system

Abstract

The system according to the embodiment aims to efficiently generate images to be used in proposals and meeting materials, thereby reducing costs. According to an embodiment, the system includes a generation unit, an application unit, and a management unit. The generation unit generates an image. The application unit applies the image generated by the generation unit to a proposal or meeting materials. The management unit maintains the consistency of the image applied by the application unit.

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] Previous technology had the problem that finding the right images for a proposal or meeting materials was time-consuming and costly.

[0005] The system according to the embodiment aims to efficiently generate images to be used in proposals and meeting materials, thereby reducing costs. [Means for solving the problem]

[0006] A system according to an embodiment includes a generating unit, an applying unit, and a managing unit. The generating unit generates an image. The applying unit applies the image generated by the generating unit to a proposal or a meeting document. The managing unit maintains consistency of the image applied by the applying unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate images to be used in proposals and meeting materials, thereby reducing costs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The image generation system of an embodiment of the present invention achieves annual cost savings of 30 million yen by using only images generated by a generative AI for proposals and meeting materials. This image generation system significantly reduces creation time by using image generation AI to generate images and then applying the generated images to proposals and meeting materials. Furthermore, images generated by the generative AI can be customized to specific themes and styles, enabling more effective visual communication. Furthermore, it is important to emphasize that using generative AI can avoid copyright issues. This not only eliminates the need to search for images on external copyright-free sites, but also reduces the risk of copyright infringement. Images generated by the generative AI are always up to date with the latest trends and designs, improving the quality of proposals and meeting materials. This makes it easier to attract audience attention and enhance the effectiveness of presentations. Furthermore, using generative AI allows for image consistency. Using images with the same style and theme across different documents and proposals allows for a unified brand image. Furthermore, the generative AI is multilingual. It can generate appropriate images for proposals and meeting materials in different languages, making it suitable for global business expansion. Considering these points, using generative AI offers many benefits, including cost reduction, improved quality of proposals and meeting materials, unified brand image, and global support. For example, an image can be generated using image generation AI. The generated image can then be applied to proposals and meeting materials. This significantly reduces the time it takes to create proposals and meeting materials. Furthermore, images generated by generative AI can be customized to fit specific themes and styles, enabling more effective visual communication. It's also important to emphasize that using generative AI can avoid copyright issues. This not only eliminates the need to search for images on external copyright-free sites, but also reduces the risk of copyright infringement. Images generated by generative AI can always keep up with the latest trends and designs, improving the quality of proposals and meeting materials. This makes it easier to attract audience attention and enhances the effectiveness of presentations.Furthermore, using generative AI can maintain image consistency. Using images with the same style and theme across different documents and proposals can unify the brand image. In addition, generative AI can also support multiple languages. It can generate appropriate images even for proposals and meeting materials in different languages, making it suitable for global business expansion. Considering these points, using generative AI not only reduces costs but also offers many benefits, such as improving the quality of proposals and meeting materials, unifying the brand image, and global compatibility. As a result, image generation systems can shorten the time it takes to create proposals and meeting materials, thereby reducing costs.

[0029] An image generation system according to an embodiment includes a generation unit, an application unit, and a management unit. The generation unit generates an image. The generation unit generates the image using, for example, a generation AI. The generation AI can generate the image using technologies such as deep learning and GAN (generative artificial network). For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image suitable for a proposal," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image suitable for conference materials," and acquire the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches a specific theme or style," and acquire the generated image. The application unit applies the generated image to a proposal or conference material. For example, the application unit inserts the generated image at a specific position in a proposal. The application unit can also insert the generated image at a specific position in the conference material. The application unit can also adjust the size of the generated image and apply it to a proposal or conference material. For example, the application unit inserts the generated image into the cover of a proposal. The application unit can also insert the generated image into a slide in the conference material. Furthermore, the application unit can adjust the format of the generated image and apply it to the proposal or conference material. The management unit maintains consistency of the applied image. For example, the management unit unifies the color tone of the generated image. The management unit can also unify the font of the generated image. Furthermore, the management unit can unify the layout of the generated image. For example, the management unit matches the color tone of the generated image with other parts of the proposal or conference material. The management unit can also match the font of the generated image with other parts of the proposal or conference material. Furthermore, the management unit can also match the layout of the generated image with other parts of the proposal or conference material. As a result, the image generation system according to the embodiment can shorten the time required to create proposals or conference materials and achieve cost reduction.

[0030] The generation unit can customize the theme or style of the image using the generation AI. For example, the generation unit inputs a prompt to the generation AI, such as "Generate an image that matches a specific theme," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Generate an image that matches a specific style," and acquires the generated image. The generation AI can customize, for example, the color tone, font, design pattern, etc. For example, the generation unit inputs a prompt to the generation AI, such as "Generate an image that matches a blue theme," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Generate an image that matches a modern style," and acquires the generated image. Furthermore, the generation unit can input a prompt to the generation AI, such as "Generate an image that matches a specific design pattern," and acquire the generated image. This enables more effective visual communication by customizing the theme or style of the image using the generation AI.

[0031] The application unit can apply the generated image to a multilingual proposal or conference material. For example, the application unit applies the generated image to a multilingual proposal. For example, the application unit can also apply the generated image to multilingual conference material. Multilingual proposals or conference materials include different languages, such as English, Japanese, and French. For example, the application unit applies the generated image to an English proposal. The application unit can also apply the generated image to Japanese conference materials. Furthermore, the application unit can also apply the generated image to a French proposal. In this way, applying the generated image to multilingual proposals or conference materials can accommodate global business development.

