System

Abstract

An object of the system according to the embodiment is to generate and provide an original image based on a user's request.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives an input of a desired condition or an ideal image from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates an original image based on the information analyzed by the analysis unit. The providing unit provides the image generated by the generating unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional techniques, it is difficult to easily generate original images based on the user's wishes, and there is a risk of copyright issues arising.

[0005] The system according to the embodiment aims to generate and provide an original image based on the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of desired conditions or an ideal image from a user. The analysis unit analyzes the information received by the reception unit. The generation unit generates an original image based on the information analyzed by the analysis unit. The provision unit provides the image generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide an original image based on the user's wishes. [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) An image generation system according to an embodiment of the present invention accepts a user's desired conditions and ideal image as input, and a generation AI analyzes the input to generate an original image. The generated image is self-created and copyright-free, so it can be used for documents. For example, a user inputs their desired conditions and ideal image into the image generation system. For example, the user may input conditions such as "a landscape with a blue sky and green grass" or "a city night view." This information is input into the generation AI. The image generation system then uses the generation AI to analyze the input information. The generation AI understands the user's desired conditions and ideal image and generates an original image based on that. For example, based on the condition "a landscape with a blue sky and green grass," the generation AI generates an image depicting that landscape. Because the generated image is customized based on the user's preferences, it can be used for documents and presentations. For example, it can be used as a background image for a business presentation or an illustration for educational materials. Furthermore, because the generated image is self-created and copyright-free, it can be used freely. This allows users to easily create original images and improve the quality of their documents and presentations. This allows the image generation system to allow users to easily create original images and improve the quality of their materials and presentations. For example, even users with no design knowledge can use the generative AI to create professional-looking images. Furthermore, because the generated images are self-created, they can be used without worrying about copyright issues.

[0029] An image generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of desired conditions or an ideal image from a user. The user can input specific conditions or an ideal image in detail, such as, for example, a "scenery with a blue sky and green grass" or a "night view of a city." The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, uses an image analysis algorithm to understand the user's desired conditions or ideal image and passes the information to the generation unit based on the understanding. The generation unit generates an original image based on the information analyzed by the analysis unit using a generation AI. The generation AI generates images using, for example, technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The generation unit, for example, uses a GAN to generate realistic images based on the user's desired conditions. The generation unit can also generate a variety of images based on the user's desired conditions using a VAE. The provision unit provides the image generated by the generation unit to the user. The provision unit provides the generated image to the user in a digital format, for example. The providing unit can also provide the generated image in a print format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at high resolution and provide it to the user. This allows the image generation system according to the embodiment to generate and provide an original image based on the user's wishes.

[0030] The reception unit allows the user to input desired conditions and ideal images. The reception unit allows the user to input, for example, the desired conditions and ideal images in detail. For example, the user can input specific conditions such as "a landscape with a blue sky and green grassland" or "a night view of the city." The reception unit can also save the information input by the user and reuse it later. For example, if the user wants to reuse conditions that they input in the past, the reception unit can call up and reuse that information. This allows the user to input detailed conditions and ideal images.

[0031] The analysis unit can pass information to the generation unit based on the user's desired conditions and ideal image. The analysis unit, for example, understands the user's desired conditions and ideal image and passes information to the generation unit based on that. The analysis unit, for example, uses an image analysis algorithm to analyze the user's desired conditions and ideal image. The analysis unit can also generate information to be passed to the generation unit based on information input by the user. For example, the analysis unit passes information for drawing a landscape based on the user's input condition of "a landscape with a blue sky and green grass." This allows information to be passed to the generation unit based on the user's wishes.

[0032] The generation unit can generate original images using a generative AI. The generation unit generates original images using, for example, a generative AI. The generative AI generates images using technologies such as a GAN (Generative Adversarial Network) or a VAE (Variational Autoencoder). The generation unit uses, for example, a GAN to generate realistic images based on the user's desired conditions. The generation unit can also use a VAE to generate a variety of images based on the user's desired conditions. This makes it possible to generate original images using the generative AI.

[0033] The providing unit can provide the generated image to the user. For example, the providing unit provides the generated image to the user. For example, the providing unit provides the generated image to the user in a digital format. The providing unit can also provide the generated image in a printed format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at a high resolution and provide it to the user. In this way, the generated image can be provided to the user.

[0034] The providing unit can provide the generated image in a format suitable for use in materials and presentations. For example, the providing unit provides the generated image in a format suitable for use in materials and presentations. For example, the providing unit provides the generated image in a PDF format or a PPT format. The providing unit can also provide the generated image in an image file format. For example, the providing unit provides the generated image in a JPEG format or a PNG format. This allows the generated image to be provided in a format suitable for use in materials, presentations, etc.

[0035] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. The reception unit, for example, automatically displays desired conditions and ideal images that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest desired conditions and ideal images to be used in a specific time period from the user's past input history. This improves user convenience by suggesting the optimal input method based on the user's past input history.

