System

Abstract

An object of a system according to an embodiment is to enable a robot to immediately dance by analyzing characteristics of music.SOLUTION: A system includes an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the characteristics, tempo, and chorus of music. The generation part generates a dance motion file on the basis of the result analyzed by the analysis part. The control unit controls the robot based on the motion file generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult for a robot to instantly dance to music it had never seen before, and creating the motions took time.

[0005] The system according to the embodiment aims to analyze the characteristics of music and enable a robot to instantly dance. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a generation unit, and a control unit. The analysis unit analyzes the characteristics, tempo, and chorus of the music. The generation unit generates a dance motion file based on the results of the analysis by the analysis unit. The control unit controls the robot based on the motion file generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the characteristics of music and enable the robot to dance instantly. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention enables a robot to dance to the rhythm of music it hears for the first time. This system uses an existing music analysis Web API to analyze the music's atmosphere, rhythm, and chorus. A generation AI then generates a dance motion file, and the robot then dances according to the motion. For example, the system analyzes the music's tempo, beat, melody line, and other characteristics to identify the music's characteristics. Based on the analysis results, the generation AI then generates a motion file containing steps that match the rhythm and dynamic movements that match the chorus. The generated motion file is then sent to the robot, and the robot then dances according to the motion. This allows the robot to instantly adapt and dance to music it hears for the first time. This significantly streamlines motion creation time. For example, compared to traditional manual motion creation, the use of generation AI allows for faster and more accurate generation of dance motions. Furthermore, because the robot can instantly adapt to music it hears for the first time, this system is expected to be useful in entertainment and events.

[0029] A dance control system according to an embodiment includes an analysis unit, a generation unit, and a control unit. The analysis unit analyzes music characteristics, tempo, and chorus. For example, the analysis unit analyzes the tempo, beat, and melody line of the music. The analysis unit can also analyze music characteristics such as the genre, tone, and volume. The analysis unit can also perform a structural analysis of the music to identify the location of the chorus. The generation unit generates a dance motion file based on the analysis results by the analysis unit. For example, the generation unit generates steps that match the rhythm. The generation unit can also generate a motion file that includes dynamic movements that match the chorus. The generation unit can also design appropriate dance motions that match the characteristics of the music. The control unit controls a robot based on the motion file generated by the generation unit. For example, the control unit controls the robot's movements based on the generated motion file. The control unit can also adjust the robot's movements in real time. The control unit can also establish a feedback loop to accurately control the robot's movements. This enables the dance control system according to an embodiment to instantly dance to music that the robot has never seen before.

[0030] The analysis unit can analyze the tempo, beat, and melody line of music. For example, the analysis unit analyzes the tempo of music. The tempo is measured in beats per minute (BPM). The analysis unit can also analyze the beat of music. The beat is detected by analyzing the rhythmic pattern of music. The analysis unit can also analyze the melody line of music. The melody line is identified by analyzing changes in pitch and the continuity of notes. This enables detailed analysis of music. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input music data into a generation AI and have the generation AI analyze the tempo, beat, and melody line.

[0031] The generation unit can generate a motion file including steps that match the rhythm and dynamic movements that match the chorus. The generation unit generates, for example, steps that match the rhythm. The steps that match the rhythm are generated using a step generation algorithm based on a rhythm pattern. The generation unit can also generate a motion file including dynamic movements that match the chorus. The dynamic movements that match the chorus are generated based on a method for adjusting the intensity and speed of the movements. Furthermore, the generation unit can design appropriate dance motions that match the characteristics of the music. This makes it possible to generate appropriate dance motions that match the music. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the results of music analysis into the generation AI and have the generation AI generate steps that match the rhythm and dynamic movements that match the chorus.

[0032] The control unit can control the robot based on the generated motion file. The control unit controls the robot's movements based on, for example, the generated motion file. The generated motion file includes motion capture data, animation data, and the like. The control unit can also adjust the robot's movements in real time. Furthermore, the control unit can also establish a feedback loop for accurately controlling the robot's movements. This allows the robot to be accurately controlled based on the generated motion file. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the generated motion file into AI and have the AI ​​control the robot's movements.

[0033] When analyzing music, the analysis unit can optimize the analysis algorithm based on past music data. For example, the analysis unit can learn tempo and beat patterns based on previously analyzed music data, thereby improving analysis accuracy. The analysis unit can also extract rhythm patterns unique to a particular genre from past music data and reflect them in the analysis. Furthermore, the analysis unit can optimize an algorithm that more accurately identifies the position of the chorus by referencing past music data. This makes it possible to improve analysis accuracy by utilizing past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past music data into a generation AI and have the generation AI optimize the analysis algorithm.

