system

Abstract

We provide a system that identifies and corrects bias and errors in AI-generated images. [Solution] A data processing system (10) operating as a platform for addressing bias issues in AI-generated images functionally comprises an upload unit, an analysis unit, a feedback unit, a correction unit, and a re-evaluation unit. The upload unit uploads images generated by a generating AI. The analysis unit analyzes the images uploaded by the upload unit and identifies bias or errors. The feedback unit provides feedback based on the bias or errors identified by the analysis unit. The correction unit corrects the images based on the feedback provided by the feedback unit. The re-evaluation unit re-evaluates the images corrected by the correction unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently identify and correct biases and errors contained in AI-generated images.

[0005] The system of the embodiment aims to identify and correct biases and errors in AI-generated images. [Means for solving the problem]

[0006] A system according to an embodiment includes an upload unit, an analysis unit, a feedback unit, a correction unit, and a re-evaluation unit. The upload unit uploads images generated by the generation AI. The analysis unit analyzes the images uploaded by the upload unit and identifies biases or errors. The feedback unit provides feedback based on the biases or errors identified by the analysis unit. The correction unit corrects the images based on the feedback provided by the feedback unit. The re-evaluation unit re-evaluates the images corrected by the correction unit. [Effects of the Invention]

[0007] Systems according to embodiments can identify and correct biases and errors in AI-generated images. [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) A platform embodying the present invention addresses the issue of bias in AI-generated images. In this system, artists upload images generated by a generative AI to the platform, where the AI ​​analyzes the images to identify biases and errors. For example, if the images contain bias against a specific race or gender, the AI ​​will point out these biases. Furthermore, human evaluators review the images and supplement the AI's findings. This allows for the identification of biases and errors from both AI and human perspectives. The artist then fine-tunes the images based on feedback from the AI ​​and human evaluators. For example, they can reduce bias by modifying or removing specific elements. Finally, the artist re-uploads the corrected images to the platform for re-evaluation. Repeating this process results in a more fair and ethical artwork. This platform promotes exchanges of opinions between artists and evaluators, not only improving the quality of artwork but also increasing artists' motivation. Furthermore, it helps address bias issues in an AI-based society and contributes to the realization of a more fair and ethical society. Evaluators are experts with specific backgrounds, such as scholars with expertise in gender studies and racial issues, or art experts. This ensures the quality of the evaluations. As an example of the specific corrections that artists make after receiving feedback, if a work contains bias against a particular race, the artist will correct or delete that part to reduce bias. The process for reevaluation after corrections is for the artist to re-upload the corrected image to the platform and undergo a clear reevaluation process. This allows the platform to improve the quality and motivation of artists' artwork. It also addresses bias issues in AI society and contributes to the realization of a more fair and ethical society.

[0029] A platform according to an embodiment includes an uploading unit, an analysis unit, a feedback unit, a correction unit, and a re-evaluation unit. The uploading unit uploads images generated by the generative AI. For example, the uploading unit allows an artist to upload images generated by the generative AI to the platform. The analysis unit analyzes the images uploaded by the uploading unit to identify biases and errors. The analysis unit can identify, for example, prejudice against a particular race or gender. The analysis unit analyzes the images using AI to identify biases and errors. For example, the analysis unit can analyze specific elements in the images and point out biases and errors. The feedback unit provides feedback based on the biases and errors identified by the analysis unit. For example, the feedback unit can provide feedback from both AI and human evaluators. The feedback unit generates feedback using AI, and the human evaluators complement the feedback. For example, the feedback unit can provide additional comments or correction suggestions for biases and errors pointed out by the AI. The correction unit corrects the images based on the feedback provided by the feedback unit. For example, the correction unit can reduce bias by correcting or deleting specific elements. The correction unit provides tools for artists to correct images after receiving feedback. For example, the correction unit may provide image editing software, allowing artists to modify or remove specific elements. The re-evaluation unit re-evaluates the images corrected by the correction unit. For example, the re-evaluation unit may re-analyze the corrected images and evaluate whether bias or error has been reduced. The re-evaluation unit re-evaluates the corrected images using AI, and a human evaluator confirms the results. For example, the re-evaluation unit may analyze the corrected images using AI, evaluate whether bias or error has been reduced, and a human evaluator confirms the results. This allows the platform according to the embodiment to improve the quality and motivation of artists' artworks. It can also address bias issues in an AI society and contribute to realizing a more fair and ethical society.

[0030] The analysis unit can identify bias against a specific race or gender. The analysis unit can identify bias against a specific race or gender, for example, using AI. For example, the analysis unit can analyze specific elements in an image and determine whether it contains bias against a specific race or gender. The analysis unit can also allow a human evaluator to provide additional comments or corrections to biases pointed out by the AI. For example, the analysis unit can allow a human evaluator to review the content of bias pointed out by the AI ​​and provide additional comments or corrections. This makes it possible to reduce bias by identifying bias against a specific race or gender. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without using AI. For example, the analysis unit can allow a human evaluator to review the content of bias pointed out by the AI ​​and provide additional comments or corrections.

[0031] The feedback unit can provide feedback from both AI and human evaluators. For example, the feedback unit generates feedback using AI, and human evaluators complement the content. For example, the feedback unit can have human evaluators provide additional comments or suggested corrections for biases or errors pointed out by the AI. Furthermore, by providing feedback from both AI and human evaluators, the feedback unit can more accurately identify biases and errors. For example, the feedback unit can have human evaluators review the content of biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections. This makes it possible to identify biases and errors from the perspectives of both the AI ​​and the human evaluators. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit can have human evaluators review the content of biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections.

[0032] The correction unit can reduce bias by correcting or deleting specific elements. For example, the correction unit provides tools for artists to correct images after receiving feedback. For example, the correction unit provides image editing software, allowing artists to correct or delete specific elements. The correction unit can also reduce bias by correcting or deleting specific elements. For example, if an image contains bias against a specific race, the correction unit can reduce bias by correcting or deleting that part. In this way, bias can be reduced by correcting or deleting specific elements. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can have an artist review the biases or errors pointed out by AI and provide correction suggestions.

[0033] The re-evaluation unit can re-evaluate the corrected image. For example, the re-evaluation unit can re-analyze the corrected image and evaluate whether bias or error has been reduced. The re-evaluation unit uses AI to re-evaluate the corrected image, and a human evaluator confirms the results. For example, the re-evaluation unit can use AI to analyze the corrected image, evaluate whether bias or error has been reduced, and a human evaluator confirms the results. In this way, by re-evaluating the corrected image, it is possible to confirm that bias has been reduced. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, or may be performed without using AI. For example, the re-evaluation unit can have a human evaluator review the content of the bias or error pointed out by the AI ​​and provide additional comments or suggested corrections.

