System

Abstract

An object of the system according to the embodiment is to reduce the influence of the bias in the generation AI and provide a fair service.SOLUTION: A system according to an embodiment includes a bias check unit, a model generation unit, an evidence presentation unit, and an ethics association unit. The bias check unit performs a bias check based on discomfort. The model generation unit studies and develops a large-scale image model specialized in Japan. The evidence presentation unit presents biased evidence. The ethics department addresses the issue of AI ethics.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately address the effects of bias in generative AI, and there is a problem of bias against Asians being particularly apparent.

[0005] The system according to the embodiment aims to reduce the influence of bias in the generative AI and provide a fair service. [Means for solving the problem]

[0006] The system according to the embodiment includes a bias check unit, a model generation unit, an evidence presentation unit, and an ethics response unit. The bias check unit performs bias checks based on discomfort. The model generation unit researches and develops large-scale image models specialized for Japan. The evidence presentation unit presents bias evidence. The ethics response unit addresses issues related to AI ethics. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the influence of bias in the generation AI and provide a fair service. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The anti-bias system according to an embodiment of the present invention is a system that addresses bias issues in generative AI and realizes fair and ethical AI systems. As a result, the anti-bias system can present bias evidence for bias issues in generative AI and address issues in AI ethics.

[0029] An anti-bias system according to an embodiment includes a bias check unit, a model generation unit, an evidence presentation unit, and an ethics response unit. The bias check unit performs bias checks based on discomfort. For example, the generation AI checks whether an image is uncomfortable based on user feedback and surveys. Furthermore, if a user feels that an image is uncomfortable, the generation AI analyzes the feedback and identifies the elements that cause the discomfort. The model generation unit researches and develops large-scale image models specialized for Japan. For example, the generation AI collects image data including traditional Japanese clothing, landscapes, and scenes from daily life, and trains the model based on this data. The evidence presentation unit presents bias evidence. For example, the generation AI visually indicates which parts of the generated image are affected by bias. The ethics response unit addresses issues related to AI ethics. For example, the generation AI selects training data and improves algorithms to prevent bias. This allows the anti-bias system according to an embodiment to address bias issues in the generation AI and realize a fair and ethical AI system. For example, by using a large-scale image model specialized for Japan, generative AI can generate images with minimal bias against Asians, eliminating unfairness in services. Generative AI can also increase AI transparency by presenting bias evidence, thereby gaining user trust. Furthermore, by addressing issues of AI ethics, generative AI can build socially acceptable AI systems.

[0030] The bias check unit can learn the user's past feedback history and identify individual discomfort patterns. For example, the generation AI stores the user's past feedback history in a database and uses a machine learning algorithm to identify individual discomfort patterns. For example, it learns discomfort caused by specific images or situations. The bias check unit also builds a system in which the generation AI analyzes the user's feedback history and identifies individual discomfort patterns. For example, it identifies elements that cause discomfort based on past feedback data. The bias check unit also develops an algorithm in which the generation AI learns the user's feedback history and identifies individual discomfort patterns. For example, it analyzes discomfort caused by specific keywords or phrases. This identifies discomfort patterns based on the user's past feedback and improves the accuracy of the bias check.

[0031] The bias check unit can collect feedback from users in different cultures and analyze discomfort patterns for each culture. For example, the bias check unit builds a system in which the generation AI collects feedback from users in different cultures and analyzes discomfort patterns for each culture. For example, it identifies discomfort elements specific to each culture. The bias check unit also develops an algorithm in which the generation AI analyzes feedback data from users in different cultures and identifies discomfort patterns for each culture. For example, it analyzes discomfort elements based on cultural background. The bias check unit also builds a database in which the generation AI collects feedback from users in different cultures and analyzes discomfort patterns for each culture. For example, it identifies discomfort elements based on feedback data from each culture. This allows discomfort patterns to be analyzed based on feedback from users in different cultures, improving the accuracy of the bias check.

[0032] The bias check unit can build a feedback loop in which elements that cause discomfort are automatically corrected and the image is re-presented to the user. For example, the bias check unit may develop a system that builds a feedback loop in which the generation AI automatically corrects elements that cause discomfort and re-presents the image to the user. For example, it may modify specific parts of the image and re-present it. The bias check unit may also develop an algorithm that allows the generation AI to automatically correct elements that cause discomfort and re-present the image to the user. For example, it may change the color tone or composition and re-present the image. The bias check unit may also develop a database that builds a feedback loop in which the generation AI automatically corrects elements that cause discomfort and re-presents the image to the user. For example, it may compare images before and after modification and re-present them. This automatically corrects elements that cause discomfort and re-presents them to the user, improving the accuracy of the bias check.

