system

Abstract

An object of the system according to the embodiment is to correct a user's text in real time and prevent misunderstanding and trouble.SOLUTION: A system includes a language analysis unit, a correction unit, an application providing unit, and an SNS function addition unit. The language analysis unit analyzes the text of the user. The proofreading unit proofreads the text analyzed by the language analysis unit in real time. The application providing unit provides the text proofread by the proofreading unit to the user. The SNS function adding unit provides the text proofread by the proofreading unit to the SNS.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies make it difficult to proofread users' text for errors or inappropriate expressions in real time, which can lead to misunderstandings and problems.

[0005] The system according to the embodiment aims to correct a user's text in real time to prevent misunderstandings and problems. [Means for solving the problem]

[0006] The system according to the embodiment includes a language analysis unit, a proofreading unit, an application providing unit, and an SNS function adding unit. The language analysis unit analyzes a user's text. The proofreading unit proofreads the text analyzed by the language analysis unit in real time. The application providing unit provides the user with the text proofread by the proofreading unit. The SNS function adding unit provides the text proofread by the proofreading unit to the SNS. [Effects of the Invention]

[0007] The system according to the embodiment can correct the user's text in real time to prevent misunderstandings and problems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system that uses generative AI to prevent misunderstandings and trouble. This system utilizes advanced language processing and real-time proofreading functions to contribute to smoother communication. As a result, the system uses generative AI to prevent misunderstandings and trouble, and can contribute to smoother communication.

[0029] A system according to an embodiment includes a language analysis unit, a proofreading unit, an application providing unit, and an SNS function adding unit. The language analysis unit analyzes a user's text. For example, it analyzes messages and comments entered by the user to detect potentially misleading or inappropriate expressions. The language analysis unit can also analyze the user's past posting history and generate a language model optimized for each individual user. For example, a generation AI collects the user's past posting history and analyzes frequently occurring expressions and phrases. The proofreading unit proofreads the text analyzed by the language analysis unit in real time. For example, it changes potentially misleading expressions to clearer expressions and corrects inappropriate expressions to appropriate expressions. The proofreading unit can also analyze the user's input speed and typing pattern to improve proofreading accuracy in real time. For example, the generation AI analyzes the user's input speed and typing pattern to improve proofreading accuracy in real time. The application providing unit provides the text proofread by the proofreading unit to the user. For example, it provides a user app equipped with the generation AI, analyzes the text entered by the user in real time, detects potentially misleading or inappropriate expressions, and suggests corrections. The app providing unit can also analyze a user's past message history and make proofreading suggestions optimized for each individual user. For example, the app collects a user's past message history and analyzes frequently used expressions and phrases. The SNS function adding unit provides the text proofread by the proofreading unit to the SNS. For example, a generation AI can be incorporated into the SNS to analyze text before the user posts, detect potentially misleading or inappropriate expressions, and suggest corrections. The SNS function adding unit can also analyze trends and buzzwords on the SNS and make proofreading suggestions that correspond to the latest linguistic expressions. For example, the generation AI can analyze trends and buzzwords on the SNS in real time and make proofreading suggestions that correspond to the latest linguistic expressions. As a result, the system according to the embodiment can prevent misunderstandings and troubles using the generation AI and contribute to smoother communication. For example, the generation AI can analyze text before a user posts on the SNS and suggest appropriate corrections, allowing the user to send a message without causing misunderstandings.Furthermore, by using the user app, users can check the content of emails and messages in advance and make appropriate corrections, allowing them to communicate with peace of mind.

[0030] The language analysis unit can analyze a user's past posting history and generate a language model optimized for each individual user. For example, the language analysis unit uses a generation AI to collect a user's past posting history and analyze frequently used expressions and phrases. This generates an individual language model for the user and suggests optimal expressions for future posts. The language analysis unit also uses a generation AI to learn the user's preferences and tendencies for specific topics and themes based on the user's past posting history. This allows it to suggest appropriate expressions for content that interests the user. The language analysis unit also uses a generation AI to analyze a user's past posting history and generate an optimal language model according to a specific context or situation. For example, it makes suggestions that take into account the difference between business emails and casual messages. This allows it to generate an optimal language model based on the user's past posting history and suggest optimal expressions for future posts.

[0031] The language analysis unit can analyze the user's voice input and perform language processing that takes into account both text and voice. For example, the language analysis unit converts the user's voice input into text using a generation AI and analyzes the tone and intonation of the text and voice. This allows for a more accurate understanding of the user's intention and suggests appropriate expressions. In addition, when the user provides voice input, the generation AI analyzes the voice data in real time and performs language processing that takes into account both text and voice. For example, it makes suggestions that reflect the strength and nuance of emotions. In addition, the language analysis unit allows the generation AI to analyze both voice input and text input, building a system that comprehensively understands the user's intention. This improves the degree of agreement between voice and text and prevents misunderstandings. This allows for a more accurate understanding of the user's intention and suggests appropriate expressions.

