system

Abstract

The system according to this embodiment aims to automate the setting of sound and lighting, enabling appropriate settings even without specialized knowledge. [Solution] The system according to the embodiment comprises a collection unit, a setting unit, an analysis unit, an adjustment unit, and a linkage unit. The collection unit collects information such as the size of the stage, equipment, capacity, and humidity. The setting unit sets the sound based on the information collected by the collection unit. The analysis unit analyzes the rhythm and tempo of the music. The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. The linkage unit adjusts the lighting settings in accordance with the changes in the sound settings.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem of insufficient manpower because the settings of acoustics and lighting require specialized knowledge and the process is complicated.

[0005] The system according to the embodiment aims to automate the settings of acoustics and lighting and perform appropriate settings even without specialized knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a setting unit, an analysis unit, an adjustment unit, and a coordination unit. The data collection unit collects information such as the size of the stage, equipment, capacity, and humidity. The setting unit performs sound settings based on the information collected by the data collection unit. The analysis unit analyzes the rhythm and tempo of the music. The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. The coordination unit adjusts the lighting settings in accordance with changes in the sound settings. [Effects of the Invention]

[0007] The system according to this embodiment automates the setting of sound and lighting, allowing for appropriate settings to be made even without specialized knowledge. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The stage production system according to an embodiment of the present invention is an AI agent for automating the settings of sound and lighting, which are essential for live performances and stage shows. This stage production system collects detailed information such as the size of the stage, equipment, capacity, and humidity, and inputs this information into the AI ​​agent. The AI ​​agent uses a model that has learned human sensibilities to automatically perform optimal sound settings. For example, if the stage is large and has a large capacity, it adjusts the sound balance so that sound reaches all audience members evenly. Also, if the humidity is high, the way sound travels changes, so it makes adjustments accordingly. Next, the lighting settings will be described. The AI ​​agent is used to automate real-time stage lighting in sync with the music. The AI ​​agent analyzes the rhythm and tempo of the music and adjusts the color and brightness of the lighting accordingly. For example, during exciting parts of the music, the lighting is brightened to enhance the audience's excitement. During quiet parts, the lighting is dimmed to create a calm atmosphere. In this way, the AI ​​agent of the present invention automates the settings of sound and lighting, enabling high-quality stage production even without specialized knowledge. This reduces the complexity of the sound design process and solves the problem of labor shortages. This allows the stage production system to automate sound and lighting settings, enabling high-quality stage productions even without specialized knowledge.

[0029] The stage production system according to this embodiment comprises a data collection unit, a setting unit, an analysis unit, an adjustment unit, and a coordination unit. The data collection unit collects information such as the size of the stage, equipment, capacity, and humidity. For example, the data collection unit collects information such as the width, depth, and height of the stage. The data collection unit can also collect information such as sound equipment, lighting equipment, and video equipment. Furthermore, the data collection unit can also collect information on capacity and humidity. For example, the data collection unit collects the number of seats and standing occupants and measures relative humidity and absolute humidity. The setting unit performs sound settings based on the information collected by the data collection unit. For example, the setting unit performs sound settings such as volume, sound quality, and echo. Furthermore, the setting unit can also adjust the balance of the sound based on the collected information. For example, if the stage is large and the capacity is large, the setting unit adjusts the balance of the sound so that the sound reaches all spectators equally. The analysis unit analyzes the rhythm and tempo of the music. For example, the analysis unit analyzes rhythm and tempo such as BPM (beats per minute) and time signature. Furthermore, the analysis unit can also use AI to analyze the rhythm and tempo of the music. For example, the analysis unit can use an AI model to analyze the rhythm and tempo of the music. The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. The adjustment unit adjusts lighting such as brightness, color, and illumination angle. The adjustment unit can also adjust the color and brightness of the lighting in accordance with the rhythm and tempo of the music. For example, the adjustment unit brightens the lighting during climactic parts of the music to enhance the audience's excitement. The coordination unit adjusts the lighting settings in response to changes in the sound settings. For example, the coordination unit adjusts the color and brightness of the lighting in response to changes in the sound settings. The coordination unit can also adjust the lighting pattern in response to changes in the sound settings. For example, the coordination unit adjusts the color of the lighting in real time in response to changes in the sound settings. As a result, the stage production system according to this embodiment automates the settings of sound and lighting, enabling high-quality stage production even without specialized knowledge.

[0030] The data collection department gathers information such as stage size, equipment, capacity, and humidity. Specifically, it collects physical dimensions such as the width, depth, and height of the stage, providing basic data for optimizing the stage layout and equipment placement. The data collection department also collects detailed information on sound equipment, lighting equipment, and video equipment. For example, by collecting information on the type and placement of sound equipment, the location and type of lighting equipment, and the resolution and installation location of video equipment, it provides important data for formulating an overall stage production plan. Furthermore, the data collection department also collects information on capacity and humidity. Specifically, it counts the number of seats and standing spectators to understand the density and placement of the audience. In addition, by measuring relative humidity and absolute humidity, it is possible to understand the environmental conditions that affect sound and lighting settings. In this way, the data collection department comprehensively collects a variety of information necessary for stage production and provides it to other departments, thereby supporting the optimization of stage production.

[0031] The settings unit performs sound settings based on the information collected by the data collection unit. Specifically, it adjusts sound settings such as volume, sound quality, and echo to optimize the overall acoustic environment of the stage. For example, if the stage is large and has a large capacity, it adjusts the sound balance to ensure that sound reaches all audience members evenly. In volume settings, it adjusts the output of each speaker to ensure sound uniformity. In sound quality settings, it uses an equalizer to adjust the balance of bass, midrange, and treble to achieve clear and rich sound. In echo settings, it adjusts the strength and duration of the echo according to the size and shape of the stage to provide a natural sound effect. The settings unit can also adjust the sound balance based on the collected information. For example, it considers sound reflection and absorption according to the shape of the stage and the arrangement of the audience to create the optimal acoustic environment. In this way, the settings unit can perform detailed settings to maintain a high quality overall acoustic environment of the stage based on the information provided by the data collection unit.

[0032] The analysis unit analyzes the rhythm and tempo of music. Specifically, it analyzes rhythm and tempo such as BPM (beats per minute) and time signature to gain a detailed understanding of the music's characteristics. The analysis uses AI to process data in real time and accurately analyze the rhythm and tempo of music. For example, the analysis unit uses an AI model to analyze the rhythm and tempo of music. The AI ​​model has the ability to learn from a large amount of music data and recognize rhythm and tempo patterns. This allows the analysis unit to quickly and accurately analyze the rhythm and tempo of music and provide information necessary for stage production. Furthermore, the analysis unit can analyze not only the rhythm and tempo of music, but also its structure and emotional elements. For example, it can identify the climaxes and quiet parts of the music and plan the performance accordingly. The analysis unit can also utilize past performance data and audience reaction data to suggest more effective performances. In this way, the analysis unit can analyze the characteristics of music in detail and play an important role in improving the quality of stage production.

[0033] The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. Specifically, it adjusts lighting such as brightness, color, and beam angle to optimize the overall visual presentation of the stage. For example, it can adjust the color and brightness of the lighting to match the rhythm and tempo of the music. During climactic parts of the music, the lighting is brightened to enhance the audience's excitement. Conversely, during quieter parts, the lighting is dimmed to create a calm atmosphere. The adjustment unit can adjust not only the color and brightness of the lighting, but also the beam angle and pattern. For example, it can change the beam angle to match specific scenes in the performance to highlight particular areas on the stage. It can also create dynamic effects by changing the lighting pattern. Furthermore, the adjustment unit can adjust the movement of the lighting in real time based on the information provided by the analysis unit. This allows the adjustment unit to create dynamic lighting effects that match the rhythm and tempo of the music, providing the audience with an immersive visual experience.

[0034] The Coordination Department adjusts lighting settings in response to changes in sound settings. Specifically, it adjusts the color and brightness of the lighting in accordance with changes in sound settings to unify the overall stage production. For example, by adjusting the color of the lighting in real time in response to changes in sound settings, the sense of unity between music and lighting is enhanced. It can also adjust the lighting patterns in response to changes in sound settings. For example, if the tempo of the music speeds up, the flashing speed of the lights is increased to increase visual stimulation. Conversely, if the tempo of the music slows down, the flashing speed of the lights is slowed down to create a calmer atmosphere. By coordinating sound and lighting settings in real time, the Coordination Department optimizes the overall stage production. Furthermore, the Coordination Department can also collaborate with other departments to plan the overall stage production. For example, it can collaborate with the Data Collection Department, Setting Department, Analysis Department, and Adjustment Department to plan and execute detailed stage production plans. In this way, the Coordination Department can play a crucial role in unifying sound and lighting settings and realizing high-quality stage productions.