[0033] The model generation unit learns the culture and customs of each region in Japan and can generate images unique to that region. For example, the model generation unit constructs a system in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the festivals and traditional events of each region. The model generation unit also develops an algorithm in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the architectural styles and scenery of each region. The model generation unit also constructs a database in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the specialty products and scenery of each region. This makes it possible to generate images that reflect the culture and customs of each region in Japan.

[0034] The model generation unit can learn about historical events and people in Japan and generate images that reflect them. For example, the model generation unit builds a system in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect historical warlords from the Sengoku period or patriots from the end of the Edo period. The model generation unit also develops an algorithm in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect the historical background of the Meiji Restoration and Taisho Democracy. The model generation unit also builds a database in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect historical buildings and cultural assets. This makes it possible to generate images that reflect historical events and people in Japan.

[0035] The model generation unit can learn the style of Japanese anime and manga and generate images that reflect that style. For example, the model generation unit builds a system in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the character designs of popular anime. The model generation unit also develops an algorithm in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the panel layout and background design of manga. The model generation unit also builds a database in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the color and line drawing style of anime. This makes it possible to generate images that reflect the style of Japanese anime and manga.

[0036] The model generation unit learns seasonal scenery and events in Japan and can generate images that reflect the seasons. For example, the model generation unit builds a system in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect cherry blossoms in spring and autumn leaves. The model generation unit also develops an algorithm in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect summer fireworks displays and winter snow scenes. The model generation unit also builds a database in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect the scenery and events of each season. This makes it possible to generate images that reflect seasonal scenery and events in Japan.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The model generation unit can be equipped with a multilingual support function that enables the generation AI to accommodate users from different languages ​​and cultural backgrounds. For example, a system can be constructed in which the generation AI generates images in multiple languages, such as Japanese, English, and Chinese. The model generation unit also develops a cultural adaptation algorithm that enables the generation AI to accommodate users from different cultural backgrounds. For example, it generates images that reflect the unique design elements of each cultural background. The model generation unit also builds a multilingual database that enables the generation AI to accommodate users from different languages ​​and cultural backgrounds. For example, it collects image data specialized for each language and cultural background and trains a model based on that data. This makes it possible to generate images that are compatible with users from different languages ​​and cultural backgrounds.

[0039] The model generation unit allows the generative AI to learn the user's preferences and interests and generate personalized images. For example, the generative AI analyzes the user's past search history and browsing history to generate images based on those preferences and interests. The model generation unit also develops algorithms that allow the generative AI to learn the user's preferences and interests and generate personalized images. For example, it generates images based on a specific theme or style. The model generation unit also builds a database that allows the generative AI to learn the user's preferences and interests and generate personalized images. For example, it collects the user's past preference data and trains a model based on that data. This makes it possible to generate personalized images based on the user's preferences and interests.

[0040] The model generation unit allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, the generation AI may modify specific parts of an image based on real-time user feedback and re-present it. The model generation unit also develops an algorithm that allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, it may change the color tone or composition and re-present the image. The model generation unit also builds a database that allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, it may compare images before and after modification and re-present them. This allows the generation AI to reflect user feedback in real time and dynamically generate images, thereby improving user satisfaction.

[0041] The model generation unit enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, the generation AI analyzes the user's past preference data and builds a system that predicts future trends. The model generation unit also develops an algorithm that enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, predicting future trends for a specific theme or style. The model generation unit also builds a database that enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, predicting future trends based on past preference data. This makes it possible to predict future trends and generate future images based on the user's past preference data.

[0042] The model generation unit allows the generative AI to dynamically adjust the image generation algorithm based on user feedback. For example, the generative AI adjusts the parameters of the algorithm based on user feedback to generate more appropriate images. The model generation unit also builds a system in which the generative AI dynamically adjusts the image generation algorithm based on user feedback. For example, it analyzes feedback data in real time and adjusts the algorithm. The model generation unit also builds a database in which the generative AI dynamically adjusts the image generation algorithm based on user feedback. For example, it adjusts the algorithm based on past feedback data. This makes it possible to dynamically adjust the image generation algorithm based on user feedback and generate more appropriate images.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The bias check unit performs a bias check based on discomfort. For example, the generation AI checks whether an image is uncomfortable based on user feedback and surveys. If a user feels that an image is uncomfortable, the generation AI analyzes the feedback and identifies what elements are causing the discomfort. Step 2: The model generation department will research and develop a large-scale image model specific to Japan. For example, the generation AI will collect image data including traditional Japanese clothing, landscapes, and scenes from daily life, and train the model based on that data. Step 3: The evidence presentation unit presents bias evidence. For example, the generation AI visually shows which parts of the generated image are affected by bias. Step 4: The Ethics Department will address AI ethics issues, such as selecting training data and improving algorithms to ensure that generative AI is not influenced by bias.

