system

Abstract

The system according to this embodiment aims to efficiently perform grammatical correction and style adjustment of documents. [Solution] The system according to the embodiment comprises a reception unit, a grammar correction suggestion unit, a style adjustment suggestion unit, and a provision unit. The reception unit receives text input from the user. The grammar correction suggestion unit corrects the grammar of the text received by the reception unit. The style adjustment suggestion unit adjusts the style of the text corrected by the grammar correction suggestion unit. The provision unit provides the text adjusted by the style adjustment suggestion unit to the user.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is necessary to manually correct the grammar and adjust the style of the text, which takes time and effort.

[0005] The system according to the embodiment aims to efficiently correct the grammar and adjust the style of the text.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a grammar correction suggestion unit, a style adjustment suggestion unit, and a provision unit. The reception unit receives text input from the user. The grammar correction suggestion unit corrects the grammar of the text received by the reception unit. The style adjustment suggestion unit adjusts the style of the text corrected by the grammar correction suggestion unit. The provision unit provides the text adjusted by the style adjustment suggestion unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform grammatical correction and style adjustment of text. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The TextCraft Assistant system, according to an embodiment of the present invention, is an advanced AI tool aimed at streamlining and improving the quality of writing. This TextCraft Assistant system helps users quickly produce professional content by improving grammar and structure and enhancing expressiveness. It is specifically designed for writers, students, and business professionals, addressing all writing needs. The system significantly reduces working time by having the AI ​​suggest grammatical corrections and style adjustments. It excels in situations requiring high-quality output, providing strong support for user writing. For example, the TextCraft Assistant system becomes a partner in efficiently and effectively creating high-quality text. The TextCraft Assistant system consists of the following steps: First, the user inputs text. Next, the AI ​​suggests grammatical corrections and style adjustments, which the user then reviews and corrects. This significantly reduces working time and provides high-quality output. For example, when a writer is writing an article, the AI ​​can point out grammatical errors and suggest appropriate expressions, enabling them to quickly complete a high-quality article. Furthermore, the TextCraft Assistant system understands the user's occupation, position, and writing style, providing support to improve content quality. For example, when business professionals create presentation materials, AI can suggest specialized terminology and improve phrasing, thereby enhancing the quality of the materials. Furthermore, the TextCraft Assistant system considers the user's individual information to provide optimal suggestions. For instance, if the user is a child, the AI ​​will suggest child-friendly language and content. This allows users to efficiently create documents that meet their needs. In this way, the TextCraft Assistant system significantly reduces working time and delivers high-quality output while the AI ​​suggests grammatical corrections and style adjustments. By utilizing the TextCraft Assistant system, users can benefit from its efficient and effective partner in creating high-quality documents.This allows the TextCraft Assistant system to streamline the user's writing process and deliver high-quality text.

[0029] The TextCraft Assistant system according to this embodiment comprises a reception unit, a grammar correction suggestion unit, a style adjustment suggestion unit, and a provision unit. The reception unit receives text input from the user. The reception unit can accept text input in various formats, such as text input and voice input. The reception unit analyzes the text entered by the user and sends it to the grammar correction suggestion unit. The grammar correction suggestion unit corrects the grammar of the received text. The grammar correction suggestion unit points out grammatical errors based on, for example, the scope of application of grammatical rules and the level of automation of correction, and proposes appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. The style adjustment suggestion unit adjusts the style of the text corrected by the grammar correction suggestion unit. The style adjustment suggestion unit adjusts the style of the text, for example, by changing the format or unifying the expression, and proposes appropriate expressions. The style adjustment suggestion unit makes suggestions to improve the style of the text, such as selecting a writing style and selecting vocabulary. The service provider delivers text to the user that has been adjusted by the style adjustment suggestion service provider. The service provider delivers text in the most optimal format for the user, for example, based on display and delivery methods. The service provider considers the user's individual information and makes optimal suggestions. For example, if the user is a child, the service provider will suggest child-friendly language and content. This allows the TextCraft Assistant system to streamline the user's writing process and deliver high-quality text.

[0030] The reception desk receives text input from users. The reception desk can accept various forms of text input, such as text input and voice input. Specifically, users may directly input text using a keyboard, or voice input may be converted to text using speech recognition technology. In the case of voice input, the speech recognition engine analyzes the user's speech and converts it into accurate text data. The reception desk analyzes the text entered by the user and sends it to the grammar correction suggestion department. The analysis uses natural language processing technology to understand the structure and meaning of the text. For example, morphological analysis is performed to identify the part of speech of each word and analyze the sentence structure. This allows the reception desk to accurately understand the content of the text entered by the user and perform appropriate processing. Furthermore, the reception desk can provide a more personalized service by considering the user's input history and individual settings. For example, it can learn the trends of previously entered texts and the user's preferences, and provide appropriate suggestion functions during input. This allows users to input text efficiently, and the reception desk can respond flexibly to the user's needs.

[0031] The grammar correction suggestion unit corrects the grammar of the submitted text. For example, it identifies grammatical errors based on the scope of application of grammatical rules and the level of automation of the correction, and proposes appropriate corrections. Specifically, it analyzes the text using natural language processing technology to detect grammatical errors. For example, it detects subject-verb agreement errors, tense errors, and incorrect article usage, and proposes appropriate corrections. The grammar correction suggestion unit also has a function to automatically correct grammatical errors using AI. The AI ​​learns from a large amount of grammatical data and understands grammatical rules to make optimal corrections to the user's text. For example, the grammar correction suggestion unit analyzes the text entered by the user and corrects it to a grammatically correct form. Furthermore, the grammar correction suggestion unit can customize correction suggestions according to the user's preferred writing style and expression. For example, it will suggest more rigorous grammatical corrections to users who prefer a formal style, and more flexible corrections to users who prefer a casual style. This allows the grammar correction suggestion unit to perform grammatical corrections according to the user's needs, thereby improving the quality of the text.

[0032] The Style Adjustment Suggestion Department adjusts the style of text corrected by the Grammar Correction Suggestion Department. The Style Adjustment Suggestion Department adjusts the style of the text, for example, by changing formatting and unifying expressions, and suggests appropriate expressions. Specifically, it makes suggestions to improve the formatting and readability of the text. For example, it suggests paragraph divisions, the use of bullet points, and the setting of headings to improve the overall structure of the text. Furthermore, to ensure consistency in expression, it unifies different expressions with the same meaning to maintain consistency in the text. The Style Adjustment Suggestion Department also has a function that automatically adjusts the style of text using AI. The AI ​​learns from a large amount of text data and understands appropriate styles, performing optimal style adjustments for the user's text. For example, the Style Adjustment Suggestion Department analyzes the text entered by the user and improves the selection of writing style and vocabulary. The Style Adjustment Suggestion Department can also customize style adjustment suggestions according to the user's preferences and purpose. For example, it suggests formal expressions and the use of technical terms for users creating business documents, and friendly expressions and concise vocabulary for users creating casual documents. This allows the style adjustment suggestion unit to perform style adjustments according to user needs, thereby improving the quality of the text.

