System

Abstract

To improve work efficiency by automatically generating banners of different sizes in a system relating to an embodiment.SOLUTION: In one embodiment, a system comprises a banner generator, a text adjuster, and a logo adjuster. The banner generating unit uses the generation AI to automatically generate banners of different sizes based on the image of one pattern provided by the user. The text adjuster automatically adjusts the text size in accordance with the banner size generated by the banner generator. The logo adjustment unit automatically adjusts the position and size of the logo according to the banner size generated by the banner generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring a lot of manual work when producing large quantities of banners of different sizes, making it inefficient.

[0005] The system according to the embodiment aims to improve work efficiency by automatically generating banners of different sizes. [Means for solving the problem]

[0006] The system according to the embodiment includes a banner generation unit, a text adjustment unit, and a logo adjustment unit. The banner generation unit uses a generation AI to automatically generate banners of different sizes based on a single pattern image provided by a user. The text adjustment unit automatically adjusts the text size according to the banner size generated by the banner generation unit. The logo adjustment unit automatically adjusts the position and size of the logo according to the banner size generated by the banner generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically generate banners of different sizes, thereby improving work efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The banner generation system according to an embodiment of the present invention uses a generation AI to automatically generate banners of different sizes based on a single image pattern provided by the user, and automatically adjusts the text size and logo position and size. This allows the banner generation system to efficiently generate banners of different sizes, reducing the workload of designers.

[0029] A banner generation system according to an embodiment includes a generation AI, a banner generation unit, a text adjustment unit, and a logo adjustment unit. The generation AI automatically generates banners of different sizes based on a single image pattern provided by a user. For example, the generation AI generates a banner using a text generation AI (e.g., LLM). The generation AI can also generate banner designs using a multimodal generation AI. The generation AI can also generate banners by scaling a portion of an image. For example, the text generation AI has learned a large amount of image data and has advanced image generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses image processing technology to scale a portion of an image to generate banners of different sizes. The banner generation unit automatically adjusts the text size according to the banner size generated by the generation AI. For example, the banner generation unit increases the text size for large banners and decreases the text size for small banners. The banner generation unit can also automatically adjust the font and style of the text. The banner generation unit can also automatically adjust the alignment of the text. For example, the banner generation unit automatically adjusts the font size to improve visibility. The text adjustment unit automatically adjusts the text size according to the banner size generated by the banner generation unit. For example, the text adjustment unit increases the text size for large banners and decreases the text size for small banners. The text adjustment unit can also automatically adjust the font and style of the text. The text adjustment unit can also automatically adjust the placement of the text. For example, the text adjustment unit automatically adjusts the font size to improve visibility. The logo adjustment unit automatically adjusts the position and size of the logo according to the banner size generated by the banner generation unit. For example, the logo adjustment unit optimizes the position and size of the logo to match the design of the banner. The logo adjustment unit can also automatically adjust the color and transparency of the logo. The logo adjustment unit can also automatically adjust the placement of the logo. For example, the logo adjustment unit automatically adjusts the position of the logo to maintain consistency in the design.As a result, the banner generation system according to the embodiment can efficiently generate banners of different sizes, reducing the workload of designers. For example, the generation AI can automatically generate background colors and design patterns for banners, providing a wide variety of variations to match the theme of a campaign. The generation AI references past campaign data and incorporates the most effective design elements. The generation AI uses an emotion estimation function to incorporate design elements based on the user's emotions, generating an emotionally appealing banner.

[0030] If the image provided by the user is a horizontal banner, the banner generation unit can generate a vertical or square banner. For example, the generation AI in the banner generation unit automatically selects a background color for the banner based on the image provided by the user and generates a hue that best suits the campaign theme. For example, for a spring campaign, the generation AI selects a light pastel color. The banner generation unit also automatically generates banner design patterns based on the image provided by the user, providing a variety of variations that match the campaign theme. For example, the generation AI automatically selects a background color for the banner based on the image provided by the user and generates a hue that best suits the campaign theme. This allows for automatic generation of banners of different sizes, enabling efficient banner production while maintaining design consistency.

[0031] The text adjustment unit can increase the text size for large banners and decrease the text size for small banners. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. For example, it incorporates design elements from banners that recorded high click rates in past campaigns. Furthermore, when the generation AI generates a banner, it references past campaign data and incorporates the most effective design elements. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. This automatically adjusts the text size according to the banner size, improving visibility.

