System

Abstract

An object of a system according to an embodiment is to share content generated by a user using a generated AI and to interact with other users.SOLUTION: A system includes a generation unit, a posting unit, and a community unit. The generator generates an image or illustration based on the user's prompt using the generation AI. The posting unit posts the content generated by the generation unit. The community unit browses the content posted by the posting unit, and the users interact with each other.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 technologies leave room for improvement as they do not adequately provide a platform for sharing user-generated content and interacting with other users.

[0005] The system according to the embodiment aims to enable users to share content generated by using generation AI and interact with other users. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a posting unit, and a community unit. The generation unit generates images or illustrations using a generation AI based on user prompts. The posting unit posts the content generated by the generation unit. The community unit allows users to view the content posted by the posting unit and interact with each other. [Effects of the Invention]

[0007] The system according to the embodiment allows users to share content generated by using generation AI and interact with other users. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) The Oshikatsu service system according to an embodiment of the present invention is a system that allows users to create and post images and illustrations of their favorite idols. This system utilizes generation AI, and when the content generated by a user is sold, a portion of the sales is returned to the person they support and the creator who created it. It also has a community function that allows users to interact with each other and share information and works about their favorite idols. This allows the Oshikatsu service system to express their affection for their favorite idols and share them with other users. Furthermore, users can enjoy economic benefits from the sale of the content they generate.

[0029] A supporter activity service system according to an embodiment includes a generation unit, a posting unit, and a community unit. The generation unit uses a generation AI to generate images and illustrations based on user prompts. For example, the generation AI generates illustrations in response to a user's prompt, such as "Please generate an illustration of my favorite character smiling and posing." The generation AI can generate images and illustrations using technologies such as deep learning and GAN (generative artificial network). The posting unit posts content generated by the generation unit. For example, users can post images and illustrations they generate within the service, allowing other users to view and purchase them. The posting unit can also provide a function for automatically tagging content when it is posted, making it easier for other users to search. The community unit views the content posted by the posting unit and allows users to interact with each other. For example, users can post information about their favorite characters and leave comments on works created by other users. The community unit can also provide a function for automatically matching users with common interests by analyzing the content posted by users. This allows the supporter activity service system according to an embodiment to post content generated by users and interact with other users. For example, users can share their illustrations with the community and gain sympathy from other users. In addition, they can enjoy economic benefits by selling the content they create.

[0030] The generation unit learns the user's past generation history and generates customized images and illustrations of their favorite character that match the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past generation history and learn the user's preferences and tendencies. For example, it generates customized illustrations of their favorite character based on the style and theme of illustrations the user has previously generated. The generation unit also clarifies the specific content of the user's generation history and how it is saved. For example, it saves data on images and illustrations previously generated and identifies the user's preferences based on that data. This makes it possible to generate customized images and illustrations of their favorite character that match the user's preferences.

[0031] The generation unit receives feedback in real time and corrects and improves images and illustrations on the spot. For example, the generation unit provides a function whereby the generation AI receives user feedback in real time and corrects images and illustrations on the spot. For example, if a user provides feedback such as "I would like the colors to be a little brighter," the generation AI immediately adjusts the colors. The generation unit also clarifies the specific format and content of feedback from users. For example, it receives feedback in the form of text comments or evaluation scores, and corrects and improves images and illustrations based on that. This allows feedback to be received in real time and images and illustrations to be corrected and improved on the spot.

[0032] The generation unit generates a 3D model of the favorite character and provides an interactive function that allows the user to freely change the pose and facial expression. The generation unit, for example, uses a generation AI to generate a 3D model of the favorite character and provides an interactive function that allows the user to freely change the pose and facial expression. For example, the user can change the character's pose or customize the facial expression. The generation unit also clarifies the specific type and format of the 3D model to be generated. For example, the number of polygons, file format, etc. are specified, and the specific types and ranges of poses and facial expressions that the user can change are clarified. This makes it possible to provide an interactive function that allows the user to freely change the pose and facial expression.

[0033] The generation unit generates animations of the favorite characters, providing a function for users to create short videos. The generation unit, for example, utilizes a generation AI to generate animations of the favorite characters, providing a function for users to create short videos. For example, the user specifies the character's movements, and the generation AI generates animations based on those movements. The generation unit also clarifies the specific type and format of the animation to be generated. For example, the number of frames, file format, etc. are specified, and the specific type and format of the short videos that users can create are clarified. This makes it possible to provide a function for users to create short videos.