[0036] The reception unit can filter the input content based on the user's current project or field of interest when the user inputs desired conditions or an ideal image. For example, the reception unit filters the input content based on the user's current project or field of interest when the user inputs desired conditions or an ideal image. For example, the reception unit preferentially displays conditions and images related to a project currently underway by the user. The reception unit can also suggest related conditions and images based on the user's field of interest. The reception unit can also suggest optimal conditions and images by referring to the user's past project history. This allows for more relevant input by filtering the input content based on the user's current project or field of interest.

[0037] The reception unit can select the optimum input means according to the user's input method when inputting desired conditions or an ideal image. For example, when inputting desired conditions or an ideal image, the reception unit selects the optimum input means according to the user's input method (voice, text, image, etc.). For example, when the user inputs conditions or an image by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs conditions or an image by text, the reception unit can also prioritize support for text input. Furthermore, when the user uploads an image, the reception unit can also support the input using image recognition technology. In this way, by selecting the optimum input means according to the user's input method, input efficiency is improved.

[0038] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information when inputting desired conditions or an ideal image. For example, the reception unit preferentially accepts highly relevant input content in consideration of the user's geographical location information when inputting desired conditions or an ideal image. For example, when the user is in a specific area, the reception unit can preferentially display conditions and images related to that area. Furthermore, when the user is traveling, the reception unit can preferentially display conditions and images related to the travel destination. Furthermore, when the user is at home, the reception unit can preferentially display conditions and images related to the area around the user's home. In this way, highly relevant input content can be preferentially accepted by taking the user's geographical location information into consideration.

[0039] The reception unit can analyze the user's social media activity when the user inputs desired conditions or an ideal image, and suggest related input content. For example, when the user inputs desired conditions or an ideal image, the reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can analyze images and posts shared by the user on social media, and suggest related conditions and images. The reception unit can also suggest related conditions and images by referring to the activity of the user's friends on social media. The reception unit can also analyze the user's interests and concerns on social media, and suggest related conditions and images. In this way, related input content can be suggested by analyzing the user's social media activity.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired conditions or an ideal image. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting desired conditions or an ideal image. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. In this way, the input method can be customized by reflecting the user's past feedback.

[0041] The analysis unit can adjust the level of detail of the analysis based on the input conditions and the importance of the ideal image during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the input conditions and the importance of the ideal image during analysis. For example, the analysis unit performs a detailed analysis for important conditions and images. The analysis unit can also perform a concise analysis for less important conditions and images. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This allows for a more appropriate analysis by adjusting the level of detail of the analysis based on the input conditions and the importance of the ideal image.

[0042] The analysis unit can apply different analysis algorithms depending on the input conditions and the category of the ideal image during analysis. For example, the analysis unit applies different analysis algorithms depending on the input conditions and the category of the ideal image during analysis. For example, the analysis unit applies a landscape analysis algorithm to a landscape image. The analysis unit can also apply a night view analysis algorithm to an urban night view. The analysis unit can also apply a person analysis algorithm to an image of a person. This allows for more appropriate analysis by applying different analysis algorithms depending on the input conditions and the category of the ideal image.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] The analysis unit can determine the analysis priority based on the input conditions and the ideal image submission time during analysis. The analysis unit, for example, determines the analysis priority based on the input conditions and the ideal image submission time during analysis. The analysis unit, for example, prioritizes analysis for urgent conditions and images. The analysis unit can also prioritize analysis for conditions and images with an approaching submission deadline. The analysis unit can also determine the analysis priority based on the submission time specified by the user. In this way, by determining the analysis priority based on the input conditions and the ideal image submission time, analyses with high urgency can be prioritized.

[0045] The analysis unit can adjust the order of analysis based on the input conditions and the relevance of the ideal image during analysis. For example, the analysis unit adjusts the order of analysis based on the input conditions and the relevance of the ideal image during analysis. For example, the analysis unit prioritizes analysis of highly relevant conditions and images. The analysis unit can also postpone analysis of less relevant conditions and images. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. In this way, by adjusting the order of analysis based on the input conditions and the relevance of the ideal image, highly relevant analysis can be prioritized.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0047] The generation unit can adjust the accuracy of generation based on the input conditions and the level of detail of the ideal image at the time of generation. For example, the generation unit adjusts the accuracy of generation based on the input conditions and the level of detail of the ideal image at the time of generation. For example, the generation unit generates a high-accuracy image for detailed conditions and images. The generation unit can also generate a low-accuracy image for simple conditions and images. The generation unit can also adjust the accuracy of generation based on the level of detail specified by the user. In this way, a more appropriate image can be generated by adjusting the accuracy of generation based on the input conditions and the level of detail of the ideal image.

[0048] The generation unit can apply different generation algorithms depending on the input conditions and the category of the ideal image at the time of generation. For example, the generation unit applies different generation algorithms depending on the input conditions and the category of the ideal image at the time of generation. For example, the generation unit applies a landscape generation algorithm to a landscape image. Furthermore, the generation unit can also apply a night view generation algorithm to an urban night view. Furthermore, the generation unit can also apply a person generation algorithm to an image of a person. In this way, by applying different generation algorithms depending on the input conditions and the category of the ideal image, a more appropriate image can be generated.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also learn specific patterns from the user's past generation results and improve the accuracy of generation. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0050] The generation unit can determine the generation priority based on the input conditions and the submission time of the ideal image at the time of generation. The generation unit determines the generation priority based on the input conditions and the submission time of the ideal image, for example, at the time of generation. The generation unit, for example, prioritizes generation for urgent conditions and images. The generation unit can also prioritize generation for conditions and images with an approaching submission deadline. The generation unit can also determine the generation priority based on the submission time specified by the user. In this way, by determining the generation priority based on the input conditions and the submission time of the ideal image, generation with high urgency can be prioritized.

