System

Abstract

An object of the system according to the embodiment is to reduce the burden of producing television programs and CMs and to reduce the cost.SOLUTION: A system according to an embodiment includes a generation unit and an input unit. The generation unit generates a 2D and a 3D model using the generation AI. The input unit allows a director to input a topic of a program. The generation unit generates a talk and a motion of a performer in real time on the basis of the topic input by the input unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not sufficiently improved working conditions or reduced costs in the production of television programs and commercials, and there is room for improvement.

[0005] The system according to the embodiment aims to reduce the burden and costs involved in producing television programs and commercials. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit and an input unit. The generation unit generates 2D and 3D models using a generation AI. The input unit receives program topics from a director. The generation unit generates talk and motions of performers in real time based on the topics input by the input unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden and costs involved in producing television programs and commercials. [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 television program production system according to an embodiment of the present invention uses generative AI to reduce the burden of television program and commercial production, enabling programs to be produced at extremely low cost. This allows the television program production system to utilize generative AI to generate 2D and 3D models of announcers and entertainers, and generate talk and motion in real time based on topics input by the director.

[0029] A television program production system according to an embodiment includes a generation unit, an input unit, and another generation unit. The generation unit generates 2D and 3D models using a generation AI. For example, the generation AI uses deep learning technology to learn the facial and body movements of announcers and entertainers and generate the 2D and 3D models. The generation AI can also generate realistic models using GAN (generative artificial network). The generation AI can also generate models with specific characters and personalities. The input unit receives input from a director about the program's topic. For example, the director can input the topic in text format. The director can also input the topic using voice input. The director can also input the topic through an interface. The generation unit generates the talk and motions of the performers in real time based on the topic input by the input unit. For example, the generation AI can generate the talk of the performers using voice synthesis technology. The generation AI can also generate the movements of the performers using motion capture technology. The generation AI can also adjust the facial expressions and movements of the performers according to the viewer's emotions. As a result, the television program production system according to the embodiment uses generation AI to reduce the burden of producing television programs and commercials, enabling programs to be produced at extremely low cost.

[0030] The generation unit can learn the facial and body movements of a specific announcer and generate a 2D or 3D model of that announcer. For example, the generation unit can learn the facial feature points of a specific announcer and generate a 2D model of that face. The generation unit can also learn the body movement patterns of a specific announcer and generate a 3D model of that body. Furthermore, the generation unit can learn the facial expressions and gestures of a specific announcer and generate a model that reflects them. In this way, by learning the facial and body movements of a specific announcer and generating a 2D or 3D model of that announcer, it is possible to provide natural talk to viewers.

[0031] The generation unit can add datasets to the model to reflect the characteristics of a specific culture or region, enabling the production of region-specific programs. For example, the generation unit adds datasets that reflect the characteristics of a specific culture or region, and generates a model based on the dataset. For example, it can generate a model wearing traditional Japanese clothing. In addition, the generation AI can generate models specialized for each region by learning the culture and customs of each region. It can also generate models that incorporate the region's scenery and buildings as a background. This enables the production of region-specific programs, providing viewers with content that is more familiar to them.

[0032] The generation unit can support the production of historical dramas and period dramas by introducing an algorithm to reflect a specific historical background or period setting in the model. For example, the generation unit introduces an algorithm to reflect a specific historical background or period setting and generates a model based on that algorithm. For example, it generates a model of a samurai from the Edo period. In addition, the generation AI can generate a model specialized for that period by learning historical costumes and buildings. Furthermore, it can generate a model that incorporates scenery and buildings from a specific period into the background. This can support the production of historical dramas and period dramas, allowing for the provision of more realistic content to viewers.

[0033] The generation unit can utilize the 2D and 3D models as educational content or instructors for online courses, leading to applications in the field of education. For example, the generation unit can utilize the 2D and 3D models generated by the generative AI as instructors for educational content to teach students. For example, models of historical figures can be used in history classes. Models generated by the generative AI can also be used by instructors for online courses to give lectures in real time. For example, a model explaining a science experiment can be used. Furthermore, models generated by the generative AI can be incorporated into educational content to provide an interactive learning environment for students. For example, a model for teaching English pronunciation can be used. This can be applied to the field of education, providing a more effective learning environment for students.

[0034] The generation unit can use the model as a moderator of virtual events or online meetings, thereby enabling business applications. For example, the generation unit can use the 2D and 3D models generated by the generative AI as a moderator of a virtual event to guide the event. For example, the model can be used at a company's new product launch event. The model generated by the generative AI can also be used as a moderator of an online meeting to support the progress of the meeting. For example, a model can be used to explain the meeting agenda. Furthermore, the model generated by the generative AI can be used for business applications to provide an interactive experience for participants of virtual events or online meetings. For example, a model can be used to conduct question and answer sessions. This can be deployed for business applications, providing companies with a more effective means of communication.