[0034] The feedback unit can provide feedback from evaluators such as scholars knowledgeable in gender studies or racial issues, or art experts. For example, the feedback unit can provide feedback from evaluators such as scholars knowledgeable in gender studies or racial issues, or art experts. For example, the feedback unit can have a gender studies expert point out gender bias in an image and provide suggested corrections. The feedback unit can also have a scholar knowledgeable in racial issues point out racial bias in an image and provide suggested corrections. Furthermore, the feedback unit can have an art expert provide feedback on the composition and color tone of the image. This improves the quality of the evaluation through feedback from experts. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can have an expert review the biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections.

[0035] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit can, for example, analyze the user's past upload history and select the optimal upload method. For example, the upload unit can automatically select an upload method that the user has frequently used in the past. The upload unit can also suggest the most efficient time period based on the user's past upload history. Furthermore, the upload unit can suggest optimal upload settings based on the types of images the user has uploaded in the past. This allows the optimal upload method to be selected based on the past history. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's past upload history data into a generation AI and have the generation AI select the optimal upload method.

[0036] The uploading unit may filter images based on the user's current project or area of ​​interest when uploading them. For example, the uploading unit may filter images based on the user's current project or area of ​​interest when uploading them. For example, the uploading unit may filter images so that only images related to the project the user is currently working on are uploaded. The uploading unit may also prioritize uploading highly relevant images based on the user's area of ​​interest. Furthermore, the uploading unit may select and upload appropriate images according to the progress of the user's project. This allows images to be uploaded according to the user's project or area of ​​interest. Some or all of the above-described processing in the uploading unit may be performed using, or without, AI. For example, the uploading unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0037] When uploading images, the uploading unit can prioritize uploading highly relevant images taking into account the user's geographical location information. For example, when uploading images, the uploading unit prioritizes uploading highly relevant images taking into account the user's geographical location information. For example, when the user is in a specific area, the uploading unit can prioritize uploading images related to that area. Furthermore, when the user is traveling, the uploading unit can prioritize uploading images related to the travel destination. Furthermore, when the user is participating in a specific event, the uploading unit can prioritize uploading images related to the event. This makes it possible to upload highly relevant images based on the geographical location information. Some or all of the above-described processing in the uploading unit may be performed using AI, for example, or may be performed without using AI. For example, the uploading unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant images.

[0038] The uploading unit can analyze the user's social media activity and upload related images when uploading an image. For example, the uploading unit can analyze the user's social media activity and upload related images when uploading an image. For example, the uploading unit can upload related images based on images shared by the user on social media. The uploading unit can also select highly relevant images based on the user's social media activity. Furthermore, the uploading unit can analyze the reactions of the user's followers on social media and prioritize uploading popular images. This makes it possible to upload highly relevant images based on social media activity. Some or all of the above-described processing in the uploading unit may be performed using, or without, AI, for example. For example, the uploading unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the image during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the image during analysis. For example, the analysis unit can perform a detailed analysis on an image with high importance. The analysis unit can also perform a simplified analysis on an image with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the image. This allows the level of detail of the analysis to be adjusted according to the importance of the image. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the image during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the image during analysis. For example, the analysis unit can apply a face recognition algorithm to a person image. Furthermore, the analysis unit can apply a landscape analysis algorithm to a landscape image. Furthermore, the analysis unit can apply a product recognition algorithm to a product image. This enables analysis depending on the category of the image. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] The analysis unit can determine the analysis priority based on the time of image submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of image submission during analysis. For example, the analysis unit can prioritize analysis of recently submitted images. The analysis unit can also postpone analysis of older submitted images. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time of submission. This allows the analysis priority to be determined based on the time of submission. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image submission time data into the generation AI and have the generation AI determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the images during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the images during analysis. For example, the analysis unit can prioritize analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the images. This makes it possible to adjust the order of analysis based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] The feedback unit can adjust the level of detail of the feedback based on the importance of the bias or error when providing feedback. For example, the feedback unit can provide detailed feedback for biases or errors with high importance. The feedback unit can provide simplified feedback for biases or errors with low importance. Furthermore, the feedback unit can determine the priority of the feedback based on the importance of the bias or error. This makes it possible to provide feedback based on the importance of the bias or error. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input data on the importance of the bias or error to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0044] The feedback unit can apply different feedback algorithms depending on the evaluator's field of expertise when providing feedback. For example, the feedback unit can apply different feedback algorithms depending on the evaluator's field of expertise when providing feedback. For example, the feedback unit can apply an algorithm regarding gender bias to feedback from a gender studies expert. Furthermore, the feedback unit can apply an algorithm regarding racial bias to feedback from a racial issues expert. Furthermore, the feedback unit can apply an algorithm regarding art evaluation to feedback from an art expert. This makes it possible to provide feedback according to the evaluator's field of expertise. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, AI, for example. For example, the feedback unit can input data on the evaluator's field of expertise into the generation AI and cause the generation AI to apply the feedback algorithm.

[0045] The feedback unit can determine the priority of feedback based on the time of image submission at the time of feedback. For example, the feedback unit can determine the priority of feedback based on the time of image submission at the time of feedback. For example, the feedback unit can provide feedback preferentially to recently submitted images. Furthermore, the feedback unit can postpone images submitted earlier. Furthermore, the feedback unit can dynamically adjust the priority of feedback according to the time of submission. This makes it possible to determine the priority of feedback based on the time of submission. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input image submission time data to the generation AI and cause the generation AI to determine the priority of feedback.

[0046] The feedback unit can adjust the order of feedback based on the relevance of the images at the time of feedback. For example, the feedback unit can adjust the order of feedback based on the relevance of the images at the time of feedback. For example, the feedback unit can provide feedback preferentially to highly relevant images. Furthermore, the feedback unit can postpone providing feedback to less relevant images. Furthermore, the feedback unit can dynamically adjust the order of feedback according to the relevance of the images. This makes it possible to adjust the order of feedback based on the relevance of the images. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input image relevance data to the generation AI and cause the generation AI to adjust the order of feedback.