[0035] The learning unit collects data from past stage productions and learns from it. For example, the learning unit collects data on sound settings, lighting settings, and audience reaction data from past stage productions. The learning unit trains an AI model based on this data. For example, the learning unit inputs data from past stage productions into the AI ​​model and trains it to learn optimal sound and lighting settings. This allows the AI ​​agent to learn human sensibilities, improving the accuracy of sound and lighting settings. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data from past stage productions into a generative AI and have the generative AI learn optimal sound and lighting settings.

[0036] The detection unit detects changes in sound settings. For example, the detection unit detects changes in volume or sound quality. By detecting changes in sound settings, the detection unit can quickly adjust the lighting settings. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings using an AI model and adjust the lighting settings.

[0037] The data collection unit meticulously collects information on the stage size and equipment layout using 3D scanning technology. For example, the data collection unit 3D scans the entire stage to collect detailed information on equipment placement and stage shape. The data collection unit can also extract information necessary for sound settings based on the 3D scan data to provide an optimal sound environment. Furthermore, the data collection unit can use 3D scanning technology to collect detailed information such as the height and depth of the stage and reflect this in the sound settings. Thus, by using 3D scanning technology, detailed information about the stage can be collected and reflected in the sound settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input 3D scan data into an AI model and extract information necessary for sound settings.

[0038] The data collection unit updates the collected information in real time and responds immediately to changes. For example, if the stage conditions change, the data collection unit updates the information in real time and automatically adjusts the sound settings. The data collection unit can also update the information in real time and optimize the sound settings if the capacity increases or decreases. Furthermore, if the humidity or temperature changes, the data collection unit can update the information in real time and reflect this in the sound settings. This allows for sound settings that respond to changes by updating the information in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the real-time updated information into an AI model and automatically adjust the sound settings.

[0039] The data collection unit detects audience movements and reactions using sensors during data collection and reflects this in the sound and lighting settings. For example, the data collection unit can detect audience movements using sensors and reflect this in the sound settings. It can also detect audience reactions using sensors and reflect this in the lighting settings. Furthermore, the data collection unit can detect audience movements and reactions in real time and automatically adjust the sound and lighting settings. This allows for the optimization of sound and lighting settings by detecting audience movements and reactions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can detect audience movements and reactions using an AI model and automatically adjust the sound and lighting settings.

[0040] The data collection unit incorporates information about the external environment during data collection and utilizes it for setting up sound and lighting. For example, the data collection unit collects weather information and reflects it in the sound settings. It can also collect temperature information and reflect it in the lighting settings. Furthermore, the data collection unit can collect information about the external environment in real time and automatically adjust the sound and lighting settings. This allows for the optimization of sound and lighting settings by incorporating information about the external environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information about the external environment using an AI model and utilize it for setting up sound and lighting.

[0041] The settings unit, when setting the sound, refers to past stage production data to make the optimal settings. For example, the settings unit refers to past stage production data to make the optimal sound settings. The settings unit can also adjust the parameters of the sound settings based on past stage production data. Furthermore, the settings unit can analyze past stage production data and propose the optimal sound settings. In this way, the optimal sound settings can be made by referring to past stage production data. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI. For example, the settings unit can input past stage production data into an AI model to make the optimal sound settings.

[0042] The settings unit optimizes sound distribution by considering the placement and movement of the audience during sound setup. For example, the settings unit optimizes sound distribution by considering the placement of the audience. The settings unit can also adjust the sound distribution in real time by considering the movement of the audience. Furthermore, the settings unit can detect the placement and movement of the audience using sensors and optimize the sound distribution. This allows for the optimization of sound distribution by considering the placement and movement of the audience. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can detect the placement and movement of the audience using an AI model and optimize the sound distribution.

[0043] The settings unit tracks the position and movement of performers on stage in real time during sound setup and adjusts the sound accordingly. For example, the settings unit can track the position of performers on stage in real time and adjust the sound settings. It can also track the movement of performers on stage in real time and adjust the sound settings. Furthermore, the settings unit can detect the position and movement of performers using sensors and optimize the sound settings. This allows for optimization of sound settings by tracking the position and movement of performers on stage in real time. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can detect the position and movement of performers using an AI model and optimize the sound settings.

[0044] The settings unit performs optimal settings during sound configuration, taking into account the characteristics of different audio equipment. For example, the settings unit considers the characteristics of different audio equipment to perform optimal sound configurations. The settings unit can also analyze the characteristics of audio equipment and propose optimal sound configurations. Furthermore, the settings unit can consider the characteristics of different audio equipment in real time and adjust the sound configurations. This makes it possible to achieve optimal sound configurations by considering the characteristics of different audio equipment. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can analyze the characteristics of audio equipment using an AI model and perform optimal sound configurations.

[0045] The analysis unit improves the accuracy of its analysis of rhythm and tempo by referring to past performance data when analyzing the rhythm and tempo of music. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by referring to past performance data. The analysis unit can also optimize the rhythm and tempo analysis algorithm based on past performance data. Furthermore, the analysis unit can improve the accuracy of rhythm and tempo analysis by analyzing past performance data. As a result, the accuracy of rhythm and tempo analysis is improved by referring to past performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past performance data into an AI model to improve the accuracy of rhythm and tempo analysis.

[0046] The analysis unit applies analysis algorithms that correspond to different genres of music when analyzing the rhythm and tempo of music. For example, the analysis unit applies analysis algorithms that correspond to different genres of music to improve the accuracy of rhythm and tempo analysis. The analysis unit can also select the optimal analysis algorithm for each genre and perform rhythm and tempo analysis. Furthermore, the analysis unit can apply analysis algorithms that correspond to different genres of music in real time to perform rhythm and tempo analysis. This improves the accuracy of rhythm and tempo analysis by supporting different genres of music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply analysis algorithms that correspond to different genres of music using an AI model to perform rhythm and tempo analysis.

[0047] The analysis unit improves the accuracy of its analysis of music's rhythm and tempo by incorporating audience reaction data. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by incorporating audience reaction data. Furthermore, the analysis unit can optimize its rhythm and tempo analysis algorithms based on audience reaction data. In addition, the analysis unit can incorporate audience reaction data in real time and perform rhythm and tempo analysis. This improves the accuracy of rhythm and tempo analysis by incorporating audience reaction data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by incorporating audience reaction data using an AI model.

[0048] The analysis unit analyzes the rhythm and tempo of music while also considering the structure and melody line of the music. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by considering the structure of the music. Furthermore, the analysis unit can optimize the rhythm and tempo analysis algorithm by considering the melody line. In addition, the analysis unit can perform rhythm and tempo analysis while considering the structure and melody line of the music in real time. This improves the accuracy of rhythm and tempo analysis by considering the structure and melody line of the music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform rhythm and tempo analysis while considering the structure and melody line of the music using an AI model.

[0049] The adjustment unit performs optimal adjustments by referring to past stage production data when adjusting the lighting. For example, the adjustment unit can refer to past stage production data to perform optimal lighting adjustments. The adjustment unit can also adjust the color and brightness of the lighting based on past stage production data. Furthermore, the adjustment unit can analyze past stage production data and propose optimal lighting adjustments. This makes it possible to perform optimal lighting adjustments by referring to past stage production data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input past stage production data into an AI model to perform optimal lighting adjustments.

[0050] The adjustment unit tracks the movements of performers on stage in real time and adjusts the lighting accordingly. For example, the adjustment unit can track the movements of performers on stage in real time and adjust the lighting. The adjustment unit can also detect the performers' movements with sensors and adjust the color and brightness of the lighting. Furthermore, the adjustment unit can track the performers' movements in real time and optimize the lighting pattern. This allows for the optimization of lighting adjustments by tracking the movements of performers on stage in real time. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can detect the performers' movements using an AI model and adjust the lighting accordingly.

[0051] The adjustment unit optimizes the lighting effect by incorporating audience reaction data when adjusting the lighting. For example, the adjustment unit can incorporate audience reaction data to optimize the lighting effect. The adjustment unit can also adjust the color and brightness of the lighting based on audience reaction data. Furthermore, the adjustment unit can incorporate audience reaction data in real time and optimize the lighting pattern. In this way, the lighting effect can be optimized by incorporating audience reaction data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can incorporate audience reaction data using an AI model to optimize the lighting effect.

[0052] The adjustment unit performs optimal adjustments when adjusting the lighting, taking into account the characteristics of different lighting equipment. For example, the adjustment unit considers the characteristics of different lighting equipment and performs optimal lighting adjustments. The adjustment unit can also analyze the characteristics of lighting equipment and propose optimal lighting adjustments. Furthermore, the adjustment unit can consider the characteristics of different lighting equipment in real time and adjust the color and brightness of the lighting. This makes it possible to perform optimal lighting adjustments by considering the characteristics of different lighting equipment. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can analyze the characteristics of lighting equipment using an AI model and perform optimal lighting adjustments.

