System

Abstract

An object of the system according to the embodiment is to generate content using a voice instruction, automatically distribute the content to a plurality of platforms, and centrally manage comments and reviews of each platform.SOLUTION: A system includes a television mounted with a generation AI, a voice analysis part, a text generation part, a screen generation part, an automatic distribution part, and a comment management part. The television equipped with the generation AI analyzes the voice instruction. The voice analysis unit analyzes the voice instruction. The text generation unit generates a text based on the instruction analyzed by the voice analysis unit. The screen generation unit generates a screen based on the text generated by the text generation unit. The automatic distribution unit automatically distributes the content generated by the screen generation unit to the plurality of platforms. The comment management unit centrally manages comments or reviews of each platform.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 technologies have faced challenges in that it is difficult to generate content using voice instructions, automatically distribute it to multiple platforms, and centrally manage comments and reviews on each platform.

[0005] The system according to the embodiment aims to generate content using voice instructions, automatically distribute it to multiple platforms, and centrally manage comments and reviews on each platform. [Means for solving the problem]

[0006] The system according to the embodiment includes a television equipped with a generation AI, a voice analysis unit, a text generation unit, a screen generation unit, an automatic distribution unit, and a comment management unit. The television equipped with the generation AI analyzes voice instructions. The voice analysis unit analyzes the voice instructions. The text generation unit generates text based on the instructions analyzed by the voice analysis unit. The screen generation unit generates a screen based on the text generated by the text generation unit. The automatic distribution unit automatically distributes the content generated by the screen generation unit to multiple platforms. The comment management unit centrally manages comments or reviews on each platform. [Effects of the Invention]

[0007] The system according to the embodiment can generate content using voice instructions, automatically distribute it to multiple platforms, and centrally manage comments and reviews on each platform. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A television system according to an embodiment of the present invention is equipped with a generative AI that uses voice to generate text and images, allowing users to distribute their own personalized animations and videos. This allows the television system to automatically distribute the generated content to multiple platforms, and allows users to centrally check comments and reviews from each platform on their television.

[0029] A television system according to an embodiment includes a generation AI, a voice analysis unit, a text generation unit, a screen generation unit, an automatic distribution unit, and a comment management unit. The generation AI analyzes a user's voice instructions. For example, when a user voice-instructs, "Create a scene of cats playing in the park," the generation AI analyzes the content. The voice analysis unit generates text based on the instructions analyzed by the generation AI. For example, the voice analysis unit converts the user's instructions into text data. The text generation unit generates a screen based on the text generated by the voice analysis unit. For example, the text generation unit creates an animation based on the generated text. The screen generation unit generates a screen based on the text generated by the text generation unit. For example, the screen generation unit creates an animation based on the generated text. The automatic distribution unit automatically distributes the content generated by the screen generation unit to multiple platforms. For example, the automatic distribution unit automatically uploads the generated content to YouTube and TikTok. The comment management unit centrally manages comments and reviews on each platform. For example, the comment management unit retrieves comments from YouTube and TikTok and displays them on a television screen. As a result, the television system according to the embodiment generates text and images in response to voice instructions from the user, automatically distributes the text and images to multiple platforms, and allows comments and reviews to be viewed centrally.

[0030] The voice analysis unit learns the user's past voice instruction history and can suggest anime and videos that match the user's preferences. For example, the voice analysis unit uses a generation AI to analyze the user's past voice instruction history and suggest anime and videos based on the user's preferences. For example, if the user has frequently requested "scenes of cats playing in the park" in the past, the generation AI will suggest similar scenes. This makes it possible to suggest anime and videos based on the user's preferences.

[0031] Generative AI responds not only to voice instructions but also to gestures and touch operations, allowing for more intuitive operation to generate animations and videos. Generative AI builds a system that responds not only to voice instructions but also to gestures and touch operations. For example, a user can specify the position of a character by touching the screen. This allows for intuitive operation using gestures and touch operations.

[0032] The generation AI can provide a function that allows users to add comments and feedback to the generated animations and videos in real time. The generation AI can provide a function that allows users to add comments to the generated animations and videos in real time. For example, a user can add a comment such as "I like this scene." This allows users to add comments and feedback in real time.

[0033] The generation AI can analyze the optimal posting times for each platform and automatically set schedules and distribute content. For example, the generation AI can analyze the viewing data of each platform and identify the optimal posting times. For example, it can set a posting schedule based on peak viewing times on YouTube. This allows content to be automatically distributed at the optimal posting times.

