System

Abstract

An object of a system according to an embodiment is to correct input content of a user and to perform posting not including an inappropriate expression.SOLUTION: A system includes an input unit, a correction unit, and a posting unit. The input unit receives an input content of a user. The correction unit corrects the content received by the input unit. The posting unit posts the content corrected by the correction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there is a possibility that content posted by users may contain inappropriate language, which could lead to problems.

[0005] The system according to the embodiment aims to correct the content entered by the user so that the user can post content that does not contain inappropriate expressions. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a correction unit, and a posting unit. The input unit receives input content from a user. The correction unit corrects the content received by the input unit. The posting unit posts the content corrected by the correction unit. [Effects of the Invention]

[0007] The system according to the embodiment can correct the content entered by the user and post content that does not include inappropriate language. [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 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 SNS system according to an embodiment of the present invention is a system in which a generation AI always modifies and posts content entered by a user. In this system, the original text entered by the user is never made public, and only the content corrected by the generation AI is posted, so inappropriate language is almost never posted. As a result, the SNS system, by having the generation AI correct and post the content entered by the user, can prevent the posting of inappropriate language and avoid flame wars.

[0029] An SNS system according to an embodiment includes an input unit, a correction unit, and a posting unit. The input unit accepts user input. For example, the input can be in the form of text, image, or audio. The input unit can also temporarily store the content entered by the user. The correction unit uses a generation AI to correct the content accepted by the input unit. For example, the generation AI may use a text generation AI (e.g., LLM) to perform grammar correction and content correction. The generation AI may also use a multimodal generation AI to perform image and audio correction. The generation AI may also learn a user's past posting history and make corrections optimized for each individual user. The posting unit posts the content corrected by the correction unit to the SNS. For example, the posting unit may automatically enter the corrected text into a posting field on the SNS and press a post button. The posting unit may also upload the corrected image or audio to a posting field on the SNS and press a post button. In this way, the SNS system, by having the generation AI correct the user's input and post it, can prevent posts containing inappropriate language and avoid flame wars.

[0030] The correction unit can replace offensive words or discriminatory expressions with appropriate expressions. For example, the generation AI detects offensive words and replaces them with polite expressions. For example, replacing "stupid" with "not smart." The correction unit also detects discriminatory expressions and replaces them with neutral expressions. For example, replacing "because she's a woman" with "because she's a human." The correction unit also uses an algorithm that enables the generation AI to detect offensive words and discriminatory expressions and replace them with appropriate expressions. For example, natural language processing technology is used to detect offensive words and discriminatory expressions and replace them with appropriate expressions. This makes it possible to prevent trouble on social media by replacing offensive words and discriminatory expressions with appropriate expressions.

[0031] The correction unit can learn the user's past posting history and make corrections optimized for each individual user. For example, the correction unit uses a generation AI to analyze the user's past posting history and make corrections optimized for the specific user. For example, the correction unit learns the words and expressions frequently used by the user and makes corrections based on those. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the corrections to be more natural and appropriate by learning the user's past posting history and making corrections optimized for each individual user.

[0032] The correction unit can understand the context of the posted content and correct it to an appropriate tone or style. In the correction unit, for example, the generation AI analyzes the context of the posted content and corrects it to an appropriate tone or style. For example, in a formal context, it corrects it to a polite expression, and in a casual context, it corrects it to a friendly expression. The correction unit also uses an algorithm that enables the generation AI to understand the context of the posted content and correct it to an appropriate tone or style. For example, it uses natural language processing technology to analyze the context and correct it to an appropriate tone or style. In this way, by understanding the context of the posted content and correcting it to an appropriate tone or style, more natural and appropriate posts can be made.

[0033] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0034] The correction unit can correct image or video captions to improve the quality of visual content. For example, the correction unit uses a generation AI to analyze image or video captions and correct them to appropriate expressions. For example, it corrects typos and makes the captions grammatically correct. The correction unit also uses an algorithm to correct image or video captions using a generation AI to improve the quality of visual content. For example, it uses natural language processing technology to analyze captions and correct them to appropriate expressions. In this way, the quality of visual content can be improved by correcting image or video captions.

[0035] The correction unit can encrypt the posted content so that only the generation AI can decrypt it. The correction unit, for example, builds a system that encrypts the posted content so that only the generation AI can decrypt it. For example, the correction unit encrypts the content entered by the user, and the generation AI decrypts it and makes the corrections. The correction unit also uses an algorithm to encrypt the posted content so that only the generation AI can decrypt it. For example, the correction unit encrypts the posted content using AES encryption or RSA encryption. In this way, the user's privacy can be protected by encrypting the posted content so that only the generation AI can decrypt it.