[0053] The integrated unit adjusts the color and brightness of the lighting in real time in response to changes in the sound settings. For example, the integrated unit can adjust the color of the lighting in real time in response to changes in the sound settings. It can also adjust the brightness of the lighting in real time in response to changes in the sound settings. Furthermore, the integrated unit can also adjust the lighting pattern in real time in response to changes in the sound settings. This allows for more appropriate stage production by adjusting the color and brightness of the lighting in real time in response to changes in the sound settings. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without using AI. For example, the integrated unit can detect changes in sound settings using an AI model and adjust the color and brightness of the lighting in real time.

[0054] The integration unit performs optimal sound and lighting coordination by referring to past stage production data. For example, the integration unit can refer to past stage production data to perform optimal sound and lighting coordination. The integration unit can also adjust the sound and lighting coordination method based on past stage production data. Furthermore, the integration unit can analyze past stage production data and propose the optimal sound and lighting coordination. This makes it possible to achieve optimal sound and lighting coordination by referring to past stage production data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past stage production data into an AI model to perform optimal sound and lighting coordination.

[0055] The integration unit optimizes the effect of sound and lighting coordination by incorporating audience reaction data. For example, the integration unit can incorporate audience reaction data to optimize the effect of sound and lighting coordination. The integration unit can also adjust the method of sound and lighting coordination based on audience reaction data. Furthermore, the integration unit can incorporate audience reaction data in real time to optimize sound and lighting coordination. This allows for the optimization of sound and lighting coordination by incorporating audience reaction data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can incorporate audience reaction data using an AI model to optimize the effect of sound and lighting coordination.

[0056] The integration unit performs optimal integration of sound and lighting, taking into account the characteristics of different equipment. For example, the integration unit considers the characteristics of different equipment to perform optimal sound and lighting integration. The integration unit can also analyze the characteristics of the equipment and propose the optimal sound and lighting integration method. Furthermore, the integration unit can consider the characteristics of different equipment in real time and adjust the sound and lighting integration. This makes it possible to achieve optimal sound and lighting integration by considering the characteristics of different equipment. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can analyze the characteristics of the equipment using an AI model to perform optimal sound and lighting integration.

[0057] The learning unit optimizes the learning algorithm by referring to past stage performance data during training. For example, the learning unit optimizes the learning algorithm by referring to past stage performance data. The learning unit can also adjust the parameters of the learning algorithm based on past stage performance data. Furthermore, the learning unit can optimize the learning algorithm by analyzing past stage performance data. This improves the accuracy of the learning algorithm by referring to past stage performance data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past stage performance data into an AI model and optimize the learning algorithm.

[0058] The learning unit broadens its learning scope by incorporating stage production data from different genres during the learning process. For example, the learning unit can broaden its learning scope by incorporating stage production data from different genres. Furthermore, the learning unit can select the most suitable learning data for each genre and optimize the learning algorithm. In addition, the learning unit can broaden its learning scope by incorporating stage production data from different genres in real time. This broadens the learning scope by incorporating stage production data from different genres. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can broaden its learning scope by incorporating stage production data from different genres using an AI model.

[0059] The learning unit improves the accuracy of its learning by incorporating audience reaction data during training. For example, the learning unit can improve the accuracy of its learning by incorporating audience reaction data. Furthermore, the learning unit can optimize its learning algorithm based on audience reaction data. In addition, the learning unit can improve the accuracy of its learning by incorporating audience reaction data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can improve the accuracy of its learning by incorporating audience reaction data using an AI model.

[0060] The learning unit selects training data during training, taking into account the characteristics of different equipment. For example, the learning unit selects optimal training data by considering the characteristics of different equipment. The learning unit can also analyze the characteristics of the equipment and optimize the learning algorithm. Furthermore, the learning unit can consider the characteristics of different equipment in real time and select training data. This allows for the selection of optimal training data by considering the characteristics of different equipment. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can analyze the characteristics of the equipment using an AI model and select optimal training data.

[0061] The detection unit detects changes in sound settings in real time and responds immediately. For example, the detection unit can detect changes in sound settings in real time and immediately adjust the sound environment. The detection unit can also detect changes in sound settings in real time and reflect them in the lighting settings. Furthermore, the detection unit can detect changes in sound settings in real time and adjust them according to the audience's reaction. This allows for immediate response by detecting changes in sound settings in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings in real time using an AI model and respond immediately.

[0062] The detection unit improves detection accuracy by referring to past change history when detecting changes in sound settings. For example, the detection unit improves detection accuracy by referring to past change history of sound settings. The detection unit can also improve detection accuracy by predicting changes in sound settings based on past change history. Furthermore, the detection unit can analyze past change history and quickly detect changes in sound settings. This allows for rapid detection of changes in sound settings by referring to past change history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings by referring to past change history using an AI model.

[0063] The detection unit improves detection accuracy by incorporating audience reaction data when detecting changes in sound settings. For example, the detection unit incorporates audience reaction data to detect changes in sound settings. The detection unit can also predict changes in sound settings based on audience reaction data to improve detection accuracy. Furthermore, the detection unit can incorporate audience reaction data in real time to quickly detect changes in sound settings. This allows for rapid detection of changes in sound settings by incorporating audience reaction data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can incorporate audience reaction data using an AI model to detect changes in sound settings.

[0064] The detection unit, when detecting changes in acoustic settings, takes into account the characteristics of different equipment. For example, the detection unit considers the characteristics of different equipment to detect changes in acoustic settings. The detection unit can also analyze the characteristics of the equipment, predict changes in acoustic settings, and improve detection accuracy. Furthermore, the detection unit can consider the characteristics of different equipment in real time and quickly detect changes in acoustic settings. This allows for rapid detection of changes in acoustic settings by considering the characteristics of different equipment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can analyze the characteristics of the equipment using an AI model and detect changes in acoustic settings.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The stage production system can further adapt to different genres of music through its sound and lighting settings. For example, in a rock concert, an energetic atmosphere can be created by increasing the sound volume and using intense lighting colors. In a classical concert, a calm atmosphere can be created by lowering the sound volume and using soft lighting colors. Furthermore, in a jazz concert, a relaxed atmosphere can be created by increasing the sound echo and using warm lighting colors. This allows for sound and lighting settings tailored to different genres of music, enabling a wider variety of stage productions.

[0067] The stage production system can also detect audience movements and reactions in real time and adjust sound and lighting settings accordingly. For example, if the audience is waving, the lights can be made to flash in sync with their movements, creating a sense of unity with the audience. If the audience is listening quietly, the sound volume can be lowered and the lighting softened to create a calm atmosphere. Furthermore, if the audience is cheering, the sound volume can be increased and the lighting brightened to create an even more exciting effect. This allows for sound and lighting settings to be adjusted according to audience movements and reactions, resulting in a more dynamic stage production.

[0068] The stage production system can further incorporate information about the external environment to adjust the sound and lighting settings. For example, if it's raining, the sound echo can be increased and the lighting can be changed to a blue hue to create a rainy atmosphere. If it's sunny, the sound volume can be increased and the lighting can be changed to a brighter hue to create a sunny atmosphere. Furthermore, if the temperature is high, the sound volume can be decreased and the lighting can be changed to a cool hue to create a cooler atmosphere. This allows for sound and lighting settings to be adjusted according to information about the external environment, resulting in a more realistic stage production.

[0069] The stage production system can further optimize sound and lighting settings by referencing past stage production data. For example, by referencing sound and lighting settings that were well-received in past concerts and implementing similar settings, audience satisfaction can be increased. It can also analyze past stage production data and suggest optimal sound and lighting settings. Furthermore, it can adjust sound and lighting settings in real time based on past stage production data. This allows for more effective sound and lighting settings by utilizing past stage production data, thereby improving the quality of the stage production.

[0070] The stage production system can further adapt to different genres of music through its sound and lighting settings. For example, in a rock concert, an energetic atmosphere can be created by increasing the sound volume and using intense lighting colors. In a classical concert, a calm atmosphere can be created by lowering the sound volume and using soft lighting colors. Furthermore, in a jazz concert, a relaxed atmosphere can be created by increasing the sound echo and using warm lighting colors. This allows for sound and lighting settings tailored to different genres of music, enabling a wider variety of stage productions.

[0071] The stage production system can also detect audience movements and reactions in real time and adjust sound and lighting settings accordingly. For example, if the audience is waving, the lights can be made to flash in sync with their movements, creating a sense of unity with the audience. If the audience is listening quietly, the sound volume can be lowered and the lighting softened to create a calm atmosphere. Furthermore, if the audience is cheering, the sound volume can be increased and the lighting brightened to create an even more exciting effect. This allows for sound and lighting settings to be adjusted according to audience movements and reactions, resulting in a more dynamic stage production.