[0051] The generation unit can adjust the order of generation based on the input conditions and the relevance of the ideal image at the time of generation. For example, the generation unit adjusts the order of generation based on the input conditions and the relevance of the ideal image at the time of generation. For example, the generation unit prioritizes generation for highly relevant conditions and images. The generation unit can also postpone generation for less relevant conditions and images. The generation unit can also adjust the order of generation based on the relevance specified by the user. In this way, by adjusting the order of generation based on the input conditions and the relevance of the ideal image, it is possible to prioritize generation of highly relevant images.

[0052] The generation unit can adjust the content of the image to be generated according to the user's level of expertise at the time of generation. For example, the generation unit adjusts the content of the image to be generated according to the user's level of expertise at the time of generation. For example, if the user has specialized knowledge, the generation unit generates an image including specialized content. Furthermore, if the user does not have specialized knowledge, the generation unit can also generate an image with simple and easy-to-understand content. Furthermore, the generation unit can adjust the content of the image according to the user's level of expertise. In this way, by adjusting the content of the image according to the user's level of expertise, a more appropriate image can be generated.

[0053] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the display method used by the user in the past. The providing unit can also provide a specific display method preferentially based on the user's past usage history. The providing unit can also analyze the user's past usage history and customize the providing method. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0054] The providing unit can customize the provided content according to the user's current task when providing the content. For example, the providing unit customizes the provided content according to the user's current task when providing the content. For example, when the user is giving a presentation, the providing unit provides an image that is optimal for the presentation. Furthermore, when the user is creating educational materials, the providing unit can also provide an image that is optimal for the educational materials. Furthermore, the providing unit can customize the provided content according to the user's current task. In this way, by customizing the provided content according to the user's current task, more appropriate images can be provided.

[0055] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method by reflecting user feedback at the time of providing. For example, when a user provides feedback on a provided image, the providing unit improves the providing method based on the feedback. The providing unit can also analyze the user's past feedback and customize the providing method. The providing unit can also improve the provided content by reflecting user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0056] The providing unit can select the optimal providing method in consideration of the user's geographical location information at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the user's geographical location information at the time of providing. For example, when the user is in a specific area, the providing unit can preferentially provide images related to that area. Furthermore, when the user is traveling, the providing unit can also preferentially provide images related to the travel destination. Furthermore, when the user is at home, the providing unit can also preferentially provide images related to the area around the user's home. In this way, the optimal providing method can be selected by considering the user's geographical location information.

[0057] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit customizes the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit analyzes images and posts shared by the user on social media and provides related images. The providing unit can also provide related images by referring to the activity of the user's friends on social media. The providing unit can also analyze the user's interests and concerns on social media and provide related images. In this way, the content to be provided can be customized by analyzing the user's social media activity.

[0058] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the delivery method. In this way, the delivery method can be customized by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can also automatically suggest related past images based on the user's input. For example, if the user inputs "a landscape with a blue sky and green grass," the reception unit can display similar images generated in the past, allowing the user to refer to them. The reception unit can also make new related suggestions based on the user's previously input conditions. For example, if the user previously inputs "urban night view," the reception unit can make new suggestions such as "urban sunset view" or "urban sunrise." Furthermore, the reception unit can provide trend information on related images based on the user's input. For example, the reception unit can suggest currently popular landscapes and colors, allowing the user to generate images based on the latest trends.

[0061] The analysis unit can also pass related text information to the generation unit based on the user's input. For example, if a user inputs "a landscape with a blue sky and a green meadow," the analysis unit can pass a poem or quote related to that landscape to the generation unit and add text to the generated image. The analysis unit can also pass related music information to the generation unit based on the user's input. For example, if a user inputs "a city night view," the analysis unit can pass music that matches that night view to the generation unit and provide the music together with the generated image. The analysis unit can also pass related video information to the generation unit based on the user's input. For example, if a user inputs "a landscape with a blue sky and a green meadow," the analysis unit can pass a video related to that landscape to the generation unit and provide the video together with the generated image.

[0062] The generation unit can also generate images in multiple different styles based on the user's input. For example, if the user inputs "a landscape with a blue sky and a green meadow," the generation unit can generate images in different styles, such as a realistic landscape image, an abstract landscape image, or an anime-style landscape image. The generation unit can also generate images for different seasons or time periods based on the user's input. For example, if the user inputs "a city night view," the generation unit can generate images for different seasons or time periods, such as a summer night view, a winter night view, or an evening night view. The generation unit can also generate images from different perspectives based on the user's input. For example, if the user inputs "a landscape with a blue sky and a green meadow," the generation unit can generate images from different perspectives, such as a view from above, a view from the ground, or a distant view.

