System

Abstract

An object of the system according to the embodiment is to monetize image generation using generation AI.SOLUTION: A system includes a generation AI, an image generation unit, a disclosure unit, and an advertisement insertion unit. The generation AI generates an image based on a word input from a user. The image generation unit puts together the images generated by the generation AI. The disclosure unit discloses the images collected by the image generation unit on a website. The advertisement placement unit places an advertisement on a website.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not sufficiently monetized image generation using generative AI, and there is room for improvement.

[0005] The system according to the embodiment aims to monetize image generation using generative AI. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an image generation unit, a publishing unit, and an advertisement posting unit. The generation AI generates an image based on words input by a user. The image generation unit compiles the images generated by the generation AI. The publishing unit publishes the images compiled by the image generation unit on a website. The advertisement posting unit posts advertisements on the website. [Effects of the Invention]

[0007] The system according to the embodiment can monetize image generation using generative AI. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The image generation system according to an embodiment of the present invention is a system that uses generative AI to combine various words to generate images, which are then published on a website as free materials. Furthermore, the system aims to generate revenue by displaying advertisements on the website. This allows the image generation system to generate images using generative AI, publish them for free, and generate revenue through advertisements.

[0029] An image generation system according to an embodiment includes a generation AI, an image generation unit, a publishing unit, and an advertising unit. The generation AI generates an image based on words input by a user. For example, when a user inputs words such as "cat," "space," or "pink," the generation AI generates an image based on these words. The generation AI can generate images using a text generation AI (e.g., LLM) or a multimodal generation AI. The image generation unit compiles the images generated by the generation AI. For example, it organizes the generated images by category so that users can easily find the desired image. The publishing unit publishes the images compiled by the image generation unit on a website. For example, it is designed to allow users to download the generated images for free. The advertising unit posts advertisements on the website. For example, it posts banner ads or text ads and earns revenue when users click on the ads. This enables the image generation system to generate images using the generation AI, publish them for free, and generate revenue through advertising.

[0030] Generative AI can refer to a user's past generation history and generate images that match the user's preferences. For example, generative AI saves a user's past generation history and generates images that match the user's preferences based on that history. For example, it analyzes the style and theme of images generated in the past and generates similar images. Generative AI also refers to a user's past generation history and suggests images that match the user's preferences. For example, it generates images based on the user's preferred colors and composition. Generative AI also learns a user's past generation history and generates images that match the user's preferences based on that data. For example, it generates images based on words and themes that the user frequently uses. This makes it possible to generate images that match the user's preferences.

[0031] The generation AI has a function that allows users to select multiple different styles, allowing them to generate images by specifying the style. For example, the generation AI can add a function that allows users to select multiple styles, allowing them to generate images by specifying the style. For example, styles such as painting, photography, and anime can be selected. The generation AI can also generate images that match the style specified by the user. For example, if the user selects "painting style," the generation AI will generate a painting-style image. The generation AI can also add a style selection function that allows users to generate images by specifying the style. For example, if the user selects "anime style," the generation AI will generate an anime-style image. This allows users to generate images by specifying the style.

[0032] The generative AI is equipped with a voice input function, allowing users to generate images by inputting words by voice. For example, the generative AI can add a voice input function, allowing users to generate images by inputting words by voice. For example, if a user inputs "cat," "space," or "pink" by voice, the generative AI will generate an image based on those words. The generative AI can also use voice recognition technology to convert the words input by the user into text and generate an image based on that text. For example, if a user inputs "mountain," "river," or "blue" by voice, the generative AI will generate an image based on those words. The generative AI can also add a voice input function, allowing users to generate images by inputting words by voice. For example, if a user inputs "flower," "wind," or "green," the generative AI will generate an image based on those words. This allows users to generate images by inputting words by voice.

[0033] The generative AI can automatically generate text information related to words entered by the user and provide it along with images. For example, the generative AI automatically generates poems and quotes related to words entered by the user. For example, it generates poems and quotes related to words such as "love," "friendship," and "hope." The generative AI can also automatically generate related text information based on words entered by the user and provide it along with images. For example, it generates poems and quotes related to words such as "nature," "scenery," and "beauty." The generative AI can also add a text generation function to automatically generate text information related to words entered by the user. For example, it generates poems and quotes related to words such as "dream," "adventure," and "freedom." This allows it to provide text information related to words entered by the user along with images.