[0034] The posting unit automatically tags content when it is posted, making it easier for users to search. For example, the posting unit provides a function whereby the generation AI automatically tags content when it is posted, making it easier for other users to search. For example, tags such as "smile" and "pose" are automatically assigned to generated illustrations. The posting unit also clarifies the specific methods and criteria for automatic tagging. For example, tagging is performed using techniques such as keyword extraction and category classification. This allows tags to be automatically assigned when content is posted, making it easier for other users to search.

[0035] The posting unit can provide an algorithm that evaluates the quality of content and prioritizes displaying high-quality content. For example, the posting unit evaluates the quality of content posted by a generation AI and provides an algorithm that prioritizes displaying high-quality content. For example, the posting unit evaluates quality based on resolution and color balance. The posting unit also clarifies specific standards and methods for evaluating content quality. For example, the posting unit evaluates quality using user ratings and algorithmic analysis. This improves user satisfaction by prioritizing the display of high-quality content.

[0036] The posting unit can use a generation AI to provide a function that automatically shares content posted by a user on other social media platforms. The posting unit, for example, uses a generation AI to provide a function that automatically shares content posted by a user on other social media platforms. For example, the posting unit can automatically post illustrations generated by a user on Twitter or Instagram. The posting unit also clarifies the specific type and format of the social media platform on which the content will be shared. For example, the posting unit supports platforms such as Facebook, Twitter, and Instagram. This allows content posted by a user to be automatically shared on other social media platforms.

[0037] The posting unit can analyze a user's purchasing history and recommend related content. For example, the posting unit provides a function whereby a generating AI analyzes a user's purchasing history and recommends related content. For example, it can recommend new illustrations in a similar style based on illustrations the user has previously purchased. The posting unit also clarifies the specific details of a user's purchasing history and how it is saved. For example, it can save data such as purchased products and purchase dates and times, and use that data to recommend related content. This allows the user's purchasing history to be analyzed and related content to be recommended.

[0038] The community section can analyze the content posted by users and automatically match users with common interests. For example, the community section provides a function whereby a generation AI analyzes the content posted by users and automatically matches users with common interests. For example, it can match users who support the same character. The community section also clarifies the specific methods and criteria for identifying common interests. For example, it can match users based on common hobbies or interests. This allows users with common interests to be automatically matched.

[0039] The community department can analyze trends within the community in real time and notify users of popular topics. For example, the community department provides a function in which the generation AI analyzes trends within the community in real time and notifies users of popular topics. For example, if there is an increase in posts about a particular character or event, the community department will notify users of that topic. The community department also clarifies the specific methods and criteria for identifying trends within the community. For example, trends can be identified based on the number of posts or the number of likes. This allows trends within the community to be analyzed in real time and notify users of popular topics.

[0040] The community unit can use the generation AI to support the planning and management of events held by users within the community. For example, the community unit uses the generation AI to provide a function to support the planning and management of events held by users within the community. For example, it automates event schedule management and participant recruitment. The community unit also clarifies the specific type and format of events to be held within the community. For example, it specifies online events, offline events, etc., and supports the planning and management based on that. This allows the community unit to support the planning and management of events held by users within the community.

[0041] The community section can automatically translate user posts and promote interaction between users who speak different languages. For example, the community section provides a function in which generative AI automatically translates user posts and promotes interaction between users who speak different languages. For example, Japanese posts are automatically translated into English or Chinese. The community section also clarifies the specific methods and standards for automatic translation. For example, translation is performed using machine translation or processing of technical terms. This can promote interaction between users who speak different languages.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The generator can also provide a function that allows users to customize the costume of their favorite character. For example, if a user wants to dress a character in a specific costume, the generator AI selects the design of that costume, and generates an image or illustration of the character that reflects that costume. The generator can also provide an interface that allows users to change the color or pattern of the costume. This allows users to customize the costume of their favorite character to suit their preferences.

[0044] The generator can also provide a function that allows users to generate a story for their favorite character. For example, if a user inputs a specific scenario or setting, the generation AI generates a story based on that scenario. The generator can also provide an interface that allows users to select the progression and ending of the story. This allows users to customize the story of their favorite character to suit their preferences.

[0045] The generation unit can also provide a function that allows users to design merchandise for their favorite character. For example, a user can select an illustration of a character and design merchandise such as T-shirts or stickers using that illustration. The generation unit can also provide an interface that allows users to change the color and layout of the merchandise. This allows users to design merchandise for their favorite character to suit their preferences.