[0036] The redaction unit can limit the storage period of the original text and automatically delete it after a certain period. For example, the redaction unit adds a function to limit the storage period of the original text and automatically delete it after a certain period. For example, the original text is stored for only 30 days after posting and then automatically deleted. The redaction unit also uses an algorithm to limit the storage period of the original text and automatically delete it after a certain period. For example, the original text is automatically deleted using a timer setting or conditional deletion. This makes it possible to protect the user's privacy by limiting the storage period of the original text and automatically deleting it after a certain period.

[0037] The correction unit can also be applied to a company or organization's internal social networking site to prevent the leakage of confidential information. For example, the correction unit applies the guarantee of non-disclosure of original text to a company or organization's internal social networking site to build a system to prevent the leakage of confidential information. For example, the content posted on the internal social networking site is encrypted, and a generation AI decrypts and corrects it. The correction unit can also be applied to a company or organization's internal social networking site, using an algorithm to prevent the leakage of confidential information. For example, data encryption technology and access control technology can be used to prevent the leakage of confidential information. This can be applied to a company or organization's internal social networking site to prevent the leakage of confidential information, thereby improving the information security of the company or organization.

[0038] The correction unit can assess the risk of posts causing an uproar in advance and automatically correct posts that pose a high risk. The correction unit, for example, constructs a system in which a generation AI analyzes the content of posts and evaluates the risk of posts causing an uproar in advance. For example, it detects offensive language or discriminatory expressions and corrects them to appropriate expressions. The correction unit also uses an algorithm that enables the generation AI to assess the risk of posts causing an uproar in advance and automatically correct posts that pose a high risk. For example, it uses risk assessment technology to evaluate the risk of posts causing an uproar and makes appropriate corrections. In this way, it is possible to prevent uproars by assessing the risk of posts causing an uproar in advance and automatically correcting posts that pose a high risk.

[0039] The correction unit can learn from past flaming cases and detect and correct similar posts in advance. For example, the correction unit constructs a system in which a generation AI learns from past flaming cases and detects and corrects similar posts in advance. For example, the correction unit creates a database of past flaming cases and detects similar expressions. The correction unit also uses an algorithm in which the generation AI learns from past flaming cases and detects and corrects similar posts in advance. For example, machine learning technology is used to learn from past flaming cases and detect similar posts. In this way, by learning from past flaming cases and detecting and correcting similar posts in advance, flaming can be prevented before it happens.

[0040] The correction unit can share the flame risk assessment between different SNS platforms and perform unified risk management. The correction unit, for example, builds a system for sharing the flame risk assessment between different SNS platforms and performing unified risk management. For example, the correction unit integrates data from each platform and performs risk assessment. The correction unit also uses an algorithm for sharing the flame risk assessment between different SNS platforms and performing unified risk management. For example, the correction unit uses common risk assessment criteria to evaluate the risk of each platform and perform unified risk management. In this way, the flame risk can be effectively managed by sharing the flame risk assessment between different SNS platforms and performing unified risk management.

[0041] The correction unit can evaluate the risk of posts causing an uproar and automatically correct posts that are at a high risk. The correction unit, for example, builds a system in which a generation AI analyzes the content of posts and evaluates the risk of them causing an uproar. For example, it detects offensive language or discriminatory expressions and corrects them to appropriate expressions. The correction unit also uses an algorithm that enables the generation AI to evaluate the risk of posts causing an uproar and automatically correct posts that are at a high risk. For example, it uses risk assessment technology to evaluate the risk of posts causing an uproar and makes appropriate corrections. In this way, by evaluating the risk of posts causing an uproar and automatically correcting posts that are at a high risk, it is possible to prevent uproars from occurring.

[0042] The correction unit can learn the user's past posting history and make corrections optimized for each individual user. For example, the correction unit uses a generation AI to analyze the user's past posting history and make corrections optimized for the specific user. For example, the correction unit learns the words and expressions frequently used by the user and makes corrections based on those. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the corrections to be more natural and appropriate by learning the user's past posting history and making corrections optimized for each individual user.

[0043] The correction unit can understand the context of the posted content and correct it to an appropriate tone or style. In the correction unit, for example, the generation AI analyzes the context of the posted content and corrects it to an appropriate tone or style. For example, in a formal context, it corrects it to a polite expression, and in a casual context, it corrects it to a friendly expression. The correction unit also uses an algorithm that enables the generation AI to understand the context of the posted content and correct it to an appropriate tone or style. For example, it uses natural language processing technology to analyze the context and correct it to an appropriate tone or style. In this way, by understanding the context of the posted content and correcting it to an appropriate tone or style, more natural and appropriate posts can be made.

