates the user's emotions and adjusts the timing of receiving music analysis data based on the estimated emotions. For example, if the user is relaxed, the receiving unit may delay the reception of music analysis data to match the slow tempo of the music. If the user is excited, the receiving unit may quickly receive music analysis data to match the fast tempo of the music. If the user is stressed, the receiving unit may interrupt the reception of music analysis data and prioritize the reception of relaxing music. In this way, the receiving unit can adjust the timing of receiving music analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the receiving unit may be performed using AI or not using AI. For example, the receiving unit can use AI to perform facial recognition and voice analysis to estimate the user's emotions and acquire emotional data.

[0077] The receiving unit selects the most suitable Web API based on the music genre and receives the analysis data. For example, in the case of classical music, the receiving unit uses a specific Web API to analyze the types of instruments and playing styles. In the case of rock music, the receiving unit can use a different Web API to analyze guitar riffs and drum beats. In the case of jazz music, the receiving unit can use yet another Web API to analyze improvisation patterns. This allows the receiving unit to select the most suitable Web API according to the music genre and receive the analysis data. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can use AI to determine the music genre and select the most suitable Web API.

[0078] The receiving unit dynamically changes the type of data it receives based on the tempo and key of the music. For example, in the case of fast-tempo music, the receiving unit prioritizes receiving beat analysis data. In the case of slow-tempo music, the receiving unit can prioritize receiving mood and melody analysis data. The receiving unit can also change the type of analysis data each time the key changes, and receive the appropriate data. This allows the receiving unit to dynamically change the type of data it receives according to the tempo and key of the music. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the tempo and key of the music using AI and dynamically change the type of data it receives.

[0079] The receiving unit estimates the user's emotions and determines the priority of the music analysis data to receive based on the estimated emotions. For example, if the user is relaxed, the receiving unit may prioritize receiving mood analysis data of the song. If the user is excited, the receiving unit may prioritize receiving beat and rhythm analysis data. If the user is stressed, the receiving unit may prioritize receiving chorus analysis data. In this way, the receiving unit can determine the priority of the music analysis data to receive according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the receiving unit may be performed using AI or not using AI. For example, the receiving unit may use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0080] The receiving unit, upon receiving music analysis data, prioritizes receiving data that is highly relevant based on the user's past musical preferences. For example, the receiving unit may prioritize receiving relevant analysis data based on the genres of music the user has previously enjoyed listening to. The receiving unit may also prioritize receiving relevant analysis data based on songs by artists the user has previously enjoyed listening to. Furthermore, the receiving unit may prioritize receiving relevant analysis data based on playlists the user has previously enjoyed listening to. This allows the receiving unit to prioritize receiving data that is highly relevant based on the user's past musical preferences. Some or all of the above processing in the receiving unit may be performed using AI, or without AI. For example, the receiving unit may analyze the user's past musical preferences using AI and prioritize receiving highly relevant data.

[0081] The receiving unit receives region-specific music analysis data based on the user's geographical location information when receiving music analysis data. For example, if the user is in a specific region, the receiving unit prioritizes receiving analysis data on the traditional music of that region. If the user is traveling, the receiving unit can prioritize receiving music analysis data for the region they are visiting. If the user is in a specific city, the receiving unit can prioritize receiving analysis data related to the music scene of that city. This allows the receiving unit to receive region-specific music analysis data based on the user's geographical location information. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the user's geographical location information using AI and receive region-specific music analysis data.

[0082] The generation unit estimates the user's emotions and adjusts the method of generating robot dance motions based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a robot dance motion with gentle movements. If the user is excited, the generation unit can generate a robot dance motion with intense movements. If the user is stressed, the generation unit can also generate a robot dance motion incorporating calming movements. This allows the generation unit to adjust the method of generating robot dance motions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. The generation AI generates the optimal robot dance motion based on the user's emotions.

