System

Abstract

An object of a system according to an embodiment is to capture a reaction of an audience in real time and generate music or a dance in an impromptu manner on the basis of the reaction.SOLUTION: A system includes an audience reaction acquisition unit, an emotion analysis unit, a music generation unit, and a dance generation unit. The audience reaction acquisition unit acquires the reaction of the audience. The emotion analysis unit analyzes the reaction of the audience acquired by the audience reaction acquisition unit. The music generation unit generates music based on the reaction of the audience analyzed by the emotion analysis unit. The dance generation unit generates a dance motion based on the reaction of the audience analyzed by the emotion analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to capture audience reactions in real time and provide impromptu entertainment based on them.

[0005] The system according to the embodiment aims to capture the audience's reactions in real time and generate improvised music and dance based on them. [Means for solving the problem]

[0006] The system according to the embodiment includes an audience response acquisition unit, an emotion analysis unit, a music generation unit, and a dance generation unit. The audience response acquisition unit acquires audience responses. The emotion analysis unit analyzes the audience responses acquired by the audience response acquisition unit. The music generation unit generates music based on the audience responses analyzed by the emotion analysis unit. The dance generation unit generates dance movements based on the audience responses analyzed by the emotion analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can capture the audience's reactions in real time and generate improvised music and dance based on the reactions. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The entertainment system according to the embodiment of the present invention captures the audience's reactions in real time, and based on that, a generative AI improvises and performs music and dance. This deepens the sense of unity and empathy between the participants and the robot, providing an unprecedented enjoyment.

[0029] The entertainment system according to the embodiment includes an audience response acquisition unit, an emotion analysis unit, a music generation unit, and a dance generation unit. The audience response acquisition unit acquires audience responses. For example, it collects audience facial expressions and voices using sensors such as cameras and microphones. The audience response acquisition unit can also acquire audience movements using motion capture technology. For example, it captures audience hand and body movements in real time. The emotion analysis unit analyzes the audience responses acquired by the audience response acquisition unit. For example, the generation AI analyzes audience emotions using facial expression recognition technology. The generation AI can also determine emotions by analyzing the tone and speed of the audience's voices using audio analysis technology. The generation AI can also analyze audience movement data to determine changes in emotions. The music generation unit generates music based on the audience responses analyzed by the emotion analysis unit. For example, the generation AI can generate bright and rhythmic music if the audience is enjoying themselves. The generation AI can also generate dramatic music if the audience is surprised. The generation AI can also change the tempo and key of the music in real time to match the audience's emotions. The dance generation unit generates dance movements based on the audience's reactions analyzed by the emotion analysis unit. For example, the generation AI generates a rhythmic dance when the audience is clapping. The generation AI can also generate a dance incorporating acrobatic movements when the audience is surprised. The generation AI can also fine-tune the dance movements in real time to match the audience's emotions. This allows the entertainment system according to the embodiment to generate music and dance in real time based on the audience's reactions. For example, when the audience is enjoying themselves, the robot performs cheerful, rhythmic music and a dance, and when the audience is surprised, the robot performs dramatic music and an acrobatic dance. This deepens the sense of unity and empathy between the participants and the robot, providing an unprecedented level of enjoyment.

[0030] The audience response acquisition unit can refer to the audience's past response history and learn the preferences and tendencies of each individual audience member. For example, the generation AI stores the audience's past response history in a database and references it in real time. For example, it learns what type of music or dance a particular audience member responds to based on response data from past events. The audience response acquisition unit can also analyze the audience's past response history and identify the preferences and tendencies of each individual audience member. For example, the generation AI learns the audience's favorite music genres and dance styles based on past response data. In this way, by referring to the audience's past response history, the AI ​​can learn the preferences and tendencies of each individual audience member and provide a more personalized performance.

[0031] The audience response acquisition unit collects biometric information from audience members in real time, enabling more accurate judgment of changes in their emotions. For example, the generation AI in the audience response acquisition unit monitors the audience's heart rates in real time and analyzes changes in their emotions. For example, if an increase in heart rate indicates excitement or surprise, the performance is adjusted based on that data. The audience response acquisition unit can also collect electrodermal activity in real time to judge changes in their emotions. For example, if changes in electrodermal activity indicate tension or excitement, the performance is adjusted based on that data. The audience response acquisition unit can also monitor breathing rate in real time to judge changes in their emotions. For example, if changes in breathing rate indicate relaxation or tension, the performance is adjusted based on that data. In this way, by collecting the audience's biometric information, changes in their emotions can be more accurately judged and the performance can be adjusted.

[0032] The emotion analysis unit can collect audience members' social media posts or real-time comments and analyze changes in their emotions. For example, the generative AI collects audience members' social media posts in real time and performs emotion analysis. For example, it analyzes Twitter and Instagram posts to determine the audience's emotions. The emotion analysis unit can also collect live chat comments in real time and perform emotion analysis. For example, if an audience member comments "Amazing!" in the live chat, it can determine that this indicates excitement or surprise and adjust the performance based on that emotion. The emotion analysis unit can also analyze the text data of audience members' social media posts and comments to determine changes in their emotions. For example, if there are a lot of positive words, it can determine that this indicates joy or excitement and adjust the performance based on that emotion. In this way, by collecting audience members' social media posts and real-time comments, it can analyze changes in their emotions and adjust the performance.

[0033] The emotion analysis unit can infer individual hobbies and preferences from the audience's clothing and belongings and reflect them in the performance. For example, the generative AI analyzes the audience's clothing and infers their hobbies and preferences based on that data. For example, if an audience member is wearing a band T-shirt, the performance will play that band's songs. The emotion analysis unit can also analyze the audience's belongings and infer their hobbies and preferences based on that data. For example, if an audience member is carrying a camera, the unit can infer that their hobby is photography and provide them with a performance related to photography. The emotion analysis unit can also combine data on the audience's clothing and belongings to comprehensively infer their hobbies and preferences. For example, if an audience member is wearing sportswear, the performance will be related to sports. This allows the performance to provide more personalized entertainment by inferring the audience's hobbies and preferences from their clothing and belongings and reflecting them in the performance.