[0063] The providing unit can also send the generated image directly to the user's device. For example, if the user is using a smartphone, the providing unit can send the generated image directly to the smartphone, allowing the user to immediately use the image. The providing unit can also store the generated image in cloud storage, allowing the user to access it at any time. For example, the providing unit can store the generated image in cloud storage such as Google® Drive or Dropbox®, allowing the user to download it when needed. Furthermore, the providing unit can provide a function for directly posting the generated image to social media. For example, the providing unit can post the generated image directly to social media such as Facebook® or Instagram®, allowing the user to easily share the image.

[0064] The providing unit can also customize the generated image according to the user's preferences. For example, if the user prefers a particular color or filter, the providing unit can apply the color or filter to the generated image and provide it. The providing unit can also adjust the size and resolution of the generated image according to the user's preferences. For example, if the user prefers high-resolution images, the providing unit can provide the generated image in high resolution. The providing unit can also add text or stamps to the generated image according to the user's preferences. For example, if the user wants to add a particular message or stamp to the image, the providing unit can add the message or stamp to the generated image and provide it.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The reception unit receives input of desired conditions or ideal images from the user. For example, the user can input specific conditions or ideal images in detail, such as "a landscape with a blue sky and green grassland" or "a city night view." Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to understand the user's desired conditions and ideal image, and then passes the information to the generation unit based on that. Step 3: The generation unit uses a generation AI to generate an original image based on the information analyzed by the analysis unit. The generation AI generates images using technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The generation unit uses a GAN, for example, to generate realistic images based on the user's desired conditions. The generation unit can also use a VAE to generate a variety of images based on the user's desired conditions. Step 4: The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image to the user in a digital format. The providing unit can also provide the generated image in a printed format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at high resolution and provide it to the user.

[0067] (Example 2) An image generation system according to an embodiment of the present invention accepts a user's desired conditions and ideal image as input, and a generation AI analyzes the input to generate an original image. The generated image is self-created and copyright-free, so it can be used for documents. For example, a user inputs their desired conditions and ideal image into the image generation system. For example, the user may input conditions such as "a landscape with a blue sky and green grass" or "a city night view." This information is input into the generation AI. The image generation system then uses the generation AI to analyze the input information. The generation AI understands the user's desired conditions and ideal image and generates an original image based on that. For example, based on the condition "a landscape with a blue sky and green grass," the generation AI generates an image depicting that landscape. Because the generated image is customized based on the user's preferences, it can be used for documents and presentations. For example, it can be used as a background image for a business presentation or an illustration for educational materials. Furthermore, because the generated image is self-created and copyright-free, it can be used freely. This allows users to easily create original images and improve the quality of their documents and presentations. This allows the image generation system to allow users to easily create original images and improve the quality of their materials and presentations. For example, even users with no design knowledge can use the generative AI to create professional-looking images. Furthermore, because the generated images are self-created, they can be used without worrying about copyright issues.

[0068] An image generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives input of desired conditions or an ideal image from a user. The user can input specific conditions or an ideal image in detail, such as, for example, a "scenery with a blue sky and green grass" or a "night view of a city." The analysis unit analyzes the information received by the reception unit. The analysis unit, for example, uses an image analysis algorithm to understand the user's desired conditions or ideal image and passes the information to the generation unit based on the understanding. The generation unit generates an original image based on the information analyzed by the analysis unit using a generation AI. The generation AI generates images using, for example, technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The generation unit, for example, uses a GAN to generate realistic images based on the user's desired conditions. The generation unit can also generate a variety of images based on the user's desired conditions using a VAE. The provision unit provides the image generated by the generation unit to the user. The provision unit provides the generated image to the user in a digital format, for example. The providing unit can also provide the generated image in a print format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at high resolution and provide it to the user. This allows the image generation system according to the embodiment to generate and provide an original image based on the user's wishes.

[0069] The reception unit allows the user to input desired conditions and ideal images. The reception unit allows the user to input, for example, the desired conditions and ideal images in detail. For example, the user can input specific conditions such as "a landscape with a blue sky and green grassland" or "a night view of the city." The reception unit can also save the information input by the user and reuse it later. For example, if the user wants to reuse conditions that they input in the past, the reception unit can call up and reuse that information. This allows the user to input detailed conditions and ideal images.

[0070] The analysis unit can pass information to the generation unit based on the user's desired conditions and ideal image. The analysis unit, for example, understands the user's desired conditions and ideal image and passes information to the generation unit based on that. The analysis unit, for example, uses an image analysis algorithm to analyze the user's desired conditions and ideal image. The analysis unit can also generate information to be passed to the generation unit based on information input by the user. For example, the analysis unit passes information for drawing a landscape based on the user's input condition of "a landscape with a blue sky and green grass." This allows information to be passed to the generation unit based on the user's wishes.

[0071] The generation unit can generate original images using a generative AI. The generation unit generates original images using, for example, a generative AI. The generative AI generates images using technologies such as a GAN (Generative Adversarial Network) or a VAE (Variational Autoencoder). The generation unit uses, for example, a GAN to generate realistic images based on the user's desired conditions. The generation unit can also use a VAE to generate a variety of images based on the user's desired conditions. This makes it possible to generate original images using the generative AI.