[0034] The publishing unit has a function that allows users to rate and comment on the generated images, and can display popular images in a ranking format. The publishing unit, for example, adds a function that allows users to rate and comment on the generated images. For example, a star rating and comment section is provided so that users can post their opinions on the images. The publishing unit also builds a system that displays popular images in a ranking format based on user ratings and comments. For example, highly rated images are displayed at the top, allowing users to easily find popular images. The publishing unit also adds a function that allows users to rate and comment on the generated images, and generates rankings based on that data. For example, popular images are displayed in a ranking format based on the number of comments and rating scores. This makes it possible to display popular images in a ranking format.

[0035] The publishing unit may be equipped with an algorithm that automatically tags the generated images, making them easier for users to search. The publishing unit, for example, develops an algorithm that automatically tags the generated images. For example, it uses image analysis technology to automatically generate tags based on the content of the images. The publishing unit also builds a system that automatically tags the generated images, making them easier for users to search. For example, it automatically generates tags based on the features or theme of the images. The publishing unit also develops an algorithm that automatically tags the generated images, making them easier for users to find the images they are looking for. For example, it automatically generates tags based on the color or composition of the image. This allows images to be automatically tagged, making them easier for users to search.

[0036] The publishing unit may have a function that allows users to customize the generated image. The publishing unit provides, for example, a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, an image editing tool may be incorporated into the website, allowing users to freely edit images. The publishing unit also provides a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, a function for adjusting the brightness or contrast of an image may be added. The publishing unit also provides a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, a function for adding effects to an image may be provided. This allows users to customize the generated image.

[0037] The publishing unit can provide the generated images in formats optimized for different devices. For example, the publishing unit builds a system that provides the generated images in formats optimized for different devices. For example, the publishing unit provides images in resolutions and file formats appropriate for smartphones, tablets, and PCs. The publishing unit also provides the images in optimized formats so that users can view the generated images on different devices. For example, the publishing unit automatically adjusts the images to fit the screen size of the device. The publishing unit also provides the generated images in formats optimized for different devices so that users can comfortably view the images on any device. For example, the publishing unit employs a responsive design to change the display format of the image depending on the device. This makes it possible to provide images in formats optimized for different devices.

[0038] The publishing unit may have a function that allows users to easily share generated images on social media. For example, the publishing unit adds a function that allows users to easily share generated images on social media. For example, a share button for the image may be installed to enable sharing with one click. The publishing unit also provides a function that allows users to share generated images on social media and automatically generates a link for the image when shared. For example, this supports platforms such as Facebook and Twitter. The publishing unit also adds a function that allows users to share generated images on social media and displays a preview of the image when shared. For example, when a share button is clicked, sharing options are displayed along with a preview of the image. This allows users to easily share generated images on social media.

[0039] The ad placement unit may be equipped with an algorithm that optimizes the display position and timing of ads based on user behavior data. The ad placement unit, for example, analyzes user behavior data and develops an algorithm that optimizes the display position and timing of ads. For example, it identifies the timing when a user is most likely to respond to an ad and displays the ad at that timing. The ad placement unit also builds a system that optimizes the display position of ads based on the user's browsing history and click data. For example, it places ads on pages or sections that the user frequently visits. The ad placement unit also analyzes user behavior data in real time and develops an algorithm that dynamically adjusts the display timing of ads. For example, it displays an ad immediately after the user performs a specific action. This makes it possible to optimize the display position and timing of ads based on the user behavior data.

[0040] The advertisement publishing unit may include a system for displaying personalized advertisements to a user demographic targeted by an advertiser. The advertisement publishing unit, for example, builds a system for displaying personalized advertisements to a user demographic targeted by an advertiser. For example, the advertisements are customized based on the user's age, gender, and interests. The advertisement publishing unit also develops an algorithm for displaying personalized advertisements based on user behavior data and profile information. For example, advertisements related to products and services that the user has viewed in the past are displayed. The advertisement publishing unit also builds a system for displaying personalized advertisements to a target user demographic specified by the advertiser. For example, the advertisements are customized to suit a specific region or time period. This makes it possible to display personalized advertisements to the target user demographic.