[0034] The music generation unit can improvise new musical styles that combine different musical genres based on the audience's reactions. For example, the music generation unit uses a generative AI to analyze the audience's reactions and generate new musical styles that combine different musical genres. For example, it may improvise music that combines classical and electronica. The music generation unit can also generate music that combines jazz and rock based on the audience's reactions. For example, if the audience is relaxed, it may play music that combines jazz and rock. The music generation unit can also generate music that combines pop and hip hop based on the audience's reactions. For example, if the audience is enjoying themselves, it may play music that combines pop and hip hop. This allows the performance to always be fresh by improvising new musical styles based on the audience's reactions.

[0035] The music generation unit can change the tempo and key of the music in real time based on the audience's reactions, providing music that matches the audience's emotions. For example, the music generation unit uses a generative AI to analyze the audience's reactions and change the tempo of the music in real time. For example, if the audience is excited, the tempo can be increased to provide energetic music. The music generation unit can also change the key of the music in real time based on the audience's reactions. For example, if the audience is relaxed, the key can be lowered to provide calmer music. The music generation unit can also adjust the melody and rhythm of the music in real time to match the audience's emotions. For example, if the audience is moved, an inspiring melody can be played. This allows the tempo and key of the music to be changed in real time to match the audience's emotions, providing a more emotionally appealing performance.

[0036] The music generation unit can incorporate natural and environmental sounds into the music based on the audience's reactions, providing a more realistic performance. For example, the music generation unit uses a generation AI to analyze the audience's reactions and incorporate natural sounds into the music. For example, if the audience is relaxed, the music generation unit can incorporate birdsong or the sound of a babbling brook into the music. The music generation unit can also incorporate the sounds of wind and waves into the music based on the audience's reactions. For example, if the audience is calm, the music generation unit can incorporate the sound of wind into the music. The music generation unit can also incorporate the sounds of rain and thunder into the music based on the audience's reactions. For example, if the audience is surprised, the music generation unit can incorporate the sound of thunder into the music. In this way, by incorporating natural and environmental sounds based on the audience's reactions, a more realistic performance can be provided.

[0037] The music generation unit synchronizes visual effects to music based on the audience's reactions, providing a performance that can be enjoyed both visually and aurally. For example, the music generation unit uses generative AI to analyze the audience's reactions and synchronize visual effects to music. For example, if the audience is excited, a colorful light show is produced in time with the music. The music generation unit can also synchronize projection mapping to music based on the audience's reactions. For example, if the audience is surprised, a dynamic projection mapping is produced. The music generation unit can also synchronize a laser show to music based on the audience's reactions. For example, if the audience is enjoying themselves, a laser show is produced in time with the music. In this way, by synchronizing visual effects based on the audience's reactions, a performance can be provided that can be enjoyed both visually and aurally.

[0038] The dance generation unit incorporates new choreography and styles into the robot's dance movements based on the audience's reactions, allowing it to constantly provide fresh performances. For example, the generation AI in the dance generation unit analyzes the audience's reactions and improvises new choreography. For example, if the audience is enjoying themselves, it will incorporate rhythmic and energetic choreography. The dance generation unit can also incorporate new dance styles based on the audience's reactions. For example, if the audience is surprised, it will generate a dance that incorporates acrobatic movements. The dance generation unit can also fine-tune the dance movements in real time based on the audience's reactions. For example, if the audience is relaxed, it will adjust the movements to be slower. This allows it to constantly provide fresh performances by incorporating new choreography and styles based on the audience's reactions.

[0039] The dance generation unit can fine-tune the robot's dance movements in real time based on the audience's reactions, providing movements that match the audience's emotions. For example, the dance generation unit uses a generation AI to analyze the audience's reactions and fine-tune the robot's dance movements in real time. For example, if the audience is relaxed, the movements are adjusted to be slower. The dance generation unit can also adjust the robot's dance movements to be more energetic based on the audience's reactions. For example, if the audience is excited, the movements are made faster to provide an energetic dance. The dance generation unit can also build a system that adjusts the robot's dance movements in real time to match the audience's emotions. For example, if the audience is moved, the robot will incorporate moving movements. This allows the robot's dance movements to be fine-tuned in real time to match the audience's emotions, providing a more emotional performance.

[0040] The dance generation unit can combine light and sound effects with the robot's dance movements based on the audience's reactions to enhance the visual impact. For example, the generation AI in the dance generation unit analyzes the audience's reactions and combines light effects with the robot's dance movements. For example, if the audience is excited, a colorful light show is created to match the dance. The dance generation unit can also combine sound effects with the robot's dance movements based on the audience's reactions. For example, if the audience is surprised, dynamic sound effects are created to match the dance. The dance generation unit can also combine a laser show with the robot's dance movements based on the audience's reactions. For example, if the audience is enjoying themselves, a laser show is created to match the dance. In this way, by combining light and sound effects based on the audience's reactions, a performance with enhanced visual impact can be provided.

[0041] The dance generation unit can incorporate other performance elements into the robot's dance movements based on the audience's reactions. For example, the generation AI in the dance generation unit analyzes the audience's reactions and incorporates juggling into the robot's dance movements. For example, if the audience is surprised, the unit provides a dance that incorporates juggling. The dance generation unit can also incorporate acrobatics into the robot's dance movements based on the audience's reactions. For example, if the audience is enjoying themselves, the unit provides a dance that incorporates acrobatic movements. The dance generation unit can also build a system that combines other performance elements into the robot's dance movements based on the audience's reactions. For example, if the audience is relaxed, the unit provides a dance that incorporates soft movements. This makes it possible to incorporate other performance elements based on the audience's reactions and provide a more diverse and attractive performance.

[0042] The robot's motion control unit can learn the robot's motion history and achieve smoother, more natural motion. For example, the generation AI of the robot's motion control unit stores the robot's past motion history in a database and references it in real time. For example, it achieves smoother, more natural motion based on past performance data. The robot's motion control unit can also analyze the robot's motion history and optimize its motion. For example, the generation AI can reduce unnecessary motion based on past motion data and achieve smoother motion. The robot's motion control unit can also learn the robot's motion history and improve the accuracy of its motion. For example, the generation AI can adjust the timing and speed of motion based on past motion data and achieve more natural motion. In this way, by learning the robot's motion history, it can achieve smoother, more natural motion.