[0072] The stage production system can further incorporate information about the external environment to adjust the sound and lighting settings. For example, if it's raining, the sound echo can be increased and the lighting can be changed to a blue hue to create a rainy atmosphere. If it's sunny, the sound volume can be increased and the lighting can be changed to a brighter hue to create a sunny atmosphere. Furthermore, if the temperature is high, the sound volume can be decreased and the lighting can be changed to a cool hue to create a cooler atmosphere. This allows for sound and lighting settings to be adjusted according to information about the external environment, resulting in a more realistic stage production.

[0073] The stage production system can further optimize sound and lighting settings by referencing past stage production data. For example, by referencing sound and lighting settings that were well-received in past concerts and implementing similar settings, audience satisfaction can be increased. It can also analyze past stage production data and suggest optimal sound and lighting settings. Furthermore, it can adjust sound and lighting settings in real time based on past stage production data. This allows for more effective sound and lighting settings by utilizing past stage production data, thereby improving the quality of the stage production.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The data collection unit gathers information such as the size of the stage, equipment, capacity, and humidity. For example, it collects information such as the width, depth, and height of the stage, sound equipment, lighting equipment, video equipment, number of seats, number of standing spectators, relative humidity, and absolute humidity. Step 2: The settings unit performs sound settings based on the information collected by the data collection unit. For example, it sets sound settings such as volume, sound quality, and echo, and adjusts the sound balance according to the size of the stage and the number of people who will be seated. Step 3: The analysis unit analyzes the rhythm and tempo of the music. For example, it can analyze rhythm and tempo such as BPM (beats per minute) and time signature, and can also perform the analysis using an AI model. Step 4: The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. For example, it adjusts the brightness, color, and beam angle of the lighting, and adjusts the color and brightness of the lighting in accordance with the rhythm and tempo of the music. Step 5: The integrated unit adjusts the lighting settings in response to changes in the sound settings. For example, it adjusts the color, brightness, and pattern of the lighting in real time in response to changes in the sound settings.

[0076] (Example of form 2) The stage production system according to an embodiment of the present invention is an AI agent for automating the settings of sound and lighting, which are essential for live performances and stage shows. This stage production system collects detailed information such as the size of the stage, equipment, capacity, and humidity, and inputs this information into the AI ​​agent. The AI ​​agent uses a model that has learned human sensibilities to automatically perform optimal sound settings. For example, if the stage is large and has a large capacity, it adjusts the sound balance so that sound reaches all audience members evenly. Also, if the humidity is high, the way sound travels changes, so it makes adjustments accordingly. Next, the lighting settings will be described. The AI ​​agent is used to automate real-time stage lighting in sync with the music. The AI ​​agent analyzes the rhythm and tempo of the music and adjusts the color and brightness of the lighting accordingly. For example, during exciting parts of the music, the lighting is brightened to enhance the audience's excitement. During quiet parts, the lighting is dimmed to create a calm atmosphere. In this way, the AI ​​agent of the present invention automates the settings of sound and lighting, enabling high-quality stage production even without specialized knowledge. This reduces the complexity of the sound design process and solves the problem of labor shortages. This allows the stage production system to automate sound and lighting settings, enabling high-quality stage productions even without specialized knowledge.

[0077] The stage production system according to this embodiment comprises a data collection unit, a setting unit, an analysis unit, an adjustment unit, and a coordination unit. The data collection unit collects information such as the size of the stage, equipment, capacity, and humidity. For example, the data collection unit collects information such as the width, depth, and height of the stage. The data collection unit can also collect information such as sound equipment, lighting equipment, and video equipment. Furthermore, the data collection unit can also collect information on capacity and humidity. For example, the data collection unit collects the number of seats and standing occupants and measures relative humidity and absolute humidity. The setting unit performs sound settings based on the information collected by the data collection unit. For example, the setting unit performs sound settings such as volume, sound quality, and echo. Furthermore, the setting unit can also adjust the balance of the sound based on the collected information. For example, if the stage is large and the capacity is large, the setting unit adjusts the balance of the sound so that the sound reaches all spectators equally. The analysis unit analyzes the rhythm and tempo of the music. For example, the analysis unit analyzes rhythm and tempo such as BPM (beats per minute) and time signature. Furthermore, the analysis unit can also use AI to analyze the rhythm and tempo of the music. For example, the analysis unit can use an AI model to analyze the rhythm and tempo of the music. The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. The adjustment unit adjusts lighting such as brightness, color, and illumination angle. The adjustment unit can also adjust the color and brightness of the lighting in accordance with the rhythm and tempo of the music. For example, the adjustment unit brightens the lighting during climactic parts of the music to enhance the audience's excitement. The coordination unit adjusts the lighting settings in response to changes in the sound settings. For example, the coordination unit adjusts the color and brightness of the lighting in response to changes in the sound settings. The coordination unit can also adjust the lighting pattern in response to changes in the sound settings. For example, the coordination unit adjusts the color of the lighting in real time in response to changes in the sound settings. As a result, the stage production system according to this embodiment automates the settings of sound and lighting, enabling high-quality stage production even without specialized knowledge.

[0078] The data collection department gathers information such as stage size, equipment, capacity, and humidity. Specifically, it collects physical dimensions such as the width, depth, and height of the stage, providing basic data for optimizing the stage layout and equipment placement. The data collection department also collects detailed information on sound equipment, lighting equipment, and video equipment. For example, by collecting information on the type and placement of sound equipment, the location and type of lighting equipment, and the resolution and installation location of video equipment, it provides important data for formulating an overall stage production plan. Furthermore, the data collection department also collects information on capacity and humidity. Specifically, it counts the number of seats and standing spectators to understand the density and placement of the audience. In addition, by measuring relative humidity and absolute humidity, it is possible to understand the environmental conditions that affect sound and lighting settings. In this way, the data collection department comprehensively collects a variety of information necessary for stage production and provides it to other departments, thereby supporting the optimization of stage production.

[0079] The settings unit performs sound settings based on the information collected by the data collection unit. Specifically, it adjusts sound settings such as volume, sound quality, and echo to optimize the overall acoustic environment of the stage. For example, if the stage is large and has a large capacity, it adjusts the sound balance to ensure that sound reaches all audience members evenly. In volume settings, it adjusts the output of each speaker to ensure sound uniformity. In sound quality settings, it uses an equalizer to adjust the balance of bass, midrange, and treble to achieve clear and rich sound. In echo settings, it adjusts the strength and duration of the echo according to the size and shape of the stage to provide a natural sound effect. The settings unit can also adjust the sound balance based on the collected information. For example, it considers sound reflection and absorption according to the shape of the stage and the arrangement of the audience to create the optimal acoustic environment. In this way, the settings unit can perform detailed settings to maintain a high quality overall acoustic environment of the stage based on the information provided by the data collection unit.

[0080] The analysis unit analyzes the rhythm and tempo of music. Specifically, it analyzes rhythm and tempo such as BPM (beats per minute) and time signature to gain a detailed understanding of the music's characteristics. The analysis uses AI to process data in real time and accurately analyze the rhythm and tempo of music. For example, the analysis unit uses an AI model to analyze the rhythm and tempo of music. The AI ​​model has the ability to learn from a large amount of music data and recognize rhythm and tempo patterns. This allows the analysis unit to quickly and accurately analyze the rhythm and tempo of music and provide information necessary for stage production. Furthermore, the analysis unit can analyze not only the rhythm and tempo of music, but also its structure and emotional elements. For example, it can identify the climaxes and quiet parts of the music and plan the performance accordingly. The analysis unit can also utilize past performance data and audience reaction data to suggest more effective performances. In this way, the analysis unit can analyze the characteristics of music in detail and play an important role in improving the quality of stage production.

[0081] The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. Specifically, it adjusts lighting such as brightness, color, and beam angle to optimize the overall visual presentation of the stage. For example, it can adjust the color and brightness of the lighting to match the rhythm and tempo of the music. During climactic parts of the music, the lighting is brightened to enhance the audience's excitement. Conversely, during quieter parts, the lighting is dimmed to create a calm atmosphere. The adjustment unit can adjust not only the color and brightness of the lighting, but also the beam angle and pattern. For example, it can change the beam angle to match specific scenes in the performance to highlight particular areas on the stage. It can also create dynamic effects by changing the lighting pattern. Furthermore, the adjustment unit can adjust the movement of the lighting in real time based on the information provided by the analysis unit. This allows the adjustment unit to create dynamic lighting effects that match the rhythm and tempo of the music, providing the audience with an immersive visual experience.

[0082] The Coordination Department adjusts lighting settings in response to changes in sound settings. Specifically, it adjusts the color and brightness of the lighting in accordance with changes in sound settings to unify the overall stage production. For example, by adjusting the color of the lighting in real time in response to changes in sound settings, the sense of unity between music and lighting is enhanced. It can also adjust the lighting patterns in response to changes in sound settings. For example, if the tempo of the music speeds up, the flashing speed of the lights is increased to increase visual stimulation. Conversely, if the tempo of the music slows down, the flashing speed of the lights is slowed down to create a calmer atmosphere. By coordinating sound and lighting settings in real time, the Coordination Department optimizes the overall stage production. Furthermore, the Coordination Department can also collaborate with other departments to plan the overall stage production. For example, it can collaborate with the Data Collection Department, Setting Department, Analysis Department, and Adjustment Department to plan and execute detailed stage production plans. In this way, the Coordination Department can play a crucial role in unifying sound and lighting settings and realizing high-quality stage productions.