[0033] The service provider delivers text to users that has been adjusted by the style adjustment and suggestion service provider. The service provider delivers text in the most optimal format for the user, for example, based on the display method and delivery method. Specifically, it displays text in the optimal format according to the user's device and environment. For example, it provides display methods that support different devices such as PCs, smartphones, and tablets, so that users can comfortably read text on any device. The service provider also considers the user's individual information and makes optimal suggestions. For example, if the user is a child, it suggests expressions and content suitable for children. The service provider utilizes AI to make customized suggestions that match the user's preferences and needs. For example, it suggests optimal expressions and content based on past usage history and user feedback. Furthermore, the service provider can reliably deliver information to users using multiple delivery methods. For example, it delivers text to users via email, social media, and messaging apps. This allows the service provider to deliver text to users quickly and reliably and respond flexibly to user needs. In addition, the service provider can collect feedback from users and continuously improve the quality of the text and suggestions it provides. This allows the service provider to deliver high-quality text to users and improve user satisfaction.

[0034] The reception desk can collect information about the user's occupation and position. For example, the reception desk collects information about the user's occupation and position, such as their occupational classification and job title. Based on the information entered by the user, the reception desk provides optimal writing support. For example, if the user is a business professional, the reception desk provides business-oriented writing support. The reception desk can also provide academic writing support if the user is a student. This makes it possible to provide appropriate writing support according to the user's occupation and position. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input information about the user's occupation and position into a generating AI and have the generating AI perform optimal writing support.

[0035] The grammar correction suggestion unit can identify grammatical errors and propose appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. Based on the scope of application of grammatical rules and the level of automation of correction, the grammar correction suggestion unit identifies grammatical errors and proposes appropriate corrections. For example, the grammar correction suggestion unit improves the quality of the text by automatically correcting grammatical errors. Some or all of the above-described processes in the grammar correction suggestion unit may be performed using AI, or without AI. For example, the grammar correction suggestion unit can input user-generated text into a generation AI and have the generation AI detect and correct grammatical errors.

[0036] The style adjustment suggestion unit can adjust the style of a text and suggest appropriate expressions. For example, the style adjustment suggestion unit adjusts the style of a text and suggests appropriate expressions, such as changing the format or unifying the expression. The style adjustment suggestion unit makes suggestions to improve the style of a text, such as selecting a writing style or vocabulary. For example, the style adjustment suggestion unit enhances expressiveness by automatically adjusting the style of a text. Some or all of the above processes in the style adjustment suggestion unit may be performed using AI, or not. For example, the style adjustment suggestion unit can input text entered by the user into a generation AI and have the generation AI perform style adjustments and suggest appropriate expressions.

[0037] The service provider can make optimal suggestions by considering the user's individual information. For example, the service provider can provide optimal writing support by considering individual information such as the user's preferences and past history. The service provider makes optimal suggestions based on the information entered by the user. For example, if the user is a business professional, the service provider can provide business-oriented writing support. Also, if the user is a student, the service provider can provide academic writing support. In this way, optimal writing support can be provided based on the user's individual information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's individual information into a generating AI and have the generating AI execute optimal suggestions.

[0038] The service provider can suggest child-friendly expressions and content if the user is a child. For example, the service provider suggests child-friendly expressions and content such as simple language and educational content. The service provider makes optimal suggestions based on the information entered by the user. For example, if the service provider is a child, it suggests child-friendly expressions and content. In this way, by suggesting child-friendly expressions and content, it can provide writing support suitable for children. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, if the user is a child, the service provider can have a generation AI perform the task of suggesting child-friendly expressions and content.

[0039] The reception unit can analyze the user's past writing history and select the optimal reception method. For example, the reception unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception unit can also analyze patterns in the user's past writing and provide the optimal input interface. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past writing history. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past writing history into a generating AI and have the generating AI select the optimal reception method.

[0040] The reception unit can filter text input based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving keywords related to the user's current project. The reception unit can also prioritize receiving input on relevant topics based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate input content according to the progress of the user's current project. This can encourage the user to input appropriate text according to their projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception unit can prioritize receiving text that is highly relevant to the user's geographical location when receiving text input. For example, if the user is in a specific region, the reception unit will prioritize receiving text related to that region. If the user is traveling, the reception unit can also prioritize receiving text related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving text related to home. This allows the reception unit to prioritize receiving highly relevant text based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generation AI and have the generation AI prioritize receiving highly relevant text.

[0042] The reception unit can analyze the user's social media activity when receiving text input and accept relevant text. For example, the reception unit can prioritize accepting relevant text based on keywords frequently used by the user on social media. The reception unit can also analyze the content of the user's social media activity and prioritize accepting text on relevant topics. Furthermore, the reception unit can prioritize accepting relevant text based on the content of accounts the user follows on social media. This allows for the priority acceptance of relevant text based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI prioritize accepting relevant text.

[0043] The grammar correction suggestion unit can adjust the level of detail of corrections based on the importance of the text when making grammar correction suggestions. For example, the grammar correction suggestion unit will make detailed grammar correction suggestions for important texts. For ordinary texts, the grammar correction suggestion unit can make standard grammar correction suggestions. In addition, the grammar correction suggestion unit can make concise grammar correction suggestions for simple notes. This allows for appropriate grammar correction suggestions according to the importance of the text. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the importance of the text into a generating AI and have the generating AI adjust the level of detail of the corrections.

[0044] The grammar correction suggestion unit can apply different correction algorithms depending on the category of the document when suggesting grammar corrections. For example, in the case of a business document, the grammar correction suggestion unit can apply a business-oriented grammar correction algorithm. In the case of an academic paper, the grammar correction suggestion unit can apply an academic-oriented grammar correction algorithm. Furthermore, in the case of creative writing, the grammar correction suggestion unit can apply a creative-oriented grammar correction algorithm. This allows for the application of an appropriate grammar correction algorithm depending on the category of the document. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the category of the document into a generating AI and have the generating AI execute the application of the correction algorithm.

