System

Abstract

An object of the system according to the embodiment is to optimize illumination in real time in accordance with a motion of a player or music.SOLUTION: A system includes a sensor part, a voice analysis part, an illumination control part, and an output part. The sensor unit detects a motion of a player. The sound analysis unit detects a change in the musical piece on the basis of the information detected by the sensor unit. The illumination control unit controls illumination on the basis of the information obtained by the voice analysis unit. The output unit outputs the illumination pattern generated by the illumination control unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, lighting design required a large human workload, making it difficult to control lighting in real time in accordance with the player's movements and the music.

[0005] The system according to the embodiment aims to optimize lighting in real time in accordance with the player's movements and the music. [Means for solving the problem]

[0006] The system according to the embodiment includes a sensor unit, an audio analysis unit, a lighting control unit, and an output unit. The sensor unit detects the movement of the player. The audio analysis unit detects changes in the music based on the information detected by the sensor unit. The lighting control unit controls the lighting based on the information obtained by the audio analysis unit. The output unit outputs the lighting pattern generated by the lighting control unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize lighting in real time in accordance with the player's movements and the music. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An automatic lighting system according to an embodiment of the present invention optimizes lighting in real time in response to player movements and changes in the music. The automatic lighting system includes a sensor unit that detects player movements, an audio analysis unit that detects changes in the music, a lighting control unit that optimizes lighting, and an output unit that outputs the lighting adjustment results. For example, the automatic lighting system may detect player movements in real time using a camera or motion sensor. Next, the automatic lighting system may analyze changes in the tempo, rhythm, and volume of the music in real time using a microphone or audio analysis software. Furthermore, the automatic lighting system may use AI to generate an optimal lighting pattern in response to player movements and changes in the music. Finally, the automatic lighting system may change the color, brightness, and lighting pattern of the lighting. This allows the automatic lighting system to provide vibrant lighting in response to player movements and changes in the music, thereby improving the entertainment value of live music concerts and events.

[0029] The automatic lighting system according to the embodiment includes a sensor unit, an audio analysis unit, a lighting control unit, and an output unit. The sensor unit detects the player's movements. Examples of the player's movements include, but are not limited to, hand movements, foot movements, and whole-body movements. The sensor unit detects the player's movements in real time using, for example, a camera. The sensor unit can also detect the player's movements using a motion sensor. For example, the camera may be a fixed camera, a PTZ camera, or an infrared camera. For example, the motion sensor may be an acceleration sensor, a gyro sensor, or an infrared sensor. The audio analysis unit detects changes in the music. Examples of changes in the music include, but are not limited to, changes in tempo, rhythm, and volume. For example, the audio analysis unit analyzes changes in the tempo, rhythm, and volume of the music in real time using, for example, a microphone. The audio analysis unit can also analyze changes in the music using audio analysis software. For example, a condenser microphone, a dynamic microphone, a directional microphone, or the like may be used. For example, FFT analysis, spectral analysis, machine learning-based analysis, or the like may be used as the audio analysis software. The lighting control unit generates an optimal lighting pattern in response to the player's movements and changes in the music. Examples of lighting patterns include, but are not limited to, changes in color, brightness, and lighting patterns. The lighting control unit uses AI to generate an optimal lighting pattern in response to the player's movements and changes in the music. For example, the lighting control unit can control the spotlight to shine when a player moves to the center of the stage. The output unit outputs the lighting adjustment results. The output unit controls the lighting equipment based on instructions from the lighting control unit and adjusts the lighting in real time. For example, the output unit changes the color, brightness, and lighting pattern of the lighting. As a result, the automatic lighting system according to the embodiment can provide optimal lighting in real time in response to the player's movements and changes in the music.

[0030] The sensor unit can detect the player's movements in real time using a camera or a motion sensor. Examples of cameras include, but are not limited to, fixed cameras, PTZ cameras, and infrared cameras. The sensor unit can detect the player's movements in real time using, for example, a fixed camera. The sensor unit can also detect the player's movements using a PTZ camera. For example, a PTZ camera has pan, tilt, and zoom functions and can detect movements over a wide range. The sensor unit can also detect the player's movements even in dark places using an infrared camera. Examples of motion sensors include, but are not limited to, acceleration sensors, gyro sensors, and infrared sensors. The sensor unit can detect the player's movements in real time using, for example, an acceleration sensor. The sensor unit can also detect the player's movements using a gyro sensor. For example, a gyro sensor can detect the player's rotation and tilt. The sensor unit can also detect the player's movements using an infrared sensor. This allows the player's movements to be detected in real time with high accuracy.

[0031] The audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using a microphone and audio analysis software. Examples of microphones include, but are not limited to, condenser microphones, dynamic microphones, and directional microphones. The audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using, for example, a condenser microphone. The audio analysis unit can also analyze changes in music using a dynamic microphone. For example, dynamic microphones are resistant to high sound pressure and are suitable for live performances. The audio analysis unit can also focus on analyzing a specific sound source using a directional microphone. Examples of audio analysis software include, but are not limited to, FFT analysis, spectral analysis, and machine learning-based analysis. The audio analysis unit can analyze frequency components of music using, for example, FFT analysis. The audio analysis unit can also analyze changes in volume of music using spectral analysis. For example, spectral analysis can display sound intensity for each frequency. The audio analysis unit can also analyze changes in tempo and rhythm of music using machine learning-based analysis. This allows changes in music to be analyzed in real time and reflected in lighting control.

[0032] The lighting control unit can generate an appropriate lighting pattern according to the player's movements and changes in the music. Examples of lighting patterns include, but are not limited to, changes in color, changes in brightness, and lighting patterns. For example, the lighting control unit can turn on a spotlight when the player moves to the center of the stage. The lighting control unit can also change the color and brightness of the lighting as the tempo of the music increases. For example, as the tempo of the music increases, the lighting color can change to red and the brightness can increase. The lighting control unit can also change the lighting pattern according to the player's movements. For example, the lighting can flash when the player jumps. This makes it possible to generate an optimal lighting pattern according to the player's movements and changes in the music.