[0083] The learning unit collects data from past stage productions and learns from it. For example, the learning unit collects data on sound settings, lighting settings, and audience reaction data from past stage productions. The learning unit trains an AI model based on this data. For example, the learning unit inputs data from past stage productions into the AI ​​model and trains it to learn optimal sound and lighting settings. This allows the AI ​​agent to learn human sensibilities, improving the accuracy of sound and lighting settings. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input data from past stage productions into a generative AI and have the generative AI learn optimal sound and lighting settings.

[0084] The detection unit detects changes in sound settings. For example, the detection unit detects changes in volume or sound quality. By detecting changes in sound settings, the detection unit can quickly adjust the lighting settings. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings using an AI model and adjust the lighting settings.

[0085] The data collection unit estimates the user's emotions and prioritizes the information to collect based on the estimated emotions. For example, if the user is nervous, the data collection unit prioritizes collecting information about the stage size and equipment to improve the accuracy of the sound settings. If the user is relaxed, the data collection unit can also prioritize collecting information about the capacity and humidity to provide a comfortable sound environment. Furthermore, if the user is excited, the data collection unit can prioritize collecting information that changes in real time to perform dynamic sound settings. This allows for more appropriate sound settings by prioritizing the information to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can estimate the user's emotions using an AI model and determine the priority of the information to collect.

[0086] The data collection unit meticulously collects information on the stage size and equipment layout using 3D scanning technology. For example, the data collection unit 3D scans the entire stage to collect detailed information on equipment placement and stage shape. The data collection unit can also extract information necessary for sound settings based on the 3D scan data to provide an optimal sound environment. Furthermore, the data collection unit can use 3D scanning technology to collect detailed information such as the height and depth of the stage and reflect this in the sound settings. Thus, by using 3D scanning technology, detailed information about the stage can be collected and reflected in the sound settings. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input 3D scan data into an AI model and extract information necessary for sound settings.

[0087] The data collection unit updates the collected information in real time and responds immediately to changes. For example, if the stage conditions change, the data collection unit updates the information in real time and automatically adjusts the sound settings. The data collection unit can also update the information in real time and optimize the sound settings if the capacity increases or decreases. Furthermore, if the humidity or temperature changes, the data collection unit can update the information in real time and reflect this in the sound settings. This allows for sound settings that respond to changes by updating the information in real time. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the real-time updated information into an AI model and automatically adjust the sound settings.

[0088] The data collection unit estimates the user's emotions and adjusts the accuracy of the collected information based on the estimated emotions. For example, if the user is tense, the data collection unit increases the accuracy of the collected information and improves the accuracy of the sound settings. The data collection unit can also adjust the accuracy of the collected information to provide a comfortable sound environment if the user is relaxed. Furthermore, if the user is excited, the data collection unit can adjust the accuracy of the collected information in real time and perform dynamic sound settings. This allows for more appropriate sound settings by adjusting the accuracy of the collected information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can estimate the user's emotions using an AI model and adjust the accuracy of the collected information.

[0089] The data collection unit detects audience movements and reactions using sensors during data collection and reflects this in the sound and lighting settings. For example, the data collection unit can detect audience movements using sensors and reflect this in the sound settings. It can also detect audience reactions using sensors and reflect this in the lighting settings. Furthermore, the data collection unit can detect audience movements and reactions in real time and automatically adjust the sound and lighting settings. This allows for the optimization of sound and lighting settings by detecting audience movements and reactions. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can detect audience movements and reactions using an AI model and automatically adjust the sound and lighting settings.

[0090] The data collection unit incorporates information about the external environment during data collection and utilizes it for setting up sound and lighting. For example, the data collection unit collects weather information and reflects it in the sound settings. It can also collect temperature information and reflect it in the lighting settings. Furthermore, the data collection unit can collect information about the external environment in real time and automatically adjust the sound and lighting settings. This allows for the optimization of sound and lighting settings by incorporating information about the external environment. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information about the external environment using an AI model and utilize it for setting up sound and lighting.

[0091] The settings unit estimates the user's emotions and adjusts the sound settings parameters based on the estimated emotions. For example, if the user is tense, the settings unit adjusts the sound settings parameters to provide a relaxing sound environment. It can also adjust the sound settings parameters to provide a comfortable sound environment if the user is relaxed. Furthermore, if the user is excited, the settings unit can adjust the sound settings parameters to provide a dynamic sound environment. This allows for the provision of a more appropriate sound environment by adjusting the sound settings parameters according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the settings unit may be performed using AI, or not. For example, the settings unit can estimate the user's emotions using an AI model and adjust the sound settings parameters accordingly.

[0092] The settings unit, when setting the sound, refers to past stage production data to make the optimal settings. For example, the settings unit refers to past stage production data to make the optimal sound settings. The settings unit can also adjust the parameters of the sound settings based on past stage production data. Furthermore, the settings unit can analyze past stage production data and propose the optimal sound settings. In this way, the optimal sound settings can be made by referring to past stage production data. Some or all of the above processing in the settings unit may be performed using AI, for example, or without using AI. For example, the settings unit can input past stage production data into an AI model to make the optimal sound settings.

[0093] The settings unit optimizes sound distribution by considering the placement and movement of the audience during sound setup. For example, the settings unit optimizes sound distribution by considering the placement of the audience. The settings unit can also adjust the sound distribution in real time by considering the movement of the audience. Furthermore, the settings unit can detect the placement and movement of the audience using sensors and optimize the sound distribution. This allows for the optimization of sound distribution by considering the placement and movement of the audience. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can detect the placement and movement of the audience using an AI model and optimize the sound distribution.

[0094] The settings unit estimates the user's emotions and determines the priority of sound settings based on the estimated emotions. For example, if the user is tense, the settings unit prioritizes relaxing sound settings. It can also prioritize comfortable sound settings if the user is relaxed. Furthermore, if the user is excited, it can prioritize dynamic sound settings. This allows for a more appropriate sound environment by prioritizing sound settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the settings unit may be performed using AI, or not. For example, the settings unit can estimate the user's emotions using an AI model and determine the priority of sound settings.

[0095] The settings unit tracks the position and movement of performers on stage in real time during sound setup and adjusts the sound accordingly. For example, the settings unit can track the position of performers on stage in real time and adjust the sound settings. It can also track the movement of performers on stage in real time and adjust the sound settings. Furthermore, the settings unit can detect the position and movement of performers using sensors and optimize the sound settings. This allows for optimization of sound settings by tracking the position and movement of performers on stage in real time. Some or all of the above-described processes in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can detect the position and movement of performers using an AI model and optimize the sound settings.

[0096] The settings unit performs optimal settings during sound configuration, taking into account the characteristics of different audio equipment. For example, the settings unit considers the characteristics of different audio equipment to perform optimal sound configurations. The settings unit can also analyze the characteristics of audio equipment and propose optimal sound configurations. Furthermore, the settings unit can consider the characteristics of different audio equipment in real time and adjust the sound configurations. This makes it possible to achieve optimal sound configurations by considering the characteristics of different audio equipment. Some or all of the above processing in the settings unit may be performed using AI, for example, or without AI. For example, the settings unit can analyze the characteristics of audio equipment using an AI model and perform optimal sound configurations.

[0097] The analysis unit estimates the user's emotions and adjusts the analysis method of the music's rhythm and tempo based on the estimated emotions. For example, if the user is relaxed, the analysis unit will analyze with a relaxed rhythm and tempo. If the user is excited, the analysis unit can also analyze with a fast rhythm and tempo. Furthermore, if the user is tense, the analysis unit can also analyze with a stable rhythm and tempo. By adjusting the analysis method of the music's rhythm and tempo according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using an AI model and adjust the analysis method of the music's rhythm and tempo.

[0098] The analysis unit improves the accuracy of its analysis of rhythm and tempo by referring to past performance data when analyzing the rhythm and tempo of music. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by referring to past performance data. The analysis unit can also optimize the rhythm and tempo analysis algorithm based on past performance data. Furthermore, the analysis unit can improve the accuracy of rhythm and tempo analysis by analyzing past performance data. As a result, the accuracy of rhythm and tempo analysis is improved by referring to past performance data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past performance data into an AI model to improve the accuracy of rhythm and tempo analysis.

