System

Abstract

Systems according to embodiments are directed to providing real-time spectator reactions to users.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, and a feedback unit. The reception unit receives an input of a user. The generation unit generates a reaction of the spectator based on the information received by the reception unit. The feedback unit provides the reaction generated by the generation unit to the user.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 techniques fall short in providing real-time audience response in virtual live experiences, and there is room for improvement.

[0005] The system according to the embodiment aims to provide users with real-time audience reactions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a feedback unit. The reception unit receives user input. The generation unit generates audience reactions based on the information received by the reception unit. The feedback unit provides the reactions generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with real-time audience 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A system according to an embodiment of the present invention combines AI and VR to provide a real-time virtual karaoke concert experience. In this system, a user puts on a VR device, enters a virtual space, and starts singing karaoke. AI generates audience responses in real time. This allows the user to experience singing as if they were at a real concert. For example, a user puts on a VR device and controls their own avatar on stage. When the user starts singing, AI generates audience responses such as applause, cheers, and cheers. Furthermore, the AI ​​changes the audience's reactions according to the user's singing. This allows the user to receive feedback on their performance in real time. This system provides users with a new karaoke experience, broadening the ways in which karaoke can be enjoyed and helping more people enjoy it.

[0029] A virtual karaoke concert system according to an embodiment includes a reception unit, a generation unit, and a feedback unit. The reception unit receives user input. Examples of user input include, but are not limited to, voice input, text input, and gesture input. The reception unit receives the user's voice input using, for example, voice recognition technology. The reception unit can also use keyboard input to receive text input. The reception unit can also use a motion sensor to receive gesture input. The generation unit generates audience responses based on the information received by the reception unit using AI. Examples of audience responses include, but are not limited to, applause, cheers, booing, and the like. The generation unit generates audience applause and cheers using, for example, voice synthesis technology. The generation unit can also generate cheering messages for the audience using text generation technology. The generation unit can also generate visual responses using effect generation technology. The feedback unit provides the responses generated by the generation unit to the user. The feedback unit visually displays the audience responses to the user through, for example, a VR device. The feedback unit can also use an audio output device to let the user hear the audience's applause and cheers. Furthermore, the feedback unit can also convey the audience's cheering messages to the user by displaying text messages. In this way, the virtual karaoke concert system according to the embodiment generates audience reactions and provides feedback in real time based on user input, thereby realizing a virtual live experience.

[0030] The generation unit can change the audience's reaction in accordance with the user's singing. For example, the generation unit can intensify the audience's reaction when the user sings a high note. For example, the generation unit can also increase the audience's reaction when the user sings in rhythm. For example, the generation unit can also tone down the audience's reaction when the user sings off-key. This allows the audience's reaction to change in accordance with the user's singing, providing a more realistic live experience. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's singing data into the generation AI and cause the generation AI to generate the audience's reaction.

[0031] The feedback unit can record a user's performance and review it later. For example, the feedback unit uses a camera to record the user's performance. For example, the feedback unit saves the recorded performance so that the user can review it later. The feedback unit can also edit the recorded performance and create highlight scenes. Furthermore, the feedback unit can provide a function for sharing the recorded performance. For example, the feedback unit has a function for uploading the recorded performance to social media. This allows the user to record their own performance and review it later. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the recorded data to a generation AI and have the generation AI perform editing and create highlight scenes.

[0032] The reception unit can receive input for operating a user's avatar. The reception unit, for example, receives input for operating the avatar by the user. The reception unit, for example, receives input for operating the avatar by the user using a keyboard or mouse. The reception unit can also receive gesture input from the user using a motion sensor. Furthermore, the reception unit can also receive voice input from the user using voice recognition technology. This allows the user to receive input for operating their own avatar, enabling free operation in the virtual space. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input user gesture data to a generation AI and have the generation AI operate the avatar.

[0033] The generation unit can generate audience reactions such as applause, cheers, and cheers. The generation unit, for example, uses voice synthesis technology to generate audience applause. The generation unit can also use voice synthesis technology to generate audience cheers. The generation unit can also use text generation technology to generate audience cheering messages. Furthermore, the generation unit can also use effect generation technology to visually display audience reactions. This makes it possible to provide a sense of realism to the user by generating audience reactions such as applause, cheers, and cheers. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input audience reaction data into the generation AI and cause the generation AI to generate applause, cheers, and cheers.

[0034] The feedback unit can confirm the user's appearance through the VR device. The feedback unit, for example, displays the user's appearance through the VR device. The feedback unit, for example, uses the VR device to display the user's entire body. The feedback unit can also use the VR device to display the user's face. Furthermore, the feedback unit can also use the VR device to display the user's hand movements. This allows the user to confirm their appearance through the VR device, providing a more realistic experience. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's appearance data into a generation AI and cause the generation AI to execute processing to display the data through the VR device.

