System

Abstract

An object of a system according to an embodiment is to efficiently proofread a sentence input by a user and correct a typographical error.SOLUTION: A system includes a reception part, a calibration part, and a correction part. The receiving unit receives a sentence input by a user. The proofreading unit analyzes the text received by the reception unit and proofreads the text on the basis of the selected proofreading mode. The correction unit corrects a typographical error in the text corrected by the correction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, proofreading text entered by a user and correcting typos and omissions is a time-consuming process, and there is a risk of errors in the text occurring, particularly in business situations.

[0005] The system according to the embodiment aims to efficiently proofread text entered by a user and correct typos and omissions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a proofreading unit, and a correction unit. The reception unit receives a sentence input by a user. The proofreading unit analyzes the sentence received by the reception unit and proofreads the sentence based on a selected proofreading mode. The correction unit corrects typos and omissions in the sentence proofread by the proofreading unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently proofread text entered by a user and correct spelling errors. [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 text proofreading system according to an embodiment of the present invention uses AI to proofread text entered by a user in a messaging app. When a user enters text in a messaging app, the AI ​​analyzes the text and allows the user to select different proofreading modes, such as business, friend, or standard. This allows the user to easily create appropriate text for a given situation. For example, a user enters text in a messaging app. At this time, the user can select a specific proofreading mode. For example, when the business proofreading mode is selected, the AI ​​proofreads the entered text to use appropriate business expressions. When the friend proofreading mode is selected, the AI ​​proofreads the entered text to use casual expressions. When the standard proofreading mode is selected, the AI ​​proofreads the entered text to use standard Japanese. The AI ​​then analyzes the entered text and performs proofreading based on the selected proofreading mode. For example, in the business proofreading mode, the AI ​​proofreads the text to use honorifics and polite expressions. In the friend proofreading mode, the AI ​​proofreads the text to use familiar, casual expressions. In the standard proofreading mode, the AI ​​proofreads the text to convert dialects and slang into standard Japanese. In addition, AI also corrects typos. For example, if a user's input text contains typos or omissions, the AI ​​automatically corrects them. This allows users to create accurate, easy-to-read text. This allows the grammar proofreading system to reduce writing errors in business situations using messaging apps. For example, typos or inappropriate expressions in business communications can lead to misunderstandings, but using this system can mitigate that risk. Furthermore, using appropriate expressions in interactions with friends can also facilitate smooth communication. In this way, the AI-powered grammar proofreading system allows users to easily create appropriate sentences for each situation and, by correcting typos, provides accurate, easy-to-read text. This allows the grammar proofreading system to appropriately proofread the text entered by the user and correct typos, providing accurate, easy-to-read text.For example, typos or inappropriate expressions in business correspondence can lead to misunderstandings, but using this system can reduce that risk. Also, using appropriate expressions when communicating with friends can help ensure smooth communication.

[0029] A text proofreading system according to an embodiment includes a receiving unit, a proofreading unit, and a correction unit. The receiving unit receives text input by a user. Examples of text input by a user include, but are not limited to, text, voice input, and text extracted from an image. The receiving unit may directly receive text in text format. The receiving unit may also convert voice input into text and accept the text. The receiving unit may also extract text from an image and accept the text. For example, the receiving unit may convert voice input into text using voice recognition technology. The receiving unit may also extract text from an image using OCR technology. The proofreading unit analyzes the text received by the receiving unit and proofreads the text based on a selected proofreading mode. Proofreading is performed based on, for example, a grammar check, a style check, and a content consistency check, but is not limited to, examples. For example, the proofreading unit may perform a grammar check to ensure grammatical correctness. The proofreading unit may also perform a style check to proofread in a style corresponding to the selected proofreading mode. The proofreading unit may also perform a content consistency check to ensure consistency throughout the text. For example, the proofreading unit detects and corrects grammatical errors using a grammar checker. The style checker applies an appropriate style based on a selected proofreading mode. The content consistency checker analyzes the context and makes necessary corrections to maintain consistency throughout the sentence. The correction unit corrects typographical errors in the sentence proofread by the proofreading unit. Corrections may be performed using, for example, a spell checker or a grammar checker, but are not limited to these examples. For example, the correction unit detects and corrects typographical errors using a spell checker. The correction unit may also detect and correct grammatical errors using a grammar checker. The correction unit may also automatically correct typographical errors using AI. For example, the correction unit analyzes the entire sentence using an AI model to detect and correct typographical errors. As a result, the sentence proofreading system according to the embodiment can provide accurate and easy-to-read sentences by appropriately proofreading sentences entered by a user and correcting typographical errors.

[0030] The proofreading unit can apply honorific language and polite expressions in the business proofreading mode. In the business proofreading mode, for example, proofreading is performed to use honorific language and polite expressions. For example, the proofreading unit adds honorifics such as "desu" (thank you) and "masu" (thank you for your continued support) to the end of a sentence. The proofreading unit can also correct sentences to use polite expressions. For example, the proofreading unit applies polite expressions such as "Thank you for your continued support" and "Please confirm." This makes it possible to provide sentences suitable for business situations. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can perform business proofreading using an AI model.

[0031] The proofreading unit can apply casual expressions in the friend-only sentence proofreading mode. In the friend-only sentence proofreading mode, for example, proofreading is performed to use casual, friendly expressions. For example, the proofreading unit adds casual expressions such as "dayo" (it's you) or "ne" (you) to the end of a sentence. The proofreading unit can also correct sentences to use casual language. For example, the proofreading unit applies casual expressions such as "thank you" (thank you) and "nice to meet you." This makes it possible to provide friendly sentences. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can proofread sentences for friends using an AI model.

[0032] In the standard Japanese sentence proofreading mode, the proofreading unit can convert dialects and slang into standard Japanese. In the standard Japanese sentence proofreading mode, for example, the proofreading unit proofreads by converting dialects and slang into standard Japanese. For example, the proofreading unit converts dialects into standard Japanese. For example, it converts "Ookinii" (thank you) into "Arigatou" (thank you). The proofreading unit can also convert slang into standard Japanese. For example, it converts "Yabai" (wow) into "Sugoi" (great). This makes it possible to provide sentences in standard Japanese. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can proofread sentences into standard Japanese using an AI model.

[0033] The correction unit can automatically correct typos and omissions in the input text. The correction unit detects and corrects typos and omissions using, for example, a spell checker or a grammar checker. For example, the correction unit detects and corrects typos using a spell checker. The correction unit can also detect and correct grammatical errors using a grammar checker. The correction unit can also automatically correct typos and omissions using AI. For example, the correction unit analyzes the entire text using an AI model to detect and correct typos and omissions. This makes it possible to provide accurate text by automatically correcting typos and omissions in the input text. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can correct typos and omissions using an AI model.

[0034] The text proofreading system includes a providing unit that provides an interface for a user to select a proofreading mode. The providing unit provides the interface for the user to select the proofreading mode. The interface may be provided in the form of, for example, a drop-down menu or radio buttons, but is not limited to these examples. For example, the providing unit may allow the user to select the proofreading mode using a drop-down menu. The providing unit may also allow the user to select the proofreading mode using radio buttons. This allows the user to perform appropriate text proofreading according to the situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's selection using an AI model and provide an optimal interface.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit prioritizes reception of input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history and extracts frequently used words and input patterns. The reception unit can also predict and receive the input method that will be used during a specific time period from the user's past input history. For example, if the user tends to use voice input during a specific time period, the reception unit prioritizes receiving voice input during that time period. The reception unit can also analyze patterns of sentences the user has previously input and suggest the optimal reception method. For example, the reception unit develops an algorithm that suggests the optimal reception method based on the user's past input history. This allows the optimal reception method to be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past input history using an AI model and select the optimal reception method.