[0041] The ad placement unit can be equipped with a function to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low. The ad placement unit, for example, builds a system that monitors the effectiveness of ads in real time and automatically changes the ad content if the effectiveness is low. For example, it adjusts the ad content based on click rates and conversion rates. The ad placement unit also analyzes ad performance data in real time and develops an algorithm that automatically changes the ad content if the effectiveness is low. For example, it dynamically changes the ad text and images. The ad placement unit also adds a function to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low. For example, it adjusts the frequency and position of ad display depending on user response. This makes it possible to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low.

[0042] The ad placement unit may include a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. The ad placement unit, for example, builds a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. For example, the ad placement unit creates product images and brand images using the generation AI. The ad placement unit also uses the generation AI to generate customized images related to their products or services and uses the images as advertisements. For example, it generates advertising images tailored to the season or event. The ad placement unit also develops a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. For example, it generates personalized advertising images tailored to the user's interests. This allows an advertiser to use the generation AI to generate images related to their products or services and use the images as advertisements.

[0043] The advertisement publishing unit may include a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. The advertisement publishing unit, for example, builds a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. For example, it displays advertisements in the form of mini-games or quizzes. The advertisement publishing unit also uses interactive advertisements to increase engagement by having users participate in the advertisements. For example, it introduces a system that allows users to receive rewards by answering quizzes within the advertisements. The advertisement publishing unit also develops a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. For example, it introduces a system that allows users to receive discount coupons by clearing mini-games within the advertisements. This allows advertisers to provide interactive advertisements on their websites and increase user engagement.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The image generation system may further include a backup unit that automatically backs up images generated by a user and stores them in cloud storage. For example, images generated by a user may be automatically uploaded to cloud storage periodically. It may also be possible to enable the image to be restored from the cloud storage even if the user accidentally deletes it. Furthermore, it may also be possible to enable the user to view images stored in the cloud storage even when accessing from different devices. In this way, images generated by a user can be safely backed up and stored in cloud storage.

[0046] The image generation system may further include a print service unit that automatically transmits user-generated images to a print service and provides them as physical photographs. For example, a user can select a user-generated image and transmit it to the print service to receive it as a photo print or photo book. A user can also print images for a specific event or anniversary. Furthermore, a user can customize a user-generated image and print it as an original calendar or postcard. This allows a user-generated image to be provided as a physical photograph.

[0047] The image generation system may further include a quality evaluation unit that automatically analyzes the image generated by the user and evaluates the quality of the image. For example, the image resolution and color balance are evaluated and feedback is provided to the user. The quality may also be evaluated based on the image composition or theme. Furthermore, advice and suggestions for improving the quality of the image generated by the user may be provided. In this way, the quality of the image generated by the user can be automatically evaluated and feedback provided.

[0048] The image generation system may further include a contest entry unit that automatically enters images generated by users into contests. For example, the system may select images generated by users and automatically enter them in contests that are held periodically. It may also automatically select and enter images that match the theme or conditions of a contest. Furthermore, the system may also include a function that notifies users if they win a contest. This allows images generated by users to be automatically entered into contests.

[0049] The image generation system may further include a listing unit that automatically lists user-generated images on a marketplace. For example, the system may select a user-generated image and automatically list it on an online marketplace. It may also be possible to automatically generate pricing and descriptions when listing an image. Furthermore, the system may also include a function that notifies the user when an image generated by the user is sold. This allows the user-generated image to be automatically listed on a marketplace.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The generative AI generates an image based on the words entered by the user. For example, if the user enters words such as "cat," "space," or "pink," the generative AI generates an image based on these words. The generative AI can generate images using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The image generation unit organizes the images generated by the generation AI, for example, by organizing the generated images by category, making it easier for users to find the desired image. Step 3: The publishing unit publishes the images compiled by the image generating unit on a website, for example, designed to allow users to download the generated images for free. Step 4: The advertising department places advertisements on the website, such as banner ads and text ads, and earns revenue when users click on the ads.

[0052] (Example 2) The image generation system according to an embodiment of the present invention is a system that uses generative AI to combine various words to generate images, which are then published on a website as free materials. Furthermore, the system aims to generate revenue by displaying advertisements on the website. This allows the image generation system to generate images using generative AI, publish them for free, and generate revenue through advertisements.