[0043] A robot's motion control unit can optimize the robot's motion in real time to improve energy efficiency. For example, a generative AI can optimize the robot's motion in real time to improve energy efficiency. For example, it can reduce unnecessary motion and suppress battery consumption. The robot's motion control unit can also optimize the robot's motion and monitor energy consumption. For example, the generative AI can use a motion optimization algorithm to improve energy efficiency. The robot's motion control unit can also adjust the robot's motion in real time to minimize energy consumption. For example, the generative AI can use a motion feedback loop to improve energy efficiency. In this way, energy efficiency can be improved by optimizing the robot's motion in real time.

[0044] The robot's movement control unit can synchronize sound and light effects with the robot's movements, providing a performance that can be enjoyed both visually and aurally. For example, the robot's movement control unit uses a generative AI to synchronize sound effects with the robot's movements. For example, music and sound effects can be synchronized when the robot moves. The robot's movement control unit can also synchronize light effects with the robot's movements. For example, a colorful light show can be created when the robot moves. The robot's movement control unit can also synchronize projection mapping with the robot's movements. For example, dynamic projection mapping can be created when the robot moves. In this way, by synchronizing sound and light effects with the robot's movements, a performance can be provided that can be enjoyed both visually and aurally.

[0045] A robot's motion control unit can incorporate cooperation with other robots and devices into the robot's movements, allowing multiple robots to perform in cooperation. For example, a generation AI can synchronize the movements of multiple robots to perform a cooperative performance. For example, multiple robots can perform a dance at the same time. A robot's motion control unit can also incorporate cooperation with other devices into the robot's movements. For example, when a robot moves, it performs in cooperation with drones or lighting equipment. A robot's motion control unit can also build a system that adjusts performances in real time that incorporate cooperation with multiple robots and devices. For example, when a robot moves, it cooperates with other robots and devices to perform a complex performance. This allows for more complex and fascinating performances to be provided by incorporating cooperation with multiple robots and devices.

[0046] The interaction unit allows the robot to improvise and create new performances based on audience requests. For example, the generation AI analyzes audience requests and the robot improvises new performances based on those requests. For example, if an audience member requests a specific song, the robot will improvise a dance to match that song. The interaction unit can also improvise new music based on audience requests. For example, the robot will improvise a music genre requested by the audience. The interaction unit can also improvise new dance moves based on audience requests. For example, the robot will improvise a dance style requested by the audience. This allows the robot to deepen interaction with the audience by improvising new performances based on audience requests.

[0047] The interaction unit can enable the robot to perform interactions such as dancing with the audience based on the audience's movements. For example, the generation AI analyzes the audience's movements in real time, and the robot dances with the audience based on those movements. For example, if an audience member waves their hands, the robot will do the same. The interaction unit can also enable the robot to play games with the audience based on the audience's movements. For example, if an audience member jumps, the robot will do the same to progress the game. The interaction unit can also build a system in which the robot performs together with the audience based on the audience's movements. For example, if an audience member takes a dance step, the robot will take the same step. This allows the robot to interact based on the audience's movements, deepening the sense of unity with the audience.

[0048] The interaction unit can provide individual interactions, such as having the robot call out the audience's name and perform based on the audience's request. For example, the generation AI analyzes the audience's request, and the robot calls out the audience's name and performs based on that request. For example, if the audience requests a specific song, the robot calls out the audience's name in time with that song. The interaction unit can also provide individual messages based on the audience's request. For example, a special message can be displayed based on the audience's request. The interaction unit can also build a system in which the robot calls out the audience's name and performs based on the audience's request. For example, the robot performs a dance while calling out the audience's name in accordance with the dance style requested by the audience. This allows for individual interactions based on the audience's request, deepening the sense of unity with the audience.

[0049] The interaction unit can enable the robot to interact with the audience, such as playing games, based on the audience's movements. For example, the generation AI analyzes the audience's movements in real time, and the robot plays games with the audience based on those movements. For example, if an audience member waves their hands, the robot will do the same to progress through the game. The interaction unit can also enable the robot to dance with the audience based on the audience's movements. For example, if an audience member jumps, the robot will do the same to perform the dance. The interaction unit can also build a system in which the robot performs a performance with the audience based on the audience's movements. For example, if an audience member takes a dance step, the robot will take the same step. This allows the robot to interact based on the audience's movements, deepening the sense of unity with the audience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The music generation unit can improvise new musical styles that combine different musical genres based on the audience's reactions. For example, it can improvise music that combines classical and electronica. It can also generate music that combines jazz and rock. It can also generate music that combines pop and hip-hop. This allows the performance to always be fresh by improvising new musical styles based on the audience's reactions.

[0052] The music generation unit can incorporate natural and environmental sounds into the music based on the audience's reactions, providing a more realistic performance. For example, if the audience is relaxed, the music can incorporate the chirping of birds or the murmuring of a river. The music can also incorporate the sounds of wind and waves. Furthermore, the music can incorporate the sounds of rain and thunder. In this way, by incorporating natural and environmental sounds based on the audience's reactions, a more realistic performance can be provided.

[0053] The music generation unit can synchronize visual effects to the music based on the audience's reactions, providing a performance that can be enjoyed both visually and aurally. For example, if the audience is excited, a colorful light show can be synchronized to the music. It can also synchronize projection mapping to the music. It can also synchronize a laser show to the music. In this way, by synchronizing visual effects based on the audience's reactions, it is possible to provide a performance that can be enjoyed both visually and aurally.

[0054] The dance generation unit can incorporate new choreography and styles into the robot's dance movements based on the audience's reactions, thereby providing a constantly fresh performance. For example, if the audience is enjoying themselves, it can incorporate rhythmic and energetic choreography. Alternatively, if the audience is surprised, it can generate a dance that incorporates acrobatic movements. Furthermore, if the audience is relaxed, it can adjust the movements to be slower. This allows the robot to incorporate new choreography and styles based on the audience's reactions, thereby providing a constantly fresh performance.

[0055] The dance generation unit can combine light and sound effects with the robot's dancing movements based on the audience's reactions to enhance the visual impact. For example, if the audience is excited, a colorful light show can be created to match the dance. Sound effects can also be combined with the robot's dancing movements. Furthermore, a laser show can be created to match the dance. In this way, by combining light and sound effects based on the audience's reactions, a performance with enhanced visual impact can be provided.