[0036] When receiving a text, the reception unit can filter the text based on the user's current situation and areas of interest. For example, if the user is using the text in a business setting, the reception unit can preferentially receive business-related keywords. For example, the reception unit can identify the user's current situation and filter out business-related keywords. Furthermore, if the user is interacting with friends, the reception unit can preferentially receive casual expressions. For example, the reception unit can identify the user's areas of interest and filter out casual expressions. Furthermore, if the user is inputting a text related to a specific area of ​​interest, the reception unit can preferentially receive expressions related to that area. For example, the reception unit can develop an algorithm that identifies the user's areas of interest and filters out expressions related to that area. This allows for filtering based on the user's current situation and areas of interest, thereby allowing for the reception of highly relevant text. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can use an AI model to identify the user's current situation and areas of interest and perform filtering.

[0037] When receiving text, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user uses voice input, the reception unit receives the text using voice recognition technology. For example, the reception unit converts the voice input into text using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also receive the text using text analysis technology. For example, the reception unit analyzes the text input using text analysis technology. Furthermore, if the user uses image input, the reception unit can also receive the text using image recognition technology. For example, the reception unit extracts text from an image using image recognition technology. This enables efficient text reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's input method using an AI model and select the optimal reception means.

[0038] When receiving text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving text related to that area. For example, the reception unit identifies the user's geographical location information and filters keywords related to the area. Furthermore, when the user is traveling, the reception unit can prioritize receiving text related to the travel destination. For example, the reception unit identifies the user's geographical location information and filters keywords related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving text related to the user's home. For example, the reception unit identifies the user's geographical location information and filters keywords related to the user's home. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's geographical location information using an AI model and prioritize receiving highly relevant text.

[0039] The reception unit can analyze the user's social media activity and receive related texts when receiving texts. The reception unit, for example, analyzes content posted by the user on social media and prioritizes receiving related texts. For example, the reception unit analyzes the user's social media activity and filters the posted content. The reception unit can also analyze content posted by the user's friends on social media and prioritize receiving related texts. For example, the reception unit analyzes the user's social media activity and filters the friends' posted content. The reception unit can also analyze the user's social media activity history and prioritize receiving related texts. For example, the reception unit analyzes the user's social media activity and develops an algorithm for filtering the activity history. This allows the user's social media activity to be analyzed and related texts to be prioritized. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can analyze the user's social media activity using an AI model and receive related texts.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a text. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit analyzes the user's past feedback and develops an algorithm that suggests an optimal reception method. The reception unit can also preferentially accept specific input methods based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and filters specific input methods. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit develops an algorithm that improves the reception method based on the user's past feedback. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past feedback using an AI model and customize the reception method.

[0041] During proofreading, the proofreading unit can adjust the level of detail of the proofreading based on the importance of the text. For example, in the case of important business documents, the proofreading unit performs detailed proofreading, thoroughly checking for typos and appropriate expressions. For example, the proofreading unit performs detailed proofreading using a grammar checker or style checker. In addition, in the case of casual exchanges with friends, the proofreading unit can perform simple proofreading and focus on correcting typos. For example, the proofreading unit can use a spell checker to correct typos. In addition, in the case of standard Japanese text, the proofreading unit can also focus on converting dialects and slang. For example, the proofreading unit can proofread using an algorithm that converts dialects and slang into standard Japanese. This allows for appropriate proofreading by adjusting the level of detail of the proofreading based on the importance of the text. Some or all of the above-mentioned processes in the proofreading unit may be performed using, for example, AI, or may be performed without AI. For example, the proofreading unit can evaluate the importance of the text using an AI model and adjust the level of detail of the proofreading.

[0042] During proofreading, the proofreading unit can apply different proofreading algorithms depending on the category of the text. For example, in the case of a business document, the proofreading unit uses an algorithm that applies honorific language and polite expressions. For example, the proofreading unit applies honorific language and polite expressions appropriate for business documents. The proofreading unit can also use an algorithm that applies casual expressions in the case of interactions with friends. For example, the proofreading unit applies casual expressions appropriate for interactions with friends. The proofreading unit can also use an algorithm that converts dialects and slang into standard language in the case of a text written in standard Japanese. For example, the proofreading unit proofreads using an algorithm that converts dialects and slang into standard Japanese. This enables appropriate proofreading by applying different proofreading algorithms depending on the category of the text. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the category of the text using an AI model and apply an appropriate proofreading algorithm.

[0043] During proofreading, the proofreading unit can improve the accuracy of proofreading by referring to the user's past proofreading results. The proofreading unit, for example, applies similar expressions based on sentences that the user has proofread in the past. For example, the proofreading unit analyzes the user's past proofreading results and develops an algorithm that applies similar expressions. The proofreading unit can also preferentially apply specific expressions from the user's past proofreading results. For example, the proofreading unit analyzes the user's past proofreading results and preferentially applies specific expressions. The proofreading unit can also analyze the user's past proofreading results and improve the proofreading algorithm. For example, the proofreading unit develops an algorithm that improves the proofreading algorithm based on the user's past proofreading results. This improves the accuracy of proofreading by referring to the user's past proofreading results. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can analyze the user's past proofreading results using an AI model to improve the accuracy of proofreading.

[0044] During proofreading, the proofreading unit can determine the proofreading priority based on the time of submission of the text. For example, the proofreading unit prioritizes proofreading of business documents with upcoming deadlines. For example, the proofreading unit identifies the submission date and time and develops an algorithm that prioritizes proofreading of business documents with upcoming deadlines. The proofreading unit can also prioritize proofreading of texts that require an urgent reply in exchanges with friends. For example, the proofreading unit identifies the submission date and time and prioritizes proofreading of texts that require an urgent reply. The proofreading unit can also prioritize proofreading of standard Japanese texts related to a specific event. For example, the proofreading unit identifies the submission date and time and prioritizes proofreading of texts related to a specific event. In this way, by determining the proofreading priority based on the time of submission of the text, texts with high urgency can be prioritized for proofreading. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the submission date and determine the proofreading priority using an AI model.

[0045] During proofreading, the proofreading unit can adjust the order of proofreading based on the relevance of the text. For example, the proofreading unit prioritizes proofreading of highly relevant parts in a business document. For example, the proofreading unit may analyze the content of the text and develop an algorithm that identifies highly relevant parts. The proofreading unit can also prioritize proofreading of important parts in interactions with friends. For example, the proofreading unit may analyze the content of the text and identify important parts. The proofreading unit can also prioritize proofreading of parts related to a specific topic in standard Japanese text. For example, the proofreading unit may analyze the content of the text and identify parts related to a specific topic. This allows important parts to be proofread preferentially by adjusting the order of proofreading based on the relevance of the text. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can analyze the content of the text using an AI model, identify highly relevant parts, and adjust the order of proofreading.