[0099] The analysis unit applies analysis algorithms that correspond to different genres of music when analyzing the rhythm and tempo of music. For example, the analysis unit applies analysis algorithms that correspond to different genres of music to improve the accuracy of rhythm and tempo analysis. The analysis unit can also select the optimal analysis algorithm for each genre and perform rhythm and tempo analysis. Furthermore, the analysis unit can apply analysis algorithms that correspond to different genres of music in real time to perform rhythm and tempo analysis. This improves the accuracy of rhythm and tempo analysis by supporting different genres of music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply analysis algorithms that correspond to different genres of music using an AI model to perform rhythm and tempo analysis.

[0100] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using an AI model and adjust the display method of the analysis results.

[0101] The analysis unit improves the accuracy of its analysis of music's rhythm and tempo by incorporating audience reaction data. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by incorporating audience reaction data. Furthermore, the analysis unit can optimize its rhythm and tempo analysis algorithms based on audience reaction data. In addition, the analysis unit can incorporate audience reaction data in real time and perform rhythm and tempo analysis. This improves the accuracy of rhythm and tempo analysis by incorporating audience reaction data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by incorporating audience reaction data using an AI model.

[0102] The analysis unit analyzes the rhythm and tempo of music while also considering the structure and melody line of the music. For example, the analysis unit can improve the accuracy of rhythm and tempo analysis by considering the structure of the music. Furthermore, the analysis unit can optimize the rhythm and tempo analysis algorithm by considering the melody line. In addition, the analysis unit can perform rhythm and tempo analysis while considering the structure and melody line of the music in real time. This improves the accuracy of rhythm and tempo analysis by considering the structure and melody line of the music. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform rhythm and tempo analysis while considering the structure and melody line of the music using an AI model.

[0103] The adjustment unit estimates the user's emotions and adjusts the color and brightness of the lighting based on the estimated emotions. For example, if the user is relaxed, the adjustment unit provides soft-toned lighting. If the user is excited, the adjustment unit can also provide bright, stimulating-toned lighting. Furthermore, if the user is tense, the adjustment unit can provide calming-toned lighting. This allows for a more appropriate lighting environment to be provided by adjusting the color and brightness of the lighting according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate the user's emotions using an AI model and adjust the color and brightness of the lighting.

[0104] The adjustment unit performs optimal adjustments by referring to past stage production data when adjusting the lighting. For example, the adjustment unit can refer to past stage production data to perform optimal lighting adjustments. The adjustment unit can also adjust the color and brightness of the lighting based on past stage production data. Furthermore, the adjustment unit can analyze past stage production data and propose optimal lighting adjustments. This makes it possible to perform optimal lighting adjustments by referring to past stage production data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input past stage production data into an AI model to perform optimal lighting adjustments.

[0105] The adjustment unit tracks the movements of performers on stage in real time and adjusts the lighting accordingly. For example, the adjustment unit can track the movements of performers on stage in real time and adjust the lighting. The adjustment unit can also detect the performers' movements with sensors and adjust the color and brightness of the lighting. Furthermore, the adjustment unit can track the performers' movements in real time and optimize the lighting pattern. This allows for the optimization of lighting adjustments by tracking the movements of performers on stage in real time. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can detect the performers' movements using an AI model and adjust the lighting accordingly.

[0106] The adjustment unit estimates the user's emotions and adjusts the lighting pattern based on the estimated emotions. For example, if the user is relaxed, the adjustment unit provides a soft lighting pattern. If the user is excited, the adjustment unit can also provide a dynamic lighting pattern. Furthermore, if the user is tense, the adjustment unit can provide a calming lighting pattern. By adjusting the lighting pattern according to the user's emotions, a more appropriate lighting environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can estimate the user's emotions using an AI model and adjust the lighting pattern accordingly.

[0107] The adjustment unit optimizes the lighting effect by incorporating audience reaction data when adjusting the lighting. For example, the adjustment unit can incorporate audience reaction data to optimize the lighting effect. The adjustment unit can also adjust the color and brightness of the lighting based on audience reaction data. Furthermore, the adjustment unit can incorporate audience reaction data in real time and optimize the lighting pattern. In this way, the lighting effect can be optimized by incorporating audience reaction data. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can incorporate audience reaction data using an AI model to optimize the lighting effect.

[0108] The adjustment unit performs optimal adjustments when adjusting the lighting, taking into account the characteristics of different lighting equipment. For example, the adjustment unit considers the characteristics of different lighting equipment and performs optimal lighting adjustments. The adjustment unit can also analyze the characteristics of lighting equipment and propose optimal lighting adjustments. Furthermore, the adjustment unit can consider the characteristics of different lighting equipment in real time and adjust the color and brightness of the lighting. This makes it possible to perform optimal lighting adjustments by considering the characteristics of different lighting equipment. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can analyze the characteristics of lighting equipment using an AI model and perform optimal lighting adjustments.

[0109] The integration unit estimates the user's emotions and adjusts the coordination of sound and lighting based on the estimated emotions. For example, if the user is relaxed, the integration unit provides soft sound and lighting coordination. If the user is excited, the integration unit can also provide dynamic sound and lighting coordination. Furthermore, if the user is tense, the integration unit can provide calm sound and lighting coordination. By adjusting the coordination of sound and lighting according to the user's emotions, a more appropriate stage production becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can estimate the user's emotions using an AI model and adjust the coordination of sound and lighting.

[0110] The integrated unit adjusts the color and brightness of the lighting in real time in response to changes in the sound settings. For example, the integrated unit can adjust the color of the lighting in real time in response to changes in the sound settings. It can also adjust the brightness of the lighting in real time in response to changes in the sound settings. Furthermore, the integrated unit can also adjust the lighting pattern in real time in response to changes in the sound settings. This allows for more appropriate stage production by adjusting the color and brightness of the lighting in real time in response to changes in the sound settings. Some or all of the above processing in the integrated unit may be performed using AI, for example, or without using AI. For example, the integrated unit can detect changes in sound settings using an AI model and adjust the color and brightness of the lighting in real time.

[0111] The integration unit performs optimal sound and lighting coordination by referring to past stage production data. For example, the integration unit can refer to past stage production data to perform optimal sound and lighting coordination. The integration unit can also adjust the sound and lighting coordination method based on past stage production data. Furthermore, the integration unit can analyze past stage production data and propose the optimal sound and lighting coordination. This makes it possible to achieve optimal sound and lighting coordination by referring to past stage production data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input past stage production data into an AI model to perform optimal sound and lighting coordination.

[0112] The coordination unit estimates the user's emotions and determines the priority of sound and lighting coordination based on the estimated emotions. For example, if the user is relaxed, the coordination unit prioritizes soft sound and lighting coordination. If the user is excited, the coordination unit may also prioritize dynamic sound and lighting coordination. Furthermore, if the user is tense, the coordination unit may also prioritize calm sound and lighting coordination. This allows for more appropriate stage production by determining the priority of sound and lighting coordination according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can estimate the user's emotions using an AI model and determine the priority of sound and lighting coordination.

[0113] The integration unit optimizes the effect of sound and lighting coordination by incorporating audience reaction data. For example, the integration unit can incorporate audience reaction data to optimize the effect of sound and lighting coordination. The integration unit can also adjust the method of sound and lighting coordination based on audience reaction data. Furthermore, the integration unit can incorporate audience reaction data in real time to optimize sound and lighting coordination. This allows for the optimization of sound and lighting coordination by incorporating audience reaction data. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can incorporate audience reaction data using an AI model to optimize the effect of sound and lighting coordination.

[0114] The integration unit performs optimal integration of sound and lighting, taking into account the characteristics of different equipment. For example, the integration unit considers the characteristics of different equipment to perform optimal sound and lighting integration. The integration unit can also analyze the characteristics of the equipment and propose the optimal sound and lighting integration method. Furthermore, the integration unit can consider the characteristics of different equipment in real time and adjust the sound and lighting integration. This makes it possible to achieve optimal sound and lighting integration by considering the characteristics of different equipment. Some or all of the above processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can analyze the characteristics of the equipment using an AI model to perform optimal sound and lighting integration.

[0115] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit selects training data related to relaxed emotions. It can also select training data related to excited emotions if the user is excited. Furthermore, if the user is tense, it can select training data related to tense emotions. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can estimate the user's emotions using an AI model and select training data.

[0116] The learning unit optimizes the learning algorithm by referring to past stage performance data during training. For example, the learning unit optimizes the learning algorithm by referring to past stage performance data. The learning unit can also adjust the parameters of the learning algorithm based on past stage performance data. Furthermore, the learning unit can optimize the learning algorithm by analyzing past stage performance data. This improves the accuracy of the learning algorithm by referring to past stage performance data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past stage performance data into an AI model and optimize the learning algorithm.

[0117] The learning unit broadens its learning scope by incorporating stage production data from different genres during the learning process. For example, the learning unit can broaden its learning scope by incorporating stage production data from different genres. Furthermore, the learning unit can select the most suitable learning data for each genre and optimize the learning algorithm. In addition, the learning unit can broaden its learning scope by incorporating stage production data from different genres in real time. This broadens the learning scope by incorporating stage production data from different genres. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can broaden its learning scope by incorporating stage production data from different genres using an AI model.

