System

Abstract

An object of a system according to an embodiment is to reduce a load applied to generation of content for web advertisement.SOLUTION: A system includes a video generation unit and a text generation unit. The video generation unit receives a prompt including an instruction from a user as an input, and generates video content based on the prompt. The text generation unit receives, as an input, a prompt including an instruction from a user, and generates text content based on the prompt.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 had the problem that generating content for web advertisements placed a huge burden on the system.

[0005] The system according to the embodiment aims to reduce the load involved in generating content for web advertisements. [Means for solving the problem]

[0006] The system according to the embodiment includes a video generation unit and a text generation unit. The video generation unit receives a prompt including instructions from a user as input and generates video content based on the prompt. The text generation unit receives a prompt including instructions from a user as input and generates text content based on the prompt. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the load involved in generating content for web advertisements. [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 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 content generation system for web advertising according to an embodiment of the present invention utilizes a generation AI to reduce the burden on clients and agencies. In this system, the generation AI receives prompts containing instructions from a user as input and generates video content and text content based on the prompts. This reduces the burden on clients and agencies, enabling the content generation system for web advertising to efficiently and quickly generate high-quality content.

[0029] A content generation system for web advertising according to an embodiment includes a video generation unit and a text generation unit. The video generation unit receives a prompt containing instructions from a user as input and generates video content based on the prompt. For example, the video generation unit uses a generation AI to automatically generate a video highlighting the product's features and benefits when the AI ​​receives a prompt such as, "Create a video introducing a new product." The video generation unit also uses the generation AI to create a video scenario, select video materials, edit the video, add effects, and output the completed video. The video generation unit can also estimate a user's emotions in real time and automatically insert video scenes that elicit positive emotions. For example, the generation AI analyzes a user's facial expressions and vocal tone in real time and automatically inserts scenes that elicit positive emotions. The video generation unit can also analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the generation AI analyzes the user's past video data, identifies scenes that received a positive viewer response, and applies them to new videos. The text generation unit receives a prompt containing instructions from a user as input and generates text content based on the prompt. For example, if the generation AI receives a prompt such as "Please write an article introducing a new product," the text generation unit automatically generates an article detailing the product's features and benefits. The text generation unit also performs tasks such as structuring sentences, selecting keywords, and checking grammar, and then outputs the completed text. Furthermore, the text generation unit can also use the generation AI to estimate user emotions in real time and automatically insert expressions that elicit positive emotions. For example, the generation AI can estimate user emotions in real time and automatically insert expressions that elicit positive emotions. This allows the web advertising content generation system to reduce the burden on clients and agencies and generate high-quality content efficiently and quickly.

[0030] The video generation unit can analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the video generation unit uses a generation AI to analyze the user's past video data, identify scenes that received a good response from viewers, and apply them to new videos. For example, it can learn the composition of successful advertising videos in the past and reflect a similar composition in new videos. This allows for the generation of more effective video content by analyzing past video data and learning and applying effective scene compositions.

[0031] The video generation unit can analyze the user's voice instructions in real time and edit the video or add effects based on the voice instructions. For example, the video generation unit uses a generation AI to analyze the user's voice instructions in real time and edit the video based on the instructions. For example, a scene can be automatically cut in response to an instruction such as "shorten this scene." This allows for more effective video content to be generated by editing the video or adding effects based on the user's voice instructions.

[0032] The text generation unit can analyze the user's past writing data and automatically learn and apply the most effective writing structure. For example, the text generation unit uses a generation AI to analyze the user's past writing data and automatically learn and apply the most effective writing structure. For example, the structure of writing that has received high marks in the past can be reflected in new writing. In this way, more effective text content can be generated by analyzing past writing data and learning and applying effective writing structures.

[0033] The video generation unit can automatically generate videos optimized for each cultural sphere, taking into account the visual preferences of different cultural spheres. For example, the generation AI in the video generation unit retrieves the visual preferences of different cultural spheres from a database and generates videos based on that. For example, anime-style designs are used for the Japanese market, while realistic images are used for the Western market. This allows for the provision of more effective video content by generating videos that take into account the visual preferences of different cultural spheres.

[0034] The video generation unit can analyze the user's past viewing history and propose the optimal video style based on the viewing history. For example, the video generation unit uses a generation AI to analyze the user's past viewing history and propose the optimal video style based on the viewing history. For example, the style of a new video is determined based on the genre and style of videos previously viewed. In this way, more effective video content can be generated by analyzing the user's past viewing history and proposing the optimal video style.