[0045] (Example 2) The anti-bias system according to an embodiment of the present invention is a system that addresses bias issues in generative AI and realizes fair and ethical AI systems. As a result, the anti-bias system can present bias evidence for bias issues in generative AI and address issues in AI ethics.

[0046] An anti-bias system according to an embodiment includes a bias check unit, a model generation unit, an evidence presentation unit, and an ethics response unit. The bias check unit performs bias checks based on discomfort. For example, the generation AI checks whether an image is uncomfortable based on user feedback and surveys. Furthermore, if a user feels that an image is uncomfortable, the generation AI analyzes the feedback and identifies the elements that cause the discomfort. The model generation unit researches and develops large-scale image models specialized for Japan. For example, the generation AI collects image data including traditional Japanese clothing, landscapes, and scenes from daily life, and trains the model based on this data. The evidence presentation unit presents bias evidence. For example, the generation AI visually indicates which parts of the generated image are affected by bias. The ethics response unit addresses issues related to AI ethics. For example, the generation AI selects training data and improves algorithms to prevent bias. This allows the anti-bias system according to an embodiment to address bias issues in the generation AI and realize a fair and ethical AI system. For example, by using a large-scale image model specialized for Japan, generative AI can generate images with minimal bias against Asians, eliminating unfairness in services. Generative AI can also increase AI transparency by presenting bias evidence, thereby gaining user trust. Furthermore, by addressing issues of AI ethics, generative AI can build socially acceptable AI systems.

[0047] The bias check unit can automatically detect discomfort by analyzing a user's facial expressions and vocal tone in real time. For example, the generation AI uses a camera and microphone to analyze a user's facial expressions and vocal tone in real time to automatically detect discomfort. For example, it analyzes frowns and changes in vocal tone to identify moments of discomfort. The bias check unit also collects the user's facial expression data and uses a machine learning algorithm to detect discomfort. For example, it analyzes facial muscle movements and eye movements to identify moments of discomfort. The bias check unit also uses voice analysis technology to analyze changes in the tone and speed of the user's voice to automatically detect discomfort. For example, it analyzes changes in vocal pitch and rhythm to identify moments of discomfort. This allows the generation AI to detect user discomfort in real time and improves the accuracy of bias checks.

[0048] The bias check unit can learn the user's past feedback history and identify individual discomfort patterns. For example, the generation AI stores the user's past feedback history in a database and uses a machine learning algorithm to identify individual discomfort patterns. For example, it learns discomfort caused by specific images or situations. The bias check unit also builds a system in which the generation AI analyzes the user's feedback history and identifies individual discomfort patterns. For example, it identifies elements that cause discomfort based on past feedback data. The bias check unit also develops an algorithm in which the generation AI learns the user's feedback history and identifies individual discomfort patterns. For example, it analyzes discomfort caused by specific keywords or phrases. This identifies discomfort patterns based on the user's past feedback and improves the accuracy of the bias check.

[0049] The bias check unit uses the emotion estimation function to analyze the emotional reactions of users when they view images in real time and identify moments when they feel uncomfortable. For example, the bias check unit uses the emotion estimation function to analyze the emotional reactions of users when they view images in real time. For example, it analyzes changes in facial expressions and heart rate to identify moments when they feel uncomfortable. The bias check unit also uses the emotion estimation function to build a system that monitors the emotional reactions of users in real time and identifies moments when they feel uncomfortable. For example, it detects discomfort based on an emotion score. The bias check unit also develops an algorithm that uses the emotion estimation function to analyze the emotional reactions of users when they view images and identify moments when they feel uncomfortable. For example, it analyzes emotions using facial expression recognition technology. This allows the generation AI to analyze the emotional reactions of users in real time and identify moments when they feel uncomfortable, improving the accuracy of the bias check.