[0118] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit may set a low learning frequency. Conversely, if the user is excited, the learning unit may set a high learning frequency. Furthermore, if the user is stressed, the learning unit may set a medium learning frequency. This allows for more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can estimate the user's emotions using an AI model and adjust the learning frequency.

[0119] The learning unit improves the accuracy of its learning by incorporating audience reaction data during training. For example, the learning unit can improve the accuracy of its learning by incorporating audience reaction data. Furthermore, the learning unit can optimize its learning algorithm based on audience reaction data. In addition, the learning unit can improve the accuracy of its learning by incorporating audience reaction data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can improve the accuracy of its learning by incorporating audience reaction data using an AI model.

[0120] The learning unit selects training data during training, taking into account the characteristics of different equipment. For example, the learning unit selects optimal training data by considering the characteristics of different equipment. The learning unit can also analyze the characteristics of the equipment and optimize the learning algorithm. Furthermore, the learning unit can consider the characteristics of different equipment in real time and select training data. This allows for the selection of optimal training data by considering the characteristics of different equipment. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can analyze the characteristics of the equipment using an AI model and select optimal training data.

[0121] The detection unit estimates the user's emotions and detects changes in sound settings based on the estimated emotions. For example, if the user is relaxed, the detection unit will gradually detect changes in sound settings. The detection unit can also quickly detect changes in sound settings if the user is excited. Furthermore, if the user is tense, the detection unit can carefully detect changes in sound settings. This allows for the provision of a more appropriate sound environment by detecting changes in sound settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using AI, or not. For example, the detection unit can estimate the user's emotions using an AI model and detect changes in sound settings.

[0122] The detection unit detects changes in sound settings in real time and responds immediately. For example, the detection unit can detect changes in sound settings in real time and immediately adjust the sound environment. The detection unit can also detect changes in sound settings in real time and reflect them in the lighting settings. Furthermore, the detection unit can detect changes in sound settings in real time and adjust them according to the audience's reaction. This allows for immediate response by detecting changes in sound settings in real time. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings in real time using an AI model and respond immediately.

[0123] The detection unit improves detection accuracy by referring to past change history when detecting changes in sound settings. For example, the detection unit improves detection accuracy by referring to past change history of sound settings. The detection unit can also improve detection accuracy by predicting changes in sound settings based on past change history. Furthermore, the detection unit can analyze past change history and quickly detect changes in sound settings. This allows for rapid detection of changes in sound settings by referring to past change history. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can detect changes in sound settings by referring to past change history using an AI model.

[0124] The detection unit estimates the user's emotions and determines the priority of sound setting changes based on the estimated emotions. For example, if the user is relaxed, the detection unit may set a low priority for changing sound settings. If the user is excited, the detection unit may set a high priority for changing sound settings. Furthermore, if the user is tense, the detection unit may set a medium priority for changing sound settings. This allows for the provision of a more appropriate sound environment by determining the priority of sound setting changes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can estimate the user's emotions using an AI model and determine the priority of sound setting changes.

[0125] The detection unit improves detection accuracy by incorporating audience reaction data when detecting changes in sound settings. For example, the detection unit incorporates audience reaction data to detect changes in sound settings. The detection unit can also predict changes in sound settings based on audience reaction data to improve detection accuracy. Furthermore, the detection unit can incorporate audience reaction data in real time to quickly detect changes in sound settings. This allows for rapid detection of changes in sound settings by incorporating audience reaction data. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can incorporate audience reaction data using an AI model to detect changes in sound settings.

[0126] The detection unit, when detecting changes in acoustic settings, takes into account the characteristics of different equipment. For example, the detection unit considers the characteristics of different equipment to detect changes in acoustic settings. The detection unit can also analyze the characteristics of the equipment, predict changes in acoustic settings, and improve detection accuracy. Furthermore, the detection unit can consider the characteristics of different equipment in real time and quickly detect changes in acoustic settings. This allows for rapid detection of changes in acoustic settings by considering the characteristics of different equipment. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can analyze the characteristics of the equipment using an AI model and detect changes in acoustic settings.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The stage production system can further estimate the audience's emotions and adjust the sound and lighting settings based on those estimates. For example, if the audience is excited, the sound volume can be increased and the lighting brightened to create a more exciting atmosphere. If the audience is relaxed, the sound volume can be lowered and the lighting softened to create a calmer atmosphere. Furthermore, if the audience is moved, the sound echo can be increased and the lighting warmed to enhance the emotional impact. This allows for sound and lighting settings that respond to the audience's emotions, resulting in a more immersive stage production.

[0129] The stage production system can further adapt to different genres of music through its sound and lighting settings. For example, in a rock concert, an energetic atmosphere can be created by increasing the sound volume and using intense lighting colors. In a classical concert, a calm atmosphere can be created by lowering the sound volume and using soft lighting colors. Furthermore, in a jazz concert, a relaxed atmosphere can be created by increasing the sound echo and using warm lighting colors. This allows for sound and lighting settings tailored to different genres of music, enabling a wider variety of stage productions.

[0130] The stage production system can also detect audience movements and reactions in real time and adjust sound and lighting settings accordingly. For example, if the audience is waving, the lights can be made to flash in sync with their movements, creating a sense of unity with the audience. If the audience is listening quietly, the sound volume can be lowered and the lighting softened to create a calm atmosphere. Furthermore, if the audience is cheering, the sound volume can be increased and the lighting brightened to create an even more exciting effect. This allows for sound and lighting settings to be adjusted according to audience movements and reactions, resulting in a more dynamic stage production.

[0131] The stage production system can further incorporate information about the external environment to adjust the sound and lighting settings. For example, if it's raining, the sound echo can be increased and the lighting can be changed to a blue hue to create a rainy atmosphere. If it's sunny, the sound volume can be increased and the lighting can be changed to a brighter hue to create a sunny atmosphere. Furthermore, if the temperature is high, the sound volume can be decreased and the lighting can be changed to a cool hue to create a cooler atmosphere. This allows for sound and lighting settings to be adjusted according to information about the external environment, resulting in a more realistic stage production.

[0132] The stage production system can further optimize sound and lighting settings by referencing past stage production data. For example, by referencing sound and lighting settings that were well-received in past concerts and implementing similar settings, audience satisfaction can be increased. It can also analyze past stage production data and suggest optimal sound and lighting settings. Furthermore, it can adjust sound and lighting settings in real time based on past stage production data. This allows for more effective sound and lighting settings by utilizing past stage production data, thereby improving the quality of the stage production.

[0133] The stage production system can further estimate the audience's emotions and adjust the sound and lighting settings based on those estimates. For example, if the audience is excited, the sound volume can be increased and the lighting brightened to create a more exciting atmosphere. If the audience is relaxed, the sound volume can be lowered and the lighting softened to create a calmer atmosphere. Furthermore, if the audience is moved, the sound echo can be increased and the lighting warmed to enhance the emotional impact. This allows for sound and lighting settings that respond to the audience's emotions, resulting in a more immersive stage production.

[0134] The stage production system can further adapt to different genres of music through its sound and lighting settings. For example, in a rock concert, an energetic atmosphere can be created by increasing the sound volume and using intense lighting colors. In a classical concert, a calm atmosphere can be created by lowering the sound volume and using soft lighting colors. Furthermore, in a jazz concert, a relaxed atmosphere can be created by increasing the sound echo and using warm lighting colors. This allows for sound and lighting settings tailored to different genres of music, enabling a wider variety of stage productions.

[0135] The stage production system can also detect audience movements and reactions in real time and adjust sound and lighting settings accordingly. For example, if the audience is waving, the lights can be made to flash in sync with their movements, creating a sense of unity with the audience. If the audience is listening quietly, the sound volume can be lowered and the lighting softened to create a calm atmosphere. Furthermore, if the audience is cheering, the sound volume can be increased and the lighting brightened to create an even more exciting effect. This allows for sound and lighting settings to be adjusted according to audience movements and reactions, resulting in a more dynamic stage production.

[0136] The stage production system can further incorporate information about the external environment to adjust the sound and lighting settings. For example, if it's raining, the sound echo can be increased and the lighting can be changed to a blue hue to create a rainy atmosphere. If it's sunny, the sound volume can be increased and the lighting can be changed to a brighter hue to create a sunny atmosphere. Furthermore, if the temperature is high, the sound volume can be decreased and the lighting can be changed to a cool hue to create a cooler atmosphere. This allows for sound and lighting settings to be adjusted according to information about the external environment, resulting in a more realistic stage production.