[0044] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0045] The correction unit can improve the quality of posted content to increase the user's sense of security. In the correction unit, for example, the generation AI analyzes the user's past posting history and makes corrections optimized for the specific user. For example, the generation AI learns the user's frequently used words and expressions and makes corrections based on them. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user, making it possible to make more natural and appropriate corrections.

[0046] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0047] The correction unit can correct image or video captions to improve the quality of visual content. For example, the correction unit uses a generation AI to analyze image or video captions and correct them to appropriate expressions. For example, it corrects typos and makes the captions grammatically correct. The correction unit also uses an algorithm to correct image or video captions using a generation AI to improve the quality of visual content. For example, it uses natural language processing technology to analyze captions and correct them to appropriate expressions. In this way, the quality of visual content can be improved by correcting image or video captions.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The SNS system may further include a feedback unit that provides real-time feedback on user posts. For example, when a user enters a post, the feedback unit immediately suggests improvements and points to note. Specifically, if a post contains offensive language, the feedback unit highlights that part and suggests more appropriate wording. The feedback unit may also point out potential misunderstandings in a post and encourage users to revise it to clarify the content. Furthermore, if a post violates certain community guidelines, the feedback unit may notify the user and request corrections. This allows users to review their content before posting and make more appropriate posts.

[0050] The SNS system can also include a tagging unit that automatically assigns tags to user posts. For example, if a post relates to a specific topic, tags related to that topic are automatically assigned. Specifically, if the post is about sports, tags such as "#sports" or "#soccer" are assigned. Also, if the post relates to a specific event, the event name can be assigned as a tag. Furthermore, the tagging unit can assign tags based on the emotion or tone of the post. For example, tags such as "#happy" and "#fun" are assigned to positive content, and "#sad" and "#anger" are assigned to negative content. This makes user posts more easily discoverable and promotes more active interactions on the SNS.

[0051] The SNS system may further include a suggestion unit that automatically suggests content related to the user's posts. For example, if a user posts about a specific topic, other posts and articles related to that topic may be suggested. Specifically, if a user posts about a movie, reviews and related news articles about that movie may be suggested. The suggestion unit may also suggest related content based on the user's past posting history and interests. For example, if a user frequently posts about travel, the suggestion unit may suggest recommended tourist spots and travelogues related to the travel. Furthermore, the suggestion unit may suggest related hashtags and topics based on the user's posts. This allows users to easily find information related to their posts, making information gathering on the SNS more efficient.

[0052] The SNS system can also include a formatting unit that automatically formats user posts. For example, if a post is long, the formatting unit automatically breaks it into paragraphs and lists to make it easier to read. Specifically, if the post covers multiple topics, it creates separate paragraphs for each topic. If the post is in list format, the formatting unit can convert it into bulleted lists or numbered lists. Furthermore, the formatting unit can automatically adjust the font, font size, color, etc. of the post to make it more visually appealing. This allows users to see their posts in a more readable and easily accessible format, increasing the likelihood of them receiving more responses from other users.

[0053] The SNS system may further include a privacy setting unit that automatically suggests privacy settings for the content posted by a user. For example, if the posted content contains personal information, the privacy setting unit suggests setting the post so that only specific friends can view it. Specifically, if a user posts information including their address or phone number, the privacy setting unit suggests limiting the visibility of the post. The privacy setting unit can also suggest appropriate privacy settings based on the user's past posting history and privacy setting trends. For example, if a user has previously posted private content only to friends, the privacy setting unit can suggest similar settings. Furthermore, the privacy setting unit can also suggest privacy settings based on the emotion and tone of the posted content. This allows users to use the SNS with peace of mind while protecting their privacy.

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

[0055] Step 1: The input unit accepts user input. For example, the input can be in the form of text, image, or sound. The input unit can also temporarily store the user input. Step 2: In the correction unit, the generation AI corrects the content received by the input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to perform grammar correction and content correction. The generation AI can also use a multimodal generation AI to correct images and audio. The generation AI can also learn from users' past posting history and make corrections optimized for each individual user. Step 3: The posting unit posts the content corrected by the correction unit to the SNS. For example, the posting unit automatically enters the corrected text into the SNS posting field and presses the post button. The posting unit also uploads the corrected image or audio to the SNS posting field and presses the post button. In this way, the SNS system can prevent posts containing inappropriate language and avoid flame wars by having the generation AI correct and post the content entered by the user.

[0056] (Example 2) The SNS system according to an embodiment of the present invention is a system in which a generation AI always modifies and posts content entered by a user. In this system, the original text entered by the user is never made public, and only the content corrected by the generation AI is posted, so inappropriate language is almost never posted. As a result, the SNS system, by having the generation AI correct and post the content entered by the user, can prevent the posting of inappropriate language and avoid flame wars.