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

[0057] Step 1: The audience response acquisition unit acquires audience responses. For example, it uses sensors such as cameras and microphones to collect audience facial expressions and voices. The audience response acquisition unit can also acquire audience movements using motion capture technology. For example, it can capture audience hand and body movements in real time. Step 2: The emotion analysis unit analyzes the audience's reactions acquired by the audience reaction acquisition unit. For example, the generation AI can analyze the audience's emotions using facial expression recognition technology. The generation AI can also use voice analysis technology to analyze the tone and speed of the audience's voices to determine their emotions. The generation AI can also analyze the audience's movement data to determine changes in their emotions. Step 3: The music generation unit generates music based on the audience's reactions analyzed by the emotion analysis unit. For example, if the audience is enjoying themselves, the generation AI generates bright, rhythmic music. If the audience is surprised, the generation AI can also generate dramatic music. The generation AI can also change the tempo and key of the music in real time to match the audience's emotions. Step 4: The dance generation unit generates dance movements based on the audience's reactions analyzed by the emotion analysis unit. For example, if the audience is clapping, the generation AI generates a rhythmic dance. If the audience is surprised, the generation AI can also generate a dance that incorporates acrobatic movements. The generation AI can also fine-tune the dance movements in real time to match the audience's emotions.

[0058] (Example 2) The entertainment system according to the embodiment of the present invention captures the audience's reactions in real time, and based on that, a generative AI improvises and performs music and dance. This deepens the sense of unity and empathy between the participants and the robot, providing an unprecedented enjoyment.

[0059] The entertainment system according to the embodiment includes an audience response acquisition unit, an emotion analysis unit, a music generation unit, and a dance generation unit. The audience response acquisition unit acquires audience responses. For example, it collects audience facial expressions and voices using sensors such as cameras and microphones. The audience response acquisition unit can also acquire audience movements using motion capture technology. For example, it captures audience hand and body movements in real time. The emotion analysis unit analyzes the audience responses acquired by the audience response acquisition unit. For example, the generation AI analyzes audience emotions using facial expression recognition technology. The generation AI can also determine emotions by analyzing the tone and speed of the audience's voices using audio analysis technology. The generation AI can also analyze audience movement data to determine changes in emotions. The music generation unit generates music based on the audience responses analyzed by the emotion analysis unit. For example, the generation AI can generate bright and rhythmic music if the audience is enjoying themselves. The generation AI can also generate dramatic music if the audience is surprised. The generation AI can also change the tempo and key of the music in real time to match the audience's emotions. The dance generation unit generates dance movements based on the audience's reactions analyzed by the emotion analysis unit. For example, the generation AI generates a rhythmic dance when the audience is clapping. The generation AI can also generate a dance incorporating acrobatic movements when the audience is surprised. The generation AI can also fine-tune the dance movements in real time to match the audience's emotions. This allows the entertainment system according to the embodiment to generate music and dance in real time based on the audience's reactions. For example, when the audience is enjoying themselves, the robot performs cheerful, rhythmic music and a dance, and when the audience is surprised, the robot performs dramatic music and an acrobatic dance. This deepens the sense of unity and empathy between the participants and the robot, providing an unprecedented level of enjoyment.

[0060] The audience response acquisition unit can refer to the audience's past response history and learn the preferences and tendencies of each individual audience member. For example, the generation AI stores the audience's past response history in a database and references it in real time. For example, it learns what type of music or dance a particular audience member responds to based on response data from past events. The audience response acquisition unit can also analyze the audience's past response history and identify the preferences and tendencies of each individual audience member. For example, the generation AI learns the audience's favorite music genres and dance styles based on past response data. In this way, by referring to the audience's past response history, the AI ​​can learn the preferences and tendencies of each individual audience member and provide a more personalized performance.

[0061] The audience response acquisition unit collects biometric information from audience members in real time, enabling more accurate judgment of changes in their emotions. For example, the generation AI in the audience response acquisition unit monitors the audience's heart rates in real time and analyzes changes in their emotions. For example, if an increase in heart rate indicates excitement or surprise, the performance is adjusted based on that data. The audience response acquisition unit can also collect electrodermal activity in real time to judge changes in their emotions. For example, if changes in electrodermal activity indicate tension or excitement, the performance is adjusted based on that data. The audience response acquisition unit can also monitor breathing rate in real time to judge changes in their emotions. For example, if changes in breathing rate indicate relaxation or tension, the performance is adjusted based on that data. In this way, by collecting the audience's biometric information, changes in their emotions can be more accurately judged and the performance can be adjusted.

[0062] The emotion analysis unit can use the emotion estimation function to estimate the audience's emotions in real time and adjust the robot's performance based on those emotions. For example, the generative AI analyzes the audience's facial expressions and uses the emotion estimation function to estimate their emotions in real time. For example, it detects smiling or surprised expressions and adjusts the performance based on those emotions. The emotion analysis unit can also analyze the tone and speed of the audience's voices and estimate their emotions using the emotion estimation function. For example, if the voice tone is high or the speed is fast, it can determine that this indicates excitement or surprise, and adjust the performance based on that emotion. The emotion analysis unit can also analyze the audience's movement data and estimate their emotions using the emotion estimation function. For example, if an audience member waves their hands, it can determine that this indicates joy or excitement, and adjust the performance based on that emotion. This allows the audience's emotions to be estimated in real time and the performance to be adjusted based on that emotion.

[0063] The emotion analysis unit can collect audience members' social media posts or real-time comments and analyze changes in their emotions. For example, the generative AI collects audience members' social media posts in real time and performs emotion analysis. For example, it analyzes Twitter and Instagram posts to determine the audience's emotions. The emotion analysis unit can also collect live chat comments in real time and perform emotion analysis. For example, if an audience member comments "Amazing!" in the live chat, it can determine that this indicates excitement or surprise and adjust the performance based on that emotion. The emotion analysis unit can also analyze the text data of audience members' social media posts and comments to determine changes in their emotions. For example, if there are a lot of positive words, it can determine that this indicates joy or excitement and adjust the performance based on that emotion. In this way, by collecting audience members' social media posts and real-time comments, it can analyze changes in their emotions and adjust the performance.