[0035] The input unit allows the generation AI to automatically collect background information and data related to the topic entered by the director and complement the talk content. For example, based on the topic entered by the director, the input unit allows the generation AI to automatically collect related news articles and data to complement the talk content. For example, it may provide statistical data related to economic news. The generation AI may also automatically collect background information related to the topic entered by the director to enrich the talk content. For example, it may provide information about historical events. Furthermore, the generation AI may automatically collect visual content (images and videos) related to the topic entered by the director to complement the talk content. For example, it may provide footage related to natural disasters. In this way, the generation AI may automatically collect background information and data related to the topic entered by the director and complement the talk content, thereby enriching the content of the program.

[0036] The input unit allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression. The input unit allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a progress scenario for a news program. The generation AI can also automatically determine the flow and order of talk based on the topics input by the director and generate a scenario. For example, the generation AI can automatically generate a progress scenario for a talk show. Furthermore, the generation AI can automatically generate a scenario based on the topics input by the director and adjust the content and timing of the performers' talk. For example, the generation AI can automatically generate a progress scenario for a discussion program. This allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression, thereby improving the efficiency of program production.

[0037] The input unit can automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences. For example, the input unit allows the generation AI to automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences. For example, translating from English to Spanish. The generation AI can also automatically translate topics entered by the director into multiple languages ​​to accommodate international audiences. For example, translating from Japanese to French. Furthermore, the generation AI can translate topics entered by the director in real time, supporting the production of programs aimed at audiences of different languages. For example, translating from Chinese to English. This allows the generation AI to automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences, making it possible to accommodate global audiences.

[0038] The input unit allows the generation AI to automatically generate related visual content (images and videos) based on the topic input by the director, thereby improving the visual appeal of the program. The input unit allows the generation AI to automatically generate related images and videos based on the topic input by the director, for example, thereby improving the visual appeal of the program. For example, it generates background footage for news. The generation AI can also automatically generate visual content related to the topic input by the director, thereby improving the visual appeal of the program. For example, it can generate graphs and charts. Furthermore, the generation AI can automatically generate visual content based on the topic input by the director, thereby providing viewers with a visually appealing program. For example, it can generate animations. In this way, the generation AI can automatically generate related visual content based on the topic input by the director, improving the visual appeal of the program, thereby providing viewers with a more appealing program.

[0039] The generation unit can introduce algorithms to give talk and motions a specific character or personality, creating unique performers. For example, the generation unit can introduce algorithms to give the generation AI a specific character or personality, creating unique performers. For example, it can generate humorous characters or serious characters. Furthermore, the generation AI can learn characters and personalities and generate talk and motions that reflect those characteristics. For example, a lively character can speak energetically. Furthermore, in order to give the generation AI a specific character or personality, it can learn past data and generate talk and motions based on that. For example, it can recreate the character of a historical figure. In this way, by introducing algorithms to give the generated talk and motions a specific character or personality, and creating unique performers, it is possible to provide a more appealing program to viewers.

[0040] The generation unit can add datasets to reflect specific situations and contexts in the talk and motions, thereby realizing performances that correspond to the scenario. For example, the generation unit adds datasets to the generation AI that reflect specific situations and contexts, and generates talk and motions that correspond to the scenario. For example, it can realize a performance that corresponds to a breaking news situation. Furthermore, the generation AI can learn the situation and context and generate talk and motions that reflect those characteristics. For example, it can realize a performance that corresponds to a situation in a comedy show. Furthermore, in order to reflect specific situations and contexts, the generation AI can learn past data and generate talk and motions based on that. For example, it can realize a performance that corresponds to a situation in a historical drama. In this way, by adding datasets to reflect specific situations and contexts in the generated talk and motions and realizing performances that correspond to the scenario, it is possible to provide more appealing programs to viewers.

[0041] The generation unit can apply the talk and motion to responses from virtual assistants or customer support, and can be deployed for business purposes. For example, the generation unit can use the talk and motion generated by the generation AI as a virtual assistant to respond to user questions. For example, it can respond to customer support inquiries in real time. The talk and motion generated by the generation AI can also be used in customer support responses to provide natural responses to users. For example, explaining how to use a product. Furthermore, the talk and motion generated by the generation AI can be used in business applications to make virtual assistant and customer support responses more efficient. For example, it can provide automatic responses to FAQs. In this way, the generated talk and motion can be applied to virtual assistant and customer support responses, and deployed for business purposes, providing a more effective means of communication for companies.