[0034] When analyzing music, the analysis unit can apply different analysis methods depending on the genre of music. For example, in the case of classical music, the analysis unit may focus on melody lines and harmonies during analysis. In the case of rock music, the analysis unit may also focus on guitar riffs and drum beats during analysis. Furthermore, in the case of jazz music, the analysis unit may also focus on improvisational parts during analysis. This enables analysis according to the genre of music. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input music genre data into the generation AI and cause the generation AI to apply an analysis method according to the genre.

[0035] When analyzing music, the analysis unit can improve the analysis accuracy based on live performance data of the music. For example, the analysis unit can reflect tempo fluctuations in the analysis based on the live performance data. The analysis unit can also analyze audience reactions from the live performance data and identify exciting parts of the music. Furthermore, the analysis unit can also reflect improvisational parts in the analysis by referring to the live performance data. This makes it possible to improve the analysis accuracy by utilizing the live performance data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the live performance data into a generation AI and cause the generation AI to improve the analysis accuracy.

[0036] When analyzing music, the analysis unit can perform the analysis based on the geographical origin of the music. For example, the analysis unit can perform the analysis by emphasizing a specific rhythm pattern based on the geographical origin of the music. The analysis unit can also perform the analysis by emphasizing the sound of a specific instrument based on the geographical origin. Furthermore, the analysis unit can perform the analysis by emphasizing a specific musical style based on the geographical origin. This enables analysis that takes the geographical origin of the music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical origin data of the music to the generation AI and cause the generation AI to perform an analysis based on the geographical origin.

[0037] When analyzing music, the analysis unit can improve the analysis accuracy based on music-related literature. For example, the analysis unit extracts a specific rhythm pattern from music-related literature and reflects it in the analysis. The analysis unit can also reflect the sound of a specific instrument from music-related literature in the analysis. Furthermore, the analysis unit can reflect a specific musical style from music-related literature in the analysis. This makes it possible to improve the analysis accuracy by utilizing related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input music-related literature data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0038] When analyzing music, the analysis unit can perform the analysis based on the market value of the music. For example, the analysis unit can perform the analysis by emphasizing popular rhythm patterns based on the market value of the music. The analysis unit can also perform the analysis by emphasizing the sounds of popular instruments based on the market value of the music. Furthermore, the analysis unit can perform the analysis by emphasizing popular musical styles based on the market value of the music. This enables analysis that takes market value into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input music market value data into the generation AI and cause the generation AI to perform an analysis based on market value.

[0039] When generating dance motions, the generation unit can optimize the generation algorithm based on past dance performance data. For example, the generation unit can optimize steps that match the rhythm based on past dance performance data. The generation unit can also optimize dynamic movements that match the chorus from past dance performance data. Furthermore, the generation unit can also optimize the overall flow of movements by referencing past dance performance data. This makes it possible to improve generation accuracy by utilizing past data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past dance performance data into the generation AI and have the generation AI optimize the generation algorithm.

[0040] When generating dance motions, the generation unit can apply different motion generation methods depending on the music genre. For example, in the case of classical music, the generation unit generates dance motions including graceful movements. In addition, in the case of rock music, the generation unit can also generate dance motions including energetic movements. Furthermore, in the case of jazz music, the generation unit can also generate dance motions including improvisational movements. This makes it possible to generate motions according to the music genre. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input music genre data into the generation AI and have the generation AI apply a motion generation method according to the genre.

[0041] When generating dance motions, the generation unit can optimize the motions based on the physical constraints of the robot. For example, the generation unit generates dance motions that include natural movements, taking into account the range of motion of the robot's joints. The generation unit can also generate dance motions that include energy-efficient movements, taking into account the robot's battery life. Furthermore, the generation unit can generate dance motions that include stable movements, taking into account the robot's weight balance. This enables motion generation that takes into account the robot's physical constraints. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input physical constraint data of the robot into the generation AI and cause the generation AI to optimize the motions based on the physical constraints.

[0042] When generating dance motions, the generation unit can generate motions based on the geographical origin of the music. For example, the generation unit generates motions that reflect a specific dance style based on the geographical origin of the music. The generation unit can also generate motions that reflect a specific rhythm pattern based on the geographical origin. Furthermore, the generation unit can generate motions that reflect the movement of a specific instrument based on the geographical origin. This enables motion generation that takes geographical origin into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical origin data of the music into the generation AI and cause the generation AI to generate motions based on the geographical origin.

[0043] When generating dance motions, the generation unit can improve the accuracy of motion generation by referring to music-related literature. For example, the generation unit can extract a specific dance style from music-related literature and reflect it in the motion. The generation unit can also extract a specific rhythm pattern from music-related literature and reflect it in the motion. Furthermore, the generation unit can extract the movements of a specific instrument from music-related literature and reflect it in the motion. This makes it possible to improve the accuracy of motion generation by utilizing related literature. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input music-related literature data into the generation AI and cause the generation AI to improve the accuracy of motion generation.