[0032] The management unit can minimize the copyright risk of the generated images. For example, the management unit evaluates the copyright risk of the generated images and filters out images with a high risk. For example, the management unit can also check copyright information of the generated images and perform appropriate filtering. For example, the management unit can also set filtering criteria to reduce the copyright risk of the generated images. For example, the management unit checks license information of the generated images and performs appropriate filtering. The management unit can also reduce the copyright risk by generating original content for the generated images. This reduces the copyright risk, thereby avoiding legal risks.

[0033] The generation unit can generate images corresponding to the latest trends or designs using the generation AI. For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image that matches the latest trends," and acquires the generated image. For example, the generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches the latest designs," and acquires the generated image. For example, the generation AI can generate images by referring to information sources for the latest trends and designs. For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image that matches the latest fashion trends," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches the latest interior designs," and acquires the generated image. Furthermore, the generation unit can input a prompt to the generation AI, such as "Please generate an image that matches the latest graphic designs," and acquires the generated image. This allows the quality of proposals and meeting materials to be improved by generating images that match the latest trends and designs.

[0034] The application unit can apply the generated image in the same style or theme across different documents or proposals. The application unit, for example, applies the generated image in the same style across different documents. The application unit can also apply the generated image in the same theme across different proposals. The application unit, for example, uses a template to apply the generated image in the same style across different documents. The application unit can also apply a style guide to apply the generated image in the same theme across different proposals. The application unit, for example, uses a template to apply the generated image in the same style across different documents. The application unit can also apply a style guide to apply the generated image in the same theme across different proposals. This allows a unified brand image by applying the same style or theme across different documents or proposals.

[0035] The generation unit can generate an appropriate image by referring to the user's past usage history during generation. The generation unit, for example, generates an optimal image by referring to the user's past usage history. For example, the generation unit causes the generation AI to generate a similar image based on the theme or style of an image used by the user in the past. For example, the generation unit can also cause the generation AI to generate an optimal image based on the color tone or design that the user has preferred in the past. For example, the generation unit can also cause the generation AI to generate an optimal image by referring to the arrangement and layout of an image used by the user in the past. The generation unit, for example, generates an optimal image by referring to the user's past project data and frequency of use. For example, the generation unit causes the generation AI to generate a related image based on the user's past project data. The generation unit can also cause the generation AI to generate an optimal image based on the user's frequency of use. In this way, more appropriate images can be generated by referring to the user's past usage history.

[0036] The generation unit can customize the image based on the user's current project or area of ​​interest at the time of generation. The generation unit customizes the image based on, for example, the user's current project or area of ​​interest. For example, the generation unit generates an image that the generation AI associates with the theme of the project the user is currently working on. The generation unit can also generate an optimal image based on, for example, the user's area of ​​interest. For example, the generation unit can also generate an image that the generation AI associates with based on keywords used by the user in the current project. For example, the generation unit generates an optimal image by referring to metadata of the user's current project or tags of the area of ​​interest. For example, the generation unit generates an image that the generation AI associates with based on metadata of the user's current project. The generation unit can also generate an optimal image based on tags of the user's area of ​​interest. This allows for more appropriate images to be generated by customizing the image based on the user's current project or area of ​​interest.

[0037] The generation unit can generate highly relevant images based on the user's geographical location information at the time of generation. The generation unit generates highly relevant images, for example, by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI generates images related to that area. For example, when the user is traveling, the generation AI can also generate images related to tourist attractions and famous places at the travel destination. For example, when the user is in a specific city, the generation AI can also generate images related to the scenery and culture of that city. For example, the generation unit acquires the user's geographical location information and generates highly relevant images based on that information. For example, the generation unit generates relevant images based on the user's geographical location information. The generation unit can also generate optimal images based on the user's geographical location information. This makes it possible to generate more relevant images by taking into account the user's geographical location information.

[0038] At the time of generation, the generation unit can generate relevant images based on the user's social media activity. The generation unit, for example, analyzes the user's social media activity and generates relevant images. The generation unit, for example, causes the generation AI to generate relevant images based on content shared by the user on social media. The generation unit can also generate optimal images based on the themes of accounts the user follows on social media. The generation unit can also generate relevant images based on posts the user has "liked" on social media. The generation unit, for example, analyzes the user's social media activity and generates relevant images based on that information. The generation unit, for example, causes the generation AI to generate relevant images based on the user's social media activity. The generation unit can also generate optimal images based on the user's social media activity. In this way, more relevant images can be generated by analyzing the user's social media activity.

[0039] When applied, the application unit can adjust the arrangement of images based on the content of a proposal or a meeting document. For example, the application unit appropriately arranges the generated images in accordance with the main points of the proposal. For example, the application unit can also arrange the generated images in optimal positions in line with the flow of the meeting document. For example, the application unit can effectively arrange the generated images in accordance with the theme of the proposal or the meeting document. For example, the application unit arranges images related to important points of the proposal in appropriate positions. The application unit can also arrange images related to the main content of the meeting document in appropriate positions. Furthermore, the application unit can apply the most relevant image based on the content of the proposal or the meeting document. This enables more effective visual communication by optimizing the arrangement of images based on the content of the proposal or the meeting document.