[0042] The generation unit can use the talk and motion as game characters or virtual reality avatars, leading to applications in the entertainment field. For example, the generation unit can use the talk and motion generated by the generation AI as game characters to provide players with an interactive experience. For example, a character in a game can speak in real time. The talk and motion generated by the generation AI can also be used as virtual reality avatars to provide users with an immersive experience. For example, conversations can be realized in a VR space. Furthermore, the talk and motion generated by the generation AI can be used in the entertainment field to make game characters and virtual reality avatars more realistic. For example, the character's facial expressions and movements can be generated in real time. This allows the generated talk and motion to be used as game characters or virtual reality avatars for applications in the entertainment field, providing viewers with a more engaging experience.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The generation unit generates 2D and 3D models using a generative AI. For example, the generative AI uses deep learning technology to learn the facial and body movements of announcers and entertainers and generate the 2D and 3D models. The generative AI can also generate realistic models using a generative adversarial network (GAN). Furthermore, the generative AI can generate models with specific characters and personalities. The input unit receives program topics from a director. For example, the director can input the topics in text format. Alternatively, the director can input the topics using voice input. Furthermore, the director can input the topics through an interface. The generation unit generates the talk and motions of the performers in real time based on the topics input by the input unit. For example, the generative AI can generate the talk of the performers using voice synthesis technology. Alternatively, the generative AI can generate the movements of the performers using motion capture technology. Furthermore, the generative AI can adjust the facial expressions and movements of the performers according to the viewer's emotions. As a result, the television program production system according to the embodiment uses the generative AI to reduce the burden of producing television programs and commercials and realize programs at extremely low cost.

[0045] The generation unit can learn the facial and body movements of a specific announcer and generate a 2D or 3D model of that announcer. For example, it can learn the facial feature points of a specific announcer and generate a 2D model of that face. The generation unit can also learn the body movement patterns of a specific announcer and generate a 3D model of that body. Furthermore, the generation unit can learn the facial expressions and gestures of a specific announcer and generate a model that reflects them. In this way, by learning the facial and body movements of a specific announcer and generating a 2D or 3D model of that announcer, it is possible to provide viewers with a natural talk.

[0046] The generation unit can add datasets to the model to reflect the characteristics of a specific culture or region, enabling the production of region-specific programs. For example, a dataset that reflects the characteristics of a specific culture or region can be added, and a model can be generated based on that dataset. For example, a model wearing traditional Japanese clothing can be generated. In addition, the generation AI can learn the culture and customs of each region and generate a model specialized for that region. It can also generate a model that incorporates the region's scenery and buildings as a background. This enables the production of region-specific programs, providing content that is more familiar to viewers.

[0047] The generation unit can support the production of historical dramas and period dramas by introducing algorithms to reflect specific historical backgrounds and period settings in the models. For example, an algorithm can be introduced to reflect specific historical backgrounds and period settings, and a model can be generated based on that algorithm. For example, a model of a samurai from the Edo period can be generated. In addition, the generation AI can learn about historical costumes and buildings and generate models specialized for that period. It can also generate models that incorporate scenery and buildings from a specific era into the background. This can support the production of historical dramas and period dramas, allowing for the provision of more realistic content to viewers.

[0048] The generation unit can utilize the 2D and 3D models as instructors for educational content and online courses, leading to applications in the field of education. For example, the 2D and 3D models generated by the generative AI can be used as instructors for educational content to teach students. For example, models of historical figures can be used in history classes. Models generated by the generative AI can also be used by instructors for online courses to give lectures in real time. For example, a model explaining a science experiment can be used. Furthermore, models generated by the generative AI can be incorporated into educational content to provide an interactive learning environment for students. For example, a model for teaching English pronunciation can be used. This can be applied to the field of education, providing a more effective learning environment for students.

[0049] The generation unit can use the models as moderators of virtual events and online meetings, enabling business applications. For example, 2D and 3D models generated by the generative AI can be used as moderators of virtual events to guide the events. For example, models can be used at a company's new product launch event. Models generated by the generative AI can also be used as moderators of online meetings to support the progress of the meetings. For example, a model can be used to explain the meeting agenda. Furthermore, models generated by the generative AI can be used for business purposes to provide an interactive experience for participants in virtual events and online meetings. For example, a model can be used to conduct question and answer sessions. This can be deployed for business applications, providing companies with a more effective means of communication.