[0045] The grammar correction suggestion unit can determine the priority of corrections based on the submission date of the document when making grammar correction suggestions. For example, for documents with an approaching deadline, the grammar correction suggestion unit will prioritize suggestions that can be corrected quickly. For documents with ample time before the submission deadline, the grammar correction suggestion unit can make detailed correction suggestions. Furthermore, for documents whose submission deadline has passed, the grammar correction suggestion unit may choose not to make any correction suggestions. This allows for appropriate grammar correction suggestions to be made according to the submission date of the document. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or not. For example, the grammar correction suggestion unit can input information about the submission date of the document into a generating AI and have the generating AI determine the priority of corrections.

[0046] The grammar correction suggestion unit can adjust the order of corrections based on the relevance of the sentences when making grammar correction suggestions. For example, the grammar correction suggestion unit can prioritize correction suggestions for sentences related to important topics. For sentences related to ordinary topics, the grammar correction suggestion unit can make correction suggestions in a standard order. Furthermore, the grammar correction suggestion unit can postpone correction suggestions for sentences related to less relevant topics. This allows for appropriate grammar correction suggestions according to the relevance of the sentences. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the relevance of sentences into a generating AI and have the generating AI adjust the order of corrections.

[0047] The style adjustment suggestion unit can adjust the level of detail of style adjustments based on the importance of the text when making style adjustment suggestions. For example, the style adjustment suggestion unit will make detailed style adjustment suggestions for important texts. For ordinary texts, the style adjustment suggestion unit can make standard style adjustment suggestions. In addition, the style adjustment suggestion unit can make concise style adjustment suggestions for simple notes. This allows for appropriate style adjustment suggestions to be made according to the importance of the text. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the importance of the text into a generating AI and have the generating AI perform the adjustment of the level of detail of the style adjustments.

[0048] The style adjustment suggestion unit can apply different adjustment algorithms depending on the category of the document when suggesting style adjustments. For example, in the case of a business document, the style adjustment suggestion unit can apply a business-oriented style adjustment algorithm. In the case of an academic paper, the style adjustment suggestion unit can apply an academic-oriented style adjustment algorithm. Furthermore, in the case of creative writing, the style adjustment suggestion unit can apply a creative-oriented style adjustment algorithm. This allows the appropriate style adjustment algorithm to be applied according to the category of the document. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the category of the document into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0049] The style adjustment proposal unit can determine the priority of adjustments based on the document's submission date when proposing style adjustments. For example, for documents with approaching deadlines, the style adjustment proposal unit prioritizes proposals that can be adjusted quickly. For documents with ample time before the submission deadline, the style adjustment proposal unit can provide detailed adjustment proposals. Furthermore, for documents whose submission deadline has passed, the style adjustment proposal unit may choose not to provide any adjustment proposals. This allows for appropriate style adjustment proposals to be made according to the document's submission date. Some or all of the above processing in the style adjustment proposal unit may be performed using AI, for example, or not. For example, the style adjustment proposal unit can input information about the document's submission date into a generating AI and have the generating AI determine the priority of adjustments.

[0050] The style adjustment suggestion unit can adjust the order of adjustments based on the relevance of the text when making style adjustment suggestions. For example, the style adjustment suggestion unit can prioritize suggesting adjustments to text related to important topics. For text related to ordinary topics, the style adjustment suggestion unit can make adjustment suggestions in a standard order. Furthermore, the style adjustment suggestion unit can postpone suggesting adjustments to text related to less relevant topics. This allows for appropriate style adjustment suggestions to be made according to the relevance of the text. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the relevance of the text into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0051] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider may also suggest the optimal display layout based on the user's past operation history. Furthermore, the service provider may analyze the user's past operation history and provide the most efficient display method. This allows the service provider to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0052] The service provider can customize the displayed content based on the user's occupation and position at the time of delivery. For example, if the user is a business professional, the service provider can provide content that includes specialized terminology and expressions. If the user is a student, the service provider can provide content that includes academic terminology and expressions. Furthermore, if the user is in a creative occupation, the service provider can provide content that includes creative expressions. This allows the service provider to provide appropriate displayed content according to the user's occupation and position. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input information about the user's occupation and position into a generating AI and have the generating AI perform the customization of the displayed content.

[0053] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize displaying information related to that region. If the user is traveling, the service provider can prioritize displaying information related to the travel destination. Furthermore, if the user is at home, the service provider can prioritize displaying information related to home. This allows the service provider to provide the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0054] The service provider can analyze the user's social media activity and customize the displayed content at the time of delivery. For example, the service provider can prioritize displaying relevant information based on keywords that the user frequently uses on social media. The service provider can analyze the user's social media activity and prioritize displaying information on relevant topics. Furthermore, the service provider can prioritize displaying relevant information based on the content of accounts that the user follows on social media. This allows the service provider to provide appropriate displayed content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The TextCraft Assistant system can be enhanced with a real-time feedback feature to further streamline the user's writing process. This real-time feedback instantly points out grammatical errors and areas for stylistic improvement as the user is typing. For example, if the user is typing a long text, it can identify grammatical errors in real time and suggest appropriate corrections. It can also suggest expressions that conform to a specific style if the user is writing according to that style. Furthermore, the real-time feedback feature can adjust the frequency of feedback based on the user's typing speed. This allows users to review corrections mid-writing and efficiently produce high-quality text.

[0057] The TextCraft Assistant system can be enhanced with context recognition capabilities to assist users in their writing. This feature understands the content of the text entered by the user and provides appropriate suggestions based on that context. For example, if a user is writing a business letter, it can suggest business-related expressions and terminology. Similarly, if a user is writing an academic paper, it can suggest academic expressions and citation formats. Furthermore, the context recognition feature can provide relevant information and references based on the topic of the text entered by the user. This allows users to receive appropriate suggestions tailored to the content of their writing, enabling them to efficiently create high-quality documents.

[0058] The TextCraft Assistant system can add collaboration features to assist users in their writing process. These collaboration features allow multiple users to create and edit documents simultaneously. For example, when team members collaboratively create a project report, they can share edits and exchange opinions in real time. Similarly, when teachers and students collaborate on an academic paper, teachers can provide real-time feedback, allowing students to make revisions based on that feedback. Furthermore, the collaboration features allow users to view other users' editing history and track changes. This enables multiple users to collaborate efficiently and create high-quality documents.

[0059] The TextCraft Assistant system can add a template function to assist users in writing. The template function allows users to utilize pre-prepared templates when creating documents according to a specific format or style. For example, using a business letter template allows users to quickly create business letters. Similarly, using an academic paper template allows users to create academic papers in the appropriate format. Furthermore, using a creative writing template allows users to create creative writing in their own style. This enables users to efficiently produce high-quality documents.