[0047] The correction unit can adjust the level of detail of the correction based on the importance of the bias or error during correction. For example, the correction unit can adjust the level of detail of the correction based on the importance of the bias or error during correction. For example, the correction unit can perform detailed corrections for biases or errors with high importance. Furthermore, the correction unit can perform simplified corrections for biases or errors with low importance. Furthermore, the correction unit can determine the priority of the corrections according to the importance of the bias or error. This makes it possible to provide corrections according to the importance of the bias or error. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on the importance of the bias or error to the generation AI and cause the generation AI to adjust the level of detail of the corrections.

[0048] The correction unit can apply different correction algorithms depending on the category of the image during correction. For example, the correction unit can apply different correction algorithms depending on the category of the image during correction. For example, the correction unit can apply a face recognition algorithm to a portrait image to make correction. Furthermore, the correction unit can apply a landscape analysis algorithm to a landscape image to make correction. Furthermore, the correction unit can apply a product recognition algorithm to a product image to make correction. This enables correction according to the category of the image. Some or all of the above-mentioned processing in the correction unit can be performed using AI, for example, or can be performed without using AI. For example, the correction unit can input image category data to the generation AI and cause the generation AI to apply the correction algorithm.

[0049] The correction unit can adjust the order of corrections based on the submission dates of the images during corrections. For example, the correction unit can adjust the order of corrections based on the submission dates of the images during corrections. For example, the correction unit can prioritize corrections of recently submitted images. Furthermore, the correction unit can postpone images submitted earlier. Furthermore, the correction unit can dynamically adjust the order of corrections according to the submission dates. This makes it possible to adjust the order of corrections based on the submission dates. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input image submission date data into the generation AI and cause the generation AI to adjust the order of corrections.

[0050] The correction unit can adjust the correction procedure based on the relevance of the images during correction. For example, the correction unit can adjust the correction procedure based on the relevance of the images during correction. For example, the correction unit can prioritize correction of highly relevant images. Furthermore, the correction unit can postpone correction of less relevant images. Furthermore, the correction unit can dynamically adjust the correction procedure according to the relevance of the images. This makes it possible to adjust the correction procedure based on the relevance of the images. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input image relevance data to the generation AI and cause the generation AI to adjust the correction procedure.

[0051] The re-evaluation unit can adjust the level of detail of the re-evaluation based on the importance of the corrected image during re-evaluation. For example, the re-evaluation unit can perform a detailed re-evaluation for a corrected image with high importance. Furthermore, the re-evaluation unit can perform a simplified re-evaluation for a corrected image with low importance. Furthermore, the re-evaluation unit can determine the priority of the re-evaluation based on the importance of the corrected image. This makes it possible to provide a re-evaluation based on the importance of the corrected image. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input importance data of the corrected image to the generation AI and cause the generation AI to adjust the level of detail of the re-evaluation.

[0052] The re-evaluation unit can apply different re-evaluation algorithms depending on the category of the image during re-evaluation. For example, the re-evaluation unit can apply different re-evaluation algorithms depending on the category of the image during re-evaluation. For example, the re-evaluation unit can apply a face recognition algorithm to a portrait image to re-evaluate. Furthermore, the re-evaluation unit can apply a landscape analysis algorithm to a landscape image to re-evaluate. Furthermore, the re-evaluation unit can apply a product recognition algorithm to a product image to re-evaluate. This enables re-evaluation depending on the category of the image. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input image category data into the generation AI and cause the generation AI to apply the re-evaluation algorithm.

[0053] The re-evaluation unit can adjust the order of re-evaluation based on the submission date of the corrected image during re-evaluation. The re-evaluation unit can adjust the order of re-evaluation based on the submission date of the corrected image during re-evaluation, for example. For example, the re-evaluation unit can prioritize re-evaluating recently submitted corrected images. The re-evaluation unit can also postpone older submitted corrected images. Furthermore, the re-evaluation unit can dynamically adjust the order of re-evaluation based on the submission date. This allows the order of re-evaluation to be adjusted based on the submission date. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input submission date data of the corrected image into the generation AI and cause the generation AI to adjust the order of re-evaluation.

[0054] The re-evaluation unit can adjust the re-evaluation procedure based on the relevance of the corrected image during re-evaluation. The re-evaluation unit can, for example, adjust the re-evaluation procedure based on the relevance of the corrected image during re-evaluation. For example, the re-evaluation unit can prioritize re-evaluating corrected images with high relevance. The re-evaluation unit can also postpone re-evaluating corrected images with low relevance. Furthermore, the re-evaluation unit can dynamically adjust the re-evaluation procedure according to the relevance of the corrected image. This allows the re-evaluation procedure to be adjusted based on the relevance of the corrected image. Some or all of the above-described processing in the re-evaluation unit may be performed using, or without, AI, for example. For example, the re-evaluation unit can input relevance data of the corrected image to the generation AI and cause the generation AI to adjust the re-evaluation procedure.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The analysis unit can analyze the color balance of an image and adjust the balance if a particular color is used excessively. For example, if a particular color is used excessively, the analysis unit can suggest to the artist that the use of that color be reduced. Also, if the color balance is unbalanced, the analysis unit can suggest that the artist balance it by adding another color. Furthermore, if the color balance needs to be adjusted, the analysis unit can provide the artist with a specific method for doing so. This can optimize the color balance of the image and improve its visual appeal.

[0057] The correction unit can analyze the composition of an image and make suggestions to optimize visual balance. For example, if an element in an image is biased, the correction unit can suggest adjusting its placement. Also, if the visual focus is unclear, the correction unit can provide a suggestion to clarify the focus. Furthermore, if the composition of an image is visually unpleasant, the correction unit can suggest how to improve it. This can optimize the composition of the image and increase its visual appeal.

[0058] The upload unit can analyze the user's past upload history and suggest the optimal upload timing. For example, the upload unit can identify the time period in which the user received the most feedback in the past and encourage the user to upload during that time period. The upload unit can also suggest the upload method that the user has had the most success with in the past. Furthermore, the upload unit can automatically select the optimal upload settings from the user's past upload history. This makes it possible to provide the optimal upload method based on the user's past history.

[0059] The analysis unit can analyze the textures of an image and adjust the balance if a particular texture is used excessively. For example, if a particular texture is used excessively, the analysis unit can suggest to the artist that the use of that texture be reduced. Furthermore, if the balance of textures is unbalanced, the analysis unit can suggest that the balance be improved by adding another texture. Furthermore, if the balance of textures needs to be adjusted, the analysis unit can provide the artist with a specific method for doing so. This can optimize the texture balance of the image and improve its visual appeal.