[0044] When generating dance motions, the generation unit can generate motions based on the market value of the music. For example, the generation unit generates motions that reflect popular dance styles based on the market value of the music. The generation unit can also generate motions that reflect popular rhythm patterns based on the market value of the music. Furthermore, the generation unit can generate motions that reflect the movements of popular musical instruments based on the market value of the music. This enables motion generation that takes market value into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input market value data of the music into the generation AI and cause the generation AI to generate motions based on market value.

[0045] When controlling the robot, the control unit can optimize the control algorithm based on past control data. For example, the control unit improves the accuracy of the robot's movement based on the past control data. The control unit can also extract specific movement patterns from the past control data and reflect them in the control. Furthermore, the control unit can also optimize the flow of the robot's movement by referring to the past control data. This makes it possible to improve control accuracy by utilizing past data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input past control data into a generation AI and have the generation AI optimize the control algorithm.

[0046] When controlling the robot, the control unit can optimize the control method based on the physical constraints of the robot. For example, the control unit controls natural movement by taking into account the range of motion of the robot's joints. The control unit can also control energy-efficient movement by taking into account the battery life of the robot. Furthermore, the control unit can control stable movement by taking into account the weight balance of the robot. This enables control that takes into account the physical constraints of the robot. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input physical constraint data of the robot into the generation AI and cause the generation AI to optimize the control method based on the physical constraints.

[0047] When controlling the robot, the control unit can improve control accuracy based on live musical performance data. For example, the control unit can reflect tempo fluctuations in real time based on the live performance data. The control unit can also analyze audience reactions from the live performance data and adjust the robot's movements. Furthermore, the control unit can also reflect improvisational performance portions in real time by referring to the live performance data. This makes it possible to improve control accuracy by utilizing live performance data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input live performance data into a generation AI and cause the generation AI to improve control accuracy.

[0048] When controlling the robot, the control unit can perform control based on the geographical origin of the music. For example, the control unit controls movements that reflect a specific dance style based on the geographical origin of the music. The control unit can also control movements that reflect a specific rhythm pattern based on the geographical origin. Furthermore, the control unit can control movements that reflect the movements of a specific musical instrument based on the geographical origin. This enables control of the robot taking the geographical origin into consideration. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical origin data of the music to the generation AI and cause the generation AI to perform control based on the geographical origin.

[0049] The control unit can improve control accuracy by referring to music-related literature when controlling the robot. For example, the control unit can extract a specific dance style from music-related literature and reflect it in the movement. The control unit can also extract a specific rhythm pattern from music-related literature and reflect it in the movement. Furthermore, the control unit can extract the movement of a specific instrument from music-related literature and reflect it in the movement. This makes it possible to improve control accuracy by utilizing related literature. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input music-related literature data into the generation AI and cause the generation AI to improve control accuracy.

[0050] The control unit can control the robot based on the market value of the music. For example, the control unit can control movements that reflect popular dance styles based on the market value of the music. The control unit can also control movements that reflect popular rhythm patterns based on the market value of the music. Furthermore, the control unit can control movements that reflect the movements of popular musical instruments based on the market value of the music. This enables control of the robot taking market value into consideration. Some or all of the above-described processing in the control unit can be performed using AI, for example, or without AI. For example, the control unit can input market value data of music to the generation AI and cause the generation AI to perform control based on market value.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] When analyzing music, the analysis unit can perform the analysis based on the historical background of the music. For example, the analysis unit can perform the analysis by emphasizing rhythmic patterns that were popular in a particular era. The analysis unit can also perform the analysis by emphasizing the sounds of a particular instrument based on the historical background. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the historical background. This enables analysis that takes the historical background of the music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input historical background data of the music into the generation AI and cause the generation AI to perform an analysis based on the historical background.

[0053] When generating dance motions, the generation unit can optimize the motions based on the user's physical characteristics. For example, the generation unit generates dance motions that include natural movements by taking into account data such as the user's height, weight, and flexibility. The generation unit can also generate dance motions that include energy-efficient movements by taking into account the user's muscle strength and endurance. Furthermore, the generation unit can generate dance motions that include stable movements by taking into account the user's sense of balance. This makes it possible to generate motions that take the user's physical characteristics into account. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's physical characteristic data into the generation AI and cause the generation AI to optimize the motions based on the physical characteristics.

[0054] When controlling the robot, the control unit can optimize the control method based on environmental data. For example, the control unit adjusts the robot's movement taking into account environmental data such as the ambient temperature, humidity, and lighting conditions. The control unit can also optimize the robot's movement taking into account data such as the floor material and slope. Furthermore, the control unit can adjust the robot's movement taking into account the position and movement of surrounding obstacles. This enables control of the robot taking into account environmental data. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input environmental data to a generation AI and cause the generation AI to optimize the control method based on the environmental data.