[0057] An SNS system according to an embodiment includes an input unit, a correction unit, and a posting unit. The input unit accepts user input. For example, the input can be in the form of text, image, or audio. The input unit can also temporarily store the content entered by the user. The correction unit uses a generation AI to correct the content accepted by the input unit. For example, the generation AI may use a text generation AI (e.g., LLM) to perform grammar correction and content correction. The generation AI may also use a multimodal generation AI to perform image and audio correction. The generation AI may also learn a user's past posting history and make corrections optimized for each individual user. The posting unit posts the content corrected by the correction unit to the SNS. For example, the posting unit may automatically enter the corrected text into a posting field on the SNS and press a post button. The posting unit may also upload the corrected image or audio to a posting field on the SNS and press a post button. In this way, the SNS system, by having the generation AI correct the user's input and post it, can prevent posts containing inappropriate language and avoid flame wars.

[0058] The correction unit can replace offensive words or discriminatory expressions with appropriate expressions. For example, the generation AI detects offensive words and replaces them with polite expressions. For example, replacing "stupid" with "not smart." The correction unit also detects discriminatory expressions and replaces them with neutral expressions. For example, replacing "because she's a woman" with "because she's a human." The correction unit also uses an algorithm that enables the generation AI to detect offensive words and discriminatory expressions and replace them with appropriate expressions. For example, natural language processing technology is used to detect offensive words and discriminatory expressions and replace them with appropriate expressions. This makes it possible to prevent trouble on social media by replacing offensive words and discriminatory expressions with appropriate expressions.

[0059] The correction unit can learn the user's past posting history and make corrections optimized for each individual user. For example, the correction unit uses a generation AI to analyze the user's past posting history and make corrections optimized for the specific user. For example, the correction unit learns the words and expressions frequently used by the user and makes corrections based on those. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the corrections to be more natural and appropriate by learning the user's past posting history and making corrections optimized for each individual user.

[0060] The correction unit can understand the context of the posted content and correct it to an appropriate tone or style. In the correction unit, for example, the generation AI analyzes the context of the posted content and corrects it to an appropriate tone or style. For example, in a formal context, it corrects it to a polite expression, and in a casual context, it corrects it to a friendly expression. The correction unit also uses an algorithm that enables the generation AI to understand the context of the posted content and correct it to an appropriate tone or style. For example, it uses natural language processing technology to analyze the context and correct it to an appropriate tone or style. In this way, by understanding the context of the posted content and correcting it to an appropriate tone or style, more natural and appropriate posts can be made.

[0061] The correction unit can use the emotion estimation function to estimate the user's emotion and correct it to elicit a positive emotion. For example, the correction unit uses the emotion estimation function to analyze the user's emotion in real time and correct it to elicit a positive emotion. For example, the correction unit replaces a negative expression with a positive expression. The correction unit also uses an algorithm to estimate the user's emotion and correct it to elicit a positive emotion using the emotion estimation function. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and correct it to elicit a positive emotion. In this way, by using the emotion estimation function to estimate the user's emotion and correct it to elicit a positive emotion, the user's psychological satisfaction can be improved.

[0062] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0063] The correction unit can correct image or video captions to improve the quality of visual content. For example, the correction unit uses a generation AI to analyze image or video captions and correct them to appropriate expressions. For example, it corrects typos and makes the captions grammatically correct. The correction unit also uses an algorithm to correct image or video captions using a generation AI to improve the quality of visual content. For example, it uses natural language processing technology to analyze captions and correct them to appropriate expressions. In this way, the quality of visual content can be improved by correcting image or video captions.

[0064] The correction unit can use the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit uses the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit makes suggestions to change negative emotions to positive emotions. The correction unit also uses the emotion estimation function to analyze the emotion of the user when posting in real time and uses an algorithm to suggest appropriate corrections. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and suggest appropriate corrections. In this way, the emotion estimation function can be used to analyze the user's emotion in real time and suggest appropriate corrections, thereby making it possible to more appropriately correct the user's posted content.

[0065] The correction unit can encrypt the posted content so that only the generation AI can decrypt it. The correction unit, for example, builds a system that encrypts the posted content so that only the generation AI can decrypt it. For example, the correction unit encrypts the content entered by the user, and the generation AI decrypts it and makes the corrections. The correction unit also uses an algorithm to encrypt the posted content so that only the generation AI can decrypt it. For example, the correction unit encrypts the posted content using AES encryption or RSA encryption. In this way, the user's privacy can be protected by encrypting the posted content so that only the generation AI can decrypt it.