[0032] The logo adjustment unit can optimize the position or size of the logo to match the banner design. For example, the generation AI analyzes user emotional data and incorporates design elements that elicit positive emotions. For example, colors and shapes that make users feel happy are reflected in the banner. The logo adjustment unit also uses an emotion estimation function to incorporate design elements based on the user's emotions, generating an emotionally appealing banner. For example, the generation AI analyzes user emotional data and incorporates design elements that elicit positive emotions. This automatically optimizes the logo position and size, maintaining consistency in the banner design.

[0033] The generation AI can automatically generate background colors or design patterns for banners, providing a wide variety of variations that match the campaign theme. For example, the generation AI can automatically select a background color for a banner based on an image provided by the user, generating a hue that best suits the campaign theme. For example, for a spring campaign, the generation AI can select a light pastel color. The generation AI can also automatically generate design patterns for banners based on an image provided by the user, providing a wide variety of variations that match the campaign theme. For example, the generation AI can automatically select a background color for a banner based on an image provided by the user, generating a hue that best suits the campaign theme. This increases visual appeal by providing a wide variety of variations that match the campaign theme.

[0034] When generating banners, the generation AI can incorporate the most effective design elements based on past campaign data. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. For example, it incorporates design elements from banners that recorded high click-through rates in past campaigns. Furthermore, when generating banners, the generation AI refers to past campaign data and incorporates the most effective design elements. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. In this way, by referring to past campaign data, it is possible to generate banners that incorporate effective design elements.

[0035] The generation AI can automatically generate not only banners but also other advertising media such as posters or flyers. For example, the generation AI automatically generates not only banners but also posters and flyers based on images provided by the user. For example, it can consistently generate different advertising media using the same design theme. The generation AI can also automatically generate not only banners but also other advertising media such as posters and flyers. For example, the generation AI can automatically generate not only banners but also posters and flyers based on images provided by the user. This further improves the efficiency of advertising production by automatically generating not only banners but also other advertising media.

[0036] Generative AI can automatically generate banners that correspond to different languages ​​or cultures, making it possible to support international campaigns. Generative AI, for example, automatically generates banners that correspond to different languages. For example, it generates banners that are translated into multiple languages, such as English, French, and Chinese. Generative AI can also automatically generate banners that correspond to different cultures. For example, it generates banners that incorporate design elements that are suited to different cultures. Generative AI can also automatically generate banners that correspond to different languages ​​or cultures, making it possible to support international campaigns. For example, generative AI can automatically generate banners that correspond to different languages. This makes it possible to support international campaigns by automatically generating banners that correspond to different languages ​​and cultures.

[0037] The generative AI can automatically enlarge or reduce portions of an image to generate optimal compositions for banners of different sizes. For example, the generative AI can automatically enlarge or reduce portions of an image provided by a user to generate optimal compositions for banners of different sizes. For example, it can optimize a horizontal image for a vertical banner. The generative AI can also automatically enlarge or reduce portions of an image to generate optimal compositions for banners of different sizes. For example, the generative AI can automatically enlarge or reduce portions of an image provided by a user to generate optimal compositions for banners of different sizes. This makes it possible to generate optimal compositions for banners of different sizes by automatically enlarging or reducing portions of an image.

[0038] The generation AI can automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can automatically adjust the color tone or brightness of an image provided by a user to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can maintain the same color tone across all banners. The generation AI can also automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can automatically adjust the color tone or brightness of an image provided by a user to maintain a consistent visual appearance even across banners of different sizes. This allows the generation AI to automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes.

[0039] The generation AI can also automatically generate thumbnail images for video ads, maintaining consistency between banners and video ads. For example, the generation AI can automatically generate thumbnail images for video ads based on images provided by the user. For example, it can generate thumbnails using the same design theme as the banner. The generation AI can also automatically generate thumbnail images for video ads, maintaining consistency between banners and video ads. For example, the generation AI can automatically generate thumbnail images for video ads based on images provided by the user. This allows the consistency between banners and video ads to be maintained.

[0040] The generative AI can also automatically generate images for social media and provide banners optimized for each platform. For example, the generative AI automatically generates images for social media based on images provided by the user. For example, it generates banners optimized for Instagram and Facebook. The generative AI can also automatically generate images for social media and provide banners optimized for each platform. For example, the generative AI can automatically generate images for social media based on images provided by the user. This allows the generative AI to automatically generate images for social media and provide banners optimized for each platform.