[0060] The correction unit can analyze the resolution of the image and suggest correction at the optimal resolution. For example, if the resolution is low, the correction unit can suggest correction at a higher resolution. Also, if the resolution is too high, the correction unit can suggest adjusting it to an appropriate resolution. Furthermore, if the resolution needs to be adjusted, the correction unit can provide the artist with a specific method for doing so. This allows the image resolution to be optimized and the visual appeal to be improved.

[0061] The upload unit may select an optimal upload server in consideration of the user's geographical location information. For example, if the user is in a specific area, the upload unit may select a server closest to that area. If the user is traveling, the upload unit may select a server closest to the user's travel destination. Furthermore, if the user is participating in a specific event, the upload unit may select a server related to the event. In this way, the optimal upload server may be selected based on the geographical location information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The upload unit uploads the image generated by the generative AI. For example, an artist can upload an image generated by the generative AI to the platform. Step 2: The analysis unit analyzes the images uploaded by the upload unit to identify biases and errors. For example, it can identify prejudice against a particular race or gender. The analysis unit uses AI to analyze the images and identifies specific elements within the images to identify biases and errors. Step 3: The feedback section provides feedback based on the biases and errors identified by the analysis section. For example, feedback from both the AI ​​and human evaluators can be provided, with the human evaluators providing additional comments and suggestions for corrections to biases and errors identified by the AI. Step 4: The Correction Department corrects the image based on the feedback provided by the Feedback Department, for example, by correcting or removing certain elements to reduce bias. The Correction Department provides tools for artists to correct the image based on the feedback and uses image editing software to correct or remove certain elements. Step 5: The re-evaluation unit re-evaluates the images corrected by the correction unit. For example, it re-analyzes the corrected images and evaluates whether bias and errors have been reduced. The re-evaluation unit uses AI to re-evaluate the corrected images, and a human evaluator confirms the results.

[0064] (Example 2) A platform embodying the present invention addresses the issue of bias in AI-generated images. In this system, artists upload images generated by a generative AI to the platform, where the AI ​​analyzes the images to identify biases and errors. For example, if the images contain bias against a specific race or gender, the AI ​​will point out these biases. Furthermore, human evaluators review the images and supplement the AI's findings. This allows for the identification of biases and errors from both AI and human perspectives. The artist then fine-tunes the images based on feedback from the AI ​​and human evaluators. For example, they can reduce bias by modifying or removing specific elements. Finally, the artist re-uploads the corrected images to the platform for re-evaluation. Repeating this process results in a more fair and ethical artwork. This platform promotes exchanges of opinions between artists and evaluators, not only improving the quality of artwork but also increasing artists' motivation. Furthermore, it helps address bias issues in an AI-based society and contributes to the realization of a more fair and ethical society. Evaluators are experts with specific backgrounds, such as scholars with expertise in gender studies and racial issues, or art experts. This ensures the quality of the evaluations. As an example of the specific corrections that artists make after receiving feedback, if a work contains bias against a particular race, the artist will correct or delete that part to reduce bias. The process for reevaluation after corrections is for the artist to re-upload the corrected image to the platform and undergo a clear reevaluation process. This allows the platform to improve the quality and motivation of artists' artwork. It also addresses bias issues in AI society and contributes to the realization of a more fair and ethical society.

[0065] A platform according to an embodiment includes an uploading unit, an analysis unit, a feedback unit, a correction unit, and a re-evaluation unit. The uploading unit uploads images generated by the generative AI. For example, the uploading unit allows an artist to upload images generated by the generative AI to the platform. The analysis unit analyzes the images uploaded by the uploading unit to identify biases and errors. The analysis unit can identify, for example, prejudice against a particular race or gender. The analysis unit analyzes the images using AI to identify biases and errors. For example, the analysis unit can analyze specific elements in the images and point out biases and errors. The feedback unit provides feedback based on the biases and errors identified by the analysis unit. For example, the feedback unit can provide feedback from both AI and human evaluators. The feedback unit generates feedback using AI, and the human evaluators complement the feedback. For example, the feedback unit can provide additional comments or correction suggestions for biases and errors pointed out by the AI. The correction unit corrects the images based on the feedback provided by the feedback unit. For example, the correction unit can reduce bias by correcting or deleting specific elements. The correction unit provides tools for artists to correct images after receiving feedback. For example, the correction unit may provide image editing software, allowing artists to modify or remove specific elements. The re-evaluation unit re-evaluates the images corrected by the correction unit. For example, the re-evaluation unit may re-analyze the corrected images and evaluate whether bias or error has been reduced. The re-evaluation unit re-evaluates the corrected images using AI, and a human evaluator confirms the results. For example, the re-evaluation unit may analyze the corrected images using AI, evaluate whether bias or error has been reduced, and a human evaluator confirms the results. This allows the platform according to the embodiment to improve the quality and motivation of artists' artworks. It can also address bias issues in an AI society and contribute to realizing a more fair and ethical society.

[0066] The analysis unit can identify bias against a specific race or gender. The analysis unit can identify bias against a specific race or gender, for example, using AI. For example, the analysis unit can analyze specific elements in an image and determine whether it contains bias against a specific race or gender. The analysis unit can also allow a human evaluator to provide additional comments or corrections to biases pointed out by the AI. For example, the analysis unit can allow a human evaluator to review the content of bias pointed out by the AI ​​and provide additional comments or corrections. This makes it possible to reduce bias by identifying bias against a specific race or gender. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, or can be performed without using AI. For example, the analysis unit can allow a human evaluator to review the content of bias pointed out by the AI ​​and provide additional comments or corrections.

[0067] The feedback unit can provide feedback from both AI and human evaluators. For example, the feedback unit generates feedback using AI, and human evaluators complement the content. For example, the feedback unit can have human evaluators provide additional comments or suggested corrections for biases or errors pointed out by the AI. Furthermore, by providing feedback from both AI and human evaluators, the feedback unit can more accurately identify biases and errors. For example, the feedback unit can have human evaluators review the content of biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections. This makes it possible to identify biases and errors from the perspectives of both the AI ​​and the human evaluators. Some or all of the above-mentioned processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit can have human evaluators review the content of biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections.

[0068] The correction unit can reduce bias by correcting or deleting specific elements. For example, the correction unit provides tools for artists to correct images after receiving feedback. For example, the correction unit provides image editing software, allowing artists to correct or delete specific elements. The correction unit can also reduce bias by correcting or deleting specific elements. For example, if an image contains bias against a specific race, the correction unit can reduce bias by correcting or deleting that part. In this way, bias can be reduced by correcting or deleting specific elements. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can have an artist review the biases or errors pointed out by AI and provide correction suggestions.