[0060] The TextCraft Assistant system can add a voice input function to assist users in writing. This voice input function allows users to input text using their voice. For example, if a user wants to write without using their hands, they can use voice input to enter text. Furthermore, if a user wants to write while on the go, they can use voice input to efficiently create text. In addition, the voice input function can adjust the input speed according to the user's speaking speed. This allows users to efficiently create text using their voice.

[0061] The TextCraft Assistant system can add a translation function to assist users in writing. This function can translate user-entered text into other languages. For example, if a user writes a document in English, it can be translated into Japanese. Similarly, if a user writes a document in French, it can be translated into English. Furthermore, the translation function can understand the context of the user's input and provide an appropriate translation. This enables users to create high-quality documents in multiple languages.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception unit receives text input from the user. The reception unit can accept text input in various formats, such as text input and voice input. The reception unit analyzes the text entered by the user and sends it to the grammar correction suggestion unit. Step 2: The grammar correction suggestion unit corrects the grammar of the submitted text. The grammar correction suggestion unit identifies grammatical errors based on factors such as the scope of application of grammatical rules and the level of automation of corrections, and proposes appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. Step 3: The Style Adjustment Proposal Unit adjusts the style of the text corrected by the Grammar Correction Proposal Unit. The Style Adjustment Proposal Unit adjusts the style of the text, for example, by changing the format or unifying the expression, and proposes appropriate expressions. The Style Adjustment Proposal Unit makes suggestions to improve the style of the text, such as selecting a writing style or vocabulary. Step 4: The delivery unit provides the user with the text that has been adjusted by the style adjustment suggestion unit. The delivery unit provides the text in the most suitable format for the user, for example, based on the display method and delivery method. The delivery unit considers the user's individual information and makes optimal suggestions. For example, if the user is a child, the delivery unit will suggest expressions and content suitable for children.

[0064] (Example of form 2) The TextCraft Assistant system, according to an embodiment of the present invention, is an advanced AI tool aimed at streamlining and improving the quality of writing. This TextCraft Assistant system helps users quickly produce professional content by improving grammar and structure and enhancing expressiveness. It is specifically designed for writers, students, and business professionals, addressing all writing needs. The system significantly reduces working time by having the AI ​​suggest grammatical corrections and style adjustments. It excels in situations requiring high-quality output, providing strong support for user writing. For example, the TextCraft Assistant system becomes a partner in efficiently and effectively creating high-quality text. The TextCraft Assistant system consists of the following steps: First, the user inputs text. Next, the AI ​​suggests grammatical corrections and style adjustments, which the user then reviews and corrects. This significantly reduces working time and provides high-quality output. For example, when a writer is writing an article, the AI ​​can point out grammatical errors and suggest appropriate expressions, enabling them to quickly complete a high-quality article. Furthermore, the TextCraft Assistant system understands the user's occupation, position, and writing style, providing support to improve content quality. For example, when business professionals create presentation materials, AI can suggest specialized terminology and improve phrasing, thereby enhancing the quality of the materials. Furthermore, the TextCraft Assistant system considers the user's individual information to provide optimal suggestions. For instance, if the user is a child, the AI ​​will suggest child-friendly language and content. This allows users to efficiently create documents that meet their needs. In this way, the TextCraft Assistant system significantly reduces working time and delivers high-quality output while the AI ​​suggests grammatical corrections and style adjustments. By utilizing the TextCraft Assistant system, users can benefit from its efficient and effective partner in creating high-quality documents.This allows the TextCraft Assistant system to streamline the user's writing process and deliver high-quality text.

[0065] The TextCraft Assistant system according to this embodiment comprises a reception unit, a grammar correction suggestion unit, a style adjustment suggestion unit, and a provision unit. The reception unit receives text input from the user. The reception unit can accept text input in various formats, such as text input and voice input. The reception unit analyzes the text entered by the user and sends it to the grammar correction suggestion unit. The grammar correction suggestion unit corrects the grammar of the received text. The grammar correction suggestion unit points out grammatical errors based on, for example, the scope of application of grammatical rules and the level of automation of correction, and proposes appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. The style adjustment suggestion unit adjusts the style of the text corrected by the grammar correction suggestion unit. The style adjustment suggestion unit adjusts the style of the text, for example, by changing the format or unifying the expression, and proposes appropriate expressions. The style adjustment suggestion unit makes suggestions to improve the style of the text, such as selecting a writing style and selecting vocabulary. The service provider delivers text to the user that has been adjusted by the style adjustment suggestion service provider. The service provider delivers text in the most optimal format for the user, for example, based on display and delivery methods. The service provider considers the user's individual information and makes optimal suggestions. For example, if the user is a child, the service provider will suggest child-friendly language and content. This allows the TextCraft Assistant system to streamline the user's writing process and deliver high-quality text.

[0066] The reception desk receives text input from users. The reception desk can accept various forms of text input, such as text input and voice input. Specifically, users may directly input text using a keyboard, or voice input may be converted to text using speech recognition technology. In the case of voice input, the speech recognition engine analyzes the user's speech and converts it into accurate text data. The reception desk analyzes the text entered by the user and sends it to the grammar correction suggestion department. The analysis uses natural language processing technology to understand the structure and meaning of the text. For example, morphological analysis is performed to identify the part of speech of each word and analyze the sentence structure. This allows the reception desk to accurately understand the content of the text entered by the user and perform appropriate processing. Furthermore, the reception desk can provide a more personalized service by considering the user's input history and individual settings. For example, it can learn the trends of previously entered texts and the user's preferences, and provide appropriate suggestion functions during input. This allows users to input text efficiently, and the reception desk can respond flexibly to the user's needs.

[0067] The grammar correction suggestion unit corrects the grammar of the submitted text. For example, it identifies grammatical errors based on the scope of application of grammatical rules and the level of automation of the correction, and proposes appropriate corrections. Specifically, it analyzes the text using natural language processing technology to detect grammatical errors. For example, it detects subject-verb agreement errors, tense errors, and incorrect article usage, and proposes appropriate corrections. The grammar correction suggestion unit also has a function to automatically correct grammatical errors using AI. The AI ​​learns from a large amount of grammatical data and understands grammatical rules to make optimal corrections to the user's text. For example, the grammar correction suggestion unit analyzes the text entered by the user and corrects it to a grammatically correct form. Furthermore, the grammar correction suggestion unit can customize correction suggestions according to the user's preferred writing style and expression. For example, it will suggest more rigorous grammatical corrections to users who prefer a formal style, and more flexible corrections to users who prefer a casual style. This allows the grammar correction suggestion unit to perform grammatical corrections according to the user's needs, thereby improving the quality of the text.