[0035] The reception unit can analyze the user's past input history and select an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. For example, if the user has frequently used text input in the past, the reception unit can also preferentially suggest text input. For example, if the user has frequently used gesture input in the past, the reception unit can also preferentially suggest gesture input. In this way, the optimal input method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input history data to a generation AI and cause the generation AI to select an optimal input method.

[0036] The reception unit can analyze the user's current environmental sound and receive input while performing noise cancellation. For example, when the user's surroundings are noisy, the reception unit enables noise cancellation and receives voice input. For example, when the user's surroundings are quiet, the reception unit can also disable noise cancellation and receive natural voice input. For example, the reception unit can automatically adjust the intensity of noise cancellation according to the user's surrounding environmental sound. This allows for more accurate input reception by analyzing the user's current environmental sound and performing noise cancellation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to adjust the noise canceling.

[0037] The reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit enables voice recognition and accepts the input. For example, if the user selects gesture input, the reception unit can also enable gesture recognition and accept the input. For example, if the user selects text input, the reception unit can also enable keyboard input and accept the input. This allows for smoother input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0038] The reception unit can preferentially accept highly relevant inputs based on the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially accepts inputs related to that area. For example, when the user is traveling, the reception unit can also preferentially accept inputs related to the travel destination. For example, when the user is at home, the reception unit can also preferentially accept inputs related to the home. In this way, by taking the user's geographical location information into consideration, highly relevant inputs can be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant inputs.

[0039] The reception unit can analyze the user's social media activity and receive related input. The reception unit, for example, preferentially receives related input based on content shared by the user on social media. The reception unit can also preferentially receive related input, for example, by referring to the activity of the user's friends on social media. The reception unit can also analyze the content posted by the user on social media and preferentially receive related input. In this way, related input can be received by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related input.

[0040] The reception unit can customize the input method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, if the user has preferred text input in the past, the reception unit can also preferentially suggest text input. For example, if the user has preferred gesture input in the past, the reception unit can also preferentially suggest gesture input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the input method.

[0041] The generation unit can change the audience's reaction based on the pitch and rhythm of the user's singing. For example, the generation unit can tone down the audience's reaction when the user sings off-key. For example, the generation unit can also increase the audience's reaction when the user sings rhythmically. For example, the generation unit can also enhance the audience's reaction when the user sings a high note. This makes it possible to provide a more realistic live experience by changing the audience's reaction based on the pitch and rhythm of the user's singing. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input the user's singing data into the generation AI and have the generation AI execute changes in the audience's reaction.

[0042] The generation unit can enhance the audience's reaction according to a specific part of the user's performance. For example, the generation unit can enhance the audience's cheers when the user hits a high note. For example, the generation unit can enhance the audience's applause when the user hits a low note. For example, the generation unit can enhance the audience's reaction when the user performs a specific movement. This makes it possible to provide a more realistic live experience by enhancing the audience's reaction according to a specific part of the user's performance. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's performance data into the generation AI and cause the generation AI to enhance the audience's reaction.

[0043] The generation unit can generate more realistic audience reactions by referencing the user's past performance data. The generation unit, for example, customizes audience reactions based on the user's past performance data. The generation unit can also analyze the user's past performance data and generate optimal audience reactions. The generation unit can also make audience reactions more realistic by referring to the user's past performance data. In this way, more realistic audience reactions can be generated by referencing the user's past performance data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past performance data into the generation AI and cause the generation AI to generate audience reactions.

[0044] The generation unit can generate region-specific audience reactions based on the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate region-specific audience reactions. For example, if the user is overseas, the generation unit can also generate country-specific audience reactions. For example, if the user is in their hometown, the generation unit can also generate local-specific audience reactions. In this way, region-specific audience reactions can be generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input geographical location information data to the generation AI and cause the generation AI to generate region-specific audience reactions.

[0045] The generation unit can analyze the user's social media activity and generate related audience responses. The generation unit can generate audience responses based on, for example, content shared by the user on social media. The generation unit can also generate audience responses by referring to, for example, the activities of the user's friends on social media. The generation unit can also generate audience responses by analyzing, for example, content posted by the user on social media. In this way, related audience responses can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input social media activity data into the generation AI and cause the generation AI to generate audience responses.

[0046] The generation unit can customize the audience reactions by reflecting the user's past feedback. The generation unit customizes the audience reactions based on, for example, the audience reactions that the user has previously preferred. The generation unit can also analyze the user's past feedback and generate optimal audience reactions. The generation unit can also make the audience reactions realistic by referring to the user's past feedback. In this way, the audience reactions can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past feedback data into the generation AI and cause the generation AI to customize the audience reactions.