[0069] The re-evaluation unit can re-evaluate the corrected image. For example, the re-evaluation unit can re-analyze the corrected image and evaluate whether bias or error has been reduced. The re-evaluation unit uses AI to re-evaluate the corrected image, and a human evaluator confirms the results. For example, the re-evaluation unit can use AI to analyze the corrected image, evaluate whether bias or error has been reduced, and a human evaluator confirms the results. In this way, by re-evaluating the corrected image, it is possible to confirm that bias has been reduced. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, or may be performed without using AI. For example, the re-evaluation unit can have a human evaluator review the content of the bias or error pointed out by the AI ​​and provide additional comments or suggested corrections.

[0070] The feedback unit can provide feedback from evaluators such as scholars knowledgeable in gender studies or racial issues, or art experts. For example, the feedback unit can provide feedback from evaluators such as scholars knowledgeable in gender studies or racial issues, or art experts. For example, the feedback unit can have a gender studies expert point out gender bias in an image and provide suggested corrections. The feedback unit can also have a scholar knowledgeable in racial issues point out racial bias in an image and provide suggested corrections. Furthermore, the feedback unit can have an art expert provide feedback on the composition and color tone of the image. This improves the quality of the evaluation through feedback from experts. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can have an expert review the biases or errors pointed out by the AI ​​and provide additional comments or suggested corrections.

[0071] The upload unit can estimate a user's emotions and adjust the timing of image uploads based on the estimated user emotions. The upload unit, for example, estimates a user's emotions and adjusts the timing of image uploads based on the estimated user emotions. For example, if a user is feeling stressed, the upload unit can prompt the user to upload at a time when the user is able to relax. Furthermore, if the user is relaxed, the upload unit can prompt the user to upload immediately. Furthermore, if the user is in a hurry, the upload unit can provide a simplified procedure for quickly completing the upload. This allows images to be uploaded at the optimal timing according to the user's emotions. The emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the upload unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the upload unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The upload unit can analyze the user's past upload history and select the optimal upload method. The upload unit can, for example, analyze the user's past upload history and select the optimal upload method. For example, the upload unit can automatically select an upload method that the user has frequently used in the past. The upload unit can also suggest the most efficient time period based on the user's past upload history. Furthermore, the upload unit can suggest optimal upload settings based on the types of images the user has uploaded in the past. This allows the optimal upload method to be selected based on the past history. Some or all of the above-described processing in the upload unit may be performed using, for example, AI, or may be performed without using AI. For example, the upload unit can input the user's past upload history data into a generation AI and have the generation AI select the optimal upload method.

[0073] The uploading unit may filter images based on the user's current project or area of ​​interest when uploading them. For example, the uploading unit may filter images based on the user's current project or area of ​​interest when uploading them. For example, the uploading unit may filter images so that only images related to the project the user is currently working on are uploaded. The uploading unit may also prioritize uploading highly relevant images based on the user's area of ​​interest. Furthermore, the uploading unit may select and upload appropriate images according to the progress of the user's project. This allows images to be uploaded according to the user's project or area of ​​interest. Some or all of the above-described processing in the uploading unit may be performed using, or without, AI. For example, the uploading unit may input the user's project data and area of ​​interest data into a generation AI and have the generation AI perform the filtering.

[0074] The upload unit can estimate a user's emotions and determine the priority of images to be uploaded based on the estimated user emotions. The upload unit, for example, estimates a user's emotions and determines the priority of images to be uploaded based on the estimated user emotions. For example, if the user is relaxed, the upload unit can prioritize uploading images of high importance. Furthermore, if the user is stressed, the upload unit can postpone uploading images of low importance. Furthermore, if the user is in a hurry, the upload unit can upload the most important images first. This allows the priority of images to be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the upload unit may be performed using AI, for example, or without AI. For example, the upload unit can input user emotion data into the generation AI and cause the generation AI to estimate emotions.

[0075] When uploading images, the uploading unit can prioritize uploading highly relevant images taking into account the user's geographical location information. For example, when uploading images, the uploading unit prioritizes uploading highly relevant images taking into account the user's geographical location information. For example, when the user is in a specific area, the uploading unit can prioritize uploading images related to that area. Furthermore, when the user is traveling, the uploading unit can prioritize uploading images related to the travel destination. Furthermore, when the user is participating in a specific event, the uploading unit can prioritize uploading images related to the event. This makes it possible to upload highly relevant images based on the geographical location information. Some or all of the above-described processing in the uploading unit may be performed using AI, for example, or may be performed without using AI. For example, the uploading unit can input the user's geographical location information data to a generation AI and cause the generation AI to select highly relevant images.

[0076] The uploading unit can analyze the user's social media activity and upload related images when uploading an image. For example, the uploading unit can analyze the user's social media activity and upload related images when uploading an image. For example, the uploading unit can upload related images based on images shared by the user on social media. The uploading unit can also select highly relevant images based on the user's social media activity. Furthermore, the uploading unit can analyze the reactions of the user's followers on social media and prioritize uploading popular images. This makes it possible to upload highly relevant images based on social media activity. Some or all of the above-described processing in the uploading unit may be performed using, or without, AI, for example. For example, the uploading unit can input the user's social media activity data into a generation AI and cause the generation AI to select related images.

[0077] The analysis unit can estimate the user's emotions and adjust the bias and error identification criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the bias and error identification criteria based on the estimated user emotions. For example, the analysis unit can identify detailed biases and errors when the user is relaxed. Furthermore, the analysis unit can identify only important biases and errors when the user is stressed. Furthermore, the analysis unit can apply criteria that allow for quick identification when the user is in a hurry. This allows the bias and error identification criteria to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the image during analysis. The analysis unit can, for example, adjust the level of detail of the analysis based on the importance of the image during analysis. For example, the analysis unit can perform a detailed analysis on an image with high importance. The analysis unit can also perform a simplified analysis on an image with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the image. This allows the level of detail of the analysis to be adjusted according to the importance of the image. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the image during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of the image during analysis. For example, the analysis unit can apply a face recognition algorithm to a person image. Furthermore, the analysis unit can apply a landscape analysis algorithm to a landscape image. Furthermore, the analysis unit can apply a product recognition algorithm to a product image. This enables analysis depending on the category of the image. Some or all of the above-mentioned processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image category data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This makes it possible to provide a display method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0081] The analysis unit can determine the analysis priority based on the time of image submission during analysis. The analysis unit can, for example, determine the analysis priority based on the time of image submission during analysis. For example, the analysis unit can prioritize analysis of recently submitted images. The analysis unit can also postpone analysis of older submitted images. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time of submission. This allows the analysis priority to be determined based on the time of submission. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or can be performed without using AI. For example, the analysis unit can input image submission time data into the generation AI and have the generation AI determine the analysis priority.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the images during analysis. The analysis unit can, for example, adjust the order of analysis based on the relevance of the images during analysis. For example, the analysis unit can prioritize analysis of highly relevant images. The analysis unit can also postpone analysis of less relevant images. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the images. This makes it possible to adjust the order of analysis based on the relevance of the images. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input image relevance data to the generation AI and cause the generation AI to adjust the order of analysis.