[0050] The input unit allows the generation AI to automatically collect background information and data related to the topic entered by the director and complement the talk content. For example, based on the topic entered by the director, the generation AI automatically collects related news articles and data to complement the talk content. For example, it can provide statistical data related to economic news. The generation AI can also automatically collect background information related to the topic entered by the director to enrich the talk content. For example, it can provide information about historical events. Furthermore, the generation AI can automatically collect related visual content (images and videos) to complement the talk content. For example, it can provide footage of natural disasters. In this way, the generation AI can automatically collect related background information and data based on the topic entered by the director and complement the talk content, thereby enriching the content of the program.

[0051] The input unit allows the generation AI to automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a progression scenario for a news program. The generation AI can also automatically determine the flow and order of talk based on the topics entered by the director and generate a scenario. For example, the generation AI can automatically generate a progression scenario for a talk show. Furthermore, the generation AI can automatically generate a scenario based on the topics entered by the director and adjust the content and timing of the participants' talk. For example, the generation AI can automatically generate a progression scenario for a discussion program. This allows the generation AI to automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression, thereby improving the efficiency of program production.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The generator uses generative AI to generate 2D and 3D models. For example, the generative AI uses deep learning technology to learn the facial and body movements of announcers and entertainers, and then generates 2D and 3D models of them. The generative AI can also generate realistic models using GAN (generative artificial network). Furthermore, the generative AI can generate models with specific characters and personalities. Step 2: The director inputs the program topic in the input unit. For example, the director can input the topic in text format. The director can also input the topic using voice input. The director can also input the topic through an interface. Step 3: The generation unit generates the talk and motions of the performers in real time based on the topic input by the input unit. For example, the generation AI generates the talk of the performers using voice synthesis technology. The generation AI can also generate the movements of the performers using motion capture technology. Furthermore, the generation AI can adjust the facial expressions and movements of the performers according to the emotions of the viewers.

[0054] (Example 2) A television program production system according to an embodiment of the present invention uses generative AI to reduce the burden of television program and commercial production, enabling programs to be produced at extremely low cost. This allows the television program production system to utilize generative AI to generate 2D and 3D models of announcers and entertainers, and generate talk and motion in real time based on topics input by the director.

[0055] A television program production system according to an embodiment includes a generation unit, an input unit, and another generation unit. The generation unit generates 2D and 3D models using a generation AI. For example, the generation AI uses deep learning technology to learn the facial and body movements of announcers and entertainers and generate the 2D and 3D models. The generation AI can also generate realistic models using GAN (generative artificial network). The generation AI can also generate models with specific characters and personalities. The input unit receives input from a director about the program's topic. For example, the director can input the topic in text format. The director can also input the topic using voice input. The director can also input the topic through an interface. The generation unit generates the talk and motions of the performers in real time based on the topic input by the input unit. For example, the generation AI can generate the talk of the performers using voice synthesis technology. The generation AI can also generate the movements of the performers using motion capture technology. The generation AI can also adjust the facial expressions and movements of the performers according to the viewer's emotions. As a result, the television program production system according to the embodiment uses generation AI to reduce the burden of producing television programs and commercials, enabling programs to be produced at extremely low cost.

[0056] The generation unit can learn the facial and body movements of a specific announcer and generate a 2D or 3D model of that announcer. For example, the generation unit can learn the facial feature points of a specific announcer and generate a 2D model of that face. The generation unit can also learn the body movement patterns of a specific announcer and generate a 3D model of that body. Furthermore, the generation unit can learn the facial expressions and gestures of a specific announcer and generate a model that reflects them. In this way, by learning the facial and body movements of a specific announcer and generating a 2D or 3D model of that announcer, it is possible to provide natural talk to viewers.

[0057] The generation unit incorporates an emotion estimation function to reflect specific emotional states in 2D and 3D models, enabling it to generate models that correspond to the viewer's emotions. For example, the generation unit uses the emotion estimation function to analyze the viewer's emotions and adjust the model's facial expressions and movements based on the results. For example, if the viewer is happy, the generation unit can change the model's facial expression to a smile. Alternatively, if the viewer is sad, the generation unit can change the model's facial expression to a sad one. Furthermore, the generation unit can collect viewer emotional data in real time and adjust the model's emotional state based on that data. This allows the generation of models that correspond to the viewer's emotions, providing more natural conversation and motion.

[0058] The generation unit can add datasets to the model to reflect the characteristics of a specific culture or region, enabling the production of region-specific programs. For example, the generation unit adds datasets that reflect the characteristics of a specific culture or region, and generates a model based on the dataset. For example, it can generate a model wearing traditional Japanese clothing. In addition, the generation AI can generate models specialized for each region by learning the culture and customs of each region. It can also generate models that incorporate the region's scenery and buildings as a background. This enables the production of region-specific programs, providing viewers with content that is more familiar to them.