[0046] During proofreading, the proofreading unit can adjust the use of technical terms in the proofreading according to the user's level of expertise. For example, in business documents, the proofreading unit uses appropriate technical terms according to the user's level of expertise. For example, the proofreading unit analyzes the user's occupation and past comments to develop an algorithm to identify appropriate technical terms. The proofreading unit can also use casual expressions according to the user's level of expertise in interactions with friends. For example, the proofreading unit analyzes the user's past comments to identify appropriate casual expressions. The proofreading unit can also use appropriate expressions in standard Japanese sentences according to the user's level of expertise. For example, the proofreading unit analyzes the user's occupation and past comments to identify appropriate expressions. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing appropriate expressions. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the user's level of expertise using an AI model and adjust the use of technical terms.

[0047] During correction, the correction unit can analyze the user's past input history and select the optimal correction method. For example, the correction unit prioritizes correcting expressions that the user has used frequently in the past. For example, the correction unit analyzes the user's past input history and develops an algorithm that identifies frequently used words and expressions. The correction unit can also prioritize correcting specific expressions from the user's past input history. For example, the correction unit analyzes the user's past input history and prioritizes correcting specific expressions. The correction unit can also analyze the user's past input history and suggest the optimal correction method. For example, the correction unit develops an algorithm that suggests the optimal correction method based on the user's past input history. In this way, the optimal correction method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's past input history using an AI model and select the optimal correction method.

[0048] The correction unit can customize the correction method based on the user's current situation during correction. For example, if the user is in a business setting, the correction unit prioritizes correcting business-related expressions. For example, the correction unit identifies the user's current situation and filters out business-related expressions. The correction unit can also prioritize correcting casual expressions when the user is interacting with friends. For example, the correction unit identifies the user's current situation and filters out casual expressions. The correction unit can also prioritize correcting expressions appropriate to the situation when the user is in a specific situation. For example, the correction unit develops an algorithm that identifies the user's current situation and filters out expressions appropriate to the situation. This enables appropriate correction by customizing the correction method based on the user's current situation. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can identify the user's current situation using an AI model and customize the correction method.

[0049] The correction unit can improve the correction method by reflecting user feedback during correction. For example, the correction unit suggests an optimal correction method based on feedback provided by the user. For example, the correction unit analyzes the user's feedback and develops an algorithm that suggests an optimal correction method. The correction unit can also preferentially apply a specific correction method based on the user's past feedback. For example, the correction unit analyzes the user's feedback and preferentially applies a specific correction method. The correction unit can also analyze the user's feedback and improve the correction algorithm. For example, the correction unit develops an algorithm that improves the correction algorithm based on the user's feedback. In this way, the correction method can be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's feedback using an AI model and improve the correction method.

[0050] The correction unit can select the optimal correction method by taking into account the user's geographical location information when correcting. For example, when the user is in a specific area, the correction unit prioritizes correcting expressions related to that area. For example, the correction unit identifies the user's geographical location information and filters expressions related to that area. Furthermore, when the user is traveling, the correction unit can also prioritize correcting expressions related to travel destinations. For example, the correction unit identifies the user's geographical location information and filters expressions related to travel destinations. Furthermore, when the user is at home, the correction unit can also prioritize correcting expressions related to their home. For example, the correction unit identifies the user's geographical location information and filters expressions related to their home. In this way, the optimal correction method can be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can identify the user's geographical location information using an AI model and select the optimal correction method.

[0051] When correcting, the correction unit can analyze the user's social media activity and suggest correction measures. The correction unit, for example, analyzes content posted by the user on social media and prioritizes correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and filters the posted content. The correction unit can also analyze content posted by the user's friends on social media and prioritize correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and filters the friends' posted content. The correction unit can also analyze the user's social media activity history and prioritize correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and develops an algorithm for filtering the activity history. In this way, by analyzing the user's social media activity, relevant expressions can be prioritized and corrected. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's social media activity using an AI model and suggest correction measures.

[0052] The correction unit can customize the correction method by reflecting the user's past feedback when correcting. For example, the correction unit suggests an optimal correction method based on feedback provided by the user in the past. For example, the correction unit analyzes the user's feedback and develops an algorithm that suggests an optimal correction method. The correction unit can also preferentially apply a specific correction method based on the user's past feedback. For example, the correction unit analyzes the user's feedback and preferentially applies a specific correction method. The correction unit can also analyze the user's feedback and improve the correction algorithm. For example, the correction unit develops an algorithm that improves the correction algorithm based on the user's feedback. In this way, the optimal correction method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's feedback using an AI model and customize the correction method.

[0053] When displaying an interface, the providing unit can select an optimal display method by referring to the user's past operation history. The providing unit, for example, prioritizes displaying an interface design that the user has frequently used in the past. For example, the providing unit analyzes the user's past operation history and develops an algorithm that identifies frequently used designs. The providing unit can also prioritize displaying a specific design based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and prioritizes displaying a specific design. The providing unit can also analyze the user's past operation history and propose an optimal interface design. For example, the providing unit develops an algorithm that proposes an optimal interface design based on the user's past operation history. This makes it possible to provide an optimal interface display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's past operation history using an AI model and select an optimal display method.

[0054] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is using the interface in a business setting, the providing unit can prioritize displaying business-related information. For example, the providing unit can identify the user's current task and filter out business-related information. Furthermore, if the user is interacting with friends, the providing unit can prioritize displaying casual information. For example, the providing unit can identify the user's current task and filter out casual information. Furthermore, if the user is performing a specific task, the providing unit can prioritize displaying information related to the task. For example, the providing unit can develop an algorithm that identifies the user's current task and filters out information related to the task. This allows the display content to be customized according to the user's current task, thereby providing highly relevant information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's current task using an AI model and customize the display content.

[0055] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying an interface. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit identifies the user's device information and provides a display method optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartwatch. In this way, the optimal interface display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's device information using an AI model and select the optimal display method.

[0056] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying an interface. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit identifies the user's device information and provides a display method optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartwatch. In this way, the optimal interface display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's device information using an AI model and select the optimal display method.

[0057] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. The providing unit, for example, automatically sets the interface language based on the language setting of the user's device. For example, the providing unit identifies the user's language setting and provides an interface corresponding to that language. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides an interface for the user to switch languages. Furthermore, if the user selects a specific language, the providing unit can provide the interface in that language. For example, the providing unit sets the interface language based on the user's selection. This makes it possible to provide an interface that is easy for the user to understand by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's language setting using an AI model and make the display content multilingual.

[0058] The providing unit may analyze the user's social media activity and provide related information when displaying the interface. The providing unit may, for example, provide information about places where the user has checked in on social media. For example, the providing unit may analyze the user's social media activity and filter information related to the checked-in places. The providing unit may also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit may analyze the user's social media activity and filter information related to the posted content. The providing unit may also provide information about related places and events based on the activities of the user's friends on social media. For example, the providing unit may develop an algorithm that analyzes the user's social media activity and filters information related to the friends' activities. This allows the user's social media activity to be analyzed and related information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's social media activity using an AI model and provide related information.

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

[0060] The reception unit can analyze the user's input speed and adjust the method of receiving text depending on the input speed. For example, if the user is inputting quickly, the reception unit can receive text in real time and immediately start proofreading. Alternatively, if the user is inputting slowly, the reception unit can receive the text all at once after input is complete and proofread it. Furthermore, if the user's input speed fluctuates, the reception unit can dynamically adjust the reception method depending on the fluctuation. This makes it possible to provide a flexible reception method that corresponds to the user's input speed.