[0137] The stage production system can further optimize sound and lighting settings by referencing past stage production data. For example, by referencing sound and lighting settings that were well-received in past concerts and implementing similar settings, audience satisfaction can be increased. It can also analyze past stage production data and suggest optimal sound and lighting settings. Furthermore, it can adjust sound and lighting settings in real time based on past stage production data. This allows for more effective sound and lighting settings by utilizing past stage production data, thereby improving the quality of the stage production.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The data collection unit gathers information such as the size of the stage, equipment, capacity, and humidity. For example, it collects information such as the width, depth, and height of the stage, sound equipment, lighting equipment, video equipment, number of seats, number of standing spectators, relative humidity, and absolute humidity. Step 2: The settings unit performs sound settings based on the information collected by the data collection unit. For example, it sets sound settings such as volume, sound quality, and echo, and adjusts the sound balance according to the size of the stage and the number of people who will be seated. Step 3: The analysis unit analyzes the rhythm and tempo of the music. For example, it can analyze rhythm and tempo such as BPM (beats per minute) and time signature, and can also perform the analysis using an AI model. Step 4: The adjustment unit adjusts the lighting based on the information analyzed by the analysis unit. For example, it adjusts the brightness, color, and beam angle of the lighting, and adjusts the color and brightness of the lighting in accordance with the rhythm and tempo of the music. Step 5: The integrated unit adjusts the lighting settings in response to changes in the sound settings. For example, it adjusts the color, brightness, and pattern of the lighting in real time in response to changes in the sound settings.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, setting unit, analysis unit, adjustment unit, coordination unit, learning unit, and detection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information on the size of the stage and equipment using the camera 42 and sensors of the smart device 14, and processes the collected information using the specific processing unit 290 of the data processing unit 12. The setting unit performs sound settings using the specific processing unit 290 of the data processing unit 12, and the analysis unit analyzes the rhythm and tempo of the music using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the lighting using the control unit 46A of the smart device 14, and the coordination unit adjusts the lighting settings in accordance with changes in the sound settings using the specific processing unit 290 of the data processing unit 12. The learning unit learns past stage performance data using the specific processing unit 290 of the data processing unit 12, and the detection unit detects changes in the sound settings using the sensors of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, setting unit, analysis unit, adjustment unit, coordination unit, learning unit, and detection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information on the size of the stage and equipment using the camera 42 and sensors of the smart glasses 214, and processes the collected information using the specific processing unit 290 of the data processing unit 12. The setting unit performs sound settings using the specific processing unit 290 of the data processing unit 12, and the analysis unit analyzes the rhythm and tempo of the music using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the lighting using the control unit 46A of the smart glasses 214, and the coordination unit adjusts the lighting settings in accordance with changes in the sound settings using the specific processing unit 290 of the data processing unit 12. The learning unit learns past stage performance data using the specific processing unit 290 of the data processing unit 12, and the detection unit detects changes in the sound settings using the sensors of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, setting unit, analysis unit, adjustment unit, coordination unit, learning unit, and detection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information on the size of the stage and equipment using the camera 42 and sensors of the headset terminal 314, and processes the collected information using the specific processing unit 290 of the data processing unit 12. The setting unit performs sound settings using the specific processing unit 290 of the data processing unit 12, and the analysis unit analyzes the rhythm and tempo of the music using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the lighting using the control unit 46A of the headset terminal 314, and the coordination unit adjusts the lighting settings in accordance with changes in the sound settings using the specific processing unit 290 of the data processing unit 12. The learning unit learns past stage performance data using the specific processing unit 290 of the data processing unit 12, and the detection unit detects changes in the sound settings using the sensors of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0183] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0185] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0192] Each of the multiple elements described above, including the collection unit, setting unit, analysis unit, adjustment unit, coordination unit, learning unit, and detection unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects information on the stage size and equipment using the camera 42 and sensors of the robot 414, and processes the collected information using the specific processing unit 290 of the data processing unit 12. The setting unit performs sound settings using the specific processing unit 290 of the data processing unit 12, and the analysis unit analyzes the rhythm and tempo of the music using the specific processing unit 290 of the data processing unit 12. The adjustment unit adjusts the lighting using the control unit 46A of the robot 414, and the coordination unit adjusts the lighting settings in accordance with changes in the sound settings using the specific processing unit 290 of the data processing unit 12. The learning unit learns past stage performance data using the specific processing unit 290 of the data processing unit 12, and the detection unit detects changes in the sound settings using the sensors of the robot 414. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0193] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0195] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0196] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0197] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0202] 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.

[0203] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0204] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0205] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0206] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0210] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0211] (Note 1) A collection unit that collects information such as stage size, equipment, capacity, and humidity, A setting unit that performs sound settings based on the information collected by the aforementioned collection unit, An analysis unit that analyzes the rhythm and tempo of the music, An adjustment unit adjusts the lighting based on the information analyzed by the aforementioned analysis unit, It includes a control unit that adjusts lighting settings in accordance with changes in sound settings. A system characterized by the following features. (Note 2) It also includes a learning unit that collects data from past stage productions and uses that data to learn from them. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes a detection unit that detects changes in sound settings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The stage size and equipment layout are collected in detail using 3D scanning technology. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The collected information is updated in real time, and changes are addressed immediately. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the accuracy of the information collected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During collection, sensors detect audience movements and reactions, and this information is used to adjust sound and lighting settings. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, information about the external environment is also incorporated and used to adjust sound and lighting settings. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned setting unit is, It estimates the user's emotions and adjusts the audio settings parameters based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned setting unit is, When setting up the sound system, the system references past stage production data to determine the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned setting unit is, When setting up the sound system, the sound distribution is optimized by taking into account the audience's placement and movement. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned setting unit is, It estimates the user's emotions and prioritizes sound settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned setting unit is, During sound setup, the system tracks the position and movements of performers on stage in real time and adjusts the sound accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned setting unit is, When setting up the sound system, take into account the characteristics of different sound equipment to make the optimal settings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method of music rhythm and tempo based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing the rhythm and tempo of music, past performance data is referenced to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, When analyzing the rhythm and tempo of music, we apply analysis algorithms that are compatible with different genres of music. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing the rhythm and tempo of music, audience reaction data is incorporated to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, When analyzing the rhythm and tempo of music, the musical structure and melody line are also taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, It estimates the user's emotions and adjusts the lighting color and brightness based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, When adjusting the lighting, we refer to past stage production data to make optimal adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, When adjusting the lighting, the system tracks the movements of the performers on stage in real time and adjusts the lighting accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, It estimates the user's emotions and adjusts the lighting pattern based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, When adjusting the lighting, audience reaction data is incorporated to optimize the lighting effect. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, When adjusting the lighting, the optimal adjustments are made considering the characteristics of different lighting equipment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the coordination of sound and lighting based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, The lighting color and brightness are adjusted in real time in response to changes in the sound settings. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, When coordinating sound and lighting, past stage production data is referenced to ensure optimal coordination. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of sound and lighting coordination based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, When coordinating sound and lighting, audience reaction data is incorporated to optimize the effectiveness of the coordination. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, When coordinating sound and lighting, the optimal coordination is achieved by considering the characteristics of different equipment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past stage performance data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, During learning, incorporate stage production data from different genres to broaden the scope of learning. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, During the learning process, audience reaction data is incorporated to improve the accuracy of the learning. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned learning unit, During training, select training data while considering the characteristics of different equipment. The system described in Appendix 1, characterized by the features described herein. (Note 40) The detection unit is It estimates the user's emotions and detects changes in sound settings based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The detection unit is It detects changes in sound settings in real time and responds immediately. The system described in Appendix 1, characterized by the features described herein. (Note 42) The detection unit is When detecting changes in sound settings, the system improves detection accuracy by referring to past change history. The system described in Appendix 1, characterized by the features described herein. (Note 43) The detection unit is It estimates the user's emotions and prioritizes changes to sound settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The detection unit is When detecting changes in sound settings, audience reaction data is incorporated to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 45) The detection unit is When detecting changes in sound settings, the detection process takes into account the characteristics of different equipment. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects information such as stage size, equipment, capacity, and humidity, A setting unit that performs sound settings based on the information collected by the aforementioned collection unit, An analysis unit that analyzes the rhythm and tempo of the music, An adjustment unit adjusts the lighting based on the information analyzed by the aforementioned analysis unit, It includes a control unit that adjusts lighting settings in accordance with changes in sound settings. A system characterized by the following features.

2. It also includes a learning unit that collects data from past stage productions and uses that data to learn from them. The system according to feature 1.

3. It also includes a detection unit that detects changes in sound settings. The system according to feature 1.

4. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

5. The aforementioned collection unit is The stage size and equipment layout are collected in detail using 3D scanning technology. The system according to feature 1.

6. The aforementioned collection unit is The collected information is updated in real time, and changes are addressed immediately. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the accuracy of the information collected based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is During collection, sensors detect audience movements and reactions, and this information is used to adjust sound and lighting settings. The system according to feature 1.

9. The aforementioned collection unit is During data collection, information about the external environment is also incorporated and used to adjust sound and lighting settings. The system according to feature 1.

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