[0035] The text generation unit takes into account the linguistic preferences of different cultural spheres and can automatically generate sentences optimized for each cultural sphere. For example, the generation AI in the text generation unit retrieves the linguistic preferences of different cultural spheres from a database and generates sentences based on that. For example, it uses a lot of honorific language for the Japanese market and casual expressions for the Western market. This allows for the generation of sentences that take into account the linguistic preferences of different cultural spheres, making it possible to provide more effective text content.

[0036] The text generation unit can analyze the user's past reading history and suggest the optimal writing style based on the reading history. For example, the text generation unit uses a generation AI to analyze the user's past reading history and suggest the optimal writing style based on the reading history. For example, it determines the style of a new writing style based on the genre and style of books read in the past. This allows for the generation of more effective text content by analyzing the user's past reading history and suggesting the optimal writing style.

[0037] The video generation unit can analyze the user's past customization history and automatically learn and apply the most effective customization pattern. For example, the video generation unit uses a generation AI to analyze the user's past customization history and automatically learn and apply the most effective customization pattern. For example, customization patterns that have received high ratings in the past can be reflected in new content. This allows the user's past customization history to be analyzed and the optimal customization pattern to be learned and applied, thereby generating more effective video content.

[0038] The video generation unit can analyze the user's voice instructions in real time and customize based on the voice instructions. For example, the video generation unit uses a generation AI to analyze the user's voice instructions in real time and customize based on the instructions. For example, it changes the color in response to an instruction such as "make this part red." This allows for customization based on the user's voice instructions, making it possible to generate more effective video content.

[0039] The video generation unit can automatically generate customized content optimized for each target demographic, taking into account the preferences of different target demographics. For example, the generation AI in the video generation unit retrieves the preferences of different target demographics from a database and customizes based on that. For example, a pop design could be used for younger demographics, while a more subdued design could be used for seniors. This allows for customization that takes into account the preferences of different target demographics, making it possible to provide more effective video content.

[0040] The video generation unit can analyze the user's past purchasing history and suggest optimal customization based on the purchasing history. For example, the generation AI in the video generation unit analyzes the user's past purchasing history and suggests optimal customization based on the purchasing history. For example, it suggests designs and colors related to products purchased in the past. In this way, by analyzing the user's past purchasing history and suggesting optimal customization, more effective video content can be generated.

[0041] The text generation unit can analyze the user's past translation history and automatically learn and apply the most effective translation pattern. For example, the generation AI in the text generation unit analyzes the user's past translation history and automatically learns and applies the most effective translation pattern. For example, translation patterns that have received high ratings in the past are reflected in new translations. This allows the user's past translation history to be analyzed and the optimal translation pattern to be learned and applied, thereby generating more effective text content.

[0042] The text generation unit can analyze the user's voice instructions in real time and translate based on the voice instructions. For example, the generation AI in the text generation unit analyzes the user's voice instructions in real time and translates based on the instructions. For example, in response to an instruction such as "Translate this part into French," it translates into French. This makes it possible to generate more effective text content by translating based on the user's voice instructions.

[0043] The text generation unit takes into account the linguistic preferences of different cultural spheres and can automatically generate translations optimized for each cultural sphere. For example, the generation AI in the text generation unit retrieves the linguistic preferences of different cultural spheres from a database and performs translation based on that. For example, it may use a lot of honorific language for the Japanese market and casual expressions for the Western market. This allows for translations that take into account the linguistic preferences of different cultural spheres, making it possible to provide more effective text content.

[0044] The text generation unit can analyze the user's past language learning history and suggest the optimal translation based on the learning history. For example, the generation AI in the text generation unit analyzes the user's past language learning history and suggests the optimal translation based on the learning history. For example, it selects appropriate translation expressions based on the level and style of the language the user has learned. This allows for the generation of more effective text content by analyzing the user's past language learning history and suggesting the optimal translation.

[0045] The video generation unit can analyze the user's past update history and automatically learn and apply the most effective update pattern. For example, the video generation unit uses a generation AI to analyze the user's past update history and automatically learn and apply the most effective update pattern. For example, update patterns that have received high ratings in the past are reflected in new content. This allows the user's past update history to be analyzed and the optimal update pattern to be learned and applied, thereby generating more effective video content.