[0064] The emotion analysis unit can infer individual hobbies and preferences from the audience's clothing and belongings and reflect them in the performance. For example, the generative AI analyzes the audience's clothing and infers their hobbies and preferences based on that data. For example, if an audience member is wearing a band T-shirt, the performance will play that band's songs. The emotion analysis unit can also analyze the audience's belongings and infer their hobbies and preferences based on that data. For example, if an audience member is carrying a camera, the unit can infer that their hobby is photography and provide them with a performance related to photography. The emotion analysis unit can also combine data on the audience's clothing and belongings to comprehensively infer their hobbies and preferences. For example, if an audience member is wearing sportswear, the performance will be related to sports. This allows the performance to provide more personalized entertainment by inferring the audience's hobbies and preferences from their clothing and belongings and reflecting them in the performance.

[0065] The emotion analysis unit uses the emotion estimation function to estimate the audience's emotions in real time and provide personalized messages or surprises to the audience based on those emotions. For example, the emotion analysis unit uses a generative AI to estimate the audience's emotions in real time and provide personalized messages based on those emotions. For example, if the audience is enjoying themselves, the message "Thank you for enjoying the show!" is displayed. The emotion analysis unit can also estimate the audience's emotions and provide surprises based on those emotions. For example, if the audience is surprised, a special performance is presented. The emotion analysis unit can also analyze the audience's emotional data and build a system that provides personalized messages or surprises based on those emotions. For example, if the audience is moved, an inspiring message is displayed. This allows the audience to feel a deeper sense of unity by providing personalized messages or surprises based on their emotions.

[0066] The music generation unit can improvise new musical styles that combine different musical genres based on the audience's reactions. For example, the music generation unit uses a generative AI to analyze the audience's reactions and generate new musical styles that combine different musical genres. For example, it may improvise music that combines classical and electronica. The music generation unit can also generate music that combines jazz and rock based on the audience's reactions. For example, if the audience is relaxed, it may play music that combines jazz and rock. The music generation unit can also generate music that combines pop and hip hop based on the audience's reactions. For example, if the audience is enjoying themselves, it may play music that combines pop and hip hop. This allows the performance to always be fresh by improvising new musical styles based on the audience's reactions.

[0067] The music generation unit can change the tempo and key of the music in real time based on the audience's reactions, providing music that matches the audience's emotions. For example, the music generation unit uses a generative AI to analyze the audience's reactions and change the tempo of the music in real time. For example, if the audience is excited, the tempo can be increased to provide energetic music. The music generation unit can also change the key of the music in real time based on the audience's reactions. For example, if the audience is relaxed, the key can be lowered to provide calmer music. The music generation unit can also adjust the melody and rhythm of the music in real time to match the audience's emotions. For example, if the audience is moved, an inspiring melody can be played. This allows the tempo and key of the music to be changed in real time to match the audience's emotions, providing a more emotionally appealing performance.

[0068] The music generation unit can use the emotion estimation function to estimate the audience's emotions in real time and adjust the musical melody and rhythm based on those emotions. For example, the music generation unit uses a generative AI to estimate the audience's emotions in real time and adjust the musical melody based on those emotions. For example, if the audience is moved, an emotional melody is played. The music generation unit can also estimate the audience's emotions and adjust the musical rhythm based on those emotions. For example, if the audience is enjoying themselves, rhythmic music is played. The music generation unit can also build a system that analyzes the audience's emotional data and adjusts the musical melody and rhythm based on those emotions. For example, if the audience is surprised, a dramatic melody is played. This allows the performance to be more emotionally appealing by adjusting the musical melody and rhythm based on the audience's emotions.

[0069] The music generation unit can incorporate natural and environmental sounds into the music based on the audience's reactions, providing a more realistic performance. For example, the music generation unit uses a generation AI to analyze the audience's reactions and incorporate natural sounds into the music. For example, if the audience is relaxed, the music generation unit can incorporate birdsong or the sound of a babbling brook into the music. The music generation unit can also incorporate the sounds of wind and waves into the music based on the audience's reactions. For example, if the audience is calm, the music generation unit can incorporate the sound of wind into the music. The music generation unit can also incorporate the sounds of rain and thunder into the music based on the audience's reactions. For example, if the audience is surprised, the music generation unit can incorporate the sound of thunder into the music. In this way, by incorporating natural and environmental sounds based on the audience's reactions, a more realistic performance can be provided.

[0070] The music generation unit synchronizes visual effects to music based on the audience's reactions, providing a performance that can be enjoyed both visually and aurally. For example, the music generation unit uses generative AI to analyze the audience's reactions and synchronize visual effects to music. For example, if the audience is excited, a colorful light show is produced in time with the music. The music generation unit can also synchronize projection mapping to music based on the audience's reactions. For example, if the audience is surprised, a dynamic projection mapping is produced. The music generation unit can also synchronize a laser show to music based on the audience's reactions. For example, if the audience is enjoying themselves, a laser show is produced in time with the music. In this way, by synchronizing visual effects based on the audience's reactions, a performance can be provided that can be enjoyed both visually and aurally.

[0071] The music generation unit can use the emotion estimation function to estimate the audience's emotions in real time and improvise musical lyrics and messages based on those emotions. For example, the music generation unit uses a generative AI to estimate the audience's emotions in real time and improvise lyrics based on those emotions. For example, if the audience is moved, it creates moving lyrics. The music generation unit can also estimate the audience's emotions and improvise messages based on those emotions. For example, if the audience is enjoying themselves, it creates a fun message. The music generation unit can also build a system that analyzes the audience's emotional data and improvises lyrics and messages based on those emotions. For example, if the audience is surprised, it creates a message of surprise. This allows for improvising musical lyrics and messages based on the audience's emotions, providing a more emotionally appealing performance.

[0072] The dance generation unit incorporates new choreography and styles into the robot's dance movements based on the audience's reactions, allowing it to constantly provide fresh performances. For example, the generation AI in the dance generation unit analyzes the audience's reactions and improvises new choreography. For example, if the audience is enjoying themselves, it will incorporate rhythmic and energetic choreography. The dance generation unit can also incorporate new dance styles based on the audience's reactions. For example, if the audience is surprised, it will generate a dance that incorporates acrobatic movements. The dance generation unit can also fine-tune the dance movements in real time based on the audience's reactions. For example, if the audience is relaxed, it will adjust the movements to be slower. This allows it to constantly provide fresh performances by incorporating new choreography and styles based on the audience's reactions.