[0040] When applied, the application unit can automatically select highly relevant images for proposals and meeting materials in different languages. For example, the application unit automatically selects appropriate images for proposals and meeting materials in different languages. For example, the application unit automatically selects images that are easy to understand in English-speaking countries for an English proposal. For example, the application unit can also automatically select images that are suitable for Japanese culture for Japanese meeting materials. For example, the application unit can automatically select images suitable for each language for a multilingual proposal. For example, the application unit uses a language model to automatically select highly relevant images for proposals and meeting materials in different languages. For example, the application unit can also evaluate translation accuracy to automatically select highly relevant images for proposals and meeting materials in different languages. This makes it possible to respond to global business expansion by automatically selecting appropriate images for proposals and meeting materials in different languages.

[0041] When applying the image priority, the application unit can determine the priority of images based on the submission date of the proposal or the meeting materials. For example, when the submission deadline of the proposal is approaching, the application unit can preferentially apply images related to important points. For example, when the presentation date of the meeting materials is approaching, the application unit can also preferentially apply images related to main contents. For example, the application unit can also preferentially apply the most effective image based on the submission date of the proposal or the meeting materials. For example, when the submission deadline of the proposal is approaching, the application unit can preferentially apply images related to important points. Furthermore, when the presentation date of the meeting materials is approaching, the application unit can also preferentially apply images related to main contents. Furthermore, the application unit can also preferentially apply the most effective image based on the submission date of the proposal or the meeting materials. Thus, by determining the priority of images based on the submission date of the proposal or the meeting materials, more effective visual communication is possible.

[0042] When applying, the application unit can adjust the application range of images based on the relevance of the proposal or the conference materials. For example, the application unit applies images related to the main theme of the proposal widely. For example, the application unit can also apply images related to important points of the conference materials to an appropriate range. For example, the application unit can apply the most relevant image based on the content of the proposal or the conference materials. For example, the application unit applies images related to the main theme of the proposal widely. Furthermore, the application unit can also apply images related to important points of the conference materials to an appropriate range. Furthermore, the application unit can apply the most relevant image based on the content of the proposal or the conference materials. In this way, by adjusting the application range of images based on the relevance of the proposal or the conference materials, more effective visual communication is possible.

[0043] During management, the management unit can select an appropriate management method based on the usage history of the generated images. The management unit selects the optimal management method based on, for example, the frequency of use of the generated images. The management unit can also analyze, for example, the usage history of the generated images and select the most effective management method. The management unit can also select the optimal management method based on, for example, the usage pattern of the generated images. The management unit selects the optimal management method based on, for example, the frequency of use of the generated images. The management unit can also analyze the usage history of the generated images and select the most effective management method. Furthermore, the management unit can select the optimal management method based on the usage pattern of the generated images. In this way, a more appropriate management method can be selected by referring to the usage history of the generated images.

[0044] The management unit can perform filtering to minimize copyright risks of generated images during management. For example, the management unit evaluates the copyright risks of generated images and filters out images with high risks. For example, the management unit can also check copyright information of generated images and perform appropriate filtering. For example, the management unit can also set filtering criteria to reduce copyright risks of generated images. For example, the management unit checks license information of generated images and performs appropriate filtering. The management unit can also reduce copyright risks by generating original content for generated images. This reduces copyright risks of generated images, thereby avoiding legal risks.

[0045] During management, the management unit can select a management method based on the geographical distribution of the generated images. The management unit, for example, analyzes the geographical distribution of the generated images and selects an optimal management method. The management unit can also select an effective management method based on the geographical distribution of the generated images. The management unit can also select an appropriate management method by taking into account the geographical distribution of the generated images. The management unit, for example, analyzes the geographical distribution of the generated images and selects an optimal management method. The management unit can also select an effective management method based on the geographical distribution of the generated images. Furthermore, the management unit can also select an appropriate management method by taking into account the geographical distribution of the generated images. In this way, a more appropriate management method can be selected by taking into account the geographical distribution of the generated images.

[0046] The management unit can improve the accuracy of management by referring to related literature of the generated image during management. The management unit, for example, improves the accuracy of management by referring to related literature of the generated image. The management unit can also, for example, analyze related literature of the generated image and select an optimal management method. The management unit can also, for example, select an effective management method based on related literature of the generated image. The management unit, for example, improves the accuracy of management by referring to related literature of the generated image. Furthermore, the management unit can also analyze related literature of the generated image and select an optimal management method. Furthermore, the management unit can also select an effective management method based on related literature of the generated image. In this way, by referring to related literature of the generated image, the accuracy of management can be improved.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The generation unit can refer to the user's past project data and adjust the theme and style of the image to be generated. For example, the generation AI generates a related image based on the theme of a project the user has previously undertaken. The generation unit can also generate an optimal image based on the style and color tone of images the user has previously used. Furthermore, the generation unit can refer to successful examples of the user's past projects and generate similar images. This makes it possible to generate more appropriate images by utilizing the user's past project data.

[0049] The management unit can select the optimal management method based on the frequency of use of the generated images. For example, if a generated image is used frequently, it can be managed as a priority. Images that are used less frequently can also be archived. Furthermore, the management unit can analyze the usage history of the generated images and select the most effective management method. This allows a more appropriate management method to be selected based on the frequency of use of the generated images.