[0047] The feedback unit can enhance feedback for a specific part of the user's performance. For example, if the user hits a high note, the feedback unit can enhance feedback for that part. For example, if the user hits a low note, the feedback unit can also enhance feedback for that part. For example, if the user performs a specific movement, the feedback unit can enhance feedback for that part. In this way, by enhancing feedback for a specific part of the user's performance, more specific feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's performance data into a generation AI and cause the generation AI to enhance the feedback.

[0048] The feedback unit can provide more specific feedback by referring to the user's past performance data. The feedback unit, for example, provides feedback on specific areas for improvement based on the user's past performance data. The feedback unit can also, for example, analyze the user's past performance data and provide optimal feedback. The feedback unit can also, for example, provide specific advice by referring to the user's past performance data. In this way, more specific feedback can be provided by referring to the user's past performance data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past performance data into a generation AI and cause the generation AI to provide specific feedback.

[0049] The feedback unit can analyze the user's feedback history and select an appropriate feedback method. The feedback unit can provide optimal feedback based on, for example, feedback methods that the user has preferred in the past. The feedback unit can also analyze the user's past feedback history and select the most effective feedback method, for example. The feedback unit can also provide customized feedback by referring to, for example, the user's feedback history. In this way, the optimal feedback method can be selected by analyzing the user's feedback history. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input feedback history data to a generation AI and cause the generation AI to select a feedback method.

[0050] The feedback unit can provide region-specific feedback based on the user's geographical location information. For example, when the user is in a specific region, the feedback unit can provide region-specific feedback. For example, when the user is overseas, the feedback unit can also provide country-specific feedback. For example, when the user is in his / her hometown, the feedback unit can also provide local-specific feedback. In this way, region-specific feedback can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information data to a generation AI and cause the generation AI to provide region-specific feedback.

[0051] The feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit can provide relevant feedback based on content shared by the user on social media. For example, the feedback unit can also provide relevant feedback by referring to the activity of the user's friends on social media. For example, the feedback unit can analyze the content posted by the user on social media and provide relevant feedback. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input social media activity data to a generation AI and cause the generation AI to generate feedback.

[0052] The feedback unit can customize the feedback method by reflecting the user's past feedback. The feedback unit can provide optimal feedback based on, for example, feedback methods that the user has preferred in the past. The feedback unit can also analyze, for example, the user's past feedback history and select the most effective feedback method. The feedback unit can also provide customized feedback by referring to, for example, the user's feedback history. In this way, the feedback method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and cause the generation AI to customize the feedback method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can monitor the user's health condition and accept input at an appropriate timing. For example, if the user's heart rate is high, the reception unit can accept input after allowing time for relaxation. If the user's blood pressure is high, the reception unit can encourage the user to take a break and accept input after the break. Furthermore, if the user's body temperature is high, the reception unit can provide feedback encouraging cooling down and then accept input. In this way, the reception timing of input can be adjusted according to the user's health condition, allowing input to be accepted at a more appropriate timing.

[0055] The generation unit not only changes the audience's reaction according to the user's singing, but also generates a dance performance of the audience based on the tempo of the user's singing. For example, if the user sings at a fast tempo, the audience's dancing will also be fast. If the user sings at a slow tempo, the audience's dancing will also be slow. Furthermore, if the user changes the rhythm, the audience's dancing can change accordingly. In this way, by generating a dance performance of the audience according to the user's singing, a more realistic live experience can be provided.

[0056] The feedback unit not only records the user's performance, but also provides automatic evaluation of the recorded performance by AI. For example, the AI ​​may evaluate the accuracy of the user's pitch and rhythm and display a score. The AI ​​may also evaluate the user's expressiveness and emotional expression and provide feedback. Furthermore, the AI ​​may specifically point out areas for improvement in the user's performance and offer advice. This allows users to improve their performance more effectively by recording their own performance and receiving evaluation and feedback from AI.

[0057] The reception unit not only receives inputs for operating the user's avatar, but also inputs for customizing the user's avatar's clothing and accessories. For example, the reception unit receives inputs from the user selecting clothing for the avatar using a keyboard or mouse. The reception unit can also receive inputs from the user selecting accessories for the avatar using a motion sensor. Furthermore, the reception unit can also receive inputs from the user customizing the avatar's appearance using voice recognition technology. This allows the user to freely customize their avatar, making their experience in the virtual space more unique.

[0058] The generation unit can not only generate the audience's reactions of applause, cheers, and cheering, but also generate the audience's facial expressions and gestures. For example, it can generate the audience's facial expressions of clapping with a smile. It can also generate the audience's surprised expressions of cheering. It can also generate the audience's gesture of waving their hands in support. This can provide the user with a more realistic experience by visually enriching the audience's reactions.