[0046] The video generation unit can analyze the user's voice instructions in real time and update based on the voice instructions. For example, the generation AI in the video generation unit analyzes the user's voice instructions in real time and updates based on the instructions. For example, it changes the color in response to an instruction such as "make this part red." This makes it possible to generate more effective video content by updating based on the user's voice instructions.

[0047] The video generation unit can automatically generate updates optimized for each market, taking into account trends in different markets. For example, the generation AI retrieves trends in different markets from a database and updates based on them. For example, the latest anime trends are reflected in the Japanese market, and the latest movie trends in the European and American markets. This allows for updates that take into account trends in different markets, making it possible to provide more effective video content.

[0048] The video generation unit can analyze the user's past advertising campaign history and propose optimal updates based on the campaign history. For example, the video generation unit uses a generation AI to analyze the user's past advertising campaign history and propose optimal updates based on the campaign history. For example, elements of past successful campaigns are reflected in a new campaign. This allows for the generation of more effective video content by analyzing the user's past advertising campaign history and proposing optimal updates.

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

[0050] The video generation unit can analyze the user's gaze tracking data and automatically adjust the focus of the video based on the movement of the user's gaze. For example, if the user focuses their gaze on a specific area, an effect that emphasizes that area can be added. It can also automatically change the camera angle of the video based on the movement of the user's gaze. This allows for more effective video content to be generated by adjusting the focus of the video according to the user's gaze.

[0051] The video generation unit can analyze a user's past purchase history and suggest optimal video content based on that purchase history. For example, it can generate videos related to products previously purchased to attract the user's interest. It can also customize the video's scenario and characters based on the purchase history. This allows the system to utilize the user's purchase history to generate more effective video content.

[0052] The video generation unit can analyze a user's past viewing history and suggest the most suitable video genre based on the viewing history. For example, it can determine the genre of a new video based on the genre and style of videos previously viewed. It can also customize the video's scenario and characters based on the viewing history. This makes it possible to generate more effective video content by utilizing the user's viewing history.

[0053] The video generation unit can analyze a user's past search history and suggest optimal video themes based on that search history. For example, it can generate videos related to previously searched keywords to attract the user's interest. It can also customize the video's scenario and characters based on the search history. This allows the system to utilize the user's search history to generate more effective video content.

[0054] The video generation unit can analyze a user's past social media activity and suggest optimal video content based on their activity history. For example, it can generate videos related to previously posted content or posts that received positive responses, attracting the user's interest. It can also customize the video's scenario and characters based on social media activity. This allows the system to utilize the user's social media activity to generate more effective video content.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The video generation unit receives a prompt containing instructions from the user as input and generates video content based on that prompt. For example, if the generation AI receives the prompt, "Create a video introducing a new product," it automatically generates a video highlighting the product's features and benefits. The video generation unit then creates a video scenario, selects video materials, edits the video, adds effects, and outputs the completed video. Furthermore, the video generation unit can estimate the user's emotions in real time and automatically insert video scenes that evoke positive emotions. For example, the generation AI analyzes the user's facial expressions and vocal tone in real time and automatically inserts scenes that evoke positive emotions. The video generation unit can also analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the generation AI analyzes the user's past video data, identifies scenes that received a positive viewer response, and applies them to new videos. Step 2: The text generation unit receives a prompt containing instructions from the user as input and generates text content based on that prompt. For example, if the generation AI receives the prompt "Please write an article introducing a new product," it will automatically generate an article that details the product's features and benefits. The text generation unit then performs tasks such as structuring sentences, selecting keywords, and checking grammar, and outputs the completed text. Furthermore, the text generation unit can also use the generation AI to estimate the user's emotions in real time and automatically insert expressions that evoke positive emotions. For example, the generation AI can estimate the user's emotions in real time and automatically insert expressions that evoke positive emotions.

[0057] (Example 2) A content generation system for web advertising according to an embodiment of the present invention utilizes a generation AI to reduce the burden on clients and agencies. In this system, the generation AI receives prompts containing instructions from a user as input and generates video content and text content based on the prompts. This reduces the burden on clients and agencies, enabling the content generation system for web advertising to efficiently and quickly generate high-quality content.