[0033] The output unit can change the color, brightness, and lighting pattern of the lighting. Examples of lighting colors include, but are not limited to, RGB colors, specific color temperatures, etc. The output unit can change the color of the lighting using, for example, RGB colors. The output unit can also change the color of the lighting by setting a specific color temperature. For example, the output unit can set a warm color temperature to make the lighting warmer. Examples of brightness include, but are not limited to, lumens, the use of an illuminance sensor, etc. The output unit can change the brightness of the lighting by adjusting the lumen value. The output unit can also automatically adjust the brightness of the lighting using an illuminance sensor. For example, the output unit can adjust the brightness of the lighting according to the ambient brightness. Examples of lighting patterns include, but are not limited to, blinking, fade-in / fade-out, random lighting, etc. The output unit can set a blinking pattern to make the lighting blink. The output unit can also set a fade-in / fade-out pattern to gradually brighten or dim the lighting. For example, the output unit can set a random lighting pattern to turn the lighting on randomly. This allows you to change the color, brightness, and lighting pattern of the lights in real time.

[0034] The sensor unit can analyze the player's past motion data and select an appropriate detection algorithm. Past motion data includes, but is not limited to, the player's past motion patterns, the speed and direction of the motion, and the like. For example, the sensor unit can analyze the player's past motion patterns and select an algorithm that prioritizes detection of the most frequently performed motion. The sensor unit can also select an algorithm that improves detection accuracy for a specific motion from the player's past motion data. For example, the sensor unit can select an optimal detection algorithm based on the player's past motion data, depending on the speed and direction of the motion. Methods for collecting past motion data include, for example, using a database or applying a machine learning algorithm. This allows the optimal detection algorithm to be selected based on the player's past motion data.

[0035] When detecting motion, the sensor unit can perform filtering taking into account the influence of a player's clothing and props. Examples of the influence of clothing and props include, but are not limited to, color, shape, and specific patterns. For example, if a player is wearing a specific clothing item, the sensor unit can detect motion taking into account the color and shape of the clothing. Furthermore, if a player is using a prop, the sensor unit can filter the prop's movement to detect only the player's motion. For example, the sensor unit can analyze the influence of a player's clothing and props in real time to improve the accuracy of motion detection. Filtering methods include, for example, using an image processing algorithm and removing specific features. This allows motion detection to take into account the influence of a player's clothing and props.

[0036] When detecting movement, the sensor unit can dynamically adjust the detection range according to the speed and direction of the player's movement. Examples of the speed of movement include, but are not limited to, using a speed sensor or calculating the distance traveled between frames. For example, if the player is moving at high speed, the sensor unit widens the detection range to track the movement. Furthermore, if the player is moving slowly, the sensor unit can narrow the detection range to improve accuracy. For example, the sensor unit dynamically adjusts the detection range according to the direction of the player's movement to perform optimal detection. Examples of the direction of movement include using a direction sensor or estimating direction through image analysis. This allows the detection range to be dynamically adjusted according to the speed and direction of the player's movement.

[0037] During audio analysis, the audio analysis unit can adjust the level of detail of the analysis based on the importance of the song. Examples of the importance of the song include, but are not limited to, the popularity of the song and the importance of the event. For example, the audio analysis unit performs detailed audio analysis for an important song. The audio analysis unit can also perform normal audio analysis for a general song. For example, the audio analysis unit performs simplified audio analysis for a song with low importance. Examples of the level of detail of the analysis include the analysis resolution and the number of analysis items. This allows the level of detail of the analysis to be adjusted based on the importance of the song.

[0038] During audio analysis, the audio analysis unit can apply different analysis algorithms depending on the category of the music. Examples of music categories include, but are not limited to, genre, tempo, and rhythm pattern. For example, the audio analysis unit applies a specific analysis algorithm to classical music. The audio analysis unit can also apply a different analysis algorithm to rock music. For example, the audio analysis unit applies yet another analysis algorithm to pop music. Analysis algorithms include, for example, a tempo analysis algorithm and a rhythm analysis algorithm. This allows the optimal analysis algorithm to be applied depending on the category of the music.

[0039] During voice analysis, the voice analysis unit can improve the accuracy of the analysis by referring to the player's past voice analysis results. Past voice analysis results include, but are not limited to, the player's past voice analysis results and specific patterns. The voice analysis unit improves the accuracy of the analysis, for example, based on the player's past voice analysis results. The voice analysis unit can also extract specific patterns from the player's past voice analysis results and reflect them in the analysis. For example, the voice analysis unit analyzes the player's past voice analysis results and proposes an optimal analysis method. Methods for collecting past voice analysis results include, for example, using a database or using them as training data for a machine learning model. In this way, the accuracy of the analysis is improved by referring to the player's past voice analysis results.

[0040] The lighting control unit can improve the accuracy of lighting control by taking into account the interrelationships between players' movements. Examples of interrelationships between movements include, but are not limited to, synchronization of multiple movements and coordinated patterns of movements. For example, when a player performs multiple movements on a stage, the lighting control unit provides lighting according to each movement. Furthermore, when a player's movements are continuous, the lighting control unit can adjust lighting according to the flow of the movements. For example, the lighting control unit analyzes the interrelationships between players' movements in real time and performs optimal lighting control. Examples of control accuracy include feedback control and real-time adjustment algorithms. This improves the accuracy of lighting control by taking into account the interrelationships between players' movements.