[0059] The feedback unit not only checks the user's appearance through the VR device, but also analyzes the user's posture and movements in real time and provides feedback. For example, if the user's posture is poor, it can provide feedback to correct the posture. If the user's movements are awkward, it can provide feedback to encourage smoother movements. Furthermore, if the user's movements are natural, it can provide positive feedback. This allows the user to check and improve their posture and movements in real time, thereby achieving better performance.

[0060] The reception unit can analyze the user's past input history and select an appropriate input method, as well as analyze performance trends based on the user's past input history to provide optimal performance advice. For example, if the user has frequently used high notes in the past, advice on how to use high notes effectively can be provided. If the user has placed importance on rhythm in the past, advice on rhythm can also be provided. Furthermore, if the user has previously preferred a particular genre, advice on that genre can also be provided. In this way, optimal performance advice can be provided by analyzing the user's past input history.

[0061] The reception unit can analyze the user's current environmental sounds, accept input while performing noise cancellation, and provide appropriate music and sound effects based on the user's environmental sounds. For example, if the user's surroundings are noisy, it can provide music for relaxation. If the user's surroundings are quiet, it can also provide music to increase concentration. Furthermore, it can automatically adjust sound effects according to the user's surrounding environmental sounds. This allows the user to have a more comfortable experience by analyzing the user's current environmental sounds and providing appropriate music and sound effects.

[0062] The reception unit not only selects an appropriate input means according to the user's input method, but also provides optimal performance advice based on the user's input method. For example, if the user selects voice input, the reception unit provides advice on vocalization tips and vocal techniques. If the user selects gesture input, the reception unit can also provide advice on how to use gestures. Furthermore, if the user selects text input, the reception unit can also provide advice on how to express lyrics. In this way, optimal performance advice according to the user's input method can be provided, thereby achieving a more effective performance.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives user input. User input includes voice input, text input, gesture input, etc. For example, voice input from the user can be received using voice recognition technology, text input can be received using keyboard input, and gesture input can be received using a motion sensor. Step 2: The generation unit generates audience reactions based on the information received by the reception unit. The audience reactions include applause, cheers, booing, etc. For example, the applause and cheers of the audience can be generated using speech synthesis technology, cheering messages of the audience can be generated using text generation technology, and visual reactions can be generated using effects generation technology. Step 3: The feedback unit provides the reaction generated by the generation unit to the user. For example, the feedback unit can visually display the audience's reaction to the user through a VR device, can let the user hear the audience's applause and cheers using an audio output device, or can convey the audience's cheering messages to the user by displaying a text message.

[0065] (Example 2) A system according to an embodiment of the present invention combines AI and VR to provide a real-time virtual karaoke concert experience. In this system, a user puts on a VR device, enters a virtual space, and starts singing karaoke. AI generates audience responses in real time. This allows the user to experience singing as if they were at a real concert. For example, a user puts on a VR device and controls their own avatar on stage. When the user starts singing, AI generates audience responses such as applause, cheers, and cheers. Furthermore, the AI ​​changes the audience's reactions according to the user's singing. This allows the user to receive feedback on their performance in real time. This system provides users with a new karaoke experience, broadening the ways in which karaoke can be enjoyed and helping more people enjoy it.

[0066] A virtual karaoke concert system according to an embodiment includes a reception unit, a generation unit, and a feedback unit. The reception unit receives user input. Examples of user input include, but are not limited to, voice input, text input, and gesture input. The reception unit receives the user's voice input using, for example, voice recognition technology. The reception unit can also use keyboard input to receive text input. The reception unit can also use a motion sensor to receive gesture input. The generation unit generates audience responses based on the information received by the reception unit using AI. Examples of audience responses include, but are not limited to, applause, cheers, booing, and the like. The generation unit generates audience applause and cheers using, for example, voice synthesis technology. The generation unit can also generate cheering messages for the audience using text generation technology. The generation unit can also generate visual responses using effect generation technology. The feedback unit provides the responses generated by the generation unit to the user. The feedback unit visually displays the audience responses to the user through, for example, a VR device. The feedback unit can also use an audio output device to let the user hear the audience's applause and cheers. Furthermore, the feedback unit can also convey the audience's cheering messages to the user by displaying text messages. In this way, the virtual karaoke concert system according to the embodiment generates audience reactions and provides feedback in real time based on user input, thereby realizing a virtual live experience.

[0067] The generation unit can change the audience's reaction in accordance with the user's singing. For example, the generation unit can intensify the audience's reaction when the user sings a high note. For example, the generation unit can also increase the audience's reaction when the user sings in rhythm. For example, the generation unit can also tone down the audience's reaction when the user sings off-key. This allows the audience's reaction to change in accordance with the user's singing, providing a more realistic live experience. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's singing data into the generation AI and cause the generation AI to generate the audience's reaction.