[0058] A content generation system for web advertising according to an embodiment includes a video generation unit and a text generation unit. The video generation unit receives a prompt containing instructions from a user as input and generates video content based on the prompt. For example, the video generation unit uses a generation AI to automatically generate a video highlighting the product's features and benefits when the AI ​​receives a prompt such as, "Create a video introducing a new product." The video generation unit also uses the generation AI to create a video scenario, select video materials, edit the video, add effects, and output the completed video. The video generation unit can also estimate a user's emotions in real time and automatically insert video scenes that elicit positive emotions. For example, the generation AI analyzes a user's facial expressions and vocal tone in real time and automatically inserts scenes that elicit positive emotions. The video generation unit can also analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the generation AI analyzes the user's past video data, identifies scenes that received a positive viewer response, and applies them to new videos. The text generation unit receives a prompt containing instructions from a user as input and generates text content based on the prompt. For example, if the generation AI receives a prompt such as "Please write an article introducing a new product," the text generation unit automatically generates an article detailing the product's features and benefits. The text generation unit also performs tasks such as structuring sentences, selecting keywords, and checking grammar, and then outputs the completed text. Furthermore, the text generation unit can also use the generation AI to estimate user emotions in real time and automatically insert expressions that elicit positive emotions. For example, the generation AI can estimate user emotions in real time and automatically insert expressions that elicit positive emotions. This allows the web advertising content generation system to reduce the burden on clients and agencies and generate high-quality content efficiently and quickly.

[0059] The video generation unit can estimate the user's emotions in real time and automatically insert video scenes that elicit positive emotions. For example, when the generation AI generates a video, the video generation unit analyzes the user's facial expressions and vocal tone in real time and automatically inserts scenes that elicit positive emotions. For example, it can detect the moment the user smiles and emphasize that scene. This allows for the automatic insertion of video scenes that correspond to the user's emotions, making it possible to generate more effective video content.

[0060] The video generation unit can analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the video generation unit uses a generation AI to analyze the user's past video data, identify scenes that received a good response from viewers, and apply them to new videos. For example, it can learn the composition of successful advertising videos in the past and reflect a similar composition in new videos. This allows for the generation of more effective video content by analyzing past video data and learning and applying effective scene compositions.

[0061] The video generation unit can analyze the user's voice instructions in real time and edit the video or add effects based on the voice instructions. For example, the video generation unit uses a generation AI to analyze the user's voice instructions in real time and edit the video based on the instructions. For example, a scene can be automatically cut in response to an instruction such as "shorten this scene." This allows for more effective video content to be generated by editing the video or adding effects based on the user's voice instructions.

[0062] The text generation unit can estimate the user's emotions in real time and automatically insert expressions that elicit positive emotions. For example, the text generation unit uses a generation AI to estimate the user's emotions in real time and automatically insert expressions that elicit positive emotions. For example, if the user is happy, expressions that emphasize joy are used. This allows for the automatic insertion of expressions that correspond to the user's emotions, making it possible to generate more effective text content.

[0063] The text generation unit can analyze the user's past writing data and automatically learn and apply the most effective writing structure. For example, the text generation unit uses a generation AI to analyze the user's past writing data and automatically learn and apply the most effective writing structure. For example, the structure of writing that has received high marks in the past can be reflected in new writing. In this way, more effective text content can be generated by analyzing past writing data and learning and applying effective writing structures.

[0064] The video generation unit can automatically generate videos optimized for each cultural sphere, taking into account the visual preferences of different cultural spheres. For example, the generation AI in the video generation unit retrieves the visual preferences of different cultural spheres from a database and generates videos based on that. For example, anime-style designs are used for the Japanese market, while realistic images are used for the Western market. This allows for the provision of more effective video content by generating videos that take into account the visual preferences of different cultural spheres.

[0065] The video generation unit can analyze the user's past viewing history and propose the optimal video style based on the viewing history. For example, the video generation unit uses a generation AI to analyze the user's past viewing history and propose the optimal video style based on the viewing history. For example, the style of a new video is determined based on the genre and style of videos previously viewed. In this way, more effective video content can be generated by analyzing the user's past viewing history and proposing the optimal video style.

[0066] The video generation unit can estimate the user's emotions and automatically select and insert music and sound effects according to the emotions. For example, the video generation unit uses a generation AI to estimate the user's emotions in real time and automatically select and insert music according to the emotions. For example, if the user is moved, it inserts moving music, and if the user is excited, it inserts up-tempo music. In this way, more effective video content can be generated by automatically selecting and inserting music and sound effects according to the user's emotions.