[0073] The dance generation unit can fine-tune the robot's dance movements in real time based on the audience's reactions, providing movements that match the audience's emotions. For example, the dance generation unit uses a generation AI to analyze the audience's reactions and fine-tune the robot's dance movements in real time. For example, if the audience is relaxed, the movements are adjusted to be slower. The dance generation unit can also adjust the robot's dance movements to be more energetic based on the audience's reactions. For example, if the audience is excited, the movements are made faster to provide an energetic dance. The dance generation unit can also build a system that adjusts the robot's dance movements in real time to match the audience's emotions. For example, if the audience is moved, the robot will incorporate moving movements. This allows the robot's dance movements to be fine-tuned in real time to match the audience's emotions, providing a more emotional performance.

[0074] The dance generation unit can use the emotion estimation function to estimate the audience's emotions in real time and adjust the robot's dance movements based on those emotions. For example, the dance generation unit uses a generation AI to estimate the audience's emotions in real time and adjust the robot's dance movements based on those emotions. For example, if the audience is moved, the dance generation unit can incorporate moving movements. The dance generation unit can also estimate the audience's emotions and adjust the robot's dance movements to be more energetic based on those emotions. For example, if the audience is excited, the movements can be made faster to provide an energetic dance. The dance generation unit can also build a system that analyzes the audience's emotional data and adjusts the robot's dance movements based on those emotions. For example, if the audience is relaxed, the movements can be adjusted to be slower. This allows the robot's dance movements to be adjusted based on the audience's emotions, providing a more emotional performance.

[0075] The dance generation unit can combine light and sound effects with the robot's dance movements based on the audience's reactions to enhance the visual impact. For example, the generation AI in the dance generation unit analyzes the audience's reactions and combines light effects with the robot's dance movements. For example, if the audience is excited, a colorful light show is created to match the dance. The dance generation unit can also combine sound effects with the robot's dance movements based on the audience's reactions. For example, if the audience is surprised, dynamic sound effects are created to match the dance. The dance generation unit can also combine a laser show with the robot's dance movements based on the audience's reactions. For example, if the audience is enjoying themselves, a laser show is created to match the dance. In this way, by combining light and sound effects based on the audience's reactions, a performance with enhanced visual impact can be provided.

[0076] The dance generation unit can incorporate other performance elements into the robot's dance movements based on the audience's reactions. For example, the generation AI in the dance generation unit analyzes the audience's reactions and incorporates juggling into the robot's dance movements. For example, if the audience is surprised, the unit provides a dance that incorporates juggling. The dance generation unit can also incorporate acrobatics into the robot's dance movements based on the audience's reactions. For example, if the audience is enjoying themselves, the unit provides a dance that incorporates acrobatic movements. The dance generation unit can also build a system that combines other performance elements into the robot's dance movements based on the audience's reactions. For example, if the audience is relaxed, the unit provides a dance that incorporates soft movements. This makes it possible to incorporate other performance elements based on the audience's reactions and provide a more diverse and attractive performance.

[0077] The dance generation unit can use the emotion estimation function to estimate the audience's emotions in real time and synchronize the robot's dance movements with those of the audience based on those emotions. For example, the dance generation unit uses a generation AI to estimate the audience's emotions in real time and synchronize the robot's dance movements with those of the audience based on those emotions. For example, if the audience is moved, the robot incorporates moving movements. The dance generation unit can also estimate the audience's emotions and adjust the robot's dance movements to be more energetic based on those emotions. For example, if the audience is excited, the movements can be made faster to provide an energetic dance. The dance generation unit can also analyze the audience's emotional data and build a system that synchronizes the robot's dance movements with those of the audience based on those emotions. For example, if the audience is relaxed, the movements can be adjusted to be slower. This allows the robot's dance movements to be synchronized with those of the audience based on their emotions, providing a more unified performance.

[0078] The robot's motion control unit can learn the robot's motion history and achieve smoother, more natural motion. For example, the generation AI of the robot's motion control unit stores the robot's past motion history in a database and references it in real time. For example, it achieves smoother, more natural motion based on past performance data. The robot's motion control unit can also analyze the robot's motion history and optimize its motion. For example, the generation AI can reduce unnecessary motion based on past motion data and achieve smoother motion. The robot's motion control unit can also learn the robot's motion history and improve the accuracy of its motion. For example, the generation AI can adjust the timing and speed of motion based on past motion data and achieve more natural motion. In this way, by learning the robot's motion history, it can achieve smoother, more natural motion.

[0079] A robot's motion control unit can optimize the robot's motion in real time to improve energy efficiency. For example, a generative AI can optimize the robot's motion in real time to improve energy efficiency. For example, it can reduce unnecessary motion and suppress battery consumption. The robot's motion control unit can also optimize the robot's motion and monitor energy consumption. For example, the generative AI can use a motion optimization algorithm to improve energy efficiency. The robot's motion control unit can also adjust the robot's motion in real time to minimize energy consumption. For example, the generative AI can use a motion feedback loop to improve energy efficiency. In this way, energy efficiency can be improved by optimizing the robot's motion in real time.

[0080] The robot's movement control unit can use an emotion estimation function to estimate the audience's emotions in real time and adjust the robot's movement based on those emotions. For example, the robot's movement control unit uses a generative AI to estimate the audience's emotions in real time and adjust the robot's movement based on those emotions. For example, if the audience is moved, the robot incorporates an emotional movement. The robot's movement control unit can also estimate the audience's emotions and adjust the robot's movement to be more energetic based on those emotions. For example, if the audience is excited, the robot's movements can be made faster to provide more energetic movements. The robot's movement control unit can also build a system that analyzes the audience's emotional data and adjusts the robot's movement based on those emotions. For example, if the audience is relaxed, the robot's movements can be adjusted to be more relaxed. This allows the robot's movement to be adjusted based on the audience's emotions, providing a more emotional performance.

[0081] The robot's movement control unit can synchronize sound and light effects with the robot's movements, providing a performance that can be enjoyed both visually and aurally. For example, the robot's movement control unit uses a generative AI to synchronize sound effects with the robot's movements. For example, music and sound effects can be synchronized when the robot moves. The robot's movement control unit can also synchronize light effects with the robot's movements. For example, a colorful light show can be created when the robot moves. The robot's movement control unit can also synchronize projection mapping with the robot's movements. For example, dynamic projection mapping can be created when the robot moves. In this way, by synchronizing sound and light effects with the robot's movements, a performance can be provided that can be enjoyed both visually and aurally.