[0068] The Style Adjustment Suggestion Department adjusts the style of text corrected by the Grammar Correction Suggestion Department. The Style Adjustment Suggestion Department adjusts the style of the text, for example, by changing formatting and unifying expressions, and suggests appropriate expressions. Specifically, it makes suggestions to improve the formatting and readability of the text. For example, it suggests paragraph divisions, the use of bullet points, and the setting of headings to improve the overall structure of the text. Furthermore, to ensure consistency in expression, it unifies different expressions with the same meaning to maintain consistency in the text. The Style Adjustment Suggestion Department also has a function that automatically adjusts the style of text using AI. The AI ​​learns from a large amount of text data and understands appropriate styles, performing optimal style adjustments for the user's text. For example, the Style Adjustment Suggestion Department analyzes the text entered by the user and improves the selection of writing style and vocabulary. The Style Adjustment Suggestion Department can also customize style adjustment suggestions according to the user's preferences and purpose. For example, it suggests formal expressions and the use of technical terms for users creating business documents, and friendly expressions and concise vocabulary for users creating casual documents. This allows the style adjustment suggestion unit to perform style adjustments according to user needs, thereby improving the quality of the text.

[0069] The service provider delivers text to users that has been adjusted by the style adjustment and suggestion service provider. The service provider delivers text in the most optimal format for the user, for example, based on the display method and delivery method. Specifically, it displays text in the optimal format according to the user's device and environment. For example, it provides display methods that support different devices such as PCs, smartphones, and tablets, so that users can comfortably read text on any device. The service provider also considers the user's individual information and makes optimal suggestions. For example, if the user is a child, it suggests expressions and content suitable for children. The service provider utilizes AI to make customized suggestions that match the user's preferences and needs. For example, it suggests optimal expressions and content based on past usage history and user feedback. Furthermore, the service provider can reliably deliver information to users using multiple delivery methods. For example, it delivers text to users via email, social media, and messaging apps. This allows the service provider to deliver text to users quickly and reliably and respond flexibly to user needs. In addition, the service provider can collect feedback from users and continuously improve the quality of the text and suggestions it provides. This allows the service provider to deliver high-quality text to users and improve user satisfaction.

[0070] The reception desk can collect information about the user's occupation and position. For example, the reception desk collects information about the user's occupation and position, such as their occupational classification and job title. Based on the information entered by the user, the reception desk provides optimal writing support. For example, if the user is a business professional, the reception desk provides business-oriented writing support. The reception desk can also provide academic writing support if the user is a student. This makes it possible to provide appropriate writing support according to the user's occupation and position. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input information about the user's occupation and position into a generating AI and have the generating AI perform optimal writing support.

[0071] The grammar correction suggestion unit can identify grammatical errors and propose appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. Based on the scope of application of grammatical rules and the level of automation of correction, the grammar correction suggestion unit identifies grammatical errors and proposes appropriate corrections. For example, the grammar correction suggestion unit improves the quality of the text by automatically correcting grammatical errors. Some or all of the above-described processes in the grammar correction suggestion unit may be performed using AI, or without AI. For example, the grammar correction suggestion unit can input user-generated text into a generation AI and have the generation AI detect and correct grammatical errors.

[0072] The style adjustment suggestion unit can adjust the style of a text and suggest appropriate expressions. For example, the style adjustment suggestion unit adjusts the style of a text and suggests appropriate expressions, such as changing the format or unifying the expression. The style adjustment suggestion unit makes suggestions to improve the style of a text, such as selecting a writing style or vocabulary. For example, the style adjustment suggestion unit enhances expressiveness by automatically adjusting the style of a text. Some or all of the above processes in the style adjustment suggestion unit may be performed using AI, or not. For example, the style adjustment suggestion unit can input text entered by the user into a generation AI and have the generation AI perform style adjustments and suggest appropriate expressions.

[0073] The service provider can make optimal suggestions by considering the user's individual information. For example, the service provider can provide optimal writing support by considering individual information such as the user's preferences and past history. The service provider makes optimal suggestions based on the information entered by the user. For example, if the user is a business professional, the service provider can provide business-oriented writing support. Also, if the user is a student, the service provider can provide academic writing support. In this way, optimal writing support can be provided based on the user's individual information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's individual information into a generating AI and have the generating AI execute optimal suggestions.

[0074] The service provider can suggest child-friendly expressions and content if the user is a child. For example, the service provider suggests child-friendly expressions and content such as simple language and educational content. The service provider makes optimal suggestions based on the information entered by the user. For example, if the service provider is a child, it suggests child-friendly expressions and content. In this way, by suggesting child-friendly expressions and content, it can provide writing support suitable for children. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, if the user is a child, the service provider can have a generation AI perform the task of suggesting child-friendly expressions and content.

[0075] The reception unit can estimate the user's emotions and adjust the timing of text input based on the estimated emotions. For example, if the user is stressed, the reception unit can prompt text input at a time when the user can relax. If the user is focused, the reception unit can immediately accept text input. Furthermore, if the user is tired, the reception unit can suggest a break and prompt text input after the break. This allows for prompting text input at the appropriate time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0076] The reception unit can analyze the user's past writing history and select the optimal reception method. For example, the reception unit can prioritize suggesting input methods that the user has frequently used in the past (such as voice input or text input). The reception unit can also analyze patterns in the user's past writing and provide the optimal input interface. Furthermore, the reception unit can suggest the optimal input method for a specific time period based on the user's past writing history. This allows the reception unit to provide the optimal reception method based on the user's past history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past writing history into a generating AI and have the generating AI select the optimal reception method.

[0077] The reception unit can filter text input based on the user's current projects and areas of interest. For example, the reception unit can prioritize receiving keywords related to the user's current project. The reception unit can also prioritize receiving input on relevant topics based on the user's areas of interest. Furthermore, the reception unit can suggest appropriate input content according to the progress of the user's current project. This can encourage the user to input appropriate text according to their projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input information about the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0078] The reception unit can estimate the user's emotions and determine the priority of the messages to be received based on the estimated emotions. For example, if the user is anxious, the reception unit will prioritize receiving important messages. If the user is relaxed, the reception unit can receive messages with normal priority. Also, if the user is tired, the reception unit can prioritize receiving simple messages. This allows for prioritizing important messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The reception unit can prioritize receiving text that is highly relevant to the user's geographical location when receiving text input. For example, if the user is in a specific region, the reception unit will prioritize receiving text related to that region. If the user is traveling, the reception unit can also prioritize receiving text related to the travel destination. Furthermore, if the user is at home, the reception unit can prioritize receiving text related to home. This allows the reception unit to prioritize receiving highly relevant text based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location information into a generation AI and have the generation AI prioritize receiving highly relevant text.