[0050] The bias check unit can collect feedback from users in different cultures and analyze discomfort patterns for each culture. For example, the bias check unit builds a system in which the generation AI collects feedback from users in different cultures and analyzes discomfort patterns for each culture. For example, it identifies discomfort elements specific to each culture. The bias check unit also develops an algorithm in which the generation AI analyzes feedback data from users in different cultures and identifies discomfort patterns for each culture. For example, it analyzes discomfort elements based on cultural background. The bias check unit also builds a database in which the generation AI collects feedback from users in different cultures and analyzes discomfort patterns for each culture. For example, it identifies discomfort elements based on feedback data from each culture. This allows discomfort patterns to be analyzed based on feedback from users in different cultures, improving the accuracy of the bias check.

[0051] The bias check unit can build a feedback loop in which elements that cause discomfort are automatically corrected and the image is re-presented to the user. For example, the bias check unit may develop a system that builds a feedback loop in which the generation AI automatically corrects elements that cause discomfort and re-presents the image to the user. For example, it may modify specific parts of the image and re-present it. The bias check unit may also develop an algorithm that allows the generation AI to automatically correct elements that cause discomfort and re-present the image to the user. For example, it may change the color tone or composition and re-present the image. The bias check unit may also develop a database that builds a feedback loop in which the generation AI automatically corrects elements that cause discomfort and re-presents the image to the user. For example, it may compare images before and after modification and re-present them. This automatically corrects elements that cause discomfort and re-presents them to the user, improving the accuracy of the bias check.

[0052] The bias check unit uses the emotion estimation function to correct elements of images that users find unpleasant in real time, thereby eliciting positive emotions. For example, the bias check unit builds a system in which the generation AI uses the emotion estimation function to correct elements of images that users find unpleasant in real time. For example, changing the color tone or composition of the image to elicit positive emotions. The bias check unit also develops an algorithm in which the generation AI uses the emotion estimation function to correct elements of images that users find unpleasant in real time. For example, deleting specific elements to elicit positive emotions. The bias check unit also builds a database in which the generation AI uses the emotion estimation function to correct elements of images that users find unpleasant in real time. For example, comparing images before and after correction to elicit positive emotions. This allows the generation AI to correct elements of images that users find unpleasant in real time and elicit positive emotions, thereby improving the accuracy of the bias check.

[0053] The model generation unit learns the culture and customs of each region in Japan and can generate images unique to that region. For example, the model generation unit constructs a system in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the festivals and traditional events of each region. The model generation unit also develops an algorithm in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the architectural styles and scenery of each region. The model generation unit also constructs a database in which the generation AI learns the culture and customs of each region in Japan and generates images unique to that region. For example, it generates images that reflect the specialty products and scenery of each region. This makes it possible to generate images that reflect the culture and customs of each region in Japan.

[0054] The model generation unit can learn about historical events and people in Japan and generate images that reflect them. For example, the model generation unit builds a system in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect historical warlords from the Sengoku period or patriots from the end of the Edo period. The model generation unit also develops an algorithm in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect the historical background of the Meiji Restoration and Taisho Democracy. The model generation unit also builds a database in which the generative AI learns about historical events and people in Japan and generates images that reflect them. For example, it generates images that reflect historical buildings and cultural assets. This makes it possible to generate images that reflect historical events and people in Japan.

[0055] The model generation unit can use the emotion estimation function to identify image elements that Japanese users particularly like and generate images that emphasize them. For example, the model generation unit builds a system in which the generation AI uses the emotion estimation function to identify image elements that Japanese users particularly like and generate images that emphasize them. For example, elements such as cherry blossoms and Mount Fuji are emphasized. The model generation unit also develops an algorithm in which the generation AI uses the emotion estimation function to identify image elements that Japanese users particularly like and generate images that emphasize them. For example, elements of Japanese-style architecture and gardens are emphasized. The model generation unit also builds a database in which the generation AI uses the emotion estimation function to identify image elements that Japanese users particularly like and generate images that emphasize them. For example, elements of traditional clothing and food culture are emphasized. This makes it possible to generate images that emphasize image elements that Japanese users particularly like.

[0056] The model generation unit can learn the style of Japanese anime and manga and generate images that reflect that style. For example, the model generation unit builds a system in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the character designs of popular anime. The model generation unit also develops an algorithm in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the panel layout and background design of manga. The model generation unit also builds a database in which a generative AI learns the style of Japanese anime and manga and generates images that reflect that style. For example, it generates images that reflect the color and line drawing style of anime. This makes it possible to generate images that reflect the style of Japanese anime and manga.