[0034] Generative AI can analyze the algorithms of each platform and automatically generate the optimal tags and descriptions to reach a wider audience. Generative AI, for example, analyzes the algorithms of each platform and builds a system that automatically generates the optimal tags. For example, it analyzes YouTube's algorithm and suggests tags that are easy for viewers to search for. This makes it possible to automatically generate the optimal tags and descriptions.

[0035] When performing automatic distribution, the generation AI can provide an automatic conversion function to accommodate the different formats of each platform. For example, the generation AI builds a system that provides an automatic conversion function to accommodate the different formats of each platform. For example, it automatically converts a horizontal video for YouTube into a vertical video for TikTok. This makes it possible to automatically convert to accommodate different formats.

[0036] The generation AI can analyze viewing data after distribution and provide a feedback function that suggests improvements for the next distribution. For example, the generation AI can build a system that analyzes viewing data after distribution and provides a feedback function that suggests improvements for the next distribution. For example, it can analyze viewer viewing time and dropout points and suggest improvements. This makes it possible to suggest improvements for the next distribution.

[0037] Generative AI can analyze comments and reviews on each platform and automatically classify feedback into positive and negative. Generative AI, for example, can analyze comments and reviews on each platform and build a system that automatically classifies feedback into positive and negative. For example, it can use natural language processing technology to analyze the sentiment of comments. This makes it possible to automatically classify feedback into positive and negative.

[0038] Generative AI can analyze the content of comments and reviews and automatically generate specific improvement suggestions for users. For example, generative AI can analyze the content of comments and reviews and build a system that automatically generates specific improvement suggestions for users. For example, it can suggest scene changes or additions based on viewer feedback. This makes it possible to automatically generate specific improvement suggestions.

[0039] Generative AI can analyze comments and reviews, identify the topics that users are most interested in, and suggest new content related to those topics. Generative AI can, for example, build a system that analyzes comments and reviews and identifies the topics that users are most interested in. For example, it can extract keywords that are frequently included in viewer comments. This can then suggest new content related to the topics that users are most interested in.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The TV system can also provide a function that allows users to add effects and music in real time to animations and videos generated based on the user's voice instructions. For example, if a user instructs "Add a fireworks effect to this scene," the generation AI will add that effect in real time. Also, if a user instructs "Add up-tempo music to this scene," the generation AI can select appropriate music and add it to the scene. This allows users to customize content in a more interactive way.

[0042] The television system can also provide a collaboration function that allows users to collaborate with other users in editing and producing animations and videos they have created. For example, user B can add character movements to a scene created by user A, and user C can change the background. This allows multiple users to collaborate in creating a single piece of content.

[0043] The TV system can also provide a function that allows viewers to vote or take surveys in real time for user-generated animations and videos. For example, viewers can vote to decide "how a character should act in the next scene." Viewer opinions can also be collected through surveys and reflected in the next content production. This allows for viewer participation in content.

[0044] The television system can also provide a function that allows viewers to add comments in real time to user-generated animations and videos. For example, a viewer can add a comment such as "This scene is interesting." Or, a viewer can add a comment such as "I like this character." This allows the system to provide content that reflects viewers' real-time reactions.

[0045] The TV system can also provide a function that allows viewers to add effects and music in real time to user-generated animations and videos. For example, if a viewer instructs, "Add a fireworks effect to this scene," the generation AI will add that effect in real time. Or, if a viewer instructs, "Add up-tempo music to this scene," the generation AI can select appropriate music and add it to the scene. This allows viewers to customize content in a more interactive way.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The generation AI analyzes the user's voice instructions. For example, if the user gives a voice command such as "Create a scene of cats playing in a park," the generation AI analyzes the content. Step 2: The voice analysis unit generates text based on the instructions analyzed by the generation AI. For example, the voice analysis unit converts the user's instructions into text data. Step 3: The text generation unit generates a screen based on the text generated by the speech analysis unit. For example, the text generation unit creates an animation based on the generated text. Step 4: The screen generator generates a screen based on the text generated by the text generator. For example, the screen generator creates an animation based on the generated text. Step 5: The automatic distribution unit automatically distributes the content generated by the screen generation unit to multiple platforms. For example, the automatic distribution unit automatically uploads the generated content to YouTube and TikTok. Step 6: The comment management department centrally manages comments and reviews from each platform. For example, the comment management department retrieves comments from YouTube and TikTok and displays them on a TV screen.