[0053] An image generation system according to an embodiment includes a generation AI, an image generation unit, a publishing unit, and an advertising unit. The generation AI generates an image based on words input by a user. For example, when a user inputs words such as "cat," "space," or "pink," the generation AI generates an image based on these words. The generation AI can generate images using a text generation AI (e.g., LLM) or a multimodal generation AI. The image generation unit compiles the images generated by the generation AI. For example, it organizes the generated images by category so that users can easily find the desired image. The publishing unit publishes the images compiled by the image generation unit on a website. For example, it is designed to allow users to download the generated images for free. The advertising unit posts advertisements on the website. For example, it posts banner ads or text ads and earns revenue when users click on the ads. This enables the image generation system to generate images using the generation AI, publish them for free, and generate revenue through advertising.

[0054] Generative AI can infer emotions based on words entered by the user and generate images that match those emotions. For example, generative AI infers emotions from words entered by the user. For example, it infers emotions such as "happiness," "fun," or "sadness," and generates images with colors and compositions that match those emotions. Generative AI can also infer emotions based on words entered by the user and generate images with themes and styles that match those emotions. For example, it generates images that correspond to emotions such as "love," "friendship," or "loneliness." Generative AI can also use its emotion inference function to analyze emotions in response to words entered by the user in real time and generate images that match those emotions. For example, it can generate images that correspond to emotions such as "surprise," "fear," or "joy." This allows it to generate images that match the user's emotions.

[0055] Generative AI can refer to a user's past generation history and generate images that match the user's preferences. For example, generative AI saves a user's past generation history and generates images that match the user's preferences based on that history. For example, it analyzes the style and theme of images generated in the past and generates similar images. Generative AI also refers to a user's past generation history and suggests images that match the user's preferences. For example, it generates images based on the user's preferred colors and composition. Generative AI also learns a user's past generation history and generates images that match the user's preferences based on that data. For example, it generates images based on words and themes that the user frequently uses. This makes it possible to generate images that match the user's preferences.

[0056] The generation AI has a function that allows users to select multiple different styles, allowing them to generate images by specifying the style. For example, the generation AI can add a function that allows users to select multiple styles, allowing them to generate images by specifying the style. For example, styles such as painting, photography, and anime can be selected. The generation AI can also generate images that match the style specified by the user. For example, if the user selects "painting style," the generation AI will generate a painting-style image. The generation AI can also add a style selection function that allows users to generate images by specifying the style. For example, if the user selects "anime style," the generation AI will generate an anime-style image. This allows users to generate images by specifying the style.

[0057] The generative AI is equipped with a voice input function, allowing users to generate images by inputting words by voice. For example, the generative AI can add a voice input function, allowing users to generate images by inputting words by voice. For example, if a user inputs "cat," "space," or "pink" by voice, the generative AI will generate an image based on those words. The generative AI can also use voice recognition technology to convert the words input by the user into text and generate an image based on that text. For example, if a user inputs "mountain," "river," or "blue" by voice, the generative AI will generate an image based on those words. The generative AI can also add a voice input function, allowing users to generate images by inputting words by voice. For example, if a user inputs "flower," "wind," or "green," the generative AI will generate an image based on those words. This allows users to generate images by inputting words by voice.

[0058] The generative AI can automatically generate text information related to words entered by the user and provide it along with images. For example, the generative AI automatically generates poems and quotes related to words entered by the user. For example, it generates poems and quotes related to words such as "love," "friendship," and "hope." The generative AI can also automatically generate related text information based on words entered by the user and provide it along with images. For example, it generates poems and quotes related to words such as "nature," "scenery," and "beauty." The generative AI can also add a text generation function to automatically generate text information related to words entered by the user. For example, it generates poems and quotes related to words such as "dream," "adventure," and "freedom." This allows it to provide text information related to words entered by the user along with images.

[0059] The generative AI can use an emotion estimation function to infer emotions based on words entered by the user and simultaneously generate music that matches those emotions. For example, the generative AI can add an emotion estimation function to infer emotions from words entered by the user and generate music that matches those emotions. For example, it can generate music that corresponds to emotions such as "happiness," "fun," and "sad." The generative AI can also infer emotions based on words entered by the user and simultaneously generate music that matches those emotions. For example, it can generate music that corresponds to emotions such as "love," "friendship," and "loneliness." The generative AI can also use the emotion estimation function to analyze emotions in response to words entered by the user in real time and generate music that matches those emotions. For example, it can generate music that corresponds to emotions such as "surprise," "fear," and "joy." This allows it to simultaneously generate music that matches the user's emotions.