[0059] The generation unit can support the production of historical dramas and period dramas by introducing an algorithm to reflect a specific historical background or period setting in the model. For example, the generation unit introduces an algorithm to reflect a specific historical background or period setting and generates a model based on that algorithm. For example, it generates a model of a samurai from the Edo period. In addition, the generation AI can generate a model specialized for that period by learning historical costumes and buildings. Furthermore, it can generate a model that incorporates scenery and buildings from a specific period into the background. This can support the production of historical dramas and period dramas, allowing for the provision of more realistic content to viewers.

[0060] The generation unit can utilize the 2D and 3D models as educational content or instructors for online courses, leading to applications in the field of education. For example, the generation unit can utilize the 2D and 3D models generated by the generative AI as instructors for educational content to teach students. For example, models of historical figures can be used in history classes. Models generated by the generative AI can also be used by instructors for online courses to give lectures in real time. For example, a model explaining a science experiment can be used. Furthermore, models generated by the generative AI can be incorporated into educational content to provide an interactive learning environment for students. For example, a model for teaching English pronunciation can be used. This can be applied to the field of education, providing a more effective learning environment for students.

[0061] The generation unit can use the model as a moderator of virtual events or online meetings, thereby enabling business applications. For example, the generation unit can use the 2D and 3D models generated by the generative AI as a moderator of a virtual event to guide the event. For example, the model can be used at a company's new product launch event. The model generated by the generative AI can also be used as a moderator of an online meeting to support the progress of the meeting. For example, a model can be used to explain the meeting agenda. Furthermore, the model generated by the generative AI can be used for business applications to provide an interactive experience for participants of virtual events or online meetings. For example, a model can be used to conduct question and answer sessions. This can be deployed for business applications, providing companies with a more effective means of communication.

[0062] The generation unit can monitor the viewer's emotional reactions to the model in real time and adjust the model's facial expressions and behavior according to the viewer's emotions. For example, the generation unit can use an emotion estimation function to monitor the viewer's emotional reactions in real time and adjust the facial expressions and behavior of the model generated by the generation AI. For example, if the viewer is laughing, the model will also smile. The generation AI can also collect viewer emotional data and change the model's facial expressions and behavior in real time based on that data. For example, if the viewer is surprised, the model will also have a surprised expression. Furthermore, by utilizing the emotion estimation function, it is possible to build a system in which the generation AI automatically adjusts the model's facial expressions and behavior according to the viewer's emotions. For example, if the viewer is sad, the model will also have a sad expression. This makes it possible to provide a more natural performance by adjusting the model's facial expressions and behavior according to the viewer's emotions.

[0063] The input unit allows the generation AI to automatically collect background information and data related to the topic entered by the director and complement the talk content. For example, based on the topic entered by the director, the input unit allows the generation AI to automatically collect related news articles and data to complement the talk content. For example, it may provide statistical data related to economic news. The generation AI may also automatically collect background information related to the topic entered by the director to enrich the talk content. For example, it may provide information about historical events. Furthermore, the generation AI may automatically collect visual content (images and videos) related to the topic entered by the director to complement the talk content. For example, it may provide footage related to natural disasters. In this way, the generation AI may automatically collect background information and data related to the topic entered by the director and complement the talk content, thereby enriching the content of the program.

[0064] The input unit allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression. The input unit allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a progress scenario for a news program. The generation AI can also automatically determine the flow and order of talk based on the topics input by the director and generate a scenario. For example, the generation AI can automatically generate a progress scenario for a talk show. Furthermore, the generation AI can automatically generate a scenario based on the topics input by the director and adjust the content and timing of the performers' talk. For example, the generation AI can automatically generate a progress scenario for a discussion program. This allows the generation AI to automatically generate a scenario based on the topics input by the director, ensuring a smooth program progression, thereby improving the efficiency of program production.

[0065] The input unit allows the generation AI to use its emotion estimation function to predict viewers' emotional reactions to topics input by the director and adjust the talk content accordingly. For example, the input unit allows the generation AI to use its emotion estimation function to predict viewers' emotional reactions to topics input by the director and adjust the talk content accordingly. For example, it may prioritize topics that viewers are likely to be interested in. The generation AI can also predict viewers' emotional reactions based on the topics input by the director and adjust the talk content accordingly. For example, it may select topics to which viewers will have a positive reaction. Furthermore, the generation AI can use its emotion estimation function to predict viewers' emotional reactions to topics input by the director and adjust the talk content in real time. For example, it may avoid topics to which viewers will have a negative reaction. In this way, the generation AI can use its emotion estimation function to predict viewers' emotional reactions to topics input by the director and adjust the talk content accordingly, thereby providing viewers with a more appealing program.