[0068] The feedback unit can record a user's performance and review it later. For example, the feedback unit uses a camera to record the user's performance. For example, the feedback unit saves the recorded performance so that the user can review it later. The feedback unit can also edit the recorded performance and create highlight scenes. Furthermore, the feedback unit can provide a function for sharing the recorded performance. For example, the feedback unit has a function for uploading the recorded performance to social media. This allows the user to record their own performance and review it later. Some or all of the above-mentioned processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input the recorded data to a generation AI and have the generation AI perform editing and create highlight scenes.

[0069] The reception unit can receive input for operating a user's avatar. The reception unit, for example, receives input for operating the avatar by the user. The reception unit, for example, receives input for operating the avatar by the user using a keyboard or mouse. The reception unit can also receive gesture input from the user using a motion sensor. Furthermore, the reception unit can also receive voice input from the user using voice recognition technology. This allows the user to receive input for operating their own avatar, enabling free operation in the virtual space. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input user gesture data to a generation AI and have the generation AI operate the avatar.

[0070] The generation unit can generate audience reactions such as applause, cheers, and cheers. The generation unit, for example, uses voice synthesis technology to generate audience applause. The generation unit can also use voice synthesis technology to generate audience cheers. The generation unit can also use text generation technology to generate audience cheering messages. Furthermore, the generation unit can also use effect generation technology to visually display audience reactions. This makes it possible to provide a sense of realism to the user by generating audience reactions such as applause, cheers, and cheers. Some or all of the above-described processing in the generation unit may be performed using, or without, AI, for example. For example, the generation unit can input audience reaction data into the generation AI and cause the generation AI to generate applause, cheers, and cheers.

[0071] The feedback unit can confirm the user's appearance through the VR device. The feedback unit, for example, displays the user's appearance through the VR device. The feedback unit, for example, uses the VR device to display the user's entire body. The feedback unit can also use the VR device to display the user's face. Furthermore, the feedback unit can also use the VR device to display the user's hand movements. This allows the user to confirm their appearance through the VR device, providing a more realistic experience. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input the user's appearance data into a generation AI and cause the generation AI to execute processing to display the data through the VR device.

[0072] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated user emotions. For example, if the user is nervous, the reception unit may allow the user time to relax before accepting input. For example, if the user is excited, the reception unit may immediately accept input and start performance. For example, if the user is tired, the reception unit may encourage the user to take a break and accept input after the break. This allows input reception to be performed at a more appropriate timing by adjusting the timing of input reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using an AI, for example, or without an AI. For example, the reception unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of input reception.

[0073] The reception unit can analyze the user's past input history and select an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit can preferentially suggest voice input. For example, if the user has frequently used text input in the past, the reception unit can also preferentially suggest text input. For example, if the user has frequently used gesture input in the past, the reception unit can also preferentially suggest gesture input. In this way, the optimal input method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input history data to a generation AI and cause the generation AI to select an optimal input method.

[0074] The reception unit can analyze the user's current environmental sound and receive input while performing noise cancellation. For example, when the user's surroundings are noisy, the reception unit enables noise cancellation and receives voice input. For example, when the user's surroundings are quiet, the reception unit can also disable noise cancellation and receive natural voice input. For example, the reception unit can automatically adjust the intensity of noise cancellation according to the user's surrounding environmental sound. This allows for more accurate input reception by analyzing the user's current environmental sound and performing noise cancellation. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input environmental sound data to a generation AI and cause the generation AI to adjust the noise canceling.

[0075] The reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit enables voice recognition and accepts the input. For example, if the user selects gesture input, the reception unit can also enable gesture recognition and accept the input. For example, if the user selects text input, the reception unit can also enable keyboard input and accept the input. This allows for smoother input by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0076] The reception unit can estimate the user's emotions and prioritize inputs based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize inputs for relaxation. For example, if the user is excited, the reception unit can prioritize inputs related to immediate performance. For example, if the user is tired, the reception unit can prioritize inputs encouraging the user to take a break. This allows for more appropriate inputs to be received by prioritizing inputs according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the inputs.

[0077] The reception unit can preferentially accept highly relevant inputs based on the user's geographical location information. For example, when the user is in a specific area, the reception unit preferentially accepts inputs related to that area. For example, when the user is traveling, the reception unit can also preferentially accept inputs related to the travel destination. For example, when the user is at home, the reception unit can also preferentially accept inputs related to the home. In this way, by taking the user's geographical location information into consideration, highly relevant inputs can be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input geographical location information data to a generation AI and cause the generation AI to select highly relevant inputs.