[0067] The text generation unit takes into account the linguistic preferences of different cultural spheres and can automatically generate sentences optimized for each cultural sphere. For example, the generation AI in the text generation unit retrieves the linguistic preferences of different cultural spheres from a database and generates sentences based on that. For example, it uses a lot of honorific language for the Japanese market and casual expressions for the Western market. This allows for the generation of sentences that take into account the linguistic preferences of different cultural spheres, making it possible to provide more effective text content.

[0068] The text generation unit can analyze the user's past reading history and suggest the optimal writing style based on the reading history. For example, the text generation unit uses a generation AI to analyze the user's past reading history and suggest the optimal writing style based on the reading history. For example, it determines the style of a new writing style based on the genre and style of books read in the past. This allows for the generation of more effective text content by analyzing the user's past reading history and suggesting the optimal writing style.

[0069] The text generation unit can estimate the user's emotions and automatically select and insert keywords and phrases according to the emotions. For example, the text generation unit uses a generation AI to estimate the user's emotions in real time and automatically select and insert keywords according to the emotions. For example, if the user is happy, keywords such as "great" or "the best" are used. This allows for the automatic selection and insertion of keywords and phrases according to the user's emotions, thereby generating more effective text content.

[0070] The video generation unit can estimate the user's emotions in real time and automatically suggest customizations that elicit positive emotions. For example, the video generation unit uses a generation AI to estimate the user's emotions in real time and automatically suggest customizations that elicit positive emotions. For example, if the user is happy, bright colors and cheerful music will be suggested. This makes it possible to generate more effective video content by automatically suggesting customizations that correspond to the user's emotions.

[0071] The video generation unit can analyze the user's past customization history and automatically learn and apply the most effective customization pattern. For example, the video generation unit uses a generation AI to analyze the user's past customization history and automatically learn and apply the most effective customization pattern. For example, customization patterns that have received high ratings in the past can be reflected in new content. This allows the user's past customization history to be analyzed and the optimal customization pattern to be learned and applied, thereby generating more effective video content.

[0072] The video generation unit can analyze the user's voice instructions in real time and customize based on the voice instructions. For example, the video generation unit uses a generation AI to analyze the user's voice instructions in real time and customize based on the instructions. For example, it changes the color in response to an instruction such as "make this part red." This allows for customization based on the user's voice instructions, making it possible to generate more effective video content.

[0073] The video generation unit can automatically generate customized content optimized for each target demographic, taking into account the preferences of different target demographics. For example, the generation AI in the video generation unit retrieves the preferences of different target demographics from a database and customizes based on that. For example, a pop design could be used for younger demographics, while a more subdued design could be used for seniors. This allows for customization that takes into account the preferences of different target demographics, making it possible to provide more effective video content.

[0074] The video generation unit can analyze the user's past purchasing history and suggest optimal customization based on the purchasing history. For example, the generation AI in the video generation unit analyzes the user's past purchasing history and suggests optimal customization based on the purchasing history. For example, it suggests designs and colors related to products purchased in the past. In this way, by analyzing the user's past purchasing history and suggesting optimal customization, more effective video content can be generated.

[0075] The video generation unit can estimate the user's emotions and automatically select and suggest customization options according to the emotions. For example, the video generation unit uses a generation AI to estimate the user's emotions in real time and automatically select and suggest customization options according to the emotions. For example, if the user is happy, bright colors and cheerful music are suggested. This allows for the generation of more effective video content by automatically selecting and suggesting customization options according to the user's emotions.

[0076] The text generation unit can estimate the user's emotions in real time and automatically select translated expressions that elicit positive emotions. For example, the text generation unit uses a generation AI to estimate the user's emotions in real time and automatically select translated expressions that elicit positive emotions. For example, if the user is happy, translated expressions that emphasize joy are used. This allows for the automatic selection of translated expressions that correspond to the user's emotions, making it possible to generate more effective text content.

[0077] The text generation unit can analyze the user's past translation history and automatically learn and apply the most effective translation pattern. For example, the generation AI in the text generation unit analyzes the user's past translation history and automatically learns and applies the most effective translation pattern. For example, translation patterns that have received high ratings in the past are reflected in new translations. This allows the user's past translation history to be analyzed and the optimal translation pattern to be learned and applied, thereby generating more effective text content.

[0078] The text generation unit can analyze the user's voice instructions in real time and translate based on the voice instructions. For example, the generation AI in the text generation unit analyzes the user's voice instructions in real time and translates based on the instructions. For example, in response to an instruction such as "Translate this part into French," it translates into French. This makes it possible to generate more effective text content by translating based on the user's voice instructions.