[0050] The generation unit can generate highly relevant images based on the user's geographical location information. For example, if the user is in a specific area, the generation AI can generate images related to that area. Also, if the user is traveling, the generation AI can generate images related to tourist attractions and famous places at the travel destination. Furthermore, if the user is in a specific city, the generation AI can generate images related to the scenery and culture of that city. In this way, by taking the user's geographical location information into consideration, more relevant images can be generated.

[0051] The application unit can adjust the image layout based on the content of a proposal or meeting materials. For example, the application unit can appropriately layout the generated image in accordance with the main points of the proposal. The application unit can also optimally layout the generated image in accordance with the flow of the meeting materials. Furthermore, the application unit can effectively layout the generated image in accordance with the theme of the proposal or meeting materials. This allows for more effective visual communication by optimizing the layout of images based on the content of the proposal or meeting materials.

[0052] The generation unit can generate relevant images based on the user's social media activity. For example, the generation AI generates relevant images based on content shared by the user on social media. The generation AI can also generate optimal images based on the themes of accounts the user follows on social media. Furthermore, the generation AI can generate relevant images based on posts the user has "liked" on social media. This makes it possible to generate more relevant images by analyzing the user's social media activity.

[0053] The management unit can perform filtering to minimize copyright risks of generated images. For example, the management unit evaluates the copyright risks of generated images and filters out images with high risks. The management unit can also check copyright information of generated images and perform appropriate filtering. Furthermore, the management unit can check license information of generated images and perform appropriate filtering. This reduces the copyright risks of generated images, thereby avoiding legal risks.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The generator generates an image. The generator uses a generative AI to generate an image, utilizing technologies such as deep learning and GAN (generative artificial network). The generator inputs prompts to the generative AI, such as "Please generate an image suitable for a proposal" or "Please generate an image suitable for meeting materials," and obtains the generated image. Step 2: The application unit applies the generated image to the proposal or the meeting materials. The application unit can insert the generated image at a specific position in the proposal or the meeting materials and adjust the size and format. For example, the generated image can be inserted into the cover of the proposal or the slide of the meeting materials. Step 3: The management department ensures consistency in the applied images. The management department ensures consistency by matching the color tone, font, and layout of the generated images with the rest of the proposal or meeting materials. This improves the overall sense of unity in the proposal or meeting materials.

[0056] (Example 2) The image generation system of an embodiment of the present invention achieves annual cost savings of 30 million yen by using only images generated by a generative AI for proposals and meeting materials. This image generation system significantly reduces creation time by using image generation AI to generate images and then applying the generated images to proposals and meeting materials. Furthermore, images generated by the generative AI can be customized to specific themes and styles, enabling more effective visual communication. Furthermore, it is important to emphasize that using generative AI can avoid copyright issues. This not only eliminates the need to search for images on external copyright-free sites, but also reduces the risk of copyright infringement. Images generated by the generative AI are always up to date with the latest trends and designs, improving the quality of proposals and meeting materials. This makes it easier to attract audience attention and enhance the effectiveness of presentations. Furthermore, using generative AI allows for image consistency. Using images with the same style and theme across different documents and proposals allows for a unified brand image. Furthermore, the generative AI is multilingual. It can generate appropriate images for proposals and meeting materials in different languages, making it suitable for global business expansion. Considering these points, using generative AI offers many benefits, including cost reduction, improved quality of proposals and meeting materials, unified brand image, and global support. For example, an image can be generated using image generation AI. The generated image can then be applied to proposals and meeting materials. This significantly reduces the time it takes to create proposals and meeting materials. Furthermore, images generated by generative AI can be customized to fit specific themes and styles, enabling more effective visual communication. It's also important to emphasize that using generative AI can avoid copyright issues. This not only eliminates the need to search for images on external copyright-free sites, but also reduces the risk of copyright infringement. Images generated by generative AI can always keep up with the latest trends and designs, improving the quality of proposals and meeting materials. This makes it easier to attract audience attention and enhances the effectiveness of presentations.Furthermore, using generative AI can maintain image consistency. Using images with the same style and theme across different documents and proposals can unify the brand image. In addition, generative AI can also support multiple languages. It can generate appropriate images even for proposals and meeting materials in different languages, making it suitable for global business expansion. Considering these points, using generative AI not only reduces costs but also offers many benefits, such as improving the quality of proposals and meeting materials, unifying the brand image, and global compatibility. As a result, image generation systems can shorten the time it takes to create proposals and meeting materials, thereby reducing costs.

[0057] An image generation system according to an embodiment includes a generation unit, an application unit, and a management unit. The generation unit generates an image. The generation unit generates the image using, for example, a generation AI. The generation AI can generate the image using technologies such as deep learning and GAN (generative artificial network). For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image suitable for a proposal," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image suitable for conference materials," and acquire the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches a specific theme or style," and acquire the generated image. The application unit applies the generated image to a proposal or conference material. For example, the application unit inserts the generated image at a specific position in a proposal. The application unit can also insert the generated image at a specific position in the conference material. The application unit can also adjust the size of the generated image and apply it to a proposal or conference material. For example, the application unit inserts the generated image into the cover of a proposal. The application unit can also insert the generated image into a slide in the conference material. Furthermore, the application unit can adjust the format of the generated image and apply it to the proposal or conference material. The management unit maintains consistency of the applied image. For example, the management unit unifies the color tone of the generated image. The management unit can also unify the font of the generated image. Furthermore, the management unit can unify the layout of the generated image. For example, the management unit matches the color tone of the generated image with other parts of the proposal or conference material. The management unit can also match the font of the generated image with other parts of the proposal or conference material. Furthermore, the management unit can also match the layout of the generated image with other parts of the proposal or conference material. As a result, the image generation system according to the embodiment can shorten the time required to create proposals or conference materials and achieve cost reduction.