[0057] The model generation unit learns seasonal scenery and events in Japan and can generate images that reflect the seasons. For example, the model generation unit builds a system in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect cherry blossoms in spring and autumn leaves. The model generation unit also develops an algorithm in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect summer fireworks displays and winter snow scenes. The model generation unit also builds a database in which a generation AI learns seasonal scenery and events in Japan and generates images that reflect the seasons. For example, it generates images that reflect the scenery and events of each season. This makes it possible to generate images that reflect seasonal scenery and events in Japan.

[0058] The model generation unit can use the emotion estimation function to analyze the emotions Japanese users have toward specific seasons or events and generate images that reflect those emotions. For example, the model generation unit builds a system in which the generation AI uses the emotion estimation function to analyze the emotions Japanese users have toward specific seasons or events and generates images that reflect those emotions. For example, it reflects emotions toward cherry blossoms in spring. The model generation unit also uses the emotion estimation function to develop an algorithm to analyze the emotions Japanese users have toward specific seasons or events and generate images that reflect those emotions. For example, it reflects emotions toward summer fireworks displays. The model generation unit also uses the emotion estimation function to build a database in which the generation AI analyzes the emotions Japanese users have toward specific seasons or events and generates images that reflect those emotions. For example, it reflects emotions toward autumn leaves or winter snowy scenery. This makes it possible to generate images that reflect the emotions Japanese users have toward specific seasons or events.

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

[0060] The bias check unit can monitor the user's physiological responses and detect discomfort. For example, the generation AI can monitor the user's heart rate and electrodermal response in real time to identify the moment when discomfort is felt. The bias check unit also builds a system in which the generation AI analyzes the user's brain wave data to detect discomfort. For example, discomfort is detected based on specific brain wave patterns. The bias check unit also develops an algorithm in which the generation AI analyzes the user's breathing patterns to detect discomfort. For example, changes in breathing rhythm and depth can be analyzed to identify the moment when discomfort is felt. This enables discomfort to be detected based on the user's physiological responses, improving the accuracy of the bias check.

[0061] The model generation unit can be equipped with a multilingual support function that enables the generation AI to accommodate users from different languages ​​and cultural backgrounds. For example, a system can be constructed in which the generation AI generates images in multiple languages, such as Japanese, English, and Chinese. The model generation unit also develops a cultural adaptation algorithm that enables the generation AI to accommodate users from different cultural backgrounds. For example, it generates images that reflect the unique design elements of each cultural background. The model generation unit also builds a multilingual database that enables the generation AI to accommodate users from different languages ​​and cultural backgrounds. For example, it collects image data specialized for each language and cultural background and trains a model based on that data. This makes it possible to generate images that are compatible with users from different languages ​​and cultural backgrounds.

[0062] The bias check unit can analyze a user's social media activity and identify individual patterns of discomfort. For example, the generation AI analyzes a user's social media posts and comments to identify elements that cause discomfort. The bias check unit also develops an algorithm that analyzes a user's reactions on social media and identifies individual patterns of discomfort. For example, it analyzes negative reactions to specific topics or images. The bias check unit also analyzes a user's social media activity and builds a database that identifies individual patterns of discomfort. For example, it identifies elements that cause discomfort based on past posts and comments. This makes it possible to identify patterns of discomfort based on a user's social media activity and improve the accuracy of bias checks.

[0063] The model generation unit allows the generative AI to learn the user's preferences and interests and generate personalized images. For example, the generative AI analyzes the user's past search history and browsing history to generate images based on those preferences and interests. The model generation unit also develops algorithms that allow the generative AI to learn the user's preferences and interests and generate personalized images. For example, it generates images based on a specific theme or style. The model generation unit also builds a database that allows the generative AI to learn the user's preferences and interests and generate personalized images. For example, it collects the user's past preference data and trains a model based on that data. This makes it possible to generate personalized images based on the user's preferences and interests.

[0064] The bias check unit uses the emotion estimation function to analyze environmental factors when a user feels discomfort and identify the cause of bias. For example, the generation AI monitors changes in sound and light around the user to identify the moment the user felt discomfort. The bias check unit also uses the emotion estimation function to develop an algorithm that analyzes the user's environmental factors and identifies elements that cause discomfort. For example, discomfort is detected based on specific sound or light patterns. The bias check unit also uses the emotion estimation function to analyze the user's environmental factors and build a database that identifies elements that cause discomfort. For example, elements that cause discomfort are identified based on past environmental data. This allows discomfort to be identified based on the user's environmental factors, improving the accuracy of bias checks.