[0061] The correction unit can adjust the length of the sentence based on the user's input. For example, if the user is inputting a short message, the correction unit can correct the message to keep it concise. If the user is inputting a detailed explanation, the correction unit can adjust the explanation to an appropriate length to make it easier to understand. Furthermore, if the user is inputting a question, the correction unit can add information necessary to clarify the question. This makes it possible to provide sentences of an appropriate length according to the user's intention.

[0062] The providing unit can analyze the user's past selection history and suggest the optimal proofreading mode. For example, if the user has frequently used the business proofreading mode in the past, the providing unit can preferentially suggest the business proofreading mode. Also, if the user has frequently used the friend proofreading mode, the providing unit can also suggest the friend proofreading mode. Furthermore, if the user tends to use a specific proofreading mode during a specific time period, the providing unit can also suggest the optimal proofreading mode for that time period. In this way, the optimal proofreading mode can be provided based on the user's past selection history.

[0063] The proofreading unit can adjust the style of the text based on the user's input. For example, if the user is inputting formal text, the proofreading unit can proofread the text to maintain that style. Also, if the user is inputting casual text, the proofreading unit can proofread the text to maintain the casual style. Furthermore, if the user is inputting technical content, the proofreading unit can proofread the text to make it more professional. This makes it possible to provide text in an appropriate style according to the user's input.

[0064] The providing unit can customize the display content of the interface according to the user's current task. For example, if the user is using the interface in a business setting, business-related information can be displayed preferentially. Also, if the user is interacting with friends, casual information can be displayed preferentially. Furthermore, if the user is performing a specific task, information related to that task can be displayed preferentially. This makes it possible to provide appropriate information according to the user's current task.

[0065] The proofreading unit can proofread to maintain consistency in the text based on the user's input. For example, if the user is writing about the same topic across multiple paragraphs, the proofreading unit can proofread to maintain consistency in the topic. Also, if the user is mixing different topics, the proofreading unit can proofread to clearly separate each topic. Furthermore, if the user is inputting a dialogue-style text, the proofreading unit can proofread to maintain a natural flow of the dialogue. This makes it possible to provide a consistent text according to the user's input.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives text input by the user. The text input by the user can be text, voice input, or text extracted from an image. The reception unit can directly receive text in text format, convert voice input into text using voice recognition technology, or extract text from an image using OCR technology and then receive it. Step 2: The proofreading unit analyzes the text received by the reception unit and proofreads the text based on the selected proofreading mode. Proofreading is performed based on grammar checks, style checks, content consistency checks, etc. For example, a grammar checker is used to detect and correct grammatical errors. The style checker proofreads the text in a style according to the selected proofreading mode, and the content consistency checker analyzes the context to maintain consistency throughout the text and makes any necessary corrections. Step 3: The correction department corrects typos in the text proofread by the proofreading department. Corrections can be done using spell checkers and grammar checkers, or they can be automatically corrected using AI. For example, an AI model can be used to analyze the entire text, detect typos, and correct them.

[0068] (Example 2) A text proofreading system according to an embodiment of the present invention uses AI to proofread text entered by a user in a messaging app. When a user enters text in a messaging app, the AI ​​analyzes the text and allows the user to select different proofreading modes, such as business, friend, or standard. This allows the user to easily create appropriate text for a given situation. For example, a user enters text in a messaging app. At this time, the user can select a specific proofreading mode. For example, when the business proofreading mode is selected, the AI ​​proofreads the entered text to use appropriate business expressions. When the friend proofreading mode is selected, the AI ​​proofreads the entered text to use casual expressions. When the standard proofreading mode is selected, the AI ​​proofreads the entered text to use standard Japanese. The AI ​​then analyzes the entered text and performs proofreading based on the selected proofreading mode. For example, in the business proofreading mode, the AI ​​proofreads the text to use honorifics and polite expressions. In the friend proofreading mode, the AI ​​proofreads the text to use familiar, casual expressions. In the standard proofreading mode, the AI ​​proofreads the text to convert dialects and slang into standard Japanese. In addition, AI also corrects typos. For example, if a user's input text contains typos or omissions, the AI ​​automatically corrects them. This allows users to create accurate, easy-to-read text. This allows the grammar proofreading system to reduce writing errors in business situations using messaging apps. For example, typos or inappropriate expressions in business communications can lead to misunderstandings, but using this system can mitigate that risk. Furthermore, using appropriate expressions in interactions with friends can also facilitate smooth communication. In this way, the AI-powered grammar proofreading system allows users to easily create appropriate sentences for each situation and, by correcting typos, provides accurate, easy-to-read text. This allows the grammar proofreading system to appropriately proofread the text entered by the user and correct typos, providing accurate, easy-to-read text.For example, typos or inappropriate expressions in business correspondence can lead to misunderstandings, but using this system can reduce that risk. Also, using appropriate expressions when communicating with friends can help ensure smooth communication.

[0069] A text proofreading system according to an embodiment includes a receiving unit, a proofreading unit, and a correction unit. The receiving unit receives text input by a user. Examples of text input by a user include, but are not limited to, text, voice input, and text extracted from an image. The receiving unit may directly receive text in text format. The receiving unit may also convert voice input into text and accept the text. The receiving unit may also extract text from an image and accept the text. For example, the receiving unit may convert voice input into text using voice recognition technology. The receiving unit may also extract text from an image using OCR technology. The proofreading unit analyzes the text received by the receiving unit and proofreads the text based on a selected proofreading mode. Proofreading is performed based on, for example, a grammar check, a style check, and a content consistency check, but is not limited to, examples. For example, the proofreading unit may perform a grammar check to ensure grammatical correctness. The proofreading unit may also perform a style check to proofread in a style corresponding to the selected proofreading mode. The proofreading unit may also perform a content consistency check to ensure consistency throughout the text. For example, the proofreading unit detects and corrects grammatical errors using a grammar checker. The style checker applies an appropriate style based on a selected proofreading mode. The content consistency checker analyzes the context and makes necessary corrections to maintain consistency throughout the sentence. The correction unit corrects typographical errors in the sentence proofread by the proofreading unit. Corrections may be performed using, for example, a spell checker or a grammar checker, but are not limited to these examples. For example, the correction unit detects and corrects typographical errors using a spell checker. The correction unit may also detect and correct grammatical errors using a grammar checker. The correction unit may also automatically correct typographical errors using AI. For example, the correction unit analyzes the entire sentence using an AI model to detect and correct typographical errors. As a result, the sentence proofreading system according to the embodiment can provide accurate and easy-to-read sentences by appropriately proofreading sentences entered by a user and correcting typographical errors.

[0070] The proofreading unit can apply honorific language and polite expressions in the business proofreading mode. In the business proofreading mode, for example, proofreading is performed to use honorific language and polite expressions. For example, the proofreading unit adds honorifics such as "desu" (thank you) and "masu" (thank you for your continued support) to the end of a sentence. The proofreading unit can also correct sentences to use polite expressions. For example, the proofreading unit applies polite expressions such as "Thank you for your continued support" and "Please confirm." This makes it possible to provide sentences suitable for business situations. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can perform business proofreading using an AI model.

[0071] The proofreading unit can apply casual expressions in the friend-only sentence proofreading mode. In the friend-only sentence proofreading mode, for example, proofreading is performed to use casual, friendly expressions. For example, the proofreading unit adds casual expressions such as "dayo" (it's you) or "ne" (you) to the end of a sentence. The proofreading unit can also correct sentences to use casual language. For example, the proofreading unit applies casual expressions such as "thank you" (thank you) and "nice to meet you." This makes it possible to provide friendly sentences. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can proofread sentences for friends using an AI model.