[0079] The text generation unit takes into account the linguistic preferences of different cultural spheres and can automatically generate translations optimized for each cultural sphere. For example, the generation AI in the text generation unit retrieves the linguistic preferences of different cultural spheres from a database and performs translation based on that. For example, it may use a lot of honorific language for the Japanese market and casual expressions for the Western market. This allows for translations that take into account the linguistic preferences of different cultural spheres, making it possible to provide more effective text content.

[0080] The text generation unit can analyze the user's past language learning history and suggest the optimal translation based on the learning history. For example, the generation AI in the text generation unit analyzes the user's past language learning history and suggests the optimal translation based on the learning history. For example, it selects appropriate translation expressions based on the level and style of the language the user has learned. This allows for the generation of more effective text content by analyzing the user's past language learning history and suggesting the optimal translation.

[0081] The text generation unit can estimate the user's emotions and automatically select and insert translated expressions according to the emotions. For example, the generation AI in the text generation unit estimates the user's emotions in real time and automatically selects and inserts translated expressions according to the emotions. For example, if the user is happy, a translated expression that emphasizes joy is used. This allows for the automatic selection and insertion of translated expressions according to the user's emotions, thereby generating more effective text content.

[0082] The video generation unit can estimate the user's emotions in real time and automatically suggest updates that will elicit positive emotions. For example, the video generation unit uses a generation AI to estimate the user's emotions in real time and automatically suggest updates that will elicit positive emotions. For example, if the user is happy, it will suggest bright colors and cheerful music. This makes it possible to generate more effective video content by automatically suggesting updates that correspond to the user's emotions.

[0083] The video generation unit can analyze the user's past update history and automatically learn and apply the most effective update pattern. For example, the video generation unit uses a generation AI to analyze the user's past update history and automatically learn and apply the most effective update pattern. For example, update patterns that have received high ratings in the past are reflected in new content. This allows the user's past update history to be analyzed and the optimal update pattern to be learned and applied, thereby generating more effective video content.

[0084] The video generation unit can analyze the user's voice instructions in real time and update based on the voice instructions. For example, the generation AI in the video generation unit analyzes the user's voice instructions in real time and updates based on the instructions. For example, it changes the color in response to an instruction such as "make this part red." This makes it possible to generate more effective video content by updating based on the user's voice instructions.

[0085] The video generation unit can automatically generate updates optimized for each market, taking into account trends in different markets. For example, the generation AI retrieves trends in different markets from a database and updates based on them. For example, the latest anime trends are reflected in the Japanese market, and the latest movie trends in the European and American markets. This allows for updates that take into account trends in different markets, making it possible to provide more effective video content.

[0086] The video generation unit can analyze the user's past advertising campaign history and propose optimal updates based on the campaign history. For example, the video generation unit uses a generation AI to analyze the user's past advertising campaign history and propose optimal updates based on the campaign history. For example, elements of past successful campaigns are reflected in a new campaign. This allows for the generation of more effective video content by analyzing the user's past advertising campaign history and proposing optimal updates.

[0087] The video generation unit can estimate the user's emotions and automatically select and insert update content according to the emotions. For example, the generation AI of the video generation unit estimates the user's emotions in real time and automatically selects and inserts update content according to the emotions. For example, if the user is happy, bright colors and cheerful music are suggested. This allows for the automatic selection and insertion of update content according to the user's emotions, thereby generating more effective video content.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The video generation unit can analyze the user's gaze tracking data and automatically adjust the focus of the video based on the movement of the user's gaze. For example, if the user focuses their gaze on a specific area, an effect that emphasizes that area can be added. It can also automatically change the camera angle of the video based on the movement of the user's gaze. This allows for more effective video content to be generated by adjusting the focus of the video according to the user's gaze.

[0090] The image generation unit can estimate the user's emotions and automatically adjust the color tone of the image according to the emotion. For example, if the user is relaxed, warm colors are used, and if the user is tense, cool colors are used. The image generation unit can also adjust the brightness and contrast of the image according to the emotion. This allows for more effective image content to be generated by adjusting the color tone according to the user's emotions.

[0091] The video generation unit can analyze a user's past purchase history and suggest optimal video content based on that purchase history. For example, it can generate videos related to products previously purchased to attract the user's interest. It can also customize the video's scenario and characters based on the purchase history. This allows the system to utilize the user's purchase history to generate more effective video content.