[0055] When analyzing music, the analysis unit can perform the analysis based on the cultural background of the music. For example, the analysis unit can perform the analysis by emphasizing rhythmic patterns unique to a particular culture. The analysis unit can also perform the analysis by emphasizing the sounds of a particular instrument based on the cultural background. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the cultural background. This enables analysis that takes into account the cultural background of the music. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input cultural background data of the music into the generation AI and cause the generation AI to perform an analysis based on the cultural background.

[0056] When analyzing music, the analysis unit can perform the analysis based on the psychological impact of the music. For example, the analysis unit can perform the analysis by emphasizing the psychological impact of a specific rhythm pattern. The analysis unit can also perform the analysis by emphasizing the sound of a specific instrument based on the psychological impact. Furthermore, the analysis unit can perform the analysis by emphasizing a specific musical style based on the psychological impact. This enables analysis that takes into account the psychological impact of music. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input psychological impact data of music to a generation AI and cause the generation AI to perform an analysis based on the psychological impact.

[0057] When analyzing music, the analysis unit can perform the analysis based on the social impact of the music. For example, the analysis unit can perform the analysis by emphasizing the social impact of a particular rhythm pattern. The analysis unit can also perform the analysis by emphasizing the sound of a particular instrument based on the social impact. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the social impact. This enables analysis that takes the social impact of music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input social impact data of music into the generation AI and cause the generation AI to perform an analysis based on the social impact.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The analysis unit analyzes the music's characteristics, tempo, and chorus. Specifically, it analyzes the tempo, beat, and melody line of the music, as well as characteristics such as the music's genre, tone, and volume. It also analyzes the music's structure to identify the location of the chorus. Step 2: The generator generates a dance motion file based on the results of the analysis by the analyzer. Specifically, it generates steps that match the rhythm and generates a motion file that includes dynamic movements that match the chorus. It also designs appropriate dance motions that match the characteristics of the music. Step 3: The control unit controls the robot based on the motion file generated by the generator. Specifically, the control unit controls the robot's movements based on the generated motion file, adjusts the robot's movements in real time, and establishes a feedback loop to accurately control the robot's movements.

[0060] (Example 2) A system according to an embodiment of the present invention enables a robot to dance to the rhythm of music it hears for the first time. This system uses an existing music analysis Web API to analyze the music's atmosphere, rhythm, and chorus. A generation AI then generates a dance motion file, and the robot then dances according to the motion. For example, the system analyzes the music's tempo, beat, melody line, and other characteristics to identify the music's characteristics. Based on the analysis results, the generation AI then generates a motion file containing steps that match the rhythm and dynamic movements that match the chorus. The generated motion file is then sent to the robot, and the robot then dances according to the motion. This allows the robot to instantly adapt and dance to music it hears for the first time. This significantly streamlines motion creation time. For example, compared to traditional manual motion creation, the use of generation AI allows for faster and more accurate generation of dance motions. Furthermore, because the robot can instantly adapt to music it hears for the first time, this system is expected to be useful in entertainment and events.

[0061] A dance control system according to an embodiment includes an analysis unit, a generation unit, and a control unit. The analysis unit analyzes music characteristics, tempo, and chorus. For example, the analysis unit analyzes the tempo, beat, and melody line of the music. The analysis unit can also analyze music characteristics such as the genre, tone, and volume. The analysis unit can also perform a structural analysis of the music to identify the location of the chorus. The generation unit generates a dance motion file based on the analysis results by the analysis unit. For example, the generation unit generates steps that match the rhythm. The generation unit can also generate a motion file that includes dynamic movements that match the chorus. The generation unit can also design appropriate dance motions that match the characteristics of the music. The control unit controls a robot based on the motion file generated by the generation unit. For example, the control unit controls the robot's movements based on the generated motion file. The control unit can also adjust the robot's movements in real time. The control unit can also establish a feedback loop to accurately control the robot's movements. This enables the dance control system according to an embodiment to instantly dance to music that the robot has never seen before.

[0062] The analysis unit can analyze the tempo, beat, and melody line of music. For example, the analysis unit analyzes the tempo of music. The tempo is measured in beats per minute (BPM). The analysis unit can also analyze the beat of music. The beat is detected by analyzing the rhythmic pattern of music. The analysis unit can also analyze the melody line of music. The melody line is identified by analyzing changes in pitch and the continuity of notes. This enables detailed analysis of music. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input music data into a generation AI and have the generation AI analyze the tempo, beat, and melody line.

[0063] The generation unit can generate a motion file including steps that match the rhythm and dynamic movements that match the chorus. The generation unit generates, for example, steps that match the rhythm. The steps that match the rhythm are generated using a step generation algorithm based on a rhythm pattern. The generation unit can also generate a motion file including dynamic movements that match the chorus. The dynamic movements that match the chorus are generated based on a method for adjusting the intensity and speed of the movements. Furthermore, the generation unit can design appropriate dance motions that match the characteristics of the music. This makes it possible to generate appropriate dance motions that match the music. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the results of music analysis into the generation AI and have the generation AI generate steps that match the rhythm and dynamic movements that match the chorus.