[0066] The input unit can automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences. For example, the input unit allows the generation AI to automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences. For example, translating from English to Spanish. The generation AI can also automatically translate topics entered by the director into multiple languages ​​to accommodate international audiences. For example, translating from Japanese to French. Furthermore, the generation AI can translate topics entered by the director in real time, supporting the production of programs aimed at audiences of different languages. For example, translating from Chinese to English. This allows the generation AI to automatically translate topics entered by the director into different languages, supporting the production of programs aimed at international audiences, making it possible to accommodate global audiences.

[0067] The input unit allows the generation AI to automatically generate related visual content (images and videos) based on the topic input by the director, thereby improving the visual appeal of the program. The input unit allows the generation AI to automatically generate related images and videos based on the topic input by the director, for example, thereby improving the visual appeal of the program. For example, it generates background footage for news. The generation AI can also automatically generate visual content related to the topic input by the director, thereby improving the visual appeal of the program. For example, it can generate graphs and charts. Furthermore, the generation AI can automatically generate visual content based on the topic input by the director, thereby providing viewers with a visually appealing program. For example, it can generate animations. In this way, the generation AI can automatically generate related visual content based on the topic input by the director, improving the visual appeal of the program, thereby providing viewers with a more appealing program.

[0068] The input unit can provide real-time feedback on viewers' emotional reactions to topics input by the director and adjust the program content according to the viewers' emotions. For example, the input unit can use an emotion estimation function to provide real-time feedback on viewers' emotional reactions to topics input by the director and adjust the program content. For example, it can prioritize topics that viewers are likely to be interested in. The generation AI can also analyze viewers' emotional reactions to topics input by the director in real time and feed back the results to adjust the program content. For example, it can select topics to which viewers have a positive reaction. Furthermore, the generation AI can provide real-time feedback on viewers' emotional reactions to topics input by the director and adjust the program content according to the viewers' emotions using the emotion estimation function. For example, it can avoid topics to which viewers have a negative reaction. In this way, by providing real-time feedback on viewers' emotional reactions to topics input by the director and adjusting the program content according to the viewers' emotions, it is possible to provide a more appealing program to viewers.

[0069] The generation unit uses an emotion estimation function to reflect the viewer's emotional reactions in real time in the talk and motions, enabling a more natural performance. For example, the generation unit uses the emotion estimation function to reflect the viewer's emotional reactions in real time in the talk and motions generated by the generation AI, enabling a more natural performance. For example, if the viewer is laughing, the performer will also smile. The emotion estimation function can also be used to collect viewer's emotional reactions in real time, and the generation AI can adjust the performer's talk and motions based on that. For example, if the viewer is surprised, the performer will also have a surprised expression. Furthermore, a system can be built in which the generation AI analyzes viewer's emotional data in real time and adjusts the performer's talk and motions based on the results. For example, if the viewer is sad, the performer will also have a sad expression. This allows the generation unit to reflect the viewer's emotional reactions in real time in the talk and motions generated by the generation AI, enabling a more natural performance, thereby providing a more appealing program to viewers.

[0070] The generation unit can introduce algorithms to give talk and motions a specific character or personality, creating unique performers. For example, the generation unit can introduce algorithms to give the generation AI a specific character or personality, creating unique performers. For example, it can generate humorous characters or serious characters. Furthermore, the generation AI can learn characters and personalities and generate talk and motions that reflect those characteristics. For example, a lively character can speak energetically. Furthermore, in order to give the generation AI a specific character or personality, it can learn past data and generate talk and motions based on that. For example, it can recreate the character of a historical figure. In this way, by introducing algorithms to give the generated talk and motions a specific character or personality, and creating unique performers, it is possible to provide a more appealing program to viewers.

[0071] The generation unit can add datasets to reflect specific situations and contexts in the talk and motions, thereby realizing performances that correspond to the scenario. For example, the generation unit adds datasets to the generation AI that reflect specific situations and contexts, and generates talk and motions that correspond to the scenario. For example, it can realize a performance that corresponds to a breaking news situation. Furthermore, the generation AI can learn the situation and context and generate talk and motions that reflect those characteristics. For example, it can realize a performance that corresponds to a situation in a comedy show. Furthermore, in order to reflect specific situations and contexts, the generation AI can learn past data and generate talk and motions based on that. For example, it can realize a performance that corresponds to a situation in a historical drama. In this way, by adding datasets to reflect specific situations and contexts in the generated talk and motions and realizing performances that correspond to the scenario, it is possible to provide more appealing programs to viewers.