[0058] The generation unit can customize the theme or style of the image using the generation AI. For example, the generation unit inputs a prompt to the generation AI, such as "Generate an image that matches a specific theme," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Generate an image that matches a specific style," and acquires the generated image. The generation AI can customize, for example, the color tone, font, design pattern, etc. For example, the generation unit inputs a prompt to the generation AI, such as "Generate an image that matches a blue theme," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Generate an image that matches a modern style," and acquires the generated image. Furthermore, the generation unit can input a prompt to the generation AI, such as "Generate an image that matches a specific design pattern," and acquire the generated image. This enables more effective visual communication by customizing the theme or style of the image using the generation AI.

[0059] The application unit can apply the generated image to a multilingual proposal or conference material. For example, the application unit applies the generated image to a multilingual proposal. For example, the application unit can also apply the generated image to multilingual conference material. Multilingual proposals or conference materials include different languages, such as English, Japanese, and French. For example, the application unit applies the generated image to an English proposal. The application unit can also apply the generated image to Japanese conference materials. Furthermore, the application unit can also apply the generated image to a French proposal. In this way, applying the generated image to multilingual proposals or conference materials can accommodate global business development.

[0060] The management unit can minimize the copyright risk of the generated images. For example, the management unit evaluates the copyright risk of the generated images and filters out images with a high risk. For example, the management unit can also check copyright information of the generated images and perform appropriate filtering. For example, the management unit can also set filtering criteria to reduce the copyright risk of the generated images. For example, the management unit checks license information of the generated images and performs appropriate filtering. The management unit can also reduce the copyright risk by generating original content for the generated images. This reduces the copyright risk, thereby avoiding legal risks.

[0061] The generation unit can generate images corresponding to the latest trends or designs using the generation AI. For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image that matches the latest trends," and acquires the generated image. For example, the generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches the latest designs," and acquires the generated image. For example, the generation AI can generate images by referring to information sources for the latest trends and designs. For example, the generation unit inputs a prompt to the generation AI, such as "Please generate an image that matches the latest fashion trends," and acquires the generated image. The generation unit can also input a prompt to the generation AI, such as "Please generate an image that matches the latest interior designs," and acquires the generated image. Furthermore, the generation unit can input a prompt to the generation AI, such as "Please generate an image that matches the latest graphic designs," and acquires the generated image. This allows the quality of proposals and meeting materials to be improved by generating images that match the latest trends and designs.

[0062] The application unit can apply the generated image in the same style or theme across different documents or proposals. The application unit, for example, applies the generated image in the same style across different documents. The application unit can also apply the generated image in the same theme across different proposals. The application unit, for example, uses a template to apply the generated image in the same style across different documents. The application unit can also apply a style guide to apply the generated image in the same theme across different proposals. The application unit, for example, uses a template to apply the generated image in the same style across different documents. The application unit can also apply a style guide to apply the generated image in the same theme across different proposals. This allows a unified brand image by applying the same style or theme across different documents or proposals.

[0063] The generation unit can estimate the user's emotions and adjust the theme or style of the generated image based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the theme or style of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate an image with calm colors and a soft design. For example, if the user is nervous, the generation AI can generate an image with a simple, highly visible design. For example, if the user is excited, the generation AI can generate an image with a vivid, dynamic design. The generation unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the generation unit can analyze the user's text data and estimate the emotion using text analysis technology. This allows more appropriate images to be generated by adjusting the theme or style of the image based on the user's emotions.

[0064] The generation unit can generate an appropriate image by referring to the user's past usage history during generation. The generation unit, for example, generates an optimal image by referring to the user's past usage history. For example, the generation unit causes the generation AI to generate a similar image based on the theme or style of an image used by the user in the past. For example, the generation unit can also cause the generation AI to generate an optimal image based on the color tone or design that the user has preferred in the past. For example, the generation unit can also cause the generation AI to generate an optimal image by referring to the arrangement and layout of an image used by the user in the past. The generation unit, for example, generates an optimal image by referring to the user's past project data and frequency of use. For example, the generation unit causes the generation AI to generate a related image based on the user's past project data. The generation unit can also cause the generation AI to generate an optimal image based on the user's frequency of use. In this way, more appropriate images can be generated by referring to the user's past usage history.

[0065] The generation unit can customize the image based on the user's current project or area of ​​interest at the time of generation. The generation unit customizes the image based on, for example, the user's current project or area of ​​interest. For example, the generation unit generates an image that the generation AI associates with the theme of the project the user is currently working on. The generation unit can also generate an optimal image based on, for example, the user's area of ​​interest. For example, the generation unit can also generate an image that the generation AI associates with based on keywords used by the user in the current project. For example, the generation unit generates an optimal image by referring to metadata of the user's current project or tags of the area of ​​interest. For example, the generation unit generates an image that the generation AI associates with based on metadata of the user's current project. The generation unit can also generate an optimal image based on tags of the user's area of ​​interest. This allows for more appropriate images to be generated by customizing the image based on the user's current project or area of ​​interest.