[0041] The generation AI can also automatically adjust the font or style of the text to provide the optimal text expression for the banner design. For example, the generation AI automatically selects the optimal text font for the banner design. For example, it selects a font that matches the theme of the campaign. The generation AI can also automatically adjust the font or style of the text to provide the optimal text expression for the banner design. For example, the generation AI automatically selects the optimal text font for the banner design. As a result, by automatically adjusting the font or style of the text, it can provide the optimal text expression for the banner design.

[0042] The generation AI can automatically adjust the color or transparency of the logo to make it harmonize with the overall banner design. For example, the generation AI can automatically adjust the color of the logo to match the banner design. For example, it can select a color that has the optimal contrast with the background color. The generation AI can also automatically adjust the color and transparency of the logo to make it harmonize with the overall banner design. For example, the generation AI can automatically adjust the color of the logo to match the banner design. In this way, the color and transparency of the logo can be automatically adjusted to harmonize with the overall banner design.

[0043] The generative AI can automatically adjust not only the text and logo, but also the icons or graphic elements. For example, the generative AI will automatically adjust the size and position of an icon to match the banner design. For example, it will position it taking into account the balance with the text and logo. The generative AI can also automatically adjust not only the text and logo, but also the icons and graphic elements. For example, the generative AI will automatically adjust the size and position of an icon to match the banner design. This allows the banner design to be optimized by automatically adjusting not only the text and logo, but also the icons and graphic elements.

[0044] The generation AI can automatically generate banners optimized for different devices (smartphones, tablets, or PCs). The generation AI, for example, automatically generates banners optimized for smartphones. For example, it adjusts the size of text and logos to fit the smartphone screen size. The generation AI also automatically generates banners optimized for different devices (smartphones, tablets, PCs). For example, the generation AI automatically generates banners optimized for smartphones. This allows for the automatic generation of banners optimized for different devices, making it possible to provide advertisements that are appropriate for each device.

[0045] The generative AI can also automatically adjust the entire banner layout to provide optimal visual balance. The generative AI can, for example, automatically adjust the placement of text, logos, and images. The generative AI can also automatically adjust the entire banner layout to provide optimal visual balance. For example, the generative AI can automatically adjust the entire banner layout to provide optimal visual balance. This makes it possible to automatically adjust the entire banner layout to provide optimal visual balance.

[0046] The generation AI can also automatically generate animation effects for banners, enabling dynamic advertising presentations. The generation AI, for example, automatically generates animation effects for banners, providing dynamic advertising presentations. For example, it generates animations in which text or logos move smoothly. The generation AI can also automatically generate animation effects for banners, enabling dynamic advertising presentations. For example, the generation AI can automatically generate animation effects for banners, providing dynamic advertising presentations. In this way, the automatic generation of animation effects for banners enables dynamic advertising presentations.

[0047] The generation AI can also automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can generate printing data using the same design theme. The generation AI can also automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can automatically generate printing data for banners, unifying online and offline advertisements. This allows the generation AI to unify online and offline advertisements by automatically generating printing data for banners.

[0048] The generative AI can also automatically generate variations of banners for A / B testing and select the optimal design. The generative AI can, for example, automatically generate variations of banners for A / B testing and select the optimal design. For example, it can try different text or logo placements. The generative AI can also automatically generate variations of banners for A / B testing and select the optimal design. For example, the generative AI can automatically generate variations of banners for A / B testing and select the optimal design. This allows the optimal design to be selected by automatically generating variations of banners for A / B testing.

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

[0050] The banner generation system can also analyze user behavior data and suggest the most effective banner placement. For example, it determines the optimal placement based on the position and size of banners that users have clicked on in the past. It can also analyze users' browsing history and incorporate design elements that will attract their attention. This makes it possible to utilize user behavior data to generate more effective banners.

[0051] The banner generation system also has a voice recognition function, allowing users to generate banners by giving voice instructions. For example, if a user says, "Create a banner for the spring campaign," the system will automatically generate a banner that matches the spring theme. The system can also adjust the placement of text and logos based on voice instructions. This makes it possible to generate banners more intuitively by utilizing the voice recognition function.

[0052] The banner generation system can also utilize the user's geographical location information to generate region-specific banners. For example, it can generate banners incorporating design elements and colors that are popular in a particular region. It can also automatically generate banners tailored to local events or seasons. By generating region-specific banners, it is possible to provide advertisements that are more appealing to the target audience.

[0053] The banner generation system can also analyze a user's purchase history and generate banners that highlight related products and services. For example, it can generate banners that introduce new products related to products the user has previously purchased. It can also generate banners that suggest products and services that the user may be interested in. This makes it possible to provide more personalized banners by utilizing the user's purchase history.