[0041] The lighting control unit can control the lighting based on the player's attribute information. The attribute information includes, but is not limited to, for example, age, gender, and experience level. The lighting control unit provides optimal lighting depending on, for example, the player's age and gender. The lighting control unit can also adjust the color and brightness of the lighting depending on the player's costume and props. For example, the lighting control unit analyzes the player's attribute information in real time and performs optimal lighting control. Control methods include, for example, a method for adjusting the color and brightness of the lighting, and a control algorithm based on movement. In this way, optimal lighting control is performed taking into account the player's attribute information.

[0042] When controlling the lighting, the lighting control unit can weight the control based on the frequency of the player's movements. The frequency of movements includes, but is not limited to, for example, the number of movements within a certain period of time and the frequency of movement patterns. For example, the lighting control unit can weight the lighting more highly for movements that the player makes frequently. The lighting control unit can also weight the lighting less highly for movements that the player makes infrequently. For example, the lighting control unit analyzes the frequency of the player's movements in real time and performs optimal lighting control. Examples of weighting include weighting according to the importance of the movement and weighting based on the frequency of the movement. In this way, the lighting control is weighted based on the frequency of the player's movements.

[0043] The output unit can improve the accuracy of the output by taking into account the interrelationships between the players' movements. Examples of interrelationships between movements include, but are not limited to, synchronization of multiple movements and coordinated patterns of movements. For example, when a player performs multiple movements on a stage, the output unit outputs lighting corresponding to each movement. Furthermore, when the player's movements are continuous, the output unit can also output lighting in accordance with the flow of the movements. For example, the output unit analyzes the interrelationships between the players' movements in real time and outputs optimal lighting. Examples of output accuracy include feedback control and real-time adjustment algorithms. This improves the accuracy of the output by taking into account the interrelationships between the players' movements.

[0044] The output unit may output the information taking into consideration the player's attribute information. The attribute information may include, but is not limited to, for example, age, gender, and experience level. The output unit may output optimal lighting depending on, for example, the player's age and gender. The output unit may also adjust the color and brightness of the lighting depending on the player's costume and props. For example, the output unit may analyze the player's attribute information in real time and output optimal lighting. This allows optimal lighting output to be performed taking into consideration the player's attribute information.

[0045] The output unit can weight the output based on the frequency of the player's movements when outputting. The frequency of movements includes, but is not limited to, the number of movements within a certain period of time, the frequency of movement patterns, and the like. For example, the output unit can weight the lighting more heavily for movements that the player makes frequently, and output the lighting. The output unit can also weight the lighting less heavily for movements that the player makes infrequently. For example, the output unit can analyze the frequency of the player's movements in real time and output optimal lighting. Examples of weighting include weighting according to the importance of the movement and weighting based on the frequency of the movement. In this way, the lighting output is weighted based on the frequency of the player's movements.

[0046] The output unit may output the light while taking into consideration the geographical distribution of the stage. Examples of the geographical distribution of the stage include, but are not limited to, the shape of the stage and the placement of the audience. For example, the output unit may concentrate and output lighting for a performance taking place in the center of the stage. The output unit may also distribute and output lighting for a performance taking place at the edge of the stage. For example, the output unit may adjust and output lighting intensity and color for a specific area of ​​the stage. This allows optimal lighting output to be performed while taking into consideration the geographical distribution of the stage.

[0047] The output unit may improve the accuracy of the output by referring to related literature during output. Examples of related literature include, but are not limited to, academic papers, patent documents, and technical reports. For example, the output unit may improve the output algorithm by referring to the latest research papers on lighting output. The output unit may also adjust the color and brightness of the lighting by referring to specialized books on lighting design. For example, the output unit may optimize the output method by referring to patent documents on lighting output. This improves the accuracy of the output by referring to the related literature.

[0048] The output unit can output the information taking into consideration the market value of the lighting. Market value includes, but is not limited to, for example, sales price, demand forecast, and evaluation of competing products. For example, when using expensive lighting equipment, the output unit can output information that makes the most of its characteristics. Furthermore, when using general lighting equipment, the output unit can also output information that emphasizes cost performance. For example, the output unit can analyze the market value of the lighting equipment in real time and propose the optimal output method. This allows for optimal lighting output taking into consideration the market value of the lighting.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The automatic lighting system can further include a learning unit that learns user preferences. The learning unit can learn user preferences based on past lighting settings and user feedback and reflect them in the next lighting settings. For example, if a user prefers a particular color or brightness, that information can be learned and used as a priority the next time the lighting is set. The learning unit can also track changes in user preferences and dynamically adjust lighting settings. This allows the system to provide optimal lighting according to the user's preferences.

[0051] The sensor unit can further include an environmental sensor that detects changes in the environment. The environmental sensor can detect environmental parameters such as temperature, humidity, and illuminance in real time, and control lighting in response to changes in the environment, as well as the player's movements and music. For example, if the environmental sensor detects high temperatures, it can adjust the brightness of the lighting to reduce heat. Also, if the environmental sensor detects low illuminance, it can increase the brightness of the lighting. This allows optimal lighting to be provided in response to changes in the environment.

[0052] The lighting control unit can further control the lighting taking into consideration the player's health condition. Health conditions include, but are not limited to, heart rate, blood pressure, and stress level. The lighting control unit can monitor the player's health condition and provide lighting according to the health condition. For example, if the player's heart rate is high, the lighting control unit can reduce the brightness of the lighting to enhance relaxation. Also, if the player's stress level is high, the lighting color can be changed to a warmer color to promote relaxation. This allows the lighting to be optimally tailored to the player's health condition.

[0053] The sensor unit may further include a prediction unit that predicts the player's movements. The prediction unit predicts the player's next movement based on past movement data and current movement trends, and can adjust the sensitivity and detection range of the sensor based on the prediction. For example, if the player repeatedly jumps, the sensor's sensitivity can be increased by predicting the next jump. Also, if the player tends to move in a specific direction, the sensor's detection range can be expanded in that direction. This allows for more accurate detection of the player's movements.