[0066] The generation unit can estimate the user's emotions and adjust the color tone or brightness of the generated image based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the color tone or brightness of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can generate an image with calm colors and soft brightness. For example, if the user is nervous, the generation AI can generate an image with simple, highly visible colors and brightness. For example, if the user is excited, the generation AI can generate an image with vivid, dynamic colors and brightness. The generation unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can analyze the user's text data and estimate the emotions using text analysis technology. This allows for the generation of a more appropriate image by adjusting the color tone and brightness of the image based on the user's emotions.

[0067] The generation unit can generate highly relevant images based on the user's geographical location information at the time of generation. The generation unit generates highly relevant images, for example, by taking into account the user's geographical location information. For example, when the user is in a specific area, the generation AI generates images related to that area. For example, when the user is traveling, the generation AI can also generate images related to tourist attractions and famous places at the travel destination. For example, when the user is in a specific city, the generation AI can also generate images related to the scenery and culture of that city. For example, the generation unit acquires the user's geographical location information and generates highly relevant images based on that information. For example, the generation unit generates relevant images based on the user's geographical location information. The generation unit can also generate optimal images based on the user's geographical location information. This makes it possible to generate more relevant images by taking into account the user's geographical location information.

[0068] At the time of generation, the generation unit can generate relevant images based on the user's social media activity. The generation unit, for example, analyzes the user's social media activity and generates relevant images. The generation unit, for example, causes the generation AI to generate relevant images based on content shared by the user on social media. The generation unit can also generate optimal images based on the themes of accounts the user follows on social media. The generation unit can also generate relevant images based on posts the user has "liked" on social media. The generation unit, for example, analyzes the user's social media activity and generates relevant images based on that information. The generation unit, for example, causes the generation AI to generate relevant images based on the user's social media activity. The generation unit can also generate optimal images based on the user's social media activity. In this way, more relevant images can be generated by analyzing the user's social media activity.

[0069] The application unit can estimate the user's emotion and adjust the image application method based on the estimated user's emotion. For example, the application unit can estimate the user's emotion and adjust the image application method based on the estimated user's emotion. For example, if the user is relaxed, the application unit can apply the generated images in a soft arrangement. For example, if the user is nervous, the application unit can also apply the generated images in a simple, highly visible arrangement. For example, if the user is excited, the application unit can also apply the generated images in a dynamic arrangement. For example, the application unit uses techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotion. For example, the application unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The application unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the application unit can analyze the user's text data and estimate the emotion using text analysis technology. This makes it possible to apply more appropriate images by adjusting the image application method based on the user's emotion.

[0070] When applied, the application unit can adjust the arrangement of images based on the content of a proposal or a meeting document. For example, the application unit appropriately arranges the generated images in accordance with the main points of the proposal. For example, the application unit can also arrange the generated images in optimal positions in line with the flow of the meeting document. For example, the application unit can effectively arrange the generated images in accordance with the theme of the proposal or the meeting document. For example, the application unit arranges images related to important points of the proposal in appropriate positions. The application unit can also arrange images related to the main content of the meeting document in appropriate positions. Furthermore, the application unit can apply the most relevant image based on the content of the proposal or the meeting document. This enables more effective visual communication by optimizing the arrangement of images based on the content of the proposal or the meeting document.

[0071] When applied, the application unit can automatically select highly relevant images for proposals and meeting materials in different languages. For example, the application unit automatically selects appropriate images for proposals and meeting materials in different languages. For example, the application unit automatically selects images that are easy to understand in English-speaking countries for an English proposal. For example, the application unit can also automatically select images that are suitable for Japanese culture for Japanese meeting materials. For example, the application unit can automatically select images suitable for each language for a multilingual proposal. For example, the application unit uses a language model to automatically select highly relevant images for proposals and meeting materials in different languages. For example, the application unit can also evaluate translation accuracy to automatically select highly relevant images for proposals and meeting materials in different languages. This makes it possible to respond to global business expansion by automatically selecting appropriate images for proposals and meeting materials in different languages.

[0072] The application unit can estimate the user's emotion and adjust the application order of images based on the estimated user's emotion. For example, the application unit can estimate the user's emotion and adjust the application order of images based on the estimated user's emotion. For example, if the user is relaxed, the application unit can apply the generated images in a relaxed order. For example, if the user is nervous, the application unit can apply the generated images in a simple, highly visible order. For example, if the user is excited, the application unit can apply the generated images in a dynamic order. For example, the application unit can use techniques such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotion. For example, the application unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The application unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the application unit can analyze the user's text data and estimate the emotion using text analysis technology. This makes it possible to apply more appropriate images by adjusting the application order of images based on the user's emotion.

[0073] When applying the image priority, the application unit can determine the priority of images based on the submission date of the proposal or the meeting materials. For example, when the submission deadline of the proposal is approaching, the application unit can preferentially apply images related to important points. For example, when the presentation date of the meeting materials is approaching, the application unit can also preferentially apply images related to main contents. For example, the application unit can also preferentially apply the most effective image based on the submission date of the proposal or the meeting materials. For example, when the submission deadline of the proposal is approaching, the application unit can preferentially apply images related to important points. Furthermore, when the presentation date of the meeting materials is approaching, the application unit can also preferentially apply images related to main contents. Furthermore, the application unit can also preferentially apply the most effective image based on the submission date of the proposal or the meeting materials. Thus, by determining the priority of images based on the submission date of the proposal or the meeting materials, more effective visual communication is possible.