[0072] In the standard Japanese sentence proofreading mode, the proofreading unit can convert dialects and slang into standard Japanese. In the standard Japanese sentence proofreading mode, for example, the proofreading unit proofreads by converting dialects and slang into standard Japanese. For example, the proofreading unit converts dialects into standard Japanese. For example, it converts "Ookinii" (thank you) into "Arigatou" (thank you). The proofreading unit can also convert slang into standard Japanese. For example, it converts "Yabai" (wow) into "Sugoi" (great). This makes it possible to provide sentences in standard Japanese. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can proofread sentences into standard Japanese using an AI model.

[0073] The correction unit can automatically correct typos and omissions in the input text. The correction unit detects and corrects typos and omissions using, for example, a spell checker or a grammar checker. For example, the correction unit detects and corrects typos using a spell checker. The correction unit can also detect and correct grammatical errors using a grammar checker. The correction unit can also automatically correct typos and omissions using AI. For example, the correction unit analyzes the entire text using an AI model to detect and correct typos and omissions. This makes it possible to provide accurate text by automatically correcting typos and omissions in the input text. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can correct typos and omissions using an AI model.

[0074] The text proofreading system includes a providing unit that provides an interface for a user to select a proofreading mode. The providing unit provides the interface for the user to select the proofreading mode. The interface may be provided in the form of, for example, a drop-down menu or radio buttons, but is not limited to these examples. For example, the providing unit may allow the user to select the proofreading mode using a drop-down menu. The providing unit may also allow the user to select the proofreading mode using radio buttons. This allows the user to perform appropriate text proofreading according to the situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's selection using an AI model and provide an optimal interface.

[0075] The reception unit can estimate the user's emotions and adjust the timing of receiving text based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive text immediately after input and begin proofreading it quickly. For example, the reception unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Alternatively, if the user is relaxed, the reception unit can accept and proofread the text a short time after completing input. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Alternatively, if the user is in a hurry, the reception unit can accept text even in the middle of input and proofread it in real time. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. This allows the timing of receiving text to be adjusted according to the user's emotions, thereby allowing text to be received at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit may estimate the user's emotions using an AI model and adjust the reception timing.

[0076] The reception unit can analyze the user's past input history and select the optimal reception method. For example, the reception unit prioritizes reception of input methods (such as voice and text) that the user has frequently used in the past. For example, the reception unit analyzes the user's past input history and extracts frequently used words and input patterns. The reception unit can also predict and receive the input method that will be used during a specific time period from the user's past input history. For example, if the user tends to use voice input during a specific time period, the reception unit prioritizes receiving voice input during that time period. The reception unit can also analyze patterns of sentences the user has previously input and suggest the optimal reception method. For example, the reception unit develops an algorithm that suggests the optimal reception method based on the user's past input history. This allows the optimal reception method to be selected by analyzing the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past input history using an AI model and select the optimal reception method.

[0077] When receiving a text, the reception unit can filter the text based on the user's current situation and areas of interest. For example, if the user is using the text in a business setting, the reception unit can preferentially receive business-related keywords. For example, the reception unit can identify the user's current situation and filter out business-related keywords. Furthermore, if the user is interacting with friends, the reception unit can preferentially receive casual expressions. For example, the reception unit can identify the user's areas of interest and filter out casual expressions. Furthermore, if the user is inputting a text related to a specific area of ​​interest, the reception unit can preferentially receive expressions related to that area. For example, the reception unit can develop an algorithm that identifies the user's areas of interest and filters out expressions related to that area. This allows for filtering based on the user's current situation and areas of interest, thereby allowing for the reception of highly relevant text. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can use an AI model to identify the user's current situation and areas of interest and perform filtering.

[0078] When receiving text, the reception unit can select the optimal reception means depending on the user's input method. For example, if the user uses voice input, the reception unit receives the text using voice recognition technology. For example, the reception unit converts the voice input into text using voice recognition technology. Furthermore, if the user uses text input, the reception unit can also receive the text using text analysis technology. For example, the reception unit analyzes the text input using text analysis technology. Furthermore, if the user uses image input, the reception unit can also receive the text using image recognition technology. For example, the reception unit extracts text from an image using image recognition technology. This enables efficient text reception by selecting the optimal reception means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's input method using an AI model and select the optimal reception means.

[0079] The reception unit can estimate the user's emotions and determine the priority of received sentences based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize receiving important sentences. For example, the reception unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the reception unit can prioritize receiving casual sentences. For example, the reception unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the reception unit can prioritize receiving short sentences. For example, the reception unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. Thus, by determining the priority of received sentences based on the user's emotions, important sentences can be prioritized. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may use an AI model to estimate the user's emotions and determine the priority of the sentences to be received.

[0080] When receiving text, the reception unit can prioritize receiving highly relevant text by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes receiving text related to that area. For example, the reception unit identifies the user's geographical location information and filters keywords related to the area. Furthermore, when the user is traveling, the reception unit can prioritize receiving text related to the travel destination. For example, the reception unit identifies the user's geographical location information and filters keywords related to the travel destination. Furthermore, when the user is at home, the reception unit can prioritize receiving text related to the user's home. For example, the reception unit identifies the user's geographical location information and filters keywords related to the user's home. In this way, highly relevant text can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can identify the user's geographical location information using an AI model and prioritize receiving highly relevant text.

[0081] The reception unit can analyze the user's social media activity and receive related texts when receiving texts. The reception unit, for example, analyzes content posted by the user on social media and prioritizes receiving related texts. For example, the reception unit analyzes the user's social media activity and filters the posted content. The reception unit can also analyze content posted by the user's friends on social media and prioritize receiving related texts. For example, the reception unit analyzes the user's social media activity and filters the friends' posted content. The reception unit can also analyze the user's social media activity history and prioritize receiving related texts. For example, the reception unit analyzes the user's social media activity and develops an algorithm for filtering the activity history. This allows the user's social media activity to be analyzed and related texts to be prioritized. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can analyze the user's social media activity using an AI model and receive related texts.

[0082] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a text. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. For example, the reception unit analyzes the user's past feedback and develops an algorithm that suggests an optimal reception method. The reception unit can also preferentially accept specific input methods based on the user's past feedback. For example, the reception unit analyzes the user's past feedback and filters specific input methods. The reception unit can also analyze the user's past feedback and improve the reception method. For example, the reception unit develops an algorithm that improves the reception method based on the user's past feedback. This makes it possible to provide an optimal reception method by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can analyze the user's past feedback using an AI model and customize the reception method.

[0083] The proofreading unit can estimate the user's emotions and adjust the proofreading expression based on the estimated user emotions. For example, if the user is nervous, the proofreading unit can use polite and calm expressions to proofread. For example, the proofreading unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, the proofreading unit can use casual and friendly expressions to proofread. For example, the proofreading unit can record the user's voice and estimate the emotion using voice analysis technology. Also, if the user is in a hurry, the proofreading unit can use concise and to-the-point expressions to proofread. For example, the proofreading unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate proofreading by adjusting the proofreading expression according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit may use an AI model to estimate the user's emotions and adjust the way the proofreading is expressed.