[0060] The publishing unit has a function that allows users to rate and comment on the generated images, and can display popular images in a ranking format. The publishing unit, for example, adds a function that allows users to rate and comment on the generated images. For example, a star rating and comment section is provided so that users can post their opinions on the images. The publishing unit also builds a system that displays popular images in a ranking format based on user ratings and comments. For example, highly rated images are displayed at the top, allowing users to easily find popular images. The publishing unit also adds a function that allows users to rate and comment on the generated images, and generates rankings based on that data. For example, popular images are displayed in a ranking format based on the number of comments and rating scores. This makes it possible to display popular images in a ranking format.

[0061] The publishing unit may be equipped with an algorithm that automatically tags the generated images, making them easier for users to search. The publishing unit, for example, develops an algorithm that automatically tags the generated images. For example, it uses image analysis technology to automatically generate tags based on the content of the images. The publishing unit also builds a system that automatically tags the generated images, making them easier for users to search. For example, it automatically generates tags based on the features or theme of the images. The publishing unit also develops an algorithm that automatically tags the generated images, making them easier for users to find the images they are looking for. For example, it automatically generates tags based on the color or composition of the image. This allows images to be automatically tagged, making them easier for users to search.

[0062] The publishing unit may have a function that allows users to customize the generated image. The publishing unit provides, for example, a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, an image editing tool may be incorporated into the website, allowing users to freely edit images. The publishing unit also provides a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, a function for adjusting the brightness or contrast of an image may be added. The publishing unit also provides a function that allows users to customize the generated image by adjusting the color or applying a filter. For example, a function for adding effects to an image may be provided. This allows users to customize the generated image.

[0063] The publishing unit can provide the generated images in formats optimized for different devices. For example, the publishing unit builds a system that provides the generated images in formats optimized for different devices. For example, the publishing unit provides images in resolutions and file formats appropriate for smartphones, tablets, and PCs. The publishing unit also provides the images in optimized formats so that users can view the generated images on different devices. For example, the publishing unit automatically adjusts the images to fit the screen size of the device. The publishing unit also provides the generated images in formats optimized for different devices so that users can comfortably view the images on any device. For example, the publishing unit employs a responsive design to change the display format of the image depending on the device. This makes it possible to provide images in formats optimized for different devices.

[0064] The publishing unit may have a function that allows users to easily share generated images on social media. For example, the publishing unit adds a function that allows users to easily share generated images on social media. For example, a share button for the image may be installed to enable sharing with one click. The publishing unit also provides a function that allows users to share generated images on social media and automatically generates a link for the image when shared. For example, this supports platforms such as Facebook and Twitter. The publishing unit also adds a function that allows users to share generated images on social media and displays a preview of the image when shared. For example, when a share button is clicked, sharing options are displayed along with a preview of the image. This allows users to easily share generated images on social media.

[0065] The publishing unit can use the emotion estimation function to collect emotional responses to images generated by users and optimize the display order of the images based on the data. The publishing unit, for example, uses the emotion estimation function to collect emotional responses to images generated by users and builds a system that optimizes the display order of images based on the data. For example, images with a high number of positive emotional responses are displayed at the top. The publishing unit also develops an algorithm that optimizes the display order of generated images based on the user's emotional response data. For example, images with a high emotional score are preferentially displayed. The publishing unit also uses the emotion estimation function to collect emotional responses to images generated by users and optimizes the display order of images based on the data. For example, the display order of images is dynamically changed depending on the user's emotional response. This makes it possible to optimize the display order of images based on the user's emotional response.

[0066] The ad placement unit may be equipped with an algorithm that optimizes the display position and timing of ads based on user behavior data. The ad placement unit, for example, analyzes user behavior data and develops an algorithm that optimizes the display position and timing of ads. For example, it identifies the timing when a user is most likely to respond to an ad and displays the ad at that timing. The ad placement unit also builds a system that optimizes the display position of ads based on the user's browsing history and click data. For example, it places ads on pages or sections that the user frequently visits. The ad placement unit also analyzes user behavior data in real time and develops an algorithm that dynamically adjusts the display timing of ads. For example, it displays an ad immediately after the user performs a specific action. This makes it possible to optimize the display position and timing of ads based on the user behavior data.