[0083] The generation unit generates different dance styles based on the beat and rhythm of the music. For example, if the music has a strong beat, the generation unit can generate hip-hop style dance motions. If the music has a complex rhythm, the generation unit can generate contemporary dance style motions. If the music has a simple beat and rhythm, the generation unit can also generate classical ballet style motions. This allows the generation unit to generate different dance styles depending on the beat and rhythm of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal dance style based on the beat and rhythm of the music.

[0084] The generation unit customizes the robot's facial expressions and movements according to the mood of the music. For example, if the music has a cheerful mood, the generation unit can make the robot's facial expression smile and its movements lively. If the music has a calm mood, the generation unit can make the robot's facial expression gentle and its movements slow. If the music has a sad mood, the generation unit can make the robot's facial expression sad and its movements slow. In this way, the generation unit can customize the robot's facial expressions and movements according to the mood of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal facial expressions and movements for the robot based on the mood of the music.

[0085] The generation unit estimates the user's emotions and adjusts the complexity of the robot dance motion it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a simple robot dance motion. If the user is excited, the generation unit can generate a more complex robot dance motion. If the user is stressed, the generation unit can also generate a simple robot dance motion incorporating calming movements. This allows the generation unit to adjust the complexity of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of generation AI include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using a generation AI. The generation AI generates the optimal robot dance motion based on the user's emotions.

[0086] The generation unit generates motions in which multiple robots dance in coordination based on music analysis data. The generation unit generates motions in which multiple robots dance in coordination based on music analysis data. For example, the generation unit generates dance motions in which multiple robots move in sync to the beat. The generation unit can generate coordinated dance motions in which different robots perform different movements based on the rhythm. The generation unit can also generate story-driven dance motions in which robots work together according to the atmosphere of the song. In this way, the generation unit can generate motions in which multiple robots dance in coordination. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal motion for multiple robots to dance in coordination based on music analysis data.

[0087] The generation unit dynamically adjusts the speed of the robot's movements according to the tempo of the music. For example, if the music has a fast tempo, the generation unit will increase the speed of the robot's movements. If the music has a slow tempo, the generation unit can decrease the speed of the robot's movements. The generation unit can also adjust the speed of the robot's movements each time the tempo changes. In this way, the generation unit can dynamically adjust the speed of the robot's movements according to the tempo of the music. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI optimally adjusts the speed of the robot's movements based on the tempo of the music.

[0088] The execution unit estimates the user's emotions and adjusts the timing of the robot dance motion based on the estimated emotions. The execution unit estimates the user's emotions and adjusts the timing of the robot dance motion based on the estimated emotions. For example, if the user is relaxed, the execution unit can perform the robot dance motion at a relaxed pace. If the user is excited, the execution unit can perform the robot dance motion at a rapid pace. If the user is stressed, the execution unit can also perform the robot dance motion at a calming pace. In this way, the execution unit can adjust the timing of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0089] The execution unit provides real-time motion data feedback to improve the robot's motion accuracy. For example, the execution unit can provide real-time feedback of data from sensors during robot operation to improve motion accuracy. The execution unit can also provide real-time feedback of video data from cameras during robot operation to improve motion accuracy. Furthermore, the execution unit can incorporate real-time user feedback during robot operation to improve motion accuracy. This allows the execution unit to improve the robot's motion accuracy. Some or all of the above-described processes in the execution unit may be performed using AI or without AI. For example, the execution unit can analyze data from sensors and cameras using AI and provide real-time feedback.

[0090] The execution unit adds a function to detect obstacles during robot operation and automatically avoid them. The execution unit adds a function to detect obstacles during robot operation and automatically avoid them. For example, the execution unit can detect obstacles with sensors during robot operation and automatically avoid them. The execution unit can detect obstacles with cameras during robot operation and automatically avoid them. The execution unit can also avoid obstacles based on user instructions during robot operation. This allows the execution unit to avoid obstacles during robot operation. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze data from sensors and cameras using AI to detect and avoid obstacles.