[0066] The redaction unit can limit the storage period of the original text and automatically delete it after a certain period. For example, the redaction unit adds a function to limit the storage period of the original text and automatically delete it after a certain period. For example, the original text is stored for only 30 days after posting and then automatically deleted. The redaction unit also uses an algorithm to limit the storage period of the original text and automatically delete it after a certain period. For example, the original text is automatically deleted using a timer setting or conditional deletion. This makes it possible to protect the user's privacy by limiting the storage period of the original text and automatically deleting it after a certain period.

[0067] The correction unit can use the emotion estimation function to analyze the emotion of the user when posting in real time and correct emotional posts with particular care. The correction unit, for example, uses the emotion estimation function to analyze the emotion of the user when posting in real time and correct emotional posts with particular care. For example, the correction unit corrects emotional posts to expressions that soften emotions such as anger or sadness. The correction unit also uses the emotion estimation function to analyze the emotion of the user when posting in real time and uses an algorithm for correcting emotional posts with particular care. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and correct emotional posts with particular care. In this way, by using the emotion estimation function to analyze the user's emotion in real time and correcting emotional posts with particular care, the user's posted content can be more appropriately corrected.

[0068] The correction unit can also be applied to a company or organization's internal social networking site to prevent the leakage of confidential information. For example, the correction unit applies the guarantee of non-disclosure of original text to a company or organization's internal social networking site to build a system to prevent the leakage of confidential information. For example, the content posted on the internal social networking site is encrypted, and a generation AI decrypts and corrects it. The correction unit can also be applied to a company or organization's internal social networking site, using an algorithm to prevent the leakage of confidential information. For example, data encryption technology and access control technology can be used to prevent the leakage of confidential information. This can be applied to a company or organization's internal social networking site to prevent the leakage of confidential information, thereby improving the information security of the company or organization.

[0069] The correction unit can use the emotion estimation function to analyze the emotion of the user when posting in real time and correct emotional posts with particular care. The correction unit, for example, uses the emotion estimation function to analyze the emotion of the user when posting in real time and correct emotional posts with particular care. For example, the correction unit corrects emotional posts to expressions that soften emotions such as anger or sadness. The correction unit also uses the emotion estimation function to analyze the emotion of the user when posting in real time and uses an algorithm for correcting emotional posts with particular care. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and correct emotional posts with particular care. In this way, by using the emotion estimation function to analyze the user's emotion in real time and correcting emotional posts with particular care, the user's posted content can be more appropriately corrected.

[0070] The correction unit can assess the risk of posts causing an uproar in advance and automatically correct posts that pose a high risk. The correction unit, for example, constructs a system in which a generation AI analyzes the content of posts and evaluates the risk of posts causing an uproar in advance. For example, it detects offensive language or discriminatory expressions and corrects them to appropriate expressions. The correction unit also uses an algorithm that enables the generation AI to assess the risk of posts causing an uproar in advance and automatically correct posts that pose a high risk. For example, it uses risk assessment technology to evaluate the risk of posts causing an uproar and makes appropriate corrections. In this way, it is possible to prevent uproars by assessing the risk of posts causing an uproar in advance and automatically correcting posts that pose a high risk.

[0071] The correction unit can learn from past flaming cases and detect and correct similar posts in advance. For example, the correction unit constructs a system in which a generation AI learns from past flaming cases and detects and corrects similar posts in advance. For example, the correction unit creates a database of past flaming cases and detects similar expressions. The correction unit also uses an algorithm in which the generation AI learns from past flaming cases and detects and corrects similar posts in advance. For example, machine learning technology is used to learn from past flaming cases and detect similar posts. In this way, by learning from past flaming cases and detecting and correcting similar posts in advance, flaming can be prevented before it happens.

[0072] The correction unit can use the emotion estimation function to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care. The correction unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and revise emotional posts that pose a high risk of causing a flare-up with particular care. For example, the correction unit revises the posts to expressions that soften emotions such as anger or sadness. The correction unit also uses the emotion estimation function to analyze the user's emotions and uses an algorithm to revise emotional posts that pose a high risk of causing a flare-up with particular care. For example, the emotion analysis algorithm is used to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care. In this way, by using the emotion estimation function to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care, it is possible to prevent flare-ups from occurring.

[0073] The correction unit can share the flame risk assessment between different SNS platforms and perform unified risk management. The correction unit, for example, builds a system for sharing the flame risk assessment between different SNS platforms and performing unified risk management. For example, the correction unit integrates data from each platform and performs risk assessment. The correction unit also uses an algorithm for sharing the flame risk assessment between different SNS platforms and performing unified risk management. For example, the correction unit uses common risk assessment criteria to evaluate the risk of each platform and perform unified risk management. In this way, the flame risk can be effectively managed by sharing the flame risk assessment between different SNS platforms and performing unified risk management.