[0046] The generation unit can also provide a function that allows users to design a figure of their favorite character. For example, the user can select the character's pose and facial expression, and generate data for 3D printing based on that design. The generation unit can also provide an interface that allows users to change the color and material of the figure. This allows users to design a figure of their favorite character to suit their preferences.

[0047] The generation unit can also provide a function that allows the user to design a cosplay costume for their favorite character. For example, the user selects a design for the character's costume, and generates a cosplay costume pattern based on that design. The generation unit can also provide an interface that allows the user to change the color and material of the costume. This allows the user to design a cosplay costume for their favorite character according to their preferences.

[0048] The generation unit can also provide a function that allows the user to design accessories for their favorite character. For example, the user can select an illustration of a character and design accessories (such as key chains or badges) using that illustration. The generation unit can also provide an interface that allows the user to change the color or shape of the accessories. This allows the user to design accessories for their favorite character according to their preferences.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generation unit uses a generative AI to generate images and illustrations based on user prompts. For example, the generative AI generates illustrations based on a user prompt such as, "I want an illustration of my favorite character smiling and posing." The generative AI can also generate images and illustrations using technologies such as deep learning and GAN (generative artificial network). Step 2: The posting unit posts the content generated by the generation unit. For example, a user can post images or illustrations they have generated within the service, which can then be viewed and purchased by other users. The posting unit can also provide a function that automatically tags content when it is posted, making it easier for other users to search for it. Step 3: The community section browses the content posted by the posting section, and users interact with each other. For example, users can post information about their favorites or leave comments on works created by other users. The community section can also analyze the content posted by users and provide a function to automatically match users with common interests.

[0051] (Example 2) The Oshikatsu service system according to an embodiment of the present invention is a system that allows users to create and post images and illustrations of their favorite idols. This system utilizes generation AI, and when the content generated by a user is sold, a portion of the sales is returned to the person they support and the creator who created it. It also has a community function that allows users to interact with each other and share information and works about their favorite idols. This allows the Oshikatsu service system to express their affection for their favorite idols and share them with other users. Furthermore, users can enjoy economic benefits from the sale of the content they generate.

[0052] A supporter activity service system according to an embodiment includes a generation unit, a posting unit, and a community unit. The generation unit uses a generation AI to generate images and illustrations based on user prompts. For example, the generation AI generates illustrations in response to a user's prompt, such as "Please generate an illustration of my favorite character smiling and posing." The generation AI can generate images and illustrations using technologies such as deep learning and GAN (generative artificial network). The posting unit posts content generated by the generation unit. For example, users can post images and illustrations they generate within the service, allowing other users to view and purchase them. The posting unit can also provide a function for automatically tagging content when it is posted, making it easier for other users to search. The community unit views the content posted by the posting unit and allows users to interact with each other. For example, users can post information about their favorite characters and leave comments on works created by other users. The community unit can also provide a function for automatically matching users with common interests by analyzing the content posted by users. This allows the supporter activity service system according to an embodiment to post content generated by users and interact with other users. For example, users can share their illustrations with the community and gain sympathy from other users. In addition, they can enjoy economic benefits by selling the content they create.

[0053] The generation unit is equipped with an emotion estimation function that generates images and illustrations that reflect the emotions of the favorite character based on a prompt. For example, the generation unit uses a generation AI to estimate the emotions of the favorite character based on a prompt entered by the user, and generates images and illustrations that reflect those emotions. For example, in response to a prompt such as "Please generate an illustration of my favorite character smiling happily," the generation AI generates an illustration of the favorite character smiling. The generation unit also uses the emotion estimation function to clarify the specific type of emotion and how it is expressed. For example, it estimates emotions such as joy, sadness, and anger, and generates images and illustrations based on those emotions. This makes it possible to generate images and illustrations that reflect the emotions of the favorite character based on the user's prompt.

[0054] The generation unit learns the user's past generation history and generates customized images and illustrations of their favorite character that match the user's preferences. For example, the generation unit uses a generation AI to analyze the user's past generation history and learn the user's preferences and tendencies. For example, it generates customized illustrations of their favorite character based on the style and theme of illustrations the user has previously generated. The generation unit also clarifies the specific content of the user's generation history and how it is saved. For example, it saves data on images and illustrations previously generated and identifies the user's preferences based on that data. This makes it possible to generate customized images and illustrations of their favorite character that match the user's preferences.