[0072] The providing unit can provide the generated image to the user. For example, the providing unit provides the generated image to the user. For example, the providing unit provides the generated image to the user in a digital format. The providing unit can also provide the generated image in a printed format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at a high resolution and provide it to the user. In this way, the generated image can be provided to the user.

[0073] The providing unit can provide the generated image in a format suitable for use in materials and presentations. For example, the providing unit provides the generated image in a format suitable for use in materials and presentations. For example, the providing unit provides the generated image in a PDF format or a PPT format. The providing unit can also provide the generated image in an image file format. For example, the providing unit provides the generated image in a JPEG format or a PNG format. This allows the generated image to be provided in a format suitable for use in materials, presentations, etc.

[0074] The reception unit can estimate the user's emotions and adjust the input method for the desired conditions and ideal image based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and adjust the input method for the desired conditions and ideal image based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to quickly input the desired conditions and ideal image. This allows for more appropriate input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The reception unit can analyze the user's past input history and suggest the optimal input method. The reception unit, for example, analyzes the user's past input history and suggests the optimal input method. The reception unit, for example, automatically displays desired conditions and ideal images that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest desired conditions and ideal images to be used in a specific time period from the user's past input history. This improves user convenience by suggesting the optimal input method based on the user's past input history.

[0076] The reception unit can filter the input content based on the user's current project or field of interest when the user inputs desired conditions or an ideal image. For example, the reception unit filters the input content based on the user's current project or field of interest when the user inputs desired conditions or an ideal image. For example, the reception unit preferentially displays conditions and images related to a project currently underway by the user. The reception unit can also suggest related conditions and images based on the user's field of interest. The reception unit can also suggest optimal conditions and images by referring to the user's past project history. This allows for more relevant input by filtering the input content based on the user's current project or field of interest.

[0077] The reception unit can select the optimum input means according to the user's input method when inputting desired conditions or an ideal image. For example, when inputting desired conditions or an ideal image, the reception unit selects the optimum input means according to the user's input method (voice, text, image, etc.). For example, when the user inputs conditions or an image by voice, the reception unit supports the input using voice recognition technology. Furthermore, when the user inputs conditions or an image by text, the reception unit can also prioritize support for text input. Furthermore, when the user uploads an image, the reception unit can also support the input using image recognition technology. In this way, by selecting the optimum input means according to the user's input method, input efficiency is improved.

[0078] The reception unit can estimate the user's emotions and determine the priority of input content based on the estimated user emotions. For example, the reception unit can estimate the user's emotions and determine the priority of input content based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize input of important conditions and images. Furthermore, when the user is relaxed, the reception unit can prioritize input of detailed conditions and images. Furthermore, when the user is in a hurry, the reception unit can prioritize input of the most important conditions and images. In this way, by determining the priority of input content according to the user's emotions, more important content can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0079] The reception unit can preferentially accept highly relevant input content in consideration of the user's geographical location information when inputting desired conditions or an ideal image. For example, the reception unit preferentially accepts highly relevant input content in consideration of the user's geographical location information when inputting desired conditions or an ideal image. For example, when the user is in a specific area, the reception unit can preferentially display conditions and images related to that area. Furthermore, when the user is traveling, the reception unit can preferentially display conditions and images related to the travel destination. Furthermore, when the user is at home, the reception unit can preferentially display conditions and images related to the area around the user's home. In this way, highly relevant input content can be preferentially accepted by taking the user's geographical location information into consideration.

[0080] The reception unit can analyze the user's social media activity when the user inputs desired conditions or an ideal image, and suggest related input content. For example, when the user inputs desired conditions or an ideal image, the reception unit can analyze the user's social media activity and suggest related input content. For example, the reception unit can analyze images and posts shared by the user on social media, and suggest related conditions and images. The reception unit can also suggest related conditions and images by referring to the activity of the user's friends on social media. The reception unit can also analyze the user's interests and concerns on social media, and suggest related conditions and images. In this way, related input content can be suggested by analyzing the user's social media activity.

[0081] The reception unit can customize the input method by reflecting the user's past feedback when inputting desired conditions or an ideal image. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting desired conditions or an ideal image. For example, the reception unit suggests an optimal input method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input method. In this way, the input method can be customized by reflecting the user's past feedback.

[0082] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually stimulating analysis results when the user is excited. In this way, by adjusting the way the analysis is presented according to the user's emotion, more appropriate analysis results can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the input conditions and the importance of the ideal image during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the input conditions and the importance of the ideal image during analysis. For example, the analysis unit performs a detailed analysis for important conditions and images. The analysis unit can also perform a concise analysis for less important conditions and images. The analysis unit can also adjust the level of detail of the analysis based on the importance specified by the user. This allows for a more appropriate analysis by adjusting the level of detail of the analysis based on the input conditions and the importance of the ideal image.