[0032] The proofreading unit can analyze a user's input speed or typing pattern and improve proofreading accuracy in real time. For example, the proofreading unit uses a generation AI to analyze a user's input speed or typing pattern and improve proofreading accuracy in real time. For example, if the input speed is fast, it suggests concise expressions. The proofreading unit also analyzes a user's typing pattern and the generation AI improves proofreading accuracy in real time. For example, it detects input errors of specific keys and suggests appropriate corrections. The proofreading unit also builds a system in which the generation AI analyzes a user's input speed or typing pattern and improves proofreading accuracy in real time. For example, it suggests appropriate expressions according to the input speed. This makes it possible to improve proofreading accuracy based on the user's input speed or typing pattern.

[0033] The proofreading unit can understand the user's context and suggest appropriate corrections based on the context. In the proofreading unit, for example, the generation AI analyzes the context of the user's input text and suggests appropriate corrections based on the context. For example, the proofreading unit makes corrections that take into account the difference between a business email and a casual message. In addition, to understand the context of the user's input text, the generation AI analyzes related keywords and phrases and suggests appropriate corrections. For example, it suggests expressions related to a specific topic. In addition, the proofreading unit builds a system in which the generation AI understands the user's context and suggests appropriate corrections based on the context. For example, it suggests appropriate expressions based on the context. This makes it possible to suggest appropriate corrections based on the user's context and prevent misunderstandings.

[0034] The proofreading unit also applies a real-time proofreading function to voice input, preventing misunderstandings of voice messages. In the proofreading unit, for example, the generation AI analyzes voice input in real time and performs appropriate proofreading. For example, to prevent misunderstandings of voice messages, the proofreading unit suggests corrections that take into account the tone and intonation of the voice. In addition, in order to apply the real-time proofreading function to voice input, the generation AI converts voice data into text and suggests appropriate corrections. For example, corrections are made to clarify the content of the voice message. In addition, the proofreading unit builds a system in which the generation AI analyzes voice input in real time and performs appropriate proofreading to prevent misunderstandings of voice messages. For example, corrections that reflect the nuances of the voice are suggested. This makes it possible to prevent misunderstandings of voice messages.

[0035] The proofreading unit can also apply the real-time proofreading function to visual data, automatically correcting captions for images or videos. For example, the proofreading unit uses a generation AI to analyze the content of images or videos and automatically correct captions in real time. For example, it changes image descriptions and video captions to appropriate expressions. In order to apply the real-time proofreading function to visual data, the proofreading unit uses image recognition technology to analyze the content of images and propose appropriate captions. For example, it proposes appropriate captions that match the content of the image. The proofreading unit also builds a system in which the generation AI analyzes the content of videos and automatically corrects captions in real time. For example, it provides appropriate captions for each scene in the video. This makes it possible to automatically correct captions for images and videos.

[0036] The application providing unit can analyze a user's past message history and make proofreading suggestions optimized for each individual user. For example, the application providing unit allows the application to collect the user's past message history and analyze frequently occurring expressions and phrases. This generates an individual language model for the user and suggests optimal expressions for future messages. The application providing unit also allows the application to learn the user's preferences and tendencies for specific topics or themes based on the user's past message history. This suggests appropriate expressions for content that interests the user. The application providing unit also allows the application to analyze the user's past message history and generate an optimal language model according to a specific context or situation. For example, suggestions are made that take into account the difference between business emails and casual messages. This allows optimal proofreading suggestions to be made based on the user's past message history.

[0037] The application providing unit can analyze the user's voice input and make proofreading suggestions that take both the voice and the text into consideration. For example, the application providing unit converts the user's voice input into text and analyzes the text and the tone and intonation of the voice. This allows the application to understand the user's intention more accurately and suggest appropriate expressions. Furthermore, when the user provides voice input, the application providing unit analyzes the voice data in real time and makes proofreading suggestions that take both the text and the voice into consideration. For example, the application providing unit makes suggestions that reflect the strength and nuance of emotions. Furthermore, the application providing unit builds a system in which the application analyzes both the voice input and the text input and comprehensively understands the user's intention. This improves the degree of agreement between the voice and the text and prevents misunderstandings. This allows the application to make proofreading suggestions that take both the user's voice input and the text into consideration.

[0038] The application providing unit provides a translation function between different languages, thereby facilitating intercultural communication. For example, the application providing unit allows the application to automatically translate text input by a user into a different language, thereby facilitating intercultural communication. For example, the application providing unit translates from English to Japanese and suggests appropriate expressions. Furthermore, when translating between different languages, the application providing unit provides translations that take cultural nuances and backgrounds into account. For example, the application providing unit provides translations that reflect differences in expressions in specific cultures. Furthermore, the application providing unit allows the application to translate between different languages, thereby enabling users to smoothly communicate between different cultures. For example, the application providing unit provides translations of business emails and casual messages. This allows for smooth intercultural communication.

[0039] The application providing unit can analyze visual data and make proofreading suggestions that take into account the relevance between text and visual data. For example, the application providing unit analyzes the content of images and videos and generates text related to the images. For example, it automatically generates image descriptions and video captions. Furthermore, in order to make proofreading suggestions that take into account the relevance between visual data and text data, the application providing unit analyzes the content of images using image recognition technology and suggests appropriate text. For example, it suggests appropriate captions that match the content of the images. Furthermore, the application providing unit analyzes the content of videos and generates text that corresponds to the scenes in the videos. For example, it provides appropriate captions and descriptions for each video scene. This makes it possible to make proofreading suggestions that take into account the relevance between visual data and text.