[0084] During proofreading, the proofreading unit can adjust the level of detail of the proofreading based on the importance of the text. For example, in the case of important business documents, the proofreading unit performs detailed proofreading, thoroughly checking for typos and appropriate expressions. For example, the proofreading unit performs detailed proofreading using a grammar checker or style checker. In addition, in the case of casual exchanges with friends, the proofreading unit can perform simple proofreading and focus on correcting typos. For example, the proofreading unit can use a spell checker to correct typos. In addition, in the case of standard Japanese text, the proofreading unit can also focus on converting dialects and slang. For example, the proofreading unit can proofread using an algorithm that converts dialects and slang into standard Japanese. This allows for appropriate proofreading by adjusting the level of detail of the proofreading based on the importance of the text. Some or all of the above-mentioned processes in the proofreading unit may be performed using, for example, AI, or may be performed without AI. For example, the proofreading unit can evaluate the importance of the text using an AI model and adjust the level of detail of the proofreading.

[0085] During proofreading, the proofreading unit can apply different proofreading algorithms depending on the category of the text. For example, in the case of a business document, the proofreading unit uses an algorithm that applies honorific language and polite expressions. For example, the proofreading unit applies honorific language and polite expressions appropriate for business documents. The proofreading unit can also use an algorithm that applies casual expressions in the case of interactions with friends. For example, the proofreading unit applies casual expressions appropriate for interactions with friends. The proofreading unit can also use an algorithm that converts dialects and slang into standard language in the case of a text written in standard Japanese. For example, the proofreading unit proofreads using an algorithm that converts dialects and slang into standard Japanese. This enables appropriate proofreading by applying different proofreading algorithms depending on the category of the text. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the category of the text using an AI model and apply an appropriate proofreading algorithm.

[0086] During proofreading, the proofreading unit can improve the accuracy of proofreading by referring to the user's past proofreading results. The proofreading unit, for example, applies similar expressions based on sentences that the user has proofread in the past. For example, the proofreading unit analyzes the user's past proofreading results and develops an algorithm that applies similar expressions. The proofreading unit can also preferentially apply specific expressions from the user's past proofreading results. For example, the proofreading unit analyzes the user's past proofreading results and preferentially applies specific expressions. The proofreading unit can also analyze the user's past proofreading results and improve the proofreading algorithm. For example, the proofreading unit develops an algorithm that improves the proofreading algorithm based on the user's past proofreading results. This improves the accuracy of proofreading by referring to the user's past proofreading results. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can analyze the user's past proofreading results using an AI model to improve the accuracy of proofreading.

[0087] The proofreading unit can estimate the user's emotions and adjust the length of the proofreading based on the estimated user emotions. For example, if the user is nervous, the proofreading unit performs short, concise proofreading to the point. For example, the proofreading unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the proofreading unit can perform longer proofreading with detailed explanations. For example, the proofreading unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the proofreading unit can perform concise, quick proofreading. For example, the proofreading unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for appropriate proofreading by adjusting the length of the proofreading according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit may use an AI model to estimate the user's emotions and adjust the length of the proofreading.

[0088] During proofreading, the proofreading unit can determine the proofreading priority based on the time of submission of the text. For example, the proofreading unit prioritizes proofreading of business documents with upcoming deadlines. For example, the proofreading unit identifies the submission date and time and develops an algorithm that prioritizes proofreading of business documents with upcoming deadlines. The proofreading unit can also prioritize proofreading of texts that require an urgent reply in exchanges with friends. For example, the proofreading unit identifies the submission date and time and prioritizes proofreading of texts that require an urgent reply. The proofreading unit can also prioritize proofreading of standard Japanese texts related to a specific event. For example, the proofreading unit identifies the submission date and time and prioritizes proofreading of texts related to a specific event. In this way, by determining the proofreading priority based on the time of submission of the text, texts with high urgency can be prioritized for proofreading. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the submission date and determine the proofreading priority using an AI model.

[0089] During proofreading, the proofreading unit can adjust the order of proofreading based on the relevance of the text. For example, the proofreading unit prioritizes proofreading of highly relevant parts in a business document. For example, the proofreading unit may analyze the content of the text and develop an algorithm that identifies highly relevant parts. The proofreading unit can also prioritize proofreading of important parts in interactions with friends. For example, the proofreading unit may analyze the content of the text and identify important parts. The proofreading unit can also prioritize proofreading of parts related to a specific topic in standard Japanese text. For example, the proofreading unit may analyze the content of the text and identify parts related to a specific topic. This allows important parts to be proofread preferentially by adjusting the order of proofreading based on the relevance of the text. Some or all of the above-described processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can analyze the content of the text using an AI model, identify highly relevant parts, and adjust the order of proofreading.

[0090] During proofreading, the proofreading unit can adjust the use of technical terms in the proofreading according to the user's level of expertise. For example, in business documents, the proofreading unit uses appropriate technical terms according to the user's level of expertise. For example, the proofreading unit analyzes the user's occupation and past comments to develop an algorithm to identify appropriate technical terms. The proofreading unit can also use casual expressions according to the user's level of expertise in interactions with friends. For example, the proofreading unit analyzes the user's past comments to identify appropriate casual expressions. The proofreading unit can also use appropriate expressions in standard Japanese sentences according to the user's level of expertise. For example, the proofreading unit analyzes the user's occupation and past comments to identify appropriate expressions. This allows the use of technical terms to be adjusted according to the user's level of expertise, thereby providing appropriate expressions. Some or all of the above-mentioned processing in the proofreading unit may be performed using, for example, AI, or may be performed without using AI. For example, the proofreading unit can identify the user's level of expertise using an AI model and adjust the use of technical terms.

[0091] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user emotions. For example, if the user is nervous, the correction unit uses polite and calm expressions to correct the user's expressions. For example, the correction unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Also, if the user is relaxed, the correction unit can use casual and friendly expressions to correct the user's expressions. For example, the correction unit records the user's voice and estimates the user's emotions using voice analysis technology. Also, if the user is in a hurry, the correction unit can use concise and to-the-point expressions to correct the user's expressions. For example, the correction unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for appropriate correction by adjusting the correction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit may use an AI model to estimate the user's emotions and adjust the correction method.

[0092] During correction, the correction unit can analyze the user's past input history and select the optimal correction method. For example, the correction unit prioritizes correcting expressions that the user has used frequently in the past. For example, the correction unit analyzes the user's past input history and develops an algorithm that identifies frequently used words and expressions. The correction unit can also prioritize correcting specific expressions from the user's past input history. For example, the correction unit analyzes the user's past input history and prioritizes correcting specific expressions. The correction unit can also analyze the user's past input history and suggest the optimal correction method. For example, the correction unit develops an algorithm that suggests the optimal correction method based on the user's past input history. In this way, the optimal correction method can be selected by analyzing the user's past input history. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's past input history using an AI model and select the optimal correction method.

[0093] The correction unit can customize the correction method based on the user's current situation during correction. For example, if the user is in a business setting, the correction unit prioritizes correcting business-related expressions. For example, the correction unit identifies the user's current situation and filters out business-related expressions. The correction unit can also prioritize correcting casual expressions when the user is interacting with friends. For example, the correction unit identifies the user's current situation and filters out casual expressions. The correction unit can also prioritize correcting expressions appropriate to the situation when the user is in a specific situation. For example, the correction unit develops an algorithm that identifies the user's current situation and filters out expressions appropriate to the situation. This enables appropriate correction by customizing the correction method based on the user's current situation. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can identify the user's current situation using an AI model and customize the correction method.