[0054] The audio analysis unit can further perform analysis according to the genre of the music. Examples of music genres include, but are not limited to, classical, rock, and pop. The audio analysis unit can identify the genre of the music and apply an analysis algorithm according to the genre. For example, in the case of classical music, the analysis can be performed with emphasis on subtle sound changes. In addition, in the case of rock music, the analysis can be performed with emphasis on changes in rhythm and beat. This allows for optimal audio analysis according to the genre of the music.

[0055] The output unit can further output light taking into consideration the energy efficiency of the lighting. Energy efficiency includes, but is not limited to, for example, power consumption and the lifespan of the lighting. The output unit can monitor the energy efficiency of the lighting in real time and provide efficient lighting output. For example, if power consumption is high, the output unit can adjust the brightness of the lighting to reduce energy consumption. Also, if the lifespan of the lighting is approaching, the frequency of use of the lighting can be reduced. This allows for optimal lighting output taking energy efficiency into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The sensor unit detects the player's movements. Player movements include hand movements, foot movements, and whole-body movements. The sensor unit detects the player's movements in real time using a camera or motion sensor. For example, a fixed camera, a PTZ camera, an infrared camera, an acceleration sensor, a gyro sensor, an infrared sensor, etc. may be used. Step 2: The audio analysis unit detects changes in the music based on the information detected by the sensor unit. Changes in the music include changes in tempo, rhythm, and volume. The audio analysis unit analyzes changes in the music's tempo, rhythm, and volume in real time using a microphone and audio analysis software. For example, condenser microphones, dynamic microphones, directional microphones, FFT analysis, spectral analysis, and machine learning-based analysis may be used. Step 3: The lighting control unit controls the lighting based on the information obtained by the audio analysis unit. The lighting control unit generates the optimal lighting pattern according to the player's movements and changes in the music. The lighting pattern includes changes in color, brightness, lighting patterns, etc. The lighting control unit uses AI to generate the optimal lighting pattern according to the player's movements and changes in the music. Step 4: The output unit outputs the lighting pattern generated by the lighting control unit. The output unit controls the lighting devices based on instructions from the lighting control unit and adjusts the lighting in real time. For example, it changes the color, brightness, and lighting pattern of the lighting.

[0058] (Example 2) An automatic lighting system according to an embodiment of the present invention optimizes lighting in real time in response to player movements and changes in the music. The automatic lighting system includes a sensor unit that detects player movements, an audio analysis unit that detects changes in the music, a lighting control unit that optimizes lighting, and an output unit that outputs the lighting adjustment results. For example, the automatic lighting system may detect player movements in real time using a camera or motion sensor. Next, the automatic lighting system may analyze changes in the tempo, rhythm, and volume of the music in real time using a microphone or audio analysis software. Furthermore, the automatic lighting system may use AI to generate an optimal lighting pattern in response to player movements and changes in the music. Finally, the automatic lighting system may change the color, brightness, and lighting pattern of the lighting. This allows the automatic lighting system to provide vibrant lighting in response to player movements and changes in the music, thereby improving the entertainment value of live music concerts and events.

[0059] The automatic lighting system according to the embodiment includes a sensor unit, an audio analysis unit, a lighting control unit, and an output unit. The sensor unit detects the player's movements. Examples of the player's movements include, but are not limited to, hand movements, foot movements, and whole-body movements. The sensor unit detects the player's movements in real time using, for example, a camera. The sensor unit can also detect the player's movements using a motion sensor. For example, the camera may be a fixed camera, a PTZ camera, or an infrared camera. For example, the motion sensor may be an acceleration sensor, a gyro sensor, or an infrared sensor. The audio analysis unit detects changes in the music. Examples of changes in the music include, but are not limited to, changes in tempo, rhythm, and volume. For example, the audio analysis unit analyzes changes in the tempo, rhythm, and volume of the music in real time using, for example, a microphone. The audio analysis unit can also analyze changes in the music using audio analysis software. For example, a condenser microphone, a dynamic microphone, a directional microphone, or the like may be used. For example, FFT analysis, spectral analysis, machine learning-based analysis, or the like may be used as the audio analysis software. The lighting control unit generates an optimal lighting pattern in response to the player's movements and changes in the music. Examples of lighting patterns include, but are not limited to, changes in color, brightness, and lighting patterns. The lighting control unit uses AI to generate an optimal lighting pattern in response to the player's movements and changes in the music. For example, the lighting control unit can control the spotlight to shine when a player moves to the center of the stage. The output unit outputs the lighting adjustment results. The output unit controls the lighting equipment based on instructions from the lighting control unit and adjusts the lighting in real time. For example, the output unit changes the color, brightness, and lighting pattern of the lighting. As a result, the automatic lighting system according to the embodiment can provide optimal lighting in real time in response to the player's movements and changes in the music.

[0060] The sensor unit can detect the player's movements in real time using a camera or a motion sensor. Examples of cameras include, but are not limited to, fixed cameras, PTZ cameras, and infrared cameras. The sensor unit can detect the player's movements in real time using, for example, a fixed camera. The sensor unit can also detect the player's movements using a PTZ camera. For example, a PTZ camera has pan, tilt, and zoom functions and can detect movements over a wide range. The sensor unit can also detect the player's movements even in dark places using an infrared camera. Examples of motion sensors include, but are not limited to, acceleration sensors, gyro sensors, and infrared sensors. The sensor unit can detect the player's movements in real time using, for example, an acceleration sensor. The sensor unit can also detect the player's movements using a gyro sensor. For example, a gyro sensor can detect the player's rotation and tilt. The sensor unit can also detect the player's movements using an infrared sensor. This allows the player's movements to be detected in real time with high accuracy.