[0040] The SNS function addition unit can analyze trends and buzzwords on SNS and make proofreading suggestions that correspond to the latest language expressions. In the SNS function addition unit, for example, the generation AI analyzes trends and buzzwords on SNS in real time and makes proofreading suggestions that correspond to the latest language expressions. For example, it makes suggestions to use buzzwords and new slang appropriately. The SNS function addition unit also analyzes trends on SNS and the generation AI proposes corrections to the user's posted content that reflect the latest language expressions. For example, it proposes expressions that match the trends. In addition, the SNS function addition unit builds a system in which the generation AI analyzes buzzwords and trends on SNS and proposes appropriate corrections to the user's posted content. For example, it proposes expressions based on the latest trends. This makes it possible to make proofreading suggestions that correspond to trends and buzzwords on SNS.

[0041] The SNS function addition unit can analyze the reactions of the user's followers and friends and suggest expressions that are most likely to garner sympathy. In the SNS function addition unit, for example, the generation AI analyzes the reactions of the user's followers and friends and suggests expressions that are most likely to garner sympathy. For example, it suggests expressions that receive a lot of positive reactions based on past reaction data. The SNS function addition unit also analyzes the reactions of the user's followers and friends, and builds a system in which the generation AI suggests expressions that are likely to garner sympathy. For example, it analyzes reactions to specific expressions and suggests appropriate modifications. In addition, the SNS function addition unit also analyzes the reactions of the user's followers and friends and suggests expressions that are most likely to garner sympathy. For example, it suggests expressions that are likely to garner sympathy based on reactions to past posts. In this way, it is possible to analyze the reactions of the user's followers and friends and suggest expressions that are likely to garner sympathy.

[0042] The SNS function addition unit can analyze captions of images and videos on SNS and make revision suggestions that take into account the relevance between text and visual data. In the SNS function addition unit, for example, a generation AI analyzes captions of images and videos on SNS and makes revision suggestions that take into account the relevance between text and visual data. For example, it suggests appropriate captions that match the content of the image. In addition, the SNS function addition unit analyzes captions of images and videos on SNS and a generation AI suggests corrections that take into account the relevance between visual data and text. For example, it suggests captions that match the scenes in the video. In addition, the SNS function addition unit builds a system in which a generation AI analyzes captions of images and videos on SNS and makes revision suggestions that take into account the relevance between text and visual data. For example, it suggests appropriate captions based on the content of the image. This makes it possible to analyze captions of images and videos on SNS and make revision suggestions that take into account the relevance between text and visual data.

[0043] The SNS function addition unit provides a translation function between different languages ​​on the SNS, thereby facilitating intercultural communication. For example, the SNS function addition unit allows a generation AI to automatically translate user posts into different languages, thereby facilitating intercultural communication. For example, the generation AI translates from English to Japanese and suggests appropriate expressions. When translating between different languages, the SNS function addition unit also allows the generation AI to provide translations that take cultural nuances and background into consideration. For example, the generation AI provides translations that reflect differences in expressions in specific cultures. The SNS function addition unit also allows the generation AI to translate between different languages, thereby enabling users to smoothly communicate between different cultures. For example, the generation AI provides translations for business emails and casual messages. This allows a translation function between different languages ​​on the SNS to facilitate intercultural communication.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The system can provide suggestions for maintaining consistency in style and tone, not just grammatical accuracy, for user input text. For example, it suggests maintaining a formal tone in business emails and a relaxed tone in casual messages. If the user needs to follow a specific style guide, it can also suggest revisions based on those guidelines. Furthermore, the system can learn specific phrases and phrasing used by the user in the past and suggest consistent language based on that. This allows users to communicate with a consistent style and tone.

[0046] The system can suggest corrections to the user's input text that take into account cultural background and region-specific expressions. For example, it can make suggestions that reflect differences in expressions in different regions and cultures. The system can also suggest corrections to avoid taboos or sensitive topics in specific cultures or regions. Furthermore, when the user communicates with people from different cultures, the system can prevent misunderstandings and trouble by suggesting appropriate expressions. This allows users to communicate smoothly across cultures.

[0047] The system can suggest expressions specific to a specific industry or field of expertise to the user's input text. For example, it suggests technical terms and appropriate expressions in the medical field, and legal expressions and terminology in the legal field. The system can also learn the latest trends and terminology in a specific industry or field of expertise and suggest corrections based on that information. Furthermore, the system can prevent misunderstandings and problems by suggesting appropriate expressions when a user communicates in a specific industry or field of expertise. This allows users to communicate professionally smoothly.

[0048] The system can provide a function to emphasize specific keywords or phrases in the text entered by the user. For example, it can automatically highlight important information or points that the user wants to emphasize. The system can also provide related information or suggestions when the user enters specific keywords or phrases. Furthermore, the system can effectively communicate information by emphasizing important parts in the text entered by the user. This allows the user to effectively communicate important information.

[0049] The system can provide real-time translation of user input text. For example, it can automatically translate input text when users communicate in different languages. The system can also apply proofreading to the translated text and suggest appropriate expressions. Furthermore, the system can prevent misunderstandings by providing translations that take into account cultural nuances and backgrounds between different languages. This allows users to communicate smoothly in different languages.