[0094] The correction unit can improve the correction method by reflecting user feedback during correction. For example, the correction unit suggests an optimal correction method based on feedback provided by the user. For example, the correction unit analyzes the user's feedback and develops an algorithm that suggests an optimal correction method. The correction unit can also preferentially apply a specific correction method based on the user's past feedback. For example, the correction unit analyzes the user's feedback and preferentially applies a specific correction method. The correction unit can also analyze the user's feedback and improve the correction algorithm. For example, the correction unit develops an algorithm that improves the correction algorithm based on the user's feedback. In this way, the correction method can be improved by reflecting the user's feedback. Some or all of the above-mentioned processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's feedback using an AI model and improve the correction method.

[0095] The correction unit can estimate the user's emotions and determine the priority of corrections based on the estimated user emotions. For example, if the user is nervous, the correction unit can prioritize important corrections. For example, the correction unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the correction unit can prioritize casual corrections. For example, the correction unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the correction unit can prioritize short corrections. For example, the correction unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the priority of corrections to be determined according to the user's emotions, thereby prioritizing important corrections. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit may use an AI model to estimate the user's emotions and determine the priority of corrections.

[0096] The correction unit can select the optimal correction method by taking into account the user's geographical location information when correcting. For example, when the user is in a specific area, the correction unit prioritizes correcting expressions related to that area. For example, the correction unit identifies the user's geographical location information and filters expressions related to that area. Furthermore, when the user is traveling, the correction unit can also prioritize correcting expressions related to travel destinations. For example, the correction unit identifies the user's geographical location information and filters expressions related to travel destinations. Furthermore, when the user is at home, the correction unit can also prioritize correcting expressions related to their home. For example, the correction unit identifies the user's geographical location information and filters expressions related to their home. In this way, the optimal correction method can be selected by taking the user's geographical location information into account. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can identify the user's geographical location information using an AI model and select the optimal correction method.

[0097] When correcting, the correction unit can analyze the user's social media activity and suggest correction measures. The correction unit, for example, analyzes content posted by the user on social media and prioritizes correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and filters the posted content. The correction unit can also analyze content posted by the user's friends on social media and prioritize correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and filters the friends' posted content. The correction unit can also analyze the user's social media activity history and prioritize correcting relevant expressions. For example, the correction unit analyzes the user's social media activity and develops an algorithm for filtering the activity history. In this way, by analyzing the user's social media activity, relevant expressions can be prioritized and corrected. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's social media activity using an AI model and suggest correction measures.

[0098] The correction unit can customize the correction method by reflecting the user's past feedback when correcting. For example, the correction unit suggests an optimal correction method based on feedback provided by the user in the past. For example, the correction unit analyzes the user's feedback and develops an algorithm that suggests an optimal correction method. The correction unit can also preferentially apply a specific correction method based on the user's past feedback. For example, the correction unit analyzes the user's feedback and preferentially applies a specific correction method. The correction unit can also analyze the user's feedback and improve the correction algorithm. For example, the correction unit develops an algorithm that improves the correction algorithm based on the user's feedback. In this way, the optimal correction method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the correction unit may be performed using, for example, AI, or may be performed without using AI. For example, the correction unit can analyze the user's feedback using an AI model and customize the correction method.

[0099] The providing unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide an interface with subdued colors to reduce visual stress. For example, the providing unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if the user is having fun, the providing unit can provide an interface with bright colors to make input work more enjoyable. For example, the providing unit can record the user's voice and estimate the emotion using voice analysis technology. Furthermore, if the user is tired, the providing unit can provide a simple, highly visible interface to make input work easier. For example, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This can reduce visual stress by adjusting the interface display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model to estimate the user's emotions and adjust the display method of the interface.

[0100] When displaying an interface, the providing unit can select an optimal display method by referring to the user's past operation history. The providing unit, for example, prioritizes displaying an interface design that the user has frequently used in the past. For example, the providing unit analyzes the user's past operation history and develops an algorithm that identifies frequently used designs. The providing unit can also prioritize displaying a specific design based on the user's past operation history. For example, the providing unit analyzes the user's past operation history and prioritizes displaying a specific design. The providing unit can also analyze the user's past operation history and propose an optimal interface design. For example, the providing unit develops an algorithm that proposes an optimal interface design based on the user's past operation history. This makes it possible to provide an optimal interface display method by referring to the user's past operation history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze the user's past operation history using an AI model and select an optimal display method.

[0101] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, if the user is using the interface in a business setting, the providing unit can prioritize displaying business-related information. For example, the providing unit can identify the user's current task and filter out business-related information. Furthermore, if the user is interacting with friends, the providing unit can prioritize displaying casual information. For example, the providing unit can identify the user's current task and filter out casual information. Furthermore, if the user is performing a specific task, the providing unit can prioritize displaying information related to the task. For example, the providing unit can develop an algorithm that identifies the user's current task and filters out information related to the task. This allows the display content to be customized according to the user's current task, thereby providing highly relevant information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's current task using an AI model and customize the display content.

[0102] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying an interface. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit identifies the user's device information and provides a display method optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartwatch. In this way, the optimal interface display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's device information using an AI model and select the optimal display method.

[0103] The providing unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, if the user is nervous, the providing unit provides simple and intuitive operation procedures. For example, the providing unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. For example, the providing unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the providing unit can provide quick operation procedures. For example, the providing unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the user's emotions using an emotion estimation algorithm. This improves ease of operation by adjusting the interface operation procedures according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model to estimate the user's emotions and adjust the operation procedure of the interface.

[0104] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying an interface. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartphone. Furthermore, if the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. For example, the providing unit identifies the user's device information and provides a display method optimized for the tablet. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit identifies the user's device information and provides a display method optimized for the smartwatch. In this way, the optimal interface display method can be provided by taking the user's device information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's device information using an AI model and select the optimal display method.

[0105] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. The providing unit, for example, automatically sets the interface language based on the language setting of the user's device. For example, the providing unit identifies the user's language setting and provides an interface corresponding to that language. The providing unit can also provide a language switching function when the user uses multiple languages. For example, the providing unit provides an interface for the user to switch languages. Furthermore, if the user selects a specific language, the providing unit can provide the interface in that language. For example, the providing unit sets the interface language based on the user's selection. This makes it possible to provide an interface that is easy for the user to understand by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can identify the user's language setting using an AI model and make the display content multilingual.