[0074] The correction unit can evaluate the risk of posts causing an uproar and automatically correct posts that are at a high risk. The correction unit, for example, builds a system in which a generation AI analyzes the content of posts and evaluates the risk of them causing an uproar. For example, it detects offensive language or discriminatory expressions and corrects them to appropriate expressions. The correction unit also uses an algorithm that enables the generation AI to evaluate the risk of posts causing an uproar and automatically correct posts that are at a high risk. For example, it uses risk assessment technology to evaluate the risk of posts causing an uproar and makes appropriate corrections. In this way, by evaluating the risk of posts causing an uproar and automatically correcting posts that are at a high risk, it is possible to prevent uproars from occurring.

[0075] The correction unit can use the emotion estimation function to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care. The correction unit, for example, uses the emotion estimation function to analyze the user's emotions in real time and revise emotional posts that pose a high risk of causing a flare-up with particular care. For example, the correction unit revises the posts to expressions that soften emotions such as anger or sadness. The correction unit also uses the emotion estimation function to analyze the user's emotions and uses an algorithm to revise emotional posts that pose a high risk of causing a flare-up with particular care. For example, the emotion analysis algorithm is used to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care. In this way, by using the emotion estimation function to analyze the user's emotions and revise emotional posts that pose a high risk of causing a flare-up with particular care, it is possible to prevent flare-ups from occurring.

[0076] The correction unit can learn the user's past posting history and make corrections optimized for each individual user. For example, the correction unit uses a generation AI to analyze the user's past posting history and make corrections optimized for the specific user. For example, the correction unit learns the words and expressions frequently used by the user and makes corrections based on those. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the corrections to be more natural and appropriate by learning the user's past posting history and making corrections optimized for each individual user.

[0077] The correction unit can understand the context of the posted content and correct it to an appropriate tone or style. In the correction unit, for example, the generation AI analyzes the context of the posted content and corrects it to an appropriate tone or style. For example, in a formal context, it corrects it to a polite expression, and in a casual context, it corrects it to a friendly expression. The correction unit also uses an algorithm that enables the generation AI to understand the context of the posted content and correct it to an appropriate tone or style. For example, it uses natural language processing technology to analyze the context and correct it to an appropriate tone or style. In this way, by understanding the context of the posted content and correcting it to an appropriate tone or style, more natural and appropriate posts can be made.

[0078] The correction unit can use the emotion estimation function to estimate the user's emotion and correct it to elicit a positive emotion. For example, the correction unit uses the emotion estimation function to analyze the user's emotion in real time and correct it to elicit a positive emotion. For example, the correction unit replaces a negative expression with a positive expression. The correction unit also uses an algorithm to estimate the user's emotion and correct it to elicit a positive emotion using the emotion estimation function. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and correct it to elicit a positive emotion. In this way, by using the emotion estimation function to estimate the user's emotion and correct it to elicit a positive emotion, the user's psychological satisfaction can be improved.

[0079] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0080] The correction unit can use the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit uses the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit makes suggestions to change negative emotions to positive emotions. The correction unit also uses the emotion estimation function to analyze the emotion of the user when posting in real time and uses an algorithm to suggest appropriate corrections. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and suggest appropriate corrections. In this way, the emotion estimation function can be used to analyze the user's emotion in real time and suggest appropriate corrections, thereby making it possible to more appropriately correct the user's posted content.

[0081] The correction unit can improve the quality of posted content to increase the user's sense of security. In the correction unit, for example, the generation AI analyzes the user's past posting history and makes corrections optimized for the specific user. For example, the generation AI learns the user's frequently used words and expressions and makes corrections based on them. The correction unit also uses an algorithm that allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user. For example, machine learning technology is used to learn the user's posting history and make optimized corrections. This allows the generation AI to learn the user's past posting history and make corrections optimized for each individual user, making it possible to make more natural and appropriate corrections.

[0082] The correction unit can estimate the user's emotions and correct them to elicit positive emotions. The correction unit, for example, uses an emotion estimation function to analyze the user's emotions in real time and correct them to elicit positive emotions. For example, the correction unit replaces negative expressions with positive expressions. The correction unit also uses an algorithm to estimate the user's emotions and correct them to elicit positive emotions using the emotion estimation function. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotions and correct them to elicit positive emotions. In this way, by using the emotion estimation function to estimate the user's emotions and correct them to elicit positive emotions, the user's psychological satisfaction can be improved.