[0050] The system can provide information related to a specific topic or theme in response to a user's input text. For example, when a user inputs a question about a specific topic, the system can automatically provide information and reference materials related to that topic. The system can also suggest related topics or themes based on the text the user inputs. Furthermore, the system can facilitate communication by providing information that helps the user gain a deeper understanding of a specific topic. This allows users to quickly obtain the information they need and communicate effectively.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The language analysis unit analyzes the user's text. For example, it analyzes messages and comments entered by the user to detect potentially misleading or inappropriate expressions. The language analysis unit can also analyze the user's past posting history and generate a language model optimized for each individual user. For example, the generation AI collects the user's past posting history and analyzes frequently used expressions and phrases. Step 2: The proofreading unit proofreads the text analyzed by the language analysis unit in real time. For example, it changes potentially misleading expressions to clearer ones, and corrects inappropriate expressions to appropriate ones. The proofreading unit can also analyze the user's input speed and typing patterns to improve the accuracy of proofreading in real time. For example, the generative AI analyzes the user's input speed and typing patterns to improve the accuracy of proofreading in real time. Step 3: The app provider provides the user with the text proofread by the proofreading unit. For example, the app provider provides a user app equipped with a generative AI that analyzes the text entered by the user in real time, detects potentially misleading or inappropriate expressions, and suggests corrections. The app provider can also analyze the user's past message history and make proofreading suggestions optimized for each individual user. For example, the app collects the user's past message history and analyzes frequently used expressions and phrases. Step 4: The SNS function addition unit provides the text proofread by the proofreading unit to the SNS side. For example, by incorporating a generation AI into the SNS side, it can analyze the text before the user posts, detect expressions that may be misleading or inappropriate, and suggest corrections. The SNS function addition unit can also analyze trends and buzzwords on SNS and make proofreading suggestions that correspond to the latest language expressions. For example, the generation AI can analyze trends and buzzwords on SNS in real time and make proofreading suggestions that correspond to the latest language expressions.

[0053] (Example 2) A system according to an embodiment of the present invention is a system that uses generative AI to prevent misunderstandings and trouble. This system utilizes advanced language processing and real-time proofreading functions to contribute to smoother communication. As a result, the system uses generative AI to prevent misunderstandings and trouble, and can contribute to smoother communication.

[0054] A system according to an embodiment includes a language analysis unit, a proofreading unit, an application providing unit, and an SNS function adding unit. The language analysis unit analyzes a user's text. For example, it analyzes messages and comments entered by the user to detect potentially misleading or inappropriate expressions. The language analysis unit can also analyze the user's past posting history and generate a language model optimized for each individual user. For example, a generation AI collects the user's past posting history and analyzes frequently occurring expressions and phrases. The proofreading unit proofreads the text analyzed by the language analysis unit in real time. For example, it changes potentially misleading expressions to clearer expressions and corrects inappropriate expressions to appropriate expressions. The proofreading unit can also analyze the user's input speed and typing pattern to improve proofreading accuracy in real time. For example, the generation AI analyzes the user's input speed and typing pattern to improve proofreading accuracy in real time. The application providing unit provides the text proofread by the proofreading unit to the user. For example, it provides a user app equipped with the generation AI, analyzes the text entered by the user in real time, detects potentially misleading or inappropriate expressions, and suggests corrections. The app providing unit can also analyze a user's past message history and make proofreading suggestions optimized for each individual user. For example, the app collects a user's past message history and analyzes frequently used expressions and phrases. The SNS function adding unit provides the text proofread by the proofreading unit to the SNS. For example, a generation AI can be incorporated into the SNS to analyze text before the user posts, detect potentially misleading or inappropriate expressions, and suggest corrections. The SNS function adding unit can also analyze trends and buzzwords on the SNS and make proofreading suggestions that correspond to the latest linguistic expressions. For example, the generation AI can analyze trends and buzzwords on the SNS in real time and make proofreading suggestions that correspond to the latest linguistic expressions. As a result, the system according to the embodiment can prevent misunderstandings and troubles using the generation AI and contribute to smoother communication. For example, the generation AI can analyze text before a user posts on the SNS and suggest appropriate corrections, allowing the user to send a message without causing misunderstandings.Furthermore, by using the user app, users can check the content of emails and messages in advance and make appropriate corrections, allowing them to communicate with peace of mind.

[0055] The language analysis unit can analyze a user's past posting history and generate a language model optimized for each individual user. For example, the language analysis unit uses a generation AI to collect a user's past posting history and analyze frequently used expressions and phrases. This generates an individual language model for the user and suggests optimal expressions for future posts. The language analysis unit also uses a generation AI to learn the user's preferences and tendencies for specific topics and themes based on the user's past posting history. This allows it to suggest appropriate expressions for content that interests the user. The language analysis unit also uses a generation AI to analyze a user's past posting history and generate an optimal language model according to a specific context or situation. For example, it makes suggestions that take into account the difference between business emails and casual messages. This allows it to generate an optimal language model based on the user's past posting history and suggest optimal expressions for future posts.