[0054] The banner generation system can also analyze users' social media activity and generate banners that match trends. For example, it can generate banners that incorporate design elements based on current trends and topics. It can also generate banners that match the style of influencers that users follow. This allows the system to provide banners that match social media trends, thereby providing advertisements that are more appealing to the target audience.

[0055] The banner generation system can also analyze the user's device usage and suggest the optimal timing for displaying the banner. For example, it can display the banner according to the time of day when the user is using their smartphone. It can also display the banner when the user is using a specific app. This allows for more effective advertising by providing the timing for displaying the banner according to the user's device usage.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The banner generation unit uses generation AI to automatically generate banners of different sizes based on a single image pattern provided by the user. The generation AI can generate banners by enlarging or reducing parts of an image using text generation AI or multimodal generation AI. For example, text generation AI has learned from large amounts of image data and has advanced image generation capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Step 2: The text adjuster automatically adjusts the text size according to the banner size generated by the banner generator. For example, it increases the text size for large banners and decreases the text size for small banners. It can also automatically adjust the font, style, and position of the text, improving visibility. Step 3: The logo adjustment section automatically adjusts the logo position and size according to the banner size generated by the banner generation section. For example, the logo position and size can be optimized to match the banner design, and the logo color, transparency, and placement can also be automatically adjusted. This ensures consistency in the design.

[0058] (Example 2) The banner generation system according to an embodiment of the present invention uses a generation AI to automatically generate banners of different sizes based on a single image pattern provided by the user, and automatically adjusts the text size and logo position and size. This allows the banner generation system to efficiently generate banners of different sizes, reducing the workload of designers.

[0059] A banner generation system according to an embodiment includes a generation AI, a banner generation unit, a text adjustment unit, and a logo adjustment unit. The generation AI automatically generates banners of different sizes based on a single image pattern provided by a user. For example, the generation AI generates a banner using a text generation AI (e.g., LLM). The generation AI can also generate banner designs using a multimodal generation AI. The generation AI can also generate banners by scaling a portion of an image. For example, the text generation AI has learned a large amount of image data and has advanced image generation capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses image processing technology to scale a portion of an image to generate banners of different sizes. The banner generation unit automatically adjusts the text size according to the banner size generated by the generation AI. For example, the banner generation unit increases the text size for large banners and decreases the text size for small banners. The banner generation unit can also automatically adjust the font and style of the text. The banner generation unit can also automatically adjust the alignment of the text. For example, the banner generation unit automatically adjusts the font size to improve visibility. The text adjustment unit automatically adjusts the text size according to the banner size generated by the banner generation unit. For example, the text adjustment unit increases the text size for large banners and decreases the text size for small banners. The text adjustment unit can also automatically adjust the font and style of the text. The text adjustment unit can also automatically adjust the placement of the text. For example, the text adjustment unit automatically adjusts the font size to improve visibility. The logo adjustment unit automatically adjusts the position and size of the logo according to the banner size generated by the banner generation unit. For example, the logo adjustment unit optimizes the position and size of the logo to match the design of the banner. The logo adjustment unit can also automatically adjust the color and transparency of the logo. The logo adjustment unit can also automatically adjust the placement of the logo. For example, the logo adjustment unit automatically adjusts the position of the logo to maintain consistency in the design.As a result, the banner generation system according to the embodiment can efficiently generate banners of different sizes, reducing the workload of designers. For example, the generation AI can automatically generate background colors and design patterns for banners, providing a wide variety of variations to match the theme of a campaign. The generation AI references past campaign data and incorporates the most effective design elements. The generation AI uses an emotion estimation function to incorporate design elements based on the user's emotions, generating an emotionally appealing banner.

[0060] If the image provided by the user is a horizontal banner, the banner generation unit can generate a vertical or square banner. For example, the generation AI in the banner generation unit automatically selects a background color for the banner based on the image provided by the user and generates a hue that best suits the campaign theme. For example, for a spring campaign, the generation AI selects a light pastel color. The banner generation unit also automatically generates banner design patterns based on the image provided by the user, providing a variety of variations that match the campaign theme. For example, the generation AI automatically selects a background color for the banner based on the image provided by the user and generates a hue that best suits the campaign theme. This allows for automatic generation of banners of different sizes, enabling efficient banner production while maintaining design consistency.

[0061] The text adjustment unit can increase the text size for large banners and decrease the text size for small banners. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. For example, it incorporates design elements from banners that recorded high click rates in past campaigns. Furthermore, when the generation AI generates a banner, it references past campaign data and incorporates the most effective design elements. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. This automatically adjusts the text size according to the banner size, improving visibility.