[0080] The reception unit can analyze the user's social media activity when receiving text input and accept relevant text. For example, the reception unit can prioritize accepting relevant text based on keywords frequently used by the user on social media. The reception unit can also analyze the content of the user's social media activity and prioritize accepting text on relevant topics. Furthermore, the reception unit can prioritize accepting relevant text based on the content of accounts the user follows on social media. This allows for the priority acceptance of relevant text based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI prioritize accepting relevant text.

[0081] The grammar correction suggestion unit can estimate the user's emotions and adjust its grammar correction suggestion method based on the estimated emotions. For example, if the user is stressed, the grammar correction suggestion unit can provide concise and easy-to-understand grammar correction suggestions. If the user is relaxed, the grammar correction suggestion unit can provide detailed grammar correction suggestions. Furthermore, if the user is in a hurry, the grammar correction suggestion unit can provide grammar correction suggestions that can be corrected quickly. This allows for appropriate grammar correction suggestions to be provided 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or not using AI. For example, the grammar correction suggestion unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0082] The grammar correction suggestion unit can adjust the level of detail of corrections based on the importance of the text when making grammar correction suggestions. For example, the grammar correction suggestion unit will make detailed grammar correction suggestions for important texts. For ordinary texts, the grammar correction suggestion unit can make standard grammar correction suggestions. In addition, the grammar correction suggestion unit can make concise grammar correction suggestions for simple notes. This allows for appropriate grammar correction suggestions according to the importance of the text. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the importance of the text into a generating AI and have the generating AI adjust the level of detail of the corrections.

[0083] The grammar correction suggestion unit can apply different correction algorithms depending on the category of the document when suggesting grammar corrections. For example, in the case of a business document, the grammar correction suggestion unit can apply a business-oriented grammar correction algorithm. In the case of an academic paper, the grammar correction suggestion unit can apply an academic-oriented grammar correction algorithm. Furthermore, in the case of creative writing, the grammar correction suggestion unit can apply a creative-oriented grammar correction algorithm. This allows for the application of an appropriate grammar correction algorithm depending on the category of the document. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the category of the document into a generating AI and have the generating AI execute the application of the correction algorithm.

[0084] The grammar correction suggestion unit can estimate the user's emotions and determine the priority of grammar corrections based on the estimated emotions. For example, if the user is anxious, the grammar correction suggestion unit will prioritize suggesting important grammar corrections. If the user is relaxed, the grammar correction suggestion unit can suggest grammar corrections with normal priority. Also, if the user is tired, the grammar correction suggestion unit can prioritize suggesting simple grammar corrections. This allows for prioritizing important grammar corrections 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, or not using AI. For example, the grammar correction suggestion unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0085] The grammar correction suggestion unit can determine the priority of corrections based on the submission date of the document when making grammar correction suggestions. For example, for documents with an approaching deadline, the grammar correction suggestion unit will prioritize suggestions that can be corrected quickly. For documents with ample time before the submission deadline, the grammar correction suggestion unit can make detailed correction suggestions. Furthermore, for documents whose submission deadline has passed, the grammar correction suggestion unit may choose not to make any correction suggestions. This allows for appropriate grammar correction suggestions to be made according to the submission date of the document. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or not. For example, the grammar correction suggestion unit can input information about the submission date of the document into a generating AI and have the generating AI determine the priority of corrections.

[0086] The grammar correction suggestion unit can adjust the order of corrections based on the relevance of the sentences when making grammar correction suggestions. For example, the grammar correction suggestion unit can prioritize correction suggestions for sentences related to important topics. For sentences related to ordinary topics, the grammar correction suggestion unit can make correction suggestions in a standard order. Furthermore, the grammar correction suggestion unit can postpone correction suggestions for sentences related to less relevant topics. This allows for appropriate grammar correction suggestions according to the relevance of the sentences. Some or all of the above processing in the grammar correction suggestion unit may be performed using AI, for example, or without AI. For example, the grammar correction suggestion unit can input information about the relevance of sentences into a generating AI and have the generating AI adjust the order of corrections.

[0087] The style adjustment suggestion unit can estimate the user's emotions and adjust the style adjustment suggestion method based on the estimated user emotions. For example, if the user is stressed, the style adjustment suggestion unit can provide simple and easy-to-understand style adjustment suggestions. If the user is relaxed, the style adjustment suggestion unit can provide detailed style adjustment suggestions. Furthermore, if the user is in a hurry, the style adjustment suggestion unit can provide style adjustment suggestions that can be adjusted quickly. In this way, appropriate style adjustment suggestions can be provided according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or not using AI. For example, the style adjustment suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The style adjustment suggestion unit can adjust the level of detail of style adjustments based on the importance of the text when making style adjustment suggestions. For example, the style adjustment suggestion unit will make detailed style adjustment suggestions for important texts. For ordinary texts, the style adjustment suggestion unit can make standard style adjustment suggestions. In addition, the style adjustment suggestion unit can make concise style adjustment suggestions for simple notes. This allows for appropriate style adjustment suggestions to be made according to the importance of the text. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the importance of the text into a generating AI and have the generating AI perform the adjustment of the level of detail of the style adjustments.

[0089] The style adjustment suggestion unit can apply different adjustment algorithms depending on the category of the document when suggesting style adjustments. For example, in the case of a business document, the style adjustment suggestion unit can apply a business-oriented style adjustment algorithm. In the case of an academic paper, the style adjustment suggestion unit can apply an academic-oriented style adjustment algorithm. Furthermore, in the case of creative writing, the style adjustment suggestion unit can apply a creative-oriented style adjustment algorithm. This allows the appropriate style adjustment algorithm to be applied according to the category of the document. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the category of the document into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0090] The style adjustment suggestion unit can estimate the user's emotions and determine the priority of style adjustments based on the estimated emotions. For example, if the user is anxious, the style adjustment suggestion unit will prioritize suggesting important style adjustments. If the user is relaxed, the style adjustment suggestion unit can suggest style adjustments with normal priority. Furthermore, if the user is tired, the style adjustment suggestion unit can prioritize suggesting simple style adjustments. This allows for the prioritization of important style adjustments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, or not using AI. For example, the style adjustment suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The style adjustment proposal unit can determine the priority of adjustments based on the document's submission date when proposing style adjustments. For example, for documents with approaching deadlines, the style adjustment proposal unit prioritizes proposals that can be adjusted quickly. For documents with ample time before the submission deadline, the style adjustment proposal unit can provide detailed adjustment proposals. Furthermore, for documents whose submission deadline has passed, the style adjustment proposal unit may choose not to provide any adjustment proposals. This allows for appropriate style adjustment proposals to be made according to the document's submission date. Some or all of the above processing in the style adjustment proposal unit may be performed using AI, for example, or not. For example, the style adjustment proposal unit can input information about the document's submission date into a generating AI and have the generating AI determine the priority of adjustments.