[0067] The advertisement publishing unit may include a system for displaying personalized advertisements to a user demographic targeted by an advertiser. The advertisement publishing unit, for example, builds a system for displaying personalized advertisements to a user demographic targeted by an advertiser. For example, the advertisements are customized based on the user's age, gender, and interests. The advertisement publishing unit also develops an algorithm for displaying personalized advertisements based on user behavior data and profile information. For example, advertisements related to products and services that the user has viewed in the past are displayed. The advertisement publishing unit also builds a system for displaying personalized advertisements to a target user demographic specified by the advertiser. For example, the advertisements are customized to suit a specific region or time period. This makes it possible to display personalized advertisements to the target user demographic.

[0068] The ad placement unit can be equipped with a function to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low. The ad placement unit, for example, builds a system that monitors the effectiveness of ads in real time and automatically changes the ad content if the effectiveness is low. For example, it adjusts the ad content based on click rates and conversion rates. The ad placement unit also analyzes ad performance data in real time and develops an algorithm that automatically changes the ad content if the effectiveness is low. For example, it dynamically changes the ad text and images. The ad placement unit also adds a function to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low. For example, it adjusts the frequency and position of ad display depending on user response. This makes it possible to monitor the effectiveness of ads in real time and automatically change the ad content if the effectiveness is low.

[0069] The ad placement unit may include a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. The ad placement unit, for example, builds a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. For example, the ad placement unit creates product images and brand images using the generation AI. The ad placement unit also uses the generation AI to generate customized images related to their products or services and uses the images as advertisements. For example, it generates advertising images tailored to the season or event. The ad placement unit also develops a system in which an advertiser uses a generation AI to generate images related to their products or services and uses the images as advertisements. For example, it generates personalized advertising images tailored to the user's interests. This allows an advertiser to use the generation AI to generate images related to their products or services and use the images as advertisements.

[0070] The advertisement publishing unit may include a system in which an advertiser uses an emotion estimation function to display advertisements tailored to a user's emotions. The advertisement publishing unit, for example, introduces a system in which an advertiser uses the emotion estimation function to display advertisements tailored to a user's emotions. For example, when a user has positive emotions, advertisements with enjoyable content are displayed. The advertisement publishing unit also uses the emotion estimation function to build a system that customizes advertisement content based on a user's emotions. For example, when a user is relaxing, advertisements for products with a relaxing effect are displayed. The advertisement publishing unit also develops a system in which an advertiser uses the emotion estimation function to display advertisements tailored to a user's emotions. For example, when a user is feeling stressed, an advertisement for a product with a refreshing effect is displayed. This allows the advertiser to use the emotion estimation function to display advertisements tailored to a user's emotions.

[0071] The advertisement publishing unit may include a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. The advertisement publishing unit, for example, builds a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. For example, it displays advertisements in the form of mini-games or quizzes. The advertisement publishing unit also uses interactive advertisements to increase engagement by having users participate in the advertisements. For example, it introduces a system that allows users to receive rewards by answering quizzes within the advertisements. The advertisement publishing unit also develops a system that allows advertisers to provide interactive advertisements on their websites and increase user engagement. For example, it introduces a system that allows users to receive discount coupons by clearing mini-games within the advertisements. This allows advertisers to provide interactive advertisements on their websites and increase user engagement.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The image generation system can further include a suggestion unit that estimates the user's emotions and suggests image themes and styles based on those emotions. For example, if the user is feeling "happy," images with bright colors and positive themes can be suggested. If the user is seeking "relaxation," images of natural landscapes and calm colors can be suggested. Furthermore, if the user is feeling "creative," images of abstract art and unique designs can be suggested. In this way, images that match the user's emotions can be suggested.

[0074] The image generation system can further include an advertisement customization unit that estimates the user's emotions and customizes the content of advertisements based on those emotions. For example, if the user is feeling "happy," advertisements containing positive messages can be displayed. If the user is feeling "stressed," advertisements for products with a relaxing effect can be displayed. Furthermore, if the user is "excited," advertisements with energetic content can be displayed. In this way, advertisements that match the user's emotions can be displayed.