[0092] The video generation unit can estimate the user's emotions and automatically adjust the tempo of the video according to the emotions. For example, if the user is excited, it generates a video with a fast tempo, and if the user is relaxed, it generates a video with a slow tempo. It can also adjust the length of video cuts and the speed at which scenes change according to the emotions. In this way, more effective video content can be generated by adjusting the tempo according to the user's emotions.

[0093] The video generation unit can analyze a user's past viewing history and suggest the most suitable video genre based on the viewing history. For example, it can determine the genre of a new video based on the genre and style of videos previously viewed. It can also customize the video's scenario and characters based on the viewing history. This makes it possible to generate more effective video content by utilizing the user's viewing history.

[0094] The video generation unit can estimate the user's emotions and automatically adjust the volume of the video according to the emotions. For example, if the user is relaxed, the volume can be lowered, and if the user is excited, the volume can be raised. It can also select the type of music or sound effects according to the emotions. In this way, more effective video content can be generated by adjusting the volume according to the user's emotions.

[0095] The video generation unit can analyze a user's past search history and suggest optimal video themes based on that search history. For example, it can generate videos related to previously searched keywords to attract the user's interest. It can also customize the video's scenario and characters based on the search history. This allows the system to utilize the user's search history to generate more effective video content.

[0096] The video generation unit can estimate the user's emotions and automatically select and apply a video filter according to the emotion. For example, if the user is happy, a bright filter is applied, and if the user is sad, a dark filter is applied. The strength of the filter can also be adjusted according to the emotion. This allows for the generation of more effective video content by selecting a filter according to the user's emotions.

[0097] The video generation unit can analyze a user's past social media activity and suggest optimal video content based on their activity history. For example, it can generate videos related to previously posted content or posts that received positive responses, attracting the user's interest. It can also customize the video's scenario and characters based on social media activity. This allows the system to utilize the user's social media activity to generate more effective video content.

[0098] The video generation unit can estimate the user's emotions and automatically select and apply video effects according to the emotions. For example, if the user is excited, a dynamic effect can be applied, and if the user is relaxed, a gentle effect can be applied. The intensity of the effect can also be adjusted according to the emotion. This allows for the generation of more effective video content by selecting effects according to the user's emotions.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The video generation unit receives a prompt containing instructions from the user as input and generates video content based on that prompt. For example, if the generation AI receives the prompt, "Create a video introducing a new product," it automatically generates a video highlighting the product's features and benefits. The video generation unit then creates a video scenario, selects video materials, edits the video, adds effects, and outputs the completed video. Furthermore, the video generation unit can estimate the user's emotions in real time and automatically insert video scenes that evoke positive emotions. For example, the generation AI analyzes the user's facial expressions and vocal tone in real time and automatically inserts scenes that evoke positive emotions. The video generation unit can also analyze past video data provided by the user and automatically learn and apply the most effective scene composition. For example, the generation AI analyzes the user's past video data, identifies scenes that received a positive viewer response, and applies them to new videos. Step 2: The text generation unit receives a prompt containing instructions from the user as input and generates text content based on that prompt. For example, if the generation AI receives the prompt "Please write an article introducing a new product," it will automatically generate an article that details the product's features and benefits. The text generation unit then performs tasks such as structuring sentences, selecting keywords, and checking grammar, and outputs the completed text. Furthermore, the text generation unit can also use the generation AI to estimate the user's emotions in real time and automatically insert expressions that evoke positive emotions. For example, the generation AI can estimate the user's emotions in real time and automatically insert expressions that evoke positive emotions.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

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

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

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

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

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

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

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

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

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

[0159] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

[0161] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

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

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

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

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

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

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

[0168] 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 video generation unit that receives a prompt including an instruction from a user as an input and generates video content based on the prompt; a text generation unit that receives as input a prompt including an instruction from a user and generates text content based on the prompt; A system characterized by:

2. The image generation unit The system estimates the user's emotions in real time and automatically inserts video scenes that elicit positive emotions.

2. The system of claim 1.

3. The text generation unit The system estimates the user's emotions in real time and automatically inserts expressions that elicit positive emotions.

2. The system of claim 1.

4. The image generation unit The user's emotions are estimated, and music and sound effects corresponding to the emotions are automatically selected and inserted.

2. The system of claim 1.

5. The text generation unit The system estimates the user's emotions in real time and automatically selects translations that elicit positive emotions.

2. The system of claim 1.

Images