[0056] The language analysis unit can analyze the user's voice input and perform language processing that takes into account both text and voice. For example, the language analysis unit converts the user's voice input into text using a generation AI and analyzes the tone and intonation of the text and voice. This allows for a more accurate understanding of the user's intention and suggests appropriate expressions. In addition, when the user provides voice input, the generation AI analyzes the voice data in real time and performs language processing that takes into account both text and voice. For example, it makes suggestions that reflect the strength and nuance of emotions. In addition, the language analysis unit allows the generation AI to analyze both voice input and text input, building a system that comprehensively understands the user's intention. This improves the degree of agreement between voice and text and prevents misunderstandings. This allows for a more accurate understanding of the user's intention and suggests appropriate expressions.

[0057] The language analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest appropriate expressions according to that emotion. For example, the language analysis unit uses a generative AI to analyze the user's input text and identify the user's emotional state using the emotion estimation function. For example, it detects emotions such as joy, sadness, and anger and suggests appropriate expressions. The language analysis unit also analyzes the emotional nuances of the text entered by the user in real time and provides appropriate feedback using the emotion estimation function. For example, it suggests expressions that emphasize positive emotions. The language analysis unit also uses the emotion estimation function to build a system that suggests appropriate expressions according to the user's emotional state. For example, it suggests positive expressions for a user with negative emotions. This makes it possible to suggest appropriate expressions according to the user's emotional state and facilitate communication.

[0058] The proofreading unit can analyze a user's input speed or typing pattern and improve proofreading accuracy in real time. For example, the proofreading unit uses a generation AI to analyze a user's input speed or typing pattern and improve proofreading accuracy in real time. For example, if the input speed is fast, it suggests concise expressions. The proofreading unit also analyzes a user's typing pattern and the generation AI improves proofreading accuracy in real time. For example, it detects input errors of specific keys and suggests appropriate corrections. The proofreading unit also builds a system in which the generation AI analyzes a user's input speed or typing pattern and improves proofreading accuracy in real time. For example, it suggests appropriate expressions according to the input speed. This makes it possible to improve proofreading accuracy based on the user's input speed or typing pattern.

[0059] The proofreading unit can understand the user's context and suggest appropriate corrections based on the context. In the proofreading unit, for example, the generation AI analyzes the context of the user's input text and suggests appropriate corrections based on the context. For example, the proofreading unit makes corrections that take into account the difference between a business email and a casual message. In addition, to understand the context of the user's input text, the generation AI analyzes related keywords and phrases and suggests appropriate corrections. For example, it suggests expressions related to a specific topic. In addition, the proofreading unit builds a system in which the generation AI understands the user's context and suggests appropriate corrections based on the context. For example, it suggests appropriate expressions based on the context. This makes it possible to suggest appropriate corrections based on the user's context and prevent misunderstandings.

[0060] The proofreading unit uses the emotion estimation function to make proofreading suggestions based on the user's emotional state, allowing the user to send messages with more positive emotions. In the proofreading unit, for example, the generation AI analyzes the user's input text and uses the emotion estimation function to identify the user's emotional state. For example, the proofreading unit suggests positive expressions for a user with negative emotions. The proofreading unit also uses the emotion estimation function to make proofreading suggestions based on the user's emotional state. For example, it suggests expressions that emphasize positive emotions. The proofreading unit also builds a system in which the generation AI analyzes the user's emotional state and uses the emotion estimation function to make appropriate proofreading suggestions. For example, it suggests expressions that emphasize parts with high emotion scores. This allows the user to send messages with more positive emotions.

[0061] The proofreading unit also applies a real-time proofreading function to voice input, preventing misunderstandings of voice messages. In the proofreading unit, for example, the generation AI analyzes voice input in real time and performs appropriate proofreading. For example, to prevent misunderstandings of voice messages, the proofreading unit suggests corrections that take into account the tone and intonation of the voice. In addition, in order to apply the real-time proofreading function to voice input, the generation AI converts voice data into text and suggests appropriate corrections. For example, corrections are made to clarify the content of the voice message. In addition, the proofreading unit builds a system in which the generation AI analyzes voice input in real time and performs appropriate proofreading to prevent misunderstandings of voice messages. For example, corrections that reflect the nuances of the voice are suggested. This makes it possible to prevent misunderstandings of voice messages.

[0062] The proofreading unit can also apply the real-time proofreading function to visual data, automatically correcting captions for images or videos. For example, the proofreading unit uses a generation AI to analyze the content of images or videos and automatically correct captions in real time. For example, it changes image descriptions and video captions to appropriate expressions. In order to apply the real-time proofreading function to visual data, the proofreading unit uses image recognition technology to analyze the content of images and propose appropriate captions. For example, it proposes appropriate captions that match the content of the image. The proofreading unit also builds a system in which the generation AI analyzes the content of videos and automatically corrects captions in real time. For example, it provides appropriate captions for each scene in the video. This makes it possible to automatically correct captions for images and videos.