[0083] The feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the feedback expression method based on the estimated user's emotion. For example, if the user is nervous, the feedback unit can provide gentle feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide concise feedback. This makes it possible to provide feedback according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0084] The feedback unit can adjust the level of detail of the feedback based on the importance of the bias or error when providing feedback. For example, the feedback unit can provide detailed feedback for biases or errors with high importance. The feedback unit can provide simplified feedback for biases or errors with low importance. Furthermore, the feedback unit can determine the priority of the feedback based on the importance of the bias or error. This makes it possible to provide feedback based on the importance of the bias or error. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input data on the importance of the bias or error to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0085] The feedback unit can apply different feedback algorithms depending on the evaluator's field of expertise when providing feedback. For example, the feedback unit can apply different feedback algorithms depending on the evaluator's field of expertise when providing feedback. For example, the feedback unit can apply an algorithm regarding gender bias to feedback from a gender studies expert. Furthermore, the feedback unit can apply an algorithm regarding racial bias to feedback from a racial issues expert. Furthermore, the feedback unit can apply an algorithm regarding art evaluation to feedback from an art expert. This makes it possible to provide feedback according to the evaluator's field of expertise. Some or all of the above-mentioned processing in the feedback unit can be performed using, or without, AI, for example. For example, the feedback unit can input data on the evaluator's field of expertise into the generation AI and cause the generation AI to apply the feedback algorithm.

[0086] The feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit can estimate the user's emotion and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit can provide short and to-the-point feedback when the user is nervous. The feedback unit can provide detailed feedback when the user is relaxed. Furthermore, the feedback unit can provide concise feedback when the user is in a hurry. This allows the length of the feedback to be adjusted according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the feedback unit can be performed using an AI, for example, or without an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The feedback unit can determine the priority of feedback based on the time of image submission at the time of feedback. For example, the feedback unit can determine the priority of feedback based on the time of image submission at the time of feedback. For example, the feedback unit can provide feedback preferentially to recently submitted images. Furthermore, the feedback unit can postpone images submitted earlier. Furthermore, the feedback unit can dynamically adjust the priority of feedback according to the time of submission. This makes it possible to determine the priority of feedback based on the time of submission. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input image submission time data to the generation AI and cause the generation AI to determine the priority of feedback.

[0088] The feedback unit can adjust the order of feedback based on the relevance of the images at the time of feedback. For example, the feedback unit can adjust the order of feedback based on the relevance of the images at the time of feedback. For example, the feedback unit can provide feedback preferentially to highly relevant images. Furthermore, the feedback unit can postpone providing feedback to less relevant images. Furthermore, the feedback unit can dynamically adjust the order of feedback according to the relevance of the images. This makes it possible to adjust the order of feedback based on the relevance of the images. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit can input image relevance data to the generation AI and cause the generation AI to adjust the order of feedback.

[0089] The correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, the correction unit can estimate the user's emotion and adjust the correction method based on the estimated user's emotion. For example, the correction unit can provide a simple and intuitive correction method when the user is nervous. The correction unit can provide detailed correction options when the user is relaxed. Furthermore, the correction unit can provide a quick correction method when the user is in a hurry. This makes it possible to provide a correction method that suits the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit can be performed using an AI, for example, or without an AI. For example, the correction unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0090] The correction unit can adjust the level of detail of the correction based on the importance of the bias or error during correction. For example, the correction unit can adjust the level of detail of the correction based on the importance of the bias or error during correction. For example, the correction unit can perform detailed corrections for biases or errors with high importance. Furthermore, the correction unit can perform simplified corrections for biases or errors with low importance. Furthermore, the correction unit can determine the priority of the corrections according to the importance of the bias or error. This makes it possible to provide corrections according to the importance of the bias or error. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can input data on the importance of the bias or error to the generation AI and cause the generation AI to adjust the level of detail of the corrections.

[0091] The correction unit can apply different correction algorithms depending on the category of the image during correction. For example, the correction unit can apply different correction algorithms depending on the category of the image during correction. For example, the correction unit can apply a face recognition algorithm to a portrait image to make correction. Furthermore, the correction unit can apply a landscape analysis algorithm to a landscape image to make correction. Furthermore, the correction unit can apply a product recognition algorithm to a product image to make correction. This enables correction according to the category of the image. Some or all of the above-mentioned processing in the correction unit can be performed using AI, for example, or can be performed without using AI. For example, the correction unit can input image category data to the generation AI and cause the generation AI to apply the correction algorithm.

[0092] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. For example, the correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. For example, if the user is nervous, the correction unit can prioritize corrections with high importance. Furthermore, if the user is relaxed, the correction unit can prioritize detailed corrections. Furthermore, if the user is in a hurry, the correction unit can prioritize parts that can be corrected quickly. This makes it possible to provide correction priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the correction unit may be performed using an AI, for example, or without an AI. For example, the correction unit can input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The correction unit can adjust the order of corrections based on the submission dates of the images during corrections. For example, the correction unit can adjust the order of corrections based on the submission dates of the images during corrections. For example, the correction unit can prioritize corrections of recently submitted images. Furthermore, the correction unit can postpone images submitted earlier. Furthermore, the correction unit can dynamically adjust the order of corrections according to the submission dates. This makes it possible to adjust the order of corrections based on the submission dates. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input image submission date data into the generation AI and cause the generation AI to adjust the order of corrections.

[0094] The correction unit can adjust the correction procedure based on the relevance of the images during correction. For example, the correction unit can adjust the correction procedure based on the relevance of the images during correction. For example, the correction unit can prioritize correction of highly relevant images. Furthermore, the correction unit can postpone correction of less relevant images. Furthermore, the correction unit can dynamically adjust the correction procedure according to the relevance of the images. This makes it possible to adjust the correction procedure based on the relevance of the images. Some or all of the above-described processing in the correction unit may be performed using AI, for example, or may be performed without using AI. For example, the correction unit can input image relevance data to the generation AI and cause the generation AI to adjust the correction procedure.