[0061] The audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using a microphone and audio analysis software. Examples of microphones include, but are not limited to, condenser microphones, dynamic microphones, and directional microphones. The audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using, for example, a condenser microphone. The audio analysis unit can also analyze changes in music using a dynamic microphone. For example, dynamic microphones are resistant to high sound pressure and are suitable for live performances. The audio analysis unit can also focus on analyzing a specific sound source using a directional microphone. Examples of audio analysis software include, but are not limited to, FFT analysis, spectral analysis, and machine learning-based analysis. The audio analysis unit can analyze frequency components of music using, for example, FFT analysis. The audio analysis unit can also analyze changes in volume of music using spectral analysis. For example, spectral analysis can display sound intensity for each frequency. The audio analysis unit can also analyze changes in tempo and rhythm of music using machine learning-based analysis. This allows changes in music to be analyzed in real time and reflected in lighting control.

[0062] The lighting control unit can generate an appropriate lighting pattern according to the player's movements and changes in the music. Examples of lighting patterns include, but are not limited to, changes in color, changes in brightness, and lighting patterns. For example, the lighting control unit can turn on a spotlight when the player moves to the center of the stage. The lighting control unit can also change the color and brightness of the lighting as the tempo of the music increases. For example, as the tempo of the music increases, the lighting color can change to red and the brightness can increase. The lighting control unit can also change the lighting pattern according to the player's movements. For example, the lighting can flash when the player jumps. This makes it possible to generate an optimal lighting pattern according to the player's movements and changes in the music.

[0063] The output unit can change the color, brightness, and lighting pattern of the lighting. Examples of lighting colors include, but are not limited to, RGB colors, specific color temperatures, etc. The output unit can change the color of the lighting using, for example, RGB colors. The output unit can also change the color of the lighting by setting a specific color temperature. For example, the output unit can set a warm color temperature to make the lighting warmer. Examples of brightness include, but are not limited to, lumens, the use of an illuminance sensor, etc. The output unit can change the brightness of the lighting by adjusting the lumen value. The output unit can also automatically adjust the brightness of the lighting using an illuminance sensor. For example, the output unit can adjust the brightness of the lighting according to the ambient brightness. Examples of lighting patterns include, but are not limited to, blinking, fade-in / fade-out, random lighting, etc. The output unit can set a blinking pattern to make the lighting blink. The output unit can also set a fade-in / fade-out pattern to gradually brighten or dim the lighting. For example, the output unit can set a random lighting pattern to turn the lighting on randomly. This allows you to change the color, brightness, and lighting pattern of the lights in real time.

[0064] The sensor unit can estimate the player's emotions and adjust the accuracy of movement detection based on the estimated player's emotions. Examples of player emotions include, but are not limited to, tension, relaxation, and excitement. For example, if the player is nervous, the sensor unit increases the sensitivity of the sensor to detect even subtle movements. Furthermore, if the player is relaxed, the sensor unit can return the sensitivity to normal to detect natural movements. For example, if the player is excited, the sensor unit adjusts the sensitivity of the sensor to respond to sudden movements. The player's emotions are estimated using, for example, facial expression recognition, voice tone analysis, changes in heart rate, etc. This allows the accuracy of movement detection to be adjusted according to the player's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI using an emotion estimation function. The generative AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0065] The sensor unit can analyze the player's past motion data and select an appropriate detection algorithm. Past motion data includes, but is not limited to, the player's past motion patterns, the speed and direction of the motion, and the like. For example, the sensor unit can analyze the player's past motion patterns and select an algorithm that prioritizes detection of the most frequently performed motion. The sensor unit can also select an algorithm that improves detection accuracy for a specific motion from the player's past motion data. For example, the sensor unit can select an optimal detection algorithm based on the player's past motion data, depending on the speed and direction of the motion. Methods for collecting past motion data include, for example, using a database or applying a machine learning algorithm. This allows the optimal detection algorithm to be selected based on the player's past motion data.

[0066] When detecting motion, the sensor unit can perform filtering taking into account the influence of a player's clothing and props. Examples of the influence of clothing and props include, but are not limited to, color, shape, and specific patterns. For example, if a player is wearing a specific clothing item, the sensor unit can detect motion taking into account the color and shape of the clothing. Furthermore, if a player is using a prop, the sensor unit can filter the prop's movement to detect only the player's motion. For example, the sensor unit can analyze the influence of a player's clothing and props in real time to improve the accuracy of motion detection. Filtering methods include, for example, using an image processing algorithm and removing specific features. This allows motion detection to take into account the influence of a player's clothing and props.

[0067] When detecting movement, the sensor unit can dynamically adjust the detection range according to the speed and direction of the player's movement. Examples of the speed of movement include, but are not limited to, using a speed sensor or calculating the distance traveled between frames. For example, if the player is moving at high speed, the sensor unit widens the detection range to track the movement. Furthermore, if the player is moving slowly, the sensor unit can narrow the detection range to improve accuracy. For example, the sensor unit dynamically adjusts the detection range according to the direction of the player's movement to perform optimal detection. Examples of the direction of movement include using a direction sensor or estimating direction through image analysis. This allows the detection range to be dynamically adjusted according to the speed and direction of the player's movement.

[0068] The audio analysis unit can estimate the player's emotions and adjust the way the audio analysis is presented based on the estimated player's emotions. Examples of player emotions include, but are not limited to, relaxed, hurried, and excited. For example, if the player is relaxed, the audio analysis unit can perform audio analysis at a slow tempo. Furthermore, if the player is in a hurry, the audio analysis unit can also perform audio analysis quickly. For example, if the player is excited, the audio analysis unit can perform audio analysis with visually stimulating effects. The audio analysis presentation method includes, for example, a method for visualizing the analysis results and a method for providing audio feedback. This allows the way the audio analysis is presented to be adjusted according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] During audio analysis, the audio analysis unit can adjust the level of detail of the analysis based on the importance of the song. Examples of the importance of the song include, but are not limited to, the popularity of the song and the importance of the event. For example, the audio analysis unit performs detailed audio analysis for an important song. The audio analysis unit can also perform normal audio analysis for a general song. For example, the audio analysis unit performs simplified audio analysis for a song with low importance. Examples of the level of detail of the analysis include the analysis resolution and the number of analysis items. This allows the level of detail of the analysis to be adjusted based on the importance of the song.