[0048] (Example 2) A television system according to an embodiment of the present invention is equipped with a generative AI that uses voice to generate text and images, allowing users to distribute their own personalized animations and videos. This allows the television system to automatically distribute the generated content to multiple platforms, and allows users to centrally check comments and reviews from each platform on their television.

[0049] A television system according to an embodiment includes a generation AI, a voice analysis unit, a text generation unit, a screen generation unit, an automatic distribution unit, and a comment management unit. The generation AI analyzes a user's voice instructions. For example, when a user voice-instructs, "Create a scene of cats playing in the park," the generation AI analyzes the content. The voice analysis unit generates text based on the instructions analyzed by the generation AI. For example, the voice analysis unit converts the user's instructions into text data. The text generation unit generates a screen based on the text generated by the voice analysis unit. For example, the text generation unit creates an animation based on the generated text. The screen generation unit generates a screen based on the text generated by the text generation unit. For example, the screen generation unit creates an animation based on the generated text. The automatic distribution unit automatically distributes the content generated by the screen generation unit to multiple platforms. For example, the automatic distribution unit automatically uploads the generated content to YouTube and TikTok. The comment management unit centrally manages comments and reviews on each platform. For example, the comment management unit retrieves comments from YouTube and TikTok and displays them on a television screen. As a result, the television system according to the embodiment generates text and images in response to voice instructions from the user, automatically distributes the text and images to multiple platforms, and allows comments and reviews to be viewed centrally.

[0050] The voice analysis unit learns the user's past voice instruction history and can suggest anime and videos that match the user's preferences. For example, the voice analysis unit uses a generation AI to analyze the user's past voice instruction history and suggest anime and videos based on the user's preferences. For example, if the user has frequently requested "scenes of cats playing in the park" in the past, the generation AI will suggest similar scenes. This makes it possible to suggest anime and videos based on the user's preferences.

[0051] The voice analysis unit analyzes the tone and speed of the user's voice and can automatically generate scenes and character expressions that correspond to the user's emotions. For example, the voice analysis unit allows the generation AI to analyze the tone and speed of the user's voice and automatically generate scenes that correspond to the user's emotions. For example, if the user gives instructions in an excited voice, the generation AI will generate an action scene. This makes it possible to automatically generate scenes and character expressions that correspond to the user's emotions.

[0052] The voice analysis unit uses the emotion estimation function to analyze the emotions of users when creating animations or videos in real time, and can suggest scenes that will elicit positive emotions. For example, the voice analysis unit uses the emotion estimation function to analyze the emotions of users when creating animations or videos in real time, and can suggest scenes that will elicit positive emotions. For example, if the user has a happy expression, the generation AI will suggest happy scenes. This makes it possible to suggest scenes that match the user's emotions.

[0053] Generative AI responds not only to voice instructions but also to gestures and touch operations, allowing for more intuitive operation to generate animations and videos. Generative AI builds a system that responds not only to voice instructions but also to gestures and touch operations. For example, a user can specify the position of a character by touching the screen. This allows for intuitive operation using gestures and touch operations.

[0054] The generation AI can provide a function that allows users to add comments and feedback to the generated animations and videos in real time. The generation AI can provide a function that allows users to add comments to the generated animations and videos in real time. For example, a user can add a comment such as "I like this scene." This allows users to add comments and feedback in real time.

[0055] The emotion estimation function analyzes the emotional reactions of other users to content created by a user and can make suggestions to improve the content based on the feedback. The emotion estimation function, for example, builds a system that analyzes the emotional reactions of other users to content created by a user. For example, it analyzes the facial expressions and voices of viewers and calculates an emotion score. This makes it possible to make suggestions to improve the content based on the emotional reactions of other users.

[0056] The generation AI can analyze the optimal posting times for each platform and automatically set schedules and distribute content. For example, the generation AI can analyze the viewing data of each platform and identify the optimal posting times. For example, it can set a posting schedule based on peak viewing times on YouTube. This allows content to be automatically distributed at the optimal posting times.

[0057] Generative AI can analyze the algorithms of each platform and automatically generate the optimal tags and descriptions to reach a wider audience. Generative AI, for example, analyzes the algorithms of each platform and builds a system that automatically generates the optimal tags. For example, it analyzes YouTube's algorithm and suggests tags that are easy for viewers to search for. This makes it possible to automatically generate the optimal tags and descriptions.