[0064] The control unit can control the robot based on the generated motion file. The control unit controls the robot's movements based on, for example, the generated motion file. The generated motion file includes motion capture data, animation data, and the like. The control unit can also adjust the robot's movements in real time. Furthermore, the control unit can also establish a feedback loop for accurately controlling the robot's movements. This allows the robot to be accurately controlled based on the generated motion file. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the generated motion file into AI and have the AI ​​control the robot's movements.

[0065] The analysis unit can estimate the user's emotions and adjust the music analysis method based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, biometric data, etc. The analysis unit can also adjust the music analysis method based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit may focus on the tempo and beat of the music during analysis. If the user is excited, the analysis unit may emphasize the dynamic parts of the music during analysis. If the user is sad, the analysis unit may focus on the melody line during analysis. This enables music analysis according to the user's emotions. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the user's emotion data into the generation AI and cause the generation AI to adjust the music analysis method based on the emotion.

[0066] When analyzing music, the analysis unit can optimize the analysis algorithm based on past music data. For example, the analysis unit can learn tempo and beat patterns based on previously analyzed music data, thereby improving analysis accuracy. The analysis unit can also extract rhythm patterns unique to a particular genre from past music data and reflect them in the analysis. Furthermore, the analysis unit can optimize an algorithm that more accurately identifies the position of the chorus by referencing past music data. This makes it possible to improve analysis accuracy by utilizing past data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past music data into a generation AI and have the generation AI optimize the analysis algorithm.

[0067] When analyzing music, the analysis unit can apply different analysis methods depending on the genre of music. For example, in the case of classical music, the analysis unit may focus on melody lines and harmonies during analysis. In the case of rock music, the analysis unit may also focus on guitar riffs and drum beats during analysis. Furthermore, in the case of jazz music, the analysis unit may also focus on improvisational parts during analysis. This enables analysis according to the genre of music. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input music genre data into the generation AI and cause the generation AI to apply an analysis method according to the genre.

[0068] When analyzing music, the analysis unit can improve the analysis accuracy based on live performance data of the music. For example, the analysis unit can reflect tempo fluctuations in the analysis based on the live performance data. The analysis unit can also analyze audience reactions from the live performance data and identify exciting parts of the music. Furthermore, the analysis unit can also reflect improvisational parts in the analysis by referring to the live performance data. This makes it possible to improve the analysis accuracy by utilizing the live performance data. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the live performance data into a generation AI and cause the generation AI to improve the analysis accuracy.

[0069] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, biometric data, etc. The analysis unit can also adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is relaxed, the analysis results are displayed in visually calm colors. If the user is excited, the analysis results are displayed in visually vivid colors. If the user is sad, the analysis results are displayed in visually subdued colors. This makes it possible to display the analysis results according to the user's emotions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the display method of the analysis results based on the emotion.

[0070] When analyzing music, the analysis unit can perform the analysis based on the geographical origin of the music. For example, the analysis unit can perform the analysis by emphasizing a specific rhythm pattern based on the geographical origin of the music. The analysis unit can also perform the analysis by emphasizing the sound of a specific instrument based on the geographical origin. Furthermore, the analysis unit can perform the analysis by emphasizing a specific musical style based on the geographical origin. This enables analysis that takes the geographical origin of the music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the geographical origin data of the music to the generation AI and cause the generation AI to perform an analysis based on the geographical origin.

[0071] When analyzing music, the analysis unit can improve the analysis accuracy based on music-related literature. For example, the analysis unit extracts a specific rhythm pattern from music-related literature and reflects it in the analysis. The analysis unit can also reflect the sound of a specific instrument from music-related literature in the analysis. Furthermore, the analysis unit can reflect a specific musical style from music-related literature in the analysis. This makes it possible to improve the analysis accuracy by utilizing related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input music-related literature data into the generation AI and cause the generation AI to improve the analysis accuracy.

[0072] When analyzing music, the analysis unit can perform the analysis based on the market value of the music. For example, the analysis unit can perform the analysis by emphasizing popular rhythm patterns based on the market value of the music. The analysis unit can also perform the analysis by emphasizing the sounds of popular instruments based on the market value of the music. Furthermore, the analysis unit can perform the analysis by emphasizing popular musical styles based on the market value of the music. This enables analysis that takes market value into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input music market value data into the generation AI and cause the generation AI to perform an analysis based on market value.

[0073] The generation unit can estimate the user's emotion and adjust the dance motion generation method based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, biometric data, etc. The generation unit can also adjust the dance motion generation method based on the estimated user's emotion. For example, if the user is relaxed, dance motions including slow movements are generated. If the user is excited, dance motions including dynamic movements are generated. If the user is sad, dance motions including gentle movements are generated. This makes it possible to generate dance motions according to the user's emotion. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the dance motion generation method based on the emotion.