[0095] The re-evaluation unit can estimate the user's emotions and adjust the re-evaluation criteria based on the estimated user's emotions. For example, the re-evaluation unit can estimate the user's emotions and adjust the re-evaluation criteria based on the estimated user's emotions. For example, the re-evaluation unit can provide simple and intuitive re-evaluation criteria when the user is nervous. Furthermore, the re-evaluation unit can provide detailed re-evaluation criteria when the user is relaxed. Furthermore, the re-evaluation unit can provide criteria that allow for quick re-evaluation when the user is in a hurry. This allows for providing re-evaluation criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the re-evaluation unit can be performed using AI, for example, or without AI. For example, the re-evaluation unit can input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0096] The re-evaluation unit can adjust the level of detail of the re-evaluation based on the importance of the corrected image during re-evaluation. For example, the re-evaluation unit can perform a detailed re-evaluation for a corrected image with high importance. Furthermore, the re-evaluation unit can perform a simplified re-evaluation for a corrected image with low importance. Furthermore, the re-evaluation unit can determine the priority of the re-evaluation based on the importance of the corrected image. This makes it possible to provide a re-evaluation based on the importance of the corrected image. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input importance data of the corrected image to the generation AI and cause the generation AI to adjust the level of detail of the re-evaluation.

[0097] The re-evaluation unit can apply different re-evaluation algorithms depending on the category of the image during re-evaluation. For example, the re-evaluation unit can apply different re-evaluation algorithms depending on the category of the image during re-evaluation. For example, the re-evaluation unit can apply a face recognition algorithm to a portrait image to re-evaluate. Furthermore, the re-evaluation unit can apply a landscape analysis algorithm to a landscape image to re-evaluate. Furthermore, the re-evaluation unit can apply a product recognition algorithm to a product image to re-evaluate. This enables re-evaluation depending on the category of the image. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input image category data into the generation AI and cause the generation AI to apply the re-evaluation algorithm.

[0098] The re-evaluation unit can estimate the user's emotions and determine the priority of re-evaluation based on the estimated user's emotions. The re-evaluation unit, for example, estimates the user's emotions and determines the priority of re-evaluation based on the estimated user's emotions. For example, if the user is nervous, the re-evaluation unit can prioritize re-evaluation of high importance. Furthermore, if the user is relaxed, the re-evaluation unit can prioritize detailed re-evaluation. Furthermore, if the user is in a hurry, the re-evaluation unit can prioritize parts that can be re-evaluated quickly. This makes it possible to provide a priority of re-evaluation according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the re-evaluation unit may be performed using an AI, for example, or without an AI. For example, the re-evaluation unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0099] The re-evaluation unit can adjust the order of re-evaluation based on the submission date of the corrected image during re-evaluation. The re-evaluation unit can adjust the order of re-evaluation based on the submission date of the corrected image during re-evaluation, for example. For example, the re-evaluation unit can prioritize re-evaluating recently submitted corrected images. The re-evaluation unit can also postpone older submitted corrected images. Furthermore, the re-evaluation unit can dynamically adjust the order of re-evaluation based on the submission date. This allows the order of re-evaluation to be adjusted based on the submission date. Some or all of the above-described processing in the re-evaluation unit may be performed using AI, for example, or may be performed without using AI. For example, the re-evaluation unit can input submission date data of the corrected image into the generation AI and cause the generation AI to adjust the order of re-evaluation.

[0100] The re-evaluation unit can adjust the re-evaluation procedure based on the relevance of the corrected image during re-evaluation. The re-evaluation unit can, for example, adjust the re-evaluation procedure based on the relevance of the corrected image during re-evaluation. For example, the re-evaluation unit can prioritize re-evaluating corrected images with high relevance. The re-evaluation unit can also postpone re-evaluating corrected images with low relevance. Furthermore, the re-evaluation unit can dynamically adjust the re-evaluation procedure according to the relevance of the corrected image. This allows the re-evaluation procedure to be adjusted based on the relevance of the corrected image. Some or all of the above-described processing in the re-evaluation unit may be performed using, or without, AI, for example. For example, the re-evaluation unit can input relevance data of the corrected image to the generation AI and cause the generation AI to adjust the re-evaluation procedure. === Hard Collateral 1-1 === Each of the above-described elements, including the upload unit, analysis unit, feedback unit, correction unit, and re-evaluation unit, may be implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the upload unit may be implemented by the control unit 46A of the smart device 14, allowing an artist to upload images generated by the generative AI to the platform. The analysis unit may be implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded images and identifying biases and errors. The feedback unit may be implemented, for example, by the specific processing unit 290 of the data processing device 12, providing feedback from both AI and human evaluators. The correction unit may be implemented, for example, by the control unit 46A of the smart device 14, providing tools for the artist to correct images based on the feedback. The re-evaluation unit may be implemented, for example, by the specific processing unit 290 of the data processing device 12, re-evaluating the corrected images. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned upload unit, analysis unit, feedback unit, correction unit, and re-evaluation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the upload unit is implemented by the control unit 46A of the smart glasses 214, allowing an artist to upload images generated by the generative AI to the platform. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded images and identifying biases and errors. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, providing feedback from both AI and human evaluators. The correction unit is implemented, for example, by the control unit 46A of the smart glasses 214, providing tools for the artist to correct images based on feedback. The re-evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, re-evaluating the corrected images. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned upload unit, analysis unit, feedback unit, correction unit, and re-evaluation unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the upload unit is implemented by the control unit 46A of the headset-type terminal 314, allowing an artist to upload images generated by the generation AI to the platform. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded images and identifying biases and errors. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, providing feedback from both AI and human evaluators. The correction unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, providing tools for the artist to correct images in response to feedback. The re-evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, re-evaluating the corrected images. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned upload unit, analysis unit, feedback unit, correction unit, and re-evaluation unit, is implemented, for example, by at least one of the robot 414 and the data processing device 12. For example, the upload unit is implemented by the control unit 46A of the robot 414, allowing an artist to upload images generated by the generative AI to the platform. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzing the uploaded images and identifying biases and errors. The feedback unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, providing feedback from both AI and human evaluators. The correction unit is implemented, for example, by the control unit 46A of the robot 414, providing tools for the artist to correct images based on feedback. The re-evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, re-evaluating the corrected images.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The analysis unit can analyze the color balance of an image and adjust the balance if a particular color is used excessively. For example, if a particular color is used excessively, the analysis unit can suggest to the artist that the use of that color be reduced. Also, if the color balance is unbalanced, the analysis unit can suggest that the artist balance it by adding another color. Furthermore, if the color balance needs to be adjusted, the analysis unit can provide the artist with a specific method for doing so. This can optimize the color balance of the image and improve its visual appeal.