[0082] A robot's motion control unit can incorporate cooperation with other robots and devices into the robot's movements, allowing multiple robots to perform in cooperation. For example, a generation AI can synchronize the movements of multiple robots to perform a cooperative performance. For example, multiple robots can perform a dance at the same time. A robot's motion control unit can also incorporate cooperation with other devices into the robot's movements. For example, when a robot moves, it performs in cooperation with drones or lighting equipment. A robot's motion control unit can also build a system that adjusts performances in real time that incorporate cooperation with multiple robots and devices. For example, when a robot moves, it cooperates with other robots and devices to perform a complex performance. This allows for more complex and fascinating performances to be provided by incorporating cooperation with multiple robots and devices.

[0083] The robot's movement control unit can use an emotion estimation function to estimate the audience's emotions in real time and coordinate the robot's movements with those of the audience based on those emotions. For example, a generative AI can estimate the audience's emotions in real time and coordinate the robot's movements with those of the audience based on those emotions. For example, if the audience is moved, the robot can incorporate moving movements. The robot's movement control unit can also estimate the audience's emotions and adjust the robot's movements to be more energetic based on those emotions. For example, if the audience is excited, the robot can speed up the movements to provide more energetic movements. The robot's movement control unit can also analyze the audience's emotional data and build a system that coordinates the robot's movements with those of the audience based on those emotions. For example, if the audience is relaxed, the robot can adjust the movements to be more relaxed. This allows the robot's movements to be coordinated with the audience based on their emotions, providing a more unified performance.

[0084] The interaction unit allows the robot to improvise and create new performances based on audience requests. For example, the generation AI analyzes audience requests and the robot improvises new performances based on those requests. For example, if an audience member requests a specific song, the robot will improvise a dance to match that song. The interaction unit can also improvise new music based on audience requests. For example, the robot will improvise a music genre requested by the audience. The interaction unit can also improvise new dance moves based on audience requests. For example, the robot will improvise a dance style requested by the audience. This allows the robot to deepen interaction with the audience by improvising new performances based on audience requests.

[0085] The interaction unit can enable the robot to perform interactions such as dancing with the audience based on the audience's movements. For example, the generation AI analyzes the audience's movements in real time, and the robot dances with the audience based on those movements. For example, if an audience member waves their hands, the robot will do the same. The interaction unit can also enable the robot to play games with the audience based on the audience's movements. For example, if an audience member jumps, the robot will do the same to progress the game. The interaction unit can also build a system in which the robot performs together with the audience based on the audience's movements. For example, if an audience member takes a dance step, the robot will take the same step. This allows the robot to interact based on the audience's movements, deepening the sense of unity with the audience.

[0086] The interaction unit uses the emotion estimation function to estimate the audience's emotions in real time, allowing the robot to provide personalized messages or surprises to the audience based on those emotions. For example, the generation AI in the interaction unit estimates the audience's emotions in real time, and the robot provides personalized messages based on those emotions. For example, if the audience is enjoying themselves, the message "Thank you for having fun!" is displayed. The interaction unit can also estimate the audience's emotions and allow the robot to provide surprises based on those emotions. For example, if the audience is surprised, the robot will perform a special performance. The interaction unit can also analyze the audience's emotional data and build a system that allows the robot to provide personalized messages or surprises based on those emotions. For example, if the audience is moved, an inspiring message is displayed. This allows the robot to provide personalized messages and surprises based on the audience's emotions, deepening the sense of unity with the audience.

[0087] The interaction unit can provide individual interactions, such as having the robot call out the audience's name and perform based on the audience's request. For example, the generation AI analyzes the audience's request, and the robot calls out the audience's name and performs based on that request. For example, if the audience requests a specific song, the robot calls out the audience's name in time with that song. The interaction unit can also provide individual messages based on the audience's request. For example, a special message can be displayed based on the audience's request. The interaction unit can also build a system in which the robot calls out the audience's name and performs based on the audience's request. For example, the robot performs a dance while calling out the audience's name in accordance with the dance style requested by the audience. This allows for individual interactions based on the audience's request, deepening the sense of unity with the audience.

[0088] The interaction unit can enable the robot to interact with the audience, such as playing games, based on the audience's movements. For example, the generation AI analyzes the audience's movements in real time, and the robot plays games with the audience based on those movements. For example, if an audience member waves their hands, the robot will do the same to progress through the game. The interaction unit can also enable the robot to dance with the audience based on the audience's movements. For example, if an audience member jumps, the robot will do the same to perform the dance. The interaction unit can also build a system in which the robot performs a performance with the audience based on the audience's movements. For example, if an audience member takes a dance step, the robot will take the same step. This allows the robot to interact based on the audience's movements, deepening the sense of unity with the audience.

[0089] The interaction unit uses the emotion estimation function to estimate the audience's emotions in real time, and the robot can interact with the audience to show emotional empathy based on those emotions. For example, the interaction unit uses a generation AI to estimate the audience's emotions in real time, and the robot can interact with the audience to show emotional empathy based on those emotions. For example, if the audience is moved, the robot can display an emotional message. The interaction unit can also estimate the audience's emotions and provide a special performance based on those emotions. For example, if the audience is surprised, the robot can perform a surprising performance. The interaction unit can also analyze the audience's emotional data and build a system in which the robot interacts with the audience to show emotional empathy based on those emotions. For example, if the audience is enjoying themselves, the robot can display a cheerful message. This allows for emotional empathy based on the audience's emotions, deepening the sense of unity with the audience.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The audience response acquisition unit collects audience biometric information in real time, allowing for more accurate assessment of emotional changes. For example, if an increase in heart rate indicates excitement or surprise, the performance can be adjusted based on that data. Also, if changes in electrodermal activity indicate tension or excitement, the performance can be adjusted based on that data. Furthermore, if changes in breathing rate indicate relaxation or tension, the performance can be adjusted based on that data. In this way, by collecting audience biometric information, emotional changes can be more accurately assessed and performance can be adjusted.