[0083] The correction unit can automatically translate posts in different languages ​​and promote international use of SNS. For example, the correction unit allows the generation AI to automatically translate the content of posts, enabling posts in different languages. For example, it translates English posts into Japanese and Japanese posts into English. The correction unit also uses an algorithm that allows the generation AI to automatically translate posts in different languages ​​and promote international use of SNS. For example, it uses a translation algorithm to automatically translate the content of posts. This allows international use of SNS to be promoted by automatically translating posts in different languages.

[0084] The correction unit can correct image or video captions to improve the quality of visual content. For example, the correction unit uses a generation AI to analyze image or video captions and correct them to appropriate expressions. For example, it corrects typos and makes the captions grammatically correct. The correction unit also uses an algorithm to correct image or video captions using a generation AI to improve the quality of visual content. For example, it uses natural language processing technology to analyze captions and correct them to appropriate expressions. In this way, the quality of visual content can be improved by correcting image or video captions.

[0085] The correction unit can use the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit uses the emotion estimation function to analyze the emotion of the user when posting in real time and suggest appropriate corrections. For example, the correction unit makes suggestions to change negative emotions to positive emotions. The correction unit also uses the emotion estimation function to analyze the emotion of the user when posting in real time and uses an algorithm to suggest appropriate corrections. For example, the correction unit uses an emotion analysis algorithm to analyze the user's emotion and suggest appropriate corrections. In this way, the emotion estimation function can be used to analyze the user's emotion in real time and suggest appropriate corrections, thereby making it possible to more appropriately correct the user's posted content.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The SNS system may further include a feedback unit that provides real-time feedback on user posts. For example, when a user enters content to post, the feedback unit immediately suggests improvements and points to note. Specifically, if a post contains offensive language, the feedback unit highlights that part and suggests more appropriate wording. The feedback unit may also point out potential misunderstandings in a post and encourage users to revise it to clarify the content. Furthermore, if a post violates certain community guidelines, the feedback unit may notify the user and request corrections. This allows users to review their content before posting and make more appropriate posts.

[0088] The SNS system can also include a tagging unit that automatically assigns tags to user posts. For example, if a post relates to a specific topic, tags related to that topic are automatically assigned. Specifically, if the post is about sports, tags such as "#sports" or "#soccer" are assigned. Also, if the post relates to a specific event, the event name can be assigned as a tag. Furthermore, the tagging unit can assign tags based on the emotion or tone of the post. For example, tags such as "#happy" and "#fun" are assigned to positive content, and "#sad" and "#anger" are assigned to negative content. This makes user posts more easily discoverable and promotes more active interactions on the SNS.

[0089] The SNS system may further include a suggestion unit that automatically suggests content related to the user's posts. For example, if a user posts about a specific topic, other posts and articles related to that topic may be suggested. Specifically, if a user posts about a movie, reviews and related news articles about that movie may be suggested. The suggestion unit may also suggest related content based on the user's past posting history and interests. For example, if a user frequently posts about travel, the suggestion unit may suggest recommended tourist spots and travelogues related to the travel. Furthermore, the suggestion unit may suggest related hashtags and topics based on the user's posts. This allows users to easily find information related to their posts, making information gathering on the SNS more efficient.

[0090] The SNS system can also include a formatting unit that automatically formats user posts. For example, if a post is long, the formatting unit automatically breaks it into paragraphs and lists to make it easier to read. Specifically, if the post covers multiple topics, it creates separate paragraphs for each topic. If the post is in list format, the formatting unit can convert it into bulleted lists or numbered lists. Furthermore, the formatting unit can automatically adjust the font, font size, color, etc. of the post to make it more visually appealing. This allows users to see their posts in a more readable and easily accessible format, increasing the likelihood of them receiving more responses from other users.

[0091] The SNS system may further include a privacy setting unit that automatically suggests privacy settings for the content posted by a user. For example, if the posted content contains personal information, the privacy setting unit suggests setting the post so that only specific friends can view it. Specifically, if a user posts information including their address or phone number, the privacy setting unit suggests limiting the visibility of the post. The privacy setting unit can also suggest appropriate privacy settings based on the user's past posting history and privacy setting trends. For example, if a user has previously posted private content only to friends, the privacy setting unit can suggest similar settings. Furthermore, the privacy setting unit can also suggest privacy settings based on the emotion and tone of the posted content. This allows users to use the SNS with peace of mind while protecting their privacy.

[0092] The SNS system may further include an emoticon suggestion unit that estimates a user's emotions and suggests appropriate emoticons and stickers based on the estimated emotions. For example, if a user has positive emotions, the emoticon suggestion unit suggests emoticons such as smiley faces and hearts. Specifically, if a user posts expressing joy or gratitude, an emoticon that matches that emotion is automatically displayed. The emoticon suggestion unit may also suggest emoticons of encouragement or comfort if a user has negative emotions. For example, if a user posts expressing sadness or anger, an emoticon that matches that emotion is displayed. Furthermore, the emoticon suggestion unit may suggest emoticons that are optimal for each individual user based on the user's past emoticon usage history. This allows users to express their emotions more effectively, facilitating smooth communication on the SNS.