[0091] The execution unit estimates the user's emotions and adjusts the execution order of the robot dance motions based on the estimated user emotions. The execution unit estimates the user's emotions and adjusts the execution order of the robot dance motions based on the estimated user emotions. For example, if the user is relaxed, the execution unit may execute slow movements first. If the user is excited, the execution unit may execute fast movements first. If the user is stressed, the execution unit may also execute motions that incorporate soothing movements first. In this way, the execution unit can adjust the execution order of the robot dance motions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit may use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0092] The execution unit adjusts the robot's movements in real time in response to changes in the music while the robot is operating. For example, if the tempo of the music speeds up, the execution unit can speed up the robot's movements. If the tempo of the music slows down, the execution unit can slow down the robot's movements. If the mood of the music changes, the execution unit can adjust the robot's movements accordingly. In this way, the execution unit can adjust the robot's movements in real time in response to changes in the music. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze changes in the music using AI and adjust the robot's movements in real time.

[0093] The execution unit performs coordinated actions in conjunction with other robots and devices while the robot is operating. For example, the execution unit can enable multiple robots to operate in sync and perform a coordinated dance performance. The execution unit can also enable the robot to operate in time with music while coordinating with lighting and sound devices to enhance the performance. Furthermore, the execution unit can enable the robot to cooperate with other smart devices and perform coordinated actions based on user instructions. This allows the execution unit to perform coordinated actions in conjunction with other robots and devices. Some or all of the above-described processes in the execution unit may be performed using AI or not. For example, the execution unit can control its coordination with other robots and devices using AI to achieve coordinated action.

[0094] The identification unit estimates the user's emotions and adjusts the method for identifying the type of analysis data based on the estimated user emotions. For example, if the user is relaxed, the identification unit may prioritize identifying mood analysis data for songs. If the user is excited, the identification unit may prioritize identifying beat and rhythm analysis data. If the user is stressed, the identification unit may also prioritize identifying chorus analysis data. This allows the identification unit to adjust the method for identifying the type of analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit is performed using generative AI. The generative AI identifies the optimal type of analysis data based on the user's emotions.

[0095] The identification unit dynamically changes the type of analysis data it identifies based on the genre of music. For example, in the case of classical music, the identification unit identifies analysis data on instrument types and playing styles. In the case of rock music, the identification unit can identify analysis data on guitar riffs and drum beats. In the case of jazz music, the identification unit can also identify analysis data on improvisation patterns. This allows the identification unit to dynamically change the type of analysis data according to the genre of music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the genre of music using AI and dynamically change the type of analysis data it identifies.

[0096] The identification unit improves the accuracy of the analysis data it identifies based on the tempo and key of the music. The identification unit improves the accuracy of the analysis data it identifies based on the tempo and key of the music. For example, in the case of fast-tempo music, the identification unit can improve the accuracy of the beat analysis data. In the case of slow-tempo music, the identification unit can improve the accuracy of the mood and melody analysis data. The identification unit can also adjust the accuracy of the analysis data each time the key changes. In this way, the identification unit can improve the accuracy of the analysis data according to the tempo and key of the music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the tempo and key of the music using AI to improve the accuracy of the analysis data it identifies.

[0097] The identification unit estimates the user's emotions and determines the priority of the analysis data to identify based on the estimated user emotions. For example, if the user is relaxed, the identification unit may prioritize identifying music atmosphere analysis data. If the user is excited, the identification unit may prioritize identifying beat and rhythm analysis data. If the user is stressed, the identification unit may also prioritize identifying chorus analysis data. In this way, the identification unit can determine the priority of analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the identification unit is performed using generative AI. The generative AI determines the optimal priority of analysis data based on the user's emotions.

[0098] The identification unit dynamically changes the type of data to identify based on the music analysis data. For example, if the music has a prominent beat, the identification unit may prioritize identifying beat analysis data. If the music has a complex rhythm, the identification unit may prioritize identifying rhythm analysis data. If the music's atmosphere is important, the identification unit may also prioritize identifying atmosphere analysis data. This allows the identification unit to dynamically change the type of data to identify based on the music analysis data. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit may analyze the music analysis data using AI and dynamically change the type of data to identify.