[0078] The reception unit can analyze the user's social media activity and receive related input. The reception unit, for example, preferentially receives related input based on content shared by the user on social media. The reception unit can also preferentially receive related input, for example, by referring to the activity of the user's friends on social media. The reception unit can also analyze the content posted by the user on social media and preferentially receive related input. In this way, related input can be received by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input social media activity data to a generation AI and cause the generation AI to select related input.

[0079] The reception unit can customize the input method by reflecting the user's past feedback. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. For example, if the user has preferred text input in the past, the reception unit can also preferentially suggest text input. For example, if the user has preferred gesture input in the past, the reception unit can also preferentially suggest gesture input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI and cause the generation AI to customize the input method.

[0080] The generation unit can estimate the user's emotions and adjust the audience's reactions based on the estimated user's emotions. For example, if the user is nervous, the generation unit can tone down the audience's reactions. For example, if the user is excited, the generation unit can also excite the audience's reactions. For example, if the user is relaxed, the generation unit can make the audience's reactions natural. This allows the audience's reactions to be adjusted according to the user's emotions, providing a more realistic live experience. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the audience's reactions.

[0081] The generation unit can change the audience's reaction based on the pitch and rhythm of the user's singing. For example, the generation unit can tone down the audience's reaction when the user sings off-key. For example, the generation unit can also increase the audience's reaction when the user sings rhythmically. For example, the generation unit can also enhance the audience's reaction when the user sings a high note. This makes it possible to provide a more realistic live experience by changing the audience's reaction based on the pitch and rhythm of the user's singing. Some or all of the above-described processing in the generation unit can be performed using, or without, AI, for example. For example, the generation unit can input the user's singing data into the generation AI and have the generation AI execute changes in the audience's reaction.

[0082] The generation unit can enhance the audience's reaction according to a specific part of the user's performance. For example, the generation unit can enhance the audience's cheers when the user hits a high note. For example, the generation unit can enhance the audience's applause when the user hits a low note. For example, the generation unit can enhance the audience's reaction when the user performs a specific movement. This makes it possible to provide a more realistic live experience by enhancing the audience's reaction according to a specific part of the user's performance. Some or all of the above-described processing in the generation unit may be performed using, or without, AI. For example, the generation unit can input the user's performance data into the generation AI and cause the generation AI to enhance the audience's reaction.

[0083] The generation unit can generate more realistic audience reactions by referencing the user's past performance data. The generation unit, for example, customizes audience reactions based on the user's past performance data. The generation unit can also analyze the user's past performance data and generate optimal audience reactions. The generation unit can also make audience reactions more realistic by referring to the user's past performance data. In this way, more realistic audience reactions can be generated by referencing the user's past performance data. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past performance data into the generation AI and cause the generation AI to generate audience reactions.

[0084] The generation unit can estimate the user's emotions and select the type of audience reaction based on the estimated user emotions. For example, if the user is nervous, the generation unit can tone down the audience's applause. For example, if the user is excited, the generation unit can also increase the audience's cheers. For example, if the user is relaxed, the generation unit can make the audience reaction more natural. This allows for a more realistic live experience by selecting the type of audience reaction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI select the type of audience reaction.

[0085] The generation unit can generate region-specific audience reactions based on the user's geographical location information. For example, if the user is in a specific region, the generation unit can generate region-specific audience reactions. For example, if the user is overseas, the generation unit can also generate country-specific audience reactions. For example, if the user is in their hometown, the generation unit can also generate local-specific audience reactions. In this way, region-specific audience reactions can be generated by taking the user's geographical location information into consideration. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input geographical location information data to the generation AI and cause the generation AI to generate region-specific audience reactions.

[0086] The generation unit can analyze the user's social media activity and generate related audience responses. The generation unit can generate audience responses based on, for example, content shared by the user on social media. The generation unit can also generate audience responses by referring to, for example, the activities of the user's friends on social media. The generation unit can also generate audience responses by analyzing, for example, content posted by the user on social media. In this way, related audience responses can be generated by analyzing the user's social media activity. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input social media activity data into the generation AI and cause the generation AI to generate audience responses.

[0087] The generation unit can customize the audience reactions by reflecting the user's past feedback. The generation unit customizes the audience reactions based on, for example, the audience reactions that the user has previously preferred. The generation unit can also analyze the user's past feedback and generate optimal audience reactions. The generation unit can also make the audience reactions realistic by referring to the user's past feedback. In this way, the audience reactions can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input past feedback data into the generation AI and cause the generation AI to customize the audience reactions.

[0088] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback to relax. For example, if the user is excited, the feedback unit can also provide positive feedback. For example, if the user is tired, the feedback unit can provide feedback encouraging the user to take a break. This allows the content of the feedback to be adjusted according to the user's emotions, thereby providing more appropriate feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content of the feedback.