[0065] The model generation unit allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, the generation AI may modify specific parts of an image based on real-time user feedback and re-present it. The model generation unit also develops an algorithm that allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, it may change the color tone or composition and re-present the image. The model generation unit also builds a database that allows the generation AI to reflect user feedback in real time and dynamically generate images. For example, it may compare images before and after modification and re-present them. This allows the generation AI to reflect user feedback in real time and dynamically generate images, thereby improving user satisfaction.

[0066] The bias check unit uses the emotion estimation function to analyze the user's physical reactions when they feel discomfort and can identify the cause of bias. For example, the generation AI monitors the user's body temperature and sweat rate to identify the moment they felt discomfort. The bias check unit also uses the emotion estimation function to develop an algorithm that analyzes the user's physical reactions and identifies factors that cause discomfort. For example, discomfort can be detected based on specific body temperature or sweat patterns. The bias check unit also uses the emotion estimation function to analyze the user's physical reactions and build a database that identifies factors that cause discomfort. For example, factors that cause discomfort can be identified based on past physical data. This makes it possible to identify discomfort based on the user's physical reactions and improve the accuracy of bias checks.

[0067] The model generation unit enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, the generation AI analyzes the user's past preference data and builds a system that predicts future trends. The model generation unit also develops an algorithm that enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, predicting future trends for a specific theme or style. The model generation unit also builds a database that enables the generation AI to predict trends and generate future images based on the user's past preference data. For example, predicting future trends based on past preference data. This makes it possible to predict future trends and generate future images based on the user's past preference data.

[0068] The bias check unit uses the emotion estimation function to analyze the psychological factors that cause a user to feel uncomfortable and identify the cause of bias. For example, the generation AI monitors the user's psychological stress level and identifies the moment the user felt uncomfortable. The bias check unit also uses the emotion estimation function to develop an algorithm that analyzes the user's psychological factors and identifies the elements that cause discomfort. For example, discomfort is detected based on a specific stress level or psychological pattern. The bias check unit also uses the emotion estimation function to analyze the user's psychological factors and build a database that identifies the elements that cause discomfort. For example, elements that cause discomfort are identified based on past psychological data. This makes it possible to identify discomfort based on the user's psychological factors and improve the accuracy of bias checks.

[0069] The model generation unit allows the generative AI to dynamically adjust the image generation algorithm based on user feedback. For example, the generative AI adjusts the parameters of the algorithm based on user feedback to generate more appropriate images. The model generation unit also builds a system in which the generative AI dynamically adjusts the image generation algorithm based on user feedback. For example, it analyzes feedback data in real time and adjusts the algorithm. The model generation unit also builds a database in which the generative AI dynamically adjusts the image generation algorithm based on user feedback. For example, it adjusts the algorithm based on past feedback data. This makes it possible to dynamically adjust the image generation algorithm based on user feedback and generate more appropriate images.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The bias check unit performs a bias check based on discomfort. For example, the generation AI checks whether an image is uncomfortable based on user feedback and surveys. If a user feels that an image is uncomfortable, the generation AI analyzes the feedback and identifies what elements are causing the discomfort. Step 2: The model generation department will research and develop a large-scale image model specific to Japan. For example, the generation AI will collect image data including traditional Japanese clothing, landscapes, and scenes from daily life, and train the model based on that data. Step 3: The evidence presentation unit presents bias evidence. For example, the generation AI visually shows which parts of the generated image are affected by bias. Step 4: The Ethics Department will address AI ethics issues, such as selecting training data and improving algorithms to ensure that generative AI is not influenced by bias.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0101] 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.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a bias check unit that performs discomfort-based bias checks; A model generation department that researches and develops large-scale image models specialized for Japan, and an evidence presentation unit that presents bias evidence; Ethics department to tackle AI ethics issues A system characterized by:

2. The bias check unit Analyzes the user's facial expressions and tone of voice in real time to automatically detect discomfort 2. The system of claim 1.

3. The bias check unit Learn from users' past feedback history to identify individual discomfort patterns 2. The system of claim 1.

4. The bias check unit Analyzes users' emotional reactions in real time when viewing images and identifies moments of discomfort 2. The system of claim 1.

5. The bias check unit Collecting feedback from users in different cultures and analyzing discomfort patterns for each culture 2. The system of claim 1.

Images