[0072] The generation unit can apply the talk and motion to responses from virtual assistants or customer support, and can be deployed for business purposes. For example, the generation unit can use the talk and motion generated by the generation AI as a virtual assistant to respond to user questions. For example, it can respond to customer support inquiries in real time. The talk and motion generated by the generation AI can also be used in customer support responses to provide natural responses to users. For example, explaining how to use a product. Furthermore, the talk and motion generated by the generation AI can be used in business applications to make virtual assistant and customer support responses more efficient. For example, it can provide automatic responses to FAQs. In this way, the generated talk and motion can be applied to virtual assistant and customer support responses, and deployed for business purposes, providing a more effective means of communication for companies.

[0073] The generation unit can use the talk and motion as game characters or virtual reality avatars, leading to applications in the entertainment field. For example, the generation unit can use the talk and motion generated by the generation AI as game characters to provide players with an interactive experience. For example, a character in a game can speak in real time. The talk and motion generated by the generation AI can also be used as virtual reality avatars to provide users with an immersive experience. For example, conversations can be realized in a VR space. Furthermore, the talk and motion generated by the generation AI can be used in the entertainment field to make game characters and virtual reality avatars more realistic. For example, the character's facial expressions and movements can be generated in real time. This allows the generated talk and motion to be used as game characters or virtual reality avatars for applications in the entertainment field, providing viewers with a more engaging experience.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The generation unit generates 2D and 3D models using a generative AI. For example, the generative AI uses deep learning technology to learn the facial and body movements of announcers and entertainers and generate the 2D and 3D models. The generative AI can also generate realistic models using a generative adversarial network (GAN). Furthermore, the generative AI can generate models with specific characters and personalities. The input unit receives program topics from a director. For example, the director can input the topics in text format. Alternatively, the director can input the topics using voice input. Furthermore, the director can input the topics through an interface. The generation unit generates the talk and motions of the performers in real time based on the topics input by the input unit. For example, the generative AI can generate the talk of the performers using voice synthesis technology. Alternatively, the generative AI can generate the movements of the performers using motion capture technology. Furthermore, the generative AI can adjust the facial expressions and movements of the performers according to the viewer's emotions. As a result, the television program production system according to the embodiment uses the generative AI to reduce the burden of producing television programs and commercials and realize programs at extremely low cost.

[0076] The generation unit can learn the facial and body movements of a specific announcer and generate a 2D or 3D model of that announcer. For example, it can learn the facial feature points of a specific announcer and generate a 2D model of that face. The generation unit can also learn the body movement patterns of a specific announcer and generate a 3D model of that body. Furthermore, the generation unit can learn the facial expressions and gestures of a specific announcer and generate a model that reflects them. In this way, by learning the facial and body movements of a specific announcer and generating a 2D or 3D model of that announcer, it is possible to provide viewers with a natural talk.

[0077] The generation unit incorporates an emotion estimation function to reflect specific emotional states in 2D and 3D models, enabling it to generate models that correspond to the viewer's emotions. For example, the emotion estimation function can be used to analyze the viewer's emotions and adjust the model's facial expressions and movements based on the results. For example, if the viewer is happy, the model's facial expression can be changed to a smile. Alternatively, if the viewer is sad, the model's facial expression can be made sad. Furthermore, it is possible to collect viewer emotional data in real time and adjust the model's emotional state based on that data. This allows for the generation of models that correspond to the viewer's emotions, providing more natural conversation and motion.

[0078] The generation unit can add datasets to the model to reflect the characteristics of a specific culture or region, enabling the production of region-specific programs. For example, a dataset that reflects the characteristics of a specific culture or region can be added, and a model can be generated based on that dataset. For example, a model wearing traditional Japanese clothing can be generated. In addition, the generation AI can learn the culture and customs of each region and generate a model specialized for that region. It can also generate a model that incorporates the region's scenery and buildings as a background. This enables the production of region-specific programs, providing content that is more familiar to viewers.

[0079] The generation unit can support the production of historical dramas and period dramas by introducing algorithms to reflect specific historical backgrounds and period settings in the models. For example, an algorithm can be introduced to reflect specific historical backgrounds and period settings, and a model can be generated based on that algorithm. For example, a model of a samurai from the Edo period can be generated. In addition, the generation AI can learn about historical costumes and buildings and generate models specialized for that period. It can also generate models that incorporate scenery and buildings from a specific era into the background. This can support the production of historical dramas and period dramas, allowing for the provision of more realistic content to viewers.