[0062] The logo adjustment unit can optimize the position or size of the logo to match the banner design. For example, the generation AI analyzes user emotional data and incorporates design elements that elicit positive emotions. For example, colors and shapes that make users feel happy are reflected in the banner. The logo adjustment unit also uses an emotion estimation function to incorporate design elements based on the user's emotions, generating an emotionally appealing banner. For example, the generation AI analyzes user emotional data and incorporates design elements that elicit positive emotions. This automatically optimizes the logo position and size, maintaining consistency in the banner design.

[0063] The generation AI can automatically generate background colors or design patterns for banners, providing a wide variety of variations that match the campaign theme. For example, the generation AI can automatically select a background color for a banner based on an image provided by the user, generating a hue that best suits the campaign theme. For example, for a spring campaign, the generation AI can select a light pastel color. The generation AI can also automatically generate design patterns for banners based on an image provided by the user, providing a wide variety of variations that match the campaign theme. For example, the generation AI can automatically select a background color for a banner based on an image provided by the user, generating a hue that best suits the campaign theme. This increases visual appeal by providing a wide variety of variations that match the campaign theme.

[0064] When generating banners, the generation AI can incorporate the most effective design elements based on past campaign data. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. For example, it incorporates design elements from banners that recorded high click-through rates in past campaigns. Furthermore, when generating banners, the generation AI refers to past campaign data and incorporates the most effective design elements. For example, the generation AI analyzes past campaign data and extracts the most effective design elements. In this way, by referring to past campaign data, it is possible to generate banners that incorporate effective design elements.

[0065] The generation AI can use its emotion estimation function to incorporate design elements based on the user's emotions and generate emotionally appealing banners. For example, the generation AI can analyze the user's emotional data and incorporate design elements that elicit positive emotions. For example, colors and shapes that make the user feel happy can be reflected in the banner. The generation AI can also use its emotion estimation function to incorporate design elements based on the user's emotions and generate emotionally appealing banners. For example, the generation AI can analyze the user's emotional data and incorporate design elements that elicit positive emotions. In this way, by incorporating design elements based on the user's emotions, it is possible to generate emotionally appealing banners.

[0066] The generation AI can automatically generate not only banners but also other advertising media such as posters or flyers. For example, the generation AI automatically generates not only banners but also posters and flyers based on images provided by the user. For example, it can consistently generate different advertising media using the same design theme. The generation AI can also automatically generate not only banners but also other advertising media such as posters and flyers. For example, the generation AI can automatically generate not only banners but also posters and flyers based on images provided by the user. This further improves the efficiency of advertising production by automatically generating not only banners but also other advertising media.

[0067] Generative AI can automatically generate banners that correspond to different languages ​​or cultures, making it possible to support international campaigns. Generative AI, for example, automatically generates banners that correspond to different languages. For example, it generates banners that are translated into multiple languages, such as English, French, and Chinese. Generative AI can also automatically generate banners that correspond to different cultures. For example, it generates banners that incorporate design elements that are suited to different cultures. Generative AI can also automatically generate banners that correspond to different languages ​​or cultures, making it possible to support international campaigns. For example, generative AI can automatically generate banners that correspond to different languages. This makes it possible to support international campaigns by automatically generating banners that correspond to different languages ​​and cultures.

[0068] The generation AI can use the emotion estimation function to suggest, in real time, a banner design that will evoke the most positive emotions in the user. For example, the generation AI uses the emotion estimation function to suggest, in real time, a banner design that will evoke the most positive emotions in the user. For example, it analyzes the user's facial expressions and voice and selects the optimal design. The generation AI also uses the emotion estimation function to suggest, in real time, a banner design that will evoke the most positive emotions in the user. For example, it uses the emotion estimation function to suggest, in real time, a banner design that will evoke the most positive emotions in the user. In this way, by suggesting, in real time, a design that will evoke the most positive emotions in the user, it is possible to generate an emotionally appealing banner.

[0069] The generative AI can automatically enlarge or reduce portions of an image to generate optimal compositions for banners of different sizes. For example, the generative AI can automatically enlarge or reduce portions of an image provided by a user to generate optimal compositions for banners of different sizes. For example, it can optimize a horizontal image for a vertical banner. The generative AI can also automatically enlarge or reduce portions of an image to generate optimal compositions for banners of different sizes. For example, the generative AI can automatically enlarge or reduce portions of an image provided by a user to generate optimal compositions for banners of different sizes. This makes it possible to generate optimal compositions for banners of different sizes by automatically enlarging or reducing portions of an image.