[0074] When generating dance motions, the generation unit can optimize the generation algorithm based on past dance performance data. For example, the generation unit can optimize steps that match the rhythm based on past dance performance data. The generation unit can also optimize dynamic movements that match the chorus from past dance performance data. Furthermore, the generation unit can also optimize the overall flow of movements by referencing past dance performance data. This makes it possible to improve generation accuracy by utilizing past data. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past dance performance data into the generation AI and have the generation AI optimize the generation algorithm.

[0075] When generating dance motions, the generation unit can apply different motion generation methods depending on the music genre. For example, in the case of classical music, the generation unit generates dance motions including graceful movements. In addition, in the case of rock music, the generation unit can also generate dance motions including energetic movements. Furthermore, in the case of jazz music, the generation unit can also generate dance motions including improvisational movements. This makes it possible to generate motions according to the music genre. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input music genre data into the generation AI and have the generation AI apply a motion generation method according to the genre.

[0076] When generating dance motions, the generation unit can optimize the motions based on the physical constraints of the robot. For example, the generation unit generates dance motions that include natural movements, taking into account the range of motion of the robot's joints. The generation unit can also generate dance motions that include energy-efficient movements, taking into account the robot's battery life. Furthermore, the generation unit can generate dance motions that include stable movements, taking into account the robot's weight balance. This enables motion generation that takes into account the robot's physical constraints. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input physical constraint data of the robot into the generation AI and cause the generation AI to optimize the motions based on the physical constraints.

[0077] The generation unit can estimate the user's emotion and determine the priority of motions to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion. The user's emotion is estimated using facial expression recognition, voice analysis, biometric data, etc. The generation unit can also determine the priority of motions to be generated based on the estimated user's emotion. For example, if the user is relaxed, a slow motion is generated with priority. If the user is excited, a dynamic motion is generated with priority. If the user is sad, a gentle motion is generated with priority. This allows the priority of motions to be determined according to the user's emotion. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user's emotion data into the generation AI and cause the generation AI to determine the priority of motions based on the emotion.

[0078] When generating dance motions, the generation unit can generate motions based on the geographical origin of the music. For example, the generation unit generates motions that reflect a specific dance style based on the geographical origin of the music. The generation unit can also generate motions that reflect a specific rhythm pattern based on the geographical origin. Furthermore, the generation unit can generate motions that reflect the movement of a specific instrument based on the geographical origin. This enables motion generation that takes geographical origin into consideration. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input geographical origin data of the music into the generation AI and cause the generation AI to generate motions based on the geographical origin.

[0079] When generating dance motions, the generation unit can improve the accuracy of motion generation by referring to music-related literature. For example, the generation unit can extract a specific dance style from music-related literature and reflect it in the motion. The generation unit can also extract a specific rhythm pattern from music-related literature and reflect it in the motion. Furthermore, the generation unit can extract the movements of a specific instrument from music-related literature and reflect it in the motion. This makes it possible to improve the accuracy of motion generation by utilizing related literature. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the generation unit can input music-related literature data into the generation AI and cause the generation AI to improve the accuracy of motion generation.

[0080] When generating dance motions, the generation unit can generate motions based on the market value of the music. For example, the generation unit generates motions that reflect popular dance styles based on the market value of the music. The generation unit can also generate motions that reflect popular rhythm patterns based on the market value of the music. Furthermore, the generation unit can generate motions that reflect the movements of popular musical instruments based on the market value of the music. This enables motion generation that takes market value into consideration. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input market value data of the music into the generation AI and cause the generation AI to generate motions based on market value.

[0081] The control unit can estimate the user's emotions and adjust the robot's control method based on the estimated user's emotions. The control unit, for example, estimates the user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, biometric data, etc. The control unit can also adjust the robot's control method based on the estimated user's emotions. For example, if the user is relaxed, the robot's movements can be adjusted to be relaxed. If the user is excited, the robot's movements can be adjusted to be dynamic. If the user is sad, the robot's movements can be adjusted to be gentle. This makes it possible to control the robot according to the user's emotions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's emotion data into a generation AI and cause the generation AI to adjust the robot's control method based on the emotion.

[0082] When controlling the robot, the control unit can optimize the control algorithm based on past control data. For example, the control unit improves the accuracy of the robot's movement based on the past control data. The control unit can also extract specific movement patterns from the past control data and reflect them in the control. Furthermore, the control unit can also optimize the flow of the robot's movement by referring to the past control data. This makes it possible to improve control accuracy by utilizing past data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input past control data into a generation AI and have the generation AI optimize the control algorithm.

[0083] When controlling the robot, the control unit can optimize the control method based on the physical constraints of the robot. For example, the control unit controls natural movement by taking into account the range of motion of the robot's joints. The control unit can also control energy-efficient movement by taking into account the battery life of the robot. Furthermore, the control unit can control stable movement by taking into account the weight balance of the robot. This enables control that takes into account the physical constraints of the robot. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input physical constraint data of the robot into the generation AI and cause the generation AI to optimize the control method based on the physical constraints.