[0093] The SNS system may further include a media suggestion unit that estimates a user's emotions and suggests appropriate music and videos based on the estimated emotions. For example, if a user feels like relaxing, the unit suggests music and videos with a relaxing effect. Specifically, if a user posts that they are feeling stressed, the unit displays relaxing music or a meditative video that matches the user's emotions. The media suggestion unit may also suggest uplifting music and videos if a user feels like cheering up. For example, if a user posts that they are feeling depressed, the unit displays uplifting music or an encouraging video that matches the user's emotions. Furthermore, the media suggestion unit may suggest music and videos that are optimal for each individual user based on the user's past media viewing history. This allows users to enjoy media that matches their emotions, improving psychological satisfaction.

[0094] The SNS system may further include an activity suggestion unit that estimates a user's emotions and suggests appropriate activities based on the estimated emotions. For example, if a user is feeling stressed, the activity suggestion unit suggests relaxing activities. Specifically, if a user posts that they are feeling tired, activities such as yoga or meditation that match that emotion are displayed. The activity suggestion unit may also suggest active activities if a user is feeling energetic. For example, if a user posts that they are full of energy, activities such as running or dancing that match that emotion are displayed. Furthermore, the activity suggestion unit may suggest activities that are optimal for each individual user based on the user's past activity history. This allows users to enjoy activities that match their emotions, improving their physical and mental health.

[0095] The SNS system may further include a message suggestion unit that estimates a user's emotions and suggests appropriate messages based on the estimated emotions. For example, if a user is feeling sad, an encouraging message may be suggested. Specifically, if a user posts about experiencing heartbreak or failure, an encouraging message that matches the user's emotions may be displayed. The message suggestion unit may also suggest a congratulatory message if the user is feeling happy. For example, if a user posts about experiencing success or joy, a congratulatory message that matches the user's emotions may be displayed. Furthermore, the message suggestion unit may suggest messages that are optimal for each individual user based on the user's past message history. This allows users to receive messages that match their emotions, enriching communication on the SNS.

[0096] The SNS system may further include an advice unit that estimates a user's emotions and provides appropriate advice based on the estimated emotions. For example, if a user is feeling stressed, the advice unit provides stress relief advice. Specifically, if a user posts that they are feeling stressed due to work or relationships, the advice unit suggests a stress relief method that suits that emotion. Furthermore, if a user is feeling positive emotions, the advice unit can provide advice on how to maintain those emotions. For example, if a user posts that they are feeling success or joy, the advice unit displays advice on how to maintain those emotions. Furthermore, the advice unit can provide advice that is optimal for each individual user based on the user's past advice history. This allows users to receive advice that suits their emotions and obtain psychological support.

[0097] The processing flow of the second embodiment will be briefly explained below.

[0098] Step 1: The input unit accepts user input. For example, the input can be in the form of text, image, or sound. The input unit can also temporarily store the user input. Step 2: In the correction unit, the generation AI corrects the content received by the input unit. For example, the generation AI uses a text generation AI (e.g., LLM) to perform grammar correction and content correction. The generation AI can also use a multimodal generation AI to correct images and audio. The generation AI can also learn from users' past posting history and make corrections optimized for each individual user. Step 3: The posting unit posts the content corrected by the correction unit to the SNS. For example, the posting unit automatically enters the corrected text into the SNS posting field and presses the post button. The posting unit also uploads the corrected image or audio to the SNS posting field and presses the post button. In this way, the SNS system can prevent posts containing inappropriate language and avoid flame wars by having the generation AI correct and post the content entered by the user.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0103] 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.

[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 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.

[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. 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.

[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 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.

[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 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.

[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 AI 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 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.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[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 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.

[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 (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).

[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] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 AI 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.

[0131] 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.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 AI 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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."

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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]

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

Claims

1. an input unit that accepts user input; a correction unit that corrects the content received by the input unit; a posting unit that posts the content corrected by the correction unit. A system characterized by:

2. The correction unit Automatically translate posts in different languages ​​to promote international social media use 2. The system of claim 1.

3. The correction unit Encrypt the content of posts so that only the generating AI can decrypt them 2. The system of claim 1.

4. The correction unit Evaluate posts' risk of causing controversy in advance and automatically edit posts with high risk 2. The system of claim 1.

5. The correction unit Using an emotion estimation function, the user's emotion is estimated and modified to elicit positive emotions.

2. The system of claim 1.

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