[0099] The identification unit customizes the types of analysis data to identify according to the mood of the music. For example, in the case of music with a cheerful mood, the identification unit identifies beat and rhythm analysis data. In the case of music with a calm mood, the identification unit can identify mood analysis data for the song. In the case of music with a sad mood, the identification unit can also identify chorus analysis data. In this way, the identification unit can customize the types of analysis data according to the mood of the music. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can analyze the mood of the music using AI and customize the types of analysis data to identify.

[0100] The adjustment unit estimates the user's emotions and modifies the robot dance motion adjustment method based on the estimated user emotions. The adjustment unit estimates the user's emotions and modifies the robot dance motion adjustment method based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit may adjust to slower movements. If the user is excited, the adjustment unit may adjust to more vigorous movements. If the user is stressed, the adjustment unit may also incorporate calming movements. In this way, the adjustment unit can change the robot dance motion adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the adjustment unit is performed using the generative AI. The generative AI determines the optimal robot dance motion adjustment method based on the user's emotions.

[0101] The adjustment unit adjusts the robot's movements in real time based on the beat and rhythm of the music while it is operating. For example, if the music has a strong beat, the adjustment unit adjusts the robot's movements to match the beat. If the music has a complex rhythm, the adjustment unit can adjust the robot's movements to match the rhythm. The adjustment unit can also adjust the robot's movements whenever the tempo of the song changes. This allows the adjustment unit to adjust the robot's movements in real time according to the beat and rhythm of the music. Some or all of the above processing in the adjustment unit may be performed using AI, or not. For example, the adjustment unit can analyze the beat and rhythm of the music using AI and adjust the robot's movements in real time.

[0102] The adjustment unit automatically adjusts the robot's movements in response to changes in the environment while it is operating. For example, the adjustment unit adjusts the robot's movements in response to changes in lighting. The adjustment unit can also adjust the robot's movements in response to changes in sound. The adjustment unit can also adjust the robot's movements in response to the movements of people in the surrounding environment. This allows the adjustment unit to automatically adjust the robot's movements in response to changes in the environment. Some or all of the above-described processes in the adjustment unit may be performed using AI, or they may not. For example, the adjustment unit can analyze environmental changes using AI and automatically adjust the robot's movements.

[0103] The adjustment unit estimates the user's emotions and determines the adjustment frequency of the robot dance motion based on the estimated user emotions. The adjustment unit estimates the user's emotions and determines the adjustment frequency of the robot dance motion based on the estimated user emotions. For example, if the user is relaxed, the adjustment unit may lower the adjustment frequency. If the user is excited, the adjustment unit may increase the adjustment frequency. If the user is stressed, the adjustment unit may set the adjustment frequency to a moderate level. In this way, the adjustment unit can determine the adjustment frequency of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment unit is performed using the generative AI. The generative AI determines the optimal adjustment frequency of the robot dance motion based on the user's emotions.

[0104] The adjustment unit adjusts the robot's movements in coordination with other robots and devices while the robot is operating. The adjustment unit adjusts the robot's movements in coordination with other robots and devices while the robot is operating. For example, the adjustment unit adjusts the movements so that multiple robots operate in sync. The adjustment unit can adjust the robot's movements in coordination with lighting and sound devices while the robot is operating in time with music. The adjustment unit can also adjust the robot's movements in coordination with other smart devices based on user instructions. This allows the adjustment unit to adjust movements in coordination with other robots and devices. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can control coordination with other robots and devices using AI to adjust movements.

[0105] The adjustment unit adjusts the robot's movements in real time in response to changes in the music while the robot is operating. For example, if the tempo of the music speeds up, the adjustment unit can speed up the robot's movements. If the tempo of the music slows down, the adjustment unit can slow down the robot's movements. If the mood of the music changes, the adjustment unit can adjust the robot's movements accordingly. In this way, the adjustment unit can adjust the robot's movements in real time in response to changes in the music. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can analyze changes in the music using AI and adjust the robot's movements in real time.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The generation unit can add effects to the robot's movements based on music analysis data. For example, it can add light effects to the robot's movements in time with the beat. It can also add color changes to the robot's movements in time with the rhythm. Furthermore, it can add smoke or fog effects to the robot's movements to match the mood of the song. In this way, the generation unit can add effects to the robot's movements based on music analysis data, enhancing the visual presentation. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal effects based on the music analysis data.