[0092] The audience reaction acquisition unit can collect audience social media posts or real-time comments and analyze changes in emotions. For example, it can analyze Twitter and Instagram posts to determine audience emotions. It can also collect live chat comments in real time and perform emotion analysis. For example, if an audience member comments "Amazing!" in the live chat, it can be determined that this indicates excitement or surprise, and the performance can be adjusted based on that emotion. It can also analyze the text data of audience social media posts and comments to determine changes in emotions. For example, if there are a lot of positive words, it can be determined that this indicates joy or excitement, and the performance can be adjusted based on that emotion. In this way, by collecting audience social media posts and real-time comments, it is possible to analyze changes in emotions and adjust the performance.

[0093] The emotion analysis unit can infer individual hobbies and preferences from the clothing and belongings of audience members and reflect them in the performance. For example, if an audience member is wearing a band T-shirt, the unit can play that band's songs. If an audience member is carrying a camera, the unit can infer that photography is their hobby and provide a performance related to photography. Furthermore, if an audience member is wearing sportswear, the unit can provide a performance related to sports. In this way, by inferring the hobbies and preferences of audience members from their clothing and belongings and reflecting them in the performance, more personalized entertainment can be provided.

[0094] The emotion analysis unit uses the emotion estimation function to estimate the audience's emotions in real time and provide them with personalized messages or surprises based on those emotions. For example, if the audience is enjoying themselves, it can display the message "Thank you for having fun!". If the audience is surprised, it can also present a special performance. Furthermore, if the audience is moved, it can display an inspiring message. This allows the audience to feel a deeper sense of unity by providing personalized messages and surprises based on their emotions.

[0095] The music generation unit uses the emotion estimation function to estimate the audience's emotions in real time and adjust the musical melody and rhythm based on those emotions. For example, if the audience is moved, an emotional melody can be played. If the audience is enjoying themselves, rhythmic music can be played. Furthermore, if the audience is surprised, a dramatic melody can be played. In this way, by adjusting the musical melody and rhythm based on the audience's emotions, a more emotional performance can be provided.

[0096] The music generation unit can improvise new musical styles that combine different musical genres based on the audience's reactions. For example, it can improvise music that combines classical and electronica. It can also generate music that combines jazz and rock. It can also generate music that combines pop and hip-hop. This allows the performance to always be fresh by improvising new musical styles based on the audience's reactions.

[0097] The music generation unit can incorporate natural and environmental sounds into the music based on the audience's reactions, providing a more realistic performance. For example, if the audience is relaxed, the music can incorporate the chirping of birds or the murmuring of a river. The music can also incorporate the sounds of wind and waves. Furthermore, the music can incorporate the sounds of rain and thunder. In this way, by incorporating natural and environmental sounds based on the audience's reactions, a more realistic performance can be provided.

[0098] The music generation unit can synchronize visual effects to the music based on the audience's reactions, providing a performance that can be enjoyed both visually and aurally. For example, if the audience is excited, a colorful light show can be synchronized to the music. It can also synchronize projection mapping to the music. It can also synchronize a laser show to the music. In this way, by synchronizing visual effects based on the audience's reactions, it is possible to provide a performance that can be enjoyed both visually and aurally.

[0099] The dance generation unit can incorporate new choreography and styles into the robot's dance movements based on the audience's reactions, thereby providing a constantly fresh performance. For example, if the audience is enjoying themselves, it can incorporate rhythmic and energetic choreography. Alternatively, if the audience is surprised, it can generate a dance that incorporates acrobatic movements. Furthermore, if the audience is relaxed, it can adjust the movements to be slower. This allows the robot to incorporate new choreography and styles based on the audience's reactions, thereby providing a constantly fresh performance.

[0100] The dance generation unit can combine light and sound effects with the robot's dancing movements based on the audience's reactions to enhance the visual impact. For example, if the audience is excited, a colorful light show can be created to match the dance. Sound effects can also be combined with the robot's dancing movements. Furthermore, a laser show can be created to match the dance. In this way, by combining light and sound effects based on the audience's reactions, a performance with enhanced visual impact can be provided.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The audience response acquisition unit acquires audience responses. For example, it uses sensors such as cameras and microphones to collect audience facial expressions and voices. The audience response acquisition unit can also acquire audience movements using motion capture technology. For example, it can capture audience hand and body movements in real time. Step 2: The emotion analysis unit analyzes the audience's reactions acquired by the audience reaction acquisition unit. For example, the generation AI can analyze the audience's emotions using facial expression recognition technology. The generation AI can also use voice analysis technology to analyze the tone and speed of the audience's voices to determine their emotions. The generation AI can also analyze the audience's movement data to determine changes in their emotions. Step 3: The music generation unit generates music based on the audience's reactions analyzed by the emotion analysis unit. For example, if the audience is enjoying themselves, the generation AI generates bright, rhythmic music. If the audience is surprised, the generation AI can also generate dramatic music. The generation AI can also change the tempo and key of the music in real time to match the audience's emotions. Step 4: The dance generation unit generates dance movements based on the audience's reactions analyzed by the emotion analysis unit. For example, if the audience is clapping, the generation AI generates a rhythmic dance. If the audience is surprised, the generation AI can also generate a dance that incorporates acrobatic movements. The generation AI can also fine-tune the dance movements in real time to match the audience's emotions.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0105] 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.

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

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

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

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

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

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

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

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

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

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

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

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

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an audience reaction acquisition unit that acquires audience reactions; an emotion analysis unit that analyzes the audience reactions acquired by the audience reaction acquisition unit; a music generation unit that generates music based on the audience's reactions analyzed by the emotion analysis unit; a dance generation unit that generates dance movements based on the reactions of the audience analyzed by the emotion analysis unit. A system characterized by:

2. The audience response acquisition unit Collecting audience biometric information in real time to more accurately determine emotional changes 2. The system of claim 1.

3. The emotion analysis unit Collect audience social media posts or real-time comments and analyze changes in sentiment 2. The system of claim 1.

4. The music generation unit Improvise new musical styles that combine different musical genres based on audience reactions 2. The system of claim 1.

5. The dance generation unit Based on audience reactions, new choreography and styles are incorporated into the robot's dance moves, providing a constantly fresh performance.

2. The system of claim 1.

6. The robot's motion control unit Estimate audience emotions in real time and adjust robot behavior based on those emotions 2. The system of claim 1.

7. The interaction section is The robot estimates the audience's emotions in real time and delivers personalized messages and surprises based on those emotions.

2. The system of claim 1.

8. The emotion analysis unit Estimate audience emotions in real time and adjust the robot's performance based on those emotions 2. The system of claim 1.

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