[0070] The generation AI can automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can automatically adjust the color tone or brightness of an image provided by a user to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can maintain the same color tone across all banners. The generation AI can also automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes. For example, the generation AI can automatically adjust the color tone or brightness of an image provided by a user to maintain a consistent visual appearance even across banners of different sizes. This allows the generation AI to automatically adjust the color tone or brightness of an image to maintain a consistent visual appearance even across banners of different sizes.

[0071] The generation AI can use the emotion estimation function to apply image filtering or effects based on the user's emotions. For example, the generation AI uses the emotion estimation function to filter images based on the user's emotions. For example, it applies a filter that makes the user feel happy. The generation AI also uses the emotion estimation function to apply image filtering or effects based on the user's emotions. For example, it uses the emotion estimation function to filter images based on the user's emotions. In this way, by applying filtering or effects based on the user's emotions, it is possible to generate an emotionally appealing banner.

[0072] The generation AI can also automatically generate thumbnail images for video ads, maintaining consistency between banners and video ads. For example, the generation AI can automatically generate thumbnail images for video ads based on images provided by the user. For example, it can generate thumbnails using the same design theme as the banner. The generation AI can also automatically generate thumbnail images for video ads, maintaining consistency between banners and video ads. For example, the generation AI can automatically generate thumbnail images for video ads based on images provided by the user. This allows the consistency between banners and video ads to be maintained.

[0073] The generative AI can also automatically generate images for social media and provide banners optimized for each platform. For example, the generative AI automatically generates images for social media based on images provided by the user. For example, it generates banners optimized for Instagram and Facebook. The generative AI can also automatically generate images for social media and provide banners optimized for each platform. For example, the generative AI can automatically generate images for social media based on images provided by the user. This allows the generative AI to automatically generate images for social media and provide banners optimized for each platform.

[0074] The generation AI can use the emotion estimation function to select an image that the user most emotionally empathizes with and apply it to the banner. For example, the generation AI uses the emotion estimation function to select an image that the user most emotionally empathizes with and apply it to the banner. For example, an image that the user feels joy is applied to the banner. The generation AI can also use the emotion estimation function to select an image that the user most emotionally empathizes with and apply it to the banner. For example, the emotion estimation function can be used to select an image that the user most emotionally empathizes with and apply it to the banner. In this way, by selecting an image that the user most emotionally empathizes with and applying it to the banner, an emotionally appealing banner can be generated.

[0075] The generation AI can also automatically adjust the font or style of the text to provide the optimal text expression for the banner design. For example, the generation AI automatically selects the optimal text font for the banner design. For example, it selects a font that matches the theme of the campaign. The generation AI can also automatically adjust the font or style of the text to provide the optimal text expression for the banner design. For example, the generation AI automatically selects the optimal text font for the banner design. As a result, by automatically adjusting the font or style of the text, it can provide the optimal text expression for the banner design.

[0076] The generation AI can automatically adjust the color or transparency of the logo to make it harmonize with the overall banner design. For example, the generation AI can automatically adjust the color of the logo to match the banner design. For example, it can select a color that has the optimal contrast with the background color. The generation AI can also automatically adjust the color and transparency of the logo to make it harmonize with the overall banner design. For example, the generation AI can automatically adjust the color of the logo to match the banner design. In this way, the color and transparency of the logo can be automatically adjusted to harmonize with the overall banner design.

[0077] The generative AI can use the emotion estimation function to propose text or logo designs based on the user's emotions. The generative AI can, for example, use the emotion estimation function to propose text designs based on the user's emotions. For example, it can select fonts and colors that bring joy to the user. The generative AI can also use the emotion estimation function to propose text or logo designs based on the user's emotions. For example, it can use the emotion estimation function to propose text designs based on the user's emotions. This makes it possible to generate emotionally appealing banners by proposing text or logo designs based on the user's emotions.

[0078] The generative AI can automatically adjust not only the text and logo, but also the icons or graphic elements. For example, the generative AI will automatically adjust the size and position of an icon to match the banner design. For example, it will position it taking into account the balance with the text and logo. The generative AI can also automatically adjust not only the text and logo, but also the icons and graphic elements. For example, the generative AI will automatically adjust the size and position of an icon to match the banner design. This allows the banner design to be optimized by automatically adjusting not only the text and logo, but also the icons and graphic elements.