[0075] The image generation system can further include a filter application unit that estimates the user's emotion and automatically applies filters and effects to the image based on that emotion. For example, if the user feels "sad," a monochrome or sepia filter can be applied. If the user feels "happy," a vivid color or pop effect can be applied. Furthermore, if the user wants to "relax," a soft focus or pastel filter can be applied. This allows filters and effects that match the user's emotion to be automatically applied.

[0076] The image generation system can further include a layout adjustment unit that estimates the user's emotions and adjusts the layout and design of the image based on those emotions. For example, if the user is "excited," a dynamic layout and bold design can be suggested. If the user is seeking "calmness," a simple and balanced layout can be suggested. Furthermore, if the user is "in a creative mood," a unique and artistic layout can be suggested. This allows the layout and design to be adjusted to match the user's emotions.

[0077] The image generation system can further include a caption generation unit that estimates the user's emotions and automatically generates captions and explanatory text for images based on those emotions. For example, if the user is feeling "happy," a caption containing a positive message can be generated. If the user is feeling "sad," a caption that reflects that emotion can also be generated. Furthermore, if the user is "excited," a caption containing an energetic message can also be generated. This makes it possible to automatically generate captions and explanatory text that match the user's emotions.

[0078] The image generation system may further include a backup unit that automatically backs up images generated by a user and stores them in cloud storage. For example, images generated by a user may be automatically uploaded to cloud storage periodically. It may also be possible to enable the image to be restored from the cloud storage even if the user accidentally deletes it. Furthermore, it may also be possible to enable the user to view images stored in the cloud storage even when accessing from different devices. In this way, images generated by a user can be safely backed up and stored in cloud storage.

[0079] The image generation system may further include a print service unit that automatically transmits user-generated images to a print service and provides them as physical photographs. For example, a user can select a user-generated image and transmit it to the print service to receive it as a photo print or photo book. A user can also print images for a specific event or anniversary. Furthermore, a user can customize a user-generated image and print it as an original calendar or postcard. This allows a user-generated image to be provided as a physical photograph.

[0080] The image generation system may further include a quality evaluation unit that automatically analyzes the image generated by the user and evaluates the quality of the image. For example, the image resolution and color balance are evaluated and feedback is provided to the user. The quality may also be evaluated based on the image composition or theme. Furthermore, advice and suggestions for improving the quality of the image generated by the user may be provided. In this way, the quality of the image generated by the user can be automatically evaluated and feedback provided.

[0081] The image generation system may further include a contest entry unit that automatically enters images generated by users into contests. For example, the system may select images generated by users and automatically enter them in contests that are held periodically. It may also automatically select and enter images that match the theme or conditions of a contest. Furthermore, the system may also include a function that notifies users if they win a contest. This allows images generated by users to be automatically entered into contests.

[0082] The image generation system may further include a listing unit that automatically lists user-generated images on a marketplace. For example, the system may select a user-generated image and automatically list it on an online marketplace. It may also be possible to automatically generate pricing and descriptions when listing an image. Furthermore, the system may also include a function that notifies the user when an image generated by the user is sold. This allows the user-generated image to be automatically listed on a marketplace.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The generative AI generates an image based on the words entered by the user. For example, if the user enters words such as "cat," "space," or "pink," the generative AI generates an image based on these words. The generative AI can generate images using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The image generation unit organizes the images generated by the generation AI, for example, by organizing the generated images by category, making it easier for users to find the desired image. Step 3: The publishing unit publishes the images compiled by the image generating unit on a website, for example, designed to allow users to download the generated images for free. Step 4: The advertising department places advertisements on the website, such as banner ads and text ads, and earns revenue when users click on the ads.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation AI that generates images based on words entered by the user using a generation AI; an image generation unit that compiles images generated by the generation AI; a publishing unit that publishes the images compiled by the image generating unit on a website; an advertisement posting unit that posts advertisements on the website; A system characterized by:

2. The generated AI is Estimating an emotion based on the word input by the user and generating the image that matches the emotion 2. The system of claim 1.

3. The generated AI is The image is generated according to the user's preferences by referring to the user's past generation history.

2. The system of claim 1.

4. The generated AI is A function for selecting a plurality of different styles is provided, and the user can generate the image by specifying the style.

2. The system of claim 1.

5. The generated AI is A voice input function is provided, so that the user can input the words by voice to generate the image.

2. The system of claim 1.

Images