[0084] The analysis unit can apply different analysis algorithms depending on the input conditions and the category of the ideal image during analysis. For example, the analysis unit applies different analysis algorithms depending on the input conditions and the category of the ideal image during analysis. For example, the analysis unit applies a landscape analysis algorithm to a landscape image. The analysis unit can also apply a night view analysis algorithm to an urban night view. The analysis unit can also apply a person analysis algorithm to an image of a person. This allows for more appropriate analysis by applying different analysis algorithms depending on the input conditions and the category of the ideal image.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, the analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows for adjusting the length of the analysis according to the user's emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0087] The analysis unit can determine the analysis priority based on the input conditions and the ideal image submission time during analysis. The analysis unit, for example, determines the analysis priority based on the input conditions and the ideal image submission time during analysis. The analysis unit, for example, prioritizes analysis for urgent conditions and images. The analysis unit can also prioritize analysis for conditions and images with an approaching submission deadline. The analysis unit can also determine the analysis priority based on the submission time specified by the user. In this way, by determining the analysis priority based on the input conditions and the ideal image submission time, analyses with high urgency can be prioritized.

[0088] The analysis unit can adjust the order of analysis based on the input conditions and the relevance of the ideal image during analysis. For example, the analysis unit adjusts the order of analysis based on the input conditions and the relevance of the ideal image during analysis. For example, the analysis unit prioritizes analysis of highly relevant conditions and images. The analysis unit can also postpone analysis of less relevant conditions and images. The analysis unit can also adjust the order of analysis based on the relevance specified by the user. In this way, by adjusting the order of analysis based on the input conditions and the relevance of the ideal image, highly relevant analysis can be prioritized.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides analysis results that make heavy use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand.

[0090] The generation unit can estimate the user's emotion and adjust the style of the image to be generated based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the style of the image to be generated based on the estimated user's emotion. For example, if the user is relaxed, the generation unit can generate an image with soft colors. If the user is excited, the generation unit can also generate an image with vivid colors. If the user is stressed, the generation unit can also generate an image with subdued colors. In this way, by adjusting the image style according to the user's emotion, a more appropriate image can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] The generation unit can adjust the accuracy of generation based on the input conditions and the level of detail of the ideal image at the time of generation. For example, the generation unit adjusts the accuracy of generation based on the input conditions and the level of detail of the ideal image at the time of generation. For example, the generation unit generates a high-accuracy image for detailed conditions and images. The generation unit can also generate a low-accuracy image for simple conditions and images. The generation unit can also adjust the accuracy of generation based on the level of detail specified by the user. In this way, a more appropriate image can be generated by adjusting the accuracy of generation based on the input conditions and the level of detail of the ideal image.

[0092] The generation unit can apply different generation algorithms depending on the input conditions and the category of the ideal image at the time of generation. For example, the generation unit applies different generation algorithms depending on the input conditions and the category of the ideal image at the time of generation. For example, the generation unit applies a landscape generation algorithm to a landscape image. Furthermore, the generation unit can also apply a night view generation algorithm to an urban night view. Furthermore, the generation unit can also apply a person generation algorithm to an image of a person. In this way, by applying different generation algorithms depending on the input conditions and the category of the ideal image, a more appropriate image can be generated.

[0093] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit improves the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit adjusts the generation algorithm based on the user's past generation results. The generation unit can also learn specific patterns from the user's past generation results and improve the accuracy of generation. The generation unit can also adjust the level of detail of generation by referring to the user's past generation results. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0094] The generation unit can estimate the user's emotion and adjust the size of the image to be generated based on the estimated user's emotion. For example, the generation unit can estimate the user's emotion and adjust the size of the image to be generated based on the estimated user's emotion. For example, the generation unit can generate a large-sized image when the user is relaxed. The generation unit can also generate a small-sized image when the user is in a hurry. The generation unit can also generate an image of an appropriate size when the user is stressed. In this way, by adjusting the size of the image according to the user's emotion, a more appropriate image can be generated. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0095] The generation unit can determine the generation priority based on the input conditions and the submission time of the ideal image at the time of generation. The generation unit determines the generation priority based on the input conditions and the submission time of the ideal image, for example, at the time of generation. The generation unit, for example, prioritizes generation for urgent conditions and images. The generation unit can also prioritize generation for conditions and images with an approaching submission deadline. The generation unit can also determine the generation priority based on the submission time specified by the user. In this way, by determining the generation priority based on the input conditions and the submission time of the ideal image, generation with high urgency can be prioritized.

[0096] The generation unit can adjust the order of generation based on the input conditions and the relevance of the ideal image at the time of generation. For example, the generation unit adjusts the order of generation based on the input conditions and the relevance of the ideal image at the time of generation. For example, the generation unit prioritizes generation for highly relevant conditions and images. The generation unit can also postpone generation for less relevant conditions and images. The generation unit can also adjust the order of generation based on the relevance specified by the user. In this way, by adjusting the order of generation based on the input conditions and the relevance of the ideal image, it is possible to prioritize generation of highly relevant images.

[0097] The generation unit can adjust the content of the image to be generated according to the user's level of expertise at the time of generation. For example, the generation unit adjusts the content of the image to be generated according to the user's level of expertise at the time of generation. For example, if the user has specialized knowledge, the generation unit generates an image including specialized content. Furthermore, if the user does not have specialized knowledge, the generation unit can also generate an image with simple and easy-to-understand content. Furthermore, the generation unit can adjust the content of the image according to the user's level of expertise. In this way, by adjusting the content of the image according to the user's level of expertise, a more appropriate image can be generated.