[0074] When applying, the application unit can adjust the application range of images based on the relevance of the proposal or the conference materials. For example, the application unit applies images related to the main theme of the proposal widely. For example, the application unit can also apply images related to important points of the conference materials to an appropriate range. For example, the application unit can apply the most relevant image based on the content of the proposal or the conference materials. For example, the application unit applies images related to the main theme of the proposal widely. Furthermore, the application unit can also apply images related to important points of the conference materials to an appropriate range. Furthermore, the application unit can apply the most relevant image based on the content of the proposal or the conference materials. In this way, by adjusting the application range of images based on the relevance of the proposal or the conference materials, more effective visual communication is possible.

[0075] The management unit can estimate the user's emotions and adjust the image management method based on the estimated user's emotions. For example, the management unit can estimate the user's emotions and adjust the image management method based on the estimated user's emotions. For example, when the user is relaxed, the management unit flexibly manages the generated images. For example, when the user is nervous, the management unit can manage the generated images in a simple and highly visible manner. For example, when the user is excited, the management unit can manage the generated images in a dynamic manner. For example, to estimate the user's emotions, the management unit uses technologies such as facial expression recognition, voice analysis, and text analysis. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The management unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the management unit can analyze the user's text data and estimate the emotions using text analysis technology. This enables more appropriate image management by adjusting the image management method based on the user's emotions.

[0076] During management, the management unit can select an appropriate management method based on the usage history of the generated images. The management unit selects the optimal management method based on, for example, the frequency of use of the generated images. The management unit can also analyze, for example, the usage history of the generated images and select the most effective management method. The management unit can also select the optimal management method based on, for example, the usage pattern of the generated images. The management unit selects the optimal management method based on, for example, the frequency of use of the generated images. The management unit can also analyze the usage history of the generated images and select the most effective management method. Furthermore, the management unit can select the optimal management method based on the usage pattern of the generated images. In this way, a more appropriate management method can be selected by referring to the usage history of the generated images.

[0077] The management unit can perform filtering to minimize copyright risks of generated images during management. For example, the management unit evaluates the copyright risks of generated images and filters out images with high risks. For example, the management unit can also check copyright information of generated images and perform appropriate filtering. For example, the management unit can also set filtering criteria to reduce copyright risks of generated images. For example, the management unit checks license information of generated images and performs appropriate filtering. The management unit can also reduce copyright risks by generating original content for generated images. This reduces copyright risks of generated images, thereby avoiding legal risks.

[0078] The management unit can estimate the user's emotions and determine the management priority of images based on the estimated user emotions. For example, the management unit can estimate the user's emotions and determine the management priority of images based on the estimated user emotions. For example, when the user is relaxed, the management unit flexibly manages generated images. For example, when the user is nervous, the management unit can manage generated images in a simple, highly visible manner. For example, when the user is excited, the management unit can manage generated images in a dynamic manner. For example, to estimate the user's emotions, the management unit uses technologies such as facial expression recognition, voice analysis, and text analysis. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The management unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the management unit can analyze the user's text data and estimate the emotions using text analysis technology. This enables more appropriate image management by determining the management priority of images based on the user's emotions.

[0079] During management, the management unit can select a management method based on the geographical distribution of the generated images. The management unit, for example, analyzes the geographical distribution of the generated images and selects an optimal management method. The management unit can also select an effective management method based on the geographical distribution of the generated images. The management unit can also select an appropriate management method by taking into account the geographical distribution of the generated images. The management unit, for example, analyzes the geographical distribution of the generated images and selects an optimal management method. The management unit can also select an effective management method based on the geographical distribution of the generated images. Furthermore, the management unit can also select an appropriate management method by taking into account the geographical distribution of the generated images. In this way, a more appropriate management method can be selected by taking into account the geographical distribution of the generated images.

[0080] The management unit can improve the accuracy of management by referring to related literature of the generated image during management. The management unit, for example, improves the accuracy of management by referring to related literature of the generated image. The management unit can also, for example, analyze related literature of the generated image and select an optimal management method. The management unit can also, for example, select an effective management method based on related literature of the generated image. The management unit, for example, improves the accuracy of management by referring to related literature of the generated image. Furthermore, the management unit can also analyze related literature of the generated image and select an optimal management method. Furthermore, the management unit can also select an effective management method based on related literature of the generated image. In this way, by referring to related literature of the generated image, the accuracy of management can be improved. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, application unit, and management unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart device 14 or the processor 28 of the data processing device 12, and generates an image using a generation AI. The application unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and applies the generated image to a proposal or meeting materials. The management unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and maintains the consistency of the applied image. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generation unit, application unit, and management unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the smart glasses 214 or the processor 28 of the data processing device 12, and generates an image using a generation AI. The application unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and applies the generated image to a proposal or meeting materials. The management unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and maintains the consistency of the applied image. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, application unit, and management unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the headset type terminal 314 or the processor 28 of the data processing device 12, and generates an image using a generation AI. The application unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and applies the generated image to a proposal or conference material. The management unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and maintains the consistency of the applied image. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, application unit, and management unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the processor 46 of the robot 414 or the processor 28 of the data processing device 12, and generates an image using generative AI. The application unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and applies the generated image to a proposal or conference materials. The management unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and maintains the consistency of the applied image.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The generation unit can refer to the user's past project data and adjust the theme and style of the image to be generated. For example, the generation AI generates a related image based on the theme of a project the user has previously undertaken. The generation unit can also generate an optimal image based on the style and color tone of images the user has previously used. Furthermore, the generation unit can refer to successful examples of the user's past projects and generate similar images. This makes it possible to generate more appropriate images by utilizing the user's past project data.