[0063] The proofreading unit can use the emotion estimation function to analyze the emotional nuances of text entered by the user in real time and provide appropriate feedback. For example, the proofreading unit uses a generation AI to analyze the text entered by the user in real time and identify the emotional nuances using the emotion estimation function. For example, it suggests expressions that emphasize positive emotions. The proofreading unit also analyzes the emotional nuances of text entered by the user in real time and provides appropriate feedback using the emotion estimation function. For example, it suggests positive expressions for a user with negative emotions. The proofreading unit also uses the emotion estimation function to build a system that provides appropriate feedback according to the user's emotional state. For example, it suggests expressions that emphasize parts with high emotion scores. This makes it possible to analyze the emotional nuances of text entered by the user in real time and provide appropriate feedback.

[0064] The application providing unit can analyze a user's past message history and make proofreading suggestions optimized for each individual user. For example, the application providing unit allows the application to collect the user's past message history and analyze frequently occurring expressions and phrases. This generates an individual language model for the user and suggests optimal expressions for future messages. The application providing unit also allows the application to learn the user's preferences and tendencies for specific topics or themes based on the user's past message history. This suggests appropriate expressions for content that interests the user. The application providing unit also allows the application to analyze the user's past message history and generate an optimal language model according to a specific context or situation. For example, suggestions are made that take into account the difference between business emails and casual messages. This allows optimal proofreading suggestions to be made based on the user's past message history.

[0065] The application providing unit can analyze the user's voice input and make proofreading suggestions that take both the voice and the text into consideration. For example, the application providing unit converts the user's voice input into text and analyzes the text and the tone and intonation of the voice. This allows the application to understand the user's intention more accurately and suggest appropriate expressions. Furthermore, when the user provides voice input, the application providing unit analyzes the voice data in real time and makes proofreading suggestions that take both the text and the voice into consideration. For example, the application providing unit makes suggestions that reflect the strength and nuance of emotions. Furthermore, the application providing unit builds a system in which the application analyzes both the voice input and the text input and comprehensively understands the user's intention. This improves the degree of agreement between the voice and the text and prevents misunderstandings. This allows the application to make proofreading suggestions that take both the user's voice input and the text into consideration.

[0066] The application providing unit can use the emotion estimation function to make appropriate proofreading suggestions according to the user's emotional state. For example, the application providing unit analyzes text input by the user and identifies the user's emotional state using the emotion estimation function. For example, emotions such as joy, sadness, and anger are detected and appropriate expressions are suggested. The application providing unit also analyzes the emotional nuances of the text input by the user in real time and provides appropriate feedback using the emotion estimation function. For example, expressions that emphasize positive emotions are suggested. The application providing unit also uses the emotion estimation function to build a system that suggests appropriate expressions according to the user's emotional state. For example, positive expressions are suggested for a user with negative emotions. This makes it possible to make appropriate proofreading suggestions according to the user's emotional state.

[0067] The application providing unit provides a translation function between different languages, thereby facilitating intercultural communication. For example, the application providing unit allows the application to automatically translate text input by a user into a different language, thereby facilitating intercultural communication. For example, the application providing unit translates from English to Japanese and suggests appropriate expressions. Furthermore, when translating between different languages, the application providing unit provides translations that take cultural nuances and backgrounds into account. For example, the application providing unit provides translations that reflect differences in expressions in specific cultures. Furthermore, the application providing unit allows the application to translate between different languages, thereby enabling users to smoothly communicate between different cultures. For example, the application providing unit provides translations of business emails and casual messages. This allows for smooth intercultural communication.

[0068] The application providing unit can analyze visual data and make proofreading suggestions that take into account the relevance between text and visual data. For example, the application providing unit analyzes the content of images and videos and generates text related to the images. For example, it automatically generates image descriptions and video captions. Furthermore, in order to make proofreading suggestions that take into account the relevance between visual data and text data, the application providing unit analyzes the content of images using image recognition technology and suggests appropriate text. For example, it suggests appropriate captions that match the content of the images. Furthermore, the application providing unit analyzes the content of videos and generates text that corresponds to the scenes in the videos. For example, it provides appropriate captions and descriptions for each video scene. This makes it possible to make proofreading suggestions that take into account the relevance between visual data and text.

[0069] The application providing unit can use the emotion estimation function to analyze the emotional nuances of text entered by a user in real time and provide appropriate feedback. For example, the application providing unit analyzes the text entered by a user in real time and identifies the emotional nuances using the emotion estimation function. For example, the application providing unit suggests expressions that emphasize positive emotions. The application providing unit also analyzes the emotional nuances of text entered by a user in real time and provides appropriate feedback using the emotion estimation function. For example, the application providing unit suggests positive expressions for a user with negative emotions. The application providing unit also uses the emotion estimation function to build a system that provides appropriate feedback according to the user's emotional state. For example, the application providing unit suggests expressions that emphasize parts with high emotion scores. This makes it possible to analyze the emotional nuances of text entered by a user in real time and provide appropriate feedback.

[0070] The SNS function addition unit can analyze trends and buzzwords on SNS and make proofreading suggestions that correspond to the latest language expressions. In the SNS function addition unit, for example, the generation AI analyzes trends and buzzwords on SNS in real time and makes proofreading suggestions that correspond to the latest language expressions. For example, it makes suggestions to use buzzwords and new slang appropriately. The SNS function addition unit also analyzes trends on SNS and the generation AI proposes corrections to the user's posted content that reflect the latest language expressions. For example, it proposes expressions that match the trends. In addition, the SNS function addition unit builds a system in which the generation AI analyzes buzzwords and trends on SNS and proposes appropriate corrections to the user's posted content. For example, it proposes expressions based on the latest trends. This makes it possible to make proofreading suggestions that correspond to trends and buzzwords on SNS.