[0108] The receiving unit can prioritize receiving highly relevant data based on the user's past dance performance data when receiving music analysis data. For example, it can prioritize receiving relevant analysis data based on the dance styles the user has preferred to dance in the past. The receiving unit can also prioritize receiving relevant analysis data based on the beats and rhythms of songs the user has preferred to dance to in the past. The receiving unit can also prioritize receiving relevant analysis data based on the atmosphere of performances the user has preferred to dance in the past. In this way, the receiving unit can prioritize receiving highly relevant data based on the user's past dance performance data. Some or all of the above processing in the receiving unit may be performed using AI or not. For example, the receiving unit can analyze the user's past dance performance data using AI and prioritize receiving highly relevant data.

[0109] The generation unit can add interactive elements to the robot's movements based on music analysis data. For example, it can add a movement where the robot waves to the user in time with the beat. It can also add a movement where the robot claps to the user in time with the rhythm. Furthermore, it can add a movement where the robot bows to the user in accordance with the mood of the song. In this way, the generation unit can add interactive elements to the robot's movements based on music analysis data, thereby increasing engagement with the user. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal interactive movements based on the music analysis data.

[0110] The execution unit can analyze the user's movements in real time while the robot is operating and synchronize the robot's movements with the user's movements. For example, if the user raises their hand, the robot will also raise its hand. Similarly, if the user takes a step, the robot can also take a step. Furthermore, if the user rotates, the robot can also rotate. In this way, the execution unit can execute robot movements that are synchronized with the user's movements in real time. Some or all of the above processing in the execution unit may be performed using AI or not. For example, the execution unit can analyze the user's movements using AI and synchronize the robot's movements in real time.

[0111] The generation unit can add storytelling elements to the robot's movements based on music analysis data. For example, it can add movements where the robot acts out a part of a story in time with the beat. It can also add movements where the robot acts out characters from a story in time with the rhythm. Furthermore, it can add movements where the robot recreates scenes from a story in accordance with the mood of the music. In this way, the generation unit can add storytelling elements to the robot's movements based on music analysis data, thereby enhancing the visual presentation. Some or all of the above processing in the generation unit is performed using a generation AI. The generation AI generates the optimal storytelling elements based on the music analysis data.

[0112] The receiving unit can estimate the user's emotions and adjust the method of receiving music analysis data based on the estimated user emotions. For example, if the user is relaxed, the receiving unit can receive music analysis data slowly and match the music to a relaxing atmosphere. If the user is excited, the receiving unit can receive music analysis data quickly and match the music to a fast tempo. If the user is stressed, the receiving unit can also interrupt the reception of music analysis data and prioritize the reception of relaxing music. In this way, the receiving unit can adjust the method of receiving music analysis data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the receiving unit may be performed using AI or not using AI. For example, the receiving unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0113] The generation unit can estimate the user's emotions and change the style of the robot dance motion based on the estimated emotions. For example, if the user is relaxed, it can generate a relaxed style of robot dance motion. If the user is excited, the generation unit can generate an energetic style of robot dance motion. If the user is stressed, the generation unit can also generate a calming style of robot dance motion. In this way, the generation unit can change the style of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit is performed using the generation AI. The generation AI generates the optimal style of robot dance motion based on the user's emotions.

[0114] The execution unit can estimate the user's emotions and adjust the execution speed of the robot dance motion based on the estimated user emotions. For example, if the user is relaxed, the robot dance motion will be executed at a slow speed. If the user is excited, the execution unit can execute the robot dance motion at a fast speed. If the user is stressed, the execution unit can also execute the robot dance motion at a calming speed. In this way, the execution unit can adjust the execution speed of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0115] The generation unit can estimate the user's emotions and adjust the complexity of the robot dance motion based on the estimated emotions. For example, if the user is relaxed, it can generate a robot dance motion with simple movements. If the user is excited, the generation unit can generate a robot dance motion with complex movements. If the user is stressed, the generation unit can also generate a simple robot dance motion incorporating calming movements. In this way, the generation unit can adjust the complexity of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit is performed using the generation AI. The generation AI generates the optimal robot dance motion based on the user's emotions.