[0070] During audio analysis, the audio analysis unit can apply different analysis algorithms depending on the category of the music. Examples of music categories include, but are not limited to, genre, tempo, and rhythm pattern. For example, the audio analysis unit applies a specific analysis algorithm to classical music. The audio analysis unit can also apply a different analysis algorithm to rock music. For example, the audio analysis unit applies yet another analysis algorithm to pop music. Analysis algorithms include, for example, a tempo analysis algorithm and a rhythm analysis algorithm. This allows the optimal analysis algorithm to be applied depending on the category of the music.

[0071] During voice analysis, the voice analysis unit can improve the accuracy of the analysis by referring to the player's past voice analysis results. Past voice analysis results include, but are not limited to, the player's past voice analysis results and specific patterns. The voice analysis unit improves the accuracy of the analysis, for example, based on the player's past voice analysis results. The voice analysis unit can also extract specific patterns from the player's past voice analysis results and reflect them in the analysis. For example, the voice analysis unit analyzes the player's past voice analysis results and proposes an optimal analysis method. Methods for collecting past voice analysis results include, for example, using a database or using them as training data for a machine learning model. In this way, the accuracy of the analysis is improved by referring to the player's past voice analysis results.

[0072] The lighting control unit can estimate the player's emotions and adjust lighting control standards based on the estimated player's emotions. Examples of player emotions include, but are not limited to, relaxation, excitement, and tension. For example, the lighting control unit provides soft lighting when the player is relaxed. Furthermore, the lighting control unit can also provide bright and stimulating lighting when the player is excited. For example, the lighting control unit provides subdued lighting when the player is tense. Lighting control standards include, for example, lighting color and brightness setting standards, and adjustment methods according to movement and emotions. This allows the lighting control standards to be adjusted according to the player's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] The lighting control unit can improve the accuracy of lighting control by taking into account the interrelationships between players' movements. Examples of interrelationships between movements include, but are not limited to, synchronization of multiple movements and coordinated patterns of movements. For example, when a player performs multiple movements on a stage, the lighting control unit provides lighting according to each movement. Furthermore, when a player's movements are continuous, the lighting control unit can adjust lighting according to the flow of the movements. For example, the lighting control unit analyzes the interrelationships between players' movements in real time and performs optimal lighting control. Examples of control accuracy include feedback control and real-time adjustment algorithms. This improves the accuracy of lighting control by taking into account the interrelationships between players' movements.

[0074] The lighting control unit can control the lighting based on the player's attribute information. The attribute information includes, but is not limited to, for example, age, gender, and experience level. The lighting control unit provides optimal lighting depending on, for example, the player's age and gender. The lighting control unit can also adjust the color and brightness of the lighting depending on the player's costume and props. For example, the lighting control unit analyzes the player's attribute information in real time and performs optimal lighting control. Control methods include, for example, a method for adjusting the color and brightness of the lighting, and a control algorithm based on movement. In this way, optimal lighting control is performed taking into account the player's attribute information.

[0075] When controlling the lighting, the lighting control unit can weight the control based on the frequency of the player's movements. The frequency of movements includes, but is not limited to, for example, the number of movements within a certain period of time and the frequency of movement patterns. For example, the lighting control unit can weight the lighting more highly for movements that the player makes frequently. The lighting control unit can also weight the lighting less highly for movements that the player makes infrequently. For example, the lighting control unit analyzes the frequency of the player's movements in real time and performs optimal lighting control. Examples of weighting include weighting according to the importance of the movement and weighting based on the frequency of the movement. In this way, the lighting control is weighted based on the frequency of the player's movements.

[0076] The output unit can estimate the player's emotions and determine the priority of lighting to be output based on the estimated player's emotions. Examples of player emotions include, but are not limited to, tension, relaxation, and excitement. For example, if the player is tensioned, the output unit can prioritize calm lighting. Furthermore, if the player is relaxed, the output unit can prioritize soft lighting. For example, if the player is excited, the output unit can prioritize bright and stimulating lighting. Examples of lighting priorities include priorities based on the intensity of emotions and priorities based on the importance of movements. This allows the priority of lighting to be determined according to the player's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The output unit can improve the accuracy of the output by taking into account the interrelationships between the players' movements. Examples of interrelationships between movements include, but are not limited to, synchronization of multiple movements and coordinated patterns of movements. For example, when a player performs multiple movements on a stage, the output unit outputs lighting corresponding to each movement. Furthermore, when the player's movements are continuous, the output unit can also output lighting in accordance with the flow of the movements. For example, the output unit analyzes the interrelationships between the players' movements in real time and outputs optimal lighting. Examples of output accuracy include feedback control and real-time adjustment algorithms. This improves the accuracy of the output by taking into account the interrelationships between the players' movements.

[0078] The output unit may output the information taking into consideration the player's attribute information. The attribute information may include, but is not limited to, for example, age, gender, and experience level. The output unit may output optimal lighting depending on, for example, the player's age and gender. The output unit may also adjust the color and brightness of the lighting depending on the player's costume and props. For example, the output unit may analyze the player's attribute information in real time and output optimal lighting. This allows optimal lighting output to be performed taking into consideration the player's attribute information.

[0079] The output unit can weight the output based on the frequency of the player's movements when outputting. The frequency of movements includes, but is not limited to, the number of movements within a certain period of time, the frequency of movement patterns, and the like. For example, the output unit can weight the lighting more heavily for movements that the player makes frequently, and output the lighting. The output unit can also weight the lighting less heavily for movements that the player makes infrequently. For example, the output unit can analyze the frequency of the player's movements in real time and output optimal lighting. Examples of weighting include weighting according to the importance of the movement and weighting based on the frequency of the movement. In this way, the lighting output is weighted based on the frequency of the player's movements.