[0071] The SNS function addition unit can analyze the reactions of the user's followers and friends and suggest expressions that are most likely to garner sympathy. In the SNS function addition unit, for example, the generation AI analyzes the reactions of the user's followers and friends and suggests expressions that are most likely to garner sympathy. For example, it suggests expressions that receive a lot of positive reactions based on past reaction data. The SNS function addition unit also analyzes the reactions of the user's followers and friends, and builds a system in which the generation AI suggests expressions that are likely to garner sympathy. For example, it analyzes reactions to specific expressions and suggests appropriate modifications. In addition, the SNS function addition unit also analyzes the reactions of the user's followers and friends and suggests expressions that are most likely to garner sympathy. For example, it suggests expressions that are likely to garner sympathy based on reactions to past posts. In this way, it is possible to analyze the reactions of the user's followers and friends and suggest expressions that are likely to garner sympathy.

[0072] The SNS function addition unit can analyze captions of images and videos on SNS and make revision suggestions that take into account the relevance between text and visual data. In the SNS function addition unit, for example, a generation AI analyzes captions of images and videos on SNS and makes revision suggestions that take into account the relevance between text and visual data. For example, it suggests appropriate captions that match the content of the image. In addition, the SNS function addition unit analyzes captions of images and videos on SNS and a generation AI suggests corrections that take into account the relevance between visual data and text. For example, it suggests captions that match the scenes in the video. In addition, the SNS function addition unit builds a system in which a generation AI analyzes captions of images and videos on SNS and makes revision suggestions that take into account the relevance between text and visual data. For example, it suggests appropriate captions based on the content of the image. This makes it possible to analyze captions of images and videos on SNS and make revision suggestions that take into account the relevance between text and visual data.

[0073] The SNS function addition unit provides a translation function between different languages ​​on the SNS, thereby facilitating intercultural communication. For example, the SNS function addition unit allows a generation AI to automatically translate user posts into different languages, thereby facilitating intercultural communication. For example, the generation AI translates from English to Japanese and suggests appropriate expressions. When translating between different languages, the SNS function addition unit also allows the generation AI to provide translations that take cultural nuances and background into consideration. For example, the generation AI provides translations that reflect differences in expressions in specific cultures. The SNS function addition unit also allows the generation AI to translate between different languages, thereby enabling users to smoothly communicate between different cultures. For example, the generation AI provides translations for business emails and casual messages. This allows a translation function between different languages ​​on the SNS to facilitate intercultural communication.

[0074] The SNS function adding unit can use the emotion estimation function to analyze the emotional nuances of text entered by a user in real time and provide appropriate feedback. For example, the SNS function adding unit uses a generation AI to analyze the text entered by a user in real time and identify the emotional nuances using the emotion estimation function. For example, it suggests expressions that emphasize positive emotions. The SNS function adding unit also analyzes the emotional nuances of text entered by a user in real time and provides appropriate feedback using the emotion estimation function. For example, it suggests positive expressions for a user with negative emotions. The SNS function adding unit also uses the emotion estimation function to build a system that provides appropriate feedback according to the user's emotional state. For example, it suggests expressions that emphasize parts with high emotion scores. This makes it possible to analyze the emotional nuances of text entered by a user in real time and provide appropriate feedback.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The system can provide suggestions for maintaining consistency in style and tone, not just grammatical accuracy, for user input text. For example, it suggests maintaining a formal tone in business emails and a relaxed tone in casual messages. If the user needs to follow a specific style guide, it can also suggest revisions based on those guidelines. Furthermore, the system can learn specific phrases and phrasing used by the user in the past and suggest consistent language based on that. This allows users to communicate with a consistent style and tone.

[0077] The system can suggest corrections to the user's input text that take into account cultural background and region-specific expressions. For example, it can make suggestions that reflect differences in expressions in different regions and cultures. The system can also suggest corrections to avoid taboos or sensitive topics in specific cultures or regions. Furthermore, when the user communicates with people from different cultures, the system can prevent misunderstandings and trouble by suggesting appropriate expressions. This allows users to communicate smoothly across cultures.

[0078] The system can suggest expressions specific to a specific industry or field of expertise to the user's input text. For example, it suggests technical terms and appropriate expressions in the medical field, and legal expressions and terminology in the legal field. The system can also learn the latest trends and terminology in a specific industry or field of expertise and suggest corrections based on that information. Furthermore, the system can prevent misunderstandings and problems by suggesting appropriate expressions when a user communicates in a specific industry or field of expertise. This allows users to communicate professionally smoothly.

[0079] The system uses its emotion estimation function to analyze the user's emotional state based on the text they input, and can suggest appropriate expressions based on that emotion. For example, if the user is feeling angry or sad, the system can suggest expressions that will alleviate that emotion. Similarly, if the user is feeling happy or excited, the system can suggest expressions that will emphasize that emotion. Furthermore, the system can provide appropriate feedback based on the user's emotional state, enabling the user to communicate with more positive emotions. This allows the system to suggest appropriate expressions based on the user's emotional state, facilitating smooth communication.