[0084] When controlling the robot, the control unit can improve control accuracy based on live musical performance data. For example, the control unit can reflect tempo fluctuations in real time based on the live performance data. The control unit can also analyze audience reactions from the live performance data and adjust the robot's movements. Furthermore, the control unit can also reflect improvisational performance portions in real time by referring to the live performance data. This makes it possible to improve control accuracy by utilizing live performance data. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input live performance data into a generation AI and cause the generation AI to improve control accuracy.

[0085] The control unit can estimate the user's emotions and determine the robot's action priorities based on the estimated user's emotions. The control unit, for example, estimates the user's emotions. The user's emotions are estimated using facial expression recognition, voice analysis, biometric data, etc. The control unit can also determine the robot's action priorities based on the estimated user's emotions. For example, if the user is relaxed, the control unit prioritizes slow movements. If the user is excited, the control unit prioritizes dynamic movements. If the user is sad, the control unit prioritizes gentle movements. This allows the robot's action priorities to be determined according to the user's emotions. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input the user's emotion data into a generation AI and cause the generation AI to determine the robot's action priorities based on the emotions.

[0086] When controlling the robot, the control unit can perform control based on the geographical origin of the music. For example, the control unit controls movements that reflect a specific dance style based on the geographical origin of the music. The control unit can also control movements that reflect a specific rhythm pattern based on the geographical origin. Furthermore, the control unit can control movements that reflect the movements of a specific musical instrument based on the geographical origin. This enables control of the robot taking the geographical origin into consideration. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input geographical origin data of the music to the generation AI and cause the generation AI to perform control based on the geographical origin.

[0087] The control unit can improve control accuracy by referring to music-related literature when controlling the robot. For example, the control unit can extract a specific dance style from music-related literature and reflect it in the movement. The control unit can also extract a specific rhythm pattern from music-related literature and reflect it in the movement. Furthermore, the control unit can extract the movement of a specific instrument from music-related literature and reflect it in the movement. This makes it possible to improve control accuracy by utilizing related literature. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input music-related literature data into the generation AI and cause the generation AI to improve control accuracy.

[0088] The control unit can control the robot based on the market value of the music. For example, the control unit can control movements that reflect popular dance styles based on the market value of the music. The control unit can also control movements that reflect popular rhythm patterns based on the market value of the music. Furthermore, the control unit can control movements that reflect the movements of popular musical instruments based on the market value of the music. This enables control of the robot taking market value into consideration. Some or all of the above-described processing in the control unit can be performed using AI, for example, or without AI. For example, the control unit can input market value data of music to the generation AI and cause the generation AI to perform control based on market value. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and control unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the control unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and control unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the control unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and control unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the control unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, generation unit, and control unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the control unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When analyzing music, the analysis unit can perform the analysis based on the historical background of the music. For example, the analysis unit can perform the analysis by emphasizing rhythmic patterns that were popular in a particular era. The analysis unit can also perform the analysis by emphasizing the sounds of a particular instrument based on the historical background. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the historical background. This enables analysis that takes the historical background of the music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input historical background data of the music into the generation AI and cause the generation AI to perform an analysis based on the historical background.

[0091] When generating dance motions, the generation unit can optimize the motions based on the user's physical characteristics. For example, the generation unit generates dance motions that include natural movements by taking into account data such as the user's height, weight, and flexibility. The generation unit can also generate dance motions that include energy-efficient movements by taking into account the user's muscle strength and endurance. Furthermore, the generation unit can generate dance motions that include stable movements by taking into account the user's sense of balance. This makes it possible to generate motions that take the user's physical characteristics into account. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input the user's physical characteristic data into the generation AI and cause the generation AI to optimize the motions based on the physical characteristics.

[0092] When controlling the robot, the control unit can optimize the control method based on environmental data. For example, the control unit adjusts the robot's movement taking into account environmental data such as the ambient temperature, humidity, and lighting conditions. The control unit can also optimize the robot's movement taking into account data such as the floor material and slope. Furthermore, the control unit can adjust the robot's movement taking into account the position and movement of surrounding obstacles. This enables control of the robot taking into account environmental data. Some or all of the above-described processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input environmental data to a generation AI and cause the generation AI to optimize the control method based on the environmental data.

[0093] When analyzing music, the analysis unit can perform the analysis based on the cultural background of the music. For example, the analysis unit can perform the analysis by emphasizing rhythmic patterns unique to a particular culture. The analysis unit can also perform the analysis by emphasizing the sounds of a particular instrument based on the cultural background. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the cultural background. This enables analysis that takes into account the cultural background of the music. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input cultural background data of the music into the generation AI and cause the generation AI to perform an analysis based on the cultural background.