[0079] The generation AI can automatically generate banners optimized for different devices (smartphones, tablets, or PCs). The generation AI, for example, automatically generates banners optimized for smartphones. For example, it adjusts the size of text and logos to fit the smartphone screen size. The generation AI also automatically generates banners optimized for different devices (smartphones, tablets, PCs). For example, the generation AI automatically generates banners optimized for smartphones. This allows for the automatic generation of banners optimized for different devices, making it possible to provide advertisements that are appropriate for each device.

[0080] The generation AI can use the emotion estimation function to suggest in real time the placement of text or a logo that will evoke the most positive emotion in the user. For example, the generation AI uses the emotion estimation function to suggest in real time the placement of text that will evoke the most positive emotion in the user. For example, the generation AI analyzes the user's facial expressions and voice and selects the optimal placement. The generation AI also uses the emotion estimation function to suggest in real time the placement of text or a logo that will evoke the most positive emotion in the user. For example, the generation AI uses the emotion estimation function to suggest in real time the placement of text that will evoke the most positive emotion in the user. This makes it possible to generate an emotionally appealing banner by suggesting in real time the placement of text or a logo that will evoke the most positive emotion in the user.

[0081] The generative AI can also automatically adjust the entire banner layout to provide optimal visual balance. The generative AI can, for example, automatically adjust the placement of text, logos, and images. The generative AI can also automatically adjust the entire banner layout to provide optimal visual balance. For example, the generative AI can automatically adjust the entire banner layout to provide optimal visual balance. This makes it possible to automatically adjust the entire banner layout to provide optimal visual balance.

[0082] The generation AI can also automatically generate animation effects for banners, enabling dynamic advertising presentations. The generation AI, for example, automatically generates animation effects for banners, providing dynamic advertising presentations. For example, it generates animations in which text or logos move smoothly. The generation AI can also automatically generate animation effects for banners, enabling dynamic advertising presentations. For example, the generation AI can automatically generate animation effects for banners, providing dynamic advertising presentations. In this way, the automatic generation of animation effects for banners enables dynamic advertising presentations.

[0083] The generation AI can use the emotion estimation function to propose a layout or animation based on the user's emotions. For example, the generation AI uses the emotion estimation function to propose a layout based on the user's emotions. For example, it selects a layout that makes the user feel happy. The generation AI also uses the emotion estimation function to propose a layout or animation based on the user's emotions. For example, it uses the emotion estimation function to propose a layout based on the user's emotions. In this way, by proposing a layout or animation based on the user's emotions, it is possible to generate an emotionally appealing banner.

[0084] The generation AI can also automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can generate printing data using the same design theme. The generation AI can also automatically generate printing data for banners, unifying online and offline advertisements. For example, the generation AI can automatically generate printing data for banners, unifying online and offline advertisements. This allows the generation AI to unify online and offline advertisements by automatically generating printing data for banners.

[0085] The generative AI can also automatically generate variations of banners for A / B testing and select the optimal design. The generative AI can, for example, automatically generate variations of banners for A / B testing and select the optimal design. For example, it can try different text or logo placements. The generative AI can also automatically generate variations of banners for A / B testing and select the optimal design. For example, the generative AI can automatically generate variations of banners for A / B testing and select the optimal design. This allows the optimal design to be selected by automatically generating variations of banners for A / B testing.

[0086] The generation AI can use the emotion estimation function to suggest in real time the layout or animation that will evoke the most positive emotion in the user. For example, the generation AI uses the emotion estimation function to suggest in real time the layout that will evoke the most positive emotion in the user. For example, the generation AI analyzes the user's facial expressions and voice to select the optimal layout. The generation AI also uses the emotion estimation function to suggest in real time the layout or animation that will evoke the most positive emotion in the user. For example, the generation AI uses the emotion estimation function to suggest in real time the layout that will evoke the most positive emotion in the user. This makes it possible to generate an emotionally appealing banner by suggesting in real time the layout or animation that will evoke the most positive emotion in the user.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The banner generation system can also analyze user behavior data and suggest the most effective banner placement. For example, it determines the optimal placement based on the position and size of banners that users have clicked on in the past. It can also analyze users' browsing history and incorporate design elements that will attract their attention. This makes it possible to utilize user behavior data to generate more effective banners.

[0089] The banner generation system also has a voice recognition function, allowing users to generate banners by giving voice instructions. For example, if a user says, "Create a banner for the spring campaign," the system will automatically generate a banner that matches the spring theme. The system can also adjust the placement of text and logos based on voice instructions. This makes it possible to generate banners more intuitively by utilizing the voice recognition function.