[0058] The emotion estimation function analyzes the emotions of users when they stream and can suggest the timing and content of streams that will elicit positive emotions. The emotion estimation function, for example, builds a system that analyzes the emotions of users when they stream in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. This makes it possible to suggest the timing and content of streams that will elicit positive emotions.

[0059] When performing automatic distribution, the generation AI can provide an automatic conversion function to accommodate the different formats of each platform. For example, the generation AI builds a system that provides an automatic conversion function to accommodate the different formats of each platform. For example, it automatically converts a horizontal video for YouTube into a vertical video for TikTok. This makes it possible to automatically convert to accommodate different formats.

[0060] The generation AI can analyze viewing data after distribution and provide a feedback function that suggests improvements for the next distribution. For example, the generation AI can build a system that analyzes viewing data after distribution and provides a feedback function that suggests improvements for the next distribution. For example, it can analyze viewer viewing time and dropout points and suggest improvements. This makes it possible to suggest improvements for the next distribution.

[0061] The emotion estimation function analyzes viewers' emotional reactions after a broadcast and can make suggestions to optimize the content of the next broadcast. The emotion estimation function, for example, builds a system that analyzes viewers' emotional reactions after a broadcast in real time. For example, it analyzes viewers' facial expressions and voices and calculates an emotion score. This makes it possible to make suggestions to optimize the content of the next broadcast.

[0062] Generative AI can analyze comments and reviews on each platform and automatically classify feedback into positive and negative. Generative AI, for example, can analyze comments and reviews on each platform and build a system that automatically classifies feedback into positive and negative. For example, it can use natural language processing technology to analyze the sentiment of comments. This makes it possible to automatically classify feedback into positive and negative.

[0063] Generative AI can analyze the content of comments and reviews and automatically generate specific improvement suggestions for users. For example, generative AI can analyze the content of comments and reviews and build a system that automatically generates specific improvement suggestions for users. For example, it can suggest scene changes or additions based on viewer feedback. This makes it possible to automatically generate specific improvement suggestions.

[0064] The emotion estimation function can analyze users' emotional reactions to comments and reviews and suggest feedback that elicits positive emotions. The emotion estimation function can, for example, build a system that analyzes users' emotional reactions to comments and reviews in real time. For example, it can analyze the user's facial expressions and voice and calculate an emotion score. This can then suggest feedback that elicits positive emotions.

[0065] Generative AI can analyze comments and reviews, identify the topics that users are most interested in, and suggest new content related to those topics. Generative AI can, for example, build a system that analyzes comments and reviews and identifies the topics that users are most interested in. For example, it can extract keywords that are frequently included in viewer comments. This can then suggest new content related to the topics that users are most interested in.

[0066] The emotion estimation function analyzes viewers' emotional reactions to comments and reviews, and can identify content that users are most likely to empathize with. The emotion estimation function, for example, builds a system that analyzes viewers' emotional reactions to comments and reviews in real time. For example, it analyzes viewers' facial expressions and voices and calculates an emotion score. This makes it possible to identify content that users are most likely to empathize with.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The TV system can also provide a function that allows users to add effects and music in real time to animations and videos generated based on the user's voice instructions. For example, if a user instructs "Add a fireworks effect to this scene," the generation AI will add that effect in real time. Also, if a user instructs "Add up-tempo music to this scene," the generation AI can select appropriate music and add it to the scene. This allows users to customize content in a more interactive way.

[0069] The television system can also provide a collaboration function that allows users to collaborate with other users in editing and producing animations and videos they have created. For example, user B can add character movements to a scene created by user A, and user C can change the background. This allows multiple users to collaborate in creating a single piece of content.

[0070] The TV system can also provide a function that allows viewers to vote or take surveys in real time for user-generated animations and videos. For example, viewers can vote to decide "how a character should act in the next scene." Viewer opinions can also be collected through surveys and reflected in the next content production. This allows for viewer participation in content.

[0071] The television system can also provide a function that allows viewers to add comments in real time to user-generated animations and videos. For example, a viewer can add a comment such as "This scene is interesting." Or, a viewer can add a comment such as "I like this character." This allows the system to provide content that reflects viewers' real-time reactions.

[0072] The TV system can also provide a function that allows viewers to add effects and music in real time to user-generated animations and videos. For example, if a viewer instructs, "Add a fireworks effect to this scene," the generation AI will add that effect in real time. Or, if a viewer instructs, "Add up-tempo music to this scene," the generation AI can select appropriate music and add it to the scene. This allows viewers to customize content in a more interactive way.