[0055] The generation unit receives feedback in real time and corrects and improves images and illustrations on the spot. For example, the generation unit provides a function whereby the generation AI receives user feedback in real time and corrects images and illustrations on the spot. For example, if a user provides feedback such as "I would like the colors to be a little brighter," the generation AI immediately adjusts the colors. The generation unit also clarifies the specific format and content of feedback from users. For example, it receives feedback in the form of text comments or evaluation scores, and corrects and improves images and illustrations based on that. This allows feedback to be received in real time and images and illustrations to be corrected and improved on the spot.

[0056] The generation unit generates a 3D model of the favorite character and provides an interactive function that allows the user to freely change the pose and facial expression. The generation unit, for example, uses a generation AI to generate a 3D model of the favorite character and provides an interactive function that allows the user to freely change the pose and facial expression. For example, the user can change the character's pose or customize the facial expression. The generation unit also clarifies the specific type and format of the 3D model to be generated. For example, the number of polygons, file format, etc. are specified, and the specific types and ranges of poses and facial expressions that the user can change are clarified. This makes it possible to provide an interactive function that allows the user to freely change the pose and facial expression.

[0057] The generation unit generates animations of the favorite characters, providing a function for users to create short videos. The generation unit, for example, utilizes a generation AI to generate animations of the favorite characters, providing a function for users to create short videos. For example, the user specifies the character's movements, and the generation AI generates animations based on those movements. The generation unit also clarifies the specific type and format of the animation to be generated. For example, the number of frames, file format, etc. are specified, and the specific type and format of the short videos that users can create are clarified. This makes it possible to provide a function for users to create short videos.

[0058] The generation unit uses an emotion estimation function to automatically generate backgrounds and situations that reflect the emotions a user feels toward their favorite character. For example, if a user inputs, "I want a scene drawn of my favorite character looking happy," the generation AI generates a happy situation. The generation unit also clarifies the specific types and formats of the automatically generated backgrounds and situations. For example, the generation unit specifies backgrounds such as landscapes or interiors, and situations such as everyday scenes or special events. This allows the automatic generation of backgrounds and situations that reflect the emotions a user feels toward their favorite character.

[0059] The posting unit automatically tags content when it is posted, making it easier for users to search. For example, the posting unit provides a function whereby the generation AI automatically tags content when it is posted, making it easier for other users to search. For example, tags such as "smile" and "pose" are automatically assigned to generated illustrations. The posting unit also clarifies the specific methods and criteria for automatic tagging. For example, tagging is performed using techniques such as keyword extraction and category classification. This allows tags to be automatically assigned when content is posted, making it easier for other users to search.

[0060] The posting unit can provide an algorithm that evaluates the quality of content and prioritizes displaying high-quality content. For example, the posting unit evaluates the quality of content posted by a generation AI and provides an algorithm that prioritizes displaying high-quality content. For example, the posting unit evaluates quality based on resolution and color balance. The posting unit also clarifies specific standards and methods for evaluating content quality. For example, the posting unit evaluates quality using user ratings and algorithmic analysis. This improves user satisfaction by prioritizing the display of high-quality content.

[0061] The posting unit can use the emotion estimation function to analyze the emotion a user expresses when posting and provide advice to draw out positive emotions. The posting unit, for example, uses the emotion estimation function to analyze the emotion a user expresses when posting and provides advice to draw out positive emotions. For example, the posting unit may advise the user to check the emotion score before posting and add a positive comment. The posting unit also clarifies the specific types of positive emotions and how to express them. For example, the posting unit analyzes emotions such as joy and satisfaction and provides advice based on the analysis. This provides advice to enable the user to post with positive emotions.

[0062] The posting unit can use a generation AI to provide a function that automatically shares content posted by a user on other social media platforms. The posting unit, for example, uses a generation AI to provide a function that automatically shares content posted by a user on other social media platforms. For example, the posting unit can automatically post illustrations generated by a user on Twitter or Instagram. The posting unit also clarifies the specific type and format of the social media platform on which the content will be shared. For example, the posting unit supports platforms such as Facebook, Twitter, and Instagram. This allows content posted by a user to be automatically shared on other social media platforms.

[0063] The posting unit can analyze a user's purchasing history and recommend related content. For example, the posting unit provides a function whereby a generating AI analyzes a user's purchasing history and recommends related content. For example, it can recommend new illustrations in a similar style based on illustrations the user has previously purchased. The posting unit also clarifies the specific details of a user's purchasing history and how it is saved. For example, it can save data such as purchased products and purchase dates and times, and use that data to recommend related content. This allows the user's purchasing history to be analyzed and related content to be recommended.