[0089] The feedback unit can enhance feedback for a specific part of the user's performance. For example, if the user hits a high note, the feedback unit can enhance feedback for that part. For example, if the user hits a low note, the feedback unit can also enhance feedback for that part. For example, if the user performs a specific movement, the feedback unit can enhance feedback for that part. In this way, by enhancing feedback for a specific part of the user's performance, more specific feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's performance data into a generation AI and cause the generation AI to enhance the feedback.

[0090] The feedback unit can provide more specific feedback by referring to the user's past performance data. The feedback unit, for example, provides feedback on specific areas for improvement based on the user's past performance data. The feedback unit can also, for example, analyze the user's past performance data and provide optimal feedback. The feedback unit can also, for example, provide specific advice by referring to the user's past performance data. In this way, more specific feedback can be provided by referring to the user's past performance data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past performance data into a generation AI and cause the generation AI to provide specific feedback.

[0091] The feedback unit can analyze the user's feedback history and select an appropriate feedback method. The feedback unit can provide optimal feedback based on, for example, feedback methods that the user has preferred in the past. The feedback unit can also analyze the user's past feedback history and select the most effective feedback method, for example. The feedback unit can also provide customized feedback by referring to, for example, the user's feedback history. In this way, the optimal feedback method can be selected by analyzing the user's feedback history. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input feedback history data to a generation AI and cause the generation AI to select a feedback method.

[0092] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can prioritize providing feedback to relax. For example, if the user is excited, the feedback unit can also prioritize providing positive feedback. For example, if the user is tired, the feedback unit can also prioritize providing feedback encouraging the user to take a break. This allows for more appropriate feedback to be provided by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of feedback.

[0093] The feedback unit can provide region-specific feedback based on the user's geographical location information. For example, when the user is in a specific region, the feedback unit can provide region-specific feedback. For example, when the user is overseas, the feedback unit can also provide country-specific feedback. For example, when the user is in his / her hometown, the feedback unit can also provide local-specific feedback. In this way, region-specific feedback can be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the feedback unit can be performed using AI, for example, or without AI. For example, the feedback unit can input geographical location information data to a generation AI and cause the generation AI to provide region-specific feedback.

[0094] The feedback unit can analyze the user's social media activity and provide relevant feedback. For example, the feedback unit can provide relevant feedback based on content shared by the user on social media. For example, the feedback unit can also provide relevant feedback by referring to the activity of the user's friends on social media. For example, the feedback unit can analyze the content posted by the user on social media and provide relevant feedback. In this way, relevant feedback can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input social media activity data to a generation AI and cause the generation AI to generate feedback.

[0095] The feedback unit can customize the feedback method by reflecting the user's past feedback. The feedback unit can provide optimal feedback based on, for example, feedback methods that the user has preferred in the past. The feedback unit can also analyze, for example, the user's past feedback history and select the most effective feedback method. The feedback unit can also provide customized feedback by referring to, for example, the user's feedback history. In this way, the feedback method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input past feedback data into a generation AI and cause the generation AI to customize the feedback method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's voice input or text input using the microphone 38B or touch panel 38A of the smart device 14. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates audience reactions using AI. The feedback unit provides the audience reactions to the user using, for example, the display 40A or speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates audience reactions using AI. The feedback unit provides the audience reactions to the user using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit receives a user's voice input using the microphone 238 of the headset type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates audience reactions using AI. The feedback unit provides the audience reactions to the user using, for example, the speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a voice input from the user using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates audience reactions using AI. The feedback unit provides the audience reactions to the user using, for example, the speaker 240 of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can monitor the user's health condition and accept input at an appropriate timing. For example, if the user's heart rate is high, the reception unit can accept input after allowing time for relaxation. If the user's blood pressure is high, the reception unit can encourage the user to take a break and accept input after the break. Furthermore, if the user's body temperature is high, the reception unit can provide feedback encouraging cooling down and then accept input. In this way, the reception timing of input can be adjusted according to the user's health condition, allowing input to be accepted at a more appropriate timing.

[0098] The generation unit not only changes the audience's reaction according to the user's singing, but also generates a dance performance of the audience based on the tempo of the user's singing. For example, if the user sings at a fast tempo, the audience's dancing will also be fast. If the user sings at a slow tempo, the audience's dancing will also be slow. Furthermore, if the user changes the rhythm, the audience's dancing can change accordingly. In this way, by generating a dance performance of the audience according to the user's singing, a more realistic live experience can be provided.

[0099] The feedback unit not only records the user's performance, but also provides automatic evaluation of the recorded performance by AI. For example, the AI ​​may evaluate the accuracy of the user's pitch and rhythm and display a score. The AI ​​may also evaluate the user's expressiveness and emotional expression and provide feedback. Furthermore, the AI ​​may specifically point out areas for improvement in the user's performance and offer advice. This allows users to improve their performance more effectively by recording their own performance and receiving evaluation and feedback from AI.