[0092] The style adjustment suggestion unit can adjust the order of adjustments based on the relevance of the text when making style adjustment suggestions. For example, the style adjustment suggestion unit can prioritize suggesting adjustments to text related to important topics. For text related to ordinary topics, the style adjustment suggestion unit can make adjustment suggestions in a standard order. Furthermore, the style adjustment suggestion unit can postpone suggesting adjustments to text related to less relevant topics. This allows for appropriate style adjustment suggestions to be made according to the relevance of the text. Some or all of the above processing in the style adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the style adjustment suggestion unit can input information about the relevance of the text into a generating AI and have the generating AI perform the adjustment of the order of adjustments.

[0093] The service provider can estimate the user's emotions and adjust the display method of the text based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display method that gets straight to the point. This allows the service provider to deliver text in an appropriate display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The service provider can select the optimal display method by referring to the user's past operation history at the time of service provision. For example, the service provider may prioritize providing display methods that the user has previously preferred. The service provider may also suggest the optimal display layout based on the user's past operation history. Furthermore, the service provider may analyze the user's past operation history and provide the most efficient display method. This allows the service provider to provide the optimal display method based on the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past operation history into a generating AI and have the generating AI select the optimal display method.

[0095] The service provider can customize the displayed content based on the user's occupation and position at the time of delivery. For example, if the user is a business professional, the service provider can provide content that includes specialized terminology and expressions. If the user is a student, the service provider can provide content that includes academic terminology and expressions. Furthermore, if the user is in a creative occupation, the service provider can provide content that includes creative expressions. This allows the service provider to provide appropriate displayed content according to the user's occupation and position. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input information about the user's occupation and position into a generating AI and have the generating AI perform the customization of the displayed content.

[0096] The service provider can estimate the user's emotions and determine the priority of the texts to be provided based on the estimated emotions. For example, if the user is anxious, the service provider will prioritize providing important texts. If the user is relaxed, the service provider can provide texts with normal priority. Furthermore, if the user is tired, the service provider can prioritize providing simple texts. This allows the service provider to prioritize providing important texts 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 is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The service provider can select the optimal display method at the time of delivery, taking into account the user's geographical location information. For example, if the user is in a specific region, the service provider can prioritize displaying information related to that region. If the user is traveling, the service provider can prioritize displaying information related to the travel destination. Furthermore, if the user is at home, the service provider can prioritize displaying information related to home. This allows the service provider to provide the optimal display method based on the user's geographical location information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal display method.

[0098] The service provider can analyze the user's social media activity and customize the displayed content at the time of delivery. For example, the service provider can prioritize displaying relevant information based on keywords that the user frequently uses on social media. The service provider can analyze the user's social media activity and prioritize displaying information on relevant topics. Furthermore, the service provider can prioritize displaying relevant information based on the content of accounts that the user follows on social media. This allows the service provider to provide appropriate displayed content based on the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the customization of the displayed content.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The TextCraft Assistant system can be enhanced with a real-time feedback feature to further streamline the user's writing process. This real-time feedback instantly points out grammatical errors and areas for stylistic improvement as the user is typing. For example, if the user is typing a long text, it can identify grammatical errors in real time and suggest appropriate corrections. It can also suggest expressions that conform to a specific style if the user is writing according to that style. Furthermore, the real-time feedback feature can adjust the frequency of feedback based on the user's typing speed. This allows users to review corrections mid-writing and efficiently produce high-quality text.

[0101] The TextCraft Assistant system can be enhanced with context recognition capabilities to assist users in their writing. This feature understands the content of the text entered by the user and provides appropriate suggestions based on that context. For example, if a user is writing a business letter, it can suggest business-related expressions and terminology. Similarly, if a user is writing an academic paper, it can suggest academic expressions and citation formats. Furthermore, the context recognition feature can provide relevant information and references based on the topic of the text entered by the user. This allows users to receive appropriate suggestions tailored to the content of their writing, enabling them to efficiently create high-quality documents.

[0102] The TextCraft Assistant system can estimate the user's emotions and adjust the tone of the text based on those emotions. For example, if the user is stressed, the system can suggest text in a gentle, relaxing tone. If the user is excited, the system can suggest text in an energetic tone that reflects that excitement. Furthermore, if the user is sad, the system can suggest text in a comforting tone. This allows for the creation of text with an appropriate tone that matches the user's emotions, thereby enhancing the expressiveness of the writing.

[0103] The TextCraft Assistant system can add collaboration features to assist users in their writing process. These collaboration features allow multiple users to create and edit documents simultaneously. For example, when team members collaboratively create a project report, they can share edits and exchange opinions in real time. Similarly, when teachers and students collaborate on an academic paper, teachers can provide real-time feedback, allowing students to make revisions based on that feedback. Furthermore, the collaboration features allow users to view other users' editing history and track changes. This enables multiple users to collaborate efficiently and create high-quality documents.

[0104] The TextCraft Assistant system can estimate a user's emotions and personalize the content of their writing based on those emotions. For example, if a user is happy, the system can suggest positive content. If a user is feeling anxious, the system can suggest reassuring content. Furthermore, if a user is angry, the system can suggest calming content. This allows for the creation of appropriate content tailored to the user's emotions, thereby enhancing the effectiveness of the writing.

[0105] The TextCraft Assistant system can add a template function to assist users in writing. The template function allows users to utilize pre-prepared templates when creating documents according to a specific format or style. For example, using a business letter template allows users to quickly create business letters. Similarly, using an academic paper template allows users to create academic papers in the appropriate format. Furthermore, using a creative writing template allows users to create creative writing in their own style. This enables users to efficiently produce high-quality documents.

[0106] The TextCraft Assistant system can estimate the user's emotions and adjust the text feedback based on those emotions. For example, if the user is stressed, the system can provide feedback in gentle language. If the user is relaxed, the system can provide detailed feedback. Furthermore, if the user is in a hurry, the system can provide concise feedback. This allows the system to provide appropriate feedback according to the user's emotions, effectively supporting the user's writing process.