[0064] The posting unit can use the emotion estimation function to collect the user's emotional reactions to the content they have purchased and provide feedback that will be useful for their next purchase. For example, the posting unit can use the emotion estimation function to collect the user's emotional reactions to the content they have purchased and provide feedback that will be useful for their next purchase based on that data. For example, if the user expresses positive emotions toward an illustration they have purchased, the posting unit can recommend illustrations in a similar style. The posting unit also clarifies the specific types of the user's emotional reactions and how they are collected. For example, the posting unit can collect emotional reactions using survey results or facial expression recognition, and provide feedback based on that. This allows the user to collect the user's emotional reactions to the content they have purchased and provide feedback that will be useful for their next purchase.

[0065] The community section can analyze the content posted by users and automatically match users with common interests. For example, the community section provides a function whereby a generation AI analyzes the content posted by users and automatically matches users with common interests. For example, it can match users who support the same character. The community section also clarifies the specific methods and criteria for identifying common interests. For example, it can match users based on common hobbies or interests. This allows users with common interests to be automatically matched.

[0066] The community department can analyze trends within the community in real time and notify users of popular topics. For example, the community department provides a function in which the generation AI analyzes trends within the community in real time and notifies users of popular topics. For example, if there is an increase in posts about a particular character or event, the community department will notify users of that topic. The community department also clarifies the specific methods and criteria for identifying trends within the community. For example, trends can be identified based on the number of posts or the number of likes. This allows trends within the community to be analyzed in real time and notify users of popular topics.

[0067] The community unit can use the emotion estimation function to analyze the emotional reactions to the user's posts and make suggestions to promote positive interactions. For example, the community unit uses the emotion estimation function to analyze the emotional reactions to the user's posts and make suggestions to promote positive interactions. For example, the community unit encourages other users to leave comments on posts in which the user has expressed positive emotions. The community unit also clarifies specific types of positive interactions and methods for promoting them. For example, the community unit makes suggestions based on constructive comments, encouraging messages, etc. In this way, the community unit analyzes the emotional reactions to the user's posts and makes suggestions to promote positive interactions.

[0068] The community unit can use the generation AI to support the planning and management of events held by users within the community. For example, the community unit uses the generation AI to provide a function to support the planning and management of events held by users within the community. For example, it automates event schedule management and participant recruitment. The community unit also clarifies the specific type and format of events to be held within the community. For example, it specifies online events, offline events, etc., and supports the planning and management based on that. This allows the community unit to support the planning and management of events held by users within the community.

[0069] The community section can automatically translate user posts and promote interaction between users who speak different languages. For example, the community section provides a function in which generative AI automatically translates user posts and promotes interaction between users who speak different languages. For example, Japanese posts are automatically translated into English or Chinese. The community section also clarifies the specific methods and standards for automatic translation. For example, translation is performed using machine translation or processing of technical terms. This can promote interaction between users who speak different languages.

[0070] The community unit can use the emotion estimation function to analyze the emotions felt by the user within the community and provide support to reduce negative emotions. For example, the community unit can use the emotion estimation function to analyze the emotions felt by the user within the community and provide support to reduce negative emotions. For example, if the user is feeling stressed, the community unit can suggest content to help the user relax. The community unit can also clarify the specific types of negative emotions and how to reduce them. For example, the community unit can analyze stress, anxiety, etc. and provide support based on that analysis. This allows the community unit to provide support to reduce negative emotions felt by the user within the community.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The generator can also provide a function that allows users to customize the costume of their favorite character. For example, if a user wants to dress a character in a specific costume, the generator AI selects the design of that costume, and generates an image or illustration of the character that reflects that costume. The generator can also provide an interface that allows users to change the color or pattern of the costume. This allows users to customize the costume of their favorite character to suit their preferences.

[0073] The generator may also provide a function that allows users to generate the voice of their favorite character. For example, when a user inputs a specific line, the generator AI plays that line in the voice of their favorite character. The generator may also provide an interface that allows users to adjust the tone and emotion of the voice. This allows users to customize the voice of their favorite character and enjoy a more realistic experience.

[0074] The generator can also provide a function that allows users to generate a story for their favorite character. For example, if a user inputs a specific scenario or setting, the generation AI generates a story based on that scenario. The generator can also provide an interface that allows users to select the progression and ending of the story. This allows users to customize the story of their favorite character to suit their preferences.