[0098] The providing unit can estimate the user's emotion and adjust the display method of the image to be provided based on the estimated user's emotion. For example, the providing unit can estimate the user's emotion and adjust the display method of the image to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide a display method with soft colors. If the user is excited, the providing unit can also provide a display method with vivid colors. If the user is stressed, the providing unit can also provide a display method with calm colors. This allows for a more appropriate display by adjusting the image display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] The providing unit can select the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method by referring to the user's past usage history at the time of providing. For example, the providing unit selects the optimal providing method based on the display method used by the user in the past. The providing unit can also provide a specific display method preferentially based on the user's past usage history. The providing unit can also analyze the user's past usage history and customize the providing method. In this way, the optimal providing method can be selected by referring to the user's past usage history.

[0100] The providing unit can customize the provided content according to the user's current task when providing the content. For example, the providing unit customizes the provided content according to the user's current task when providing the content. For example, when the user is giving a presentation, the providing unit provides an image that is optimal for the presentation. Furthermore, when the user is creating educational materials, the providing unit can also provide an image that is optimal for the educational materials. Furthermore, the providing unit can customize the provided content according to the user's current task. In this way, by customizing the provided content according to the user's current task, more appropriate images can be provided.

[0101] The providing unit can improve the providing method by reflecting user feedback at the time of providing. For example, the providing unit improves the providing method by reflecting user feedback at the time of providing. For example, when a user provides feedback on a provided image, the providing unit improves the providing method based on the feedback. The providing unit can also analyze the user's past feedback and customize the providing method. The providing unit can also improve the provided content by reflecting user feedback. In this way, the providing method can be improved by reflecting user feedback.

[0102] The providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user emotions. For example, the providing unit can estimate the user's emotions and determine the priority of images to be provided based on the estimated user emotions. For example, when the user is relaxed, the providing unit can prioritize providing important images. Furthermore, when the user is in a hurry, the providing unit can prioritize providing the most important images. Furthermore, when the user is feeling stressed, the providing unit can provide images with a moderate priority. In this way, by determining the priority of images according to the user's emotions, more important images can be prioritized. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] The providing unit can select the optimal providing method in consideration of the user's geographical location information at the time of providing. For example, the providing unit selects the optimal providing method in consideration of the user's geographical location information at the time of providing. For example, when the user is in a specific area, the providing unit can preferentially provide images related to that area. Furthermore, when the user is traveling, the providing unit can also preferentially provide images related to the travel destination. Furthermore, when the user is at home, the providing unit can also preferentially provide images related to the area around the user's home. In this way, the optimal providing method can be selected by considering the user's geographical location information.

[0104] The providing unit can customize the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit customizes the content to be provided by analyzing the user's social media activity at the time of providing. For example, the providing unit analyzes images and posts shared by the user on social media and provides related images. The providing unit can also provide related images by referring to the activity of the user's friends on social media. The providing unit can also analyze the user's interests and concerns on social media and provide related images. In this way, the content to be provided can be customized by analyzing the user's social media activity.

[0105] The providing unit can customize the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing the information. For example, the providing unit suggests an optimal delivery method based on feedback provided by the user in the past. The providing unit can also preferentially suggest a specific delivery method based on the user's past feedback. The providing unit can also analyze the user's past feedback and customize the delivery method. In this way, the delivery method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input of desired conditions and ideal images from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an original image based on the analyzed information. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides the generated image to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input of desired conditions and ideal images from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an original image based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides the generated image to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input of desired conditions and ideal images from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an original image based on the analyzed information. The provision unit is realized, for example, by the display 343 of the headset-type terminal 314 and provides the generated image to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input of desired conditions and ideal images from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the information received by the reception unit. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an original image based on the analyzed information. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides the generated image to the user.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The reception unit can also automatically suggest related past images based on the user's input. For example, if the user inputs "a landscape with a blue sky and green grass," the reception unit can display similar images generated in the past, allowing the user to refer to them. The reception unit can also make new related suggestions based on the user's previously input conditions. For example, if the user previously inputs "urban night view," the reception unit can make new suggestions such as "urban sunset view" or "urban sunrise." Furthermore, the reception unit can provide trend information on related images based on the user's input. For example, the reception unit can suggest currently popular landscapes and colors, allowing the user to generate images based on the latest trends.

[0108] The analysis unit can also pass related text information to the generation unit based on the user's input. For example, if a user inputs "a landscape with a blue sky and a green meadow," the analysis unit can pass a poem or quote related to that landscape to the generation unit and add text to the generated image. The analysis unit can also pass related music information to the generation unit based on the user's input. For example, if a user inputs "a city night view," the analysis unit can pass music that matches that night view to the generation unit and provide the music together with the generated image. The analysis unit can also pass related video information to the generation unit based on the user's input. For example, if a user inputs "a landscape with a blue sky and a green meadow," the analysis unit can pass a video related to that landscape to the generation unit and provide the video together with the generated image.