[0103] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is depressed, the feedback unit can provide feedback including encouraging words. If the user is excited, the feedback unit can provide calm advice. Furthermore, if the user is feeling anxious, the feedback unit can provide feedback that gives a sense of security. In this way, it is possible to provide appropriate feedback according to the user's emotions.

[0104] The correction unit can analyze the composition of an image and make suggestions to optimize visual balance. For example, if an element in an image is biased, the correction unit can suggest adjusting its placement. Also, if the visual focus is unclear, the correction unit can provide a suggestion to clarify the focus. Furthermore, if the composition of an image is visually unpleasant, the correction unit can suggest how to improve it. This can optimize the composition of the image and increase its visual appeal.

[0105] The re-evaluation unit can estimate the user's emotions and adjust the re-evaluation feedback based on the estimated user's emotions. For example, the re-evaluation unit can provide detailed feedback when the user is confident. For example, the re-evaluation unit can provide concise and positive feedback when the user is anxious. For example, the re-evaluation unit can provide quick and understandable feedback when the user is in a hurry. In this way, the re-evaluation feedback can be provided according to the user's emotions.

[0106] The upload unit can analyze the user's past upload history and suggest the optimal upload timing. For example, the upload unit can identify the time period in which the user received the most feedback in the past and encourage the user to upload during that time period. The upload unit can also suggest the upload method that the user has had the most success with in the past. Furthermore, the upload unit can automatically select the optimal upload settings from the user's past upload history. This makes it possible to provide the optimal upload method based on the user's past history.

[0107] The analysis unit can analyze the textures of an image and adjust the balance if a particular texture is used excessively. For example, if a particular texture is used excessively, the analysis unit can suggest to the artist that the use of that texture be reduced. Furthermore, if the balance of textures is unbalanced, the analysis unit can suggest that the balance be improved by adding another texture. Furthermore, if the balance of textures needs to be adjusted, the analysis unit can provide the artist with a specific method for doing so. This can optimize the texture balance of the image and improve its visual appeal.

[0108] The feedback unit can estimate the user's emotion and adjust the timing of the feedback based on the estimated user's emotion. For example, if the user is relaxed, the feedback unit can provide feedback immediately. If the user is feeling stressed, the feedback unit can provide feedback at a time when the user is able to relax. Furthermore, if the user is in a hurry, the feedback unit can provide feedback quickly. This makes it possible to provide feedback at optimal timing according to the user's emotion.

[0109] The correction unit can analyze the resolution of the image and suggest correction at the optimal resolution. For example, if the resolution is low, the correction unit can suggest correction at a higher resolution. Also, if the resolution is too high, the correction unit can suggest adjusting it to an appropriate resolution. Furthermore, if the resolution needs to be adjusted, the correction unit can provide the artist with a specific method for doing so. This allows the image resolution to be optimized and the visual appeal to be improved.

[0110] The re-evaluation unit can estimate the user's emotion and adjust the level of detail of the re-evaluation based on the estimated user's emotion. For example, the re-evaluation unit can provide a detailed re-evaluation when the user is relaxed. Furthermore, the re-evaluation unit can provide a concise re-evaluation when the user is stressed. Furthermore, the re-evaluation unit can provide a re-evaluation that can be quickly understood when the user is in a hurry. In this way, the level of detail of the re-evaluation can be provided according to the user's emotion.

[0111] The upload unit may select an optimal upload server in consideration of the user's geographical location information. For example, if the user is in a specific area, the upload unit may select a server closest to that area. If the user is traveling, the upload unit may select a server closest to the user's travel destination. Furthermore, if the user is participating in a specific event, the upload unit may select a server related to the event. In this way, the optimal upload server may be selected based on the geographical location information.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The upload unit uploads the image generated by the generative AI. For example, an artist can upload an image generated by the generative AI to the platform. Step 2: The analysis unit analyzes the images uploaded by the upload unit to identify biases and errors. For example, it can identify prejudice against a particular race or gender. The analysis unit uses AI to analyze the images and identifies specific elements within the images to identify biases and errors. Step 3: The feedback section provides feedback based on the biases and errors identified by the analysis section. For example, feedback from both the AI ​​and human evaluators can be provided, with the human evaluators providing additional comments and suggestions for corrections to biases and errors identified by the AI. Step 4: The Correction Department corrects the image based on the feedback provided by the Feedback Department, for example, by correcting or removing certain elements to reduce bias. The Correction Department provides tools for artists to correct the image based on the feedback and uses image editing software to correct or remove certain elements. Step 5: The re-evaluation unit re-evaluates the images corrected by the correction unit. For example, it re-analyzes the corrected images and evaluates whether bias and errors have been reduced. The re-evaluation unit uses AI to re-evaluate the corrected images, and a human evaluator confirms the results.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 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.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0128] 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.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The 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.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] 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.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0144] 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.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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. an upload unit that uploads images generated by the generation AI; an analysis unit that analyzes the images uploaded by the upload unit and identifies bias or error; a feedback unit that provides feedback based on the bias or error identified by the analysis unit; a modification unit that modifies the image based on the feedback provided by the feedback unit; a re-evaluation unit that re-evaluates the image corrected by the correction unit. A system characterized by:

2. The analysis unit Identifying bias against certain races or genders 2. The system of claim 1.

3. The feedback unit Providing feedback from both AI and human reviewers 2. The system of claim 1.

4. The correction unit Mitigate bias by modifying or removing certain elements 2. The system of claim 1.

5. The re-evaluation unit Reassess the corrected image 2. The system of claim 1.

6. The feedback unit Providing feedback from evaluators, such as scholars with expertise in gender studies or race issues, or art professionals 2. The system of claim 1.

7. The upload unit Estimate user emotions and adjust the timing of image uploads based on the estimated user emotions.

2. The system of claim 1.

8. The upload unit Analyze the user's upload history and select the optimal upload method 2. The system of claim 1.

9. The upload unit Filter images as they are uploaded based on the user's current project or area of ​​interest 2. The system of claim 1.

10. The upload unit Estimate the user's emotions and prioritize the images to be uploaded based on the estimated user emotions.

2. The system of claim 1.

Images