[0116] The execution unit can estimate the user's emotions and adjust the timing of the robot dance motion based on the estimated emotions. For example, if the user is relaxed, the robot dance motion will be executed at a relaxed pace. If the user is excited, the execution unit can execute the robot dance motion at a rapid pace. If the user is stressed, the execution unit can also execute the robot dance motion at a calming pace. In this way, the execution unit can adjust the timing of the robot dance motion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the execution unit may be performed using AI or not using AI. For example, the execution unit can use AI to perform facial recognition or voice analysis to estimate the user's emotions and obtain emotion data.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The receiving unit receives music analysis data. The receiving unit can receive analysis data such as the beat, rhythm, mood of the song, and chorus. The receiving unit uses multiple existing Web APIs to obtain this data for music analysis. Step 2: The generation unit generates robot dance motions based on the music analysis data received by the receiving unit. The generation unit generates creative robot dance motions based on data such as the received beat, rhythm, song atmosphere, and chorus. The generation unit uses a generation AI to analyze the music analysis data and generate the optimal dance motion. The generation AI uses, for example, a text generation AI (e.g., LLM) or a multimodal generation AI to generate dance motions based on the music analysis data. Step 3: The execution unit executes the robot dance motion generated by the generation unit. The execution unit executes the generated dance motion in real time, for example, so that the robot dances to the music. The execution unit includes an adjustment unit that adjusts the generated motion in real time. The adjustment unit adjusts the generated motion in real time, for example, to optimize the robot's movements.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the receiving unit, generation unit, execution unit, identification unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the receiving unit is implemented by the control unit 46A of the smart device 14 and receives music analysis data. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates robot dance motion based on the music analysis data. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the generated robot dance motion. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies the type of analysis data. The adjustment unit is implemented by the control unit 46A of the smart device 14 and adjusts the generated motion in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the receiving unit, generation unit, execution unit, identification unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit is implemented by the control unit 46A of the smart glasses 214 and receives music analysis data. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates robot dance motion based on the music analysis data. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the generated robot dance motion. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies the type of analysis data. The adjustment unit is implemented by the control unit 46A of the smart glasses 214 and adjusts the generated motion in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the receiving unit, generation unit, execution unit, identification unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the receiving unit is implemented by the control unit 46A of the headset terminal 314 and receives music analysis data. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates robot dance motion based on the music analysis data. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the generated robot dance motion. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies the type of analysis data. The adjustment unit is implemented by the control unit 46A of the headset terminal 314 and adjusts the generated motion in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the receiving unit, generation unit, execution unit, identification unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the receiving unit is implemented by the control unit 46A of the robot 414 and receives music analysis data. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates robot dance motion based on the music analysis data. The execution unit is implemented by the control unit 46A of the robot 414 and executes the generated robot dance motion. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies the type of analysis data. The adjustment unit is implemented by the control unit 46A of the robot 414 and adjusts the generated motion in real time. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) A receiving unit that receives music analysis data, A generation unit that generates robot dance motions based on music analysis data received by the receiving unit, The system comprises an execution unit that executes the robot dance motion generated by the generation unit. A system characterized by the following features. (Note 2) The receiving unit is Receive analysis data on music beats, rhythms, song atmosphere, choruses, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the received analysis data, it generates creative robot dance motions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The execution unit is, The generated robot dance motion is executed in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It includes a unit that identifies the type of analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The execution unit is, It includes an adjustment unit that adjusts the generated motion in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The receiving unit is It estimates the user's emotions and adjusts the timing of receiving music analysis data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The receiving unit is Based on the music genre, the system selects the most suitable Web API and receives analytical data. The system described in Appendix 1, characterized by the features described herein. (Note 9) The receiving unit is The type of data received is dynamically changed based on the tempo and key of the music. The system described in Appendix 1, characterized by the features described herein. (Note 10) The receiving unit is It estimates the user's emotions and determines the priority of incoming music analysis data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The receiving unit is When receiving music analysis data, the system prioritizes receiving data that is highly relevant based on the user's past musical preferences. The system described in Appendix 1, characterized by the features described herein. (Note 12) The receiving unit is When receiving music analysis data, region-specific music analysis data is received based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates the user's emotions and adjusts the robot dance motion generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is Generate different dance styles based on the beat and rhythm of the music. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Customize the robot's facial expressions and movements according to the mood of the music. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the complexity of the robot dance motion generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is Based on music analysis data, it generates motions in which multiple robots dance in coordination. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is The robot's movement speed is dynamically adjusted according to the tempo of the music. The system described in Appendix 1, characterized by the features described herein. (Note 19) The execution unit is, It estimates the user's emotions and adjusts the timing of the robot dance motion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, To improve the accuracy of robot movements, motion data is fed back in real time. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, Add a function that detects obstacles during robot operation and automatically avoids them. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, It estimates the user's emotions and adjusts the execution order of robot dance motions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, The robot adjusts its movements in real time in response to changes in the music while it is operating. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, During robot operation, it performs coordinated actions in conjunction with other robots and devices. The system described in Appendix 1, characterized by the features described herein. (Note 25) The specified part is, We estimate user sentiment and adjust the method for identifying the type of analytical data based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 26) The specified part is, Dynamically change the type of analytical data to identify based on the music genre. The system described in Appendix 2, characterized by the features described herein. (Note 27) The specified part is, Improve the accuracy of the analysis data identified based on the tempo and key of the music. The system described in Appendix 2, characterized by the features described herein. (Note 28) The specified part is, It estimates user emotions and determines the priority of analytical data to identify based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The specified part is, Dynamically change the type of data to identify based on music analysis data. The system described in Appendix 2, characterized by the features described herein. (Note 30) The specified part is, Customize the types of analytical data to identify based on the mood of the music. The system described in Appendix 2, characterized by the features described herein. (Note 31) The adjustment unit is, The system estimates the user's emotions and modifies how the robot dance motion is adjusted based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The adjustment unit is, The robot adjusts its movements in real time based on the beat and rhythm of the music while it is operating. The system described in Appendix 3, characterized by the features described herein. (Note 33) The adjustment unit is, The robot automatically adjusts its movements in response to changes in the environment while it is operating. The system described in Appendix 3, characterized by the features described herein. (Note 34) The adjustment unit is, The system estimates the user's emotions and determines the frequency of adjustments to the robot's dance motion based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The adjustment unit is, During robot operation, it coordinates its movements with other robots and devices. The system described in Appendix 3, characterized by the features described herein. (Note 36) The adjustment unit is, The robot adjusts its movements in real time in response to changes in the music while it is operating. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A receiving unit that receives music analysis data, A generation unit that generates robot dance motions based on music analysis data received by the receiving unit, The system comprises an execution unit that executes the robot dance motion generated by the generation unit. A system characterized by the following features.

2. The receiving unit is Receive analysis data on music beats, rhythms, song atmosphere, choruses, etc. The system according to feature 1.

3. The generating unit is Based on the received analysis data, it generates creative robot dance motions. The system according to feature 1.

4. The execution unit is, The generated robot dance motion is executed in real time. The system according to feature 1.

5. The generating unit is It includes a unit that identifies the type of analysis data. The system according to feature 1.

6. The execution unit is, It includes an adjustment unit that adjusts the generated motion in real time. The system according to feature 1.

7. The receiving unit is It estimates the user's emotions and adjusts the timing of receiving music analysis data based on the estimated user emotions. The system according to feature 1.

8. The receiving unit is Based on the music genre, the system selects the most suitable Web API and receives analytical data. The system according to feature 1.

9. The receiving unit is The type of data received is dynamically changed based on the tempo and key of the music. The system according to feature 1.

10. The receiving unit is It estimates the user's emotions and determines the priority of incoming music analysis data based on the estimated user emotions. The system according to feature 1.

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