[0109] The generation unit can also generate images in multiple different styles based on the user's input. For example, if the user inputs "a landscape with a blue sky and a green meadow," the generation unit can generate images in different styles, such as a realistic landscape image, an abstract landscape image, or an anime-style landscape image. The generation unit can also generate images for different seasons or time periods based on the user's input. For example, if the user inputs "a city night view," the generation unit can generate images for different seasons or time periods, such as a summer night view, a winter night view, or an evening night view. The generation unit can also generate images from different perspectives based on the user's input. For example, if the user inputs "a landscape with a blue sky and a green meadow," the generation unit can generate images from different perspectives, such as a view from above, a view from the ground, or a distant view.

[0110] The providing unit may also send the generated image directly to the user's device. For example, if the user is using a smartphone, the providing unit may send the generated image directly to the smartphone, allowing the user to immediately use the image. The providing unit may also store the generated image in cloud storage, allowing the user to access it at any time. For example, the providing unit may store the generated image in cloud storage such as Google Drive or Dropbox, allowing the user to download it when needed. Furthermore, the providing unit may provide a function for directly posting the generated image to social media. For example, the providing unit may post the generated image directly to social media such as Facebook or Instagram, allowing the user to easily share the image.

[0111] The providing unit can also customize the generated image according to the user's preferences. For example, if the user prefers a particular color or filter, the providing unit can apply the color or filter to the generated image and provide it. The providing unit can also adjust the size and resolution of the generated image according to the user's preferences. For example, if the user prefers high-resolution images, the providing unit can provide the generated image in high resolution. The providing unit can also add text or stamps to the generated image according to the user's preferences. For example, if the user wants to add a particular message or stamp to the image, the providing unit can add the message or stamp to the generated image and provide it.

[0112] The reception unit can estimate the user's emotions and automatically complete the input content based on the estimated user's emotions. For example, if the user inputs "a landscape with a blue sky and green grass," the reception unit can estimate that the user is relaxed and automatically add elements such as "birds chirping" and "gentle breeze." If the user inputs "a city night view," the reception unit can estimate that the user is excited and automatically add elements such as "neon lights" and "busy streets." If the user inputs "a quiet lake," the reception unit can estimate that the user is calm and automatically add elements such as "calm waves" and "reflections of a sunset." This allows the input content to be completed according to the user's emotions, thereby generating richer images.

[0113] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and pass more accurate information to the generation unit. If the user is in a hurry, the analysis unit can perform a concise analysis and pass the information quickly to the generation unit. Furthermore, if the user is excited, the analysis unit can pass a visually stimulating analysis result to the generation unit. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0114] The generation unit can estimate the user's emotions and adjust the theme of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image with a nature or landscape theme. If the user is excited, the generation unit can generate an image with a city or event theme. Furthermore, if the user is stressed, the generation unit can generate an image with a healing or relaxation theme. In this way, by adjusting the image theme according to the user's emotions, more appropriate images can be generated.

[0115] The providing unit can estimate the user's emotion and adjust the format of the image to be provided based on the estimated user's emotion. For example, if the user is relaxed, the providing unit can provide an image with soft colors. If the user is excited, the providing unit can provide an image with vivid colors. Furthermore, if the user is stressed, the providing unit can provide an image with calm colors. In this way, by adjusting the format of the image according to the user's emotion, more appropriate images can be provided.

[0116] The providing unit can estimate the user's emotions and adjust the timing of providing images based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide images at a slow pace. If the user is in a hurry, the providing unit can provide images quickly. Furthermore, if the user is excited, the providing unit can provide images continuously. In this way, by adjusting the timing of providing images according to the user's emotions, more appropriate images can be provided.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The reception unit receives input of desired conditions or ideal images from the user. For example, the user can input specific conditions or ideal images in detail, such as "a landscape with a blue sky and green grassland" or "a city night view." Step 2: The analysis unit analyzes the information received by the reception unit. For example, the analysis unit uses an image analysis algorithm to understand the user's desired conditions and ideal image, and then passes the information to the generation unit based on that. Step 3: The generation unit uses a generation AI to generate an original image based on the information analyzed by the analysis unit. The generation AI generates images using technologies such as a generative adversarial network (GAN) or a variational autoencoder (VAE). The generation unit uses a GAN, for example, to generate realistic images based on the user's desired conditions. The generation unit can also use a VAE to generate a variety of images based on the user's desired conditions. Step 4: The providing unit provides the image generated by the generating unit to the user. For example, the providing unit provides the generated image to the user in a digital format. The providing unit can also provide the generated image in a printed format. For example, the providing unit can provide the generated image to the user in a PDF format or a JPEG format. The providing unit can also print the generated image at high resolution and provide it to the user.

[0119] 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.

[0120] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 receiving unit that receives input of desired conditions or an ideal image from a user; an analysis unit that analyzes the information received by the reception unit; a generation unit that generates an original image based on the information analyzed by the analysis unit; a providing unit that provides the image generated by the generating unit to a user; A system characterized by:

2. The reception unit Users can input their desired conditions and ideal images.

2. The system of claim 1.

3. The analysis unit Information based on the user's desired conditions and ideal image is passed to the generation unit.

2. The system of claim 1.

4. The generation unit Generate original images using generative AI 2. The system of claim 1.

5. The providing unit Providing the generated image to the user 2. The system of claim 1.

6. The providing unit Provide the generated images in a format suitable for use in documents and presentations 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the desired conditions and ideal image input method based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input method 2. The system of claim 1.

Images