[0080] The system can analyze the user's emotional state using an emotion estimation function for the user's input text and provide appropriate feedback according to that emotion. For example, if the user has negative emotions, the system can provide positive feedback to alleviate those emotions. Also, if the user has positive emotions, the system can provide feedback to emphasize those emotions. Furthermore, by providing appropriate feedback according to the user's emotional state, the system enables the user to communicate with a more positive emotion. This allows for smoother communication by providing appropriate feedback according to the user's emotional state.

[0081] The system can analyze the user's emotional state using emotion estimation functionality for the user's input text and suggest appropriate expressions according to that emotion. For example, if the user is feeling stressed, the system can suggest expressions that will alleviate that emotion. Also, if the user is relaxed, the system can suggest expressions that will maintain that emotion. Furthermore, the system can provide appropriate feedback according to the user's emotional state, allowing the user to communicate in a more relaxed state. This allows the system to suggest appropriate expressions according to the user's emotional state and facilitate smooth communication.

[0082] The system uses its emotion estimation function to analyze the user's emotional state based on the text they input, and can suggest appropriate expressions based on that emotion. For example, if the user is feeling anxious, the system can suggest expressions that will ease that emotion. Alternatively, if the user is feeling reassured, the system can suggest expressions that will maintain that emotion. Furthermore, the system provides appropriate feedback based on the user's emotional state, allowing the user to communicate in a more reassuring state. This allows the system to suggest appropriate expressions based on the user's emotional state, facilitating smooth communication.

[0083] The system can provide a function to emphasize specific keywords or phrases in the text entered by the user. For example, it can automatically highlight important information or points that the user wants to emphasize. The system can also provide related information or suggestions when the user enters specific keywords or phrases. Furthermore, the system can effectively communicate information by emphasizing important parts in the text entered by the user. This allows the user to effectively communicate important information.

[0084] The system can provide real-time translation of user input text. For example, it can automatically translate input text when users communicate in different languages. The system can also apply proofreading to the translated text and suggest appropriate expressions. Furthermore, the system can prevent misunderstandings by providing translations that take into account cultural nuances and backgrounds between different languages. This allows users to communicate smoothly in different languages.

[0085] The system can provide information related to a specific topic or theme in response to a user's input text. For example, when a user inputs a question about a specific topic, the system can automatically provide information and reference materials related to that topic. The system can also suggest related topics or themes based on the text the user inputs. Furthermore, the system can facilitate communication by providing information that helps the user gain a deeper understanding of a specific topic. This allows users to quickly obtain the information they need and communicate effectively.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The language analysis unit analyzes the user's text. For example, it analyzes messages and comments entered by the user to detect potentially misleading or inappropriate expressions. The language analysis unit can also analyze the user's past posting history and generate a language model optimized for each individual user. For example, the generation AI collects the user's past posting history and analyzes frequently used expressions and phrases. Step 2: The proofreading unit proofreads the text analyzed by the language analysis unit in real time. For example, it changes potentially misleading expressions to clearer ones, and corrects inappropriate expressions to appropriate ones. The proofreading unit can also analyze the user's input speed and typing patterns to improve the accuracy of proofreading in real time. For example, the generative AI analyzes the user's input speed and typing patterns to improve the accuracy of proofreading in real time. Step 3: The app provider provides the user with the text proofread by the proofreading unit. For example, the app provider provides a user app equipped with a generative AI that analyzes the text entered by the user in real time, detects potentially misleading or inappropriate expressions, and suggests corrections. The app provider can also analyze the user's past message history and make proofreading suggestions optimized for each individual user. For example, the app collects the user's past message history and analyzes frequently used expressions and phrases. Step 4: The SNS function addition unit provides the text proofread by the proofreading unit to the SNS side. For example, by incorporating a generation AI into the SNS side, it can analyze the text before the user posts, detect expressions that may be misleading or inappropriate, and suggest corrections. The SNS function addition unit can also analyze trends and buzzwords on SNS and make proofreading suggestions that correspond to the latest language expressions. For example, the generation AI can analyze trends and buzzwords on SNS in real time and make proofreading suggestions that correspond to the latest language expressions.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0120] The data processing system 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a language analysis unit that analyzes a user's text; a proofreading unit that proofreads the text analyzed by the language analysis unit in real time; an application providing unit that provides the text proofread by the proofreading unit to a user; an SNS function adding unit that provides the text proofread by the proofreading unit to an SNS. A system characterized by:

2. The language analysis unit Analyzing the user's past posting history and generating a language model optimized for each individual user 2. The system of claim 1.

3. The language analysis unit Analyzing the user's voice input and performing language processing that takes into account both the text and the voice.

2. The system of claim 1.

4. The language analysis unit Analyzing the emotional state of the user and suggesting appropriate expressions according to the emotion 2. The system of claim 1.

5. The calibration unit Analyzing the user's input speed or typing pattern to improve proofreading accuracy in real time 2. The system of claim 1.

6. The calibration unit Understand the user's context and suggest appropriate modifications based on that context 2. The system of claim 1.

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