[0107] The TextCraft Assistant system can add a voice input function to assist users in writing. This voice input function allows users to input text using their voice. For example, if a user wants to write without using their hands, they can use voice input to enter text. Furthermore, if a user wants to write while on the go, they can use voice input to efficiently create text. In addition, the voice input function can adjust the input speed according to the user's speaking speed. This allows users to efficiently create text using their voice.

[0108] The TextCraft Assistant system can estimate the user's emotions and suggest revisions to their writing based on those emotions. For example, if the user is anxious, the system can offer quick revision suggestions. If the user is relaxed, the system can offer more detailed revision suggestions. Furthermore, if the user is tired, the system can offer simple revision suggestions. This allows the system to provide appropriate revision suggestions tailored to the user's emotions, effectively supporting their writing process.

[0109] The TextCraft Assistant system can add a translation function to assist users in writing. This function can translate user-entered text into other languages. For example, if a user writes a document in English, it can be translated into Japanese. Similarly, if a user writes a document in French, it can be translated into English. Furthermore, the translation function can understand the context of the user's input and provide an appropriate translation. This enables users to create high-quality documents in multiple languages.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception unit receives text input from the user. The reception unit can accept text input in various formats, such as text input and voice input. The reception unit analyzes the text entered by the user and sends it to the grammar correction suggestion unit. Step 2: The grammar correction suggestion unit corrects the grammar of the submitted text. The grammar correction suggestion unit identifies grammatical errors based on factors such as the scope of application of grammatical rules and the level of automation of corrections, and proposes appropriate corrections. The grammar correction suggestion unit detects and corrects various grammatical errors, such as subject-verb agreement and tense errors. Step 3: The Style Adjustment Proposal Unit adjusts the style of the text corrected by the Grammar Correction Proposal Unit. The Style Adjustment Proposal Unit adjusts the style of the text, for example, by changing the format or unifying the expression, and proposes appropriate expressions. The Style Adjustment Proposal Unit makes suggestions to improve the style of the text, such as selecting a writing style or vocabulary. Step 4: The delivery unit provides the user with the text that has been adjusted by the style adjustment suggestion unit. The delivery unit provides the text in the most suitable format for the user, for example, based on the display method and delivery method. The delivery unit considers the user's individual information and makes optimal suggestions. For example, if the user is a child, the delivery unit will suggest expressions and content suitable for children.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the reception unit, grammar correction suggestion unit, style adjustment suggestion unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives text input from the user. The grammar correction suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects the grammar of the received text. The style adjustment suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the style of the corrected text. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the adjusted text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, grammar correction suggestion unit, style adjustment suggestion unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives text input from the user. The grammar correction suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects the grammar of the received text. The style adjustment suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the style of the corrected text. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the adjusted text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, grammar correction suggestion unit, style adjustment suggestion unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives text input from the user. The grammar correction suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects the grammar of the received text. The style adjustment suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the style of the corrected text. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the adjusted text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, grammar correction suggestion unit, style adjustment suggestion unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives text input from the user. The grammar correction suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and corrects the grammar of the received text. The style adjustment suggestion unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the style of the corrected text. The provision unit is implemented by the control unit 46A of the robot 414 and provides the adjusted text to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception area that accepts text input from users, A grammar correction suggestion unit that corrects the grammar of the document received by the aforementioned reception unit, A style adjustment suggestion unit adjusts the style of the text corrected by the aforementioned grammar correction suggestion unit, The system includes a provisioning unit that provides the user with text adjusted by the style adjustment suggestion unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is Collect information about the user's occupation and position. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned grammar correction suggestion unit, It points out grammatical errors and suggests appropriate corrections. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned style adjustment suggestion unit is Adjust the writing style and suggest appropriate expressions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We take into account the user's individual information to provide the most suitable suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, If the user is a child, we will suggest child-friendly language and content. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of text input based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past writing history and selects the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving text input, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the messages to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting text input, the system prioritizes accepting text that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving text input, the system analyzes the user's social media activity and accepts relevant text. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned grammar correction suggestion unit, It estimates the user's emotions and adjusts the grammar correction suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned grammar correction suggestion unit, When suggesting grammatical corrections, adjust the level of detail based on the importance of the sentence. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned grammar correction suggestion unit, When suggesting grammatical corrections, different correction algorithms are applied depending on the category of the text. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned grammar correction suggestion unit, It estimates the user's emotions and determines the priority of grammatical corrections based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned grammar correction suggestion unit, When proposing grammatical corrections, prioritize the corrections based on when the document was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned grammar correction suggestion unit, When suggesting grammatical corrections, adjust the order of corrections based on the relevance of the sentences. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned style adjustment suggestion unit is It estimates the user's emotions and adjusts the style adjustment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned style adjustment suggestion unit is When suggesting style adjustments, the level of detail of the adjustments is adjusted based on the importance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned style adjustment suggestion unit is When suggesting style adjustments, different adjustment algorithms are applied depending on the category of the text. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned style adjustment suggestion unit is It estimates the user's emotions and determines the priority of style adjustments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned style adjustment suggestion unit is When proposing style adjustments, prioritize the adjustments based on when the document was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned style adjustment suggestion unit is When suggesting style adjustments, the order of adjustments is adjusted based on the relevance of the text. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the text is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the displayed content will be customized based on the user's occupation and position. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the text to be delivered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal display method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the displayed content is customized by analyzing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts text input from users, A grammar correction suggestion unit that corrects the grammar of the document received by the aforementioned reception unit, A style adjustment suggestion unit adjusts the style of the text corrected by the aforementioned grammar correction suggestion unit, The system includes a provisioning unit that provides the user with text adjusted by the style adjustment suggestion unit. A system characterized by the following features.

2. The aforementioned reception unit is Collect information about the user's occupation and position. The system according to feature 1.

3. The aforementioned grammar correction suggestion unit, It points out grammatical errors and suggests appropriate corrections. The system according to feature 1.

4. The aforementioned style adjustment suggestion unit is Adjust the writing style and suggest appropriate expressions. The system according to feature 1.

5. The aforementioned supply unit is, We take into account the user's individual information to provide the most suitable suggestions. The system according to feature 1.

6. The aforementioned supply unit is, If the user is a child, we will suggest child-friendly language and content. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of text input based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past writing history and selects the most suitable submission method. The system according to feature 1.

9. The aforementioned reception unit is When receiving text input, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of the messages to be received based on the estimated user emotions. The system according to feature 1.

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