[0106] The providing unit may analyze the user's social media activity and provide related information when displaying the interface. The providing unit may, for example, provide information about places where the user has checked in on social media. For example, the providing unit may analyze the user's social media activity and filter information related to the checked-in places. The providing unit may also analyze the user's social media posts and provide information about related tourist spots and stores. For example, the providing unit may analyze the user's social media activity and filter information related to the posted content. The providing unit may also provide information about related places and events based on the activities of the user's friends on social media. For example, the providing unit may develop an algorithm that analyzes the user's social media activity and filters information related to the friends' activities. This allows the user's social media activity to be analyzed and related information to be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may analyze the user's social media activity using an AI model and provide related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned receiving unit, proofreading unit, correction unit, and providing unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit can receive text input by a user using the receiving device 38 or the camera 42 of the smart device 14. For example, the proofreading unit is realized by the specific processing unit 290 of the data processing device 12 and proofreads the text based on a selected proofreading mode. For example, the correction unit corrects typos by the specific processing unit 290 of the data processing device 12. For example, the providing unit provides an interface for a user to select a proofreading mode using the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned receiving unit, proofreading unit, correction unit, and providing unit is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit can receive a sentence input by a user using the microphone 238 or the camera 42 of the smart glasses 214. For example, the proofreading unit is realized by the specific processing unit 290 of the data processing device 12 and proofreads the sentence based on a selected proofreading mode. For example, the correction unit corrects typos by the specific processing unit 290 of the data processing device 12. For example, the providing unit provides an interface for a user to select a proofreading mode using the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, proofreading unit, correction unit, and providing unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit can receive text input by the user using the microphone 238 or the camera 42 of the headset type terminal 314. For example, the proofreading unit is realized by the specific processing unit 290 of the data processing device 12 and proofreads the text based on the selected proofreading mode. For example, the correction unit corrects typos by the specific processing unit 290 of the data processing device 12. For example, the providing unit provides an interface that allows the user to select the proofreading mode using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, proofreading unit, correction unit, and providing unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive text input by the user using the microphone 238 or the camera 42 of the robot 414. For example, the proofreading unit is realized by the specific processing unit 290 of the data processing device 12 and proofreads the text based on the selected proofreading mode. For example, the correction unit corrects typos by the specific processing unit 290 of the data processing device 12. For example, the providing unit provides an interface using the display device of the robot 414 for the user to select the proofreading mode.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's input speed and adjust the method of receiving text depending on the input speed. For example, if the user is inputting quickly, the reception unit can receive text in real time and immediately start proofreading. Alternatively, if the user is inputting slowly, the reception unit can receive the text all at once after input is complete and proofread it. Furthermore, if the user's input speed fluctuates, the reception unit can dynamically adjust the reception method depending on the fluctuation. This makes it possible to provide a flexible reception method that corresponds to the user's input speed.

[0109] The proofreading unit can adjust the tone of the text based on the user's input. For example, if the user is inputting a text expressing gratitude, the proofreading unit can proofread the text to emphasize the tone. Also, if the user is inputting a text expressing an apology, the proofreading unit can proofread the tone to be more polite. Furthermore, if the user is inputting a text making a suggestion, the proofreading unit can proofread the tone to be more positive. In this way, it is possible to provide text with an appropriate tone according to the user's intention.

[0110] The correction unit can adjust the length of the sentence based on the user's input. For example, if the user is inputting a short message, the correction unit can correct the message to keep it concise. If the user is inputting a detailed explanation, the correction unit can adjust the explanation to an appropriate length to make it easier to understand. Furthermore, if the user is inputting a question, the correction unit can add information necessary to clarify the question. This makes it possible to provide sentences of an appropriate length according to the user's intention.

[0111] The providing unit can analyze the user's past selection history and suggest the optimal proofreading mode. For example, if the user has frequently used the business proofreading mode in the past, the providing unit can preferentially suggest the business proofreading mode. Also, if the user has frequently used the friend proofreading mode, the providing unit can also suggest the friend proofreading mode. Furthermore, if the user tends to use a specific proofreading mode during a specific time period, the providing unit can also suggest the optimal proofreading mode for that time period. In this way, the optimal proofreading mode can be provided based on the user's past selection history.

[0112] The reception unit can estimate the user's emotions and adjust the method of receiving text based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can receive text immediately after input and start proofreading quickly. Alternatively, if the user is relaxed, the reception unit can wait a short time after input is complete before receiving and proofreading the text. Furthermore, if the user is in a hurry, the reception unit can receive text even in the middle of input and proofread it in real time. This makes it possible to adjust the method of receiving text according to the user's emotions.

[0113] The proofreading unit can adjust the style of the text based on the user's input. For example, if the user is inputting formal text, the proofreading unit can proofread the text to maintain that style. Also, if the user is inputting casual text, the proofreading unit can proofread the text to maintain the casual style. Furthermore, if the user is inputting technical content, the proofreading unit can proofread the text to make it more professional. This makes it possible to provide text in an appropriate style according to the user's input.

[0114] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated user's emotions. For example, if the user is nervous, the correction unit can correct the user's comments using polite and calm expressions. If the user is relaxed, the correction unit can also correct the user's comments using casual and friendly expressions. Furthermore, if the user is in a hurry, the correction unit can also correct the user's comments using concise and to-the-point expressions. This makes it possible to provide an appropriate correction method according to the user's emotions.

[0115] The providing unit can customize the display content of the interface according to the user's current task. For example, if the user is using the interface in a business setting, business-related information can be displayed preferentially. Also, if the user is interacting with friends, casual information can be displayed preferentially. Furthermore, if the user is performing a specific task, information related to that task can be displayed preferentially. This makes it possible to provide appropriate information according to the user's current task.

[0116] The providing unit can estimate the user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, an interface with subdued colors can be provided to reduce visual stress. If the user is having fun, an interface with bright colors can be provided to make input work more enjoyable. Furthermore, if the user is tired, a simple, highly visible interface can be provided to make input work easier. In this way, an appropriate interface can be provided according to the user's emotions.

[0117] The proofreading unit can proofread to maintain consistency in the text based on the user's input. For example, if the user is writing about the same topic across multiple paragraphs, the proofreading unit can proofread to maintain consistency in the topic. Also, if the user is mixing different topics, the proofreading unit can proofread to clearly separate each topic. Furthermore, if the user is inputting a dialogue-style text, the proofreading unit can proofread to maintain a natural flow of the dialogue. This makes it possible to provide a consistent text according to the user's input.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives text input by the user. The text input by the user can be text, voice input, or text extracted from an image. The reception unit can directly receive text in text format, convert voice input into text using voice recognition technology, or extract text from an image using OCR technology and then receive it. Step 2: The proofreading unit analyzes the text received by the reception unit and proofreads the text based on the selected proofreading mode. Proofreading is performed based on grammar checks, style checks, content consistency checks, etc. For example, a grammar checker is used to detect and correct grammatical errors. The style checker proofreads the text in a style according to the selected proofreading mode, and the content consistency checker analyzes the context to maintain consistency throughout the text and makes any necessary corrections. Step 3: The correction department corrects typos in the text proofread by the proofreading department. Corrections can be done using spell checkers and grammar checkers, or they can be automatically corrected using AI. For example, an AI model can be used to analyze the entire text, detect typos, and correct them.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 reception unit that receives a sentence input by a user; a proofreading unit that analyzes the text received by the reception unit and proofreads the text based on a selected proofreading mode; a correction unit that corrects typos and omissions in the text proofread by the proofreading unit. A system characterized by:

2. The calibration unit Apply honorifics and polite expressions in business proofreading mode The system of claim 1 .

3. The calibration unit Use casual language in grammar checker mode for friends The system of claim 1 .

4. The calibration unit Standard Japanese proofreading mode converts dialects and slang to standard Japanese The system of claim 1 .

5. The correction unit Automatically correct spelling errors as you type The system of claim 1 .

6. A provision unit is provided that provides an interface for a user to select a calibration mode. The system of claim 1 .

7. The reception unit Estimate the user's emotions and adjust the timing of receiving text based on the estimated user emotions. The system of claim 1 .

8. The reception unit Analyze the user's past input history and select the optimal reception method The system of claim 1 .

9. The reception unit When receiving text, filter it based on the user's current situation and interests. The system of claim 1 .

Images