[0094] When generating dance motions, the generation unit can estimate the user's emotions and adjust the speed of the motions based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates motions at a slow speed. If the user is excited, the generation unit generates motions at a fast speed. If the user is sad, the generation unit generates motions at a gentle speed. This makes it possible to adjust the speed of the motions according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the speed of the motions based on the emotion.

[0095] When controlling the robot, the control unit can estimate the user's emotions and adjust the intensity of the robot's movements based on the estimated user emotions. For example, if the user is relaxed, the robot's movements can be adjusted gently. If the user is excited, the robot's movements can be adjusted strongly. If the user is sad, the robot's movements can be adjusted gently. This makes it possible to adjust the intensity of the robot's movements according to the user's emotions. Some or all of the above-mentioned processing in the control unit may be performed using AI, for example, or may be performed without using AI. For example, the control unit can input the user's emotion data into the generation AI and have the generation AI adjust the intensity of the robot's movements based on the emotion.

[0096] When analyzing music, the analysis unit can perform the analysis based on the psychological impact of the music. For example, the analysis unit can perform the analysis by emphasizing the psychological impact of a specific rhythm pattern. The analysis unit can also perform the analysis by emphasizing the sound of a specific instrument based on the psychological impact. Furthermore, the analysis unit can perform the analysis by emphasizing a specific musical style based on the psychological impact. This enables analysis that takes into account the psychological impact of music. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input psychological impact data of music to a generation AI and cause the generation AI to perform an analysis based on the psychological impact.

[0097] When generating dance motions, the generation unit can estimate the user's emotions and adjust the complexity of the motions based on the estimated user emotions. For example, if the user is relaxed, the generation unit generates motions including simple movements. If the user is excited, the generation unit generates motions including complex movements. If the user is sad, the generation unit generates motions including gentle movements. This makes it possible to adjust the complexity of the motions according to the user's emotions. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the complexity of the motions based on the emotion.

[0098] When controlling the robot, the control unit can estimate the user's emotions and adjust the timing of the robot's movements based on the estimated user emotions. For example, if the user is relaxed, the robot's movements can be adjusted to slower timing. If the user is excited, the robot's movements can be adjusted to faster timing. If the user is sad, the robot's movements can be adjusted to gentler timing. This makes it possible to adjust the timing of the robot's movements according to the user's emotions. Some or all of the above-mentioned processing in the control unit may be performed using, for example, AI, or may be performed without using AI. For example, the control unit can input user emotion data into a generation AI and have the generation AI adjust the timing of the robot's movements based on the emotion.

[0099] When analyzing music, the analysis unit can perform the analysis based on the social impact of the music. For example, the analysis unit can perform the analysis by emphasizing the social impact of a particular rhythm pattern. The analysis unit can also perform the analysis by emphasizing the sound of a particular instrument based on the social impact. Furthermore, the analysis unit can perform the analysis by emphasizing a particular musical style based on the social impact. This enables analysis that takes the social impact of music into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input social impact data of music into the generation AI and cause the generation AI to perform an analysis based on the social impact.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The analysis unit analyzes the music's characteristics, tempo, and chorus. Specifically, it analyzes the tempo, beat, and melody line of the music, as well as characteristics such as the music's genre, tone, and volume. It also analyzes the music's structure to identify the location of the chorus. Step 2: The generator generates a dance motion file based on the results of the analysis by the analyzer. Specifically, it generates steps that match the rhythm and generates a motion file that includes dynamic movements that match the chorus. It also designs appropriate dance motions that match the characteristics of the music. Step 3: The control unit controls the robot based on the motion file generated by the generator. Specifically, the control unit controls the robot's movements based on the generated motion file, adjusts the robot's movements in real time, and establishes a feedback loop to accurately control the robot's movements.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0156] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0157] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0158] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0159] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0160] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0161] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0162] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0163] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0164] 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.

[0165] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0166] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0167] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0168] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0169] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0170] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0173] [Explanation of symbols]

[0174] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis section that analyzes the characteristics, tempo, and chorus of the music, a generation unit that generates a dance motion file based on the analysis result by the analysis unit; a control unit that controls the robot based on the motion file generated by the generation unit. A system characterized by:

2. The analysis unit Analyze the tempo, beat, and melody line of music 2. The system of claim 1.

3. The generation unit Generate motion files that include steps that match the rhythm and dynamic movements that match the chorus 2. The system of claim 1.

4. The control unit Control the robot based on the generated motion file 2. The system of claim 1.

5. The analysis unit Estimate the user's emotions and adjust the music analysis method based on the estimated user emotions.

2. The system of claim 1.

6. The analysis unit When analyzing music, the analysis algorithm is optimized based on past music data.

2. The system of claim 1.

7. The analysis unit When analyzing music, apply different analysis methods depending on the music genre.

2. The system of claim 1.

8. The analysis unit When analyzing music, improve analysis accuracy based on live music performance data 2. The system of claim 1.

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