[0100] The reception unit not only receives inputs for operating the user's avatar, but also inputs for customizing the user's avatar's clothing and accessories. For example, the reception unit receives inputs from the user selecting clothing for the avatar using a keyboard or mouse. The reception unit can also receive inputs from the user selecting accessories for the avatar using a motion sensor. Furthermore, the reception unit can also receive inputs from the user customizing the avatar's appearance using voice recognition technology. This allows the user to freely customize their avatar, making their experience in the virtual space more unique.

[0101] The generation unit can not only generate the audience's reactions of applause, cheers, and cheering, but also generate the audience's facial expressions and gestures. For example, it can generate the audience's facial expressions of clapping with a smile. It can also generate the audience's surprised expressions of cheering. It can also generate the audience's gesture of waving their hands in support. This can provide the user with a more realistic experience by visually enriching the audience's reactions.

[0102] The feedback unit not only checks the user's appearance through the VR device, but also analyzes the user's posture and movements in real time and provides feedback. For example, if the user's posture is poor, it can provide feedback to correct the posture. If the user's movements are awkward, it can provide feedback to encourage smoother movements. Furthermore, if the user's movements are natural, it can provide positive feedback. This allows the user to check and improve their posture and movements in real time, thereby achieving better performance.

[0103] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated user emotions, as well as provide music and images that correspond to the user's emotions. For example, if the user is nervous, it can provide relaxing music and images. If the user is excited, it can provide energetic music and images. Furthermore, if the user is tired, it can provide soothing music and images. In this way, by providing music and images that correspond to the user's emotions, it is possible to provide a more comfortable experience.

[0104] The reception unit can analyze the user's past input history and select an appropriate input method, as well as analyze performance trends based on the user's past input history to provide optimal performance advice. For example, if the user has frequently used high notes in the past, advice on how to use high notes effectively can be provided. If the user has placed importance on rhythm in the past, advice on rhythm can also be provided. Furthermore, if the user has previously preferred a particular genre, advice on that genre can also be provided. In this way, optimal performance advice can be provided by analyzing the user's past input history.

[0105] The reception unit can analyze the user's current environmental sounds, accept input while performing noise cancellation, and provide appropriate music and sound effects based on the user's environmental sounds. For example, if the user's surroundings are noisy, it can provide music for relaxation. If the user's surroundings are quiet, it can also provide music to increase concentration. Furthermore, it can automatically adjust sound effects according to the user's surrounding environmental sounds. This allows the user to have a more comfortable experience by analyzing the user's current environmental sounds and providing appropriate music and sound effects.

[0106] The reception unit not only selects an appropriate input means according to the user's input method, but also provides optimal performance advice based on the user's input method. For example, if the user selects voice input, the reception unit provides advice on vocalization tips and vocal techniques. If the user selects gesture input, the reception unit can also provide advice on how to use gestures. Furthermore, if the user selects text input, the reception unit can also provide advice on how to express lyrics. In this way, optimal performance advice according to the user's input method can be provided, thereby achieving a more effective performance.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives user input. User input includes voice input, text input, gesture input, etc. For example, voice input from the user can be received using voice recognition technology, text input can be received using keyboard input, and gesture input can be received using a motion sensor. Step 2: The generation unit generates audience reactions based on the information received by the reception unit. The audience reactions include applause, cheers, booing, etc. For example, the applause and cheers of the audience can be generated using speech synthesis technology, cheering messages of the audience can be generated using text generation technology, and visual reactions can be generated using effects generation technology. Step 3: The feedback unit provides the reaction generated by the generation unit to the user. For example, the feedback unit can visually display the audience's reaction to the user through a VR device, can let the user hear the audience's applause and cheers using an audio output device, or can convey the audience's cheering messages to the user by displaying a text message.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a generation unit that generates a response from the audience based on the information received by the reception unit; a feedback unit that provides the reaction generated by the generation unit to a user. A system characterized by:

2. The generation unit Change the audience's reaction according to the user's singing 2. The system of claim 1.

3. The feedback unit User performance can be recorded and reviewed later 2. The system of claim 1.

4. The reception unit Accepts input to control the user's avatar 2. The system of claim 1.

5. The generation unit Generate audience reactions such as applause, cheers, and cheers 2. The system of claim 1.

6. The feedback unit The user's appearance can be confirmed through the VR device.

2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past input history and select the appropriate input method 2. The system of claim 1.

9. The reception unit Analyzes the user's current ambient sound and accepts input while canceling noise.

2. The system of claim 1.

10. The reception unit Select the appropriate input method depending on the user's input method 2. The system of claim 1.

Images