[0073] The TV system can estimate the user's emotions and automatically adjust the scenes in the animations and videos generated by the user based on the estimated emotions. For example, if the user gives instructions in a sad voice, the generation AI will generate a moving scene. On the other hand, if the user gives instructions in a happy voice, the generation AI will generate a happy scene. This makes it possible to automatically generate scenes according to the user's emotions.

[0074] The TV system can estimate the user's emotions and automatically adjust the facial expressions of the characters in the animations and videos generated by the user based on the estimated emotions. For example, if the user gives commands in an angry voice, the generation AI will change the character's facial expression to an angry one. Conversely, if the user gives commands in a sad voice, the generation AI will change the character's facial expression to a sad one. This makes it possible to automatically generate character facial expressions according to the user's emotions.

[0075] The TV system can estimate the user's emotions and automatically adjust the background music for the animations and videos the user generates based on the estimated emotions. For example, if the user gives instructions in a relaxed voice, the generation AI will select calm background music. On the other hand, if the user gives instructions in an excited voice, the generation AI will select up-tempo background music. This makes it possible to automatically generate background music according to the user's emotions.

[0076] The television system can estimate the user's emotions and automatically adjust the color tones of the animations and videos the user generates based on the estimated emotions. For example, if the user gives instructions in a calm voice, the generation AI will change the color tones of the scene to warmer colors. On the other hand, if the user gives instructions in an excited voice, the generation AI will change the color tones of the scene to more vivid colors. This makes it possible to automatically generate the color tones of a scene according to the user's emotions.

[0077] The television system can estimate the user's emotions and automatically adjust the tempo of the scenes in the animations and videos the user generates based on the estimated emotions. For example, if the user gives instructions in a relaxed voice, the generation AI will slow down the tempo of the scene. On the other hand, if the user gives instructions in an excited voice, the generation AI will speed up the tempo of the scene. This makes it possible to automatically generate the tempo of a scene according to the user's emotions.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The generation AI analyzes the user's voice instructions. For example, if the user gives a voice command such as "Create a scene of cats playing in a park," the generation AI analyzes the content. Step 2: The voice analysis unit generates text based on the instructions analyzed by the generation AI. For example, the voice analysis unit converts the user's instructions into text data. Step 3: The text generation unit generates a screen based on the text generated by the speech analysis unit. For example, the text generation unit creates an animation based on the generated text. Step 4: The screen generator generates a screen based on the text generated by the text generator. For example, the screen generator creates an animation based on the generated text. Step 5: The automatic distribution unit automatically distributes the content generated by the screen generation unit to multiple platforms. For example, the automatic distribution unit automatically uploads the generated content to YouTube and TikTok. Step 6: The comment management department centrally manages comments and reviews from each platform. For example, the comment management department retrieves comments from YouTube and TikTok and displays them on a TV screen.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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, in order to avoid confusion and to 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.

[0146] 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]

[0147] 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 TV equipped with generative AI, a voice analysis unit that analyzes voice instructions; a text generation unit that generates text based on the instruction analyzed by the voice analysis unit; a screen generation unit that generates a screen based on the text generated by the text generation unit; an automatic distribution unit that automatically distributes the content generated by the screen generation unit to a plurality of platforms; A comment management unit that centrally manages comments or reviews on each platform. A system characterized by:

2. The generated AI is In addition to voice commands, gesture and touch operations are also supported, allowing for more intuitive animation and video generation.

2. The system of claim 1.

3. The generated AI is Analyze the optimal posting time for each platform and automatically set a schedule for distribution 2. The system of claim 1.

4. The generated AI is When performing automatic distribution, provide an automatic conversion function to accommodate the different formats of each platform.

2. The system of claim 1.

5. The generated AI is Analyzing the comments or reviews for each of the platforms and automatically classifying the feedback as positive or negative.

2. The system of claim 1.

6. The voice analysis unit Analyzing the tone or speed of a user's voice and automatically generating scenes and character expressions that correspond to the user's emotions 2. The system of claim 1.

7. The emotion estimation function is Analyzing the emotions of users when they deliver the content, and proposing the timing or content of delivery to elicit positive emotions.

2. The system of claim 1.

8. The emotion estimation function is Analyzing viewers' emotional responses to the comments or reviews to identify the content that users are most likely to empathize with.

2. The system of claim 1.

Images