[0080] The output unit can estimate the player's emotions and adjust the lighting display method to be output based on the estimated player's emotions. Examples of player emotions include, but are not limited to, tension, relaxation, and excitement. For example, if the player is tensioned, the output unit can display lighting with a subdued color tone. Furthermore, if the player is relaxed, the output unit can also display lighting with a soft color tone. For example, if the player is excited, the output unit can display lighting with a bright and stimulating color tone. Examples of lighting display methods include color changes, brightness changes, lighting patterns, and the like. This allows the lighting display method to be adjusted according to the player's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The output unit may output the light while taking into consideration the geographical distribution of the stage. Examples of the geographical distribution of the stage include, but are not limited to, the shape of the stage and the placement of the audience. For example, the output unit may concentrate and output lighting for a performance taking place in the center of the stage. The output unit may also distribute and output lighting for a performance taking place at the edge of the stage. For example, the output unit may adjust and output lighting intensity and color for a specific area of ​​the stage. This allows optimal lighting output to be performed while taking into consideration the geographical distribution of the stage.

[0082] The output unit may improve the accuracy of the output by referring to related literature during output. Examples of related literature include, but are not limited to, academic papers, patent documents, and technical reports. For example, the output unit may improve the output algorithm by referring to the latest research papers on lighting output. The output unit may also adjust the color and brightness of the lighting by referring to specialized books on lighting design. For example, the output unit may optimize the output method by referring to patent documents on lighting output. This improves the accuracy of the output by referring to the related literature.

[0083] The output unit can output the information taking into consideration the market value of the lighting. Market value includes, but is not limited to, for example, sales price, demand forecast, and evaluation of competing products. For example, when using expensive lighting equipment, the output unit can output information that makes the most of its characteristics. Furthermore, when using general lighting equipment, the output unit can also output information that emphasizes cost performance. For example, the output unit can analyze the market value of the lighting equipment in real time and propose the optimal output method. This allows for optimal lighting output taking into consideration the market value of the lighting. === Hard Collateral 1-1 === Each of the above-described elements, including the sensor unit, audio analysis unit, lighting control unit, and output unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the sensor unit can detect the player's movements in real time using the camera 42 or motion sensor of the smart device 14. For example, the audio analysis unit can analyze changes in tempo, rhythm, and volume of the music in real time using the microphone 38B or audio analysis software of the smart device 14. For example, the lighting control unit can generate an optimal lighting pattern in response to the player's movements and changes in the music using AI via the specific processing unit 290 of the data processing device 12. For example, the output unit can change the color, brightness, and lighting pattern of the lighting via the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the sensor unit, audio analysis unit, lighting control unit, and output unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the sensor unit can detect the player's movements in real time using the camera 42 or motion sensor of the smart glasses 214. For example, the audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using the microphone 238 or audio analysis software of the smart glasses 214. For example, the lighting control unit can generate an optimal lighting pattern in response to the player's movements or changes in music using AI via the specific processing unit 290 of the data processing device 12. For example, the output unit can change the color, brightness, and lighting pattern of the lighting via the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the sensor unit, audio analysis unit, lighting control unit, and output unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the sensor unit can detect the player's movements in real time using the camera 42 or motion sensor of the headset-type terminal 314. For example, the audio analysis unit can analyze changes in tempo, rhythm, and volume of music in real time using the microphone 238 or audio analysis software of the headset-type terminal 314. For example, the lighting control unit can generate an optimal lighting pattern in response to the player's movements and changes in music using AI via the specific processing unit 290 of the data processing device 12. For example, the output unit can change the color, brightness, and lighting pattern of the lighting via the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the sensor unit, audio analysis unit, lighting control unit, and output unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the sensor unit can detect the player's movements in real time using the camera 42 or motion sensor of the robot 414. For example, the audio analysis unit can analyze changes in tempo, rhythm, and volume of the music in real time using the microphone 238 or audio analysis software of the robot 414. For example, the lighting control unit can generate an optimal lighting pattern in response to the player's movements and changes in the music using AI via the specific processing unit 290 of the data processing device 12. For example, the output unit can change the color, brightness, and lighting pattern of the lighting via the control unit 46A of the robot 414.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The automatic lighting system can further include a learning unit that learns user preferences. The learning unit can learn user preferences based on past lighting settings and user feedback and reflect them in the next lighting settings. For example, if a user prefers a particular color or brightness, that information can be learned and used as a priority the next time the lighting is set. The learning unit can also track changes in user preferences and dynamically adjust lighting settings. This allows the system to provide optimal lighting according to the user's preferences.

[0086] The sensor unit can further include an environmental sensor that detects changes in the environment. The environmental sensor can detect environmental parameters such as temperature, humidity, and illuminance in real time, and control lighting in response to changes in the environment, as well as the player's movements and music. For example, if the environmental sensor detects high temperatures, it can adjust the brightness of the lighting to reduce heat. Also, if the environmental sensor detects low illuminance, it can increase the brightness of the lighting. This allows optimal lighting to be provided in response to changes in the environment.

[0087] The audio analysis unit can further analyze the emotion of the music. Examples of emotions of the music include, but are not limited to, sadness, joy, excitement, etc. The audio analysis unit can analyze the emotion of the music and issue instructions to the lighting control unit based on the results of analyzing the emotion of the music. For example, if the music expresses a sad emotion, the color of the lighting can be changed to blue or purple. Also, if the music expresses joy, the color of the lighting can be changed to bright yellow or orange. This makes it possible to provide lighting that corresponds to the emotion of the music.