[0080] The generation unit can utilize the 2D and 3D models as instructors for educational content and online courses, leading to applications in the field of education. For example, the 2D and 3D models generated by the generative AI can be used as instructors for educational content to teach students. For example, models of historical figures can be used in history classes. Models generated by the generative AI can also be used by instructors for online courses to give lectures in real time. For example, a model explaining a science experiment can be used. Furthermore, models generated by the generative AI can be incorporated into educational content to provide an interactive learning environment for students. For example, a model for teaching English pronunciation can be used. This can be applied to the field of education, providing a more effective learning environment for students.

[0081] The generation unit can use the models as moderators of virtual events and online meetings, enabling business applications. For example, 2D and 3D models generated by the generative AI can be used as moderators of virtual events to guide the events. For example, models can be used at a company's new product launch event. Models generated by the generative AI can also be used as moderators of online meetings to support the progress of the meetings. For example, a model can be used to explain the meeting agenda. Furthermore, models generated by the generative AI can be used for business purposes to provide an interactive experience for participants in virtual events and online meetings. For example, a model can be used to conduct question and answer sessions. This can be deployed for business applications, providing companies with a more effective means of communication.

[0082] The generation unit can monitor the viewer's emotional reactions to the model in real time and adjust the model's facial expressions and behavior according to the viewer's emotions. For example, the emotion estimation function can be used to monitor the viewer's emotional reactions in real time and adjust the facial expressions and behavior of the model generated by the generation AI. For example, if the viewer is laughing, the model will also smile. The generation AI can also collect viewer emotional data and change the model's facial expressions and behavior in real time based on that data. For example, if the viewer is surprised, the model will also have a surprised expression. Furthermore, the emotion estimation function can be used to build a system in which the generation AI automatically adjusts the model's facial expressions and behavior according to the viewer's emotions. For example, if the viewer is sad, the model will also have a sad expression. This makes it possible to adjust the model's facial expressions and behavior according to the viewer's emotions, providing a more natural performance.

[0083] The input unit allows the generation AI to automatically collect background information and data related to the topic entered by the director and complement the talk content. For example, based on the topic entered by the director, the generation AI automatically collects related news articles and data to complement the talk content. For example, it can provide statistical data related to economic news. The generation AI can also automatically collect background information related to the topic entered by the director to enrich the talk content. For example, it can provide information about historical events. Furthermore, the generation AI can automatically collect related visual content (images and videos) to complement the talk content. For example, it can provide footage of natural disasters. In this way, the generation AI can automatically collect related background information and data based on the topic entered by the director and complement the talk content, thereby enriching the content of the program.

[0084] The input unit allows the generation AI to automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression. For example, the generation AI can automatically generate a progression scenario for a news program. The generation AI can also automatically determine the flow and order of talk based on the topics entered by the director and generate a scenario. For example, the generation AI can automatically generate a progression scenario for a talk show. Furthermore, the generation AI can automatically generate a scenario based on the topics entered by the director and adjust the content and timing of the participants' talk. For example, the generation AI can automatically generate a progression scenario for a discussion program. This allows the generation AI to automatically generate a scenario based on the topics entered by the director, ensuring a smooth program progression, thereby improving the efficiency of program production.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The generator uses generative AI to generate 2D and 3D models. For example, the generative AI uses deep learning technology to learn the facial and body movements of announcers and entertainers, and then generates 2D and 3D models of them. The generative AI can also generate realistic models using GAN (generative artificial network). Furthermore, the generative AI can generate models with specific characters and personalities. Step 2: The director inputs the program topic in the input unit. For example, the director can input the topic in text format. The director can also input the topic using voice input. The director can also input the topic through an interface. Step 3: The generation unit generates the talk and motions of the performers in real time based on the topic input by the input unit. For example, the generation AI generates the talk of the performers using voice synthesis technology. The generation AI can also generate the movements of the performers using motion capture technology. Furthermore, the generation AI can adjust the facial expressions and movements of the performers according to the emotions of the viewers.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0131] In the robot 414, 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 robot 414 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0154] 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 generation unit that generates 2D and 3D models using a generation AI; an input section for the director to input program topics; a generation unit that generates talk and motion of performers in real time based on the topic input by the input unit. A system characterized by:

2. The generation unit Learn the facial and body movements of a specific announcer and generate a 2D or 3D model of that announcer 2. The system of claim 1.

3. The generation unit Emotion estimation functionality is incorporated into the 2D and 3D models to reflect specific emotional states, generating models that correspond to the viewer's emotions.

2. The system of claim 1.

4. The generation unit Adding datasets to the model to reflect specific cultures and regions will enable the creation of region-specific programming.

2. The system of claim 1.

5. The generation unit The model will incorporate algorithms to reflect specific historical backgrounds and time periods, supporting the production of historical dramas and period dramas.

2. The system of claim 1.

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