[0090] The banner generation system can further estimate the user's emotion and adjust the color tone of the banner based on the estimated emotion. For example, if the user is feeling happy, a banner with bright color tones can be generated. On the other hand, if the user is feeling calm, a banner with calm color tones can be generated. In this way, by generating a banner with a color tone that corresponds to the user's emotion, it is possible to provide an emotionally appealing banner.

[0091] The banner generation system can also utilize the user's geographical location information to generate region-specific banners. For example, it can generate banners incorporating design elements and colors that are popular in a particular region. It can also automatically generate banners tailored to local events or seasons. By generating region-specific banners, it is possible to provide advertisements that are more appealing to the target audience.

[0092] The banner generation system can also estimate the user's emotions and adjust the animation effects of the banner based on the estimated emotions. For example, if the user is excited, a banner incorporating dynamic animations can be generated. On the other hand, if the user is relaxed, a banner incorporating calm animations can be generated. In this way, by incorporating animation effects according to the user's emotions, it is possible to provide an emotionally appealing banner.

[0093] The banner generation system can also analyze a user's purchase history and generate banners that highlight related products and services. For example, it can generate banners that introduce new products related to products the user has previously purchased. It can also generate banners that suggest products and services that the user may be interested in. This makes it possible to provide more personalized banners by utilizing the user's purchase history.

[0094] The banner generation system can further estimate the user's emotions and adjust the text content of the banner based on the estimated emotions. For example, if the user is feeling happy, the system can generate text containing a positive message. Alternatively, if the user is feeling anxious, the system can generate text containing a message that gives a sense of security. This allows the system to provide an emotionally appealing banner by providing text content that corresponds to the user's emotions.

[0095] The banner generation system can also analyze users' social media activity and generate banners that match trends. For example, it can generate banners that incorporate design elements based on current trends and topics. It can also generate banners that match the style of influencers that users follow. This allows the system to provide banners that match social media trends, thereby providing advertisements that are more appealing to the target audience.

[0096] The banner generation system can further estimate the user's emotions and adjust the font and style of the banner based on the estimated emotions. For example, if the user is excited, a banner using a bold font and style can be generated. On the other hand, if the user is relaxed, a banner using a soft font and style can be generated. In this way, by providing fonts and styles according to the user's emotions, it is possible to provide an emotionally appealing banner.

[0097] The banner generation system can also analyze the user's device usage and suggest the optimal timing for displaying the banner. For example, it can display the banner according to the time of day when the user is using their smartphone. It can also display the banner when the user is using a specific app. This allows for more effective advertising by providing the timing for displaying the banner according to the user's device usage.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The banner generation unit uses generation AI to automatically generate banners of different sizes based on a single image pattern provided by the user. The generation AI can generate banners by enlarging or reducing parts of an image using text generation AI or multimodal generation AI. For example, text generation AI has learned from large amounts of image data and has advanced image generation capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. Step 2: The text adjuster automatically adjusts the text size according to the banner size generated by the banner generator. For example, it increases the text size for large banners and decreases the text size for small banners. It can also automatically adjust the font, style, and position of the text, improving visibility. Step 3: The logo adjustment section automatically adjusts the logo position and size according to the banner size generated by the banner generation section. For example, the logo position and size can be optimized to match the banner design, and the logo color, transparency, and placement can also be automatically adjusted. This ensures consistency in the design.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[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 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.

[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 (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).

[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] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0144] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0166] 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]

[0167] 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 banner generation unit that uses generation AI to automatically generate banners of different sizes based on a single pattern image provided by the user; a text adjustment unit that automatically adjusts a text size according to the banner size generated by the banner generation unit; a logo adjustment unit that automatically adjusts the position and size of a logo according to the banner size generated by the banner generation unit. A system characterized by:

2. The banner generation unit If the image provided by the user is a horizontal banner, generate a vertical or square banner.

2. The system of claim 1.

3. The generated AI is In addition to the banner, other advertising media such as posters or flyers are also automatically generated.

2. The system of claim 1.

4. The generated AI is Automatically scale portions of the image to generate optimal compositions for the banner at different sizes 2. The system of claim 1.

5. The generated AI is It also automatically adjusts the font or style of the text to provide the best text presentation for the banner design.

2. The system of claim 1.

6. The generated AI is The entire layout of the banner is also automatically adjusted to provide optimal visual balance.

2. The system of claim 1.

7. The generated AI is Incorporating design elements based on the user's emotions to generate emotionally appealing banners 2. The system of claim 1.

8. The generated AI is Proposing in real time the banner design that gives the user the most positive feeling 2. The system of claim 1.

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