[0075] The generation unit can also provide a function that allows users to design merchandise for their favorite character. For example, a user can select an illustration of a character and design merchandise such as T-shirts or stickers using that illustration. The generation unit can also provide an interface that allows users to change the color and layout of the merchandise. This allows users to design merchandise for their favorite character to suit their preferences.

[0076] The generator can also provide a function that allows a user to generate music that reflects the emotions of their favorite character. For example, when a user inputs a specific emotion, the generation AI generates music based on that emotion. The generator can also provide an interface that allows the user to adjust the tempo and instruments of the music. This allows users to customize music that reflects the emotions of their favorite character and enjoy a more emotional experience.

[0077] The generation unit can also provide a function that allows users to design a figure of their favorite character. For example, the user can select the character's pose and facial expression, and generate data for 3D printing based on that design. The generation unit can also provide an interface that allows users to change the color and material of the figure. This allows users to design a figure of their favorite character to suit their preferences.

[0078] The generation unit can also use the emotion estimation function to provide a function to automatically generate a message card that reflects the feelings a user has toward their favorite character. For example, if a user inputs "I want to express my gratitude to my favorite character," the generation AI will generate a card containing a message of gratitude. The generation unit can also provide an interface that allows the user to customize the card design and message content. This allows the user to generate a message card that reflects the feelings the user has toward their favorite character.

[0079] The generation unit can also provide a function that allows the user to design a cosplay costume for their favorite character. For example, the user selects a design for the character's costume, and generates a cosplay costume pattern based on that design. The generation unit can also provide an interface that allows the user to change the color and material of the costume. This allows the user to design a cosplay costume for their favorite character according to their preferences.

[0080] The generation unit can also use the emotion estimation function to provide a function for automatically generating poems or short stories that reflect the feelings a user has toward their favorite character. For example, if a user inputs, "I want to write a poem that expresses my love for my favorite character," the generation AI will generate a poem with a love-themed theme. The generation unit can also provide an interface that allows the user to customize the content and style of the poem or short story. This allows the user to generate a poem or short story that reflects their feelings toward their favorite character.

[0081] The generation unit can also provide a function that allows the user to design accessories for their favorite character. For example, the user can select an illustration of a character and design accessories (such as key chains or badges) using that illustration. The generation unit can also provide an interface that allows the user to change the color or shape of the accessories. This allows the user to design accessories for their favorite character according to their preferences.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The generation unit uses a generative AI to generate images and illustrations based on user prompts. For example, the generative AI generates illustrations based on a user prompt such as, "I want an illustration of my favorite character smiling and posing." The generative AI can also generate images and illustrations using technologies such as deep learning and GAN (generative artificial network). Step 2: The posting unit posts the content generated by the generation unit. For example, a user can post images or illustrations they have generated within the service, which can then be viewed and purchased by other users. The posting unit can also provide a function that automatically tags content when it is posted, making it easier for other users to search for it. Step 3: The community section browses the content posted by the posting section, and users interact with each other. For example, users can post information about their favorites or leave comments on works created by other users. The community section can also analyze the content posted by users and provide a function to automatically match users with common interests.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

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

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

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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 generator that uses a generative AI to generate an image or illustration based on a user's prompts; a posting unit that posts the content generated by the generation unit; a community section where users can view the content posted by the posting section and interact with each other. A system characterized by:

2. The generation unit An emotion estimation function that generates an image or illustration that reflects the emotion of the person you love based on the prompt.

2. The system of claim 1.

3. The generation unit Generate a 3D model of the character you like and provide an interactive function that allows you to freely change the pose or facial expression of the character.

2. The system of claim 1.

4. The posting unit: Provide a function that automatically tags the content when it is posted, making it easier for users to search.

2. The system of claim 1.

5. The community section Provide a function to analyze the content posted by the users and automatically match users who share common interests with each other 2. The system of claim 1.

6. The generation unit Using an emotion estimation function, a function is provided to automatically generate a background or situation that reflects the emotions the user has toward the character of their choice.

2. The system of claim 1.

7. The posting unit: Using the emotion estimation function, the emotion expressed by the user when posting is analyzed, and advice is provided to elicit positive emotions.

2. The system of claim 1.

8. The community section Using emotion estimation capabilities to analyze the user's emotional response to the post and make suggestions to foster positive interactions 2. The system of claim 1.

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