[0083] The application unit can apply the generated image based on the user's current emotion. For example, if the user is relaxed, the application unit can apply the generated image in a soft arrangement. If the user is nervous, the application unit can apply the generated image in a simple, highly visible arrangement. Furthermore, if the user is excited, the application unit can apply the generated image in a dynamic arrangement. This allows for more effective visual communication by adjusting the image application method based on the user's emotion.

[0084] The management unit can select the optimal management method based on the frequency of use of the generated images. For example, if a generated image is used frequently, it can be managed as a priority. Images that are used less frequently can also be archived. Furthermore, the management unit can analyze the usage history of the generated images and select the most effective management method. This allows a more appropriate management method to be selected based on the frequency of use of the generated images.

[0085] The generation unit can generate highly relevant images based on the user's geographical location information. For example, if the user is in a specific area, the generation AI can generate images related to that area. Also, if the user is traveling, the generation AI can generate images related to tourist attractions and famous places at the travel destination. Furthermore, if the user is in a specific city, the generation AI can generate images related to the scenery and culture of that city. In this way, by taking the user's geographical location information into consideration, more relevant images can be generated.

[0086] The application unit can adjust the image layout based on the content of a proposal or meeting materials. For example, the application unit can appropriately layout the generated image in accordance with the main points of the proposal. The application unit can also optimally layout the generated image in accordance with the flow of the meeting materials. Furthermore, the application unit can effectively layout the generated image in accordance with the theme of the proposal or meeting materials. This allows for more effective visual communication by optimizing the layout of images based on the content of the proposal or meeting materials.

[0087] The generation unit can generate relevant images based on the user's social media activity. For example, the generation AI generates relevant images based on content shared by the user on social media. The generation AI can also generate optimal images based on the themes of accounts the user follows on social media. Furthermore, the generation AI can generate relevant images based on posts the user has "liked" on social media. This makes it possible to generate more relevant images by analyzing the user's social media activity.

[0088] The application unit can estimate the user's emotion and adjust the application order of images based on the estimated user's emotion. For example, if the user is relaxed, the generated images can be applied in a soft order. If the user is nervous, the generated images can be applied in a simple, highly visible order. Furthermore, if the user is excited, the generated images can be applied in a dynamic order. In this way, adjusting the application order of images based on the user's emotion enables more appropriate image application.

[0089] The management unit can perform filtering to minimize copyright risks of generated images. For example, the management unit evaluates the copyright risks of generated images and filters out images with high risks. The management unit can also check copyright information of generated images and perform appropriate filtering. Furthermore, the management unit can check license information of generated images and perform appropriate filtering. This reduces the copyright risks of generated images, thereby avoiding legal risks.

[0090] The generation unit can estimate the user's emotions and adjust the color tone and brightness of the generated image based on the estimated user emotions. For example, if the user is relaxed, the generation AI can generate an image with calm color tones and soft brightness. If the user is nervous, the generation AI can also generate an image with simple, highly visible color tones and brightness. Furthermore, if the user is excited, the generation AI can generate an image with vivid, dynamic color tones and brightness. This makes it possible to generate more appropriate images by adjusting the color tone and brightness of the image based on the user's emotions.

[0091] The management unit can estimate the user's emotions and determine the management priority of images based on the estimated user's emotions. For example, if the user is relaxed, the generated images can be managed flexibly. If the user is nervous, the generated images can be managed in a simple, highly visible manner. Furthermore, if the user is excited, the generated images can be managed in a dynamic manner. This allows for more appropriate image management by determining the management priority of images based on the user's emotions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The generator generates an image. The generator uses a generative AI to generate an image, utilizing technologies such as deep learning and GAN (generative artificial network). The generator inputs prompts to the generative AI, such as "Please generate an image suitable for a proposal" or "Please generate an image suitable for meeting materials," and obtains the generated image. Step 2: The application unit applies the generated image to the proposal or the meeting materials. The application unit can insert the generated image at a specific position in the proposal or the meeting materials and adjust the size and format. For example, the generated image can be inserted into the cover of the proposal or the slide of the meeting materials. Step 3: The management department ensures consistency in the applied images. The management department ensures consistency by matching the color tone, font, and layout of the generated images with the rest of the proposal or meeting materials. This improves the overall sense of unity in the proposal or meeting materials.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The 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.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 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.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates an image; an application unit that applies the image generated by the generation unit to a proposal or a meeting document; a management unit that maintains the consistency of the image applied by the application unit. A system characterized by:

2. The generation unit Customize the theme or style of your images with generative AI The system of claim 1 .

3. The application unit includes: Apply the generated image to a multilingual proposal or meeting materials The system of claim 1 .

4. The management unit Minimize copyright risks for generated images The system of claim 1 .

5. The generation unit Generative AI generates images that correspond to the latest trends or designs The system of claim 1 .

6. The application unit includes: Apply the generated images to different documents or proposals with the same style or theme The system of claim 1 .

7. The generation unit Estimating a user's emotion and adjusting the theme or style of an image to be generated based on the estimated user's emotion The system of claim 1 .

8. The generation unit When generating images, the system references the user's past usage history to generate appropriate images. The system of claim 1 .

Images