[0088] The lighting control unit can further control the lighting taking into consideration the player's health condition. Health conditions include, but are not limited to, heart rate, blood pressure, and stress level. The lighting control unit can monitor the player's health condition and provide lighting according to the health condition. For example, if the player's heart rate is high, the lighting control unit can reduce the brightness of the lighting to enhance relaxation. Also, if the player's stress level is high, the lighting color can be changed to a warmer color to promote relaxation. This allows the lighting to be optimally tailored to the player's health condition.

[0089] The output unit can also detect audience reactions and adjust the lighting accordingly. Examples of audience reactions include, but are not limited to, clapping, cheering, and movement. The output unit can detect audience reactions in real time and adjust the lighting based on the results. For example, if the audience is applauding loudly, the output unit can increase the brightness of the lighting to create excitement. Alternatively, if the audience is watching quietly, the output unit can change the color of the lighting to a more subdued color. This allows optimal lighting to be provided in response to the audience reactions.

[0090] The sensor unit may further include a prediction unit that predicts the player's movements. The prediction unit predicts the player's next movement based on past movement data and current movement trends, and can adjust the sensitivity and detection range of the sensor based on the prediction. For example, if the player repeatedly jumps, the sensor's sensitivity can be increased by predicting the next jump. Also, if the player tends to move in a specific direction, the sensor's detection range can be expanded in that direction. This allows for more accurate detection of the player's movements.

[0091] The audio analysis unit can further perform analysis according to the genre of the music. Examples of music genres include, but are not limited to, classical, rock, and pop. The audio analysis unit can identify the genre of the music and apply an analysis algorithm according to the genre. For example, in the case of classical music, the analysis can be performed with emphasis on subtle sound changes. In addition, in the case of rock music, the analysis can be performed with emphasis on changes in rhythm and beat. This allows for optimal audio analysis according to the genre of the music.

[0092] The lighting control unit can further estimate the player's emotions and adjust the color and brightness of the lighting based on the estimated player's emotions. Examples of player emotions include, but are not limited to, joy, sadness, and excitement. The lighting control unit can estimate the player's emotions in real time and adjust the color and brightness of the lighting based on the results. For example, if the player is happy, it can provide bright lighting and high brightness. On the other hand, if the player is sad, it can provide subdued lighting and low brightness. This allows optimal lighting to be provided according to the player's emotions.

[0093] The output unit can further estimate the audience's emotions and adjust the lighting display method based on the estimated audience's emotions. Examples of audience emotions include, but are not limited to, excitement, emotion, and relaxation. The output unit can estimate the audience's emotions in real time and adjust the lighting display method based on the results. For example, if the audience is excited, bright and stimulating lighting can be displayed. On the other hand, if the audience is emotional, softer lighting can be displayed. This makes it possible to provide optimal lighting according to the audience's emotions.

[0094] The output unit can further output light taking into consideration the energy efficiency of the lighting. Energy efficiency includes, but is not limited to, for example, power consumption and the lifespan of the lighting. The output unit can monitor the energy efficiency of the lighting in real time and provide efficient lighting output. For example, if power consumption is high, the output unit can adjust the brightness of the lighting to reduce energy consumption. Also, if the lifespan of the lighting is approaching, the frequency of use of the lighting can be reduced. This allows for optimal lighting output taking energy efficiency into consideration.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The sensor unit detects the player's movements. Player movements include hand movements, foot movements, and whole-body movements. The sensor unit detects the player's movements in real time using a camera or motion sensor. For example, a fixed camera, a PTZ camera, an infrared camera, an acceleration sensor, a gyro sensor, an infrared sensor, etc. may be used. Step 2: The audio analysis unit detects changes in the music based on the information detected by the sensor unit. Changes in the music include changes in tempo, rhythm, and volume. The audio analysis unit analyzes changes in the music's tempo, rhythm, and volume in real time using a microphone and audio analysis software. For example, condenser microphones, dynamic microphones, directional microphones, FFT analysis, spectral analysis, and machine learning-based analysis may be used. Step 3: The lighting control unit controls the lighting based on the information obtained by the audio analysis unit. The lighting control unit generates the optimal lighting pattern according to the player's movements and changes in the music. The lighting pattern includes changes in color, brightness, lighting patterns, etc. The lighting control unit uses AI to generate the optimal lighting pattern according to the player's movements and changes in the music. Step 4: The output unit outputs the lighting pattern generated by the lighting control unit. The output unit controls the lighting devices based on instructions from the lighting control unit and adjusts the lighting in real time. For example, it changes the color, brightness, and lighting pattern of the lighting.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0140] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0141] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0151] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0152] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0153] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0154] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0156] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0157] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0158] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0159] 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.

[0160] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0161] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0162] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0163] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0164] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0165] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0166] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0167] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0168] [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A sensor unit that detects the player's movements; an audio analysis unit that detects changes in music based on information detected by the sensor unit; a lighting control unit that controls lighting based on the information obtained by the audio analysis unit; an output unit that outputs the illumination pattern generated by the illumination control unit; A system characterized by:

2. The sensor unit Detect player movement in real time using cameras or motion sensors 2. The system of claim 1.

3. The voice analysis unit Using a microphone and audio analysis software, the tempo, rhythm, and volume of a song are analyzed in real time.

2. The system of claim 1.

4. The lighting control unit Generates appropriate lighting patterns according to the player's movements and changes in the music 2. The system of claim 1.

5. The output unit Change the color, brightness, and lighting pattern of the lights 2. The system of claim 1.

6. The sensor unit Estimate the player's emotions and adjust the accuracy of movement detection based on the estimated player's emotions.

2. The system of claim 1.

7. The sensor unit Analyze the player's past movement data and select the appropriate detection algorithm 2. The system of claim 1.

8. The sensor unit When detecting movement, filtering is performed taking into account the influence of the player's clothing and props.

2. The system of claim 1.

Images